[2/N] Fix extra warnings brought by clang-tidy-17 (#137459)

Follows #137407

Pull Request resolved: https://github.com/pytorch/pytorch/pull/137459
Approved by: https://github.com/Skylion007

aten/src/ATen/SparseCsrTensorUtils.h

@@ -144,8 +144,8 @@ class CheckSparseTensorInvariants {
   bool old_state;
 
  public:
-  CheckSparseTensorInvariants(bool state) {
-    old_state = at::globalContext().checkSparseTensorInvariants();
+  CheckSparseTensorInvariants(bool state)
+      : old_state(at::globalContext().checkSparseTensorInvariants()) {
     at::globalContext().setCheckSparseTensorInvariants(state);
   }
 

aten/src/ATen/ThreadLocalState.h

@@ -82,7 +82,7 @@ class TORCH_API ThreadLocalState {
     !defined(BUILD_LITE_INTERPRETER)
   // TLS for autocast dtypes
   std::array<at::ScalarType, at::COMPILE_TIME_MAX_DEVICE_TYPES>
-      autocast_dtypes_;
+      autocast_dtypes_{};
 #endif
 
   friend class ThreadLocalStateGuard;

aten/src/ATen/cuda/CUDAGraph.cpp

@@ -125,7 +125,7 @@ void CUDAGraph::capture_begin(MempoolId_t pool/*=0*/, cudaStreamCaptureMode capt
   // due to the capture status being updated _after_ a capture had already started.
   c10::cuda::CUDACachingAllocator::beginAllocateToPool(capture_dev_, mempool_id_, [this](cudaStream_t stream) {
       cudaStreamCaptureStatus status;
-      CaptureId_t stream_capture_id;
+      CaptureId_t stream_capture_id = 0;
       AT_CUDA_CHECK(cudaStreamGetCaptureInfo(stream, &status, &stream_capture_id));
       return status == cudaStreamCaptureStatus::cudaStreamCaptureStatusActive && stream_capture_id == capture_id_;
   });

aten/src/ATen/functorch/BatchRulesConvolution.cpp

@@ -362,6 +362,7 @@ static std::tuple<Tensor,Tensor,Tensor> convolution_backward_plumbing(
     const Tensor& grad_output_, const Tensor& input_, const Tensor& weight_,
     const c10::OptionalArrayRef<SymInt> bias_sizes_opt,
     c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed,
+    // NOLINTNEXTLINE(performance-unnecessary-value-param)
     c10::SymIntArrayRef output_padding, c10::SymInt groups, std::array<bool, 3> output_mask) {
   const auto maybe_layer = maybeCurrentDynamicLayer();
   vmap_check_escaped(maybe_layer, "convolution_backward_plumbing");

aten/src/ATen/functorch/BatchRulesIndexing.cpp

@@ -8,7 +8,7 @@
 #include <ATen/core/dispatch/Dispatcher.h>
 #include <ATen/functorch/BatchRulesHelper.h>
 
-namespace at { namespace functorch {
+namespace at::functorch {
 
 #define OP_DECOMPOSE(op)  m.impl(#op, static_cast<decltype(&ATEN_FN(op))>(native::op));
 #define OP_DECOMPOSE2(op, overload)  m.impl(#op"."#overload, static_cast<decltype(&ATEN_FN2(op, overload))>(native::op));
@@ -20,4 +20,4 @@ TORCH_LIBRARY_IMPL(aten, FuncTorchBatched, m) {
   OP_DECOMPOSE(_unsafe_masked_index_put_accumulate);
 }
 
-}}
+}

aten/src/ATen/functorch/BatchRulesModules.cpp

@@ -226,7 +226,7 @@ static Tensor one_hot_decomposition_hack(const Tensor &self, int64_t num_classes
         if (num_classes <= 0) {
             AT_ERROR("Can not infer total number of classes from empty tensor.");
         } else {
-            shape.push_back(num_classes);
+            shape.emplace_back(num_classes);
             return at::empty_symint(shape, self.options());
         }
     }
@@ -246,7 +246,7 @@ static Tensor one_hot_decomposition_hack(const Tensor &self, int64_t num_classes
     //   TORCH_CHECK(num_classes > self.max().item().toLong(), "Class values must be smaller than num_classes.");
     // }
 
-    shape.push_back(num_classes);
+    shape.emplace_back(num_classes);
     Tensor ret = at::zeros_symint(shape, self.options());
     return ret.scatter(-1, self.unsqueeze(-1), 1);
 }

aten/src/ATen/functorch/BatchRulesRandomness.cpp

@@ -213,7 +213,7 @@ static std::tuple<Tensor,Tensor> native_dropout_batching_rule(const Tensor& tens
   return std::make_tuple(output, mask);
 }
 
-static Tensor multinomial_batching_rule(const Tensor& self, const int64_t num_samples, const bool replacement, const std::optional<Generator> generator) {
+static Tensor multinomial_batching_rule(const Tensor& self, const int64_t num_samples, const bool replacement, std::optional<Generator> generator) {
   c10::impl::ExcludeDispatchKeyGuard guard(DispatchKey::FuncTorchVmapMode);
   auto maybe_layer = maybeCurrentDynamicLayer();
   const auto cur_level = maybe_layer->layerId();
@@ -237,7 +237,7 @@ static Tensor multinomial_batching_rule(const Tensor& self, const int64_t num_sa
     if (is_2D_case) {
       self_value = reshape_dim_into(0, 0, self_value);
     }
-    auto out = multinomial(self_value, num_samples, replacement, generator);
+    auto out = multinomial(self_value, num_samples, replacement, std::move(generator));
     if (is_2D_case) {
       out = reshape_dim_outof_symint(0, maybe_layer->batchSize(), out);
     }
@@ -249,7 +249,7 @@ static Tensor multinomial_batching_rule(const Tensor& self, const int64_t num_sa
   // Must be same randomness with unbatched input
   // 1D case: S -> multinomial(S) -> S
   // 2D case: MS -> multinomial(MS) -> MS
-  return multinomial(self_value, num_samples, replacement, generator);
+  return multinomial(self_value, num_samples, replacement, std::move(generator));
 }
 
 template <typename A, A a, typename C>

aten/src/ATen/functorch/LegacyVmapTransforms.cpp

@@ -102,7 +102,7 @@ static Tensor moveDimToFrontAndExpand(Tensor tensor, std::optional<int64_t> dim,
   } else {
     tensor = tensor.unsqueeze(0);
     auto expanded_sizes = tensor.sym_sizes().vec();
-    expanded_sizes[0] = size;
+    expanded_sizes[0] = std::move(size);
     tensor = tensor.expand_symint(expanded_sizes);
   }
   return tensor;

aten/src/ATen/functorch/PyTorchOperatorHacks.cpp

@@ -4,7 +4,6 @@
 #include <ATen/WrapDimUtils.h>
 #include <ATen/functorch/TensorWrapper.h>
 #include <ATen/functorch/BatchedTensorImpl.h>
-#include <ATen/ATen.h>
 #include <ATen/Dispatch.h>
 #include <c10/util/irange.h>
 #include <ATen/NamedTensorUtils.h>

aten/src/ATen/native/nested/NestedTensorBackward.cpp

@@ -9,8 +9,6 @@
 #include <ATen/NestedTensorImpl.h>
 #include <c10/core/DispatchKey.h>
 #include <ATen/native/nested/NestedTensorUtils.h>
-#include <ATen/native/nested/NestedTensorMath.h>
-#include <ATen/native/layer_norm.h>
 #include <c10/core/DeviceType.h>
 
 #include <utility>

aten/src/ATen/native/nested/NestedTensorMatmul.cpp

@@ -13,7 +13,6 @@
 #include <ATen/core/Tensor.h>
 #include <ATen/core/grad_mode.h>
 #include <ATen/native/layer_norm.h>
-#include <ATen/native/nested/NestedTensorUtils.h>
 
 namespace at::native {
 

aten/src/ATen/native/nested/cuda/NestedTensorTransformerFunctions.cpp

@@ -13,7 +13,6 @@
 #include <ATen/ops/narrow_native.h>
 #endif
 
-#include <ATen/native/NonSymbolicBC.h>
 #include <ATen/native/nested/NestedTensorTransformerFunctions.h>
 #include <ATen/native/nested/NestedTensorTransformerUtils.h>
 #include <ATen/native/nested/NestedTensorMath.h>

c10/core/CPUAllocator.cpp

@@ -114,8 +114,7 @@ class DefaultMobileCPUAllocator final : public at::Allocator {
     }
 
     auto alloc_size = PreGuardBytes + nbytes + PostGuardBytes;
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    void* data;
+    void* data = nullptr;
     auto allocator_ptr = GetThreadLocalCachingAllocator();
     auto profiling_allocator_ptr = GetThreadLocalProfilingAllocator();
     if (allocator_ptr != nullptr) {

c10/core/GeneratorImpl.cpp

@@ -88,8 +88,7 @@ static uint64_t readURandomLong() {
  *   a 32 bit number to 64 bit.
  */
 uint64_t getNonDeterministicRandom(bool is_cuda) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint64_t s;
+  uint64_t s = 0;
   if (!is_cuda) {
 #ifdef _WIN32
     s = (uint64_t)std::chrono::high_resolution_clock::now()

c10/core/SymbolicShapeMeta.cpp

@@ -186,7 +186,6 @@ SymBool SymbolicShapeMeta::compute_is_non_overlapping_and_dense_anydim() const {
   return is_contiguous() | compute_non_overlapping_and_dense();
 }
 
-// NOLINTNEXTLINE(performance-unnecessary-value-param)
 void SymbolicShapeMeta::set_numel(SymInt val) const {
   std::scoped_lock lock(mutables_);
   if (has_numel()) {

c10/core/TensorImpl.cpp

@@ -111,7 +111,6 @@ TensorImpl::TensorImpl(
     DispatchKeySet key_set,
     const caffe2::TypeMeta data_type)
     : storage_(std::move(storage)),
-
       numel_(0),
       data_type_(data_type),
       device_opt_(storage_.device()),
@@ -123,7 +122,6 @@ TensorImpl::TensorImpl(
   }
 }
 
-// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
 TensorImpl::TensorImpl(
     DispatchKeySet key_set,
     const caffe2::TypeMeta data_type,
@@ -137,7 +135,6 @@ TensorImpl::TensorImpl(
     const caffe2::TypeMeta data_type,
     std::optional<c10::Device> device_opt)
     : storage_(std::move(storage)),
-
       numel_(0),
       data_type_(data_type),
       device_opt_(device_opt) {

c10/core/impl/alloc_cpu.cpp

@@ -92,8 +92,7 @@ void* alloc_cpu(size_t nbytes) {
       "alloc_cpu() seems to have been called with negative number: ",
       nbytes);
 
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  void* data;
+  void* data = nullptr;
 #ifdef __ANDROID__
   data = memalign(gAlignment, nbytes);
   CAFFE_ENFORCE(

c10/mobile/CPUCachingAllocator.cpp

@@ -12,8 +12,7 @@ std::mutex CPUCachingAllocator::mutex_;
 ska::flat_hash_map<void*, size_t> CPUCachingAllocator::allocation_map_;
 
 inline void* CPUCachingAllocator::allocate_and_cache(const size_t bytes) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  void* ptr;
+  void* ptr = nullptr;
   try {
     ptr = c10::alloc_cpu(bytes);
   } catch (c10::Error&) {

c10/mobile/CPUProfilingAllocator.cpp

@@ -152,10 +152,8 @@ std::vector<uint64_t> formulate_greedy_allocation_plan(
       create_and_sort_mem_events(allocation_sizes, allocation_lifetimes);
   uint64_t max_offset{0};
   for (const auto& mem_event : mem_events) {
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    uint64_t alloc_offset;
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    uint64_t new_offset, new_size;
+    uint64_t alloc_offset = 0;
+    uint64_t new_offset = 0, new_size = 0;
     if (mem_event.type == EventType::Allocate) {
       auto it = free_size_to_offset.lower_bound(mem_event.size);
       if (it == free_size_to_offset.end()) {

c10/test/util/Half_test.cpp

@@ -41,17 +41,15 @@ float halfbits2float(unsigned short h) {
 unsigned short float2halfbits(float src) {
   unsigned x = c10::detail::fp32_to_bits(src);
 
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables,cppcoreguidelines-avoid-magic-numbers)
-  unsigned u = (x & 0x7fffffff), remainder, shift, lsb, lsb_s1, lsb_m1;
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  unsigned sign, exponent, mantissa;
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
+  unsigned u = (x & 0x7fffffff), shift = 0;
 
   // Get rid of +NaN/-NaN case first.
   if (u > 0x7f800000) {
     return 0x7fffU;
   }
 
-  sign = ((x >> 16) & 0x8000);
+  unsigned sign = ((x >> 16) & 0x8000);
 
   // Get rid of +Inf/-Inf, +0/-0.
   if (u > 0x477fefff) {
@@ -61,8 +59,8 @@ unsigned short float2halfbits(float src) {
     return (sign | 0x0000);
   }
 
-  exponent = ((u >> 23) & 0xff);
-  mantissa = (u & 0x7fffff);
+  unsigned exponent = ((u >> 23) & 0xff);
+  unsigned mantissa = (u & 0x7fffff);
 
   if (exponent > 0x70) {
     shift = 13;
@@ -72,12 +70,12 @@ unsigned short float2halfbits(float src) {
     exponent = 0;
     mantissa |= 0x800000;
   }
-  lsb = (1 << shift);
-  lsb_s1 = (lsb >> 1);
-  lsb_m1 = (lsb - 1);
+  unsigned lsb = (1 << shift);
+  unsigned lsb_s1 = (lsb >> 1);
+  unsigned lsb_m1 = (lsb - 1);
 
   // Round to nearest even.
-  remainder = (mantissa & lsb_m1);
+  unsigned remainder = (mantissa & lsb_m1);
   mantissa >>= shift;
   if (remainder > lsb_s1 || (remainder == lsb_s1 && (mantissa & 0x1))) {
     ++mantissa;

c10/test/util/bfloat16_test.cpp

@@ -7,17 +7,14 @@
 
 namespace {
 float float_from_bytes(uint32_t sign, uint32_t exponent, uint32_t fraction) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint32_t bytes;
-  bytes = 0;
+  uint32_t bytes = 0;
   bytes |= sign;
   bytes <<= 8;
   bytes |= exponent;
   bytes <<= 23;
   bytes |= fraction;
 
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  float res;
+  float res = 0;
   std::memcpy(&res, &bytes, sizeof(res));
   return res;
 }
@@ -160,8 +157,7 @@ TEST(BFloat16Math, NextAfterZero) {
 }
 
 float BinaryToFloat(uint32_t bytes) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  float res;
+  float res = 0;
   std::memcpy(&res, &bytes, sizeof(res));
   return res;
 }

torch/csrc/Exceptions.h

@@ -353,7 +353,6 @@ using Arg = typename invoke_traits<Func>::template arg<i>::type;
 
 template <typename Func, size_t... Is, bool release_gil>
 auto wrap_pybind_function_impl_(
-    // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
     Func&& f,
     std::index_sequence<Is...>,
     std::bool_constant<release_gil>) {

torch/csrc/Storage.cpp

@@ -482,8 +482,7 @@ static PyObject* THPStorage_get(THPStorage* self, PyObject* index) {
     return THPByteUtils_newReal(value);
     /* Slice index */
   } else if (PySlice_Check(index)) {
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    Py_ssize_t start, stop, slicelength, step;
+    Py_ssize_t start = 0, stop = 0, slicelength = 0, step = 0;
     if (PySlice_Unpack(index, &start, &stop, &step) < 0) {
       return nullptr;
     }
@@ -554,8 +553,7 @@ static int THPStorage_set(THPStorage* self, PyObject* index, PyObject* value) {
     storage_set(storage, nindex, rvalue);
     return 0;
   } else if (PySlice_Check(index)) {
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    Py_ssize_t start, stop, step;
+    Py_ssize_t start = 0, stop = 0, step = 0;
     Py_ssize_t len = static_cast<Py_ssize_t>(storage.nbytes());
     if (PySlice_Unpack(index, &start, &stop, &step) < 0) {
       return -1;

torch/csrc/StorageSharing.cpp

@@ -313,7 +313,6 @@ static PyObject* THPStorage_shareCuda(PyObject* self, PyObject* noargs) {
   THPObjectPtr _event_sync_required(Py_None);
   Py_INCREF(Py_None);
   if (storage.data()) {
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
     auto shandle =
         c10::cuda::CUDACachingAllocator::shareIpcHandle(storage.mutable_data());
     _handle = PyBytes_FromStringAndSize(
@@ -470,8 +469,7 @@ static PyObject* THPStorage_newSharedCuda(PyObject* _unused, PyObject* args) {
     }
     auto ipc_event_handle = reinterpret_cast<const cudaIpcEventHandle_t*>(
         s_ipc_event_handle.c_str());
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    cudaEvent_t event;
+    cudaEvent_t event = nullptr;
     cudaIpcOpenEventHandle(&event, *ipc_event_handle);
     C10_CUDA_CHECK(
         cudaStreamWaitEvent(c10::cuda::getCurrentCUDAStream(device), event, 0));

torch/csrc/api/include/torch/detail/TensorDataContainer.h

@@ -110,7 +110,6 @@ struct TensorDataContainer {
   // NOTE: For tensors with zero-size dimensions (e.g. `torch::tensor({{},
   // {}})`), the innermost empty braced-init-list `{}` matches the default
   // constructor of the innermost `TensorDataContainer`.
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   TensorDataContainer()
       : sizes_({0}),
         // NOTE: In Python, the dtype of tensors with zero-size dimensions (e.g.
@@ -125,12 +124,9 @@ struct TensorDataContainer {
         scalar_type_(at::k##S),                 \
         type_(TensorDataContainerType::Scalar), \
         scalar_(value) {}
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TENSOR)
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   AT_FORALL_COMPLEX_TYPES(TENSOR)
 #undef TENSOR
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   TensorDataContainer(std::initializer_list<TensorDataContainer> init_list)
       : sizes_(),
         scalar_type_(init_list.begin()->scalar_type()),
@@ -157,7 +153,7 @@ struct TensorDataContainer {
           elem.scalar_type());
     }
     sizes_.reserve(first_elem.sizes().size() + 1);
-    sizes_.push_back(init_list.size());
+    sizes_.push_back(static_cast<int64_t>(init_list.size()));
     sizes_.insert(
         sizes_.end(), first_elem.sizes().begin(), first_elem.sizes().end());
   }
@@ -174,9 +170,7 @@ struct TensorDataContainer {
       tensor_ = at::tensor(values, at::dtype(scalar_type_).device(at::kCPU)); \
     }                                                                         \
   }
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TENSOR)
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   AT_FORALL_COMPLEX_TYPES(TENSOR)
 #undef TENSOR
 
@@ -194,9 +188,7 @@ struct TensorDataContainer {
 #define TENSOR(T, S)                                \
   TensorDataContainer(const std::vector<T>& values) \
       : TensorDataContainer(at::ArrayRef<T>(values)) {}
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TENSOR)
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   AT_FORALL_COMPLEX_TYPES(TENSOR)
 #undef TENSOR
 
@@ -328,7 +320,7 @@ struct TensorDataContainer {
           " in its first dimension, but got Tensor with size ",
           tensor.sizes()[0],
           " in its first dimension");
-      size_t index = 0;
+      int64_t index = 0;
       for (const auto& elem : init_list_) {
         at::Tensor slice = tensor[index];
         elem.fill_tensor(slice);

torch/csrc/api/include/torch/nn/functional/embedding.h

@@ -133,8 +133,7 @@ inline Tensor embedding_bag(
         input_.dim());
   }
 
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  int mode_enum;
+  int mode_enum = 0;
   if (std::holds_alternative<enumtype::kSum>(mode)) {
     mode_enum = 0;
   } else if (std::holds_alternative<enumtype::kMean>(mode)) {

torch/csrc/api/include/torch/nn/functional/loss.h

@@ -47,8 +47,7 @@ inline Tensor kl_div(
     const Tensor& target,
     KLDivFuncOptions::reduction_t reduction,
     bool log_target = false) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  torch::Reduction::Reduction reduction_enum;
+  torch::Reduction::Reduction reduction_enum{};
 
   if (std::holds_alternative<enumtype::kMean>(reduction)) {
     TORCH_WARN(

torch/csrc/api/include/torch/nn/functional/vision.h

@@ -60,8 +60,7 @@ inline Tensor grid_sample(
     GridSampleFuncOptions::mode_t mode,
     GridSampleFuncOptions::padding_mode_t padding_mode,
     std::optional<bool> align_corners) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  int64_t mode_enum, padding_mode_enum;
+  int64_t mode_enum = 0, padding_mode_enum = 0;
 
   if (std::holds_alternative<enumtype::kBilinear>(mode)) {
     mode_enum = 0;

torch/csrc/api/include/torch/nn/modules/batchnorm.h

@@ -7,8 +7,6 @@
 #include <torch/nn/pimpl.h>
 #include <torch/types.h>
 
-#include <cstdint>
-
 namespace torch {
 namespace nn {
 
@@ -104,11 +102,8 @@ class BatchNormImplBase : public NormImplBase<D, Derived, BatchNormOptions> {
 
   Tensor forward(const Tensor& input) {
     this->_check_input_dim(input);
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    double exponential_average_factor;
-    if (this->options.momentum() == std::nullopt) {
-      exponential_average_factor = 0.0;
-    } else {
+    double exponential_average_factor = 0.0;
+    if (this->options.momentum().has_value()) {
       exponential_average_factor = this->options.momentum().value();
     }
 

torch/csrc/api/include/torch/nn/modules/embedding.h

@@ -70,10 +70,8 @@ class Embedding : public torch::nn::ModuleHolder<EmbeddingImpl> {
         embeddings.dim() == 2,
         "Embeddings parameter is expected to be 2-dimensional");
 
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    int64_t rows, cols;
-    rows = embeddings.size(0);
-    cols = embeddings.size(1);
+    auto rows = embeddings.size(0);
+    auto cols = embeddings.size(1);
 
     Embedding embedding(EmbeddingOptions(rows, cols)
                             ._weight(embeddings)
@@ -149,10 +147,8 @@ class EmbeddingBag : public torch::nn::ModuleHolder<EmbeddingBagImpl> {
         embeddings.dim() == 2,
         "Embeddings parameter is expected to be 2-dimensional");
 
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    int64_t rows, cols;
-    rows = embeddings.size(0);
-    cols = embeddings.size(1);
+    auto rows = embeddings.size(0);
+    auto cols = embeddings.size(1);
 
     EmbeddingBag embeddingbag(
         EmbeddingBagOptions(rows, cols)

torch/csrc/api/src/data/datasets/mnist.cpp

@@ -9,9 +9,7 @@
 #include <fstream>
 #include <string>
 
-namespace torch {
-namespace data {
-namespace datasets {
+namespace torch::data::datasets {
 namespace {
 constexpr uint32_t kTrainSize = 60000;
 constexpr uint32_t kTestSize = 10000;
@@ -36,18 +34,20 @@ constexpr uint32_t flip_endianness(uint32_t value) {
 
 uint32_t read_int32(std::ifstream& stream) {
   static const bool is_little_endian = check_is_little_endian();
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint32_t value;
+  uint32_t value = 0;
   AT_ASSERT(stream.read(reinterpret_cast<char*>(&value), sizeof value));
   return is_little_endian ? flip_endianness(value) : value;
 }
 
 uint32_t expect_int32(std::ifstream& stream, uint32_t expected) {
   const auto value = read_int32(stream);
-  // clang-format off
-  TORCH_CHECK(value == expected,
-      "Expected to read number ", expected, " but found ", value, " instead");
-  // clang-format on
+  TORCH_CHECK(
+      value == expected,
+      "Expected to read number ",
+      expected,
+      " but found ",
+      value,
+      " instead");
   return value;
 }
 
@@ -101,14 +101,15 @@ MNIST::MNIST(const std::string& root, Mode mode)
       targets_(read_targets(root, mode == Mode::kTrain)) {}
 
 Example<> MNIST::get(size_t index) {
-  return {images_[index], targets_[index]};
+  return {
+      images_[static_cast<int64_t>(index)],
+      targets_[static_cast<int64_t>(index)]};
 }
 
 std::optional<size_t> MNIST::size() const {
   return images_.size(0);
 }
 
-// NOLINTNEXTLINE(bugprone-exception-escape)
 bool MNIST::is_train() const noexcept {
   return images_.size(0) == kTrainSize;
 }
@@ -121,6 +122,4 @@ const Tensor& MNIST::targets() const {
   return targets_;
 }
 
-} // namespace datasets
-} // namespace data
-} // namespace torch
+} // namespace torch::data::datasets

torch/csrc/api/src/optim/lbfgs.cpp

@@ -176,8 +176,7 @@ std::tuple<double, Tensor> LBFGS::_directional_evaluate(
     double t,
     const Tensor& d) {
   _add_grad(t, d);
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  double loss;
+  double loss = 0;
   {
     torch::AutoGradMode enable_grad(true);
     loss = closure().item<double>();
@@ -215,12 +214,9 @@ static double _cubic_interpolate(
 
   auto d1 = (g1 + g2) - (3 * (f1 - f2) / (x1 - x2));
   auto d2_square = std::pow(d1, 2) - g1 * g2;
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  double d2;
   if (d2_square >= 0) {
-    d2 = std::sqrt(d2_square);
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    double min_pos;
+    auto d2 = std::sqrt(d2_square);
+    double min_pos = 0;
     if (x1 <= x2) {
       min_pos = x2 - ((x2 - x1) * ((g2 + d2 - d1) / (g2 - g1 + 2 * d2)));
     } else {

torch/csrc/autograd/functions/pybind.h

@@ -8,8 +8,7 @@
 #include <torch/csrc/autograd/python_cpp_function.h>
 #include <torch/csrc/autograd/python_function.h>
 
+// NOLINTNEXTLINE(misc-unused-alias-decls)
 namespace py = pybind11;
 
-namespace pybind11 {
-namespace detail {}
-} // namespace pybind11
+namespace pybind11::detail {} // namespace pybind11::detail

torch/csrc/autograd/python_function.h

@@ -13,7 +13,6 @@
 #include <optional>
 
 #include <memory>
-#include <optional>
 #include <vector>
 
 namespace torch::jit {

torch/csrc/cuda/CUDAPluggableAllocator.cpp

@@ -14,7 +14,7 @@ CUDAPluggableAllocatorDeleterContext::CUDAPluggableAllocatorDeleterContext(
     size_t size,
     int device,
     cudaStream_t stream)
-    : free_fn_(free_fn),
+    : free_fn_(std::move(free_fn)),
       data_(data),
       size_(size),
       device_(device),

torch/csrc/cuda/Module.cpp

@@ -1266,8 +1266,7 @@ static void registerCudaPluggableAllocator(PyObject* module) {
   m.def(
       "_tensors_data_ptrs_at_indices_equal",
       [](py::list& tensors, py::list& data_ptrs, py::list& indices) {
-        for (size_t i = 0, end = indices.size(); i < end; ++i) {
-          auto index = indices[i].cast<int64_t>();
+        for (auto index : indices) {
           auto t = tensors[index].cast<at::Tensor>();
           auto data_ptr = data_ptrs[index].cast<int64_t>();
           if (reinterpret_cast<int64_t>(t.data_ptr()) != data_ptr) {
@@ -1451,7 +1450,6 @@ PyObject* THCPModule_getCurrentBlasHandle_wrap(
     PyObject* self,
     PyObject* noargs) {
   HANDLE_TH_ERRORS
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
   return PyLong_FromVoidPtr(handle);
   END_HANDLE_TH_ERRORS

torch/csrc/cuda/comm.cpp

@@ -94,7 +94,6 @@ std::vector<Tensor>& broadcast_out(
 }
 
 std::vector<Tensor> broadcast(const Tensor& tensor, IntArrayRef devices) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   std::vector<Tensor> diff_device_dst_tensors;
   diff_device_dst_tensors.reserve(devices.size());
   for (auto device : devices) {
@@ -109,7 +108,6 @@ std::vector<Tensor> broadcast(const Tensor& tensor, IntArrayRef devices) {
     }
   }
   _broadcast_out_impl(tensor, diff_device_dst_tensors);
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   std::vector<Tensor> dst_tensors;
   dst_tensors.reserve(devices.size());
   auto it = diff_device_dst_tensors.begin();
@@ -172,7 +170,6 @@ tensor_list2d broadcast_coalesced(
   buffer_size = std::min(torch::cuda::nccl::get_max_count(), buffer_size);
 #endif
 
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   tensor_list2d outputs(devices.size());
   outputs[0] = tensors.vec();
   for (auto& o : outputs)
@@ -239,7 +236,6 @@ std::vector<at::Tensor>& scatter_out(
       "Expected at least one output tensor to scatter to");
   dim = at::maybe_wrap_dim(dim, tensor);
   int64_t total_size = 0;
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   std::vector<int64_t> chunk_sizes;
   chunk_sizes.reserve(out_tensors.size());
   for (const auto i : c10::irange(out_tensors.size())) {
@@ -374,7 +370,6 @@ static inline at::Tensor& _gather_out_impl(
     at::TensorList tensors,
     at::Tensor& out_tensor,
     int64_t dim) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   std::vector<int64_t> chunk_sizes;
   chunk_sizes.reserve(tensors.size());
   for (auto& tensor : tensors) {
@@ -397,7 +392,6 @@ at::Tensor& gather_out(
   auto& first = tensors.front();
   const auto first_size = first.sizes();
   dim = at::maybe_wrap_dim(dim, first);
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   std::vector<int64_t> expected_size(first_size.begin(), first_size.end());
   for (const auto i : c10::irange(tensors.size())) {
     const auto& tensor = tensors[i];
@@ -452,7 +446,6 @@ at::Tensor gather(
   auto& first = tensors.front();
   const auto first_size = first.sizes();
   dim = at::maybe_wrap_dim(dim, first);
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   std::vector<int64_t> expected_size(first_size.begin(), first_size.end());
   auto memory_format = first.suggest_memory_format();
   for (const auto i : c10::irange(tensors.size())) {

torch/csrc/cuda/nccl.cpp

@@ -263,7 +263,7 @@ struct NcclCommList {
   ~NcclCommList() {
     if (comms) {
       for (const auto i : c10::irange(ndevices)) {
-        int dummy_var;
+        int dummy_var = 0;
         if (C10_CUDA_ERROR_HANDLED(cudaGetDevice(&dummy_var)) != cudaSuccess) {
           /* there are cases when this destructor is called after the
            CUDA driver is already unloaded from the process.
@@ -366,7 +366,7 @@ void check_inputs(
   auto dtype = inputs[0].scalar_type();
 
   for (const auto i : c10::irange(len)) {
-    auto input = inputs[i];
+    const auto& input = inputs[i];
     auto output = outputs[i];
 
     check_tensor(
@@ -398,7 +398,7 @@ void check_inputs(
   auto dtype = inputs[0].scalar_type();
 
   for (const auto i : c10::irange(len)) {
-    auto input = inputs[i];
+    const auto& input = inputs[i];
 
     check_tensor(
         input,
@@ -421,25 +421,24 @@ void check_inputs(
 
 } // namespace detail
 
-AutoNcclGroup::AutoNcclGroup() {
+AutoNcclGroup::AutoNcclGroup() : comm_(nullptr), comm_nonblocking_(false) {
 #if defined(NCCL_MAJOR) && (NCCL_MAJOR < 2)
   // nccl < 2.0 cannot be called concurrently with cudaFree
   (c10::cuda::getFreeMutex())->lock();
 #endif
-  comm_nonblocking_ = false;
-  comm_ = nullptr;
+
 #if defined(NCCL_MAJOR) && (NCCL_MAJOR >= 2)
   detail::NCCL_CHECK(ncclGroupStart());
 #endif
 }
 
-AutoNcclGroup::AutoNcclGroup(ncclComm_t comm, bool comm_nonblocking) {
+AutoNcclGroup::AutoNcclGroup(ncclComm_t comm, bool comm_nonblocking)
+    : comm_(comm), comm_nonblocking_(comm_nonblocking) {
 #if defined(NCCL_MAJOR) && (NCCL_MAJOR < 2)
   // nccl < 2.0 cannot be called concurrently with cudaFree
   (c10::cuda::getFreeMutex())->lock();
 #endif
-  comm_ = comm;
-  comm_nonblocking_ = comm_nonblocking;
+
 #if defined(NCCL_MAJOR) && (NCCL_MAJOR >= 2)
   detail::NCCL_CHECK(ncclGroupStart());
 #endif
@@ -510,7 +509,7 @@ void get_unique_id(ncclUniqueId& id) {
 ncclComm_t comm_init_rank(int nranks, const ncclUniqueId& comm_id, int rank) {
 #ifdef USE_NCCL
   using namespace torch::cuda::nccl::detail;
-  ncclComm_t comm;
+  ncclComm_t comm = nullptr;
   ncclUniqueId id = comm_id;
   NCCL_CHECK(ncclCommInitRank(
       to_nccl_comm(&comm), nranks, *(to_nccl_unique_id(&id)), rank));
@@ -548,7 +547,7 @@ struct GetSecondArgType;
 
 template <typename R, typename Arg0, typename Arg1, typename... Args>
 struct GetSecondArgType<R(Arg0, Arg1, Args...)> {
-  typedef typename std::decay<Arg1>::type type;
+  typedef std::decay_t<Arg1> type;
 };
 
 constexpr auto count_max =
@@ -827,7 +826,7 @@ void all2all_single_equal_split(
     ((NCCL_MAJOR > 2) || ((NCCL_MAJOR == 2) && (NCCL_MINOR >= 7)))
   using namespace torch::cuda::nccl::detail;
 
-  int numranks;
+  int numranks = 0;
   auto type = to_nccl_data_type(input);
   size_t count = input.numel() / size;
   size_t rankdiff = input.nbytes() / size;
@@ -897,7 +896,7 @@ void all2all_single_unequal_split(
       comm,
       stream.stream()));
 #else
-  int numranks;
+  int numranks = 0;
   NCCL_CHECK(ncclCommCount(comm, &numranks));
   NCCL_CHECK(ncclGroupStart());
   for (const auto r : c10::irange(numranks)) {
@@ -1109,7 +1108,7 @@ void gather(
   using namespace torch::cuda::nccl::detail;
 
   auto comm = to_nccl_comm(_comm);
-  int numranks, cur_rank;
+  int numranks = 0, cur_rank = 0;
   NCCL_CHECK(ncclCommCount(comm, &numranks));
   NCCL_CHECK(ncclCommUserRank(comm, &cur_rank));
 
@@ -1158,7 +1157,7 @@ void scatter(
   using namespace torch::cuda::nccl::detail;
 
   auto comm = to_nccl_comm(_comm);
-  int numranks, cur_rank;
+  int numranks = 0, cur_rank = 0;
 #ifndef NCCL_HAS_COMM_NONBLOCKING
   NCCL_CHECK(ncclCommCount(comm, &numranks));
   NCCL_CHECK(ncclCommUserRank(comm, &cur_rank));

torch/csrc/distributed/autograd/rpc_messages/propagate_gradients_req.cpp

@@ -63,10 +63,8 @@ std::unique_ptr<PropagateGradientsReq> PropagateGradientsReq::fromMessage(
   bool retainGraph = tupleElements.back().toBool();
 
   // Build AutogradMetadata.
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  int64_t autogradContextId, autogradMessageId;
-  autogradMessageId = tupleElements[tupleElements.size() - 2].toInt();
-  autogradContextId = tupleElements[tupleElements.size() - 3].toInt();
+  int64_t autogradMessageId = tupleElements[tupleElements.size() - 2].toInt();
+  int64_t autogradContextId = tupleElements[tupleElements.size() - 3].toInt();
 
   AutogradMetadata autogradMetadata(autogradContextId, autogradMessageId);
 

torch/csrc/distributed/c10d/socket.cpp

@@ -820,7 +820,7 @@ bool SocketConnectOp::tryConnect(int family) {
 
   deadline_ = Clock::now() + opts_->connect_timeout();
 
-  bool retry; // NOLINT(cppcoreguidelines-init-variables)
+  bool retry = false;
   do {
     retry = false;
 

torch/csrc/distributed/rpc/agent_utils.cpp

@@ -45,8 +45,7 @@ static std::vector<std::string> splitString(
     const std::string& delim) {
   std::vector<std::string> tokens;
   size_t start = 0;
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  size_t end;
+  size_t end = 0;
   // Iterate through each delimiter
   while ((end = s.find(delim, start)) != std::string::npos) {
     tokens.emplace_back(s.substr(start, end - start));

torch/csrc/inductor/aoti_runtime/model_container.h

@@ -19,8 +19,7 @@ class AOTInductorModelContainer {
   AOTInductorModelContainer(
       size_t num_models,
       const std::string& device_str,
-      const std::optional<std::string>& cubin_dir = std::nullopt)
-      : use_secondary_(false), constant_folded_(false) {
+      const std::optional<std::string>& cubin_dir = std::nullopt) {
     constants_map_ = std::make_shared<ConstantMap>();
     constants_array_ = std::make_shared<std::vector<ConstantHandle>>();
 
@@ -413,10 +412,10 @@ class AOTInductorModelContainer {
   // If true,
   // constants_map_secondary/constant_blob_secondary/constants_array_secondary
   // is being used.
-  bool use_secondary_;
+  bool use_secondary_{false};
 
   // Determine whether we have ran constant folding
-  bool constant_folded_;
+  bool constant_folded_{false};
 
   // Holds the mapping of constants to at::Tensor.
   // The underlying data of at::Tensor is in either constant_blob_ (for CUDA).

torch/csrc/lazy/core/lazy_graph_executor.cpp

@@ -19,8 +19,6 @@
 
 #include <utility>
 
-#include <ATen/ScalarOps.h>
-
 namespace torch::lazy {
 namespace {
 

torch/csrc/lazy/core/shape_inference.h

@@ -13,8 +13,7 @@
 #include <optional>
 #include <vector>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 // Turn clang-format off, as we rely on the whole signature being on one line
 // for codegen.
 // clang-format off
@@ -120,5 +119,4 @@ TORCH_API std::vector<torch::lazy::Shape> compute_shape_diagonal_scatter(const a
 TORCH_API std::vector<torch::lazy::Shape> compute_shape_slice_scatter_symint(const at::Tensor & self, const at::Tensor & src, int64_t dim, ::std::optional<c10::SymInt> start, ::std::optional<c10::SymInt> end, c10::SymInt step);
 TORCH_API std::vector<torch::lazy::Shape> compute_shape_as_strided_scatter_symint(const at::Tensor & self, const at::Tensor & src, c10::SymIntArrayRef size, c10::SymIntArrayRef stride, ::std::optional<c10::SymInt> storage_offset);
 // clang-format on
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/python/init.cpp

@@ -18,10 +18,10 @@
 #include <torch/csrc/lazy/ts_backend/ts_lowering_context.h>
 #endif // FBCODE_CAFFE2 || OVRSOURCE
 #include <string>
+#include <utility>
 #include <vector>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 // TODO(whc) backend 'device' related APIs are not very clear, this code could
 // be simplified but it should probably be done together with
@@ -190,7 +190,7 @@ void initLazyBindings(PyObject* module) {
     return torch::lazy::getLTCForceFallback();
   });
   lazy.def("_set_force_fallback", [](std::string newval) {
-    torch::lazy::getLTCForceFallback() = newval;
+    torch::lazy::getLTCForceFallback() = std::move(newval);
   });
   lazy.def("_clear_ir_cache", []() { TrieCache::Get()->Clear(); });
   lazy.def("_dump_ir_cache", [](std::string filename) {
@@ -337,5 +337,4 @@ void initLazyBindings(PyObject* module) {
 #endif // USE_DEPLOY
 }
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/python/init.h

@@ -3,10 +3,8 @@
 #include <torch/csrc/Export.h>
 #include <torch/csrc/utils/pybind.h>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 TORCH_PYTHON_API void initLazyBindings(PyObject* module);
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/python/python_util.cpp

@@ -8,8 +8,7 @@
 #include <torch/csrc/utils/python_compat.h>
 #include <torch/csrc/utils/python_strings.h>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 std::optional<SourceLocation> GetPythonFrameTop() {
   if (!Py_IsInitialized()) {
@@ -51,5 +50,4 @@ std::vector<SourceLocation> GetPythonFrames() {
   return frames;
 }
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/python/python_util.h

@@ -4,12 +4,10 @@
 #include <optional>
 #include <vector>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 std::optional<SourceLocation> TORCH_PYTHON_API GetPythonFrameTop();
 
 std::vector<SourceLocation> TORCH_PYTHON_API GetPythonFrames();
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/ts_backend/ops/device_data.cpp

@@ -5,8 +5,7 @@
 
 #include <sstream>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 DeviceData::DeviceData(std::shared_ptr<BackendData> data)
     : TsNode(
@@ -26,7 +25,7 @@ const DeviceData* DeviceData::Cast(const Node* node) {
   return NodeCast<DeviceData>(node);
 }
 
-NodePtr DeviceData::Create(std::shared_ptr<BackendData> data) {
+NodePtr DeviceData::Create(const std::shared_ptr<BackendData>& data) {
   NodePtr node = ReuseOrMakeNode<DeviceData>(data);
   // ReuseOrMakeNode may return a reused node which has the same shape,
   // however, we need to replace the old data_ with the new one.
@@ -38,5 +37,4 @@ NodePtr DeviceData::Create(std::shared_ptr<BackendData> data) {
   return node;
 }
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/ts_backend/ops/device_data.h

@@ -4,8 +4,9 @@
 #include <torch/csrc/lazy/core/internal_ops/ltc_ops.h>
 #include <torch/csrc/lazy/ts_backend/ts_node.h>
 
-namespace torch {
-namespace lazy {
+#include <utility>
+
+namespace torch::lazy {
 
 class TORCH_API DeviceData : public TsNode {
  public:
@@ -18,7 +19,7 @@ class TORCH_API DeviceData : public TsNode {
   // A DeviceData node can be reused if the shape matches,
   // but we will substitute the actual data_ pointer under
   // the hood.
-  bool CanBeReused(std::shared_ptr<BackendData> data) const {
+  bool CanBeReused(const std::shared_ptr<BackendData>& data) const {
     return data_->shape() == data->shape();
   }
 
@@ -29,14 +30,14 @@ class TORCH_API DeviceData : public TsNode {
   }
 
   void SetData(std::shared_ptr<BackendData> data) {
-    data_ = data;
+    data_ = std::move(data);
   }
 
   static const DeviceData* Cast(const Node* node);
 
   // To reuse IR nodes, use this method to create DeviceData nodes
-  // instead of calling the constructor directly.
-  static NodePtr Create(std::shared_ptr<BackendData> data);
+  // instead of calling the constructor directconst ly.
+  static NodePtr Create(const std::shared_ptr<BackendData>& data);
 
   TSOpVector Lower(
       std::shared_ptr<torch::jit::GraphFunction> function,
@@ -46,5 +47,4 @@ class TORCH_API DeviceData : public TsNode {
   std::shared_ptr<BackendData> data_;
 };
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/ts_backend/ops/generic.cpp

@@ -1,7 +1,6 @@
 #include <torch/csrc/lazy/ts_backend/ops/generic.h>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 Generic::Generic(
     OpKind op,
@@ -32,5 +31,4 @@ Generic::Generic(OpKind op, Shape shape, size_t num_outputs, hash_t hash_seed)
     : TsNode(op, std::move(shape), num_outputs, hash_seed),
       hash_seed_(hash_seed) {}
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/ts_backend/ops/generic.h

@@ -4,8 +4,7 @@
 
 #include <torch/csrc/lazy/core/ir_builder.h>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 // Generic IR Node implementation for nodes which can simply be described by a
 // specific OpKind and a lowering function. IR nodes carrying
@@ -50,5 +49,4 @@ inline NodePtr GenericOp(
       op, operands, std::move(shape), num_outputs, hash_seed);
 }
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/lazy/ts_backend/ops/to_copy.h

@@ -2,8 +2,7 @@
 
 #include <torch/csrc/lazy/ts_backend/ts_node.h>
 
-namespace torch {
-namespace lazy {
+namespace torch::lazy {
 
 // This IR was copied from code-generated output, but the entire _to_copy
 // operator cannot be trivially code genereated since it is only desirable to
@@ -123,5 +122,4 @@ class ToCopy : public torch::lazy::TsNode {
   std::optional<at::MemoryFormat> memory_format;
 };
 
-} // namespace lazy
-} // namespace torch
+} // namespace torch::lazy

torch/csrc/multiprocessing/init.h

@@ -2,10 +2,8 @@
 
 #include <torch/csrc/python_headers.h>
 
-namespace torch {
-namespace multiprocessing {
+namespace torch::multiprocessing {
 
 PyMethodDef* python_functions();
 
-} // namespace multiprocessing
-} // namespace torch
+} // namespace torch::multiprocessing

torch/csrc/utils/byte_order.cpp

@@ -12,8 +12,7 @@
 namespace {
 
 static inline void swapBytes16(void* ptr) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint16_t output;
+  uint16_t output = 0;
   memcpy(&output, ptr, sizeof(uint16_t));
 #if defined(_MSC_VER) && !defined(_DEBUG)
   output = _byteswap_ushort(output);
@@ -28,8 +27,7 @@ static inline void swapBytes16(void* ptr) {
 }
 
 static inline void swapBytes32(void* ptr) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint32_t output;
+  uint32_t output = 0;
   memcpy(&output, ptr, sizeof(uint32_t));
 #if defined(_MSC_VER) && !defined(_DEBUG)
   output = _byteswap_ulong(output);
@@ -46,8 +44,7 @@ static inline void swapBytes32(void* ptr) {
 }
 
 static inline void swapBytes64(void* ptr) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint64_t output;
+  uint64_t output = 0;
   memcpy(&output, ptr, sizeof(uint64_t));
 #if defined(_MSC_VER)
   output = _byteswap_uint64(output);
@@ -70,8 +67,7 @@ static inline void swapBytes64(void* ptr) {
 }
 
 static inline uint16_t decodeUInt16(const uint8_t* data) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint16_t output;
+  uint16_t output = 0;
   memcpy(&output, data, sizeof(uint16_t));
   return output;
 }
@@ -83,8 +79,7 @@ static inline uint16_t decodeUInt16ByteSwapped(const uint8_t* data) {
 }
 
 static inline uint32_t decodeUInt32(const uint8_t* data) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint32_t output;
+  uint32_t output = 0;
   memcpy(&output, data, sizeof(uint32_t));
   return output;
 }
@@ -96,8 +91,7 @@ static inline uint32_t decodeUInt32ByteSwapped(const uint8_t* data) {
 }
 
 static inline uint64_t decodeUInt64(const uint8_t* data) {
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  uint64_t output;
+  uint64_t output = 0;
   memcpy(&output, data, sizeof(uint64_t));
   return output;
 }
@@ -149,6 +143,7 @@ TORCH_API void THP_decodeBuffer<c10::Half, bool>(
     bool do_byte_swap,
     size_t len) {
   for (const auto i : c10::irange(len)) {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint16_t x;
       c10::Half f;
@@ -191,6 +186,7 @@ TORCH_API void THP_decodeBuffer<float, bool>(
     bool do_byte_swap,
     size_t len) {
   for (const auto i : c10::irange(len)) {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint32_t x;
       float f;
@@ -208,6 +204,7 @@ TORCH_API void THP_decodeBuffer<double, bool>(
     bool do_byte_swap,
     size_t len) {
   for (const auto i : c10::irange(len)) {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint64_t x;
       double d;
@@ -225,10 +222,12 @@ TORCH_API void THP_decodeBuffer<c10::complex<float>, bool>(
     bool do_byte_swap,
     size_t len) {
   for (const auto i : c10::irange(len)) {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint32_t x;
       float re;
     };
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint32_t y;
       float im;
@@ -250,10 +249,12 @@ TORCH_API void THP_decodeBuffer<c10::complex<double>, bool>(
     bool do_byte_swap,
     size_t len) {
   for (const auto i : c10::irange(len)) {
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint64_t x;
       double re;
     };
+    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
     union {
       uint64_t y;
       double im;

torch/csrc/utils/disable_torch_function.cpp

@@ -343,7 +343,7 @@ inline bool array_has_torch_function(PyObject* const* args, Py_ssize_t nargs) {
 }
 
 PyObject* THPModule_has_torch_function(PyObject*, PyObject* arg) {
-  bool result; // NOLINT(cppcoreguidelines-init-variables)
+  bool result = false;
   if (PyTuple_CheckExact(arg) || PyList_CheckExact(arg)) {
     // Fast path:
     //   If we know that we have a tuple or list, we can skip an INCREF and

torch/csrc/utils/init.cpp

@@ -39,7 +39,7 @@ void initThroughputBenchmarkBindings(PyObject* module) {
              const py::kwargs& kwargs) {
             // Depending on this being ScriptModule of nn.Module we will release
             // the GIL or not further down in the stack
-            return self.runOnce(std::move(args), kwargs);
+            return self.runOnce(args, kwargs);
           })
       .def(
           "benchmark",

torch/csrc/utils/python_numbers.h

@@ -177,8 +177,7 @@ inline bool THPUtils_unpackNumberAsBool(PyObject* obj) {
     return !(real_val == 0 && imag_val == 0);
   }
 
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  int overflow;
+  int overflow = 0;
   long long value = PyLong_AsLongLongAndOverflow(obj, &overflow);
   if (value == -1 && PyErr_Occurred()) {
     throw python_error();

torch/csrc/utils/tensor_numpy.cpp

@@ -52,7 +52,6 @@ bool is_numpy_dlpack_deleter_bugged() {
 #include <ATen/ATen.h>
 #include <ATen/TensorUtils.h>
 #include <memory>
-#include <sstream>
 #include <stdexcept>
 
 using namespace at;
@@ -68,8 +67,7 @@ bool is_numpy_available() {
     }
     // Try to get exception message, print warning and return false
     std::string message = "Failed to initialize NumPy";
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    PyObject *type, *value, *traceback;
+    PyObject *type = nullptr, *value = nullptr, *traceback = nullptr;
     PyErr_Fetch(&type, &value, &traceback);
     if (auto str = value ? PyObject_Str(value) : nullptr) {
       if (auto enc_str = PyUnicode_AsEncodedString(str, "utf-8", "strict")) {
@@ -403,10 +401,8 @@ at::Tensor tensor_from_cuda_array_interface(PyObject* obj) {
   }
 
   // Extract the `obj.__cuda_array_interface__['typestr']` attribute
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  ScalarType dtype;
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  int dtype_size_in_bytes;
+  ScalarType dtype{};
+  int dtype_size_in_bytes = 0;
   {
     PyObject* py_typestr = nullptr;
     if (PyDict_GetItemStringRef(cuda_dict, "typestr", &py_typestr) < 0) {
@@ -415,8 +411,7 @@ at::Tensor tensor_from_cuda_array_interface(PyObject* obj) {
     if (py_typestr == nullptr) {
       throw TypeError("attribute `typestr` must exist");
     }
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    PyArray_Descr* descr;
+    PyArray_Descr* descr = nullptr;
     TORCH_CHECK_VALUE(
         PyArray_DescrConverter(py_typestr, &descr), "cannot parse `typestr`");
     dtype = numpy_dtype_to_aten(descr->type_num);
@@ -429,8 +424,7 @@ at::Tensor tensor_from_cuda_array_interface(PyObject* obj) {
   }
 
   // Extract the `obj.__cuda_array_interface__['data']` attribute
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  void* data_ptr;
+  void* data_ptr = nullptr;
   {
     PyObject* py_data = nullptr;
     if (PyDict_GetItemStringRef(cuda_dict, "data", &py_data) < 0) {