[5/N] Fix extra warnings brought by clang-tidy-17 (#138403)

Follows #137983
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138403
Approved by: https://github.com/ezyang

aten/src/ATen/FunctionalInverses.cpp

@@ -231,6 +231,7 @@ Tensor FunctionalInverses::slice_Tensor_inverse(const Tensor& base, const Tensor
     }
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 Tensor FunctionalInverses::split_Tensor_inverse(const Tensor& base, const Tensor& mutated_view, InverseReturnMode inverse_return_mode, int64_t mutated_view_idx, c10::SymInt split_size, int64_t dim) {
     // It would be nice if this logic could be re-used from autograd's split_backward(), but I don't think it can.
     // For functionalization, we have only have one of the tensors from the TensorList outputed by split(), and we want to layer i
@@ -452,6 +453,7 @@ Tensor FunctionalInverses::chunk_inverse(const at::Tensor & base, const at::Tens
     return split_with_sizes_inverse(base, mutated_view, inverse_return_mode, mutated_view_idx, split_sizes, dim);
 }
 
+// NOLINTNEXTLINE(performance-unnecessary-value-param)
 Tensor FunctionalInverses::narrow_inverse(const at::Tensor & base, const at::Tensor & mutated_view, InverseReturnMode inverse_return_mode, int dim, c10::SymInt start, c10::SymInt length) {
     if (inverse_return_mode == InverseReturnMode::AlwaysView) {
       // NB: assumes mutated_view is a narrowed view of base.

aten/src/ATen/core/DistributionsHelper.h

@@ -95,11 +95,9 @@ struct uniform_int_distribution {
 template <typename T>
 struct uniform_real_distribution {
 
-  C10_HOST_DEVICE inline uniform_real_distribution(T from, T to) {
+  C10_HOST_DEVICE inline uniform_real_distribution(T from, T to) : from_(from), to_(to) {
     TORCH_CHECK_IF_NOT_ON_CUDA(from <= to);
     TORCH_CHECK_IF_NOT_ON_CUDA(to - from <= std::numeric_limits<T>::max());
-    from_ = from;
-    to_ = to;
   }
 
   template <typename RNG>
@@ -186,10 +184,8 @@ DISTRIBUTION_HELPER_GENERATE_NEXT_NORMAL_METHODS(float);
 template <typename T>
 struct normal_distribution {
 
-  C10_HOST_DEVICE inline normal_distribution(T mean_in, T stdv_in) {
+  C10_HOST_DEVICE inline normal_distribution(T mean_in, T stdv_in) : mean(mean_in), stdv(stdv_in) {
     TORCH_CHECK_IF_NOT_ON_CUDA(stdv_in >= 0, "stdv_in must be positive: ", stdv_in);
-    mean = mean_in;
-    stdv = stdv_in;
   }
 
   template <typename RNG>
@@ -236,9 +232,8 @@ template <> struct DiscreteDistributionType<double> { using type = double; };
 template <typename T>
 struct bernoulli_distribution {
 
-  C10_HOST_DEVICE inline bernoulli_distribution(T p_in) {
+  C10_HOST_DEVICE inline bernoulli_distribution(T p_in) : p(p_in) {
     TORCH_CHECK_IF_NOT_ON_CUDA(p_in >= 0 && p_in <= 1);
-    p = p_in;
   }
 
   template <typename RNG>
@@ -257,9 +252,8 @@ struct bernoulli_distribution {
 template <typename T>
 struct geometric_distribution {
 
-  C10_HOST_DEVICE inline geometric_distribution(T p_in) {
+  C10_HOST_DEVICE inline geometric_distribution(T p_in) : p(p_in) {
     TORCH_CHECK_IF_NOT_ON_CUDA(p_in > 0 && p_in < 1);
-    p = p_in;
   }
 
   template <typename RNG>
@@ -317,10 +311,8 @@ struct cauchy_distribution {
 template <typename T>
 struct lognormal_distribution {
 
-  C10_HOST_DEVICE inline lognormal_distribution(T mean_in, T stdv_in) {
+  C10_HOST_DEVICE inline lognormal_distribution(T mean_in, T stdv_in) : mean(mean_in), stdv(stdv_in) {
     TORCH_CHECK_IF_NOT_ON_CUDA(stdv_in > 0);
-    mean = mean_in;
-    stdv = stdv_in;
   }
 
   template<typename RNG>

aten/src/ATen/core/ivalue.h

@@ -1163,7 +1163,7 @@ struct TORCH_API IValue final {
   // this value different (e.g. using NaN boxing), and this would make it more
   // costly to determine the tag for all types vs just determining if something
   // is a particular type. Instead we want clients to use the `isX` methods when
-  // possible. If for perf. reasons you really, absolutely, must have a jump
+  // possible. If for performance reasons you really, absolutely, must have a jump
   // table, then we can revisit this.
   enum class Tag : uint32_t {
 #define DEFINE_TAG(x) x,

aten/src/ATen/dlpack.h

@@ -32,7 +32,9 @@
 #define DLPACK_DLL
 #endif
 
+// NOLINTNEXTLINE(modernize-deprecated-headers)
 #include <stdint.h>
+// NOLINTNEXTLINE(modernize-deprecated-headers)
 #include <stddef.h>
 
 #ifdef __cplusplus

aten/src/ATen/functorch/BatchRulesReduceOps.cpp

@@ -103,7 +103,7 @@ template<
   // optional cannot be used in a template, otherwise we would use it here.
   int maybe_keepdim_arg_pos
 >
-void boxed_reduction_batch_rule(const c10::OperatorHandle& op, torch::jit::Stack* stack) {
+static void boxed_reduction_batch_rule(const c10::OperatorHandle& op, torch::jit::Stack* stack) {
   const auto& schema = op.schema();
   const auto num_returns = schema.returns().size();
   const auto num_arguments = schema.arguments().size();
@@ -357,21 +357,21 @@ static std::tuple<Tensor, std::optional<int64_t>> searchsorted_batch_rule(
     // B<...>D, B<...>V -> no change
     if (buckets_bdim.has_value() && self_bdim.has_value()) {
       auto self_ = moveBatchDimToFront(self, self_bdim);
-      auto result = at::searchsorted(buckets, self_, out_int32, right, std::move(side), sorter_);
+      auto result = at::searchsorted(buckets, self_, out_int32, right, side, sorter_);
       return std::make_tuple(std::move(result), 0);
     }
     // B<...>D, <...>V -> B<...>D, B<...>V
     if (buckets_bdim.has_value() && !self_bdim.has_value()) {
       auto self_ = moveBatchDimToFront(self, self_bdim);
       self_ = ensure_has_bdim(self_, self_bdim.has_value(), buckets.size(0));
-      auto result = at::searchsorted(buckets, self_, out_int32, right, std::move(side), sorter_);
+      auto result = at::searchsorted(buckets, self_, out_int32, right, side, sorter_);
       return std::make_tuple(std::move(result), 0);
     }
     // <...>D, B<...>V -> <...>D, <...>(BV)
     if (!buckets_bdim.has_value() && self_bdim.has_value()) {
       auto bdim_size = self.size(*self_bdim);
       auto self_ = reshape_dim_into(*self_bdim, -1, self);
-      auto result = at::searchsorted(buckets, self_, out_int32, right, std::move(side), sorter_);
+      auto result = at::searchsorted(buckets, self_, out_int32, right, side, sorter_);
       result = reshape_dim_outof(-1, bdim_size, result);
       return std::make_tuple(result, result.dim() - 2);
     }
@@ -382,7 +382,7 @@ static std::tuple<Tensor, std::optional<int64_t>> searchsorted_batch_rule(
   if (buckets_bdim.has_value() && self_bdim.has_value()) {
     auto self_ = moveBatchDimToFront(self, self_bdim);
     auto self_view_ = self_logical_rank == 0 ? self_.unsqueeze(-1) : self_.flatten(1);
-    auto result = at::searchsorted(buckets, self_view_, out_int32, right, std::move(side), sorter_);
+    auto result = at::searchsorted(buckets, self_view_, out_int32, right, side, sorter_);
     result = self_logical_rank == 0 ? result.squeeze(-1) : result.view(self_.sizes());
     return std::make_tuple(std::move(result), 0);
   }
@@ -391,13 +391,13 @@ static std::tuple<Tensor, std::optional<int64_t>> searchsorted_batch_rule(
     auto bdim_size = buckets.size(*buckets_bdim);
     auto self_ = ensure_has_bdim(self, false, bdim_size);
     auto self_view_ = self_logical_rank == 0 ? self_.unsqueeze(-1) : self_.flatten(1);
-    auto result = at::searchsorted(buckets, self_view_, out_int32, right, std::move(side), sorter_);
+    auto result = at::searchsorted(buckets, self_view_, out_int32, right, side, sorter_);
     result = self_logical_rank == 0 ? result.squeeze(-1) : result.view(self_.sizes());
     return std::make_tuple(std::move(result), 0);
   }
   // D, B* -> no change
   if (!buckets_bdim.has_value() && self_bdim.has_value()) {
-    auto result = at::searchsorted(buckets, self, out_int32, right, std::move(side), sorter_);
+    auto result = at::searchsorted(buckets, self, out_int32, right, side, sorter_);
     return std::make_tuple(std::move(result), self_bdim);
   }
   TORCH_INTERNAL_ASSERT(false);

c10/core/TensorImpl.cpp

@@ -81,11 +81,7 @@ TensorImpl::TensorImpl(
     DispatchKeySet key_set,
     const caffe2::TypeMeta data_type)
     // Use std::forward to suppress static analyzer false positive.
-    : TensorImpl(
-          std::forward<Storage>(storage),
-          key_set,
-          data_type,
-          storage.device()) {}
+    : TensorImpl(std::move(storage), key_set, data_type, storage.device()) {}
 
 // [Note: Python key removal]
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

c10/core/thread_pool.cpp

@@ -62,6 +62,7 @@ ThreadPool::~ThreadPool() {
   for (auto& t : threads_) {
     try {
       t.join();
+      // NOLINTNEXTLINE(bugprone-empty-catch)
     } catch (const std::exception&) {
     }
   }

c10/test/util/TypeIndex_test.cpp

@@ -163,7 +163,7 @@ TEST(TypeIndex, TypeComputationsAreResolved) {
   EXPECT_EQ(
       string_view::npos,
       get_fully_qualified_type_name<
-          typename std::remove_pointer<typename Type<int>::type>::type>()
+          std::remove_pointer_t<typename Type<int>::type>>()
           .find("*"));
 }
 

c10/util/Backtrace.cpp

@@ -281,6 +281,7 @@ class GetBacktraceImpl {
   }
 
  private:
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
   const bool skip_python_frames_;
   std::vector<void*> callstack_;
 };

c10/util/Gauge.h

@@ -36,6 +36,7 @@ class C10_API GaugeHandle {
   void record(int64_t value);
 
  private:
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
   detail::GaugeImpl& impl_;
 };
 

c10/util/Lazy.h

@@ -29,7 +29,7 @@ class OptimisticLazy {
   }
 
   template <class Factory>
-  T& ensure(Factory&& factory) {
+  T& ensure(const Factory& factory) {
     if (T* value = value_.load(std::memory_order_acquire)) {
       return *value;
     }

c10/util/Logging.cpp

@@ -220,6 +220,7 @@ void SetGlobalRank(int64_t rank) {
 void LogAPIUsage(const std::string& event) try {
   if (auto logger = GetAPIUsageLogger())
     (*logger)(event);
+  // NOLINTNEXTLINE(bugprone-empty-catch)
 } catch (std::bad_function_call&) {
   // static destructor race
 }
@@ -229,6 +230,7 @@ void LogAPIUsageMetadata(
     const std::map<std::string, std::string>& metadata_map) try {
   if (auto logger = GetAPIUsageMetadataLogger())
     (*logger)(context, metadata_map);
+  // NOLINTNEXTLINE(bugprone-empty-catch)
 } catch (std::bad_function_call&) {
   // static destructor race
 }
@@ -236,6 +238,7 @@ void LogAPIUsageMetadata(
 void LogPyTorchDDPUsage(const DDPLoggingData& ddpData) try {
   if (auto logger = GetDDPUsageLogger())
     (*logger)(ddpData);
+  // NOLINTNEXTLINE(bugprone-empty-catch)
 } catch (std::bad_function_call&) {
   // static destructor race
 }
@@ -245,6 +248,7 @@ bool LogAPIUsageFakeReturn(const std::string& event) try {
   if (auto logger = GetAPIUsageLogger())
     (*logger)(event);
   return true;
+  // NOLINTNEXTLINE(bugprone-empty-catch)
 } catch (std::bad_function_call&) {
   // static destructor race
   return true;

c10/util/WaitCounter.cpp

@@ -5,6 +5,7 @@
 
 #include <chrono>
 #include <memory>
+#include <string>
 #include <string_view>
 #include <unordered_map>
 #include <vector>
@@ -110,7 +111,7 @@ class WaitCounterImpl {
     return ctxs;
   }
 
-  void stop(SmallVector<intptr_t>&& ctxs) noexcept {
+  void stop(const SmallVector<intptr_t>& ctxs) noexcept {
     auto now = std::chrono::steady_clock::now();
     assert(ctxs.size() == backends_.size());
     for (size_t i = 0; i < ctxs.size(); ++i) {
@@ -155,7 +156,7 @@ WaitCounterHandle::WaitGuard WaitCounterHandle::start() {
   return WaitCounterHandle::WaitGuard(*this, impl_.start());
 }
 
-void WaitCounterHandle::stop(SmallVector<intptr_t>&& ctxs) {
-  return impl_.stop(std::move(ctxs));
+void WaitCounterHandle::stop(const SmallVector<intptr_t>& ctxs) {
+  return impl_.stop(ctxs);
 }
 } // namespace c10::monitor

c10/util/WaitCounter.h

@@ -2,7 +2,6 @@
 
 #include <chrono>
 #include <memory>
-#include <string>
 #include <string_view>
 
 #include <c10/macros/Macros.h>
@@ -61,7 +60,7 @@ class C10_API WaitCounterHandle {
 
     void stop() {
       if (auto handle = std::exchange(handle_, nullptr)) {
-        handle->stop(std::move(ctxs_));
+        handle->stop(ctxs_);
       }
     }
 
@@ -81,8 +80,9 @@ class C10_API WaitCounterHandle {
  private:
   // Stops the waiter. Each start() call should be matched by exactly one stop()
   // call.
-  void stop(SmallVector<intptr_t>&& ctxs);
+  void stop(const SmallVector<intptr_t>& ctxs);
 
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
   detail::WaitCounterImpl& impl_;
 };
 } // namespace c10::monitor

torch/csrc/inductor/aoti_runtime/device_utils.h

@@ -38,12 +38,10 @@ using DeviceStreamType = cudaStream_t;
     throw std::runtime_error("CPU runtime error"); \
   }
 
-namespace torch {
-namespace aot_inductor {
+namespace torch::aot_inductor {
 
 using DeviceStreamType = void*;
 
-} // namespace aot_inductor
-} // namespace torch
+} // namespace torch::aot_inductor
 
 #endif // USE_CUDA

torch/csrc/lazy/python/init.cpp

@@ -193,7 +193,7 @@ void initLazyBindings(PyObject* module) {
     torch::lazy::getLTCForceFallback() = std::move(newval);
   });
   lazy.def("_clear_ir_cache", []() { TrieCache::Get()->Clear(); });
-  lazy.def("_dump_ir_cache", [](std::string filename) {
+  lazy.def("_dump_ir_cache", [](const std::string& filename) {
     TrieCache::Get()->DumpToDotFile(filename);
   });
   lazy.def("_set_reuse_ir", [](bool val) { FLAGS_torch_lazy_reuse_ir = val; });

torch/csrc/utils/tensor_numpy.cpp

@@ -57,8 +57,7 @@ bool is_numpy_dlpack_deleter_bugged() {
 using namespace at;
 using namespace torch::autograd;
 
-namespace torch {
-namespace utils {
+namespace torch::utils {
 
 bool is_numpy_available() {
   static bool available = []() {
@@ -567,7 +566,6 @@ void validate_numpy_for_dlpack_deleter_bug() {
 bool is_numpy_dlpack_deleter_bugged() {
   return numpy_with_dlpack_deleter_bug_installed;
 }
-} // namespace utils
-} // namespace torch
+} // namespace torch::utils
 
 #endif // USE_NUMPY