Concat namespaces in jit code (#138976)

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/138976
Approved by: https://github.com/Skylion007

torch/csrc/jit/backends/backend.h

@@ -5,8 +5,7 @@
 #include <torch/csrc/jit/backends/backend_interface.h>
 #include <torch/custom_class.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 namespace {
 // NOLINTNEXTLINE(clang-diagnostic-unneeded-internal-declaration)
 inline c10::FunctionSchema getIsAvailableSchema() {
@@ -115,5 +114,4 @@ class backend {
   }
 };
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_debug_handler.cpp

@@ -2,8 +2,7 @@
 
 #include <stack>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 std::atomic<DebugHandleType> BackendDebugInfoRecorder::unique_debug_handle_{0};
 
@@ -33,5 +32,4 @@ BackendDebugInfoMapType BackendDebugInfoRecorder::stopRecording() {
   return handles_to_inlined_callstack_ptrs_;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_debug_handler.h

@@ -7,8 +7,7 @@
 
 #include <atomic>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 /*
  *  BackendDebugHandleManager is responsible for issuing debug handles to
@@ -136,5 +135,4 @@ class TORCH_API BackendDebugInfoRecorder {
   BackendDebugInfoMapType handles_to_inlined_callstack_ptrs_;
 };
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_debug_info.cpp

@@ -1,9 +1,7 @@
 #include <c10/macros/Macros.h>
 #include <torch/csrc/jit/backends/backend_debug_info.h>
 
-namespace torch {
-namespace jit {
-namespace backend {
+namespace torch::jit::backend {
 namespace {
 #ifdef BUILD_LITE_INTERPRETER
 static auto cls = torch::class_<PyTorchBackendDebugInfoDummy>(
@@ -18,6 +16,4 @@ static auto cls = torch::class_<PyTorchBackendDebugInfo>(
 #endif
 
 } // namespace
-} // namespace backend
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::backend

torch/csrc/jit/backends/backend_debug_info.h

@@ -5,8 +5,7 @@
 #endif
 #include <torch/custom_class.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 constexpr static auto kBackendUtilsNamespace = "backendutils";
 constexpr static auto kBackendDebugInfoClass = "BackendDebugInfo";
@@ -61,5 +60,4 @@ class PyTorchBackendDebugInfoDummy : public torch::CustomClassHolder {
   PyTorchBackendDebugInfoDummy() = default;
 };
 #endif
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_detail.h

@@ -6,8 +6,7 @@
 
 #include <functional>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 using DebugHandleType = int64_t;
 
@@ -37,5 +36,4 @@ TORCH_API Module codegen_backend_module(
     const c10::Dict<IValue, IValue>& method_compile_spec,
     const c10::DictTypePtr& any_dict_ty);
 } // namespace detail
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_init.cpp

@@ -7,8 +7,7 @@
 #include <torch/csrc/jit/python/pybind_utils.h>
 #include <torch/csrc/utils/pybind.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Get all types that are shared in the module hierarchy rooted at \p mod.
 std::unordered_set<TypePtr> getSharedModuleTypes(Module& mod) {
@@ -189,5 +188,4 @@ void initJitBackendBindings(PyObject* module) {
             "Object ", py::str(orig_module), " is not a ScriptModule"));
       });
 }
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_init.h

@@ -3,9 +3,7 @@
 #include <torch/csrc/jit/python/pybind.h>
 #include <torch/csrc/utils/pybind.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 // Initialize Python bindings for JIT to_<backend> functions.
 void initJitBackendBindings(PyObject* module);
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_interface.cpp

@@ -1,10 +1,8 @@
 #include <torch/csrc/jit/backends/backend_interface.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 PyTorchBackendInterface::PyTorchBackendInterface() noexcept = default;
 PyTorchBackendInterface::~PyTorchBackendInterface() = default;
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_interface.h

@@ -2,8 +2,7 @@
 
 #include <torch/custom_class.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Interface for a JIT backend.
 class TORCH_API PyTorchBackendInterface : public torch::CustomClassHolder {
@@ -30,5 +29,4 @@ class TORCH_API PyTorchBackendInterface : public torch::CustomClassHolder {
       c10::IValue handle,
       c10::impl::GenericList inputs) = 0;
 };
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_preprocess.h

@@ -1,8 +1,7 @@
 #pragma once
 
 #include <torch/csrc/jit/backends/backend_detail.h>
-namespace torch {
-namespace jit {
+namespace torch::jit {
 class backend_preprocess_register {
   std::string backend_name_;
 
@@ -14,5 +13,4 @@ class backend_preprocess_register {
     detail::registerBackendPreprocessFunction(name, preprocess);
   }
 };
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_resolver.cpp

@@ -2,8 +2,7 @@
 #include <torch/csrc/jit/frontend/sugared_value.h>
 #include <torch/custom_class.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 namespace {
 // Essentially ClassNamespaceValue from import_source.cpp without the
 // SourceImporterImpl reference. This helps resolve the
@@ -67,5 +66,4 @@ std::shared_ptr<Resolver> loweredModuleResolver() {
   return resolver;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/backend_resolver.h

@@ -2,9 +2,7 @@
 
 #include <torch/csrc/jit/frontend/resolver.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 // Create a Resolver for use in generating LoweredModules for specific backends.
 TORCH_API std::shared_ptr<Resolver> loweredModuleResolver();
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/backends/coreml/objc/PTMCoreMLTensorSpec.h

@@ -3,10 +3,7 @@
 
 #include <string>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace coreml {
+namespace torch::jit::mobile::coreml {
 
 struct TensorSpec {
   std::string name = "";
@@ -26,7 +23,4 @@ static inline c10::ScalarType scalar_type(const std::string& type_string) {
   return c10::ScalarType::Undefined;
 }
 
-} // namespace coreml
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::coreml

torch/csrc/jit/backends/xnnpack/executor/xnn_executor.h

@@ -8,10 +8,7 @@
 #include <memory>
 #include <vector>
 
-namespace torch {
-namespace jit {
-namespace xnnpack {
-namespace delegate {
+namespace torch::jit::xnnpack::delegate {
 
 class XNNExecutor {
  private:
@@ -68,7 +65,4 @@ class XNNExecutor {
   friend class XNNCompiler;
 };
 
-} // namespace delegate
-} // namespace xnnpack
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::xnnpack::delegate

torch/csrc/jit/codegen/cuda/interface.cpp

@@ -9,10 +9,7 @@
 #include <torch/csrc/jit/runtime/custom_operator.h>
 #include <torch/csrc/jit/runtime/register_ops_utils.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace cuda {
+namespace torch::jit::fuser::cuda {
 
 static std::atomic<bool> cuda_fusion_guard_mode{true};
 
@@ -131,7 +128,4 @@ bool skipNode(const std::string& symbol_str, bool flip) {
       getFuserInterface()->fn_skip_n(symbol_str, flip);
 }
 
-} // namespace cuda
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::cuda

torch/csrc/jit/codegen/cuda/interface.h

@@ -13,10 +13,7 @@
  * Registration is done in torch/csrc/jit/codegen/cuda/register_interface.cpp
  */
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace cuda {
+namespace torch::jit::fuser::cuda {
 
 TORCH_API std::atomic<bool>& getCudaFusionGuardMode();
 
@@ -52,7 +49,4 @@ TORCH_API bool isEnabled();
 TORCH_API bool setEnabled(bool is_enabled);
 TORCH_API bool canBeEnabled();
 
-} // namespace cuda
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::cuda

torch/csrc/jit/codegen/fuser/cpu/fused_kernel.h

@@ -13,10 +13,7 @@ namespace at {
 struct DynamicLibrary;
 }
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace cpu {
+namespace torch::jit::fuser::cpu {
 
 // Represents a compiled CPU kernel and the metadata necessary to run it
 struct TORCH_API FusedKernelCPU : public FusedKernel {
@@ -43,7 +40,4 @@ struct TORCH_API FusedKernelCPU : public FusedKernel {
   void (*kernel)(uint32_t, void**) = nullptr;
 };
 
-} // namespace cpu
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::cpu

torch/csrc/jit/codegen/fuser/cpu/resource_strings.h

@@ -2,10 +2,7 @@
 
 #include <ATen/code_template.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace cpu {
+namespace torch::jit::fuser::cpu {
 
 /*with type_as not checking type of its input, a fusion group can have non-fp32
 tensor as input. Correct code for this case is generated, however, nvrtc does
@@ -101,7 +98,4 @@ JIT_API void ${kernelName}(IndexType totalElements, void ** args) {
 }
 )");
 
-} // namespace cpu
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::cpu

torch/csrc/jit/codegen/fuser/cpu/temp_file.h

@@ -22,10 +22,7 @@
 #include <string>
 #include <vector>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace cpu {
+namespace torch::jit::fuser::cpu {
 
 #ifdef _MSC_VER
 int wmkstemps(wchar_t* tmpl, int suffix_len) {
@@ -135,7 +132,4 @@ struct TempFile {
   std::string name_;
 };
 
-} // namespace cpu
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::cpu

torch/csrc/jit/codegen/onednn/LlgaTensorImpl.cpp

@@ -4,10 +4,7 @@
 #include <c10/core/CPUAllocator.h>
 #include <torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 // Non-default dnnl::graph::allocator needs an allocator.
 // We would let it use c10::GetCPUAllocator's allocator,
@@ -152,9 +149,6 @@ at::ScalarType LlgaTensorDesc::aten_scalar_type() const {
   }
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn
 
 #endif // AT_MKLDNN_ENABLED()

torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h

@@ -6,10 +6,7 @@
 #include <oneapi/dnnl/dnnl_graph.hpp>
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 // Engine represents a device and its context. From the device kind, the engine
 // knows how to generate code for the target device and what kind of device
@@ -270,7 +267,4 @@ at::Tensor empty_llga(
 
 dnnl::graph::tensor llga_from_aten_tensor(const at::Tensor& tensor);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/decompose_silu.cpp

@@ -5,10 +5,7 @@
 #include <torch/csrc/jit/passes/dead_code_elimination.h>
 #include <torch/csrc/jit/passes/subgraph_rewrite.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 static bool shouldDecomposeSilu(Node* node) {
   if (node->kind() != aten::silu) {
@@ -59,7 +56,4 @@ void DecomposeSiluForLLGA(std::shared_ptr<Graph>& graph) {
   EliminateDeadCode(graph);
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/decompose_silu.h

@@ -2,14 +2,8 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 void DecomposeSiluForLLGA(std::shared_ptr<Graph>& graph);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/defer_size_check.cpp

@@ -2,10 +2,7 @@
 #include <torch/csrc/jit/ir/alias_analysis.h>
 #include <torch/csrc/jit/runtime/symbolic_shape_registry_util.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 class SizeCheckMover {
  private:
@@ -82,7 +79,4 @@ void DeferSizeCheck(std::shared_ptr<Graph>& graph) {
   SizeCheckMover(graph->block(), graph).run();
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/defer_size_check.h

@@ -2,14 +2,8 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 void DeferSizeCheck(std::shared_ptr<Graph>& graph);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/graph_fuser.cpp

@@ -5,10 +5,7 @@
 #include <torch/csrc/jit/passes/dead_code_elimination.h>
 #include <torch/csrc/jit/passes/utils/subgraph_utils.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 void CreateLlgaSubgraphs(std::shared_ptr<Graph>& graph) {
   AliasDb db(graph);
@@ -25,7 +22,4 @@ void CreateLlgaSubgraphs(std::shared_ptr<Graph>& graph) {
   EliminateDeadCode(graph);
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/graph_fuser.h

@@ -3,10 +3,7 @@
 #include <torch/csrc/jit/codegen/onednn/graph_helper.h>
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 struct WorkBlock : public std::pair<Node*, Node*> {
   using pair::pair;
@@ -47,7 +44,4 @@ class GraphRewriter {
 // torch/csrc/jit/passes/create_autodiff_subgraphs.cpp
 void CreateLlgaSubgraphs(std::shared_ptr<Graph>& graph);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/graph_helper.cpp

@@ -5,10 +5,7 @@
 #include <torch/csrc/jit/jit_log.h>
 #include <torch/csrc/jit/passes/utils/subgraph_utils.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 using opkind = dnnl::graph::op::kind;
 
@@ -615,7 +612,4 @@ bool LlgaNodeWrapper::useOpaqueLayout(size_t offset) const {
   return n->is(attr::output_layouts)[offset] == OPAQUE_LAYOUT;
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/graph_helper.h

@@ -5,10 +5,7 @@
 #include <torch/csrc/jit/ir/alias_analysis.h>
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 #define STRIDED_LAYOUT 0
 #define OPAQUE_LAYOUT 1
@@ -98,7 +95,4 @@ class LlgaNodeWrapper {
   Node* n;
 };
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/graph_rewriter.cpp

@@ -5,10 +5,7 @@
 #include <torch/csrc/jit/passes/dead_code_elimination.h>
 #include <torch/csrc/jit/passes/utils/subgraph_utils.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 void GraphRewriter::cleanupSubgraphs() {
   auto curNode = *block_->nodes().rbegin();
@@ -138,7 +135,4 @@ std::optional<Node*> GraphRewriter::tryMerge(Node* consumer, Node* producer) {
   return consumer;
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/guard_shape.cpp

@@ -5,10 +5,7 @@
 #include <torch/csrc/jit/passes/utils/subgraph_utils.h>
 #include <torch/csrc/jit/runtime/graph_executor.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 //! [ Note -- prepareFusionGroupAndGuardOutputs implementation ]
 //! shamelessly copying code from NNC (tensorexpr_fuser)  with very little
@@ -39,7 +36,4 @@ void prepareFusionGroupAndGuardOutputs(Block* block) {
   }
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/guard_shape.h

@@ -2,14 +2,8 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 void prepareFusionGroupAndGuardOutputs(Block* block);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/interface.cpp

@@ -16,10 +16,8 @@
 #include <torch/csrc/jit/runtime/graph_executor.h>
 #include <torch/csrc/jit/runtime/operator_options.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit {
+namespace fuser::onednn {
 
 void fuseGraph(std::shared_ptr<Graph>& g) {
   // Follow the process of the tensorexpr_fuser in profiling mode:
@@ -95,8 +93,7 @@ void fuseGraph(std::shared_ptr<Graph>& g) {
   }
 }
 
-} // namespace onednn
-} // namespace fuser
+} // namespace fuser::onednn
 
 static Operation createLlgaKernel(const Node* node) {
   auto kernel = std::make_shared<fuser::onednn::LlgaKernel>(node);
@@ -178,5 +175,4 @@ RegisterOperators oneDNNGuardOp({
         createLlgaGuardKernel,
         AliasAnalysisKind::FROM_SCHEMA),
 });
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/codegen/onednn/interface.h

@@ -3,10 +3,8 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/jit/passes/pass_manager.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit {
+namespace fuser::onednn {
 
 static std::atomic<bool> onednn_enabled{false};
 
@@ -16,8 +14,7 @@ static std::atomic<bool>& getLlgaEnabled() {
 
 C10_EXPORT void fuseGraph(std::shared_ptr<Graph>& g);
 
-} // namespace onednn
-} // namespace fuser
+} // namespace fuser::onednn
 
 struct C10_EXPORT RegisterLlgaFuseGraph
     : public PassManager<RegisterLlgaFuseGraph> {
@@ -58,5 +55,4 @@ struct C10_EXPORT RegisterLlgaFuseGraph
   }
 };
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/codegen/onednn/kernel.cpp

@@ -4,10 +4,7 @@
 #include <ATen/core/functional.h>
 #include <torch/csrc/jit/jit_log.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 using namespace dnnl::graph;
 using data_type = dnnl::graph::logical_tensor::data_type;
@@ -293,7 +290,4 @@ void LlgaKernel::run(Stack& stack) {
 #endif
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/kernel.h

@@ -10,10 +10,7 @@
 
 #include <c10/util/CallOnce.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 using ArgSpec = LlgaTensorDesc;
 using ArgSpecs = std::vector<ArgSpec>;
@@ -89,7 +86,4 @@ class LlgaKernel {
   bool is_initialized_ = false;
 };
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/layout_propagation.cpp

@@ -2,10 +2,7 @@
 #include <torch/csrc/jit/codegen/onednn/layout_propagation.h>
 #include <torch/csrc/jit/jit_log.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 static void LayoutPropagation(Node* n) {
   if (!LlgaGraphHelper::isLlgaSubgraph(n))
@@ -47,7 +44,4 @@ void PropagateLayout(const std::shared_ptr<Graph>& graph) {
   LayoutPropagation(graph->block());
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/layout_propagation.h

@@ -2,14 +2,8 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 void PropagateLayout(const std::shared_ptr<Graph>& graph);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/operator.h

@@ -4,10 +4,7 @@
 #include <torch/csrc/jit/codegen/onednn/LlgaTensorImpl.h>
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 class Operator {
  public:
@@ -146,7 +143,4 @@ class Operator {
   dnnl::graph::op::kind k;
 };
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/prepare_binary.cpp

@@ -3,10 +3,7 @@
 #include <torch/csrc/jit/passes/dead_code_elimination.h>
 #include <torch/csrc/jit/passes/shape_analysis.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 static bool compareConstValue(Value* v, double d) {
   auto ival = toIValue(v);
@@ -179,7 +176,4 @@ void PrepareBinaryForLLGA(const std::shared_ptr<Graph>& graph) {
   ConvertScalarToTensor(graph->block());
 }
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/prepare_binary.h

@@ -2,10 +2,7 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 // Prepare binary ops for LLGA
 //
@@ -20,7 +17,4 @@ namespace onednn {
 //
 void PrepareBinaryForLLGA(const std::shared_ptr<Graph>& graph);
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/codegen/onednn/register_interface.cpp

@@ -1,9 +1,6 @@
 #include <torch/csrc/jit/runtime/profiling_record.h>
 
-namespace torch {
-namespace jit {
-namespace fuser {
-namespace onednn {
+namespace torch::jit::fuser::onednn {
 
 static bool canFuseNode(const Node* node) {
   switch (node->kind()) {
@@ -48,7 +45,4 @@ class RegisterInterface {
 static RegisterInterface register_interface_;
 } // namespace
 
-} // namespace onednn
-} // namespace fuser
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::fuser::onednn

torch/csrc/jit/mobile/nnc/aot_compiler.cpp

@@ -25,10 +25,7 @@
 using namespace torch::jit;
 using namespace torch::jit::tensorexpr;
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 // TODO(mvz): temporarily disable NNC backend in mobile builds.
 /*
@@ -446,7 +443,4 @@ static c10::IValue preprocess(
 
 // static auto reg = torch::jit::backend_preprocess_register("nnc", preprocess);
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/mobile/nnc/aot_compiler.h

@@ -4,10 +4,7 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/jit/mobile/nnc/context.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 // Performs Ahead Of Time compilation of a given method in a model
 // returning the compiled function and LLVM assembly code
@@ -18,7 +15,4 @@ TORCH_API std::pair<std::unique_ptr<Function>, const std::string> aotCompile(
     const std::vector<at::ScalarType>& types,
     const std::string& kernel_func_name = "func");
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/mobile/nnc/backend.cpp

@@ -3,10 +3,7 @@
 #include <torch/csrc/jit/backends/backend.h>
 #include <torch/csrc/jit/mobile/nnc/context.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 class NNCBackend : public PyTorchBackendInterface {
  public:
@@ -55,7 +52,4 @@ namespace {
 // static const auto cls = torch::jit::backend<NNCBackend>("nnc");
 } // namespace
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/mobile/nnc/context.cpp

@@ -7,10 +7,7 @@
 
 #include <torch/csrc/jit/mobile/nnc/registry.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 constexpr int64_t kProducedNNCFileFormatVersion = 0x1L;
 
@@ -342,7 +339,4 @@ Function* CompilationUnit::find_function(const c10::QualifiedName& name) const {
   return it->second.get();
 }
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/mobile/nnc/context.h

@@ -8,10 +8,7 @@
 #include <ATen/core/ivalue.h>
 #include <c10/core/ScalarType.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 // Specify the requirements on an input tensor.
 // TODO: support input tensor with dynamic shape (PR #54982)
@@ -223,7 +220,4 @@ class TORCH_API CompilationUnit {
   std::unordered_map<c10::QualifiedName, std::unique_ptr<Function>> functions_;
 };
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/mobile/nnc/registry.cpp

@@ -1,13 +1,7 @@
 #include <torch/csrc/jit/mobile/nnc/registry.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 C10_DEFINE_REGISTRY(NNCKernelRegistry, NNCKernel);
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/mobile/nnc/registry.h

@@ -3,10 +3,7 @@
 #include <c10/util/Exception.h>
 #include <c10/util/Registry.h>
 
-namespace torch {
-namespace jit {
-namespace mobile {
-namespace nnc {
+namespace torch::jit::mobile::nnc {
 
 using nnc_kernel_function_type = int(void**);
 
@@ -40,7 +37,4 @@ inline std::unique_ptr<NNCKernel> get_nnc_kernel(const std::string& id) {
 
 } // namespace registry
 
-} // namespace nnc
-} // namespace mobile
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::mobile::nnc

torch/csrc/jit/passes/dbr_quantization/remove_redundant_aliases.cpp

@@ -5,8 +5,7 @@
 #include <torch/csrc/jit/passes/quantization/helper.h>
 #include <torch/csrc/jit/runtime/graph_iterator.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 
@@ -70,5 +69,4 @@ Module DBRQuantRemoveRedundantAliases(Module& module) {
   return module;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/dbr_quantization/remove_redundant_aliases.h

@@ -2,8 +2,7 @@
 
 #include <torch/csrc/jit/api/module.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // This function replaces instances of
 //
@@ -17,5 +16,4 @@ namespace jit {
 // on the module forward, if it's safe to do so.
 TORCH_API Module DBRQuantRemoveRedundantAliases(Module& module);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/autograd_function_process.cpp

@@ -4,8 +4,7 @@
 #include <torch/csrc/jit/passes/onnx/helper.h>
 #include <torch/csrc/jit/passes/utils/subgraph_utils.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 void convertSubgraphToSubBlock(Block* block) {
   for (auto it = block->nodes().begin(), end = block->nodes().end();
@@ -54,5 +53,4 @@ void ONNXAutogradFunctionProcess(std::shared_ptr<Graph>& graph) {
   convertSubgraphToSubBlock(graph->block());
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/autograd_function_process.h

@@ -2,10 +2,8 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 TORCH_API void ONNXAutogradFunctionProcess(std::shared_ptr<Graph>& graph);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/common.cpp

@@ -1,7 +1,6 @@
 #include <torch/csrc/jit/passes/onnx/pattern_conversion/common.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 bool IndexingPatternFinder::IsSameSource(const Node* n, const Node* m) {
   const auto source_n = n->sourceRange().source();
@@ -41,5 +40,4 @@ std::vector<Node*> IndexingPatternFinder::FetchSliceAndSelect(
   return slice_and_select_node;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/common.h

@@ -4,8 +4,7 @@
 
 // Functions used by both encapsulation and conversion.
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 struct IndexingPatternFinder {
  public:
@@ -15,5 +14,4 @@ struct IndexingPatternFinder {
   static bool IsSameSource(const Node* n, const Node* m);
 };
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/pattern_conversion.cpp

@@ -12,8 +12,7 @@
 // EDITING THIS FILE? READ THIS FIRST!
 // see Note [Edit Pattern Conversion] in pattern_conversion.h
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Converting inplace index_put to ONNX
 namespace {
@@ -392,5 +391,4 @@ std::vector<Value*> ConvertPatternFromSubblock(
   return res;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/pattern_conversion.h

@@ -3,8 +3,7 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/utils/pybind.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Introduction
 //
@@ -42,5 +41,4 @@ TORCH_API std::vector<Value*> ConvertPatternFromSubblock(
     py::dict& env,
     py::set& values_in_env);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/pattern_encapsulation.cpp

@@ -7,8 +7,7 @@
 // EDITING THIS FILE? READ THIS FIRST!
 // see Note [Edit Pattern Encapsulation] in pattern_encapsulation.h
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 
@@ -87,5 +86,4 @@ std::optional<Node*> EncapsulatePatternIntoSubblock(Node* n) {
   return std::nullopt;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/onnx/pattern_conversion/pattern_encapsulation.h

@@ -2,8 +2,7 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Introduction
 //
@@ -30,5 +29,4 @@ namespace jit {
 // pattern is stored as attr::name.
 TORCH_API std::optional<Node*> EncapsulatePatternIntoSubblock(Node* n);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/dedup_module_uses.cpp

@@ -5,8 +5,7 @@
 
 #include <stack>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 namespace {
 class ModuleUseDeduper {
  public:
@@ -125,5 +124,4 @@ void DedupModuleUses(Module& module) {
   d.dedup();
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/dedup_module_uses.h

@@ -2,8 +2,7 @@
 
 #include <torch/csrc/jit/api/module.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 /** Recursively deduplicate multiple uses of the same module by
  *  creating an instance clone for each use of the module, which means
@@ -24,5 +23,4 @@ namespace jit {
  */
 TORCH_API void DedupModuleUses(Module& module);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/finalize.cpp

@@ -16,8 +16,7 @@
 
 #include <utility>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 
@@ -275,5 +274,4 @@ Module FinalizeOnDevicePTQ(
   return module;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/finalize.h

@@ -4,8 +4,7 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/jit/passes/quantization/quantization_type.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 /** \brief Backend specific pass to fuse dequantize - op - quantize calls
  * as quantized_op calls.
@@ -59,5 +58,4 @@ TORCH_API Module FinalizeOnDevicePTQ(
     Module& module,
     QuantType quant_type,
     const std::string& method_name);
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/fusion_passes.cpp

@@ -1,8 +1,7 @@
 #include <torch/csrc/jit/passes/quantization/fusion_passes.h>
 #include <torch/csrc/jit/passes/subgraph_rewrite.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 void fuseQuantizeAddReluImpl(std::shared_ptr<Graph>& graph) {
@@ -59,5 +58,4 @@ void FuseQuantizedAddRelu(std::shared_ptr<Graph>& graph) {
   fuseQuantizeAddReluImpl(graph);
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/fusion_passes.h

@@ -2,8 +2,6 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 TORCH_API void FuseQuantizedAddRelu(std::shared_ptr<Graph>& graph);
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/helper.cpp

@@ -5,8 +5,7 @@
 
 #include <utility>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 using graph_rewrite_helper::getFuncName;
 
@@ -795,5 +794,4 @@ bool is_batchnorm3d_module(
       "__torch__.torch.nn.modules.batchnorm.BatchNorm3d");
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/helper.h

@@ -8,8 +8,7 @@
 #include <functional>
 #include <regex>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 using graph_rewrite_helper::getFuncName;
 
@@ -212,5 +211,4 @@ bool is_batchnorm3d_module(
     const Match& match,
     const std::unordered_map<std::string, Value*>& vmap);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/insert_observers.cpp

@@ -17,8 +17,7 @@
 #include <string>
 #include <utility>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 using ModuleQConfigMap = std::unordered_map<ModulePtr, std::optional<QConfig>>;
 
@@ -1720,5 +1719,4 @@ Module InsertObserversForOnDevicePTQ(
       cloned_module, observer_method_name, /* is_entry_point */ true);
   return cloned_module;
 }
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/insert_observers.h

@@ -14,8 +14,7 @@ struct hash<torch::jit::Module> {
 
 } // namespace std
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 using QConfig = std::tuple<Module, Module>;
 using QConfigDict = std::unordered_map<std::string, std::optional<QConfig>>;
@@ -64,5 +63,4 @@ TORCH_API Module InsertObserversForOnDevicePTQ(
     bool inplace,
     QuantType quant_type = QuantType::STATIC);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/insert_quant_dequant.cpp

@@ -15,8 +15,7 @@
 #include <stack>
 #include <utility>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 using graph_rewrite_helper::PatternInfo;
@@ -1841,5 +1840,4 @@ Module InsertQuantDeQuantOnDevicePTQ(
   h.propagateQuantizationOps(module);
   return module;
 }
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/insert_quant_dequant.h

@@ -4,8 +4,7 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/jit/passes/quantization/quantization_type.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 /** Replicate quantize node for prim::If blocks, so that we can match
  *  quantization patterns in prim::If blocks
@@ -42,5 +41,4 @@ TORCH_API Module InsertQuantDeQuantOnDevicePTQ(
     bool debug,
     QuantType quant_type = QuantType::STATIC);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/quantization_patterns.h

@@ -10,8 +10,7 @@
 #include <unordered_map>
 #include <utility>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 struct QuantFusionInfo {
   std::string quantized_op_name;
@@ -1260,5 +1259,4 @@ graph(%a_dequant, %w_quant, %b, %stride, %padding, %output_padding, %groups, %di
        std::move(conv_transpose2d_with_quant_prepack)}};
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/quantization_type.cpp

@@ -1,7 +1,6 @@
 #include <torch/csrc/jit/passes/quantization/quantization_type.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 std::ostream& operator<<(std::ostream& os, QuantType t) {
   switch (t) {
@@ -17,5 +16,4 @@ std::ostream& operator<<(std::ostream& os, QuantType t) {
   return os;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/quantization_type.h

@@ -2,8 +2,7 @@
 #include <cstdint>
 #include <ostream>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Quantization type (dynamic quantization, static quantization).
 // Should match the Python enum in quantize_jit.py
@@ -11,5 +10,4 @@ enum QuantType : std::uint8_t { DYNAMIC = 0, STATIC };
 
 std::ostream& operator<<(std::ostream& os, QuantType t);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/register_packed_params.cpp

@@ -7,8 +7,7 @@
 #include <torch/csrc/jit/passes/quantization/helper.h>
 #include <torch/csrc/jit/passes/quantization/register_packed_params.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 namespace {
 bool isPrepackNode(Node* n) {
@@ -144,5 +143,4 @@ std::unordered_set<std::string> RegisterPrePackParams(
   return packed_param_names;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/quantization/register_packed_params.h

@@ -4,8 +4,7 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <memory>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 using PrePackParamFilterFn = std::function<bool(Node*)>;
 
@@ -16,5 +15,4 @@ TORCH_API std::unordered_set<std::string> RegisterPrePackParams(
     const std::string& attr_prefix);
 
 TORCH_API std::string joinPaths(const std::vector<std::string>& paths);
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/check_alias_annotation.cpp

@@ -6,8 +6,7 @@
 
 #include <c10/util/irange.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 namespace {
 
 IValue deepCopy(const IValue& self) {
@@ -305,5 +304,4 @@ void checkAliasAnnotation(
   checkWrites(inputsToCheck, inputsDeepCopy);
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/check_alias_annotation.h

@@ -6,8 +6,7 @@
 #include <string>
 #include <vector>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Verify that alias annotations are correct. See impl for definition of
 // "correct".
@@ -18,5 +17,4 @@ TORCH_API void checkAliasAnnotation(
     const std::shared_ptr<Graph>& graph,
     std::vector<IValue> pythonInputs,
     const std::string& unqualifiedOpName);
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/memory_dag.cpp

@@ -4,8 +4,7 @@
 #include <algorithm>
 #include <queue>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 namespace {
 
 void makePointerToImpl(Element* from, Element* to) {
@@ -232,5 +231,4 @@ void MemoryDAG::setWildcards(
 Element* MemoryDAG::unsafeMakeFreshValue(const Value* v) {
   return makeFreshValueImpl(v, indexToElementMap_);
 }
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/memory_dag.h

@@ -16,8 +16,7 @@
 
 // Uses a compressed index representation for faster comparisons
 typedef c10::SparseBitVector<256> MemoryLocations;
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 struct Value;
 
@@ -172,5 +171,4 @@ class TORCH_API MemoryDAGBuilder {
   // the map to construct the `MemoryDAG`
   std::vector<std::unique_ptr<Element>> indexToElementMap_;
 };
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/op_registry.cpp

@@ -2,8 +2,7 @@
 
 // Location for Commonly Used Shape registries
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Requirements:
 //   dims           : preserved from the first argument
@@ -72,5 +71,4 @@ std::shared_ptr<OperatorSet> ops_one_tensor_in_shape_transform() {
   });
   return ops;
 };
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/op_registry.h

@@ -4,8 +4,7 @@
 #include <torch/csrc/jit/ir/ir.h>
 #include <memory>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 // Moved from shape_analysis.cpp
 
 // Requirements:
@@ -27,5 +26,4 @@ std::shared_ptr<OperatorSet> nn_ops_first_input_preserving();
 //   tensor inputs  : 1
 //   tensor outputs : 1
 std::shared_ptr<OperatorSet> ops_one_tensor_in_shape_transform();
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/optimization_utils.cpp

@@ -1,7 +1,6 @@
 #include <torch/csrc/jit/passes/utils/optimization_utils.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 bool nonConstantParameters(Node* n) {
   // Checks if the parameters, not including the
@@ -14,5 +13,4 @@ bool nonConstantParameters(Node* n) {
   return false;
 }
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/optimization_utils.h

@@ -3,12 +3,10 @@
 
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
+namespace torch::jit {
 
 // Checks if the parameters, not including the
 // first param are all constants.
 bool nonConstantParameters(Node* n);
 
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit

torch/csrc/jit/passes/utils/subgraph_utils.cpp

@@ -9,9 +9,7 @@
 
 #include <utility>
 
-namespace torch {
-namespace jit {
-namespace SubgraphUtils {
+namespace torch::jit::SubgraphUtils {
 namespace {
 
 bool hasSubgraph(Node* n) {
@@ -633,6 +631,4 @@ std::string generateNameForGraph(
   return truncateStrWithHash(graph_name.str(), maxlen);
 }
 
-} // namespace SubgraphUtils
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::SubgraphUtils

torch/csrc/jit/passes/utils/subgraph_utils.h

@@ -4,14 +4,11 @@
 #include <torch/csrc/jit/ir/alias_analysis.h>
 #include <torch/csrc/jit/ir/ir.h>
 
-namespace torch {
-namespace jit {
-
 // Utilities for dealing with nodes that contain subgraphs.
 //
 // They handle the complexity of editing inputs/outputs as you merge nodes in
 // and out of subgraphs.
-namespace SubgraphUtils {
+namespace torch::jit::SubgraphUtils {
 
 // Create a new subgraph node that contains only `n`. The new subgraph will have
 // `subgraphKind` as its type.
@@ -70,6 +67,4 @@ TORCH_API std::string generateNameForGraph(
     size_t maxlen = 40,
     const std::string& prefix = "fused");
 
-} // namespace SubgraphUtils
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::SubgraphUtils

torch/csrc/jit/tensorexpr/bounds_inference.h

@@ -6,9 +6,7 @@
 #include <torch/csrc/Export.h>
 #include <torch/csrc/jit/tensorexpr/mem_dependency_checker.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 class Expr;
 class Buf;
@@ -74,6 +72,4 @@ TORCH_API bool isOverlapping(
     const StorePtr& S,
     const LoadPtr& L);
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/bounds_overlap.h

@@ -6,10 +6,7 @@
 #include <utility>
 #include <vector>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
-namespace analysis {
+namespace torch::jit::tensorexpr::analysis {
 
 // A simple class containing the start and end of a range in a single dimension.
 struct TORCH_API Bound {
@@ -121,7 +118,4 @@ std::vector<IndexBounds> TORCH_API subtractIndicesBounds(
 std::vector<IndexBounds> TORCH_API
 subtractIndicesBounds(const IndexBounds& A, const IndexBounds& B);
 
-} // namespace analysis
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr::analysis

torch/csrc/jit/tensorexpr/cpp_intrinsics.h

@@ -1,8 +1,6 @@
 #pragma once
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 constexpr auto cpp_intrinsics_definition = R"(
 namespace std {
@@ -31,6 +29,4 @@ To bitcast(const From& v) {
 } // namespace std
 )";
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/cuda_random.h

@@ -1,8 +1,6 @@
 #pragma once
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 constexpr auto philox_random_string = R"(
 
@@ -99,6 +97,4 @@ __device__  __inline__ float Uint32ToFloat(unsigned int x) {
 
 )";
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/fwd_decls.h

@@ -2,9 +2,7 @@
 #include <c10/core/ScalarType.h>
 #include <memory>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 template <typename Node>
 using NodePtr = std::shared_ptr<Node>;
@@ -124,6 +122,4 @@ using SyncThreadsPtr = NodePtr<SyncThreads>;
 AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
 #undef IMM_DECLARE
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/ir.h

@@ -11,9 +11,7 @@
 
 #include <ATen/core/ivalue.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 enum CompareSelectOperation {
   kEQ = 0,
@@ -918,6 +916,4 @@ TORCH_API ExprPtr flatten_index(
     const std::vector<ExprPtr>& indices,
     const std::vector<ExprPtr>& strides);
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/ir_cloner.h

@@ -5,9 +5,7 @@
 
 #include <torch/csrc/jit/tensorexpr/ir_mutator.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 class TORCH_API IRCloner : public IRMutator {
  public:
@@ -61,6 +59,4 @@ class TORCH_API IRCloner : public IRMutator {
   StmtPtr mutate(const CondPtr& v) override;
 };
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/ir_verifier.h

@@ -3,9 +3,7 @@
 #include <torch/csrc/jit/tensorexpr/fwd_decls.h>
 #include <torch/csrc/jit/tensorexpr/ir_visitor.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 class Expr;
 class ExprHandle;
@@ -53,6 +51,4 @@ TORCH_API void verify(const StmtPtr&);
 TORCH_API void verify(const ExprPtr&);
 TORCH_API void verify(const ExprHandle&);
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/operators/conv2d.h

@@ -3,9 +3,7 @@
 #include <torch/csrc/jit/tensorexpr/operators/misc.h>
 #include <torch/csrc/jit/tensorexpr/tensor.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 // An API to compute 2D depthwise convolutions with bias.
 TORCH_API Tensor conv2d_depthwise(
@@ -100,6 +98,4 @@ Tensor computeMkldnnPrepackedConvRun(
     const std::vector<ExprHandle>& outputStrides,
     const std::optional<ScalarType>& outputType,
     at::Device device);
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/operators/matmul.h

@@ -2,9 +2,7 @@
 
 #include <torch/csrc/jit/tensorexpr/kernel.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 Tensor computeMatmul(
     const std::vector<ArgValue>& inputs,
@@ -19,6 +17,4 @@ Tensor computeAddMM(
     const std::optional<ScalarType>& outputType,
     at::Device device);
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/operators/norm.h

@@ -2,9 +2,7 @@
 
 #include <torch/csrc/jit/tensorexpr/kernel.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 Tensor computeBatchNorm(
     const std::vector<ArgValue>& inputs,
@@ -13,6 +11,4 @@ Tensor computeBatchNorm(
     const std::optional<ScalarType>& outputType,
     at::Device device);
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/operators/pointwise.h

@@ -2,9 +2,7 @@
 
 #include <torch/csrc/jit/tensorexpr/kernel.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 TORCH_API Tensor computeSign(
     const std::vector<ArgValue>& inputs,
@@ -81,6 +79,4 @@ Tensor computeScalar(
     const std::function<ExprHandle(const ExprHandle&, const ExprHandle&)>&
         innerExpr);
 
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr

torch/csrc/jit/tensorexpr/operators/quantization.h

@@ -2,9 +2,7 @@
 
 #include <torch/csrc/jit/tensorexpr/kernel.h>
 
-namespace torch {
-namespace jit {
-namespace tensorexpr {
+namespace torch::jit::tensorexpr {
 
 TORCH_API ExprHandle quantizePerTensorQParamFromArg(ArgValue arg);
 
@@ -155,6 +153,4 @@ TORCH_API Tensor computeQuantizedSigmoidExternalCall(
     const std::vector<ExprHandle>& outputStrides,
     const std::optional<ScalarType>& outputType,
     at::Device);
-} // namespace tensorexpr
-} // namespace jit
-} // namespace torch
+} // namespace torch::jit::tensorexpr