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

This reverts commit 5aa258eb09d5ecd62aea4d2bd02bbfa5eda0d554.

Reverted https://github.com/pytorch/pytorch/pull/116486 on behalf of https://github.com/izaitsevfb due to Reverting, as it depends on https://github.com/pytorch/pytorch/pull/116353, which has to be reverted ([comment](https://github.com/pytorch/pytorch/pull/116486#issuecomment-1876042948))

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() {}
+  python_error() : type(nullptr), value(nullptr), traceback(nullptr) {}
 
   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{nullptr};
-  PyObject* value{nullptr};
-  PyObject* traceback{nullptr};
+  PyObject* type;
+  PyObject* value;
+  PyObject* traceback;
 
   // Message to return to the user when 'what()' is invoked.
   std::string message;

torch/csrc/autograd/FunctionsManual.h

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

torch/csrc/autograd/InferenceMode.h

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

torch/csrc/autograd/VariableTypeUtils.h

@@ -17,9 +17,14 @@
 #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>
 
@@ -112,8 +117,8 @@ inline void rebase_history(Variable& var, std::shared_ptr<Node> grad_fn) {
 }
 
 inline void rebase_history(
-    const std::vector<Variable>& vars,
-    const std::shared_ptr<Node>& grad_fn) {
+    std::vector<Variable>&& vars,
+    std::shared_ptr<Node> grad_fn) {
   if (grad_fn) {
     for (auto& var : vars) {
       if (var.defined()) {
@@ -132,7 +137,6 @@ 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

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

torch/csrc/autograd/autograd.h

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

torch/csrc/autograd/autograd_not_implemented_fallback.h

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

torch/csrc/autograd/cpp_hook.h

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

torch/csrc/autograd/custom_function.h

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

torch/csrc/autograd/edge.h

@@ -6,7 +6,8 @@
 
 #include <c10/util/hash.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct Node;
 
@@ -37,7 +38,8 @@ struct Edge {
   /// The identifier of a particular input to the function.
   uint32_t input_nr;
 };
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch
 
 // 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

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

torch/csrc/autograd/forward_grad.h

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

torch/csrc/autograd/function.h

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

torch/csrc/autograd/function_hook.h

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

torch/csrc/autograd/grad_mode.h

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

torch/csrc/autograd/graph_task.h

@@ -6,7 +6,8 @@
 #include <torch/csrc/autograd/utils/warnings.h>
 #include <vector>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 using edge_list = std::vector<Edge>;
 struct ReadyQueue;
@@ -238,4 +239,5 @@ 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 torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/input_buffer.h

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

torch/csrc/autograd/input_metadata.h

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

torch/csrc/autograd/jit_decomp_interface.h

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

torch/csrc/autograd/profiler_kineto.h

@@ -9,15 +9,16 @@
 #include <torch/csrc/profiler/util.h>
 
 namespace torch {
-
-namespace profiler::impl {
+namespace profiler {
+namespace impl {
 struct Result;
 namespace kineto {
 struct ActivityTraceWrapper;
 } // namespace kineto
-} // namespace profiler::impl
-
-namespace autograd::profiler {
+} // namespace impl
+} // namespace profiler
+namespace autograd {
+namespace profiler {
 using experimental_event_t = std::shared_ptr<torch::profiler::impl::Result>;
 using extra_meta_t = std::unordered_map<std::string, std::string>;
 
@@ -176,13 +177,16 @@ TORCH_API void prepareProfiler(
     const torch::profiler::impl::ProfilerConfig& config,
     const std::set<torch::profiler::impl::ActivityType>& activities);
 
-} // namespace autograd::profiler
+} // namespace profiler
+} // namespace autograd
 
-namespace profiler::impl {
+namespace profiler {
+namespace impl {
 
 // Experimental.
 TORCH_API void _reportVulkanEventToProfiler(vulkan_id_t id);
 
-} // namespace profiler::impl
+} // namespace impl
+} // namespace profiler
 
 } // namespace torch

torch/csrc/autograd/profiler_legacy.h

@@ -15,7 +15,8 @@
 #include <torch/csrc/profiler/stubs/base.h>
 #include <torch/csrc/profiler/util.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct Node;
 
@@ -412,4 +413,5 @@ struct TORCH_API TLSLegacyProfilerGuard {
 };
 
 } // namespace profiler
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/profiler_python.h

@@ -1,7 +1,13 @@
 #pragma once
 
-namespace torch::autograd::profiler::python_tracer {
+namespace torch {
+namespace autograd {
+namespace profiler {
+namespace python_tracer {
 
 void init();
 
 }
+} // namespace profiler
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_autograd.h

@@ -4,11 +4,13 @@
 PyObject* THPAutograd_initExtension(PyObject* _unused, PyObject* unused);
 void THPAutograd_initFunctions();
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 PyMethodDef* python_functions();
 
 }
+} // namespace torch
 
 #include <torch/csrc/autograd/python_engine.h>
 #include <torch/csrc/autograd/python_function.h>

torch/csrc/autograd/python_cpp_function.h

@@ -8,7 +8,8 @@
 #include <torch/csrc/autograd/function.h>
 #include <torch/csrc/utils/object_ptr.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct THPCppFunction {
   PyObject_HEAD std::shared_ptr<Node> cdata;
@@ -102,4 +103,5 @@ PyObject* functionToPyObject(const std::shared_ptr<Node>& cdata);
 
 bool THPCppFunction_Check(PyObject* obj);
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_engine.h

@@ -7,7 +7,9 @@
 
 bool THPEngine_initModule(PyObject* module);
 
-namespace torch::autograd::python {
+namespace torch {
+namespace autograd {
+namespace python {
 
 struct PythonEngine : public Engine {
   static Engine& get_python_engine();
@@ -41,4 +43,6 @@ struct PythonEngine : public Engine {
   PythonEngine();
 };
 
-} // namespace torch::autograd::python
+} // namespace python
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_enum_tag.h

@@ -2,6 +2,8 @@
 
 #include <torch/csrc/python_headers.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 void initEnumTag(PyObject* module);
 }
+} // namespace torch

torch/csrc/autograd/python_fft_functions.h

@@ -1,7 +1,9 @@
 #pragma once
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 void initFFTFunctions(PyObject* module);
 
 }
+} // namespace torch

torch/csrc/autograd/python_function.h

@@ -16,11 +16,13 @@
 #include <utility>
 #include <vector>
 
-namespace torch::jit {
+namespace torch {
+namespace jit {
 struct Graph;
 }
-
-namespace torch::autograd {
+} // namespace torch
+namespace torch {
+namespace autograd {
 
 // A Function which is implemented by a Python object (i.e., a THPFunction).
 // Calls to 'apply' are forwarded to the Python method implementation.
@@ -69,7 +71,8 @@ inline bool ensure_tuple(THPObjectPtr& obj) {
   return true;
 }
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch
 
 // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
 struct THPFunction {

torch/csrc/autograd/python_hook.h

@@ -8,7 +8,8 @@ namespace torch::dynamo::autograd {
 class SwapSavedVariables;
 } // namespace torch::dynamo::autograd
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct PyFunctionTensorPreHook : public FunctionPreHook {
   PyFunctionTensorPreHook(PyObject* dict, size_t value_idx);
@@ -52,4 +53,5 @@ struct PyFunctionTensorPostAccGradHooks : public PostAccumulateGradHook {
   PyObject* dict;
 };
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_legacy_variable.h

@@ -5,8 +5,10 @@
 
 #include <torch/csrc/python_headers.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 void init_legacy_variable(PyObject* module);
 
 }
+} // namespace torch

torch/csrc/autograd/python_linalg_functions.h

@@ -1,7 +1,9 @@
 #pragma once
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 void initLinalgFunctions(PyObject* module);
 
 }
+} // namespace torch

torch/csrc/autograd/python_nested_functions.h

@@ -1,9 +1,11 @@
 #pragma once
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 PyMethodDef* get_nested_functions_manual();
 
 void initNestedFunctions(PyObject* module);
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_nn_functions.h

@@ -1,7 +1,9 @@
 #pragma once
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 void initNNFunctions(PyObject* module);
 
 }
+} // namespace torch

torch/csrc/autograd/python_saved_variable_hooks.h

@@ -10,7 +10,8 @@
 
 namespace py = pybind11;
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct PySavedVariableHooks : public SavedVariableHooks {
   PySavedVariableHooks(py::function& pack_hook, py::function& unpack_hook);
@@ -30,4 +31,5 @@ struct PyDefaultSavedVariableHooks {
   static std::unique_ptr<SavedVariableHooks> get_hooks();
 };
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_sparse_functions.h

@@ -1,7 +1,9 @@
 #pragma once
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 void initSparseFunctions(PyObject* module);
 
 }
+} // namespace torch

torch/csrc/autograd/python_special_functions.h

@@ -1,7 +1,9 @@
 #pragma once
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 void initSpecialFunctions(PyObject* module);
 
 }
+} // namespace torch

torch/csrc/autograd/python_torch_functions.h

@@ -2,7 +2,8 @@
 
 #include <vector>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 extern PyObject* THPVariableFunctionsModule;
 
@@ -24,4 +25,5 @@ inline PyObject* TypeError_to_NotImplemented_(
 
 void initTorchFunctions();
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/python_variable_indexing.h

@@ -6,7 +6,8 @@
 #include <torch/csrc/utils/pybind.h>
 #include <torch/csrc/utils/python_symnode.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct UnpackedSlice {
   c10::SymInt start;
@@ -99,4 +100,5 @@ Variable valueToTensor(
     PyObject* value,
     const at::Device& device);
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/record_function_ops.h

@@ -3,7 +3,9 @@
 #include <c10/util/Optional.h>
 #include <torch/custom_class.h>
 
-namespace torch::autograd::profiler {
+namespace torch {
+namespace autograd {
+namespace profiler {
 
 struct PythonRecordFunction : public torch::CustomClassHolder {
   at::RecordFunction record;
@@ -24,4 +26,6 @@ TORCH_API c10::intrusive_ptr<c10::ivalue::Future> _call_end_callbacks_on_fut_new
     const c10::intrusive_ptr<PythonRecordFunction>& record,
     const c10::intrusive_ptr<c10::ivalue::Future>& fut);
 
-} // namespace torch::autograd::profiler
+} // namespace profiler
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/saved_variable.h

@@ -9,7 +9,8 @@
 #include <cstdint>
 #include <memory>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 using Variable = at::Tensor;
 struct Node;
@@ -118,4 +119,5 @@ class TORCH_API SavedVariable {
       std::unique_ptr<SavedVariableHooks>&& hooks,
       const Variable& data);
 };
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/saved_variable_hooks.h

@@ -2,7 +2,8 @@
 
 #include <ATen/core/Tensor.h>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct TORCH_API SavedVariableHooks {
   virtual void call_pack_hook(const at::Tensor& tensor) = 0;
@@ -10,4 +11,5 @@ struct TORCH_API SavedVariableHooks {
   virtual ~SavedVariableHooks() = default;
 };
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/symbolic.h

@@ -4,7 +4,8 @@
 #include <torch/csrc/onnx/onnx.h>
 #include <vector>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 struct SymbolicContext {
   jit::Block* block;
@@ -14,4 +15,5 @@ struct symbolic_unconvertible : public std::runtime_error {
   using std::runtime_error::runtime_error;
 };
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch

torch/csrc/autograd/variable.h

@@ -21,7 +21,8 @@
 #include <utility>
 #include <vector>
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 /// `Variable` is exactly the same as `Tensor` (i.e. we have `using Variable =
 /// at::Tensor`). This means you can perform all the usual mathematical and
@@ -32,7 +33,8 @@ namespace torch::autograd {
 /// is to eliminate the `Variable` class in the near future.
 using Variable = at::Tensor;
 
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch
 
 // The following are all internal APIs and should not be shown in libtorch docs.
 // Therefore, we wrap the following code with `#ifndef DOXYGEN_SHOULD_SKIP_THIS
@@ -40,7 +42,8 @@ using Variable = at::Tensor;
 
 #ifndef DOXYGEN_SHOULD_SKIP_THIS
 
-namespace torch::autograd {
+namespace torch {
+namespace autograd {
 
 /// Check if this type is supported by the autograd engine.
 /// If you change this, update the doc at the top of the
@@ -841,6 +844,7 @@ namespace utils {
 TORCH_API bool has_same_meta(const Variable& base, const Variable& other);
 
 } // namespace utils
-} // namespace torch::autograd
+} // namespace autograd
+} // namespace torch
 
 #endif /* DOXYGEN_SHOULD_SKIP_THIS */

torch/csrc/functorch/init.h

@@ -1,7 +1,12 @@
-#include <Python.h>
+#include <pybind11/pybind11.h>
+#include <torch/csrc/utils/pybind.h>
 
-namespace torch::functorch::impl {
+namespace torch {
+namespace functorch {
+namespace impl {
 
 void initFuncTorchBindings(PyObject* module);
 
 }
+} // namespace functorch
+} // namespace torch

torch/csrc/jit/frontend/source_range.h

@@ -11,7 +11,8 @@
 #include <sstream>
 #include <unordered_map>
 
-namespace torch::jit {
+namespace torch {
+namespace jit {
 
 class SourceRangeUnpickler;
 struct SourceRange;
@@ -443,7 +444,8 @@ using SourceRangeRecords = std::vector<TaggedRange>;
 using SourceRangeTagMap =
     std::unordered_map<SourceRange, int64_t, SourceRangeHasher>;
 
-} // namespace torch::jit
+} // namespace jit
+} // namespace torch
 
 namespace std {
 template <>

torch/csrc/jit/frontend/tracer.h

@@ -17,7 +17,8 @@
 #include <unordered_map>
 #include <vector>
 
-namespace torch::jit {
+namespace torch {
+namespace jit {
 struct Node;
 struct Value;
 struct Graph;
@@ -381,13 +382,13 @@ TORCH_API void ensureUniqueIfOutOfPlaced(
 
 template <
     typename T,
-    typename = torch::enable_if_t<
-        (!std::is_convertible_v<torch::decay_t<T>, at::TensorList> &&
-         !std::is_convertible_v<torch::decay_t<T>, c10::List<at::Tensor>> &&
-         !std::is_convertible_v<torch::decay_t<T>, at::Tensor> &&
-         !std::is_convertible_v<
-             torch::decay_t<T>,
-             c10::intrusive_ptr<c10::ivalue::Object>>)>>
+    typename = torch::enable_if_t<(
+        !std::is_convertible<torch::decay_t<T>, at::TensorList>::value &&
+        !std::is_convertible<torch::decay_t<T>, c10::List<at::Tensor>>::value &&
+        !std::is_convertible<torch::decay_t<T>, at::Tensor>::value &&
+        !std::is_convertible<
+            torch::decay_t<T>,
+            c10::intrusive_ptr<c10::ivalue::Object>>::value)>>
 void addOutput(Node* node, T&&) {
   AT_ERROR(
       "Found an unsupported argument type ",
@@ -409,4 +410,5 @@ TORCH_API autograd::Variable getSizeOf(
 TORCH_API autograd::Variable getNumelOf(const autograd::Variable& var);
 
 } // namespace tracer
-} // namespace torch::jit
+} // namespace jit
+} // namespace torch

torch/csrc/utils/pybind.h

@@ -28,7 +28,8 @@ PYBIND11_DECLARE_HOLDER_TYPE(T, c10::intrusive_ptr<T>, true);
 PYBIND11_DECLARE_HOLDER_TYPE(T, c10::SingletonOrSharedTypePtr<T>);
 PYBIND11_DECLARE_HOLDER_TYPE(T, c10::SingletonTypePtr<T>, true);
 
-namespace pybind11::detail {
+namespace pybind11 {
+namespace detail {
 
 // torch.Tensor <-> at::Tensor conversions (without unwrapping)
 template <>
@@ -323,9 +324,11 @@ struct type_caster<c10::complex<T>> {
   }
 };
 
-} // namespace pybind11::detail
+} // namespace detail
+} // namespace pybind11
 
-namespace torch::impl {
+namespace torch {
+namespace impl {
 
 // Use this function if you have a C++ object that is used from both C++
 // and Python contexts, and you need its GIL to be released when you
@@ -381,4 +384,5 @@ inline void destroy_without_gil(T* ptr) {
   }
 }
 
-} // namespace torch::impl
+} // namespace impl
+} // namespace torch

torch/csrc/utils/tensor_memoryformats.h

@@ -4,9 +4,11 @@
 #include <torch/csrc/Export.h>
 #include <torch/csrc/utils/python_stub.h>
 
-namespace torch::utils {
+namespace torch {
+namespace utils {
 
 void initializeMemoryFormats();
 TORCH_PYTHON_API PyObject* getTHPMemoryFormat(c10::MemoryFormat);
 
-} // namespace torch::utils
+} // namespace utils
+} // namespace torch

torch/csrc/utils/tensor_numpy.h

@@ -3,7 +3,8 @@
 #include <ATen/core/Tensor.h>
 #include <torch/csrc/python_headers.h>
 
-namespace torch::utils {
+namespace torch {
+namespace utils {
 
 PyObject* tensor_to_numpy(const at::Tensor& tensor, bool force = false);
 at::Tensor tensor_from_numpy(PyObject* obj, bool warn_if_not_writeable = true);
@@ -22,4 +23,5 @@ at::Tensor tensor_from_cuda_array_interface(PyObject* obj);
 void validate_numpy_for_dlpack_deleter_bug();
 bool is_numpy_dlpack_deleter_bugged();
 
-} // namespace torch::utils
+} // namespace utils
+} // namespace torch

torch/csrc/utils/variadic.h

@@ -73,13 +73,13 @@ struct MakeIndices<0, Is...> {
 //===----------------------------------------------------------------------===//
 
 template <bool value, typename T = void>
-using enable_if_t = std::enable_if_t<value, T>;
+using enable_if_t = typename std::enable_if<value, T>::type;
 
 template <bool value, typename T = void>
 using disable_if_t = enable_if_t<!value, T>;
 
 template <typename T>
-using decay_t = std::decay_t<T>;
+using decay_t = typename std::decay<T>::type;
 
 namespace detail {
 template <bool...>
@@ -112,7 +112,7 @@ using enable_if_all_of_t = enable_if_t<all_of<values...>::value>;
 
 template <typename T, typename... Ts>
 using disable_if_contains_t =
-    enable_if_all_of_t<(!std::is_same_v<T, decay_t<Ts>>)...>;
+    enable_if_all_of_t<(!std::is_same<T, decay_t<Ts>>::value)...>;
 
 template <typename Function, typename... Ts>
 void apply(Function function, Ts&&... ts) {