More fixes and improved clang-tidy checkers (#93213)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/93213
Approved by: https://github.com/Skylion007

.clang-tidy

@@ -3,11 +3,14 @@
 InheritParentConfig: true
 Checks: '
 bugprone-*,
+-bugprone-easily-swappable-parameters,
 -bugprone-forward-declaration-namespace,
 -bugprone-macro-parentheses,
 -bugprone-lambda-function-name,
 -bugprone-reserved-identifier,
+-bugprone-swapped-arguments,
 cppcoreguidelines-*,
+-cppcoreguidelines-avoid-do-while,
 -cppcoreguidelines-avoid-magic-numbers,
 -cppcoreguidelines-avoid-non-const-global-variables,
 -cppcoreguidelines-interfaces-global-init,
@@ -30,6 +33,7 @@ misc-unused-alias-decls,
 misc-unused-using-decls,
 modernize-*,
 -modernize-concat-nested-namespaces,
+-modernize-macro-to-enum,
 -modernize-return-braced-init-list,
 -modernize-use-auto,
 -modernize-use-default-member-init,
@@ -44,5 +48,4 @@ readability-container-size-empty,
 HeaderFilterRegex: '^(c10/(?!test)|torch/csrc/(?!deploy/interpreter/cpython)).*$'
 AnalyzeTemporaryDtors: false
 WarningsAsErrors: '*'
-CheckOptions:
 ...

aten/src/ATen/core/TensorBase.h

@@ -858,7 +858,7 @@ auto TensorBase::register_hook(T&& hook) const -> TensorBase::hook_return_void_t
 
 template <typename T>
 auto TensorBase::register_hook(T&& hook) const -> TensorBase::hook_return_var_t<T> {
-  return _register_hook(std::move(hook));
+  return _register_hook(std::forward<T>(hook));
 }
 
 namespace detail {

aten/src/ATen/core/dispatch/OperatorEntry.cpp

@@ -64,7 +64,7 @@ const AnnotatedKernel& OperatorEntry::ambiguousAutogradOtherKernel() const {
   return kernel;
 }
 
-void OperatorEntry::assertSignatureIsCorrect(const CppSignature call_signature, bool has_symint) const {
+void OperatorEntry::assertSignatureIsCorrect(const CppSignature& call_signature, bool has_symint) const {
   if (has_symint) {
     if (C10_UNLIKELY(sym_cpp_signature_.has_value() && (call_signature != sym_cpp_signature_->signature))) {
       reportSignatureError(call_signature, *sym_cpp_signature_);

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

@@ -167,7 +167,7 @@ public:
     assertSignatureIsCorrect(CppSignature::make<FuncType>(), fn_has_symint<FuncType>::value);
   }
 
-  void assertSignatureIsCorrect(const CppSignature call_signature, bool has_symint) const;
+  void assertSignatureIsCorrect(const CppSignature& call_signature, bool has_symint) const;
 
   [[noreturn]] void reportError(DispatchKey dispatchKey) const;
 

aten/src/ATen/core/ivalue.h

@@ -80,7 +80,7 @@ struct StreamData3Holder : c10::intrusive_ptr_target {
     StreamData3Holder(struct c10::StreamData3 d) {
       val = d;
     }
-    StreamData3Holder() = default;
+    StreamData3Holder() = delete;
     struct c10::StreamData3 val;
 };
 
@@ -1261,12 +1261,12 @@ public:
   friend MaybeOwnedTraits<IValue>;
 
   Payload payload;
-  Tag tag;
+  Tag tag{IValue::Tag::None};
   friend struct WeakIValue;
 };
 
 struct TORCH_API WeakIValue final {
-  WeakIValue() : tag(IValue::Tag::None), is_intrusive_ptr(false) {}
+  WeakIValue() = default;
 
   WeakIValue(const WeakIValue& rhs)
       : payload(rhs.payload),
@@ -1378,8 +1378,8 @@ struct TORCH_API WeakIValue final {
  private:
   using Payload = IValue::Payload::TriviallyCopyablePayload;
   Payload payload;
-  IValue::Tag tag;
-  bool is_intrusive_ptr;
+  IValue::Tag tag{IValue::Tag::None};
+  bool is_intrusive_ptr{false};
 };
 
 // An owning pointer to a type. When the type is class type, it requires a pair

aten/src/ATen/core/jit_type.h

@@ -1001,8 +1001,8 @@ struct TORCH_API DictType : public SharedType {
 
   std::string annotation_str_impl(TypePrinter printer = nullptr) const override {
     std::stringstream ss;
-    ss << "Dict[" << getKeyType()->annotation_str(printer) << ", "
-       << getValueType()->annotation_str(std::move(printer)) << "]";
+    ss << "Dict[" << getKeyType()->annotation_str(printer) << ", ";
+    ss << getValueType()->annotation_str(std::move(printer)) << "]";
     return ss.str();
   }
 

aten/src/ATen/functorch/BatchRulesScatterOps.cpp

@@ -350,14 +350,12 @@ namespace {
   // /aten/src/ATen/native/TensorAdvancedIndexing.cpp#L294-L312
   VmapDimVector compute_indexed_shape(const Tensor &src, TensorList indices_list)
   {
-    int64_t dims_before = 0, dims_after = 0, dims_indexed = 0;
+    int64_t dims_before = 0, dims_indexed = 0;
     IntArrayRef replacement_shape;
     for (const auto dim : c10::irange(indices_list.size())) {
       if (!indices_list[dim].defined()) {
         if (dims_indexed == 0) {
           dims_before++;
-        } else {
-          dims_after++;
         }
       } else {
         dims_indexed++;

aten/src/ATen/record_function.cpp

@@ -153,7 +153,7 @@ class CacheEntry {
 
   // Includes sampling callbacks which are waiting to run.
   c10::SmallVector<CallbackAndCounter, kSoftLimitCallbacks> callbacks_;
-  RecordScope scope_;
+  RecordScope scope_{RecordScope::FUNCTION};
 
   StepCallbacks active_callbacks_;
 

c10/core/CPUAllocator.cpp

@@ -207,7 +207,11 @@ void ProfiledCPUMemoryReporter::New(void* ptr, size_t nbytes) {
   }
   if (profile_memory) {
     reportMemoryUsageToProfiler(
-        ptr, nbytes, allocated, 0, c10::Device(c10::DeviceType::CPU));
+        ptr,
+        static_cast<int64_t>(nbytes),
+        static_cast<int64_t>(allocated),
+        0,
+        c10::Device(c10::DeviceType::CPU));
   }
 }
 
@@ -242,7 +246,11 @@ void ProfiledCPUMemoryReporter::Delete(void* ptr) {
   }
   if (profile_memory) {
     reportMemoryUsageToProfiler(
-        ptr, -nbytes, allocated, 0, c10::Device(c10::DeviceType::CPU));
+        ptr,
+        -static_cast<int64_t>(nbytes),
+        static_cast<int64_t>(allocated),
+        0,
+        c10::Device(c10::DeviceType::CPU));
   }
 }
 

c10/core/Device.cpp

@@ -130,7 +130,7 @@ Device::Device(const std::string& device_string) : Device(Type::CPU) {
 
   try {
     if (!device_index_str.empty()) {
-      index_ = c10::stoi(device_index_str);
+      index_ = static_cast<c10::DeviceIndex>(c10::stoi(device_index_str));
     }
   } catch (const std::exception&) {
     TORCH_CHECK(

c10/core/TensorImpl.cpp

@@ -104,6 +104,7 @@ TensorImpl::TensorImpl(
 // the Python and PythonTLSSnapshot dispatch keys will be set and all is well.
 // The point is to delay the dispatch key setting until that point.
 
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
 TensorImpl::TensorImpl(
     ImplType type,
     Storage&& storage,
@@ -122,12 +123,14 @@ TensorImpl::TensorImpl(
   }
 }
 
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
 TensorImpl::TensorImpl(
     DispatchKeySet key_set,
     const caffe2::TypeMeta data_type,
     c10::optional<c10::Device> device_opt)
     : TensorImpl({}, key_set, data_type, device_opt) {}
 
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
 TensorImpl::TensorImpl(
     Storage&& storage,
     DispatchKeySet key_set,
@@ -864,7 +867,8 @@ void TensorImpl::Extend(int64_t num, float growthPct) {
   newCapacity[0] = std::max(
       newDims[0],
       static_cast<int64_t>(std::ceil(
-          sizes_and_strides_.size_at_unchecked(0) * (1 + growthPct / 100))));
+          static_cast<float>(sizes_and_strides_.size_at_unchecked(0)) *
+          (1 + growthPct / 100))));
   auto oldData = std::move(storage_.data_ptr());
   auto oldSize = numel_;
   Resize(std::move(newCapacity));

c10/core/impl/PyObjectSlot.cpp

@@ -26,6 +26,7 @@ PyInterpreter* PyObjectSlot::pyobj_interpreter() {
 }
 
 PyObject* PyObjectSlot::_unchecked_untagged_pyobj() const {
+  // NOLINTNEXTLINE(performance-no-int-to-ptr)
   return reinterpret_cast<PyObject*>(
       reinterpret_cast<uintptr_t>(pyobj_) & ~0x1ULL);
 }
@@ -47,10 +48,12 @@ PyInterpreter& PyObjectSlot::load_pyobj_interpreter() const {
 }
 
 bool PyObjectSlot::owns_pyobj() {
+  // NOLINTNEXTLINE(performance-no-int-to-ptr)
   return reinterpret_cast<uintptr_t>(pyobj_) & 1;
 }
 
 void PyObjectSlot::set_owns_pyobj(bool b) {
+  // NOLINTNEXTLINE(performance-no-int-to-ptr)
   pyobj_ = reinterpret_cast<PyObject*>(
       reinterpret_cast<uintptr_t>(_unchecked_untagged_pyobj()) | b);
 }

c10/core/impl/TorchDispatchModeTLS.cpp

@@ -41,7 +41,7 @@ const std::shared_ptr<SafePyObject>& TorchDispatchModeTLS::get_stack_at(
 }
 
 int64_t TorchDispatchModeTLS::stack_len() {
-  return torchDispatchModeState.stack_.size();
+  return static_cast<int64_t>(torchDispatchModeState.stack_.size());
 }
 
 const TorchDispatchModeTLS& TorchDispatchModeTLS::get_state() {

c10/core/impl/alloc_cpu.cpp

@@ -30,8 +30,8 @@ void memset_junk(void* data, size_t num) {
   static constexpr int32_t kJunkPattern = 0x7fedbeef;
   static constexpr int64_t kJunkPattern64 =
       static_cast<int64_t>(kJunkPattern) << 32 | kJunkPattern;
-  int32_t int64_count = num / sizeof(kJunkPattern64);
-  int32_t remaining_bytes = num % sizeof(kJunkPattern64);
+  auto int64_count = num / sizeof(kJunkPattern64);
+  auto remaining_bytes = num % sizeof(kJunkPattern64);
   int64_t* data_i64 = reinterpret_cast<int64_t*>(data);
   for (const auto i : c10::irange(int64_count)) {
     data_i64[i] = kJunkPattern64;

c10/macros/Macros.h

@@ -434,8 +434,7 @@ __device__ __attribute__((noinline)) __attribute__((weak)) void __assert_fail(
 // Warning: __has_trivial_copy for GCC may not always detect the non-POD
 // correctly. For example, T = std::unique_ptr may evaluate to true and be
 // treated as POD. This can cause unexpected behavior.
-#if defined(__GNUG__) && __GNUC__ < 5 && \
-    !(defined(__clang__) && defined(_LIBCPP_VERSION))
+#if defined(__GNUG__) && __GNUC__ < 5 && !defined(__clang__)
 #define C10_IS_TRIVIALLY_COPYABLE(T) __has_trivial_copy(T)
 #else
 #define C10_IS_TRIVIALLY_COPYABLE(T) std::is_trivially_copyable<T>::value

c10/util/Optional.h

@@ -501,13 +501,8 @@ class arrayref_optional_base {
       : storage_(v) {}
 
   constexpr bool initialized() const noexcept {
-    typename storage::raw repr;
-    // Cast to void* to suppress GCC's -Wclass-memaccess.
-    memcpy(
-        static_cast<void*>(&repr),
-        static_cast<const void*>(&storage_),
-        sizeof(storage_));
-    return repr.p != nullptr || repr.sz == 0;
+    return storage_.uninitialized_.p != nullptr ||
+        storage_.uninitialized_.sz == 0;
   }
 
   void setInitialized(bool init) noexcept {

functorch/csrc/dim/dim.cpp

@@ -166,7 +166,7 @@ struct Dim : public py::base<Dim> {
         return batchtensor_;
     }
 private:
-    int64_t size_;
+    int64_t size_{-1};
     at::Tensor range_;
     at::Tensor batchtensor_;
 };

torch/csrc/StorageSharing.cpp

@@ -91,10 +91,10 @@ static PyObject* THPStorage_shareFilename(PyObject* _self, PyObject* noargs) {
       "_share_filename_: only available on CPU");
   auto self = (THPStorage*)_self;
   c10::StorageImpl* storage = self->cdata;
-  // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-  THManagedMapAllocator* ctx;
+  THManagedMapAllocator* ctx =
+      THManagedMapAllocator::fromDataPtr(storage->data_ptr());
   // Storage is already in shared memory, just return a handle
-  if ((ctx = THManagedMapAllocator::fromDataPtr(storage->data_ptr()))) {
+  if (ctx) {
     // done
   } else {
     // TODO: retry on collision

torch/csrc/autograd/custom_function.h

@@ -146,7 +146,7 @@ struct TORCH_API AutogradContext {
   // weak_ptr to avoid a refcycle. Since grad_fn_ owns this AutogradContext, it
   // will always be alive when we want to use it.
   std::weak_ptr<Node> grad_fn_;
-  bool has_freed_buffers_;
+  bool has_freed_buffers_{false};
 
   void save_variables();
 

torch/csrc/autograd/python_torch_functions_manual.cpp

@@ -28,18 +28,11 @@
 #include <utility>
 #include <vector>
 
-using at::ArrayRef;
-using at::Backend;
-using at::Device;
 using at::DeviceGuard;
-using at::Dimname;
 using at::DimnameList;
-using at::Generator;
 using at::IntArrayRef;
-using at::Layout;
 using at::OptionalDeviceGuard;
 using at::Scalar;
-using at::ScalarType;
 using at::Tensor;
 using at::TensorList;
 using at::TensorOptions;

torch/csrc/distributed/rpc/tensorpipe_agent.h

@@ -97,8 +97,8 @@ struct TORCH_API TensorPipeRpcBackendOptions : public RpcBackendOptions {
         "num_worker_threads must be positive, got ",
         numWorkerThreads);
 
-    if (transports.has_value()) {
-      for (const std::string& transportName : transports.value()) {
+    if (this->transports.has_value()) {
+      for (const std::string& transportName : this->transports.value()) {
         TORCH_CHECK(
             TensorPipeTransportRegistry()->Has(transportName),
             "Unknown transport: ",
@@ -106,8 +106,8 @@ struct TORCH_API TensorPipeRpcBackendOptions : public RpcBackendOptions {
       }
     }
 
-    if (channels.has_value()) {
-      for (const std::string& channelName : channels.value()) {
+    if (this->channels.has_value()) {
+      for (const std::string& channelName : this->channels.value()) {
         TORCH_CHECK(
             TensorPipeChannelRegistry()->Has(channelName),
             "Unknown channel: ",

torch/csrc/jit/frontend/schema_type_parser.cpp

@@ -26,7 +26,6 @@ using c10::ListType;
 using c10::MemoryFormatType;
 using c10::NoneType;
 using c10::NumberType;
-using c10::OptionalType;
 using c10::QSchemeType;
 using c10::QuantizerType;
 using c10::RRefType;

torch/csrc/jit/frontend/sugared_value.h

@@ -658,15 +658,15 @@ struct TORCH_API RangeValue : SugaredValue {
   }
 
  private:
-  Value* start_;
-  Value* end_;
-  Value* step_;
+  Value* start_{};
+  Value* end_{};
+  Value* step_{};
   // a flag to determine if it's a simple range() call with only end_ from
   // arguments If true, we will not insert length calculation and index
   // derivation nodes to simplify the graph and enable more possible
   // optimizations
-  bool has_only_end_;
-  c10::optional<int64_t> static_len_ = c10::nullopt;
+  bool has_only_end_{};
+  c10::optional<int64_t> static_len_;
 };
 
 // Specialized Tree structure to matched against for special handling

torch/csrc/jit/frontend/tracer.h

@@ -179,9 +179,7 @@ inline void warn(const char* _reason, const char* _kind = nullptr) {
 TORCH_API void setWarn(warn_fn_type fn);
 
 struct TORCH_API NoWarn {
-  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
   NoWarn() : state(getTracingState()) {
-    // NOLINTNEXTLINE(*.cplusplus.UninitializedObject)
     if (state) {
       prev = state->warn;
       state->warn = false;
@@ -193,7 +191,7 @@ struct TORCH_API NoWarn {
     }
   }
   std::shared_ptr<TracingState> state;
-  bool prev;
+  bool prev{false};
 };
 
 struct WithNestedTracingFrame {

torch/csrc/jit/mobile/compatibility/backport.cpp

@@ -10,11 +10,8 @@
 namespace torch {
 namespace jit {
 
-using caffe2::serialize::FileAdapter;
 using caffe2::serialize::IStreamAdapter;
-using caffe2::serialize::PyTorchStreamReader;
 using caffe2::serialize::PyTorchStreamWriter;
-using caffe2::serialize::ReadAdapterInterface;
 
 const static BackportManager backportManager;
 

torch/csrc/jit/mobile/compatibility/backport_manager.cpp

@@ -15,11 +15,9 @@
 namespace torch {
 namespace jit {
 
-using caffe2::serialize::FileAdapter;
 using caffe2::serialize::IStreamAdapter;
 using caffe2::serialize::PyTorchStreamReader;
 using caffe2::serialize::PyTorchStreamWriter;
-using caffe2::serialize::ReadAdapterInterface;
 
 // Current support bytecode version
 namespace {

torch/csrc/jit/passes/onnx/unpack_quantized_weights.cpp

@@ -16,7 +16,6 @@
 #include <stack>
 
 using ::c10::Dispatcher;
-using ::c10::DispatchKey;
 namespace torch {
 namespace jit {
 namespace onnx {

torch/csrc/jit/runtime/argument_spec.h

@@ -101,7 +101,8 @@ struct ArgumentSpec {
     // https://github.com/zdevito/pytorch/commit/21e7200a0a0fc456bea2f10e95b1781f83933d10
     // show overhead in extra refcounting along this path
     const at::Tensor* t = reinterpret_cast<const at::Tensor*>(&input);
-    if ((arg.defined_ = t->defined())) {
+    arg.defined_ = t->defined();
+    if (arg.defined_) {
       arg.requires_grad_ = with_grad && autograd::Variable(*t).requires_grad();
       arg.dim_ = t->dim();
       // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)

torch/csrc/jit/runtime/register_distributed_ops.cpp

@@ -13,8 +13,6 @@
 #include <fmt/format.h>
 #include <stdexcept>
 
-using at::Scalar;
-using at::Tensor;
 namespace dist_autograd = torch::distributed::autograd;
 namespace dist_rpc = torch::distributed::rpc;
 

torch/csrc/utils/python_arg_parser.cpp

@@ -549,7 +549,7 @@ static void append_overloaded_arg(
     }
   }
   if (class_not_seen_yet) {
-    int arg_index = overloaded_args->size();
+    auto arg_index = overloaded_args->size();
     for (const auto j : c10::irange(arg_index)) {
       if (PyObject_IsSubclass(
               obj_type,
@@ -565,7 +565,8 @@ static void append_overloaded_arg(
     // add object to overloaded_args. If it's a subclass of another class
     // we've already seen it will be inserted before the superclass,
     // otherwise it will be inserted at the end of the array
-    overloaded_args->insert(overloaded_args->begin() + arg_index, obj);
+    overloaded_args->insert(
+        overloaded_args->begin() + static_cast<long>(arg_index), obj);
   }
 }
 
@@ -1204,19 +1205,19 @@ std::string FunctionSignature::toString() const {
 [[noreturn]] static void extra_args(
     const FunctionSignature& signature,
     Py_ssize_t nargs) {
-  const long max_pos_args = signature.max_pos_args;
-  const long min_args = signature.min_args;
+  const auto max_pos_args = signature.max_pos_args;
+  const auto min_args = signature.min_args;
   const long nargs_ = nargs;
   if (min_args != max_pos_args) {
     throw TypeError(
-        "%s() takes from %ld to %ld positional arguments but %ld were given",
+        "%s() takes from %zu to %zu positional arguments but %ld were given",
         signature.name.c_str(),
         min_args,
         max_pos_args,
         nargs_);
   }
   throw TypeError(
-      "%s() takes %ld positional argument%s but %ld %s given",
+      "%s() takes %zu positional argument%s but %ld %s given",
       signature.name.c_str(),
       max_pos_args,
       max_pos_args == 1 ? "" : "s",
@@ -1302,7 +1303,7 @@ bool FunctionSignature::parse(
     PyObject* kwargs,
     PyObject* dst[], // NOLINT
     bool raise_exception) {
-  size_t nargs = args ? PyTuple_GET_SIZE(args) : 0;
+  Py_ssize_t nargs = args ? PyTuple_GET_SIZE(args) : 0;
   auto remaining_kwargs = kwargs ? PyDict_Size(kwargs) : 0;
   size_t arg_pos = 0;
   bool allow_varargs_intlist = false;
@@ -1320,7 +1321,7 @@ bool FunctionSignature::parse(
     }
   }
 
-  if (nargs > max_pos_args && !allow_varargs_intlist) {
+  if (static_cast<size_t>(nargs) > max_pos_args && !allow_varargs_intlist) {
     if (raise_exception) {
       // foo() takes takes 2 positional arguments but 3 were given
       extra_args(*this, nargs);
@@ -1339,7 +1340,7 @@ bool FunctionSignature::parse(
   for (auto& param : params) {
     PyObject* obj = nullptr;
     bool is_kwd = false;
-    if (arg_pos < nargs) {
+    if (arg_pos < static_cast<size_t>(nargs)) {
       // extra positional args given after single positional IntArrayRef arg
       if (param.keyword_only) {
         if (raise_exception) {

torch/csrc/utils/tensor_apply.cpp

@@ -35,7 +35,7 @@ static void recursive_apply(
     int64_t dim,
     PyObject* fn,
     std::array<StridedData, N> strided_data) {
-  int64_t ndim = sizes.size();
+  int64_t ndim = static_cast<int64_t>(sizes.size());
   if (dim == ndim) {
     auto args = THPObjectPtr(PyTuple_New(N));
     if (!args)

torch/csrc/utils/tensor_flatten.cpp

@@ -29,7 +29,7 @@ std::vector<TensorGroup> take_tensors(
       tensor_size = tensor.numel() * tensor.element_size();
     }
 
-    auto& type_group = groups[type_id(tensor)];
+    auto& type_group = groups[static_cast<int64_t>(type_id(tensor))];
     type_group.tensors.push_back(tensor);
 
     if (fine_grained) {

torch/csrc/utils/tensor_list.cpp

@@ -17,8 +17,8 @@ static PyObject* recursive_to_list(
     IntArrayRef strides,
     int64_t dim,
     ScalarType scalarType,
-    int64_t elementSize) {
-  int64_t ndim = sizes.size();
+    size_t elementSize) {
+  int64_t ndim = static_cast<int64_t>(sizes.size());
   if (dim == ndim) {
     return torch::utils::load_scalar(data, scalarType);
   }

torch/csrc/utils/tensor_new.cpp

@@ -33,19 +33,14 @@
 #include <stdexcept>
 #include <vector>
 
-using at::Backend;
 using at::Device;
 using at::IntArrayRef;
-using at::kCPU;
-using at::kCUDA;
 using at::kInt;
 using at::kLong;
-using at::Scalar;
 using at::ScalarType;
 using at::Storage;
 using at::Tensor;
 using at::TensorOptions;
-using at::Type;
 using c10::optional;
 
 namespace torch {
@@ -64,7 +59,7 @@ TensorOptions build_options(
   return options;
 }
 
-void maybe_initialize_cuda(const Device device) {
+void maybe_initialize_cuda(const Device& device) {
   if (device.is_cuda()) {
     torch::utils::cuda_lazy_init();
   }
@@ -103,7 +98,7 @@ std::vector<int64_t> compute_sizes(PyObject* seq, ScalarType scalar_type) {
     if (length < 0)
       throw python_error();
     if (is_storage) {
-      length /= elementSize(scalar_type);
+      length /= static_cast<int64_t>(elementSize(scalar_type));
     }
     sizes.push_back(length);
     if (sizes.size() > MAX_DIMS) {
@@ -205,11 +200,11 @@ void recursive_store(
     IntArrayRef strides,
     int64_t dim,
     ScalarType scalarType,
-    int elementSize,
+    size_t elementSize,
     PyObject* obj) {
   TORCH_INTERNAL_ASSERT_DEBUG_ONLY(data != nullptr);
 
-  int64_t ndim = sizes.size();
+  int64_t ndim = static_cast<int64_t>(sizes.size());
   bool is_symfloat = torch::is_symfloat(obj);
   bool is_symint = torch::is_symint(obj);
   if (dim == ndim) {
@@ -374,7 +369,7 @@ Tensor internal_new_from_data(
       at::tracer::impl::NoTracerDispatchMode tracer_guard;
 
       if (isStorage(data)) {
-        ScalarType storage_scalar_type;
+        ScalarType storage_scalar_type{ScalarType::Undefined};
         bool is_typed_storage = false;
         Storage storage =
             createStorageGetType(data, storage_scalar_type, is_typed_storage);
@@ -562,6 +557,7 @@ Tensor legacy_sparse_tensor_generic_ctor_new(
     check_legacy_ctor_device(dispatch_key, deviceOptional);
     return at::empty({0}, build_options(options, scalar_type, deviceOptional));
   } else if (r.idx == 1) {
+    // NOLINTNEXTLINE(performance-no-int-to-ptr)
     auto cdata = reinterpret_cast<void*>(r.toInt64(0));
     return at::unsafeTensorFromTH(cdata, true);
   } else if (r.idx == 2) {
@@ -608,9 +604,9 @@ c10::TensorOptions typeIdWithDefault(
     int64_t device_idx,
     c10::DispatchKey dispatch_key) {
   auto options = dispatchKeyToTensorOptions(dispatch_key);
-  if (!r.isNone(device_idx)) {
+  if (!r.isNone(static_cast<int>(device_idx))) {
     // TODO: This line doesn't seem to be exercised at all in tests
-    options = options.device(r.device(device_idx).type());
+    options = options.device(r.device(static_cast<int>(device_idx)).type());
   }
   return options;
 }
@@ -655,7 +651,7 @@ Tensor legacy_tensor_generic_ctor_new(
     at::OptionalDeviceGuard device_guard(deviceOptional);
     return at::empty({0}, build_options(options, scalar_type));
   } else if (r.idx == 1) {
-    at::ScalarType storage_scalar_type;
+    at::ScalarType storage_scalar_type{at::ScalarType::Undefined};
     bool is_typed_storage = false;
     at::Storage storage = r.storage(0, storage_scalar_type, is_typed_storage);
     if (storage_scalar_type != at::ScalarType::Undefined && is_typed_storage) {
@@ -669,6 +665,7 @@ Tensor legacy_tensor_generic_ctor_new(
     }
     return new_with_storage(options, scalar_type, storage);
   } else if (r.idx == 2) {
+    // NOLINTNEXTLINE(performance-no-int-to-ptr)
     auto cdata = reinterpret_cast<void*>(r.toInt64(0));
     return at::unsafeTensorFromTH(cdata, true);
   } else if (r.idx == 3) {
@@ -786,9 +783,8 @@ Tensor indexing_tensor_from_data(
 
 class CheckSparseTensorInvariantsContext {
  public:
-  CheckSparseTensorInvariantsContext() {
-    state = at::globalContext().checkSparseTensorInvariants();
-  }
+  CheckSparseTensorInvariantsContext()
+      : state{at::globalContext().checkSparseTensorInvariants()} {}
   ~CheckSparseTensorInvariantsContext() {
     at::globalContext().setCheckSparseTensorInvariants(state);
   }

torch/csrc/utils/tensor_numpy.cpp

@@ -175,7 +175,7 @@ PyObject* tensor_to_numpy(const at::Tensor& tensor, bool force /*=false*/) {
 
   auto array = THPObjectPtr(PyArray_New(
       &PyArray_Type,
-      prepared_tensor.dim(),
+      static_cast<int>(prepared_tensor.dim()),
       sizes.data(),
       dtype,
       strides.data(),
@@ -382,6 +382,7 @@ 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;

torch/custom_class.h

@@ -197,8 +197,8 @@ class class_ : public ::torch::detail::class_base {
       GetterFunc getter_func,
       SetterFunc setter_func,
       std::string doc_string = "") {
-    torch::jit::Function* getter;
-    torch::jit::Function* setter;
+    torch::jit::Function* getter{};
+    torch::jit::Function* setter{};
 
     auto wrapped_getter =
         detail::wrap_func<CurClass, GetterFunc>(std::move(getter_func));
@@ -218,7 +218,7 @@ class class_ : public ::torch::detail::class_base {
       const std::string& name,
       GetterFunc getter_func,
       std::string doc_string = "") {
-    torch::jit::Function* getter;
+    torch::jit::Function* getter{};
 
     auto wrapped_getter =
         detail::wrap_func<CurClass, GetterFunc>(std::move(getter_func));
@@ -321,7 +321,7 @@ class class_ : public ::torch::detail::class_base {
         c10::guts::infer_function_traits_t<std::decay_t<SetStateFn>>;
     using SetStateArg = typename c10::guts::typelist::head_t<
         typename SetStateTraits::parameter_types>;
-    auto setstate_wrapper = [set_state = std::move(set_state)](
+    auto setstate_wrapper = [set_state = std::forward<SetStateFn>(set_state)](
                                 c10::tagged_capsule<CurClass> self,
                                 SetStateArg&& arg) {
       c10::intrusive_ptr<CurClass> classObj =

torch/lib/libshm/libshm.h

@@ -27,7 +27,7 @@ class THManagedMapAllocator : private THManagedMapAllocatorInit,
 
   void close() override;
 
-  ~THManagedMapAllocator() {
+  ~THManagedMapAllocator() override {
     close();
   }