[12/N] Apply clang-tidy and fix warnings in headers of torch/csrc (pytorch#116486)

This PR follows pytorch#116751.
Pull Request resolved: pytorch#116486
Approved by: https://github.com/albanD

Author: cyyever

torch/csrc/Exceptions.h

@@ -145,7 +145,7 @@ extern PyObject *THPException_FatalError, *THPException_LinAlgError,
 // Throwing this exception means that the python error flags have been already
 // set and control should be immediately returned to the interpreter.
 struct python_error : public std::exception {
-  python_error() : type(nullptr), value(nullptr), traceback(nullptr) {}
+  python_error() {}
 
   python_error(const python_error& other)
       : type(other.type),
@@ -244,9 +244,9 @@ struct python_error : public std::exception {
     PyErr_Restore(type, value, traceback);
   }
 
-  PyObject* type;
-  PyObject* value;
-  PyObject* traceback;
+  PyObject* type{nullptr};
+  PyObject* value{nullptr};
+  PyObject* traceback{nullptr};
 
   // Message to return to the user when 'what()' is invoked.
   std::string message;

torch/csrc/autograd/FunctionsManual.h

@@ -12,10 +12,7 @@
 #include <ATen/ATen.h>
 #include <torch/csrc/autograd/generated/Functions.h>
 
-namespace torch {
-namespace autograd {
-namespace generated {
-namespace details {
+namespace torch::autograd::generated::details {
 
 extern const char* kCudnnDoubleBackwardMsg;
 
@@ -1101,7 +1098,4 @@ mkldnn_rnn_layer_differentiable_backward(
 
 Tensor values_backward(const Tensor& grad, const Tensor& self);
 
-} // namespace details
-} // namespace generated
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd::generated::details

torch/csrc/autograd/InferenceMode.h

@@ -3,10 +3,8 @@
 #include <c10/core/InferenceMode.h>
 #include <torch/csrc/Export.h>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using InferenceMode = c10::InferenceMode;
 
 }
-} // namespace torch

torch/csrc/autograd/VariableTypeUtils.h

@@ -17,14 +17,9 @@
 #include <torch/csrc/autograd/jit_decomp_interface.h>
 #include <torch/csrc/utils/variadic.h>
 
-#include <array>
 #include <cstddef>
 #include <functional>
-#include <initializer_list>
 #include <memory>
-#include <stdexcept>
-#include <string>
-#include <tuple>
 #include <utility>
 #include <vector>
 
@@ -117,8 +112,8 @@ inline void rebase_history(Variable& var, std::shared_ptr<Node> grad_fn) {
 }
 
 inline void rebase_history(
-    std::vector<Variable>&& vars,
-    std::shared_ptr<Node> grad_fn) {
+    const std::vector<Variable>& vars,
+    const std::shared_ptr<Node>& grad_fn) {
   if (grad_fn) {
     for (auto& var : vars) {
       if (var.defined()) {
@@ -137,6 +132,7 @@ inline void increment_version(const at::Tensor& t) {
 
 struct Flatten : IterArgs<Flatten> {
   Flatten(variable_list& out) : out(out) {}
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-const-or-ref-data-members)
   variable_list& out;
   void operator()(const at::Tensor& x) {
     out.emplace_back(x);

torch/csrc/autograd/anomaly_mode.h

@@ -4,8 +4,7 @@
 #include <memory>
 #include <string>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 // forward declaration of Node from function.h
 struct Node;
@@ -69,5 +68,4 @@ struct TORCH_API AnomalyMetadata {
   std::shared_ptr<Node> parent_;
 };
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/autograd.h

@@ -2,8 +2,7 @@
 
 #include <torch/csrc/autograd/variable.h>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 /// Computes the sum of gradients of given tensors with respect to graph leaves.
 ///
@@ -102,5 +101,4 @@ TORCH_API uint64_t enter_dual_level();
 TORCH_API void exit_dual_level(uint64_t level);
 
 } // namespace forward_ad
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/autograd_not_implemented_fallback.h

@@ -2,8 +2,7 @@
 
 #include <torch/library.h>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 // Default DispatchKey::Autograd fallback for built-in operators.
 // Can be registered for custom operators.
@@ -30,5 +29,4 @@ enum class AutogradFallbackMode {
 TORCH_API void setAutogradFallbackMode(AutogradFallbackMode mode);
 TORCH_API AutogradFallbackMode getAutogradFallbackMode();
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/cpp_hook.h

@@ -3,8 +3,7 @@
 #include <functional>
 #include <memory>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using hooks_list =
     std::vector<std::function<at::TensorBase(const at::TensorBase&)>>;
@@ -27,5 +26,4 @@ struct CppFunctionSingleTensorPreHook : public FunctionPreHook {
   size_t value_idx_;
 };
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/custom_function.h

@@ -8,8 +8,7 @@
 #include <torch/csrc/autograd/variable.h>
 #include <vector>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using optional_variable_list = std::vector<c10::optional<Variable>>;
 using _jvp_fn_t = std::function<variable_list(variable_list, variable_list)>;
@@ -97,7 +96,7 @@ struct TORCH_API Function {
   // the parameter X.
   template <typename X = T, typename... Args>
   static auto apply(Args&&... args)
-      -> std::enable_if_t<std::is_same<X, T>::value, forward_t<X, Args...>>;
+      -> std::enable_if_t<std::is_same_v<X, T>, forward_t<X, Args...>>;
 };
 
 /// Context to save information during `forward` that can be accessed in
@@ -228,8 +227,8 @@ inline void extract_vars(
 }
 
 template <typename T>
-typename std::enable_if<std::is_same<T, variable_list>::value, T>::type
-to_output_type(std::vector<c10::optional<Variable>>& output_list) {
+std::enable_if_t<std::is_same_v<T, variable_list>, T> to_output_type(
+    std::vector<c10::optional<Variable>>& output_list) {
   // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
   variable_list result;
   std::transform(
@@ -241,8 +240,8 @@ to_output_type(std::vector<c10::optional<Variable>>& output_list) {
 }
 
 template <typename T>
-typename std::enable_if<std::is_same<T, Variable>::value, T>::type
-to_output_type(std::vector<c10::optional<Variable>>& output_list) {
+std::enable_if_t<std::is_same_v<T, Variable>, T> to_output_type(
+    std::vector<c10::optional<Variable>>& output_list) {
   return *output_list[0];
 }
 
@@ -264,7 +263,7 @@ inline std::vector<c10::optional<Variable>> to_optional(variable_list& output) {
 template <class T>
 template <typename X, typename... Args>
 auto Function<T>::apply(Args&&... args)
-    -> std::enable_if_t<std::is_same<X, T>::value, forward_t<X, Args...>> {
+    -> std::enable_if_t<std::is_same_v<X, T>, forward_t<X, Args...>> {
   const auto& functorch_tls = at::functorch::functorchTLSAccessor();
   if (functorch_tls) {
     // Function support for functorch is handled in Python.
@@ -434,5 +433,4 @@ void CppNode<T>::set_ctx_grad_fn(const std::shared_ptr<Node>& node) {
   ctx_.grad_fn_ = node;
 }
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/edge.h

@@ -6,8 +6,7 @@
 
 #include <c10/util/hash.h>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 struct Node;
 
@@ -38,8 +37,7 @@ struct Edge {
   /// The identifier of a particular input to the function.
   uint32_t input_nr;
 };
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd
 
 // The idiomatic way of enabling use of a custom type as the key of hash
 // containers in C++11. This method removes the requirement of having to pass

torch/csrc/autograd/engine.h

@@ -27,14 +27,11 @@
 #include <utility>
 #include <vector>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 struct ReadyQueue;
 }
-} // namespace torch
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 // Maximum reentrant backward depth before switching to a new thread
 // This limit is based on the TSAN's deadlock detector, where it will
@@ -291,5 +288,4 @@ struct TORCH_API Engine {
 using EngineStub = Engine& (*)();
 TORCH_API void set_default_engine_stub(EngineStub stub);
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/forward_grad.h

@@ -3,8 +3,7 @@
 #include <ATen/core/Tensor.h>
 #include <unordered_set>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 // [ Using ForwardGrad ]
 // ForwardGrad needs to be a shared_ptr to satisfy constraints of its inner
@@ -208,5 +207,4 @@ struct TORCH_API ForwardGrad : std::enable_shared_from_this<ForwardGrad> {
   mutable std::mutex mutex_;
 };
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/function.h

@@ -24,8 +24,7 @@
 #include <utility>
 #include <vector>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 struct Edge;
 struct FunctionPostHook;
@@ -757,5 +756,4 @@ struct TypeAndSize {
   at::TensorOptions options;
 };
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/function_hook.h

@@ -12,8 +12,7 @@ class SwapSavedVariables;
 
 // A hook that's called on gradients
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using Variable = at::Tensor;
 using variable_list = std::vector<Variable>;
@@ -62,5 +61,4 @@ struct TORCH_API PostAccumulateGradHook {
   }
 };
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/grad_mode.h

@@ -3,11 +3,9 @@
 #include <ATen/core/grad_mode.h>
 #include <torch/csrc/Export.h>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using GradMode = at::GradMode;
 using AutoGradMode = at::AutoGradMode;
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/graph_task.h

@@ -6,8 +6,7 @@
 #include <torch/csrc/autograd/utils/warnings.h>
 #include <vector>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using edge_list = std::vector<Edge>;
 struct ReadyQueue;
@@ -239,5 +238,4 @@ TORCH_API std::vector<Node*> get_current_graph_task_execution_order();
 TORCH_API int get_current_graph_task_id();
 void add_node_to_current_graph_task_exec_info(Node* fn);
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/input_buffer.h

@@ -13,8 +13,7 @@
 #include <c10/util/Optional.h>
 #include <torch/csrc/autograd/variable.h>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 struct InputBuffer {
   explicit InputBuffer(size_t size) : buffer(size) {}
@@ -44,5 +43,4 @@ struct InputBuffer {
   std::vector<Variable> buffer;
 };
 
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/input_metadata.h

@@ -22,8 +22,7 @@
 #include <cstdint>
 #include <utility>
 
-namespace torch {
-namespace autograd {
+namespace torch::autograd {
 
 using SymIntSmallVec = c10::SmallVector<c10::SymInt, c10::kDimVectorStaticSize>;
 using MetadataShape = std::variant<SymIntSmallVec, at::Tensor>;
@@ -109,5 +108,4 @@ struct TORCH_API InputMetadata {
   bool is_nested_ = false;
   bool was_default_constructed_ = true;
 };
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd

torch/csrc/autograd/jit_decomp_interface.h

@@ -27,9 +27,7 @@
 // For extra context, see VariableHooksInterface.h, where a similar technique
 // is used
 
-namespace torch {
-namespace autograd {
-namespace impl {
+namespace torch::autograd::impl {
 
 struct TORCH_API JitDecompInterface {
   virtual ~JitDecompInterface() = default;
@@ -49,6 +47,4 @@ struct TORCH_API JitDecompRegisterer {
   }
 };
 
-} // namespace impl
-} // namespace autograd
-} // namespace torch
+} // namespace torch::autograd::impl