[14/N] Fix extra warnings brought by clang-tidy-17 (#141644)

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141644
Approved by: https://github.com/ezyang

Co-authored-by: Eli Uriegas <1700823+seemethere@users.noreply.github.com>

aten/src/ATen/MatrixRef.h

@@ -92,8 +92,8 @@ class MatrixRef {
   /// The declaration here is extra complicated so that "arrayRef = {}"
   /// continues to select the move assignment operator.
   template <typename U>
-  // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
   std::enable_if_t<std::is_same_v<U, T>, MatrixRef<T>>& operator=(
+      // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
       U&& Temporary) = delete;
 
   /// Disallow accidental assignment from a temporary.

aten/src/ATen/cuda/CUDABlas.cpp

@@ -106,6 +106,7 @@ static hipblasStatus_t rocBLASStatusToHIPStatus(rocblas_status error)
 namespace {
 
 static cublasOperation_t _cublasOpFromChar(char op) {
+  // NOLINTNEXTLINE(bugprone-switch-missing-default-case)
   switch (op) {
     case 'n':
     case 'N':

test/cpp/api/optim.cpp

@@ -8,17 +8,16 @@
 
 #include <cmath>
 #include <cstdlib>
-#include <functional>
 #include <iostream>
 #include <memory>
-#include <random>
+#include <utility>
 #include <vector>
 
 using namespace torch::nn;
 using namespace torch::optim;
 
 template <typename OptimizerClass, typename Options>
-bool test_optimizer_xor(Options options) {
+static bool test_optimizer_xor(Options options) {
   torch::manual_seed(0);
 
   Sequential model(
@@ -30,9 +29,9 @@ bool test_optimizer_xor(Options options) {
   const int64_t kBatchSize = 200;
   const int64_t kMaximumNumberOfEpochs = 3000;
 
-  OptimizerClass optimizer(model->parameters(), options);
+  OptimizerClass optimizer(model->parameters(), std::move(options));
 
-  float running_loss = 1;
+  double running_loss = 1;
   int epoch = 0;
   while (running_loss > 0.1) {
     auto inputs = torch::empty({kBatchSize, 2});
@@ -46,8 +45,8 @@ bool test_optimizer_xor(Options options) {
 
     auto step = [&](OptimizerClass& optimizer,
                     Sequential model,
-                    torch::Tensor inputs,
-                    torch::Tensor labels) {
+                    const torch::Tensor& inputs,
+                    const torch::Tensor& labels) {
       auto closure = [&]() {
         optimizer.zero_grad();
         auto x = model->forward(inputs);
@@ -60,11 +59,10 @@ bool test_optimizer_xor(Options options) {
 
     torch::Tensor loss = step(optimizer, model, inputs, labels);
 
-    // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,cppcoreguidelines-avoid-magic-numbers,bugprone-narrowing-conversions)
-    running_loss = running_loss * 0.99 + loss.item<float>() * 0.01;
+    running_loss = running_loss * 0.99 + loss.item<double>() * 0.01;
     if (epoch > kMaximumNumberOfEpochs) {
       std::cout << "Loss is too high after epoch " << epoch << ": "
-                << running_loss << std::endl;
+                << running_loss << '\n';
       return false;
     }
     epoch++;
@@ -73,10 +71,10 @@ bool test_optimizer_xor(Options options) {
 }
 
 template <typename Parameters>
-void assign_parameter(
+static void assign_parameter(
     const Parameters& parameters,
     const char* name,
-    torch::Tensor new_tensor) {
+    const torch::Tensor& new_tensor) {
   auto parameter = parameters[name];
   parameter.set_requires_grad(false);
   parameter.flatten().copy_(new_tensor);
@@ -84,7 +82,7 @@ void assign_parameter(
 }
 
 template <typename OptimizerClass, typename Options>
-void check_exact_values(
+static void check_exact_values(
     Options options,
     std::vector<std::vector<torch::Tensor>> expected_parameters) {
   const size_t kIterations = 1001;
@@ -119,7 +117,7 @@ void check_exact_values(
   assign_parameter(
       parameters, "2.bias", torch::tensor({-0.0711}, torch::kFloat64));
 
-  auto optimizer = OptimizerClass(parameters.values(), options);
+  auto optimizer = OptimizerClass(parameters.values(), std::move(options));
   torch::Tensor input =
       torch::tensor({0.1, 0.2, 0.3, 0.4, 0.5, 0.6}, torch::kFloat64)
           .reshape({3, 2});
@@ -145,8 +143,7 @@ void check_exact_values(
             expected_parameters.at(i / kSampleEvery).at(p).to(torch::kFloat64);
         if (!computed.allclose(expected, /*rtol=*/1e-3, /*atol=*/5e-4)) {
           std::cout << "Iteration " << i << ": " << computed
-                    << " != " << expected << " (parameter " << p << ")"
-                    << std::endl;
+                    << " != " << expected << " (parameter " << p << ")" << '\n';
           ASSERT_TRUE(false);
         }
       }
@@ -166,8 +163,7 @@ TEST(OptimTest, OptimizerAccessors) {
   ASSERT_TRUE(options == options_);
   // test for param_groups() with non-const reference return
   auto& params_groups = optimizer.param_groups();
-  // NOLINTNEXTLINE(modernize-use-emplace)
-  params_groups.push_back(OptimizerParamGroup(params));
+  params_groups.emplace_back(params);
   auto& params_1 = params_groups[1].params();
   for (const auto i : c10::irange(params_1.size())) {
     torch::equal(params[i], params_1[i]);
@@ -204,7 +200,7 @@ TEST(OptimTest, OptimizerAccessors) {
 
 struct MyOptimizerOptions
     : public OptimizerCloneableOptions<MyOptimizerOptions> {
-  MyOptimizerOptions(double lr = 1.0) : lr_(lr){};
+  MyOptimizerOptions(double lr = 1.0) : lr_(lr) {}
   TORCH_ARG(double, lr) = 1.0;
 };
 
@@ -216,18 +212,16 @@ TEST(OptimTest, OldInterface) {
     }
     explicit MyOptimizer(
         std::vector<at::Tensor> params,
-        MyOptimizerOptions defaults = {})
-        : // NOLINTNEXTLINE(performance-move-const-arg)
-          Optimizer(
-              {std::move(OptimizerParamGroup(params))},
+        const MyOptimizerOptions& defaults = {})
+        : Optimizer(
+              std::move(params),
               std::make_unique<MyOptimizerOptions>(defaults)) {}
   };
   std::vector<torch::Tensor> parameters = {
       torch::ones({2, 3}), torch::zeros({2, 3}), torch::rand({2, 3})};
   {
     MyOptimizer optimizer(parameters);
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    size_t size;
+    size_t size = 0;
     OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
     ASSERT_EQ(size, parameters.size());
   }
@@ -235,8 +229,7 @@ TEST(OptimTest, OldInterface) {
     std::vector<at::Tensor> params;
     MyOptimizer optimizer(params);
 
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    size_t size;
+    size_t size = 0;
     OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
     ASSERT_EQ(size, 0);
 
@@ -255,8 +248,7 @@ TEST(OptimTest, OldInterface) {
     Linear linear(3, 4);
     MyOptimizer optimizer(linear->parameters());
 
-    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
-    size_t size;
+    size_t size = 0;
     OLD_INTERFACE_WARNING_CHECK(size = optimizer.size());
     ASSERT_EQ(size, linear->parameters().size());
   }
@@ -480,7 +472,7 @@ TEST(OptimTest, AddParameter_LBFGS) {
 
 // Check whether the learning rate of the parameter groups in the optimizer are
 // the same as the expected learning rates given in the epoch:learning rate map
-void check_lr_change(
+static void check_lr_change(
     Optimizer& optimizer,
     LRScheduler& lr_scheduler,
     std::map<unsigned, double> expected_epoch_lrs) {
@@ -512,7 +504,7 @@ void check_lr_change(
 // Very similar to check_lr_change, but for ReduceLROnPlateauScheduler
 // which does not inherit from LRScheduler and requires a metrics
 // input to step().
-void check_lr_change_for_reduce_on_plateau(
+static void check_lr_change_for_reduce_on_plateau(
     Optimizer& optimizer,
     ReduceLROnPlateauScheduler& lr_scheduler,
     std::map<unsigned, double> expected_epoch_lrs) {

torch/csrc/CudaIPCTypes.cpp

@@ -36,6 +36,10 @@ struct CudaIPCGlobalEntities {
   CudaIPCGlobalEntities() {
     alive = true;
   }
+  CudaIPCGlobalEntities(const CudaIPCGlobalEntities&) = delete;
+  CudaIPCGlobalEntities(CudaIPCGlobalEntities&&) = delete;
+  CudaIPCGlobalEntities& operator=(const CudaIPCGlobalEntities&) = delete;
+  CudaIPCGlobalEntities& operator=(CudaIPCGlobalEntities&&) = delete;
   ~CudaIPCGlobalEntities() {
     CudaIPCSentDataLimbo_.collect();
     safe_clean_current_file();
@@ -202,6 +206,7 @@ CudaIPCSentData::~CudaIPCSentData() {
       }
       cuda_ipc_global_entities.sync_events_used_--;
     }
+    // NOLINTNEXTLINE(bugprone-empty-catch)
   } catch (...) { /* No throw */
   }
 #endif

torch/csrc/Generator.cpp

@@ -30,7 +30,7 @@ using namespace torch;
 
 PyObject* THPGeneratorClass = nullptr;
 
-PyObject* THPGenerator_initDefaultGenerator(const at::Generator& cdata) {
+PyObject* THPGenerator_initDefaultGenerator(at::Generator cdata) {
   auto type = (PyTypeObject*)THPGeneratorClass;
   auto self = THPObjectPtr{type->tp_alloc(type, 0)};
   if (!self)
@@ -401,8 +401,7 @@ PyObject* THPGenerator_Wrap(const Generator& gen) {
     return obj;
   }
 
-  return THPGenerator_NewWithVar(
-      (PyTypeObject*)THPGeneratorClass, std::move(gen));
+  return THPGenerator_NewWithVar((PyTypeObject*)THPGeneratorClass, gen);
 }
 
 at::Generator THPGenerator_Unwrap(PyObject* state) {

torch/csrc/Generator.h

@@ -14,7 +14,7 @@ struct THPGenerator {
 // is borrowed. The caller should ensure that the at::Generator object lifetime
 // last at least as long as the Python wrapper.
 TORCH_PYTHON_API PyObject* THPGenerator_initDefaultGenerator(
-    const at::Generator& cdata);
+    at::Generator cdata);
 
 #define THPGenerator_Check(obj) PyObject_IsInstance(obj, THPGeneratorClass)
 

torch/csrc/PyInterpreter.cpp

@@ -159,6 +159,10 @@ class PyInterpreterHolder {
         is_main_interpreter_(
             at::impl::PythonOpRegistrationTrampoline::registerInterpreter(
                 impl_)) {}
+  PyInterpreterHolder(const PyInterpreterHolder&) = delete;
+  PyInterpreterHolder(PyInterpreterHolder&&) = delete;
+  PyInterpreterHolder& operator=(const PyInterpreterHolder&) = delete;
+  PyInterpreterHolder& operator=(PyInterpreterHolder&&) = delete;
   // NB: intentionally leaks the PyInterpreter, as there may still be
   // references to it that are live, living in objects that aren't being
   // destructed while Python is being cleaned up.

torch/csrc/api/include/torch/cuda.h

@@ -1,14 +1,14 @@
 #pragma once
 
-#include <torch/csrc/Export.h>
+#include <c10/core/Device.h>
+#include <c10/macros/Export.h>
 
-#include <cstddef>
 #include <cstdint>
 
 namespace torch::cuda {
 
 /// Returns the number of CUDA devices available.
-size_t TORCH_API device_count();
+c10::DeviceIndex TORCH_API device_count();
 
 /// Returns true if at least one CUDA device is available.
 bool TORCH_API is_available();

torch/csrc/api/include/torch/data/dataloader/base.h

@@ -37,6 +37,10 @@ class DataLoaderBase {
         main_thread_dataset_(std::move(main_thread_dataset)),
         sequencer_(new_sequencer()) {}
 
+  DataLoaderBase(const DataLoaderBase&) = delete;
+  DataLoaderBase(DataLoaderBase&&) = delete;
+  DataLoaderBase& operator=(const DataLoaderBase&) = delete;
+  DataLoaderBase& operator=(DataLoaderBase&&) = delete;
   // NOLINTNEXTLINE(bugprone-exception-escape)
   virtual ~DataLoaderBase() {
     join();

torch/csrc/api/include/torch/nn/modules/container/any_value.h

@@ -21,6 +21,7 @@ class AnyValue {
   /// behavior of move for `std::unique_ptr`.
   AnyValue(AnyValue&&) = default;
   AnyValue& operator=(AnyValue&&) = default;
+  ~AnyValue() = default;
 
   /// Copy construction and assignment is allowed.
   AnyValue(const AnyValue& other) : content_(other.content_->clone()) {}
@@ -89,6 +90,8 @@ class AnyValue {
         : type_info(type_info_) {}
     Placeholder(const Placeholder&) = default;
     Placeholder(Placeholder&&) = default;
+    Placeholder& operator=(const Placeholder&) = delete;
+    Placeholder& operator=(Placeholder&&) = delete;
     virtual ~Placeholder() = default;
     virtual std::unique_ptr<Placeholder> clone() const {
       TORCH_CHECK(false, "clone() should only be called on `AnyValue::Holder`");

torch/csrc/api/include/torch/optim/adagrad.h

@@ -41,11 +41,6 @@ struct TORCH_API AdagradParamState
   TORCH_ARG(int64_t, step) = 0;
 
  public:
-  AdagradParamState() = default;
-  AdagradParamState(const AdagradParamState&) = default;
-  AdagradParamState& operator=(const AdagradParamState&) = default;
-  AdagradParamState(AdagradParamState&&) noexcept = default;
-  AdagradParamState& operator=(AdagradParamState&&) noexcept = default;
   void serialize(torch::serialize::InputArchive& archive) override;
   void serialize(torch::serialize::OutputArchive& archive) const override;
   TORCH_API friend bool operator==(

torch/csrc/api/include/torch/optim/optimizer.h

@@ -85,6 +85,7 @@ class TORCH_API OptimizerParamGroup {
         options_(
             param_group.has_options() ? param_group.options().clone()
                                       : nullptr) {}
+  OptimizerParamGroup(OptimizerParamGroup&& param_group) = default;
   OptimizerParamGroup(std::vector<Tensor> params)
       : params_(std::move(params)) {}
   OptimizerParamGroup(
@@ -94,6 +95,9 @@ class TORCH_API OptimizerParamGroup {
 
   OptimizerParamGroup& operator=(const OptimizerParamGroup& param_group) =
       delete;
+  OptimizerParamGroup& operator=(OptimizerParamGroup&& param_group) noexcept =
+      default;
+  ~OptimizerParamGroup() = default;
   bool has_options() const;
   OptimizerOptions& options();
   const OptimizerOptions& options() const;
@@ -112,6 +116,8 @@ class TORCH_API Optimizer {
   // `state_dict` / `load_state_dict` API to copy an optimizer instead.
   Optimizer(const Optimizer& optimizer) = delete;
   Optimizer(Optimizer&& optimizer) = default;
+  Optimizer& operator=(const Optimizer& optimizer) = delete;
+  Optimizer& operator=(Optimizer&& optimizer) = default;
 
   explicit Optimizer(
       const std::vector<OptimizerParamGroup>& param_groups,

torch/csrc/api/include/torch/optim/schedulers/lr_scheduler.h

@@ -32,6 +32,7 @@ class TORCH_API LRScheduler {
  private:
   void set_optimizer_lrs(const std::vector<double>& learning_rates);
 
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
   torch::optim::Optimizer& optimizer_;
 };
 } // namespace torch::optim

torch/csrc/api/include/torch/ordered_dict.h

@@ -379,7 +379,7 @@ Value& OrderedDict<Key, Value>::insert(Key key, Value&& value) {
 template <typename Key, typename Value>
 void OrderedDict<Key, Value>::update(OrderedDict&& other) {
   reserve(size() + other.size());
-  for (auto& item : other) {
+  for (auto&& item : std::move(other)) {
     // We want to call `insert()` to prevent duplicate keys.
     insert(std::move(item.key()), std::move(item.value()));
   }

torch/csrc/api/src/cuda.cpp

@@ -4,11 +4,9 @@
 #include <c10/core/DeviceGuard.h>
 #include <c10/util/irange.h>
 
-#include <cstddef>
-
 namespace torch::cuda {
 
-size_t device_count() {
+c10::DeviceIndex device_count() {
   return at::detail::getCUDAHooks().deviceCount();
 }
 
@@ -54,7 +52,7 @@ void synchronize(int64_t device_index) {
   TORCH_CHECK(is_available(), "No CUDA GPUs are available");
   auto num_gpus = cuda::device_count();
   TORCH_CHECK(
-      device_index < 0 || static_cast<size_t>(device_index) < num_gpus,
+      device_index < 0 || device_index < num_gpus,
       "Device index out of range: ",
       device_index);
   at::detail::getCUDAHooks().deviceSynchronize(

torch/csrc/autograd/autograd_not_implemented_fallback.cpp

@@ -89,6 +89,7 @@ struct WarnNotImplemented : public Node {
   size_t num_outputs;
 };
 
+// NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
 auto WarnNotImplemented::apply(variable_list&& inputs) -> variable_list {
   auto inputsLocal = std::move(inputs);
   warnAutogradNotImplemented(op_name);

torch/csrc/autograd/custom_function.h

@@ -122,6 +122,9 @@ struct TORCH_API AutogradContext {
   AutogradContext() = default;
   AutogradContext(const AutogradContext& other) = delete;
   AutogradContext& operator=(const AutogradContext& other) = delete;
+  AutogradContext(AutogradContext&& other) = delete;
+  AutogradContext& operator=(AutogradContext&& other) = delete;
+  ~AutogradContext() = default;
 
   /// Can be used to save non-variable data for `backward`.
   ska::flat_hash_map<std::string, at::IValue> saved_data;

torch/csrc/autograd/functions/accumulate_grad.cpp

@@ -21,6 +21,7 @@ AccumulateGrad::AccumulateGrad(Variable variable_)
   add_input_metadata(variable);
 }
 
+// NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
 auto AccumulateGrad::apply(variable_list&& grads) -> variable_list {
   check_input_variables("AccumulateGrad", grads, 1, 0);
 

torch/csrc/autograd/functions/comm.cpp

@@ -109,13 +109,13 @@ variable_list Gather::apply(variable_list&& inputs) {
   }
 
   std::vector<at::Tensor> tensors;
-  tensors.reserve(inputs.size());
-  for (auto& variable : inputs) {
-    if (unsqueeze_scalars) {
+  if (unsqueeze_scalars) {
+    tensors.reserve(inputs.size());
+    for (auto& variable : inputs) {
       tensors.push_back(variable.view(1));
-    } else {
-      tensors.push_back(std::move(variable));
     }
+  } else {
+    tensors = std::move(inputs);
   }
 
   // Disable the autograd during the actual computation

torch/csrc/autograd/functions/tensor.cpp

@@ -21,21 +21,21 @@ static variable_list CopyBackwards_apply_functional(
     std::array<bool, 2> needs_input_grad,
     const c10::TensorOptions& src_options) {
   check_input_variables("CopyBackwards", grads, 1, -1, true);
-  auto grad = c10::MaybeOwned<at::Tensor>::borrowed(grads[0]);
+  auto& grad = std::move(grads)[0];
   variable_list grad_inputs(2);
-  if (grad->defined()) {
+  if (grad.defined()) {
     if (needs_input_grad[0]) {
-      grad_inputs[0] = at::zeros_like(*grad, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+      grad_inputs[0] = at::zeros_like(grad, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
     }
     if (needs_input_grad[1]) {
       // Handle R->C copies without raising a warning
       const auto src_type = src_options.dtype().toScalarType();
-      if (!c10::isComplexType(src_type) && grad->is_complex()) {
-        grad = c10::MaybeOwned<at::Tensor>::owned(at::real(grads[0]));
+      if (!c10::isComplexType(src_type) && grad.is_complex()) {
+        grad = at::real(grad);
       }
 
       at::DeviceGuard device_guard(src_options.device());
-      grad_inputs[1] = grad->to(src_options);
+      grad_inputs[1] = grad.to(src_options);
     }
   }
   return grad_inputs;
@@ -87,7 +87,7 @@ inline variable_list CopySlices::apply_impl(
     variable_list&& inputs,
     const T& call_fn) {
   check_input_variables("CopySlices", inputs, 1, -1, true);
-  auto& grad = inputs[0];
+  auto& grad = std::move(inputs)[0];
   if (!grad.defined()) {
     return variable_list(num_outputs());
   }

torch/csrc/autograd/functions/utils.cpp

@@ -6,11 +6,13 @@
 #include <torch/csrc/autograd/variable.h>
 
 #include <sstream>
+#include <utility>
 
 namespace torch::autograd {
 
 variable_list wrap_outputs(
     const variable_list& inputs,
+    // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
     tensor_list&& outputs,
     const function_constructor& ctr) {
   variable_list result;
@@ -18,7 +20,8 @@ variable_list wrap_outputs(
   if (!any_variable_requires_grad(inputs)) {
     for (auto& output : outputs) {
       if (output.defined()) {
-        result.push_back(make_variable(output, /*requires_grad=*/false));
+        result.push_back(
+            make_variable(std::move(output), /*requires_grad=*/false));
       } else {
         result.emplace_back();
       }
@@ -29,7 +32,7 @@ variable_list wrap_outputs(
     for (auto& output : outputs) {
       if (output.defined()) {
         auto variable =
-            autograd::make_variable(output, /*requires_grad=*/false);
+            autograd::make_variable(std::move(output), /*requires_grad=*/false);
         autograd::create_gradient_edge(variable, grad_fn);
         result.push_back(std::move(variable));
       } else {
@@ -50,7 +53,7 @@ void check_input_variables(
   if (required_args == -1) {
     required_args = args;
   }
-  if (inputs.size() != (size_t)args) {
+  if (inputs.size() != static_cast<size_t>(args)) {
     std::stringstream ss;
     ss << name << ": expected " << args << " arguments (got " << inputs.size();
     ss << ")";

torch/csrc/autograd/profiler_python.cpp

@@ -277,6 +277,10 @@ class ValueCache {
  public:
   ValueCache() = default;
   ValueCache(const ValueCache&) = delete;
+  ValueCache& operator==(const ValueCache&) = delete;
+  ValueCache(ValueCache&&) = default;
+  ValueCache& operator==(ValueCache&&) = delete;
+  ~ValueCache() = default;
 
   template <CallType C>
   void store(const typename Config<C>::key_t&, typename Config<C>::ephemeral_t);

torch/csrc/autograd/variable.h

@@ -883,7 +883,7 @@ inline Variable make_variable(
       } else {
         data_impl_copy->set_autograd_meta(nullptr);
       }
-      return Variable(data_impl_copy);
+      return Variable(std::move(data_impl_copy));
     }
   }
   return Variable();

torch/csrc/distributed/autograd/context/context.h

@@ -20,6 +20,7 @@ class TORCH_API DistAutogradContext {
   using GradCallback = std::function<bool(torch::Tensor&)>;
 
   explicit DistAutogradContext(int64_t contextId);
+  ~DistAutogradContext() = default;
 
   // Retrieves the autograd context id for this context.
   int64_t contextId() const;