[17/N] Fix extra warnings brought by clang-tidy-17 (#143804)

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143804
Approved by: https://github.com/Skylion007

aten/src/ATen/MatrixRef.h

@@ -92,6 +92,7 @@ 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;

aten/src/ATen/native/mkldnn/xpu/Conv.cpp

@@ -16,19 +16,18 @@ using namespace dnnl;
 using namespace at::native;
 using namespace at::native::onednn;
 
-namespace at::native {
-namespace xpu {
+namespace at::native::xpu {
 namespace impl {
 
 struct ConvParams {
   std::vector<int64_t> stride;
   std::vector<int64_t> padding;
   std::vector<int64_t> dilation;
-  bool transposed;
+  bool transposed{};
   std::vector<int64_t> output_padding;
-  int groups;
-  bool benchmark;
-  bool deterministic;
+  int64_t groups{};
+  bool benchmark{};
+  bool deterministic{};
 
   bool is_strided() const;
   bool is_dilated() const;
@@ -58,7 +57,7 @@ std::ostream& operator<<(std::ostream& out, const ConvParams& params) {
 
 bool ConvParams::is_strided() const {
   bool is_strided = false;
-  for (int s : stride) {
+  for (auto s : stride) {
     is_strided |= (s != 1);
   }
   return is_strided;
@@ -66,7 +65,7 @@ bool ConvParams::is_strided() const {
 
 bool ConvParams::is_dilated() const {
   bool is_dilated = false;
-  for (int d : dilation) {
+  for (auto d : dilation) {
     is_dilated |= (d != 1);
   }
   return is_dilated;
@@ -74,7 +73,7 @@ bool ConvParams::is_dilated() const {
 
 bool ConvParams::is_padded() const {
   bool is_padded = false;
-  for (int p : padding) {
+  for (auto p : padding) {
     is_padded |= (p != 0);
   }
   return is_padded;
@@ -82,7 +81,7 @@ bool ConvParams::is_padded() const {
 
 bool ConvParams::is_output_padding_neg() const {
   bool is_non_neg = false;
-  for (int p : output_padding) {
+  for (auto p : output_padding) {
     is_non_neg |= (p < 0);
   }
   return is_non_neg;
@@ -99,7 +98,7 @@ bool ConvParams::is_output_padding_big() const {
 
 bool ConvParams::is_padding_neg() const {
   bool is_non_neg = false;
-  for (int p : padding) {
+  for (auto p : padding) {
     is_non_neg |= (p < 0);
   }
   return is_non_neg;
@@ -107,7 +106,7 @@ bool ConvParams::is_padding_neg() const {
 
 bool ConvParams::is_stride_nonpos() const {
   bool is_nonpos = false;
-  for (int s : stride) {
+  for (auto s : stride) {
     is_nonpos |= (s <= 0);
   }
   return is_nonpos;
@@ -246,7 +245,7 @@ static void check_shape_forward(
       std::ostringstream output_ss;
       std::string separator = "";
 
-      for (int i = 0, len = input_shape.size(); i < len; ++i) {
+      for (size_t i = 0, len = input_shape.size(); i < len; ++i) {
         input_ss << separator << input_shape[i];
         kernel_ss << separator << kernel_shape[i];
         separator = " x ";
@@ -496,8 +495,8 @@ Tensor _convolution_out(
     // (padding_left, padding_right,
     //  padding_top, padding_bottom,
     //  padding_front, padding_back)
-    if (pad_nd.vec().size() > 0) {
-      for (int i = 0; i < dim; ++i) {
+    if (!pad_nd.vec().empty()) {
+      for (int64_t i = 0; i < dim; ++i) {
         padding_front_top_left[i] += pad_nd[2 * dim - 2 * i - 2]; // 4, 2, 0
         padding_back_bottom_right[i] += pad_nd[2 * dim - 2 * i - 1]; // 5, 3, 1
       }
@@ -628,8 +627,8 @@ std::tuple<Tensor, Tensor, Tensor> convolution_backward_overrideable(
 
   Tensor grad_output_, input_, weight_;
   IntArrayRef stride_, padding_, dilation_, output_padding_;
-  bool transposed_;
-  int64_t groups_;
+  bool transposed_ = false;
+  int64_t groups_ = 0;
   ConvParams params;
   if (3 == ndim) {
     grad_output_ = view4d(grad_output);
@@ -744,5 +743,4 @@ TORCH_LIBRARY_IMPL(aten, XPU, m) {
       TORCH_FN(convolution_backward_overrideable));
 }
 
-} // namespace xpu
-} // namespace at::native
+} // namespace at::native::xpu

aten/src/ATen/native/mkldnn/xpu/detail/Utils.cpp

@@ -114,7 +114,7 @@ dnnl::memory::dims get_onednn_strides(const at::Tensor& tensor) {
 }
 
 dnnl::memory::desc get_onednn_md(const at::Tensor& tensor) {
-  Tensor t = tensor.sizes().size() == 0 ? tensor.unsqueeze(0) : tensor;
+  Tensor t = tensor.sizes().empty() ? tensor.unsqueeze(0) : tensor;
   return {get_onednn_dims(t), get_onednn_dtype(t), get_onednn_strides(t)};
 }
 
@@ -130,8 +130,8 @@ bool onednn_strides_check(const Tensor& src) {
   dnnl_memory_desc_t md;
   dnnl_memory_desc_create_with_strides(&md, ndims, dims, data_type, strides);
   dnnl_format_kind_t md_fmt_kind;
-  int md_ndims;
-  int md_inner_nblks;
+  int md_ndims = 0;
+  int md_inner_nblks = 0;
   dnnl_dims_t* md_padded_dims = nullptr;
 
   dnnl_memory_desc_query(md, dnnl_query_inner_nblks_s32, &md_inner_nblks);

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

@@ -33,6 +33,8 @@ struct TORCH_XPU_API GpuEngineManager {
 
   GpuEngineManager(GpuEngineManager const&) = delete;
   GpuEngineManager& operator=(GpuEngineManager const&) = delete;
+  GpuEngineManager(GpuEngineManager&&) = default;
+  GpuEngineManager& operator=(GpuEngineManager&&) = default;
 
  protected:
   GpuEngineManager() {
@@ -44,7 +46,7 @@ struct TORCH_XPU_API GpuEngineManager {
               c10::xpu::get_raw_device(i), c10::xpu::get_device_context())));
     }
   }
-  ~GpuEngineManager() {}
+  ~GpuEngineManager() = default;
 
  private:
   std::vector<std::shared_ptr<dnnl::engine>> engine_pool;
@@ -71,6 +73,8 @@ struct TORCH_XPU_API GpuStreamManager {
 
   GpuStreamManager(GpuStreamManager const&) = delete;
   GpuStreamManager& operator=(GpuStreamManager const&) = delete;
+  GpuStreamManager(GpuStreamManager&&) = default;
+  GpuStreamManager& operator=(GpuStreamManager&&) = default;
 
  protected:
   GpuStreamManager() {
@@ -78,7 +82,7 @@ struct TORCH_XPU_API GpuStreamManager {
     TORCH_INTERNAL_ASSERT(device_count > 0);
     stream_pool.resize(device_count);
   }
-  ~GpuStreamManager() {}
+  ~GpuStreamManager() = default;
 
  private:
   using stream_hash_map =

torch/csrc/Generator.cpp

@@ -20,7 +20,7 @@ using namespace torch;
 
 PyObject* THPGeneratorClass = nullptr;
 
-PyObject* THPGenerator_initDefaultGenerator(at::Generator cdata) {
+PyObject* THPGenerator_initDefaultGenerator(const at::Generator& cdata) {
   auto type = (PyTypeObject*)THPGeneratorClass;
   auto self = THPObjectPtr{type->tp_alloc(type, 0)};
   if (!self)

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(
-    at::Generator cdata);
+    const at::Generator& cdata);
 
 #define THPGenerator_Check(obj) PyObject_IsInstance(obj, THPGeneratorClass)