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miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/tensor_list.h

@@ -0,0 +1,18 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+
+namespace at {
+class Tensor;
+}
+
+namespace torch::utils {
+
+PyObject* tensor_to_list(const at::Tensor& tensor);
+
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/tensor_memoryformats.h

@@ -0,0 +1,20 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/core/MemoryFormat.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/utils/python_stub.h>
+
+namespace torch::utils {
+
+void initializeMemoryFormats();
+
+// This methods returns a borrowed reference!
+TORCH_PYTHON_API PyObject* getTHPMemoryFormat(
+    c10::MemoryFormat /*memory_format*/);
+
+} // namespace torch::utils
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/tensor_new.h

@@ -0,0 +1,141 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+#include <torch/csrc/utils/python_arg_parser.h>
+
+#include <ATen/core/Tensor.h>
+
+namespace torch::utils {
+
+// NOTE: [torch.tensor, lift_fresh, and device movement]
+//
+// The `only_lift_cpu_tensors` flag controls what happens on torch.tensor([1, 2,
+// 3], device="cuda") (or any non-CPU devices).
+//
+// If false (default):
+// - the data gets moved into a CPU Tensor
+// - then, it gets moved to cuda (via .to)
+// - finally, we call lift_fresh() on it.
+// Steps 1 and 2 happen with all modes disabled.
+//
+// If true:
+// - the data gets moved into a CPU Tensor (with correct dtype)
+// - we call lift_fresh() on it
+// - finally, we move it to cuda (via .to)
+// Step 1 happens with all modes disabled.
+//
+// `only_lift_cpu_tensors=true` is useful to prevent CUDA initialization under
+// FakeTensorMode because it avoids moving concrete data to CUDA.
+TORCH_API bool only_lift_cpu_tensors();
+TORCH_API void set_only_lift_cpu_tensors(bool value);
+
+at::Tensor base_tensor_ctor(PyObject* args, PyObject* kwargs);
+TORCH_PYTHON_API at::Tensor legacy_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+at::Tensor legacy_tensor_new(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+at::Tensor indexing_tensor_from_data(
+    c10::TensorOptions options,
+    at::ScalarType scalar_type,
+    std::optional<at::Device> device,
+    PyObject* data);
+at::Tensor sparse_coo_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+void _validate_sparse_coo_tensor_args(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+
+at::Tensor sparse_compressed_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+at::Tensor sparse_csr_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+at::Tensor sparse_csc_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+at::Tensor sparse_bsr_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+at::Tensor sparse_bsc_tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+
+void _validate_sparse_compressed_tensor_args(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+void _validate_sparse_csr_tensor_args(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+void _validate_sparse_csc_tensor_args(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+void _validate_sparse_bsr_tensor_args(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+void _validate_sparse_bsc_tensor_args(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+
+at::Tensor tensor_ctor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+at::Tensor as_tensor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PythonArgs& r);
+at::Tensor new_tensor(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+at::Tensor new_ones(
+    c10::DispatchKey dispatch_key,
+    at::ScalarType scalar_type,
+    PyObject* args,
+    PyObject* kwargs);
+at::Tensor tensor_frombuffer(
+    PyObject* buffer,
+    at::ScalarType dtype,
+    int64_t count,
+    int64_t offset,
+    bool requires_grad);
+at::Tensor tensor_fromDLPack(PyObject* data);
+at::Tensor asarray(
+    PyObject* obj,
+    std::optional<c10::ScalarType> dtype,
+    std::optional<c10::Device> device,
+    std::optional<bool> copy,
+    bool requires_grad);
+} // namespace torch::utils
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/tensor_numpy.h

@@ -0,0 +1,37 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/core/Tensor.h>
+#include <torch/csrc/python_headers.h>
+
+namespace torch::utils {
+
+TORCH_API PyObject* tensor_to_numpy(
+    const at::Tensor& tensor,
+    bool force = false);
+
+TORCH_API at::Tensor tensor_from_numpy(
+    PyObject* obj,
+    bool warn_if_not_writeable = true);
+
+TORCH_API int aten_to_numpy_dtype(const at::ScalarType scalar_type);
+TORCH_API at::ScalarType numpy_dtype_to_aten(int dtype);
+
+TORCH_API bool is_numpy_available();
+TORCH_API bool is_numpy_int(PyObject* obj);
+TORCH_API bool is_numpy_bool(PyObject* obj);
+TORCH_API bool is_numpy_scalar(PyObject* obj);
+
+void warn_numpy_not_writeable();
+at::Tensor tensor_from_cuda_array_interface(
+    PyObject* obj,
+    std::optional<c10::Device> device_opt = std::nullopt);
+
+void validate_numpy_for_dlpack_deleter_bug();
+bool is_numpy_dlpack_deleter_bugged();
+
+} // namespace torch::utils
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/tensor_qschemes.h

@@ -0,0 +1,14 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+#include <torch/csrc/QScheme.h>
+
+namespace torch::utils {
+
+PyObject* getTHPQScheme(at::QScheme qscheme);
+void initializeQSchemes();
+
+} // namespace torch::utils
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/tensor_types.h

@@ -0,0 +1,25 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/core/DeprecatedTypeProperties.h>
+#include <c10/core/TensorOptions.h>
+#include <utility>
+#include <vector>
+
+namespace torch::utils {
+
+std::string options_to_string(const at::TensorOptions& options);
+std::string type_to_string(const at::DeprecatedTypeProperties& type);
+at::TensorOptions options_from_string(const std::string& str);
+
+// return a vector of all "declared" types, even those that weren't compiled
+std::vector<std::pair<at::Backend, at::ScalarType>> all_declared_types();
+
+// return python module name of backend, like torch.cuda, torch.foo
+const char* backend_to_string(const at::Backend& backend);
+
+} // namespace torch::utils
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/throughput_benchmark-inl.h

@@ -0,0 +1,176 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <random>
+#include <thread>
+
+#include <torch/csrc/autograd/profiler.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/utils/pybind.h>
+
+#include <ATen/Parallel.h>
+#include <ATen/autocast_mode.h>
+#include <c10/core/GradMode.h>
+#include <c10/core/impl/LocalDispatchKeySet.h>
+#include <c10/util/irange.h>
+
+namespace torch::throughput_benchmark::detail {
+
+template <class Input, class Output, class Model>
+BenchmarkExecutionStats BenchmarkHelper<Input, Output, Model>::benchmark(
+    const BenchmarkConfig& config) const {
+  CHECK(initialized_);
+  TORCH_CHECK(
+      config.num_worker_threads == 1,
+      "Only parallelization by callers is supported");
+
+  LOG(INFO) << at::get_parallel_info();
+
+  // We pre-generate inputs here for each of the threads. This allows us to
+  // safely move inputs out for each of the threads independently and thus avoid
+  // overhead from the benchmark runner itself
+  std::vector<std::vector<Input>> thread_inputs(config.num_calling_threads);
+  std::vector<size_t> input_iters(config.num_calling_threads);
+  {
+    std::random_device seeder;
+    std::mt19937 engine(seeder());
+    TORCH_CHECK(
+        !inputs_.empty(),
+        "Please provide benchmark inputs."
+        "Did you forget to call add_input()? ");
+    std::uniform_int_distribution<int> dist(0, inputs_.size() - 1);
+
+    for (const auto thread_id : c10::irange(config.num_calling_threads)) {
+      // Just in case we generate num_iters inputs for each of the threads
+      // This was if one thread does all the work we will be fine
+      for (const auto i [[maybe_unused]] :
+           c10::irange(config.num_iters + config.num_warmup_iters)) {
+        thread_inputs[thread_id].push_back(cloneInput(inputs_[dist(engine)]));
+      }
+      input_iters[thread_id] = 0;
+    }
+  }
+
+  std::mutex m;
+  std::condition_variable worker_main_cv;
+  std::condition_variable main_worker_cv;
+  // TODO: add GUARDED_BY once it is available
+  int64_t initialized{0};
+  int64_t finished{0};
+  bool start{false};
+  std::atomic<int64_t> num_attempted_iters{0};
+  std::vector<std::thread> callers;
+
+  callers.reserve(config.num_calling_threads);
+
+  static constexpr auto& DEVICES = at::autocast::_AUTOCAST_SUPPORTED_DEVICES;
+  std::array<bool, DEVICES.size()> autocast_enabled;
+  std::array<at::ScalarType, DEVICES.size()> autocast_dtype;
+  for (size_t i = 0; i < DEVICES.size(); i++) {
+    autocast_enabled[i] = at::autocast::is_autocast_enabled(DEVICES[i]);
+    autocast_dtype[i] = at::autocast::get_autocast_dtype(DEVICES[i]);
+  }
+  bool autocast_cache_enabled = at::autocast::is_autocast_cache_enabled();
+  bool tls_grad_enabled = c10::GradMode::is_enabled();
+  c10::impl::LocalDispatchKeySet tls_key_set =
+      c10::impl::tls_local_dispatch_key_set();
+
+  for (const auto thread_id : c10::irange(config.num_calling_threads)) {
+    callers.emplace_back([&, thread_id]() {
+      // We use conditional variable as a barrier to make sure each thread
+      // performs required warmeup iterations before we start measuring
+      c10::GradMode::set_enabled(tls_grad_enabled);
+      c10::impl::_force_tls_local_dispatch_key_set(tls_key_set);
+      for (size_t i = 0; i < DEVICES.size(); i++) {
+        at::autocast::set_autocast_enabled(DEVICES[i], autocast_enabled[i]);
+        at::autocast::set_autocast_dtype(DEVICES[i], autocast_dtype[i]);
+      }
+      at::autocast::set_autocast_cache_enabled(autocast_cache_enabled);
+
+      for (const auto j : c10::irange(config.num_warmup_iters)) {
+        (void)j;
+        runOnce(std::move(thread_inputs[thread_id][input_iters[thread_id]]));
+        ++input_iters[thread_id];
+      }
+      {
+        std::unique_lock<std::mutex> lock(m);
+        ++initialized;
+        worker_main_cv.notify_one();
+        // NOLINTNEXTLINE(bugprone-infinite-loop)
+        while (!start) {
+          main_worker_cv.wait(lock);
+        }
+      }
+      LOG(INFO) << "Starting forward thread " << thread_id;
+      while (num_attempted_iters.fetch_add(1) < config.num_iters) {
+        runOnce(std::move(thread_inputs[thread_id][input_iters[thread_id]]));
+        ++input_iters[thread_id];
+      }
+
+      {
+        std::unique_lock<std::mutex> lock(m);
+        ++finished;
+        worker_main_cv.notify_one();
+        LOG(INFO) << "Shutting down forward thread " << thread_id
+                  << ". Total number of finished threads: " << finished;
+      }
+    });
+  }
+
+  using Clock = std::chrono::high_resolution_clock;
+  using RecordProfile = torch::autograd::profiler::RecordProfile;
+  using TimePoint = std::chrono::time_point<Clock>;
+  TimePoint start_time;
+
+  std::unique_ptr<RecordProfile> profiler_guard;
+  {
+    std::unique_lock<std::mutex> lock(m);
+    while (initialized != config.num_calling_threads) {
+      worker_main_cv.wait(lock);
+    }
+    if (!config.profiler_output_path.empty()) {
+      LOG(INFO) << "Using Autograd profiler. Trace will be saved to "
+                << config.profiler_output_path;
+      profiler_guard =
+          std::make_unique<RecordProfile>(config.profiler_output_path);
+    }
+    LOG(INFO) << "Starting threads";
+    start = true;
+    start_time = Clock::now();
+  }
+
+  main_worker_cv.notify_all();
+  {
+    std::unique_lock<std::mutex> lock(m);
+    worker_main_cv.wait(
+        lock, [&]() { return finished == config.num_calling_threads; });
+  }
+  auto end_time = std::chrono::high_resolution_clock::now();
+  profiler_guard.reset();
+  LOG(INFO) << "Finished benchmark";
+
+  BenchmarkExecutionStats stats;
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  float total_time_ms = std::chrono::duration_cast<std::chrono::nanoseconds>(
+                            end_time - start_time)
+                            .count() /
+      1000.0 / 1000.0;
+  // We use config.num_iters instead of num_attempted_iters as it is
+  // repsesatative of the real work done. Last attempted iteration on each
+  // calling threads doesn't represent the real work (i.e. running the model)
+  stats.latency_avg_ms =
+      // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+      total_time_ms * config.num_calling_threads / config.num_iters;
+  stats.num_iters = config.num_iters;
+
+  for (auto& t : callers) {
+    t.join();
+  }
+  return stats;
+}
+
+} // namespace torch::throughput_benchmark::detail
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/throughput_benchmark.h

@@ -0,0 +1,204 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <pybind11/pybind11.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/utils/pybind.h>
+
+#include <torch/csrc/jit/python/pybind_utils.h>
+
+#include <iosfwd>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace py = pybind11;
+
+namespace torch::throughput_benchmark {
+
+/**
+ * The struct is used to provide results of a benchmark to the caller
+ * In the future all additional statistics should be added here.
+ */
+struct BenchmarkExecutionStats {
+  float latency_avg_ms{-1};
+  int64_t num_iters{-1};
+};
+
+std::ostream& operator<<(
+    std::ostream& os,
+    const BenchmarkExecutionStats& value);
+
+/**
+ * Use this struct in order to configure a throughput benchmark run.
+ * This struct should include parameters related to threading, batching, number
+ * of iterations, warm-up, etc. More configs can be added as needed.
+ * General rule here is that only things that c++ must(!) to be aware of should
+ * be here. If we can keep other parts in python, we should keep them there.
+ * This is typical for things that are not perf critical and don't affect
+ * execution statistics benchmark returns.
+ */
+struct BenchmarkConfig {
+ public:
+  // Calling threads are those threads that are calling into a module in
+  // parallel.
+  int num_calling_threads{1};
+  // Worker threads are not supported yet. This is just an example that we plan
+  // to support some sort of multi-threaded forward calls. We may change this
+  // setting in the future to support different intra and inter op parallelism
+  // which is not available in PyTorch yet
+  int num_worker_threads{1};
+  // Warmup iters are used to make sure we run a module a few times before
+  // actually measuring things. This way we avoid cold caches and any other
+  // similar problems
+  int num_warmup_iters{1};
+  // Number of iterations the benchmark should run with. This number is separate
+  // from the warmup iterations
+  int64_t num_iters{100};
+  // If set autograd profiler will be enabled. I.e. this variable would be
+  // created before the main benchmark loop (but after the warmup):
+  // RecordProfile guard(profiler_output_path);
+  std::string profiler_output_path;
+};
+
+namespace detail {
+
+/**
+ * A helper class to abstract out different models we test throughput of
+ */
+template <class Input, class Output, class Model>
+class BenchmarkHelper {
+ public:
+  BenchmarkHelper();
+  explicit BenchmarkHelper(Model model)
+      : model_(std::move(model)), initialized_(true) {}
+
+  // This method to be used in benchmark() method
+  // Note that there is no result. This way we don't have to call this under GIL
+  // even when running in the nn.Module mode. Otherwise destructor of the result
+  // would race with Python
+  void runOnce(Input&&) const;
+  // This method is to be used when calling from Python directly
+  Output runOnce(const py::args&, const py::kwargs&) const;
+  // Aggregate input in the format Model expects in order to avoid further
+  // conversions at the benchmark time
+  void addInput(py::args&&, py::kwargs&&);
+  void addInput(Input&&);
+  BenchmarkExecutionStats benchmark(const BenchmarkConfig& config) const;
+
+  bool initialized() const {
+    return initialized_;
+  }
+
+  // Destructor doesn't require the GIL because it is going to be executed on
+  // the PyThon thread
+  std::vector<Input> inputs_;
+  Model model_;
+  bool initialized_{false};
+};
+
+struct C10_HIDDEN ModuleInput {
+  ModuleInput(ModuleInput&& other) = default;
+
+  ModuleInput(const ModuleInput&) = delete;
+  ModuleInput& operator=(ModuleInput& other) = delete;
+  ModuleInput& operator=(ModuleInput&& other) = delete;
+  ~ModuleInput() = default;
+
+  ModuleInput(py::args&& args, py::kwargs&& kwargs)
+      : args(std::move(args)), kwargs(std::move(kwargs)) {}
+
+  py::args args;
+  py::kwargs kwargs;
+};
+typedef py::object ModuleOutput;
+typedef std::vector<at::IValue> ScriptModuleInput;
+typedef at::IValue ScriptModuleOutput;
+
+template <class Input>
+Input cloneInput(const Input& input);
+
+typedef BenchmarkHelper<ScriptModuleInput, at::IValue, jit::Module>
+    ScriptModuleBenchmark;
+template <>
+inline BenchmarkHelper<ScriptModuleInput, at::IValue, jit::Module>::
+    BenchmarkHelper()
+    : model_("Module", std::make_shared<jit::CompilationUnit>()),
+      initialized_(false) {}
+typedef BenchmarkHelper<ModuleInput, py::object, py::object> ModuleBenchmark;
+template <>
+inline BenchmarkHelper<ModuleInput, py::object, py::object>::BenchmarkHelper()
+    : initialized_(false) {}
+
+template <>
+void ScriptModuleBenchmark::runOnce(ScriptModuleInput&& input) const;
+
+template <>
+ScriptModuleOutput ScriptModuleBenchmark::runOnce(
+    const py::args& args,
+    const py::kwargs& kwargs) const;
+
+template <>
+void ModuleBenchmark::runOnce(ModuleInput&& input) const;
+
+template <>
+ModuleOutput ModuleBenchmark::runOnce(
+    const py::args& args,
+    const py::kwargs& kwargs) const;
+
+template <>
+void ScriptModuleBenchmark::addInput(py::args&& args, py::kwargs&& kwargs);
+template <>
+void ScriptModuleBenchmark::addInput(ScriptModuleInput&& input);
+
+template <>
+void ModuleBenchmark::addInput(py::args&& args, py::kwargs&& kwargs);
+
+} // namespace detail
+
+/**
+ * This class is a small c++ component responsible for executing a PyTorch
+ * module under an inference server like load. It can emulate multiple calling
+ * threads to a single module provided. In the future we plan to enhance this
+ * component to support inter and intra-op parallelism as well as multiple
+ * models running in a single process.
+ *
+ * For current available configurations refer to the BenchmarkConfig
+ * documentation
+ *
+ * The class supports working with either nn.Module or ScriptModule.
+ * Under the hood it just dispatches to corresponding specialization of
+ * class BenchmarkHelper<Input, Output, Model>
+ */
+class C10_HIDDEN ThroughputBenchmark {
+ public:
+  explicit ThroughputBenchmark(const jit::Module& module);
+  explicit ThroughputBenchmark(py::object module);
+
+  // Add one more input example. This input example should be in the exact
+  // format the module under test expects. It is responsibility of the module to
+  // perform any such format checks, the benchmark doesn't perform any
+  // validation of its own
+  void addInput(py::args args, py::kwargs kwargs);
+
+  // Equivalent to just running the model directly on the given input
+  py::object runOnce(const py::args& args, const py::kwargs& kwargs);
+
+  // The main method of the class allows to perform a multi-threaded benchmark
+  // It returns BenchmarkExecutionStats object with a lot of useful statistics
+  // about runtime execution. We can enhance this class in the future to provide
+  // more information to the user
+  BenchmarkExecutionStats benchmark(const BenchmarkConfig& config) const;
+
+ private:
+  detail::ScriptModuleBenchmark script_module_;
+  detail::ModuleBenchmark module_;
+};
+} // namespace torch::throughput_benchmark
+
+#include <torch/csrc/utils/throughput_benchmark-inl.h>
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/torch_dispatch_mode.h

@@ -0,0 +1,73 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/core/impl/TorchDispatchModeTLS.h>
+
+namespace torch::torch_dispatch_mode {
+
+struct StashTorchDispatchModeGuard {
+ public:
+  StashTorchDispatchModeGuard() {
+    if (c10::impl::TorchDispatchModeTLS::any_modes_set(
+            /*skip_infra_modes=*/true)) {
+      saved_mode_ = c10::impl::TorchDispatchModeTLS::pop_stack();
+    } else {
+      auto mode_and_key =
+          c10::impl::TorchDispatchModeTLS::pop_highest_infra_mode();
+      saved_mode_ = std::move(std::get<0>(mode_and_key));
+      saved_mode_key_ = std::get<1>(mode_and_key);
+    }
+  }
+
+  ~StashTorchDispatchModeGuard() {
+    if (saved_mode_key_.has_value()) {
+      c10::impl::TorchDispatchModeTLS::set_mode(
+          saved_mode_, saved_mode_key_.value());
+    } else {
+      c10::impl::TorchDispatchModeTLS::push_non_infra_mode_onto_stack(
+          std::move(saved_mode_));
+    }
+  }
+  StashTorchDispatchModeGuard(const StashTorchDispatchModeGuard&) = delete;
+  StashTorchDispatchModeGuard(StashTorchDispatchModeGuard&&) = delete;
+  StashTorchDispatchModeGuard& operator=(const StashTorchDispatchModeGuard&) =
+      delete;
+  StashTorchDispatchModeGuard& operator=(StashTorchDispatchModeGuard&&) =
+      delete;
+
+  const std::shared_ptr<c10::impl::PyObject_TorchDispatchMode>& get_cur_mode() {
+    return saved_mode_;
+  }
+
+ private:
+  std::shared_ptr<c10::impl::PyObject_TorchDispatchMode> saved_mode_;
+  std::optional<c10::impl::TorchDispatchModeKey> saved_mode_key_;
+};
+
+struct StashTorchDispatchStackGuard {
+ public:
+  StashTorchDispatchStackGuard() {
+    auto old = c10::impl::TorchDispatchModeTLS::get_state();
+    c10::impl::TorchDispatchModeTLS::set_state(std::move(saved_state_));
+    saved_state_ = std::move(old);
+  }
+  StashTorchDispatchStackGuard(const StashTorchDispatchStackGuard&) = delete;
+  StashTorchDispatchStackGuard(StashTorchDispatchStackGuard&&) = delete;
+  StashTorchDispatchStackGuard& operator=(const StashTorchDispatchStackGuard&) =
+      delete;
+  StashTorchDispatchStackGuard& operator=(StashTorchDispatchStackGuard&&) =
+      delete;
+
+  ~StashTorchDispatchStackGuard() {
+    c10::impl::TorchDispatchModeTLS::set_state(std::move(saved_state_));
+  }
+
+ private:
+  c10::impl::TorchDispatchModeTLS saved_state_;
+};
+
+} // namespace torch::torch_dispatch_mode
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/variadic.h

@@ -0,0 +1,116 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/core/Tensor.h>
+#include <ATen/core/Variadic.h>
+#include <torch/csrc/autograd/variable.h>
+
+#include <type_traits>
+#include <utility>
+
+namespace torch {
+
+using at::IterArgs;
+
+struct CountTensors : IterArgs<CountTensors> {
+  size_t out = 0;
+  void operator()(const at::Tensor& x) {
+    out += 1;
+  }
+  void operator()(const std::optional<at::Tensor>& x) {
+    out += x.has_value();
+  }
+  void operator()(at::ArrayRef<at::Tensor> xs) {
+    out += xs.size();
+  }
+};
+
+template <typename... Args>
+size_t count_tensors(Args&&... args) {
+  return CountTensors().apply(std::forward<Args>(args)...).out;
+}
+
+struct CountVariables : IterArgs<CountVariables> {
+  size_t out = 0;
+  void operator()(const autograd::Variable& x) {
+    out += 1;
+  }
+  void operator()(at::ArrayRef<autograd::Variable> xs) {
+    out += xs.size();
+  }
+};
+
+template <typename... Args>
+inline size_t count_variables(Args&&... args) {
+  return CountVariables().apply(std::forward<Args>(args)...).out;
+}
+
+//===----------------------------------------------------------------------===//
+//                std::index_sequence shim for C++11
+//===----------------------------------------------------------------------===//
+
+// A container of type-template parameter indices.
+template <size_t... Is>
+struct Indices {};
+
+// Decrements the index N, adds N-1 to the list of indices and forwards
+// whatever we already have.
+template <size_t N, size_t... Is>
+struct MakeIndices : MakeIndices<N - 1, N - 1, Is...> {};
+
+// Partial specialization that forms our base case. When N is zero, we stop
+// and define a typedef that will be visible to earlier classes due to
+// inheritance. The typedef we define is an index list containing the numbers
+// 0 through N-1.
+template <size_t... Is>
+struct MakeIndices<0, Is...> {
+  using indices = Indices<Is...>;
+};
+
+//===----------------------------------------------------------------------===//
+//                                 Utilities
+//===----------------------------------------------------------------------===//
+
+template <typename Function, typename... Ts>
+void apply(Function function, Ts&&... ts) {
+  // https://stackoverflow.com/questions/13978916/inserting-a-variadic-argument-list-into-a-vector
+  // Creates a dummy array, so that each function call is evaluated in order.
+  // `(function(), 0)` is because `function` should (!) return `void`, so
+  // according to the comma operator, it is evaluated and its result (`void`)
+  // is discarded. Then the zero is evaluated and used as an element in the
+  // array. The first zero ensures the array is not empty.
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+  int _[]{0, (function(std::forward<Ts>(ts)), 0)...};
+  (void)_;
+}
+
+template <
+    typename ReturnType,
+    typename... Ts,
+    typename Function,
+    typename Accessor>
+ReturnType unpack(Function function, Accessor accessor) {
+  return ReturnType(unpack<ReturnType, Ts...>(
+      std::move(function),
+      std::move(accessor),
+      typename MakeIndices<sizeof...(Ts)>::indices()));
+}
+
+template <
+    typename ReturnType,
+    typename... Ts,
+    typename Function,
+    typename Accessor,
+    size_t... Is>
+ReturnType unpack(
+    Function function,
+    Accessor accessor,
+    Indices<Is...> /*unused*/) {
+  return ReturnType(function(accessor.template operator()<Ts>(Is)...));
+}
+
+} // namespace torch
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/utils/verbose.h

@@ -0,0 +1,13 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+#include <torch/csrc/python_headers.h>
+
+namespace torch {
+
+void initVerboseBindings(PyObject* module);
+
+} // namespace torch
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/xpu/Event.h

@@ -0,0 +1,21 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/xpu/XPUEvent.h>
+#include <torch/csrc/Event.h>
+#include <torch/csrc/python_headers.h>
+
+struct THXPEvent : THPEvent {
+  at::xpu::XPUEvent xpu_event;
+};
+extern PyObject* THXPEventClass;
+
+void THXPEvent_init(PyObject* module);
+
+inline bool THXPEvent_Check(PyObject* obj) {
+  return THXPEventClass && PyObject_IsInstance(obj, THXPEventClass);
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/xpu/Module.h

@@ -0,0 +1,16 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+
+PyMethodDef* THXPModule_methods();
+
+namespace torch::xpu {
+
+void initModule(PyObject* module);
+
+} // namespace torch::xpu
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/xpu/Stream.h

@@ -0,0 +1,22 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/xpu/XPUStream.h>
+#include <torch/csrc/Stream.h>
+#include <torch/csrc/python_headers.h>
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+struct THXPStream : THPStream {
+  at::xpu::XPUStream xpu_stream;
+};
+extern PyObject* THXPStreamClass;
+
+void THXPStream_init(PyObject* module);
+
+inline bool THXPStream_Check(PyObject* obj) {
+  return THXPStreamClass && PyObject_IsInstance(obj, THXPStreamClass);
+}
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/csrc/xpu/XPUPluggableAllocator.h

@@ -0,0 +1,85 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <c10/xpu/XPUCachingAllocator.h>
+#include <torch/csrc/Export.h>
+
+namespace torch::xpu::XPUPluggableAllocator {
+
+struct _AllocationMetadata {
+  _AllocationMetadata() {}
+  _AllocationMetadata(
+      size_t size,
+      c10::DeviceIndex device_idx,
+      sycl::queue* queue)
+      : size(size), device_idx(device_idx), queue(queue) {}
+  size_t size{0};
+  c10::DeviceIndex device_idx{-1};
+  sycl::queue* queue{};
+};
+
+struct TORCH_PYTHON_API XPUPluggableAllocator
+    : public c10::xpu::XPUCachingAllocator::XPUAllocator {
+  XPUPluggableAllocator(
+      std::function<void*(size_t, int, sycl::queue*)> alloc_fn,
+      std::function<void(void*, size_t, int, sycl::queue*)> free_fn)
+      : alloc_fn_(std::move(alloc_fn)), free_fn_(std::move(free_fn)) {}
+
+  C10_DISABLE_COPY_AND_ASSIGN(XPUPluggableAllocator);
+
+  ~XPUPluggableAllocator() override = default;
+
+  void* malloc(size_t size, c10::DeviceIndex device, sycl::queue* stream);
+
+  c10::DataPtr allocate(size_t size) override;
+  c10::DeleterFnPtr raw_deleter() const override;
+
+  void* raw_alloc(size_t nbytes) override;
+  void raw_delete(void* ptr) override;
+  void init(c10::DeviceIndex device_count) override;
+  bool initialized() override;
+  void copy_data(void* dest, const void* src, std::size_t count) const final;
+
+  void recordStream(const c10::DataPtr&, c10::Stream stream) override;
+  void emptyCache(c10::MempoolId_t mempool_id = {0, 0}) override;
+  c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+      c10::DeviceIndex device) override;
+  void resetAccumulatedStats(c10::DeviceIndex device) override;
+  void resetPeakStats(c10::DeviceIndex device) override;
+
+  void set_init_fn(std::function<void(int)> init_fn) {
+    init_fn_ = std::move(init_fn);
+  }
+  void set_record_stream_fn(
+      std::function<void(void* ptr, sycl::queue* queue)> record_stream_fn) {
+    record_stream_fn_ = std::move(record_stream_fn);
+  }
+
+ protected:
+  std::function<void*(size_t, int, sycl::queue*)> alloc_fn_;
+  std::function<void(void*, size_t, int, sycl::queue*)> free_fn_;
+  std::function<void(int)> init_fn_;
+  std::function<void(void* ptr, sycl::queue*)> record_stream_fn_;
+  std::mutex allocator_mutex_;
+  // We do the bookkeeping here in order to simplify custom allocators
+  std::unordered_map<void*, _AllocationMetadata> allocation_metadata_;
+  bool initialized_ = false;
+};
+
+TORCH_XPU_API std::shared_ptr<c10::xpu::XPUCachingAllocator::XPUAllocator>
+getCurrentAllocator();
+
+TORCH_XPU_API std::shared_ptr<c10::xpu::XPUCachingAllocator::XPUAllocator>
+createCustomAllocator(
+    std::function<void*(size_t, int, sycl::queue*)> alloc_fn,
+    std::function<void(void*, size_t, int, sycl::queue*)> free_fn);
+
+TORCH_XPU_API void changeCurrentAllocator(
+    const std::shared_ptr<c10::xpu::XPUCachingAllocator::XPUAllocator>&
+        allocator);
+
+} // namespace torch::xpu::XPUPluggableAllocator
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/DeviceType.h

@@ -0,0 +1,128 @@
+#pragma once
+
+// This is directly synchronized with caffe2/proto/caffe2.proto, but
+// doesn't require me to figure out how to get Protobuf headers into
+// ATen/core (which would require a lot more build system hacking.)
+// If you modify me, keep me synchronized with that file.
+
+#include <torch/headeronly/macros/Export.h>
+#include <torch/headeronly/macros/Macros.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+
+namespace c10 {
+
+// These contains all device types that also have a BackendComponent
+// and therefore participate in per-backend functionality dispatch keys.
+// This is most backends except PrivateUse2 and PrivateUse3
+#define C10_FORALL_BACKEND_DEVICE_TYPES(_, extra) \
+  _(CPU, extra)                                   \
+  _(CUDA, extra)                                  \
+  _(HIP, extra)                                   \
+  _(XLA, extra)                                   \
+  _(MPS, extra)                                   \
+  _(IPU, extra)                                   \
+  _(XPU, extra)                                   \
+  _(HPU, extra)                                   \
+  _(VE, extra)                                    \
+  _(Lazy, extra)                                  \
+  _(Meta, extra)                                  \
+  _(MTIA, extra)                                  \
+  _(PrivateUse1, extra)
+
+enum class DeviceType : int8_t {
+  CPU = 0,
+  CUDA = 1, // CUDA.
+  MKLDNN = 2, // Reserved for explicit MKLDNN
+  OPENGL = 3, // OpenGL
+  OPENCL = 4, // OpenCL
+  IDEEP = 5, // IDEEP.
+  HIP = 6, // AMD HIP
+  FPGA = 7, // FPGA
+  MAIA = 8, // ONNX Runtime / Microsoft
+  XLA = 9, // XLA / TPU
+  Vulkan = 10, // Vulkan
+  Metal = 11, // Metal
+  XPU = 12, // XPU
+  MPS = 13, // MPS
+  Meta = 14, // Meta (tensors with no data)
+  HPU = 15, // HPU / HABANA
+  VE = 16, // SX-Aurora / NEC
+  Lazy = 17, // Lazy Tensors
+  IPU = 18, // Graphcore IPU
+  MTIA = 19, // Meta training and inference devices
+  PrivateUse1 = 20, // PrivateUse1 device
+  // NB: If you add more devices:
+  //  - Change the implementations of DeviceTypeName and isValidDeviceType
+  //    in c10/core/DeviceType.cpp
+  //  - Change the number below
+  COMPILE_TIME_MAX_DEVICE_TYPES = 21,
+};
+
+constexpr DeviceType kCPU = DeviceType::CPU;
+constexpr DeviceType kCUDA = DeviceType::CUDA;
+constexpr DeviceType kHIP = DeviceType::HIP;
+constexpr DeviceType kFPGA = DeviceType::FPGA;
+constexpr DeviceType kMAIA = DeviceType::MAIA;
+constexpr DeviceType kXLA = DeviceType::XLA;
+constexpr DeviceType kMPS = DeviceType::MPS;
+constexpr DeviceType kMeta = DeviceType::Meta;
+constexpr DeviceType kVulkan = DeviceType::Vulkan;
+constexpr DeviceType kMetal = DeviceType::Metal;
+constexpr DeviceType kXPU = DeviceType::XPU;
+constexpr DeviceType kHPU = DeviceType::HPU;
+constexpr DeviceType kVE = DeviceType::VE;
+constexpr DeviceType kLazy = DeviceType::Lazy;
+constexpr DeviceType kIPU = DeviceType::IPU;
+constexpr DeviceType kMTIA = DeviceType::MTIA;
+constexpr DeviceType kPrivateUse1 = DeviceType::PrivateUse1;
+
+// define explicit int constant
+constexpr int COMPILE_TIME_MAX_DEVICE_TYPES =
+    static_cast<int>(DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES);
+
+static_assert(
+    COMPILE_TIME_MAX_DEVICE_TYPES <= 21,
+    "Hey!  You seem to be adding a lot of new DeviceTypes.  The intent was "
+    "for this constant to reflect the actual number of DeviceTypes we support "
+    "in PyTorch; it's important that this number is not too large as we "
+    "use this to allocate stack arrays in some places in our code.  If you "
+    "are indeed just adding the 20th device type, feel free to change "
+    "the check to 32; but if you are adding some sort of extensible device "
+    "types registration, please be aware that you are affecting code that "
+    "this number is small.  Try auditing uses of this constant.");
+
+} // namespace c10
+
+namespace std {
+template <>
+struct hash<c10::DeviceType> {
+  std::size_t operator()(c10::DeviceType k) const {
+    return std::hash<int>()(static_cast<int>(k));
+  }
+};
+} // namespace std
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::COMPILE_TIME_MAX_DEVICE_TYPES;
+using c10::DeviceType;
+using c10::kCPU;
+using c10::kCUDA;
+using c10::kFPGA;
+using c10::kHIP;
+using c10::kHPU;
+using c10::kIPU;
+using c10::kLazy;
+using c10::kMAIA;
+using c10::kMeta;
+using c10::kMetal;
+using c10::kMPS;
+using c10::kMTIA;
+using c10::kPrivateUse1;
+using c10::kVE;
+using c10::kVulkan;
+using c10::kXLA;
+using c10::kXPU;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/Dispatch.h

@@ -0,0 +1,73 @@
+#pragma once
+
+#include <torch/headeronly/core/ScalarType.h>
+#include <torch/headeronly/macros/Macros.h>
+
+// THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL is same as
+// AT_PRIVATE_CASE_TYPE_USING_HINT but with a custom PRELUDE macro:
+#define THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL(PRELUDE, enum_type, HINT, ...) \
+  case enum_type: {                                                          \
+    PRELUDE(enum_type);                                                      \
+    using HINT [[maybe_unused]] =                                            \
+        torch::headeronly::impl::ScalarTypeToCPPTypeT<enum_type>;            \
+    return __VA_ARGS__();                                                    \
+  }
+
+// THO_DISPATCH_CASE_TMPL is same as AT_DISPATCH_CASE but with a
+// custom CASE_TYPE_USING_HINT macro:
+#define THO_DISPATCH_CASE_TMPL(CASE_TYPE_USING_HINT, enum_type, ...) \
+  CASE_TYPE_USING_HINT(enum_type, scalar_t, __VA_ARGS__)
+
+namespace detail {
+inline torch::headeronly::ScalarType scalar_type(
+    torch::headeronly::ScalarType s) {
+  return s;
+}
+} // namespace detail
+
+// THO_DISPATCH_SWITCH_TMPL is same as AT_DISPATCH_SWITCH but with
+// custom PRELUDE and CHECK_NOT_IMPLEMENTED macros:
+#define THO_DISPATCH_SWITCH_TMPL(                                           \
+    PRELUDE, CHECK_NOT_IMPLEMENTED, TYPE, NAME, ...)                        \
+  [&] {                                                                     \
+    const auto& the_type = TYPE;                                            \
+    constexpr const char* at_dispatch_name = NAME;                          \
+    /* don't use TYPE again in case it is an expensive or side-effect op */ \
+    torch::headeronly::ScalarType _st = ::detail::scalar_type(the_type);    \
+    PRELUDE(at_dispatch_name, _st);                                         \
+    C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")             \
+    switch (_st) {                                                          \
+      __VA_ARGS__                                                           \
+      default:                                                              \
+        CHECK_NOT_IMPLEMENTED(                                              \
+            false,                                                          \
+            '"',                                                            \
+            at_dispatch_name,                                               \
+            "\" not implemented for '",                                     \
+            torch::headeronly::toString(_st),                               \
+            "'");                                                           \
+    }                                                                       \
+    C10_DIAGNOSTIC_POP()                                                    \
+  }()
+
+// THO_EMPTY is a helper macro that discards its arguments.
+#define THO_EMPTY(...)
+
+// THO_PRIVATE_CASE_TYPE_USING_HINT is same as
+// AT_PRIVATE_CASE_TYPE_USING_HINT with call to macro
+// AT_PRIVATE_CHECK_SELECTIVE_BUILD removed.
+#define THO_PRIVATE_CASE_TYPE_USING_HINT(enum_type, HINT, ...) \
+  THO_PRIVATE_CASE_TYPE_USING_HINT_TMPL(THO_EMPTY, enum_type, HINT, __VA_ARGS__)
+
+// THO_DISPATCH_SWITCH is same as AT_DISPATCH_SWITCH with call to
+// macro RECORD_KERNEL_FUNCTION_DTYPE removed and using
+// STD_TORCH_CHECK instead of TORCH_CHECK_NOT_IMPLEMENTED.
+#define THO_DISPATCH_SWITCH(TYPE, NAME, ...) \
+  THO_DISPATCH_SWITCH_TMPL(THO_EMPTY, STD_TORCH_CHECK, TYPE, NAME, __VA_ARGS__)
+
+// THO_DISPATCH_CASE is same as AT_DISPATCH_CASE but using
+// THO_PRIVATE_CASE_TYPE_USING_HINT instead of
+// AT_PRIVATE_CASE_TYPE_USING_HINT.
+#define THO_DISPATCH_CASE(enum_type, ...) \
+  THO_DISPATCH_CASE_TMPL(                 \
+      THO_PRIVATE_CASE_TYPE_USING_HINT, enum_type, __VA_ARGS__)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/Dispatch_v2.h

@@ -0,0 +1,170 @@
+#pragma once
+
+#include <torch/headeronly/core/Dispatch.h>
+#include <torch/headeronly/core/ScalarType.h>
+
+// This file provides THO_DISPATCH_V2_TMPL macro that is a generalized
+// version of the original AT_DISPATCH_V2 (see ATen/Dispatch_v2.h for
+// documentation): THO_DISPATCH_V2_TMPL extends AT_DISPATCH_V2 with
+// extra DISPATCH_SWITCH and DISPATCH_CASE arguments for specifying
+// custom implementations of the original AT_DISPATCH_SWITCH and
+// AT_DISPATCH_CASE macros. Use the provided macros
+// THO_DISPATCH_SWITCH_TMPL and THO_DISPATCH_CASE_TMPL to define the
+// custom implementations of the switch and case macros, respectively.
+
+// Public API macros
+
+// THO_DISPATCH_V2_TMPL is same as AT_DISPATCH_V2 but with custom
+// DISPATCH_SWITCH and DISPATCH_CASE macro arguments:
+#define THO_DISPATCH_V2_TMPL(                              \
+    DISPATCH_SWITCH, DISPATCH_CASE, TYPE, NAME, BODY, ...) \
+  DISPATCH_SWITCH(                                         \
+      TYPE,                                                \
+      NAME,                                                \
+      THO_AP_VAR_TMPL(DISPATCH_CASE, AT_WRAP(BODY), TYPE, __VA_ARGS__))
+
+// THO_DISPATCH_V2 is same as AT_DISPATCH_V2 but using
+// THO_DISPATCH_SWITCH and THO_DISPATCH_CASE instead of
+// AT_DISPATCH_SWITCH and AT_DISPATCH_CASE, respectively.
+#define THO_DISPATCH_V2(TYPE, NAME, BODY, ...) \
+  THO_DISPATCH_V2_TMPL(                        \
+      THO_DISPATCH_SWITCH, THO_DISPATCH_CASE, TYPE, NAME, BODY, __VA_ARGS__)
+
+// Type collection macros
+
+// This macro lets you pass an arbitrary expression that may contain internal
+// commas to another macro without having the commas causing the expression
+// to be interpreted as being multiple arguments
+#define AT_WRAP(...) __VA_ARGS__
+
+#define AT_FLOAT8_TYPES                               \
+  torch::headeronly::ScalarType::Float8_e5m2,         \
+      torch::headeronly::ScalarType::Float8_e5m2fnuz, \
+      torch::headeronly::ScalarType::Float8_e4m3fn,   \
+      torch::headeronly::ScalarType::Float8_e4m3fnuz, \
+      torch::headeronly::ScalarType::Float8_e8m0fnu
+
+#define AT_INTEGRAL_TYPES                                                      \
+  torch::headeronly::ScalarType::Byte, torch::headeronly::ScalarType::Char,    \
+      torch::headeronly::ScalarType::Int, torch::headeronly::ScalarType::Long, \
+      torch::headeronly::ScalarType::Short
+#define AT_FLOATING_TYPES \
+  torch::headeronly::ScalarType::Double, torch::headeronly::ScalarType::Float
+#define AT_BAREBONES_UNSIGNED_TYPES          \
+  torch::headeronly::ScalarType::UInt16,     \
+      torch::headeronly::ScalarType::UInt32, \
+      torch::headeronly::ScalarType::UInt64
+#define AT_INTEGRAL_TYPES_V2 \
+  AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)
+#define AT_COMPLEX_TYPES                        \
+  torch::headeronly::ScalarType::ComplexDouble, \
+      torch::headeronly::ScalarType::ComplexFloat
+#define AT_QINT_TYPES                                                          \
+  torch::headeronly::ScalarType::QInt8, torch::headeronly::ScalarType::QUInt8, \
+      torch::headeronly::ScalarType::QInt32
+// NB: not *actually* all types
+#define AT_ALL_TYPES AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)
+#define AT_ALL_TYPES_AND_COMPLEX \
+  AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES)
+
+// Helper macros
+
+// THO_AP_VAR_TMPL is same as AT_AP_VAR but with a custom
+// DISPATCH_CASE macro argument:
+#define THO_AP_VAR_TMPL(C, N, T, ...) \
+  AT_EXPAND(                          \
+      AT_CONCAT(THO_AP, AT_NUM_ARGS(__VA_ARGS__))(C, AT_WRAP(N), __VA_ARGS__))
+#define AT_CONCAT(a, b) AT_CONCAT_AUX(a, b)
+#define AT_CONCAT_AUX(a, b) a##b
+#define AT_EXPAND(X) X
+
+// Ensure we never have too many scalar types for the expansion here to
+// support.  To bump this, you must regenerate the macros below.
+static_assert(static_cast<int>(torch::headeronly::ScalarType::NumOptions) < 60);
+
+// Python code to regenerate generate code below:
+#if 0
+
+num_args = 60
+
+nums = ', '.join(str(i) for i in reversed(range(num_args+1)))
+args = ', '.join(f'_{i}' for i in range(1, num_args+1))
+
+print(f'#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, {nums}))')
+print(f'#define AT_NUM_ARGS_AUX({args}, N, ...) N')
+
+for i in range(1, num_args+1):
+    args = ', '.join(f'_{i}' for i in range(1, i+1))
+    cases = ' '.join([f'C(_{j}, N)' for j in range(1, i+1)])
+    print(f'#define THO_AP{i}(C, N, {args}) {cases}')
+
+#endif
+
+// Begin generated code
+// clang-format off
+
+#define AT_NUM_ARGS(...) AT_EXPAND(AT_NUM_ARGS_AUX(__VA_ARGS__, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
+#define AT_NUM_ARGS_AUX(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60, N, ...) N
+#define THO_AP1(C, N, _1) C(_1, N)
+#define THO_AP2(C, N, _1, _2) C(_1, N) C(_2, N)
+#define THO_AP3(C, N, _1, _2, _3) C(_1, N) C(_2, N) C(_3, N)
+#define THO_AP4(C, N, _1, _2, _3, _4) C(_1, N) C(_2, N) C(_3, N) C(_4, N)
+#define THO_AP5(C, N, _1, _2, _3, _4, _5) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N)
+#define THO_AP6(C, N, _1, _2, _3, _4, _5, _6) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N)
+#define THO_AP7(C, N, _1, _2, _3, _4, _5, _6, _7) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N)
+#define THO_AP8(C, N, _1, _2, _3, _4, _5, _6, _7, _8) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N)
+#define THO_AP9(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N)
+#define THO_AP10(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N)
+#define THO_AP11(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N)
+#define THO_AP12(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N)
+#define THO_AP13(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N)
+#define THO_AP14(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N)
+#define THO_AP15(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N)
+#define THO_AP16(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N)
+#define THO_AP17(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N)
+#define THO_AP18(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N)
+#define THO_AP19(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N)
+#define THO_AP20(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N)
+#define THO_AP21(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N)
+#define THO_AP22(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N)
+#define THO_AP23(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N)
+#define THO_AP24(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N)
+#define THO_AP25(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N)
+#define THO_AP26(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N)
+#define THO_AP27(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N)
+#define THO_AP28(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N)
+#define THO_AP29(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N)
+#define THO_AP30(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N)
+#define THO_AP31(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N)
+#define THO_AP32(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N)
+#define THO_AP33(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N)
+#define THO_AP34(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N)
+#define THO_AP35(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N)
+#define THO_AP36(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N)
+#define THO_AP37(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N)
+#define THO_AP38(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N)
+#define THO_AP39(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N)
+#define THO_AP40(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N)
+#define THO_AP41(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N)
+#define THO_AP42(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N)
+#define THO_AP43(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N)
+#define THO_AP44(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N)
+#define THO_AP45(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N)
+#define THO_AP46(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N)
+#define THO_AP47(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N)
+#define THO_AP48(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N)
+#define THO_AP49(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N)
+#define THO_AP50(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N)
+#define THO_AP51(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N)
+#define THO_AP52(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N)
+#define THO_AP53(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N)
+#define THO_AP54(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N)
+#define THO_AP55(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N) C(_55, N)
+#define THO_AP56(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N) C(_55, N) C(_56, N)
+#define THO_AP57(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N) C(_55, N) C(_56, N) C(_57, N)
+#define THO_AP58(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N) C(_55, N) C(_56, N) C(_57, N) C(_58, N)
+#define THO_AP59(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N) C(_55, N) C(_56, N) C(_57, N) C(_58, N) C(_59, N)
+#define THO_AP60(C, N, _1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, _33, _34, _35, _36, _37, _38, _39, _40, _41, _42, _43, _44, _45, _46, _47, _48, _49, _50, _51, _52, _53, _54, _55, _56, _57, _58, _59, _60) C(_1, N) C(_2, N) C(_3, N) C(_4, N) C(_5, N) C(_6, N) C(_7, N) C(_8, N) C(_9, N) C(_10, N) C(_11, N) C(_12, N) C(_13, N) C(_14, N) C(_15, N) C(_16, N) C(_17, N) C(_18, N) C(_19, N) C(_20, N) C(_21, N) C(_22, N) C(_23, N) C(_24, N) C(_25, N) C(_26, N) C(_27, N) C(_28, N) C(_29, N) C(_30, N) C(_31, N) C(_32, N) C(_33, N) C(_34, N) C(_35, N) C(_36, N) C(_37, N) C(_38, N) C(_39, N) C(_40, N) C(_41, N) C(_42, N) C(_43, N) C(_44, N) C(_45, N) C(_46, N) C(_47, N) C(_48, N) C(_49, N) C(_50, N) C(_51, N) C(_52, N) C(_53, N) C(_54, N) C(_55, N) C(_56, N) C(_57, N) C(_58, N) C(_59, N) C(_60, N)
+
+// End generated code
+// clang-format on

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/Layout.h

@@ -0,0 +1,44 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Exception.h>
+
+#include <cstdint>
+#include <ostream>
+
+namespace c10 {
+
+enum class Layout : int8_t {
+  Strided,
+  Sparse,
+  SparseCsr,
+  Mkldnn,
+  SparseCsc,
+  SparseBsr,
+  SparseBsc,
+  Jagged,
+  NumOptions
+};
+
+constexpr auto kStrided = Layout::Strided;
+constexpr auto kSparse = Layout::Sparse;
+constexpr auto kSparseCsr = Layout::SparseCsr;
+constexpr auto kMkldnn = Layout::Mkldnn;
+constexpr auto kSparseCsc = Layout::SparseCsc;
+constexpr auto kSparseBsr = Layout::SparseBsr;
+constexpr auto kSparseBsc = Layout::SparseBsc;
+constexpr auto kJagged = Layout::Jagged;
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::kJagged;
+using c10::kMkldnn;
+using c10::kSparse;
+using c10::kSparseBsc;
+using c10::kSparseBsr;
+using c10::kSparseCsc;
+using c10::kSparseCsr;
+using c10::kStrided;
+using c10::Layout;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/MemoryFormat.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Exception.h>
+
+#include <cstdint>
+#include <ostream>
+
+// Memory format is not the property of a Tensor. It is the way to tell an
+// operator how the result should be organized in memory and nothing more. That
+// means memory format should never be used as return value for any tensor state
+// interrogation functions (internally and externally).
+//
+// Possible options are:
+//  Preserve:
+//    If any of the input tensors is in channels_last format, operator output
+//    should be in channels_last format
+//
+//  Contiguous:
+//    Regardless of input tensors format, the output should be contiguous
+//    Tensor.
+//
+//  ChannelsLast:
+//    Regardless of input tensors format, the output should be in channels_last
+//    format.
+
+namespace c10 {
+
+enum class MemoryFormat : int8_t {
+  Contiguous,
+  Preserve,
+  ChannelsLast,
+  ChannelsLast3d,
+  NumOptions
+};
+
+inline MemoryFormat get_contiguous_memory_format() {
+  return MemoryFormat::Contiguous;
+}
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::get_contiguous_memory_format;
+using c10::MemoryFormat;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/ScalarType.h

@@ -0,0 +1,381 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/BFloat16.h>
+#include <torch/headeronly/util/Float4_e2m1fn_x2.h>
+#include <torch/headeronly/util/Float8_e4m3fn.h>
+#include <torch/headeronly/util/Float8_e4m3fnuz.h>
+#include <torch/headeronly/util/Float8_e5m2.h>
+#include <torch/headeronly/util/Float8_e5m2fnuz.h>
+#include <torch/headeronly/util/Float8_e8m0fnu.h>
+#include <torch/headeronly/util/Half.h>
+#include <torch/headeronly/util/bits.h>
+#include <torch/headeronly/util/complex.h>
+#include <torch/headeronly/util/qint32.h>
+#include <torch/headeronly/util/qint8.h>
+#include <torch/headeronly/util/quint2x4.h>
+#include <torch/headeronly/util/quint4x2.h>
+#include <torch/headeronly/util/quint8.h>
+
+#include <cstdint>
+
+C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wswitch-enum")
+
+namespace c10 {
+
+// dummy struct for uint1 to uint7, actual functionality
+// of these dtypes will be implemented in python with Tensor subclass
+template <unsigned int N>
+struct dummy_uint1_7_t {};
+
+// dummy struct for int1 to int7, actual functionality
+// of these dtypes will be implemented in python with Tensor subclass
+template <unsigned int N>
+struct dummy_int1_7_t {};
+
+// [dtype Macros note] For the macros below:
+//
+// For users: If you want to macro some code for all non-QInt scalar types
+// (i.e. types with complete information, you probably want one of the
+// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND macros below, which are
+// designed to behave similarly to the Dispatch macros with the same name.
+//
+// For adding a new dtype: In the beginning, we had an idea that there was a
+// list of all scalar types, and you could use AT_FORALL_SCALAR_TYPES to
+// iterate over them.  But over the years we added weird types which couldn't
+// be handled uniformly everywhere and so in the end we ended up with some
+// mish-mosh of some helper macros, but mostly use sites making a call about
+// what dtypes they can or can't support.  So if you want to add a new dtype,
+// the preferred resolution is to find a dtype similar to what you want,
+// grep for it and edit all the sites you find this way.  If you need to add
+// a completely new kind of dtype, you're going to have to laboriously audit
+// all of the sites everywhere to figure out how it should work.  Consulting
+// some old PRs where we added new dtypes (check history of this file) can
+// help give you an idea where to start.
+
+// If you want to support ComplexHalf for real, add ComplexHalf
+// into this macro (and change the name).  But beware: convert()
+// doesn't work for all the conversions you need...
+//
+// TODO: To add unsigned int types here, we must define accumulate type.
+// But uint8 currently accumulates into int64, so we would have to make
+// an inconsistent choice for the larger types.  Difficult.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ(_) \
+  _(uint8_t, Byte)                                                      \
+  _(int8_t, Char)                                                       \
+  _(int16_t, Short)                                                     \
+  _(int, Int)                                                           \
+  _(int64_t, Long)                                                      \
+  _(c10::Half, Half)                                                    \
+  _(float, Float)                                                       \
+  _(double, Double)                                                     \
+  _(c10::complex<float>, ComplexFloat)                                  \
+  _(c10::complex<double>, ComplexDouble)                                \
+  _(bool, Bool)                                                         \
+  _(c10::BFloat16, BFloat16)                                            \
+  _(c10::Float8_e5m2, Float8_e5m2)                                      \
+  _(c10::Float8_e4m3fn, Float8_e4m3fn)
+
+// This macro controls many of our C++ APIs, including constructors
+// for Scalar as well as the data() and item() accessors on Tensor
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(_) \
+  _(uint8_t, Byte)                             \
+  _(int8_t, Char)                              \
+  _(int16_t, Short)                            \
+  _(int, Int)                                  \
+  _(int64_t, Long)                             \
+  _(c10::Half, Half)                           \
+  _(float, Float)                              \
+  _(double, Double)                            \
+  _(c10::complex<c10::Half>, ComplexHalf)      \
+  _(c10::complex<float>, ComplexFloat)         \
+  _(c10::complex<double>, ComplexDouble)       \
+  _(bool, Bool)                                \
+  _(c10::BFloat16, BFloat16)                   \
+  _(c10::Float8_e5m2, Float8_e5m2)             \
+  _(c10::Float8_e4m3fn, Float8_e4m3fn)         \
+  _(c10::Float8_e5m2fnuz, Float8_e5m2fnuz)     \
+  _(c10::Float8_e4m3fnuz, Float8_e4m3fnuz)     \
+  _(c10::Float8_e8m0fnu, Float8_e8m0fnu)
+
+// NB: Order matters for this macro; it is relied upon in
+// _promoteTypesLookup and the serialization format.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(_) \
+  _(uint8_t, Byte) /* 0 */                               \
+  _(int8_t, Char) /* 1 */                                \
+  _(int16_t, Short) /* 2 */                              \
+  _(int, Int) /* 3 */                                    \
+  _(int64_t, Long) /* 4 */                               \
+  _(c10::Half, Half) /* 5 */                             \
+  _(float, Float) /* 6 */                                \
+  _(double, Double) /* 7 */                              \
+  _(c10::complex<c10::Half>, ComplexHalf) /* 8 */        \
+  _(c10::complex<float>, ComplexFloat) /* 9 */           \
+  _(c10::complex<double>, ComplexDouble) /* 10 */        \
+  _(bool, Bool) /* 11 */                                 \
+  _(c10::qint8, QInt8) /* 12 */                          \
+  _(c10::quint8, QUInt8) /* 13 */                        \
+  _(c10::qint32, QInt32) /* 14 */                        \
+  _(c10::BFloat16, BFloat16) /* 15 */                    \
+  _(c10::quint4x2, QUInt4x2) /* 16 */                    \
+  _(c10::quint2x4, QUInt2x4) /* 17 */                    \
+  _(c10::bits1x8, Bits1x8) /* 18 */                      \
+  _(c10::bits2x4, Bits2x4) /* 19 */                      \
+  _(c10::bits4x2, Bits4x2) /* 20 */                      \
+  _(c10::bits8, Bits8) /* 21 */                          \
+  _(c10::bits16, Bits16) /* 22 */                        \
+  _(c10::Float8_e5m2, Float8_e5m2) /* 23 */              \
+  _(c10::Float8_e4m3fn, Float8_e4m3fn) /* 24 */          \
+  _(c10::Float8_e5m2fnuz, Float8_e5m2fnuz) /* 25 */      \
+  _(c10::Float8_e4m3fnuz, Float8_e4m3fnuz) /* 26 */      \
+  _(uint16_t, UInt16) /* 27 */                           \
+  _(uint32_t, UInt32) /* 28 */                           \
+  _(uint64_t, UInt64) /* 29 */                           \
+  _(c10::dummy_uint1_7_t<1>, UInt1) /* 30 */             \
+  _(c10::dummy_uint1_7_t<2>, UInt2) /* 31 */             \
+  _(c10::dummy_uint1_7_t<3>, UInt3) /* 32 */             \
+  _(c10::dummy_uint1_7_t<4>, UInt4) /* 33 */             \
+  _(c10::dummy_uint1_7_t<5>, UInt5) /* 34 */             \
+  _(c10::dummy_uint1_7_t<6>, UInt6) /* 35 */             \
+  _(c10::dummy_uint1_7_t<7>, UInt7) /* 36 */             \
+  _(c10::dummy_int1_7_t<1>, Int1) /* 37 */               \
+  _(c10::dummy_int1_7_t<2>, Int2) /* 38 */               \
+  _(c10::dummy_int1_7_t<3>, Int3) /* 39 */               \
+  _(c10::dummy_int1_7_t<4>, Int4) /* 40 */               \
+  _(c10::dummy_int1_7_t<5>, Int5) /* 41 */               \
+  _(c10::dummy_int1_7_t<6>, Int6) /* 42 */               \
+  _(c10::dummy_int1_7_t<7>, Int7) /* 43 */               \
+  _(c10::Float8_e8m0fnu, Float8_e8m0fnu) /* 44 */        \
+  _(c10::Float4_e2m1fn_x2, Float4_e2m1fn_x2) /* 45 */
+
+// NB: despite its generic sounding name, the macros that don't take _AND
+// are mostly only used by tensorexpr
+#define AT_FORALL_INT_TYPES(_) \
+  _(uint8_t, Byte)             \
+  _(int8_t, Char)              \
+  _(int16_t, Short)            \
+  _(int, Int)                  \
+  _(int64_t, Long)
+
+#define AT_FORALL_SCALAR_TYPES(_) \
+  _(uint8_t, Byte)                \
+  _(int8_t, Char)                 \
+  _(int16_t, Short)               \
+  _(int, Int)                     \
+  _(int64_t, Long)                \
+  _(float, Float)                 \
+  _(double, Double)
+
+// These macros are often controlling how many template instantiations we
+// create for kernels.  It is typically inappropriate to add new dtypes here,
+// instead, new types should be added to use sites on a case-by-case basis.
+// We generally are not accepting new dtypes due to binary size concerns.
+
+#define AT_FORALL_SCALAR_TYPES_AND(SCALARTYPE, _) \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE>, SCALARTYPE)
+
+#define AT_FORALL_SCALAR_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, _)   \
+  _(uint8_t, Byte)                                                 \
+  _(int8_t, Char)                                                  \
+  _(int16_t, Short)                                                \
+  _(int, Int)                                                      \
+  _(int64_t, Long)                                                 \
+  _(float, Float)                                                  \
+  _(double, Double)                                                \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE1>, \
+    SCALARTYPE1)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE2>, SCALARTYPE2)
+
+#define AT_FORALL_SCALAR_TYPES_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, _) \
+  _(uint8_t, Byte)                                                            \
+  _(int8_t, Char)                                                             \
+  _(int16_t, Short)                                                           \
+  _(int, Int)                                                                 \
+  _(int64_t, Long)                                                            \
+  _(float, Float)                                                             \
+  _(double, Double)                                                           \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE1>,            \
+    SCALARTYPE1)                                                              \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE2>,            \
+    SCALARTYPE2)                                                              \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE3>, SCALARTYPE3)
+
+#define AT_FORALL_SCALAR_TYPES_AND7(                               \
+    SCALARTYPE1,                                                   \
+    SCALARTYPE2,                                                   \
+    SCALARTYPE3,                                                   \
+    SCALARTYPE4,                                                   \
+    SCALARTYPE5,                                                   \
+    SCALARTYPE6,                                                   \
+    SCALARTYPE7,                                                   \
+    _)                                                             \
+  _(uint8_t, Byte)                                                 \
+  _(int8_t, Char)                                                  \
+  _(int16_t, Short)                                                \
+  _(int, Int)                                                      \
+  _(int64_t, Long)                                                 \
+  _(float, Float)                                                  \
+  _(double, Double)                                                \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE1>, \
+    SCALARTYPE1)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE2>, \
+    SCALARTYPE2)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE3>, \
+    SCALARTYPE3)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE4>, \
+    SCALARTYPE4)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE5>, \
+    SCALARTYPE5)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE6>, \
+    SCALARTYPE6)                                                   \
+  _(c10::impl::ScalarTypeToCPPTypeT<c10::ScalarType::SCALARTYPE7>, SCALARTYPE7)
+
+#define AT_FORALL_QINT_TYPES(_) \
+  _(c10::qint8, QInt8)          \
+  _(c10::quint8, QUInt8)        \
+  _(c10::qint32, QInt32)        \
+  _(c10::quint4x2, QUInt4x2)    \
+  _(c10::quint2x4, QUInt2x4)
+
+#define AT_FORALL_FLOAT8_TYPES(_)          \
+  _(c10::Float8_e5m2, Float8_e5m2)         \
+  _(c10::Float8_e4m3fn, Float8_e4m3fn)     \
+  _(c10::Float8_e5m2fnuz, Float8_e5m2fnuz) \
+  _(c10::Float8_e4m3fnuz, Float8_e4m3fnuz) \
+  _(c10::Float8_e8m0fnu, Float8_e8m0fnu)
+
+#define AT_FORALL_COMPLEX_TYPES(_)     \
+  _(c10::complex<float>, ComplexFloat) \
+  _(c10::complex<double>, ComplexDouble)
+
+enum class ScalarType : int8_t {
+#define DEFINE_ST_ENUM_VAL_(_1, n) n,
+  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_ST_ENUM_VAL_)
+#undef DEFINE_ENUM_ST_ENUM_VAL_
+      Undefined,
+  NumOptions
+};
+
+constexpr uint16_t NumScalarTypes =
+    static_cast<uint16_t>(ScalarType::NumOptions);
+
+// Map from C++ type to ScalarType enum
+template <typename T>
+struct CppTypeToScalarType;
+
+#define SPECIALIZE_CppTypeToScalarType(cpp_type, scalar_type)                  \
+  template <>                                                                  \
+  struct CppTypeToScalarType<cpp_type>                                         \
+      : std::                                                                  \
+            integral_constant<c10::ScalarType, c10::ScalarType::scalar_type> { \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
+
+#undef SPECIALIZE_CppTypeToScalarType
+
+namespace impl {
+
+// These are used to map ScalarTypes to C++ types.
+
+template <c10::ScalarType N>
+struct ScalarTypeToCPPType;
+
+#define SPECIALIZE_ScalarTypeToCPPType(cpp_type, scalar_type)                \
+  template <>                                                                \
+  struct ScalarTypeToCPPType<c10::ScalarType::scalar_type> {                 \
+    using type = cpp_type;                                                   \
+                                                                             \
+    /* This is a workaround for the CUDA bug which prevents */               \
+    /* ::detail::ScalarTypeToCType<T>::type being used directly due to */    \
+    /* ambiguous reference which can't to be resolved. For some reason it */ \
+    /* can't pick between at::detail and at::cuda::detail. */                \
+    /* For repro example, please see: */                                     \
+    /* https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba */    \
+    /* UPDATE: while the CUDA bug is fixed, we cannot remove the  */         \
+    /* workaround as it is BC breaking. However, it is recommended to  */    \
+    /* update any code that contains */                                      \
+    /*   decltype(ScalarTypeToCPPType<T>::t) */                              \
+    /* with */                                                               \
+    /*   ScalarTypeToCPPTypeT<T> */                                          \
+    static type t;                                                           \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_ScalarTypeToCPPType)
+
+#undef SPECIALIZE_ScalarTypeToCPPType
+
+template <c10::ScalarType N>
+using ScalarTypeToCPPTypeT = typename ScalarTypeToCPPType<N>::type;
+
+} // namespace impl
+
+inline const char* toString(ScalarType t) {
+#define DEFINE_CASE(_, name) \
+  case ScalarType::name:     \
+    return #name;
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE)
+    default:
+      return "UNKNOWN_SCALAR";
+  }
+#undef DEFINE_CASE
+}
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    c10::ScalarType scalar_type) {
+  return stream << toString(scalar_type);
+}
+
+inline bool isQIntType(ScalarType t) {
+  // Don't forget to extend this when adding new QInt types
+  return t == ScalarType::QInt8 || t == ScalarType::QUInt8 ||
+      t == ScalarType::QInt32 || t == ScalarType::QUInt4x2 ||
+      t == ScalarType::QUInt2x4;
+}
+
+inline ScalarType toUnderlying(ScalarType t) {
+  switch (t) {
+    case ScalarType::QUInt8:
+    case ScalarType::QUInt4x2:
+      [[fallthrough]];
+    case ScalarType::QUInt2x4:
+      return ScalarType::Byte;
+    case ScalarType::QInt8:
+      return ScalarType::Char;
+    case ScalarType::QInt32:
+      return ScalarType::Int;
+    default:
+      return t;
+  }
+}
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::CppTypeToScalarType;
+using c10::dummy_int1_7_t;
+using c10::dummy_uint1_7_t;
+using c10::NumScalarTypes;
+using c10::ScalarType;
+using c10::toString;
+using c10::operator<<;
+using c10::isQIntType;
+using c10::toUnderlying;
+
+namespace impl {
+using c10::impl::ScalarTypeToCPPTypeT;
+} // namespace impl
+
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+C10_DIAGNOSTIC_POP()

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/core/TensorAccessor.h

@@ -0,0 +1,462 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Exception.h>
+#include <torch/headeronly/util/HeaderOnlyArrayRef.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <iterator>
+#include <type_traits>
+
+namespace torch::headeronly {
+
+// The PtrTraits argument to the TensorAccessor/GenericPackedTensorAccessor
+// is used to enable the __restrict__ keyword/modifier for the data
+// passed to cuda.
+template <typename T>
+struct DefaultPtrTraits {
+  typedef T* PtrType;
+};
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+template <typename T>
+struct RestrictPtrTraits {
+  typedef T* __restrict__ PtrType;
+};
+#endif
+
+namespace detail {
+// Template classes in torch::headeronly::detail namespace are used
+// to construct accessor template classes with custom ArrayRef and
+// index bound check implementations. For instance,
+// at::TensorAccessor and torch::headeronly::TensorAccessor template
+// classes use c10::IntArrayRef and
+// torch::headeronly::IntHeaderOnlyArrayRef classes, respectively,
+// as return value types of sizes() and strides() methods.
+
+// TensorAccessorBase and TensorAccessor are used for both CPU and CUDA tensors.
+// For CUDA tensors it is used in device code (only). This means that we
+// restrict ourselves to functions and types available there (e.g. IntArrayRef
+// isn't).
+
+// The PtrTraits argument is only relevant to cuda to support `__restrict__`
+// pointers.
+template <
+    class ArrayRefCls,
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+class TensorAccessorBase {
+ public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
+
+  C10_HOST_DEVICE TensorAccessorBase(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : data_(data_), sizes_(sizes_), strides_(strides_) {}
+  C10_HOST ArrayRefCls sizes() const {
+    return ArrayRefCls(sizes_, N);
+  }
+  C10_HOST ArrayRefCls strides() const {
+    return ArrayRefCls(strides_, N);
+  }
+  C10_HOST_DEVICE index_t stride(index_t i) const {
+    return strides_[i];
+  }
+  C10_HOST_DEVICE index_t size(index_t i) const {
+    return sizes_[i];
+  }
+  C10_HOST_DEVICE PtrType data() {
+    return data_;
+  }
+  C10_HOST_DEVICE const PtrType data() const {
+    return data_;
+  }
+
+ protected:
+  PtrType data_;
+  const index_t* sizes_;
+  const index_t* strides_;
+};
+
+// The `TensorAccessor` is typically instantiated for CPU `Tensor`s using
+// `Tensor.accessor<T, N>()`.
+// For CUDA `Tensor`s, `GenericPackedTensorAccessor` is used on the host and
+// only indexing on the device uses `TensorAccessor`s.
+template <
+    class ArrayRefCls,
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+class TensorAccessor
+    : public TensorAccessorBase<ArrayRefCls, T, N, PtrTraits, index_t> {
+ public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
+
+  C10_HOST_DEVICE TensorAccessor(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : TensorAccessorBase<ArrayRefCls, T, N, PtrTraits, index_t>(
+            data_,
+            sizes_,
+            strides_) {}
+
+  C10_HOST_DEVICE TensorAccessor<ArrayRefCls, T, N - 1, PtrTraits, index_t>
+  operator[](index_t i) {
+    return TensorAccessor<ArrayRefCls, T, N - 1, PtrTraits, index_t>(
+        this->data_ + this->strides_[0] * i,
+        this->sizes_ + 1,
+        this->strides_ + 1);
+  }
+
+  C10_HOST_DEVICE const TensorAccessor<
+      ArrayRefCls,
+      T,
+      N - 1,
+      PtrTraits,
+      index_t>
+  operator[](index_t i) const {
+    return TensorAccessor<ArrayRefCls, T, N - 1, PtrTraits, index_t>(
+        this->data_ + this->strides_[0] * i,
+        this->sizes_ + 1,
+        this->strides_ + 1);
+  }
+};
+
+template <
+    class ArrayRefCls,
+    typename T,
+    template <typename U> class PtrTraits,
+    typename index_t>
+class TensorAccessor<ArrayRefCls, T, 1, PtrTraits, index_t>
+    : public TensorAccessorBase<ArrayRefCls, T, 1, PtrTraits, index_t> {
+ public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
+
+  C10_HOST_DEVICE TensorAccessor(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : TensorAccessorBase<ArrayRefCls, T, 1, PtrTraits, index_t>(
+            data_,
+            sizes_,
+            strides_) {}
+  C10_HOST_DEVICE T& operator[](index_t i) {
+    // NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
+    return this->data_[this->strides_[0] * i];
+  }
+  C10_HOST_DEVICE const T& operator[](index_t i) const {
+    return this->data_[this->strides_[0] * i];
+  }
+};
+
+// GenericPackedTensorAccessorBase and GenericPackedTensorAccessor are used on
+// for CUDA `Tensor`s on the host and as in contrast to `TensorAccessor`s, they
+// copy the strides and sizes on instantiation (on the host) in order to
+// transfer them on the device when calling kernels. On the device, indexing of
+// multidimensional tensors gives to `TensorAccessor`s. Use RestrictPtrTraits as
+// PtrTraits if you want the tensor's data pointer to be marked as __restrict__.
+// Instantiation from data, sizes, strides is only needed on the host and
+// std::copy isn't available on the device, so those functions are host only.
+template <
+    typename IndexBoundsCheck,
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+class GenericPackedTensorAccessorBase {
+ public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
+  C10_HOST GenericPackedTensorAccessorBase(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : data_(data_) {
+    std::copy(sizes_, sizes_ + N, std::begin(this->sizes_));
+    std::copy(strides_, strides_ + N, std::begin(this->strides_));
+  }
+
+  // if index_t is not int64_t, we want to have an int64_t constructor
+  template <
+      typename source_index_t,
+      class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
+  C10_HOST GenericPackedTensorAccessorBase(
+      PtrType data_,
+      const source_index_t* sizes_,
+      const source_index_t* strides_)
+      : data_(data_) {
+    for (size_t i = 0; i < N; ++i) {
+      this->sizes_[i] = sizes_[i];
+      this->strides_[i] = strides_[i];
+    }
+  }
+
+  C10_HOST_DEVICE index_t stride(index_t i) const {
+    return strides_[i];
+  }
+  C10_HOST_DEVICE index_t size(index_t i) const {
+    return sizes_[i];
+  }
+  C10_HOST_DEVICE PtrType data() {
+    return data_;
+  }
+  C10_HOST_DEVICE const PtrType data() const {
+    return data_;
+  }
+
+ protected:
+  PtrType data_;
+  // NOLINTNEXTLINE(*c-arrays*)
+  index_t sizes_[N];
+  // NOLINTNEXTLINE(*c-arrays*)
+  index_t strides_[N];
+  C10_HOST void bounds_check_(index_t i) const {
+    IndexBoundsCheck _(i);
+  }
+};
+
+template <
+    typename ItemAccessor,
+    typename IndexBoundsCheck,
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+class GenericPackedTensorAccessor : public GenericPackedTensorAccessorBase<
+                                        IndexBoundsCheck,
+                                        T,
+                                        N,
+                                        PtrTraits,
+                                        index_t> {
+ public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
+
+  C10_HOST GenericPackedTensorAccessor(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : GenericPackedTensorAccessorBase<
+            IndexBoundsCheck,
+            T,
+            N,
+            PtrTraits,
+            index_t>(data_, sizes_, strides_) {}
+
+  // if index_t is not int64_t, we want to have an int64_t constructor
+  template <
+      typename source_index_t,
+      class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
+  C10_HOST GenericPackedTensorAccessor(
+      PtrType data_,
+      const source_index_t* sizes_,
+      const source_index_t* strides_)
+      : GenericPackedTensorAccessorBase<
+            IndexBoundsCheck,
+            T,
+            N,
+            PtrTraits,
+            index_t>(data_, sizes_, strides_) {}
+
+  C10_DEVICE ItemAccessor operator[](index_t i) {
+    index_t* new_sizes = this->sizes_ + 1;
+    index_t* new_strides = this->strides_ + 1;
+    return ItemAccessor(
+        this->data_ + this->strides_[0] * i, new_sizes, new_strides);
+  }
+
+  C10_DEVICE const ItemAccessor operator[](index_t i) const {
+    const index_t* new_sizes = this->sizes_ + 1;
+    const index_t* new_strides = this->strides_ + 1;
+    return ItemAccessor(
+        this->data_ + this->strides_[0] * i, new_sizes, new_strides);
+  }
+
+  /// Returns a PackedTensorAccessor of the same dimension after transposing the
+  /// two dimensions given. Does not actually move elements; transposition is
+  /// made by permuting the size/stride arrays. If the dimensions are not valid,
+  /// asserts.
+  C10_HOST GenericPackedTensorAccessor<
+      ItemAccessor,
+      IndexBoundsCheck,
+      T,
+      N,
+      PtrTraits,
+      index_t>
+  transpose(index_t dim1, index_t dim2) const {
+    this->bounds_check_(dim1);
+    this->bounds_check_(dim2);
+    GenericPackedTensorAccessor<
+        ItemAccessor,
+        IndexBoundsCheck,
+        T,
+        N,
+        PtrTraits,
+        index_t>
+        result(this->data_, this->sizes_, this->strides_);
+    std::swap(result.strides_[dim1], result.strides_[dim2]);
+    std::swap(result.sizes_[dim1], result.sizes_[dim2]);
+    return result;
+  }
+};
+
+template <
+    typename ItemAccessor,
+    typename IndexBoundsCheck,
+    typename T,
+    template <typename U> class PtrTraits,
+    typename index_t>
+class GenericPackedTensorAccessor<
+    ItemAccessor,
+    IndexBoundsCheck,
+    T,
+    1,
+    PtrTraits,
+    index_t>
+    : public GenericPackedTensorAccessorBase<
+          IndexBoundsCheck,
+          T,
+          1,
+          PtrTraits,
+          index_t> {
+ public:
+  typedef typename PtrTraits<T>::PtrType PtrType;
+  C10_HOST GenericPackedTensorAccessor(
+      PtrType data_,
+      const index_t* sizes_,
+      const index_t* strides_)
+      : GenericPackedTensorAccessorBase<
+            IndexBoundsCheck,
+            T,
+            1,
+            PtrTraits,
+            index_t>(data_, sizes_, strides_) {}
+
+  // if index_t is not int64_t, we want to have an int64_t constructor
+  template <
+      typename source_index_t,
+      class = std::enable_if_t<std::is_same_v<source_index_t, int64_t>>>
+  C10_HOST GenericPackedTensorAccessor(
+      PtrType data_,
+      const source_index_t* sizes_,
+      const source_index_t* strides_)
+      : GenericPackedTensorAccessorBase<
+            IndexBoundsCheck,
+            T,
+            1,
+            PtrTraits,
+            index_t>(data_, sizes_, strides_) {}
+
+  C10_DEVICE T& operator[](index_t i) {
+    return this->data_[this->strides_[0] * i];
+  }
+  C10_DEVICE const T& operator[](index_t i) const {
+    return this->data_[this->strides_[0] * i];
+  }
+
+  // Same as in the general N-dimensional case, but note that in the
+  // 1-dimensional case the returned PackedTensorAccessor will always be an
+  // identical copy of the original
+  C10_HOST GenericPackedTensorAccessor<
+      ItemAccessor,
+      IndexBoundsCheck,
+      T,
+      1,
+      PtrTraits,
+      index_t>
+  transpose(index_t dim1, index_t dim2) const {
+    this->bounds_check_(dim1);
+    this->bounds_check_(dim2);
+    return GenericPackedTensorAccessor<
+        ItemAccessor,
+        IndexBoundsCheck,
+        T,
+        1,
+        PtrTraits,
+        index_t>(this->data_, this->sizes_, this->strides_);
+  }
+};
+
+template <size_t N, typename index_t>
+struct HeaderOnlyIndexBoundsCheck {
+  HeaderOnlyIndexBoundsCheck(index_t i) {
+    STD_TORCH_CHECK(
+        0 <= i && i < index_t{N},
+        "Index ",
+        i,
+        " is not within bounds of a tensor of dimension ",
+        N);
+  }
+};
+
+} // namespace detail
+
+// HeaderOnlyTensorAccessorBase is same as at::TensorAccessorBase
+// except sizes() and strides() return IntHeaderOnlyArrayRef instead
+// of IntArrayRef.
+template <
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+using HeaderOnlyTensorAccessorBase = detail::TensorAccessorBase<
+    torch::headeronly::IntHeaderOnlyArrayRef,
+    T,
+    N,
+    PtrTraits,
+    index_t>;
+
+// HeaderOnlyTensorAccessor is same as at::TensorAccessor except
+// sizes() and strides() return IntHeaderOnlyArrayRef instead of
+// IntArrayRef.
+template <
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+using HeaderOnlyTensorAccessor = detail::TensorAccessor<
+    torch::headeronly::IntHeaderOnlyArrayRef,
+    T,
+    N,
+    PtrTraits,
+    index_t>;
+
+// HeaderOnlyGenericPackedTensorAccessorBase is same as
+// at::GenericPackedTensorAccessorBase except sizes() and strides()
+// return IntHeaderOnlyArrayRef instead of IntArrayRef.
+template <
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+using HeaderOnlyGenericPackedTensorAccessorBase =
+    detail::GenericPackedTensorAccessorBase<
+        detail::HeaderOnlyIndexBoundsCheck<N, index_t>,
+        T,
+        N,
+        PtrTraits,
+        index_t>;
+
+// HeaderOnlyGenericPackedTensorAccessor is same as
+// at::GenericPackedTensorAccessor except sizes() and strides() return
+// IntHeaderOnlyArrayRef instead of IntArrayRef, and bounds check uses
+// STD_TORCH_CHECK instead of TORCH_CHECK_INDEX.
+template <
+    typename T,
+    size_t N,
+    template <typename U> class PtrTraits = DefaultPtrTraits,
+    typename index_t = int64_t>
+using HeaderOnlyGenericPackedTensorAccessor =
+    detail::GenericPackedTensorAccessor<
+        HeaderOnlyTensorAccessor<T, N - 1, PtrTraits, index_t>,
+        detail::HeaderOnlyIndexBoundsCheck<N, index_t>,
+        T,
+        N,
+        PtrTraits,
+        index_t>;
+
+} // namespace torch::headeronly

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/cpu/vec/intrinsics.h

@@ -0,0 +1,50 @@
+#pragma once
+#if defined(__GNUC__) && (defined(__x86_64__) || defined(__i386__))
+/* GCC or clang-compatible compiler, targeting x86/x86-64 */
+#include <x86intrin.h>
+#elif defined(__clang__) && (defined(__ARM_NEON__) || defined(__aarch64__))
+/* Clang-compatible compiler, targeting arm neon */
+#include <arm_neon.h>
+#if defined(__ARM_FEATURE_SVE)
+/* CLANG-compatible compiler, targeting ARM with SVE */
+#include <arm_sve.h>
+#endif
+#elif defined(_MSC_VER)
+/* Microsoft C/C++-compatible compiler */
+#include <intrin.h>
+#if _MSC_VER <= 1900
+#define _mm256_extract_epi64(X, Y) \
+  (_mm_extract_epi64(_mm256_extractf128_si256(X, Y >> 1), Y % 2))
+#define _mm256_extract_epi32(X, Y) \
+  (_mm_extract_epi32(_mm256_extractf128_si256(X, Y >> 2), Y % 4))
+#define _mm256_extract_epi16(X, Y) \
+  (_mm_extract_epi16(_mm256_extractf128_si256(X, Y >> 3), Y % 8))
+#define _mm256_extract_epi8(X, Y) \
+  (_mm_extract_epi8(_mm256_extractf128_si256(X, Y >> 4), Y % 16))
+#endif
+#elif defined(__GNUC__) && (defined(__ARM_NEON__) || defined(__aarch64__))
+/* GCC-compatible compiler, targeting ARM with NEON */
+#include <arm_neon.h>
+#if defined(__ARM_FEATURE_SVE)
+/* GCC-compatible compiler, targeting ARM with SVE */
+#include <arm_sve.h>
+#endif
+#elif defined(__GNUC__) && defined(__IWMMXT__)
+/* GCC-compatible compiler, targeting ARM with WMMX */
+#include <mmintrin.h>
+#elif defined(__s390x__)
+// targets Z/architecture
+// we will include vecintrin later
+#elif (defined(__GNUC__) || defined(__xlC__)) && \
+    (defined(__VEC__) || defined(__ALTIVEC__))
+/* XLC or GCC-compatible compiler, targeting PowerPC with VMX/VSX */
+#include <altivec.h>
+/* We need to undef those tokens defined by <altivec.h> to avoid conflicts
+   with the C++ types. => Can still use __bool/__vector */
+#undef bool
+#undef vector
+#undef pixel
+#elif defined(__GNUC__) && defined(__SPE__)
+/* GCC-compatible compiler, targeting PowerPC with SPE */
+#include <spe.h>
+#endif

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/cpu/vec/vec_half.h

@@ -0,0 +1,59 @@
+#pragma once
+
+#include <torch/headeronly/cpu/vec/intrinsics.h>
+#include <torch/headeronly/macros/Macros.h>
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly, vec)
+// See Note [CPU_CAPABILITY namespace]
+inline namespace CPU_CAPABILITY {
+
+#if (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+static inline uint16_t float2half_scalar(float val) {
+#if defined(CPU_CAPABILITY_AVX2)
+#if defined(_MSC_VER)
+  __m256 v = _mm256_set1_ps(val);
+  __m128i o =
+      _mm256_cvtps_ph(v, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
+  return static_cast<std::uint16_t>(_mm_cvtsi128_si32(o));
+#else
+  return _cvtss_sh(val, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
+#endif
+#elif defined(CPU_CAPABILITY_AVX512)
+  __m512 v = _mm512_set1_ps(val);
+  __m256i o =
+      _mm512_cvtps_ph(v, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC));
+  return static_cast<std::uint16_t>(
+      _mm_cvtsi128_si32(_mm256_castsi256_si128(o)));
+#endif
+}
+
+static inline float half2float_scalar(uint16_t val) {
+#if defined(CPU_CAPABILITY_AVX2)
+#if defined(_MSC_VER)
+  __m128i v = _mm_cvtsi32_si128(val);
+  __m256 o = _mm256_cvtph_ps(v);
+  return _mm256_cvtss_f32(o);
+#else
+  return _cvtsh_ss(val);
+#endif
+#elif defined(CPU_CAPABILITY_AVX512)
+  __m256i v =
+      _mm256_setr_epi16(val, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0);
+  __m512 o = _mm512_cvtph_ps(v);
+  return _mm512_cvtss_f32(o);
+#endif
+}
+
+#endif
+
+} // namespace CPU_CAPABILITY
+HIDDEN_NAMESPACE_END(torch, headeronly, vec)
+
+namespace at::vec {
+#if (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+using torch::headeronly::vec::float2half_scalar;
+using torch::headeronly::vec::half2float_scalar;
+#endif
+} // namespace at::vec

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/macros/Export.h

@@ -0,0 +1,153 @@
+#pragma once
+
+#ifndef C10_MACROS_EXPORT_H_
+#define C10_MACROS_EXPORT_H_
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+#include <torch/headeronly/macros/cmake_macros.h>
+#endif // C10_USING_CUSTOM_GENERATED_MACROS
+
+/* Header file to define the common scaffolding for exported symbols.
+ *
+ * Export is by itself a quite tricky situation to deal with, and if you are
+ * hitting this file, make sure you start with the background here:
+ * - Linux: https://gcc.gnu.org/wiki/Visibility
+ * - Windows:
+ * https://docs.microsoft.com/en-us/cpp/cpp/dllexport-dllimport?view=vs-2017
+ *
+ * Do NOT include this file directly. Instead, use c10/macros/Macros.h
+ */
+
+// You do not need to edit this part of file unless you are changing the core
+// pytorch export abstractions.
+//
+// This part defines the C10 core export and import macros. This is controlled
+// by whether we are building shared libraries or not, which is determined
+// during build time and codified in c10/core/cmake_macros.h.
+// When the library is built as a shared lib, EXPORT and IMPORT will contain
+// visibility attributes. If it is being built as a static lib, then EXPORT
+// and IMPORT basically have no effect.
+
+// As a rule of thumb, you should almost NEVER mix static and shared builds for
+// libraries that depend on c10. AKA, if c10 is built as a static library, we
+// recommend everything dependent on c10 to be built statically. If c10 is built
+// as a shared library, everything dependent on it should be built as shared. In
+// the PyTorch project, all native libraries shall use the macro
+// C10_BUILD_SHARED_LIB to check whether pytorch is building shared or static
+// libraries.
+
+// For build systems that do not directly depend on CMake and directly build
+// from the source directory (such as Buck), one may not have a cmake_macros.h
+// file at all. In this case, the build system is responsible for providing
+// correct macro definitions corresponding to the cmake_macros.h.in file.
+//
+// In such scenarios, one should define the macro
+//     C10_USING_CUSTOM_GENERATED_MACROS
+// to inform this header that it does not need to include the cmake_macros.h
+// file.
+
+#ifdef _WIN32
+#define C10_HIDDEN
+#if defined(C10_BUILD_SHARED_LIBS)
+#define C10_EXPORT __declspec(dllexport)
+#define C10_IMPORT __declspec(dllimport)
+#else
+#define C10_EXPORT
+#define C10_IMPORT
+#endif
+#else // _WIN32
+#if defined(__GNUC__)
+#define C10_EXPORT __attribute__((__visibility__("default")))
+#define C10_HIDDEN __attribute__((__visibility__("hidden")))
+#else // defined(__GNUC__)
+#define C10_EXPORT
+#define C10_HIDDEN
+#endif // defined(__GNUC__)
+#define C10_IMPORT C10_EXPORT
+#endif // _WIN32
+
+#ifdef NO_EXPORT
+#undef C10_EXPORT
+#define C10_EXPORT
+#endif
+
+// Definition of an adaptive XX_API macro, that depends on whether you are
+// building the library itself or not, routes to XX_EXPORT and XX_IMPORT.
+// Basically, you will need to do this for each shared library that you are
+// building, and the instruction is as follows: assuming that you are building
+// a library called libawesome.so. You should:
+// (1) for your cmake target (usually done by "add_library(awesome, ...)"),
+//     define a macro called AWESOME_BUILD_MAIN_LIB using
+//     target_compile_options.
+// (2) define the AWESOME_API macro similar to the one below.
+// And in the source file of your awesome library, use AWESOME_API to
+// annotate public symbols.
+
+// Here, for the C10 library, we will define the macro C10_API for both import
+// and export.
+
+// This one is being used by libc10.so
+#ifdef C10_BUILD_MAIN_LIB
+#define C10_API C10_EXPORT
+#else
+#define C10_API C10_IMPORT
+#endif
+
+// This one is being used by libtorch.so
+#ifdef CAFFE2_BUILD_MAIN_LIB
+#define TORCH_API C10_EXPORT
+#else
+#define TORCH_API C10_IMPORT
+#endif
+
+// You may be wondering why we have TORCH_CUDA_CPP_API and TORCH_CUDA_CU_API
+// belonging to the same library instead of just one TORCH_CUDA_API. Well, it
+// can indeed just be one TORCH_CUDA_API (and used to be)! TORCH_CUDA_CPP_API
+// and TORCH_CUDA_CU_API are artifacts of when we needed a split build to
+// avoid relocation marker linking errors. The context is as follows:
+//
+// Once upon a time, there _was_ only TORCH_CUDA_API. All was happy until we
+// tried to compile PyTorch for CUDA 11.1, which ran into relocation marker
+// issues when linking big binaries.
+// (https://github.com/pytorch/pytorch/issues/39968) We had two choices:
+//    (1) Stop supporting so many GPU architectures
+//    (2) Do something else
+// We chose #2 and decided to split the behemoth that was torch_cuda into two
+// smaller libraries, one with most of the core kernel functions (torch_cuda_cu)
+// and the other that had..well..everything else (torch_cuda_cpp). The idea was
+// this: instead of linking our static libraries (like the hefty
+// libcudnn_static.a) with another huge library, torch_cuda, and run into pesky
+// relocation marker issues, we could link our static libraries to a smaller
+// part of torch_cuda (torch_cuda_cpp) and avoid the issues.
+
+// libtorch_cuda.so (where torch_cuda_cu and torch_cuda_cpp are a part of the
+// same api)
+#ifdef TORCH_CUDA_BUILD_MAIN_LIB
+#define TORCH_CUDA_CPP_API C10_EXPORT
+#define TORCH_CUDA_CU_API C10_EXPORT
+#else
+#define TORCH_CUDA_CPP_API C10_IMPORT
+#define TORCH_CUDA_CU_API C10_IMPORT
+#endif
+
+#if defined(TORCH_HIP_BUILD_MAIN_LIB)
+#define TORCH_HIP_CPP_API C10_EXPORT
+#define TORCH_HIP_API C10_EXPORT
+#else
+#define TORCH_HIP_CPP_API C10_IMPORT
+#define TORCH_HIP_API C10_IMPORT
+#endif
+
+#if defined(TORCH_XPU_BUILD_MAIN_LIB)
+#define TORCH_XPU_API C10_EXPORT
+#else
+#define TORCH_XPU_API C10_IMPORT
+#endif
+
+// Enums only need to be exported on windows for non-CUDA files
+#if defined(_WIN32) && defined(__CUDACC__)
+#define C10_API_ENUM C10_API
+#else
+#define C10_API_ENUM
+#endif
+#endif // C10_MACROS_EXPORT_H_

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/macros/Macros.h

@@ -0,0 +1,694 @@
+#ifndef C10_MACROS_MACROS_H_
+#define C10_MACROS_MACROS_H_
+
+#ifdef __cplusplus
+#include <cassert>
+#else
+#include <assert.h>
+#endif
+
+/* Main entry for torch/headeronly/macros (used to be c10/macros).
+ *
+ * In your code, include torch/headeronly/macros/Macros.h directly, instead of
+ * individual files in this folder.
+ */
+
+// For build systems that do not directly depend on CMake and directly build
+// from the source directory (such as Buck), one may not have a cmake_macros.h
+// file at all. In this case, the build system is responsible for providing
+// correct macro definitions corresponding to the cmake_macros.h.in file.
+//
+// In such scenarios, one should define the macro
+//     C10_USING_CUSTOM_GENERATED_MACROS
+// to inform this header that it does not need to include the cmake_macros.h
+// file.
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+#include <torch/headeronly/macros/cmake_macros.h>
+#endif // C10_USING_CUSTOM_GENERATED_MACROS
+
+#include <torch/headeronly/macros/Export.h>
+
+#if defined(__clang__)
+#define __ubsan_ignore_float_divide_by_zero__ \
+  __attribute__((no_sanitize("float-divide-by-zero")))
+#define __ubsan_ignore_undefined__ __attribute__((no_sanitize("undefined")))
+#define __ubsan_ignore_signed_int_overflow__ \
+  __attribute__((no_sanitize("signed-integer-overflow")))
+#define __ubsan_ignore_pointer_overflow__ \
+  __attribute__((no_sanitize("pointer-overflow")))
+#define __ubsan_ignore_function__ __attribute__((no_sanitize("function")))
+#define __ubsan_ignore_float_cast_overflow__ \
+  __attribute__((no_sanitize("float-cast-overflow")))
+#else
+#define __ubsan_ignore_float_divide_by_zero__
+#define __ubsan_ignore_undefined__
+#define __ubsan_ignore_signed_int_overflow__
+#define __ubsan_ignore_pointer_overflow__
+#define __ubsan_ignore_function__
+#define __ubsan_ignore_float_cast_overflow__
+#endif
+
+// Detect address sanitizer as some stuff doesn't work with it
+#undef C10_ASAN_ENABLED
+
+// for clang
+#if defined(__has_feature)
+#if ((__has_feature(address_sanitizer)))
+#define C10_ASAN_ENABLED 1
+#endif
+#endif
+
+// for gcc
+#if defined(__SANITIZE_ADDRESS__)
+#if __SANITIZE_ADDRESS__
+#if !defined(C10_ASAN_ENABLED)
+#define C10_ASAN_ENABLED 1
+#endif
+#endif
+#endif
+
+#if !defined(C10_ASAN_ENABLED)
+#define C10_ASAN_ENABLED 0
+#endif
+
+// Detect undefined-behavior sanitizer (UBSAN)
+#undef C10_UBSAN_ENABLED
+
+// for clang or gcc >= 14
+// NB: gcc 14 adds support for Clang's __has_feature
+//   https://gcc.gnu.org/gcc-14/changes.html
+//   gcc < 14 doesn't have a macro for UBSAN
+//   (e.g. __SANITIZE_UNDEFINED__ does not exist in gcc)
+//   https://github.com/google/sanitizers/issues/765
+#if defined(__has_feature)
+#if ((__has_feature(undefined_behavior_sanitizer)))
+#define C10_UBSAN_ENABLED 1
+#endif
+#endif
+
+#if !defined(C10_UBSAN_ENABLED)
+#define C10_UBSAN_ENABLED 0
+#endif
+
+// Disable the copy and assignment operator for a class. Note that this will
+// disable the usage of the class in std containers.
+#define C10_DISABLE_COPY_AND_ASSIGN(classname) \
+  classname(const classname&) = delete;        \
+  classname& operator=(const classname&) = delete
+
+#define C10_CONCATENATE_IMPL(s1, s2) s1##s2
+#define C10_CONCATENATE(s1, s2) C10_CONCATENATE_IMPL(s1, s2)
+
+#define C10_MACRO_EXPAND(args) args
+
+#define C10_STRINGIZE_IMPL(x) #x
+#define C10_STRINGIZE(x) C10_STRINGIZE_IMPL(x)
+
+/**
+ * C10_ANONYMOUS_VARIABLE(str) introduces a new identifier which starts with
+ * str and ends with a unique number.
+ */
+#ifdef __COUNTER__
+#define C10_UID __COUNTER__
+#define C10_ANONYMOUS_VARIABLE(str) C10_CONCATENATE(str, __COUNTER__)
+#else
+#define C10_UID __LINE__
+#define C10_ANONYMOUS_VARIABLE(str) C10_CONCATENATE(str, __LINE__)
+#endif
+
+#ifdef __has_cpp_attribute
+#define C10_HAS_CPP_ATTRIBUTE(x) __has_cpp_attribute(x)
+#else
+#define C10_HAS_CPP_ATTRIBUTE(x) (0)
+#endif
+
+#ifndef FBCODE_CAFFE2
+/// DEPRECATED: Warn if a type or return value is discarded.
+#define C10_NODISCARD [[nodiscard]]
+
+/// DEPRECATED: Suppress an unused variable.
+#define C10_UNUSED [[maybe_unused]]
+#endif
+
+#if !defined(__has_attribute)
+#define __has_attribute(x) 0
+#endif
+
+// Direct port of LLVM_ATTRIBUTE_USED.
+#if __has_attribute(used)
+#define C10_USED __attribute__((__used__))
+#else
+#define C10_USED
+#endif
+
+#define C10_RESTRICT __restrict
+
+#ifdef __cplusplus
+
+// Simply define the namespace, in case a dependent library want to refer to
+// the c10 namespace but not any nontrivial files.
+namespace c10 {}
+namespace c10::cuda {}
+namespace c10::hip {}
+namespace c10::xpu {}
+
+// Since C10 is the core library for caffe2 (and aten), we will simply reroute
+// all abstractions defined in c10 to be available in caffe2 as well.
+// This is only for backwards compatibility. Please use the symbols from the
+// c10 namespace where possible.
+namespace caffe2 {
+using namespace c10;
+}
+namespace at {
+using namespace c10;
+}
+namespace at::cuda {
+using namespace c10::cuda;
+} // namespace at::cuda
+
+// WARNING!!! THIS IS A GIANT HACK!!!
+// This line means you cannot simultaneously include c10/hip
+// and c10/cuda and then use them from the at::cuda namespace.
+// This is true in practice, because HIPIFY works inplace on
+// files in ATen/cuda, so it assumes that c10::hip is available
+// from at::cuda.  This namespace makes that happen.  When
+// HIPIFY is no longer out-of-place, we can switch the cuda
+// here to hip and everyone is happy.
+namespace at::cuda {
+using namespace c10::hip;
+} // namespace at::cuda
+
+namespace at::xpu {
+using namespace c10::xpu;
+} // namespace at::xpu
+
+#endif // __cplusplus
+
+// C10_LIKELY/C10_UNLIKELY
+//
+// These macros provide parentheses, so you can use these macros as:
+//
+//    if C10_LIKELY(some_expr) {
+//      ...
+//    }
+//
+// NB: static_cast to boolean is mandatory in C++, because __builtin_expect
+// takes a long argument, which means you may trigger the wrong conversion
+// without it.
+//
+#if defined(__GNUC__) || defined(__ICL) || defined(__clang__)
+#define C10_LIKELY(expr) (__builtin_expect(static_cast<bool>(expr), 1))
+#define C10_UNLIKELY(expr) (__builtin_expect(static_cast<bool>(expr), 0))
+#else
+#define C10_LIKELY(expr) (expr)
+#define C10_UNLIKELY(expr) (expr)
+#endif
+
+/// C10_NOINLINE - Functions whose declaration is annotated with this will not
+/// be inlined.
+#ifdef __GNUC__
+#define C10_NOINLINE __attribute__((noinline))
+#elif _MSC_VER
+#define C10_NOINLINE __declspec(noinline)
+#else
+#define C10_NOINLINE
+#endif
+
+#if defined(_MSC_VER)
+#define C10_ALWAYS_INLINE __forceinline
+#elif __has_attribute(always_inline) || defined(__GNUC__)
+#define C10_ALWAYS_INLINE __attribute__((__always_inline__)) inline
+#else
+#define C10_ALWAYS_INLINE inline
+#endif
+
+// Unlike C10_ALWAYS_INLINE, C10_ALWAYS_INLINE_ATTRIBUTE can be used
+// on a lambda.
+#if defined(_MSC_VER)
+// MSVC 14.39 is reasonably recent and doesn't like
+// [[msvc::forceinline]] on a lambda, so don't try to use it.
+#define C10_ALWAYS_INLINE_ATTRIBUTE
+#elif __has_attribute(always_inline) || defined(__GNUC__)
+#define C10_ALWAYS_INLINE_ATTRIBUTE __attribute__((__always_inline__))
+#else
+#define C10_ALWAYS_INLINE_ATTRIBUTE
+#endif
+
+#if defined(_MSC_VER)
+#define C10_ATTR_VISIBILITY_HIDDEN
+#elif defined(__GNUC__)
+#define C10_ATTR_VISIBILITY_HIDDEN __attribute__((__visibility__("hidden")))
+#else
+#define C10_ATTR_VISIBILITY_HIDDEN
+#endif
+
+#define C10_ERASE C10_ALWAYS_INLINE C10_ATTR_VISIBILITY_HIDDEN
+
+#ifdef __cplusplus
+#include <cstdint>
+#else
+#include <stdint.h>
+#endif
+
+#ifdef __HIPCC__
+// Unlike CUDA, HIP requires a HIP header to be included for __host__ to work.
+// We do this #include here so that C10_HOST_DEVICE and friends will Just Work.
+// See https://github.com/ROCm/hip/issues/441
+#include <hip/hip_runtime.h>
+#endif
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+// Designates functions callable from the host (CPU) and the device (GPU)
+#define C10_HOST_DEVICE __host__ __device__
+#define C10_DEVICE __device__
+#define C10_HOST __host__
+// constants from
+// (https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#features-and-technical-specifications)
+// The maximum number of threads per multiprocessor is 1024 for Turing
+// architecture (7.5), 1536 for Geforce Ampere (8.6)/Jetson Orin (8.7), and
+// 2048 for all other architectures. You'll get warnings if you exceed these
+// constants. Hence, the following macros adjust the input values from the user
+// to resolve potential warnings.
+#if __CUDA_ARCH__ == 750
+constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 1024;
+#elif __CUDA_ARCH__ == 860 || __CUDA_ARCH__ == 870 || __CUDA_ARCH__ == 890 || \
+    __CUDA_ARCH__ == 1200
+constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 1536;
+#else
+constexpr uint32_t CUDA_MAX_THREADS_PER_SM = 2048;
+#endif
+// CUDA_MAX_THREADS_PER_BLOCK is same for all architectures currently
+constexpr uint32_t CUDA_MAX_THREADS_PER_BLOCK = 1024;
+// CUDA_THREADS_PER_BLOCK_FALLBACK is the "canonical fallback" choice of block
+// size. 256 is a good number for this fallback and should give good occupancy
+// and versatility across all architectures.
+constexpr uint32_t CUDA_THREADS_PER_BLOCK_FALLBACK = 256;
+// NOTE: if you are thinking of constexpr-ify the inputs to launch bounds, it
+//       turns out that although __launch_bounds__ can take constexpr, it
+//       can't take a constexpr that has anything to do with templates.
+//       Currently we use launch_bounds that depend on template arguments in
+//       Loops.cuh, Reduce.cuh and LossCTC.cuh. Hence, C10_MAX_THREADS_PER_BLOCK
+//       and C10_MIN_BLOCKS_PER_SM are kept as macros.
+// Suppose you were planning to write __launch_bounds__(a, b), based on your
+// performance tuning on a modern GPU. Instead, you should write
+// __launch_bounds__(C10_MAX_THREADS_PER_BLOCK(a), C10_MIN_BLOCKS_PER_SM(a, b)),
+// which will also properly respect limits on old architectures.
+#define C10_MAX_THREADS_PER_BLOCK(val)           \
+  (((val) <= CUDA_MAX_THREADS_PER_BLOCK) ? (val) \
+                                         : CUDA_THREADS_PER_BLOCK_FALLBACK)
+#define C10_MIN_BLOCKS_PER_SM(threads_per_block, blocks_per_sm)        \
+  ((((threads_per_block) * (blocks_per_sm) <= CUDA_MAX_THREADS_PER_SM) \
+        ? (blocks_per_sm)                                              \
+        : ((CUDA_MAX_THREADS_PER_SM + (threads_per_block) - 1) /       \
+           (threads_per_block))))
+// C10_LAUNCH_BOUNDS is analogous to __launch_bounds__
+#define C10_LAUNCH_BOUNDS_0 \
+  __launch_bounds__(        \
+      256, 4) // default launch bounds that should give good occupancy and
+              // versatility across all architectures.
+#define C10_LAUNCH_BOUNDS_1(max_threads_per_block) \
+  __launch_bounds__((C10_MAX_THREADS_PER_BLOCK((max_threads_per_block))))
+#define C10_LAUNCH_BOUNDS_2(max_threads_per_block, min_blocks_per_sm) \
+  __launch_bounds__(                                                  \
+      (C10_MAX_THREADS_PER_BLOCK((max_threads_per_block))),           \
+      (C10_MIN_BLOCKS_PER_SM((max_threads_per_block), (min_blocks_per_sm))))
+#else
+#define C10_HOST_DEVICE
+#define C10_HOST
+#define C10_DEVICE
+#endif
+
+#if defined(USE_ROCM)
+#define C10_HIP_HOST_DEVICE __host__ __device__
+#else
+#define C10_HIP_HOST_DEVICE
+#endif
+
+#if defined(USE_ROCM)
+// C10_WARP_SIZE is only allowed for device code.
+// Host code _must_ use at::cuda::warp_size()
+// HIP header used to define warpSize as a constexpr that was either 32 or 64
+// depending on the target device, and then always set it to 64 for host code.
+// Host pass of HIP compiler needs C10_WARP_SIZE defined to _something_ so we
+// set it to something unreasonable to trigger obvious host code errors.
+
+namespace at::cuda {
+TORCH_CUDA_CPP_API int warp_size();
+}
+#ifdef __HIPCC__
+static inline int __host__ C10_WARP_SIZE_INTERNAL() {
+  return at::cuda::warp_size();
+}
+
+static inline constexpr int __device__ C10_WARP_SIZE_INTERNAL() {
+#if defined(__GFX9__)
+  return 64;
+#else // __GFX9__
+  return 32;
+#endif // __GFX9__
+}
+#else // __HIPCC__
+static inline int C10_WARP_SIZE_INTERNAL() {
+  return at::cuda::warp_size();
+}
+#endif // __HIPCC__
+
+#define C10_WARP_SIZE (C10_WARP_SIZE_INTERNAL())
+#define C10_WARP_SIZE_STATIC 64
+
+#else // defined(USE_ROCM)
+#define C10_WARP_SIZE 32
+#endif
+
+#if defined(_MSC_VER) && _MSC_VER <= 1900
+#define __func__ __FUNCTION__
+#endif
+
+// CUDA_KERNEL_ASSERT checks the assertion
+// even when NDEBUG is defined. This is useful for important assertions in CUDA
+// code that would otherwise be suppressed when building Release.
+#if defined(__ANDROID__) || defined(__APPLE__) || defined(__FreeBSD__)
+// Those platforms do not support assert()
+#define CUDA_KERNEL_ASSERT(cond)
+#define CUDA_KERNEL_ASSERT_MSG(cond, msg)
+#define CUDA_KERNEL_ASSERT_PRINTF(cond, msg, ...)
+#define SYCL_KERNEL_ASSERT(cond)
+#elif defined(_MSC_VER)
+#if defined(NDEBUG)
+extern "C" {
+C10_IMPORT
+#if defined(__SYCL_DEVICE_ONLY__)
+extern SYCL_EXTERNAL void _wassert(
+    const wchar_t* wexpr,
+    const wchar_t* wfile,
+    unsigned line);
+#else
+#if defined(__CUDA_ARCH__)
+__host__ __device__
+#endif // __CUDA_ARCH__
+    void
+    _wassert(wchar_t const* _Message, wchar_t const* _File, unsigned _Line);
+#endif // __SYCL_DEVICE_ONLY__
+}
+#endif // NDEBUG
+#define CUDA_KERNEL_ASSERT(cond)                 \
+  if (C10_UNLIKELY(!(cond))) {                   \
+    (void)(_wassert(                             \
+               _CRT_WIDE(#cond),                 \
+               _CRT_WIDE(__FILE__),              \
+               static_cast<unsigned>(__LINE__)), \
+           0);                                   \
+  }
+// TODO: This doesn't assert the message because I (chilli) couldn't figure out
+// a nice way to convert a char* to a wchar_t*
+#define CUDA_KERNEL_ASSERT_MSG(cond, msg)        \
+  if (C10_UNLIKELY(!(cond))) {                   \
+    (void)(_wassert(                             \
+               _CRT_WIDE(#cond),                 \
+               _CRT_WIDE(__FILE__),              \
+               static_cast<unsigned>(__LINE__)), \
+           0);                                   \
+  }
+#define CUDA_KERNEL_ASSERT_PRINTF(cond, msg, ...)                     \
+  if (C10_UNLIKELY(!(cond))) {                                        \
+    (void)(printf(                                                    \
+        "[CUDA_KERNEL_ASSERT] " __FILE__ ":" C10_STRINGIZE(           \
+            __LINE__) ": %s: block: [%d,%d,%d], thread: [%d,%d,%d]: " \
+                      "Assertion failed: `" #cond "`: " msg "\n",     \
+        __func__,                                                     \
+        blockIdx.x,                                                   \
+        blockIdx.y,                                                   \
+        blockIdx.z,                                                   \
+        threadIdx.x,                                                  \
+        threadIdx.y,                                                  \
+        threadIdx.z,                                                  \
+        ##__VA_ARGS__));                                              \
+    (void)(_wassert(                                                  \
+               _CRT_WIDE(#cond),                                      \
+               _CRT_WIDE(__FILE__),                                   \
+               static_cast<unsigned>(__LINE__)),                      \
+           0);                                                        \
+  }
+#define SYCL_KERNEL_ASSERT(cond)                 \
+  if (C10_UNLIKELY(!(cond))) {                   \
+    (void)(_wassert(                             \
+               _CRT_WIDE(#cond),                 \
+               _CRT_WIDE(__FILE__),              \
+               static_cast<unsigned>(__LINE__)), \
+           0);                                   \
+  }
+#else // __APPLE__, _MSC_VER
+#if defined(NDEBUG)
+extern "C" {
+#if defined(__SYCL_DEVICE_ONLY__)
+extern SYCL_EXTERNAL void __assert_fail(
+    const char* expr,
+    const char* file,
+    unsigned int line,
+    const char* func);
+#elif (defined(__EMSCRIPTEN__))
+// As defined in assert.h in the Emscripten stdlib
+_Noreturn void __assert_fail(
+    const char* expr,
+    const char* file,
+    int line,
+    const char* func);
+#else // __SYCL_DEVICE_ONLY__
+#if (defined(__CUDA_ARCH__) && !(defined(__clang__) && defined(__CUDA__)))
+// CUDA supports __assert_fail function which are common for both device
+// and host side code.
+__host__ __device__
+#endif
+
+    // This forward declaration matching the declaration of __assert_fail
+    // exactly how it is in glibc in case parts of the program are compiled with
+    // different NDEBUG settings. Otherwise we might get 'ambiguous declaration'
+    // error. Note: On ROCm - this declaration serves for host side compilation.
+    void
+    __assert_fail(
+        const char* assertion,
+        const char* file,
+        unsigned int line,
+        const char* function) noexcept __attribute__((__noreturn__));
+
+#endif // __SYCL_DEVICE_ONLY__
+}
+#endif // NDEBUG
+// ROCm disables kernel assert by default for performance considerations.
+// Though ROCm supports __assert_fail, it uses kernel printf which has
+// a non-negligible performance impact even if the assert condition is
+// never triggered. We choose to use abort() instead which will still
+// terminate the application but without a more useful error message.
+#if !defined(C10_USE_ROCM_KERNEL_ASSERT) && defined(USE_ROCM)
+#define CUDA_KERNEL_ASSERT(cond) \
+  if C10_UNLIKELY (!(cond)) {    \
+    abort();                     \
+  }
+#define CUDA_KERNEL_ASSERT_MSG(cond, msg) \
+  if C10_UNLIKELY (!(cond)) {             \
+    abort();                              \
+  }
+#define CUDA_KERNEL_ASSERT_PRINTF(cond, msg, ...) \
+  if C10_UNLIKELY (!(cond)) {                     \
+    abort();                                      \
+  }
+#define SYCL_KERNEL_ASSERT(cond) \
+  if C10_UNLIKELY (!(cond)) {    \
+    abort();                     \
+  }
+#else
+#define CUDA_KERNEL_ASSERT(cond)                                         \
+  if (C10_UNLIKELY(!(cond))) {                                           \
+    __assert_fail(                                                       \
+        #cond, __FILE__, static_cast<unsigned int>(__LINE__), __func__); \
+  }
+#define CUDA_KERNEL_ASSERT_MSG(cond, msg)                              \
+  if (C10_UNLIKELY(!(cond))) {                                         \
+    __assert_fail(                                                     \
+        msg, __FILE__, static_cast<unsigned int>(__LINE__), __func__); \
+  }
+#define CUDA_KERNEL_ASSERT_PRINTF(cond, msg, ...)                        \
+  if (C10_UNLIKELY(!(cond))) {                                           \
+    printf(                                                            \
+        "[CUDA_KERNEL_ASSERT] " __FILE__ ":" C10_STRINGIZE(            \
+            __LINE__) ": %s: block: [%d,%d,%d], thread: [%d,%d,%d]: "  \
+            "Assertion failed: `" #cond "`: " msg "\n",                \
+        __func__,                                                      \
+        blockIdx.x,                                                    \
+        blockIdx.y,                                                    \
+        blockIdx.z,                                                    \
+        threadIdx.x,                                                   \
+        threadIdx.y,                                                   \
+        threadIdx.z,                                                   \
+        ##__VA_ARGS__); \
+    __assert_fail(                                                       \
+        #cond, __FILE__, static_cast<unsigned int>(__LINE__), __func__); \
+  }
+#define SYCL_KERNEL_ASSERT(cond)                                         \
+  if (C10_UNLIKELY(!(cond))) {                                           \
+    __assert_fail(                                                       \
+        #cond, __FILE__, static_cast<unsigned int>(__LINE__), __func__); \
+  }
+#endif //  C10_USE_ROCM_KERNEL_ASSERT && USE_ROCM
+#endif // __APPLE__
+
+// Compile-time switch to control how assertions are logged inside CUDA kernels.
+// If C10_CUDA_VERBOSE_ASSERT is defined,  CUDA_KERNEL_ASSERT_VERBOSE will
+// take addition information passed to the macro and forward them to
+// CUDA_KERNEL_ASSERT_PRINTF If C10_CUDA_VERBOSE_ASSERT is not defined,
+// CUDA_KERNEL_ASSERT_VERBOSE will behave the same as CUDA_KERNEL_ASSERT.
+#ifdef C10_ENABLE_VERBOSE_ASSERT
+#define CUDA_KERNEL_ASSERT_VERBOSE(cond, ...) \
+  CUDA_KERNEL_ASSERT_PRINTF(cond, __VA_ARGS__)
+#else
+#define CUDA_KERNEL_ASSERT_VERBOSE(cond, ...) CUDA_KERNEL_ASSERT(cond)
+#endif
+
+#ifdef __APPLE__
+#include <TargetConditionals.h>
+#endif
+
+#if defined(__ANDROID__)
+#define C10_ANDROID 1
+#define C10_MOBILE 1
+#elif (                   \
+    defined(__APPLE__) && \
+    (TARGET_IPHONE_SIMULATOR || TARGET_OS_SIMULATOR || TARGET_OS_IPHONE))
+#define C10_IOS 1
+#define C10_MOBILE 1
+#endif // ANDROID / IOS
+
+#if defined(C10_MOBILE) && C10_MOBILE
+#define C10_ALWAYS_INLINE_UNLESS_MOBILE inline
+#else
+#define C10_ALWAYS_INLINE_UNLESS_MOBILE C10_ALWAYS_INLINE
+#endif
+
+#if !defined(FBCODE_CAFFE2) && !defined(C10_NODEPRECATED)
+#define CONSTEXPR_EXCEPT_WIN_CUDA constexpr
+#define C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA constexpr
+
+#define STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(field, val) \
+  static constexpr const char field[] = val;
+#define STATIC_CONST_STR_OUT_OF_LINE_FOR_WIN_CUDA(cls, field, val)
+#endif // !defined(FBCODE_CAFFE2) && !defined(C10_NODEPRECATED)
+
+#ifndef HAS_DEMANGLE
+#if defined(__ANDROID__) || defined(_WIN32) || defined(__EMSCRIPTEN__)
+#define HAS_DEMANGLE 0
+#elif defined(__APPLE__) && \
+    (TARGET_IPHONE_SIMULATOR || TARGET_OS_SIMULATOR || TARGET_OS_IPHONE)
+#define HAS_DEMANGLE 0
+#else
+#define HAS_DEMANGLE 1
+#endif
+#endif // HAS_DEMANGLE
+
+#define _C10_PRAGMA__(string) _Pragma(#string)
+#define _C10_PRAGMA_(string) _C10_PRAGMA__(string)
+
+#ifdef __clang__
+#define C10_CLANG_DIAGNOSTIC_PUSH() _Pragma("clang diagnostic push")
+#define C10_CLANG_DIAGNOSTIC_POP() _Pragma("clang diagnostic pop")
+#define C10_CLANG_DIAGNOSTIC_IGNORE(flag) \
+  _C10_PRAGMA_(clang diagnostic ignored flag)
+#define C10_CLANG_HAS_WARNING(flag) __has_warning(flag)
+#else
+#define C10_CLANG_DIAGNOSTIC_PUSH()
+#define C10_CLANG_DIAGNOSTIC_POP()
+#define C10_CLANG_DIAGNOSTIC_IGNORE(flag)
+#define C10_CLANG_HAS_WARNING(flag) 0
+#endif
+
+#ifdef __clang__
+
+#define C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED(warning)         \
+  _C10_PRAGMA_(clang diagnostic push)                               \
+  _C10_PRAGMA_(clang diagnostic ignored "-Wunknown-warning-option") \
+  _C10_PRAGMA_(clang diagnostic ignored warning)
+
+#define C10_DIAGNOSTIC_POP() _C10_PRAGMA_(clang diagnostic pop)
+
+#elif __GNUC__
+
+#define C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED(warning) \
+  _C10_PRAGMA_(GCC diagnostic push)                         \
+  _C10_PRAGMA_(GCC diagnostic ignored "-Wpragmas")          \
+  _C10_PRAGMA_(GCC diagnostic ignored warning)
+
+#define C10_DIAGNOSTIC_POP() _C10_PRAGMA_(GCC diagnostic pop)
+
+#else
+
+#define C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED(warning)
+#define C10_DIAGNOSTIC_POP()
+
+#endif
+
+// This macro is used to find older C++ compilers
+// that don't support move optimization for return values.
+
+#if (defined(__GNUC__) && __GNUC__ < 13) || \
+    (defined(__clang_major__) && __clang_major__ < 13)
+#define C10_RETURN_MOVE_IF_OLD_COMPILER 1
+#else
+#define C10_RETURN_MOVE_IF_OLD_COMPILER 0
+#endif
+
+// The HIDDEN_NAMESPACE_BEGIN and HIDDEN_NAMESPACE_END below
+// are needed for maintaining robustness in our header APIs in
+// torch/headeronly and torch/csrc/stable under the namespaces
+// torch::headeronly and torch::stable respectively. We enforce
+// hidden visibility for these APIs because we want to enable
+// loading custom extensions compiled against different libtorch
+// versions where these APIs may have changed.
+
+// Helper macros to handle 1-3 hidden namespace levels when not windows
+#define _HIDDEN_NS_GET_MACRO(_1, _2, _3, NAME, ...) NAME
+#define _HIDDEN_NS_1(n1) namespace n1 __attribute__((visibility("hidden"))) {
+#define _HIDDEN_NS_2(n1, n2) \
+  namespace n1 {             \
+  namespace n2 __attribute__((visibility("hidden"))) {
+#define _HIDDEN_NS_3(n1, n2, n3) \
+  namespace n1::n2 {             \
+  namespace n3 __attribute__((visibility("hidden"))) {
+
+// Helper macros to close namespaces when not windows
+#define _HIDDEN_NS_END_1(n1) }
+#define _HIDDEN_NS_END_N(n1, ...) \
+  }                               \
+  }
+
+// Helper macros to join strs with :: (for win, where symbols are hidden by
+// default)
+#define _EXPAND(...) __VA_ARGS__
+#define _JOIN_GET_MACRO(_1, _2, _3, NAME, ...) NAME
+#define _JOIN_NS1(a) a
+#define _JOIN_NS2(a, b) a::b
+#define _JOIN_NS3(a, b, c) a::b::c
+
+#if !defined(HIDDEN_NAMESPACE_BEGIN)
+#if defined(__GNUG__) && !defined(_WIN32)
+#define HIDDEN_NAMESPACE_BEGIN(...) \
+  _HIDDEN_NS_GET_MACRO(             \
+      __VA_ARGS__, _HIDDEN_NS_3, _HIDDEN_NS_2, _HIDDEN_NS_1)(__VA_ARGS__)
+#else
+#define HIDDEN_NAMESPACE_BEGIN(...)  \
+  namespace _EXPAND(_JOIN_GET_MACRO( \
+      __VA_ARGS__, _JOIN_NS3, _JOIN_NS2, _JOIN_NS1)(__VA_ARGS__)) {
+#endif
+#endif
+
+#if !defined(HIDDEN_NAMESPACE_END)
+#if defined(__GNUG__) && !defined(_WIN32)
+#define HIDDEN_NAMESPACE_END(...)                                         \
+  _HIDDEN_NS_GET_MACRO(                                                   \
+      __VA_ARGS__, _HIDDEN_NS_END_N, _HIDDEN_NS_END_N, _HIDDEN_NS_END_1)( \
+      __VA_ARGS__)
+#else
+#define HIDDEN_NAMESPACE_END(...) }
+#endif
+#endif
+
+#endif // C10_MACROS_MACROS_H_

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/macros/cmake_macros.h

@@ -0,0 +1,14 @@
+#ifndef C10_MACROS_CMAKE_MACROS_H_
+#define C10_MACROS_CMAKE_MACROS_H_
+
+// Automatically generated header file for the C10 library.
+// Do not include this file directly. Instead, include torch/headeronly/macros/Macros.h.
+
+#define C10_BUILD_SHARED_LIBS
+/* #undef C10_USE_GLOG */
+/* #undef C10_USE_GFLAGS */
+/* #undef C10_USE_NUMA */
+/* #undef C10_USE_MSVC_STATIC_RUNTIME */
+/* #undef C10_USE_ROCM_KERNEL_ASSERT */
+
+#endif // C10_MACROS_CMAKE_MACROS_H_

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/BFloat16.h

@@ -0,0 +1,480 @@
+#pragma once
+
+// Defines the bloat16 type (brain floating-point). This representation uses
+// 1 bit for the sign, 8 bits for the exponent and 7 bits for the mantissa.
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/bit_cast.h>
+
+#include <cmath>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <ostream>
+
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+#include <cuda_bf16.h>
+#endif
+
+#if defined(CL_SYCL_LANGUAGE_VERSION)
+#include <CL/sycl.hpp> // for SYCL 1.2.1
+#elif defined(SYCL_LANGUAGE_VERSION)
+#include <sycl/sycl.hpp> // for SYCL 2020
+#endif
+
+namespace c10 {
+
+struct alignas(2) BFloat16 {
+  uint16_t x;
+
+  // HIP wants __host__ __device__ tag, CUDA does not
+#if defined(USE_ROCM) && defined(__HIPCC__)
+  C10_HOST_DEVICE BFloat16() = default;
+#else
+  BFloat16() = default;
+#endif
+
+  struct from_bits_t {};
+  static constexpr C10_HOST_DEVICE from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  constexpr C10_HOST_DEVICE BFloat16(
+      unsigned short bits,
+      from_bits_t /*unused*/)
+      : x(bits) {}
+  /* implicit */ inline C10_HOST_DEVICE BFloat16(float value);
+  inline C10_HOST_DEVICE operator float() const;
+
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+  inline C10_HOST_DEVICE BFloat16(const __nv_bfloat16& value);
+  explicit inline C10_HOST_DEVICE operator __nv_bfloat16() const;
+#endif
+
+#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+  inline C10_HOST_DEVICE BFloat16(const sycl::ext::oneapi::bfloat16& value);
+  explicit inline C10_HOST_DEVICE operator sycl::ext::oneapi::bfloat16() const;
+#endif
+};
+
+inline std::ostream& operator<<(std::ostream& out, const BFloat16& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+inline C10_HOST_DEVICE float f32_from_bits(uint16_t src) {
+  float res = 0;
+  uint32_t tmp = src;
+  tmp <<= 16;
+
+#if defined(USE_ROCM) && defined(__HIPCC__)
+  float* tempRes;
+
+  // We should be using memcpy in order to respect the strict aliasing rule
+  // but it fails in the HIP environment.
+  tempRes = reinterpret_cast<float*>(&tmp);
+  res = *tempRes;
+#else
+  std::memcpy(&res, &tmp, sizeof(tmp));
+#endif
+
+  return res;
+}
+
+inline C10_HOST_DEVICE uint16_t bits_from_f32(float src) {
+  uint32_t res = 0;
+
+#if defined(USE_ROCM) && defined(__HIPCC__)
+  // We should be using memcpy in order to respect the strict aliasing rule
+  // but it fails in the HIP environment.
+  uint32_t* tempRes = reinterpret_cast<uint32_t*>(&src);
+  res = *tempRes;
+#else
+  std::memcpy(&res, &src, sizeof(res));
+#endif
+
+  return res >> 16;
+}
+
+inline C10_HOST_DEVICE uint16_t round_to_nearest_even(float src) {
+#if defined(USE_ROCM) && defined(__HIPCC__)
+  if (src != src) {
+#elif defined(_MSC_VER)
+  if (isnan(src)) {
+#else
+  if (std::isnan(src)) {
+#endif
+    return UINT16_C(0x7FC0);
+  } else {
+    const uint32_t U32 = c10::bit_cast<uint32_t>(src);
+    uint32_t rounding_bias = ((U32 >> 16) & 1) + UINT32_C(0x7FFF);
+    return static_cast<uint16_t>((U32 + rounding_bias) >> 16);
+  }
+}
+
+} // namespace detail
+
+//-------- the following is copied from c10/util/BFloat16-inl.h ---------//
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+/// Constructors
+inline C10_HOST_DEVICE BFloat16::BFloat16(float value)
+    :
+#if defined(__CUDACC__) && !defined(USE_ROCM) && defined(__CUDA_ARCH__) && \
+    __CUDA_ARCH__ >= 800
+      x(__bfloat16_as_ushort(__float2bfloat16(value)))
+#elif defined(__SYCL_DEVICE_ONLY__) && \
+    defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+      x(c10::bit_cast<uint16_t>(sycl::ext::oneapi::bfloat16(value)))
+#else
+      // RNE by default
+      x(detail::round_to_nearest_even(value))
+#endif
+{
+}
+
+/// Implicit conversions
+inline C10_HOST_DEVICE BFloat16::operator float() const {
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+  return __bfloat162float(*reinterpret_cast<const __nv_bfloat16*>(&x));
+#elif defined(__SYCL_DEVICE_ONLY__) && \
+    defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+  return float(*reinterpret_cast<const sycl::ext::oneapi::bfloat16*>(&x));
+#else
+  return detail::f32_from_bits(x);
+#endif
+}
+
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+inline C10_HOST_DEVICE BFloat16::BFloat16(const __nv_bfloat16& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE BFloat16::operator __nv_bfloat16() const {
+  return *reinterpret_cast<const __nv_bfloat16*>(&x);
+}
+#endif
+
+#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+inline C10_HOST_DEVICE BFloat16::BFloat16(
+    const sycl::ext::oneapi::bfloat16& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE BFloat16::operator sycl::ext::oneapi::bfloat16() const {
+  return *reinterpret_cast<const sycl::ext::oneapi::bfloat16*>(&x);
+}
+#endif
+
+// CUDA intrinsics
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+inline C10_DEVICE BFloat16 __ldg(const BFloat16* ptr) {
+#if !defined(USE_ROCM) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+  return __ldg(reinterpret_cast<const __nv_bfloat16*>(ptr));
+#else
+  return *ptr;
+#endif
+}
+#endif
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE BFloat16
+operator+(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16
+operator-(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16
+operator*(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator/(const BFloat16& a, const BFloat16& b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator-(const BFloat16& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE BFloat16& operator+=(BFloat16& a, const BFloat16& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator-=(BFloat16& a, const BFloat16& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator*=(BFloat16& a, const BFloat16& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator/=(BFloat16& a, const BFloat16& b) {
+  a = a / b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator|(BFloat16& a, const BFloat16& b) {
+  a.x = a.x | b.x;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator^(BFloat16& a, const BFloat16& b) {
+  a.x = a.x ^ b.x;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator&(BFloat16& a, const BFloat16& b) {
+  a.x = a.x & b.x;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(BFloat16 a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(BFloat16 a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(BFloat16 a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(BFloat16 a, float b) {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, BFloat16 b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, BFloat16 b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, BFloat16 b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, BFloat16 b) {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const BFloat16& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const BFloat16& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const BFloat16& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const BFloat16& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(BFloat16 a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(BFloat16 a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(BFloat16 a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(BFloat16 a, double b) {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, BFloat16 b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, BFloat16 b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, BFloat16 b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, BFloat16 b) {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<BFloat16>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator+(int a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) + b;
+}
+inline C10_HOST_DEVICE BFloat16 operator-(int a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) - b;
+}
+inline C10_HOST_DEVICE BFloat16 operator*(int a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) * b;
+}
+inline C10_HOST_DEVICE BFloat16 operator/(int a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<BFloat16>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator+(int64_t a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) + b;
+}
+inline C10_HOST_DEVICE BFloat16 operator-(int64_t a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) - b;
+}
+inline C10_HOST_DEVICE BFloat16 operator*(int64_t a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) * b;
+}
+inline C10_HOST_DEVICE BFloat16 operator/(int64_t a, BFloat16 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<BFloat16>(a) / b;
+}
+
+// Overloading < and > operators, because std::max and std::min use them.
+
+inline C10_HOST_DEVICE bool operator>(BFloat16& lhs, BFloat16& rhs) {
+  return float(lhs) > float(rhs);
+}
+
+inline C10_HOST_DEVICE bool operator<(BFloat16& lhs, BFloat16& rhs) {
+  return float(lhs) < float(rhs);
+}
+
+C10_CLANG_DIAGNOSTIC_POP()
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+
+namespace detail {
+using c10::detail::bits_from_f32;
+using c10::detail::f32_from_bits;
+using c10::detail::round_to_nearest_even;
+} // namespace detail
+
+using c10::BFloat16;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+using c10::operator<;
+using c10::operator>;
+using c10::operator<<;
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::BFloat16> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = true;
+  static constexpr auto has_denorm = numeric_limits<float>::has_denorm;
+  static constexpr auto has_denorm_loss =
+      numeric_limits<float>::has_denorm_loss;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 8;
+  static constexpr int digits10 = 2;
+  static constexpr int max_digits10 = 4;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -125;
+  static constexpr int min_exponent10 = -37;
+  static constexpr int max_exponent = 128;
+  static constexpr int max_exponent10 = 38;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::BFloat16 min() {
+    return c10::BFloat16(0x0080, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 lowest() {
+    return c10::BFloat16(0xFF7F, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 max() {
+    return c10::BFloat16(0x7F7F, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 epsilon() {
+    return c10::BFloat16(0x3C00, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 round_error() {
+    return c10::BFloat16(0x3F00, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 infinity() {
+    return c10::BFloat16(0x7F80, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 quiet_NaN() {
+    return c10::BFloat16(0x7FC0, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 signaling_NaN() {
+    return c10::BFloat16(0x7F80, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 denorm_min() {
+    return c10::BFloat16(0x0001, c10::BFloat16::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Deprecated.h

@@ -0,0 +1,102 @@
+#pragma once
+
+/**
+ * This file provides portable macros for marking declarations
+ * as deprecated.  You should generally use C10_DEPRECATED,
+ * except when marking 'using' declarations as deprecated,
+ * in which case you should use C10_DEFINE_DEPRECATED_USING
+ * (due to portability concerns).
+ */
+
+// Sample usage:
+//
+//    C10_DEPRECATED void bad_func();
+//    struct C10_DEPRECATED BadStruct {
+//      ...
+//    };
+
+// NB: __cplusplus doesn't work for MSVC, so for now MSVC always uses
+// the "__declspec(deprecated)" implementation and not the C++14
+// "[[deprecated]]" attribute. We tried enabling "[[deprecated]]" for C++14 on
+// MSVC, but ran into issues with some older MSVC versions.
+#if (defined(__cplusplus) && __cplusplus >= 201402L)
+#define C10_DEPRECATED [[deprecated]]
+#define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
+#elif defined(__GNUC__)
+#define C10_DEPRECATED __attribute__((deprecated))
+// TODO Is there some way to implement this?
+#define C10_DEPRECATED_MESSAGE(message) __attribute__((deprecated))
+
+#elif defined(_MSC_VER)
+#define C10_DEPRECATED __declspec(deprecated)
+#define C10_DEPRECATED_MESSAGE(message) __declspec(deprecated(message))
+#else
+#warning "You need to implement C10_DEPRECATED for this compiler"
+#define C10_DEPRECATED
+#endif
+
+// Sample usage:
+//
+//    C10_DEFINE_DEPRECATED_USING(BadType, int)
+//
+//   which is the portable version of
+//
+//    using BadType [[deprecated]] = int;
+
+// technically [[deprecated]] syntax is from c++14 standard, but it works in
+// many compilers.
+#if defined(__has_cpp_attribute)
+#if __has_cpp_attribute(deprecated) && !defined(__CUDACC__)
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName [[deprecated]] = TypeThingy;
+#endif
+#endif
+
+#if defined(_MSC_VER)
+#if defined(__CUDACC__)
+// neither [[deprecated]] nor __declspec(deprecated) work on nvcc on Windows;
+// you get the error:
+//
+//    error: attribute does not apply to any entity
+//
+// So we just turn the macro off in this case.
+#if defined(C10_DEFINE_DEPRECATED_USING)
+#undef C10_DEFINE_DEPRECATED_USING
+#endif
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName = TypeThingy;
+#else
+// [[deprecated]] does work in windows without nvcc, though msc doesn't support
+// `__has_cpp_attribute` when c++14 is supported, otherwise
+// __declspec(deprecated) is used as the alternative.
+#ifndef C10_DEFINE_DEPRECATED_USING
+#if defined(_MSVC_LANG) && _MSVC_LANG >= 201402L
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName [[deprecated]] = TypeThingy;
+#else
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName = __declspec(deprecated) TypeThingy;
+#endif
+#endif
+#endif
+#endif
+
+#if !defined(C10_DEFINE_DEPRECATED_USING) && defined(__GNUC__)
+// nvcc has a bug where it doesn't understand __attribute__((deprecated))
+// declarations even when the host compiler supports it. We'll only use this gcc
+// attribute when not cuda, and when using a GCC compiler that doesn't support
+// the c++14 syntax we checked for above (available in __GNUC__ >= 5)
+#if !defined(__CUDACC__)
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName __attribute__((deprecated)) = TypeThingy;
+#else
+// using cuda + gcc < 5, neither deprecated syntax is available so turning off.
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName = TypeThingy;
+#endif
+#endif
+
+#if !defined(C10_DEFINE_DEPRECATED_USING)
+#warning "You need to implement C10_DEFINE_DEPRECATED_USING for this compiler"
+#define C10_DEFINE_DEPRECATED_USING
+#endif

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Exception.h

@@ -0,0 +1,83 @@
+#pragma once
+
+#include <torch/headeronly/macros/Export.h>
+#include <torch/headeronly/macros/Macros.h>
+
+#include <sstream>
+#include <string>
+
+namespace c10 {
+// On nvcc, C10_UNLIKELY thwarts missing return statement analysis.  In cases
+// where the unlikely expression may be a constant, use this macro to ensure
+// return statement analysis keeps working (at the cost of not getting the
+// likely/unlikely annotation on nvcc).
+// https://github.com/pytorch/pytorch/issues/21418
+//
+// Currently, this is only used in the error reporting macros below.  If you
+// want to use it more generally, move me to Macros.h
+//
+// TODO: Brian Vaughan observed that we might be able to get this to work on
+// nvcc by writing some sort of C++ overload that distinguishes constexpr inputs
+// from non-constexpr.  Since there isn't any evidence that losing C10_UNLIKELY
+// in nvcc is causing us perf problems, this is not yet implemented, but this
+// might be an interesting piece of C++ code for an intrepid bootcamper to
+// write.
+#if defined(__CUDACC__)
+#define C10_UNLIKELY_OR_CONST(e) e
+#else
+#define C10_UNLIKELY_OR_CONST(e) C10_UNLIKELY(e)
+#endif
+
+} // namespace c10
+
+// STD_TORCH_CHECK throws std::runtime_error instead of c10::Error which is
+// useful when certain headers are used in a libtorch-independent way,
+// e.g. when Vectorized<T> is used in AOTInductor generated code, or
+// for custom ops to have an ABI stable dependency on libtorch.
+#ifdef STRIP_ERROR_MESSAGES
+#define STD_TORCH_CHECK_MSG(cond, type, ...) \
+  (#cond #type " CHECK FAILED at " C10_STRINGIZE(__FILE__))
+#else // so STRIP_ERROR_MESSAGES is not defined
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly, detail)
+template <typename... Args>
+std::string stdTorchCheckMsgImpl(const char* /*msg*/, const Args&... args) {
+  // This is similar to the one in c10/util/Exception.h, but does
+  // not depend on the more complex c10::str() function. ostringstream
+  // supports fewer data types than c10::str(), but should be sufficient
+  // in the headeronly world.
+  std::ostringstream oss;
+  ((oss << args), ...);
+  return oss.str();
+}
+
+inline const char* stdTorchCheckMsgImpl(const char* msg) {
+  return msg;
+}
+// If there is just 1 user-provided C-string argument, use it.
+inline const char* stdTorchCheckMsgImpl(const char* /*msg*/, const char* args) {
+  return args;
+}
+HIDDEN_NAMESPACE_END(torch, headeronly, detail)
+
+#define STD_TORCH_CHECK_MSG(cond, type, ...)               \
+  (torch::headeronly::detail::stdTorchCheckMsgImpl(        \
+      "Expected " #cond                                    \
+      " to be true, but got false.  "                      \
+      "(Could this error message be improved?  If so, "    \
+      "please report an enhancement request to PyTorch.)", \
+      ##__VA_ARGS__))
+#endif // STRIP_ERROR_MESSAGES
+
+#define STD_TORCH_CHECK(cond, ...)                \
+  if (C10_UNLIKELY_OR_CONST(!(cond))) {           \
+    throw std::runtime_error(STD_TORCH_CHECK_MSG( \
+        cond,                                     \
+        "",                                       \
+        __func__,                                 \
+        ", ",                                     \
+        __FILE__,                                 \
+        ":",                                      \
+        __LINE__,                                 \
+        ", ",                                     \
+        ##__VA_ARGS__));                          \
+  }

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float4_e2m1fn_x2.h

@@ -0,0 +1,47 @@
+#pragma once
+#include <cstdint>
+
+#include <torch/headeronly/macros/Macros.h>
+
+/// Defines the Float4_e2m1fn_x2 type (4-bit floating-point, two elements packed
+/// into one byte). This is the FP4 dtype from the OCP MX format spec
+/// (https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf,
+/// Section 5.3.3)
+///
+/// Given two high precision values val0 and val1, here is the
+/// binary configuration of their packed representation, from MSB to LSB:
+///
+///   original value             | val1 : val0
+///   ========================================
+///   bit index (MSB==7, LSB==0) | 7654 : 3210
+///   sign/exponent/mantissa     | seem : seem
+///
+
+namespace c10 {
+
+struct alignas(1) Float4_e2m1fn_x2 {
+  uint8_t val_;
+  Float4_e2m1fn_x2() = default;
+  C10_HOST_DEVICE explicit Float4_e2m1fn_x2(uint8_t val) : val_(val) {}
+};
+
+/// Comparison operators
+inline C10_HOST_DEVICE bool operator==(
+    const Float4_e2m1fn_x2& a,
+    const Float4_e2m1fn_x2& b) {
+  return a.val_ == b.val_;
+}
+
+inline C10_HOST_DEVICE bool operator!=(
+    const Float4_e2m1fn_x2& a,
+    const Float4_e2m1fn_x2& b) {
+  return a.val_ != b.val_;
+}
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::Float4_e2m1fn_x2;
+using c10::operator==;
+using c10::operator!=;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float8_e4m3fn.h

@@ -0,0 +1,531 @@
+#pragma once
+
+/// Defines the Float8_e4m3fn type (8-bit floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration:
+/// s eeee mmm
+/// 1 sign bit
+/// 4 exponent bits
+/// 3 mantissa bits
+/// bias = 7
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2209.05433.pdf
+/// and inspired by Half implementation from pytorch/c10/util/Half.h
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/floating_point_utils.h>
+
+#if defined(__cplusplus)
+#include <cmath>
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#include <climits>
+#include <iostream>
+
+namespace c10 {
+
+struct alignas(1) Float8_e4m3fn {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e4m3fn() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e4m3fn(uint8_t bits, from_bits_t /*unused*/)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e4m3fn(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+inline std::ostream& operator<<(std::ostream& out, const Float8_e4m3fn& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E4M3FN format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline C10_HOST_DEVICE float fp8e4m3fn_to_fp32_value(uint8_t input) {
+  /*
+   * Extend the fp8 E4M3FN number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEE|MMM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31 27-30 24-26          0-23
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)input << 24;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the bits 0-30
+   * of the 32-bit word:
+   *
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEE|MMM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31  27-30 24-26      0-23
+   */
+  const uint32_t nonsign = w & UINT32_C(0x7FFFFFFF);
+  /*
+   * Renorm shift is the number of bits to shift mantissa left to make the
+   * half-precision number normalized. If the initial number is normalized, some
+   * of its high 5 bits (sign == 0 and 4-bit exponent) equals one. In this case
+   * renorm_shift == 0. If the number is denormalize, renorm_shift > 0. Note
+   * that if we shift denormalized nonsign by renorm_shift, the unit bit of
+   * mantissa will shift into exponent, turning the biased exponent into 1, and
+   * making mantissa normalized (i.e. without leading 1).
+   */
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+  uint32_t renorm_shift = __clz(nonsign);
+#elif defined(__SYCL_DEVICE_ONLY__)
+  // Note: zero is not a supported input into `__builtin_clz`
+  uint32_t renorm_shift =
+      nonsign != 0 ? __builtin_clz(nonsign) : sizeof(uint32_t) * CHAR_BIT;
+#elif defined(_MSC_VER) && !defined(__clang__)
+  unsigned long nonsign_bsr;
+  _BitScanReverse(&nonsign_bsr, (unsigned long)nonsign);
+  uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+  // Note: zero is not a supported input into `__builtin_clz`
+  uint32_t renorm_shift =
+      nonsign != 0 ? __builtin_clz(nonsign) : sizeof(uint32_t) * CHAR_BIT;
+#endif
+  renorm_shift = renorm_shift > 4 ? renorm_shift - 4 : 0;
+  /*
+   * Iff fp8e4m3fn number has all exponent and mantissa bits set to 1,
+   * the addition overflows it into bit 31, and the subsequent shift turns the
+   * high 9 bits into 1. Thus inf_nan_mask == 0x7F800000 if the fp8e4m3fn number
+   * is Nan, 0x00000000 otherwise
+   */
+  const int32_t inf_nan_mask =
+      ((int32_t)(nonsign + 0x01000000) >> 8) & INT32_C(0x7F800000);
+  /*
+   * Iff nonsign is 0, it overflows into 0xFFFFFFFF, turning bit 31
+   * into 1. Otherwise, bit 31 remains 0. The signed shift right by 31
+   * broadcasts bit 31 into all bits of the zero_mask. Thus zero_mask ==
+   * 0xFFFFFFFF if the half-precision number was zero (+0.0h or -0.0h)
+   * 0x00000000 otherwise
+   */
+  const int32_t zero_mask = (int32_t)(nonsign - 1) >> 31;
+  /*
+   * 1. Shift nonsign left by renorm_shift to normalize it (if the input
+   * was denormal)
+   * 2. Shift nonsign right by 4 so the exponent (4 bits originally)
+   * becomes an 8-bit field and 3-bit mantissa shifts into the 3 high
+   * bits of the 23-bit mantissa of IEEE single-precision number.
+   * 3. Add 0x78 to the exponent (starting at bit 23) to compensate the
+   * different in exponent bias (0x7F for single-precision number less 0x07
+   * for fp8e4m3fn number).
+   * 4. Subtract renorm_shift from the exponent (starting at bit 23) to
+   * account for renormalization. As renorm_shift is less than 0x78, this
+   * can be combined with step 3.
+   * 5. Binary OR with inf_nan_mask to turn the exponent into 0xFF if the
+   * input was NaN or infinity.
+   * 6. Binary ANDNOT with zero_mask to turn the mantissa and exponent
+   * into zero if the input was zero.
+   * 7. Combine with the sign of the input number.
+   */
+  uint32_t result = sign |
+      ((((nonsign << renorm_shift >> 4) + ((0x78 - renorm_shift) << 23)) |
+        inf_nan_mask) &
+       ~zero_mask);
+  return fp32_from_bits(result);
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E4M3FN format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e4m3fn_from_fp32_value(float f) {
+  /*
+   * Binary representation of 480.0f, which is the first value
+   * not representable in fp8e4m3fn range:
+   * 0 1111 111 - fp8e4m3fn
+   * 0 10000111 11100000000000000000000 - fp32
+   */
+  constexpr uint32_t fp8_max = UINT32_C(1087) << 20;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e4m3fn normal range
+   * into denorm representation
+   * magic number: ((127 - 7) + (23 - 3) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(141) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint8_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fp8_max) {
+    // NaN - all exponent and mantissa bits set to 1
+    result = 0x7f;
+  } else {
+    if (f_bits < (UINT32_C(121) << 23)) {
+      // Input number is smaller than 2^(-6), which is the smallest
+      // fp8e4m3fn normal number
+      f_bits =
+          fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+      result = static_cast<uint8_t>(f_bits - denorm_mask);
+    } else {
+      // resulting mantissa is odd
+      uint8_t mant_odd = (f_bits >> 20) & 1;
+
+      // update exponent, rounding bias part 1
+      f_bits += ((uint32_t)(7 - 127) << 23) + 0x7FFFF;
+
+      // rounding bias part 2
+      f_bits += mant_odd;
+
+      // take the bits!
+      result = static_cast<uint8_t>(f_bits >> 20);
+    }
+  }
+
+  result |= static_cast<uint8_t>(sign >> 24);
+  return result;
+}
+
+} // namespace detail
+
+// -------- below is copied from c10/util/Float8_e4m3fn-inl.h --------//
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e4m3fn::Float8_e4m3fn(float value)
+    : x(detail::fp8e4m3fn_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e4m3fn::operator float() const {
+  return detail::fp8e4m3fn_to_fp32_value(x);
+}
+
+/// Special values helper
+
+inline C10_HOST_DEVICE bool Float8_e4m3fn::isnan() const {
+  return (x & 0b01111111) == 0b01111111;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e4m3fn
+operator+(const Float8_e4m3fn& a, const Float8_e4m3fn& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn
+operator-(const Float8_e4m3fn& a, const Float8_e4m3fn& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn
+operator*(const Float8_e4m3fn& a, const Float8_e4m3fn& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(
+    const Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(const Float8_e4m3fn& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator+=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator-=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator*=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn& operator/=(
+    Float8_e4m3fn& a,
+    const Float8_e4m3fn& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e4m3fn a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e4m3fn a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e4m3fn a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e4m3fn a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e4m3fn b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e4m3fn b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e4m3fn b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e4m3fn b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e4m3fn& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e4m3fn& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e4m3fn& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e4m3fn& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e4m3fn a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e4m3fn a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e4m3fn a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e4m3fn a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e4m3fn b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e4m3fn b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e4m3fn b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e4m3fn b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(Float8_e4m3fn a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(Float8_e4m3fn a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(Float8_e4m3fn a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(Float8_e4m3fn a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e4m3fn>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(int a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(int a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(int a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(int a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(Float8_e4m3fn a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(Float8_e4m3fn a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(Float8_e4m3fn a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e4m3fn>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(Float8_e4m3fn a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e4m3fn>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fn operator+(int64_t a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator-(int64_t a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator*(int64_t a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fn operator/(int64_t a, Float8_e4m3fn b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fn>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e4m3fn to float.
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::Float8_e4m3fn;
+using c10::operator<<;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e4m3fn> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 4;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 3;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -5;
+  static constexpr int min_exponent10 = -1;
+  static constexpr int max_exponent = 8;
+  static constexpr int max_exponent10 = 2;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before = false;
+
+  static constexpr c10::Float8_e4m3fn min() {
+    return c10::Float8_e4m3fn(0x08, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn lowest() {
+    return c10::Float8_e4m3fn(0xFE, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn max() {
+    return c10::Float8_e4m3fn(0x7E, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn epsilon() {
+    return c10::Float8_e4m3fn(0x20, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn round_error() {
+    return c10::Float8_e4m3fn(0x30, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn quiet_NaN() {
+    return c10::Float8_e4m3fn(0x7F, c10::Float8_e4m3fn::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fn denorm_min() {
+    return c10::Float8_e4m3fn(0x01, c10::Float8_e4m3fn::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float8_e4m3fnuz.h

@@ -0,0 +1,444 @@
+#pragma once
+
+/// Defines the Float8_e4m3fnuz type (8-bit floating-point) including
+/// conversions to standard C types and basic arithmetic operations. Note that
+/// arithmetic operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration remains the same as Float8_e4m3fn:
+/// s eeee mmm
+/// 1 sign bit
+/// 4 exponent bits
+/// 3 mantissa bits
+/// The key differences versus Float8_e4m3fn are:
+/// bias = 8
+/// no infinities or negative zero
+/// NaN only when sign bit is 1, rest all 0s
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2206.02915.pdf and
+/// the existing Float8_e4m3fn implementation.
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Float8_fnuz_cvt.h>
+#include <torch/headeronly/util/floating_point_utils.h>
+
+#include <limits>
+
+#if defined(__cplusplus)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <ostream>
+
+namespace c10 {
+
+struct alignas(1) Float8_e4m3fnuz {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e4m3fnuz() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e4m3fnuz(
+      uint8_t bits,
+      from_bits_t /*unused*/)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e4m3fnuz(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+inline std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e4m3fnuz& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E4M3FNUZ format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e4m3fnuz_from_fp32_value(float f) {
+  /*
+   * Binary representation of 256.0f, which is the first value not representable
+   * (i.e. the first value which would overflow in to the sign bit, resulting in
+   * a NaN) in fp8e4m3fnuz range:
+   * 1 0000 000 - fp8e4m3fnuz
+   * 0 10000111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fnuz_max = UINT32_C(0x87) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e4m3fnuz normal range
+   * into denorm representation
+   * magic number: ((127 - 8) + (23 - 3) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(0x8C) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint32_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fnuz_max) {
+    // NaN -- sign bit set to 1, rest 0s.
+    return 0x80;
+  }
+
+  if (f_bits < (UINT32_C(0x78) << 23) /* 2^-7 in float32 */) {
+    // Input exponent is less than -7, the smallest e4m3fnuz exponent, so the
+    // number will become subnormal.
+    f_bits = fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    result = static_cast<uint8_t>(f_bits - denorm_mask);
+    if (result == 0) {
+      // fnuz types don't have negative zero.
+      return 0;
+    }
+  } else {
+    // resulting mantissa is odd
+    uint8_t mant_odd = (f_bits >> 20) & 1;
+
+    // update exponent, rounding bias part 1
+    f_bits += ((uint32_t)(8 - 127) << 23) + 0x7FFFF;
+
+    // rounding bias part 2
+    f_bits += mant_odd;
+
+    // take the bits!
+    result = static_cast<uint8_t>(f_bits >> 20);
+  }
+
+  result |= sign >> 24;
+  return result;
+}
+
+} // namespace detail
+
+//------ below is copied from c10/util/Float8_e4m3fnuz-inl.h ------//
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz::Float8_e4m3fnuz(float value)
+    : x(detail::fp8e4m3fnuz_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz::operator float() const {
+  return torch::headeronly::detail::fp8_fnuz_to_fp32_value<4, 3>(x);
+}
+
+/// Special values helper
+
+inline C10_HOST_DEVICE bool Float8_e4m3fnuz::isnan() const {
+  return x == 0b10000000;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz
+operator+(const Float8_e4m3fnuz& a, const Float8_e4m3fnuz& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz
+operator-(const Float8_e4m3fnuz& a, const Float8_e4m3fnuz& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz
+operator*(const Float8_e4m3fnuz& a, const Float8_e4m3fnuz& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator/(
+    const Float8_e4m3fnuz& a,
+    const Float8_e4m3fnuz& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator-(const Float8_e4m3fnuz& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz& operator+=(
+    Float8_e4m3fnuz& a,
+    const Float8_e4m3fnuz& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz& operator-=(
+    Float8_e4m3fnuz& a,
+    const Float8_e4m3fnuz& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz& operator*=(
+    Float8_e4m3fnuz& a,
+    const Float8_e4m3fnuz& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz& operator/=(
+    Float8_e4m3fnuz& a,
+    const Float8_e4m3fnuz& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e4m3fnuz a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e4m3fnuz a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e4m3fnuz a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e4m3fnuz a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e4m3fnuz b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e4m3fnuz b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e4m3fnuz b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e4m3fnuz b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e4m3fnuz& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e4m3fnuz& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e4m3fnuz& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e4m3fnuz& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e4m3fnuz a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e4m3fnuz a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e4m3fnuz a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e4m3fnuz a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e4m3fnuz b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e4m3fnuz b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e4m3fnuz b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e4m3fnuz b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator+(Float8_e4m3fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e4m3fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator-(Float8_e4m3fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e4m3fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator*(Float8_e4m3fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e4m3fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator/(Float8_e4m3fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e4m3fnuz>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator+(int a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator-(int a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator*(int a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator/(int a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator+(Float8_e4m3fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e4m3fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator-(Float8_e4m3fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e4m3fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator*(Float8_e4m3fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e4m3fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator/(Float8_e4m3fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e4m3fnuz>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator+(int64_t a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator-(int64_t a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator*(int64_t a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e4m3fnuz operator/(int64_t a, Float8_e4m3fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e4m3fnuz>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e4m3fnuz to float.
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::Float8_e4m3fnuz;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+using c10::operator<<;
+
+namespace detail {
+using c10::detail::fp8e4m3fnuz_from_fp32_value;
+} // namespace detail
+
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e4m3fnuz> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 4;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 3;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -6;
+  static constexpr int min_exponent10 = -1;
+  static constexpr int max_exponent = 8;
+  static constexpr int max_exponent10 = 2;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before = false;
+
+  static constexpr c10::Float8_e4m3fnuz min() {
+    return c10::Float8_e4m3fnuz(0x08, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz lowest() {
+    return c10::Float8_e4m3fnuz(0xFF, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz max() {
+    return c10::Float8_e4m3fnuz(0x7F, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz epsilon() {
+    return c10::Float8_e4m3fnuz(0x28, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz round_error() {
+    return c10::Float8_e4m3fnuz(0x38, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz infinity() {
+    // NaN (no infinities)
+    return c10::Float8_e4m3fnuz(0x80, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz quiet_NaN() {
+    return c10::Float8_e4m3fnuz(0x80, c10::Float8_e4m3fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e4m3fnuz denorm_min() {
+    return c10::Float8_e4m3fnuz(0x01, c10::Float8_e4m3fnuz::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float8_e5m2.h

@@ -0,0 +1,458 @@
+#pragma once
+
+/// Defines the Float8_e5m2 type (8-bit floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration:
+/// s eeeee mm
+/// 1 sign bit
+/// 5 exponent bits
+/// 2 mantissa bits
+/// bias = 15
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2209.05433.pdf
+/// and inspired by Half implementation from pytorch/c10/util/Half.h
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Half.h>
+
+#include <limits>
+
+namespace c10 {
+
+struct alignas(1) Float8_e5m2 {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e5m2() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e5m2(uint8_t bits, from_bits_t /*unused*/)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e5m2(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+  inline C10_HOST_DEVICE bool isinf() const;
+};
+
+inline std::ostream& operator<<(std::ostream& out, const Float8_e5m2& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E5M2 format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline C10_HOST_DEVICE float fp8e5m2_to_fp32_value(uint8_t input) {
+  /*
+   * Extend the fp8 E5M2 number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEEE|MM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31 26-30 24-25          0-23
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  uint16_t half_representation = input;
+  half_representation <<= 8;
+  return fp16_ieee_to_fp32_value(half_representation);
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E5M2 format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e5m2_from_fp32_value(float f) {
+  /*
+   * Binary representation of fp32 infinity
+   * 0 11111111 00000000000000000000000
+   */
+  constexpr uint32_t fp32_inf = UINT32_C(255) << 23;
+
+  /*
+   * Binary representation of 65536.0f, which is the first value
+   * not representable in fp8e5m2 range:
+   * 0 11111 00 - fp8e5m2
+   * 0 10001111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fp8_max = UINT32_C(143) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e5m2 normal range
+   * into denorm representation
+   * magic number: ((127 - 15) + (23 - 2) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(134) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+  uint8_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fp8_max) {
+    // NaN - all exponent and mantissa bits set to 1
+    result = f_bits > fp32_inf ? UINT8_C(0x7F) : UINT8_C(0x7C);
+  } else {
+    if (f_bits < (UINT32_C(113) << 23)) {
+      // Input number is smaller than 2^(-14), which is the smallest
+      // fp8e5m2 normal number
+      f_bits =
+          fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+      result = static_cast<uint8_t>(f_bits - denorm_mask);
+    } else {
+      // resulting mantissa is odd
+      uint32_t mant_odd = (f_bits >> 21) & 1;
+
+      // update exponent, rounding bias part 1
+      f_bits += ((uint32_t)(15 - 127) << 23) + 0xFFFFF;
+
+      // rounding bias part 2
+      f_bits += mant_odd;
+
+      // take the bits!
+      result = static_cast<uint8_t>(f_bits >> 21);
+    }
+  }
+
+  result |= static_cast<uint8_t>(sign >> 24);
+  return result;
+}
+
+} // namespace detail
+
+// -------- below is copied from c10/util/Float8_e5m2-inl.h --------//
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+#define EXP_WIDTH_FP8 5
+#define MAN_WIDTH_FP8 2
+#define EXP_BIAS_FP8 15
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e5m2::Float8_e5m2(float value)
+    : x(detail::fp8e5m2_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e5m2::operator float() const {
+  return detail::fp8e5m2_to_fp32_value(x);
+}
+
+/// Special values helpers
+
+inline C10_HOST_DEVICE bool Float8_e5m2::isnan() const {
+  return (x & 0b01111111) > 0b01111100;
+}
+
+inline C10_HOST_DEVICE bool Float8_e5m2::isinf() const {
+  return (x & 0b01111111) == 0b01111100;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e5m2
+operator+(const Float8_e5m2& a, const Float8_e5m2& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2
+operator-(const Float8_e5m2& a, const Float8_e5m2& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2
+operator*(const Float8_e5m2& a, const Float8_e5m2& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator/(
+    const Float8_e5m2& a,
+    const Float8_e5m2& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator-(const Float8_e5m2& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator+=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator-=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator*=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator/=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e5m2 a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e5m2 a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e5m2 a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e5m2 a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e5m2 b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e5m2 b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e5m2 b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e5m2 b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e5m2& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e5m2& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e5m2& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e5m2& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e5m2 a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e5m2 a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e5m2 a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e5m2 a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e5m2 b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e5m2 b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e5m2 b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e5m2 b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(Float8_e5m2 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(Float8_e5m2 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(Float8_e5m2 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(Float8_e5m2 a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e5m2>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(int a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(int a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(int a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(int a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(Float8_e5m2 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(Float8_e5m2 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(Float8_e5m2 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(Float8_e5m2 a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e5m2>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(int64_t a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(int64_t a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(int64_t a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(int64_t a, Float8_e5m2 b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e5m2 to float.
+C10_CLANG_DIAGNOSTIC_POP()
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::Float8_e5m2;
+using c10::operator<<;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+
+namespace detail {
+using c10::detail::fp8e5m2_from_fp32_value;
+using c10::detail::fp8e5m2_to_fp32_value;
+} // namespace detail
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e5m2> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 3;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 2;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -13;
+  static constexpr int min_exponent10 = -4;
+  static constexpr int max_exponent = 16;
+  static constexpr int max_exponent10 = 4;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::Float8_e5m2 min() {
+    return c10::Float8_e5m2(0x4, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 max() {
+    return c10::Float8_e5m2(0x7B, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 lowest() {
+    return c10::Float8_e5m2(0xFB, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 epsilon() {
+    return c10::Float8_e5m2(0x34, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 round_error() {
+    return c10::Float8_e5m2(0x38, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 infinity() {
+    return c10::Float8_e5m2(0x7C, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 quiet_NaN() {
+    return c10::Float8_e5m2(0x7F, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 denorm_min() {
+    return c10::Float8_e5m2(0x01, c10::Float8_e5m2::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float8_e5m2fnuz.h

@@ -0,0 +1,448 @@
+#pragma once
+
+/// Defines the Float8_e5m2fnuz type (8-bit floating-point) including
+/// conversions to standard C types and basic arithmetic operations. Note that
+/// arithmetic operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration remains the same as e5m2:
+/// s eeeee mm
+/// 1 sign bit
+/// 5 exponent bits
+/// 2 mantissa bits
+/// The key differences that e5m2fnuz brings are:
+/// bias = 16
+/// no infinities or negative zero
+/// NaN only when sign bit is 1, rest all 0s
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2206.02915.pdf and
+/// the existing Float8_e4m3fn implementation.
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Float8_fnuz_cvt.h>
+#include <torch/headeronly/util/TypeSafeSignMath.h>
+#include <torch/headeronly/util/floating_point_utils.h>
+
+#if defined(__cplusplus)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <ostream>
+
+namespace c10 {
+
+struct alignas(1) Float8_e5m2fnuz {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e5m2fnuz() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e5m2fnuz(
+      uint8_t bits,
+      from_bits_t /*unused*/)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e5m2fnuz(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+  inline C10_HOST_DEVICE bool isinf() const;
+};
+
+inline std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e5m2fnuz& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E5M2 format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e5m2fnuz_from_fp32_value(float f) {
+  /*
+   * Binary representation of 65536.0f, which is the first value not
+   * representable (i.e. the first value which would overflow in to the sign
+   * bit, resulting in a NaN) in fp8e4m3fnuz range:
+   * 1 00000 00 - fp8e5m2fnuz
+   * 0 10001111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fnuz_max = UINT32_C(0x8F) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e5m2fnuz normal range
+   * into denormalized representation.
+   * magic number: ((127 - 16) + (23 - 2) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(0x85) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+  uint32_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fnuz_max) {
+    // NaN -- sign bit set to 1, rest 0s
+    return 0x80;
+  }
+
+  if (f_bits < (UINT32_C(0x70) << 23) /* 2^-15 in float32 */) {
+    // Input exponent is less than -15, the smallest e5m2fnuz exponent, so the
+    // number will become subnormal.
+    f_bits = fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    result = static_cast<uint8_t>(f_bits - denorm_mask);
+    if (result == 0) {
+      // fnuz types don't have negative zero.
+      return 0;
+    }
+  } else {
+    // resulting mantissa is odd
+    uint8_t mant_odd = (f_bits >> 21) & 1;
+
+    // update exponent, rounding bias part 1
+    f_bits += ((uint32_t)(16 - 127) << 23) + 0xFFFFF;
+
+    // rounding bias part 2
+    f_bits += mant_odd;
+
+    // take the bits!
+    result = static_cast<uint8_t>(f_bits >> 21);
+  }
+
+  result |= sign >> 24;
+  return result;
+}
+
+} // namespace detail
+
+//------ below is copied from c10/util/Float8_e5m2fnuz-inl.h ------//
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz::Float8_e5m2fnuz(float value)
+    : x(detail::fp8e5m2fnuz_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz::operator float() const {
+  return torch::headeronly::detail::fp8_fnuz_to_fp32_value<5, 2>(x);
+}
+
+/// Special values helpers
+
+inline C10_HOST_DEVICE bool Float8_e5m2fnuz::isnan() const {
+  return x == 0b10000000;
+}
+
+inline C10_HOST_DEVICE bool Float8_e5m2fnuz::isinf() const {
+  return false;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz
+operator+(const Float8_e5m2fnuz& a, const Float8_e5m2fnuz& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz
+operator-(const Float8_e5m2fnuz& a, const Float8_e5m2fnuz& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz
+operator*(const Float8_e5m2fnuz& a, const Float8_e5m2fnuz& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(
+    const Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(const Float8_e5m2fnuz& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator+=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator-=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator*=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator/=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e5m2fnuz a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e5m2fnuz a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e5m2fnuz a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e5m2fnuz a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e5m2fnuz b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e5m2fnuz b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e5m2fnuz b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e5m2fnuz b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e5m2fnuz& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e5m2fnuz& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e5m2fnuz& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e5m2fnuz& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e5m2fnuz a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e5m2fnuz a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e5m2fnuz a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e5m2fnuz a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e5m2fnuz b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e5m2fnuz b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e5m2fnuz b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e5m2fnuz b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(Float8_e5m2fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(Float8_e5m2fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(Float8_e5m2fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(Float8_e5m2fnuz a, int b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e5m2fnuz>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(int a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(int a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(int a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(int a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(Float8_e5m2fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a + static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(Float8_e5m2fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a - static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(Float8_e5m2fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a * static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(Float8_e5m2fnuz a, int64_t b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return a / static_cast<Float8_e5m2fnuz>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(int64_t a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(int64_t a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(int64_t a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(int64_t a, Float8_e5m2fnuz b) {
+  // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)
+  return static_cast<Float8_e5m2fnuz>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e5m2fnuz to float.
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::Float8_e5m2fnuz;
+using c10::operator<<;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+
+namespace detail {
+using c10::detail::fp8e5m2fnuz_from_fp32_value;
+}
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e5m2fnuz> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 3;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 2;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -14;
+  static constexpr int min_exponent10 = -4;
+  static constexpr int max_exponent = 16;
+  static constexpr int max_exponent10 = 4;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::Float8_e5m2fnuz min() {
+    return c10::Float8_e5m2fnuz(0x04, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz max() {
+    return c10::Float8_e5m2fnuz(0x7F, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz lowest() {
+    return c10::Float8_e5m2fnuz(0xFF, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz epsilon() {
+    return c10::Float8_e5m2fnuz(0x34, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz round_error() {
+    return c10::Float8_e5m2fnuz(0x38, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz infinity() {
+    return c10::Float8_e5m2fnuz(0x80, c10::Float8_e5m2fnuz::from_bits());
+  }
+  // TODO(future): we are mapping neg_zero to both inf and NaN, this is
+  // surprising and we should figure out what to do about it.
+  static constexpr c10::Float8_e5m2fnuz quiet_NaN() {
+    return c10::Float8_e5m2fnuz(0x80, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz denorm_min() {
+    return c10::Float8_e5m2fnuz(0x01, c10::Float8_e5m2fnuz::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float8_e8m0fnu.h

@@ -0,0 +1,226 @@
+#pragma once
+
+/// Defines the Float8_e8m0fnu type (8-bit floating-point) including
+/// conversions to standard C types
+/// Binary configuration :
+/// eeeeeeee
+/// no sign bits
+/// 8 exponent bits
+/// no mantissa bits
+///
+/// This is the E8M0 dtype from the OCP MX format spec
+/// (https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf,
+/// Section 5.4.1)
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/floating_point_utils.h>
+
+// TODO(#146647): do we need to special case OPENCL?
+#if defined(__cplusplus)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <limits>
+#include <ostream>
+
+namespace c10 {
+
+struct alignas(1) Float8_e8m0fnu {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e8m0fnu() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e8m0fnu(uint8_t bits, from_bits_t /*unused*/)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e8m0fnu(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+inline std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e8m0fnu& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 e8m0fnu format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e8m0fnu_from_fp32_value(float f) {
+  // TODO(#146647): maybe rewrite without control flow
+
+  uint32_t f_bits = c10::detail::fp32_to_bits(f);
+
+  // extract the exponent
+  uint32_t exponent = (f_bits >> 23) & 0b11111111;
+
+  // special case float32 NaN and +-inf to map to e8m0 nan
+  if (exponent == 0b11111111) {
+    return exponent;
+  }
+
+  // next, we use guard, round, sticky bits and the LSB to implement round to
+  // nearest, with ties to even
+
+  // guard bit - bit 23, or 22 zero-indexed
+  uint8_t g = (f_bits & 0x400000) > 0;
+  // round bit - bit 22, or 21 zero-indexed
+  uint8_t r = (f_bits & 0x200000) > 0;
+  // sticky bit - bits 21 to 1, or 20 to 0 zero-indexed
+  uint8_t s = (f_bits & 0x1FFFFF) > 0;
+  // in casting to e8m0, LSB is the implied mantissa bit. It equals to 0 if the
+  // original float32 is denormal, and to 1 if the original float32 is normal.
+  uint8_t lsb = exponent > 0;
+
+  // implement the RNE logic
+  bool round_up = false;
+
+  // if g == 0, round down (no-op)
+  if (g == 1) {
+    if ((r == 1) || (s == 1)) {
+      // round up
+      round_up = true;
+    } else {
+      if (lsb == 1) {
+        // round up
+        round_up = true;
+      }
+      // if lsb == 0, round down (no-op)
+    }
+  }
+
+  if (round_up) {
+    // adjust exponent
+    // note that if exponent was 255 we would have already returned earlier, so
+    // we know we can add one safely without running out of bounds
+    exponent++;
+  }
+
+  return exponent;
+}
+
+} // namespace detail
+
+//------- the below is from c10/util/Float8_e8m0fnu-inl.h  ------//
+// TODO(#146647): Can we remove the below warning?
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+/// Constructors
+inline C10_HOST_DEVICE Float8_e8m0fnu::Float8_e8m0fnu(float value)
+    : x(detail::fp8e8m0fnu_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e8m0fnu::operator float() const {
+  // TODO(#146647): maybe rewrite without control flow
+
+  // if exponent is zero, need to special case to return 2^-127 instead of zero
+  if (x == 0) {
+    return c10::detail::fp32_from_bits(0x00400000);
+  }
+
+  // if exponent is NaN, need to special case to return properly encoded NaN
+  if (isnan()) {
+    return c10::detail::fp32_from_bits(0x7f800001);
+  }
+
+  // leave sign at 0, set the exponent bits, leave stored mantissa at 0
+  uint32_t res = x << 23;
+
+  return c10::detail::fp32_from_bits(res);
+}
+
+/// Special values helper
+
+inline C10_HOST_DEVICE bool Float8_e8m0fnu::isnan() const {
+  return x == 0b11111111;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e8m0fnu to float.
+C10_CLANG_DIAGNOSTIC_POP()
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::Float8_e8m0fnu;
+using c10::operator<<;
+
+namespace detail {
+using c10::detail::fp8e8m0fnu_from_fp32_value;
+} // namespace detail
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e8m0fnu> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = false;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = false;
+  static constexpr auto has_denorm_loss = false;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 1;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 1; // just a 2!
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -126;
+  static constexpr int min_exponent10 = -38;
+  static constexpr int max_exponent = 128;
+  static constexpr int max_exponent10 = 38;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before = false;
+
+  static constexpr c10::Float8_e8m0fnu min() {
+    // 2^-127
+    return c10::Float8_e8m0fnu(0b00000000, c10::Float8_e8m0fnu::from_bits());
+  }
+  static constexpr c10::Float8_e8m0fnu lowest() {
+    // 2^-127
+    return c10::Float8_e8m0fnu(0b00000000, c10::Float8_e8m0fnu::from_bits());
+  }
+  static constexpr c10::Float8_e8m0fnu max() {
+    // 254 biased, which is 127 unbiased, so 2^127
+    return c10::Float8_e8m0fnu(0b11111110, c10::Float8_e8m0fnu::from_bits());
+  }
+  static constexpr c10::Float8_e8m0fnu epsilon() {
+    // according to https://en.cppreference.com/w/cpp/types/numeric_limits, this
+    // is "the difference between 1.0 and the next representable value of the
+    // given floating-point type". The next representable value is 2.0, so the
+    // difference is 1.0 which is 2^0. 0 unbiased is 127 biased.
+    return c10::Float8_e8m0fnu(0b01111111, c10::Float8_e8m0fnu::from_bits());
+  }
+  static constexpr c10::Float8_e8m0fnu round_error() {
+    // 0.5 in float, which is 2^-1, and -1 + 127 = 126
+    return c10::Float8_e8m0fnu(0b01111110, c10::Float8_e8m0fnu::from_bits());
+  }
+  static constexpr c10::Float8_e8m0fnu quiet_NaN() {
+    return c10::Float8_e8m0fnu(0b11111111, c10::Float8_e8m0fnu::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Float8_fnuz_cvt.h

@@ -0,0 +1,69 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/floating_point_utils.h>
+
+#include <cstdint>
+
+#if defined(SYCL_LANGUAGE_VERSION)
+#include <sycl/sycl.hpp>
+#endif
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly, detail)
+
+/*
+ * Convert a 8-bit floating-point number in either f8 E4M3FNUZ or bf8 E5M2FNUZ
+ * format, in bit representation, to a 32-bit floating-point number.
+ */
+template <uint32_t we, uint32_t wm>
+inline C10_HOST_DEVICE float fp8_fnuz_to_fp32_value(uint8_t x) {
+  static_assert((we == 4 && wm == 3) || (we == 5 && wm == 2));
+  constexpr uint32_t weo = 8;
+  constexpr uint32_t wmo = 23;
+
+  if (x == 0) {
+    return 0;
+  }
+
+  if (x == 0x80) {
+    constexpr uint32_t ifNaN = 0x7F800001;
+    return fp32_from_bits(ifNaN);
+  }
+
+  uint32_t mantissa = x & ((1 << wm) - 1);
+  uint32_t exponent = (x & 0x7F) >> wm;
+
+  // subnormal input
+  if (exponent == 0) {
+    // guaranteed mantissa!=0 since cases 0x0 and 0x80 are handled above
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+    uint32_t renorm_shift = __clz(mantissa);
+#elif defined(__SYCL_DEVICE_ONLY__)
+    uint32_t renorm_shift = sycl::clz(mantissa);
+#elif defined(_MSC_VER)
+    unsigned long nonsign_bsr;
+    _BitScanReverse(&nonsign_bsr, (unsigned long)mantissa);
+    uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+    uint32_t renorm_shift = __builtin_clz(mantissa);
+#endif
+    uint32_t sh = 1 + renorm_shift - (32 - wm);
+    mantissa <<= sh;
+    exponent += 1 - sh;
+    mantissa &= ((1 << wm) - 1);
+  }
+
+  const uint32_t exp_low_cutoff = (1 << (weo - 1)) - (1 << (we - 1));
+  exponent += exp_low_cutoff - 1;
+  mantissa <<= wmo - wm;
+
+  uint32_t sign = x >> 7;
+  uint32_t retval = (sign << 31) | (exponent << 23) | mantissa;
+  return fp32_from_bits(retval);
+}
+
+HIDDEN_NAMESPACE_END(torch, headeronly, detail)
+
+namespace c10::detail {
+using torch::headeronly::detail::fp8_fnuz_to_fp32_value;
+}

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Half.h

@@ -0,0 +1,788 @@
+#pragma once
+
+/// Defines the Half type (half-precision floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32, instead of using CUDA half intrinsics.
+/// Most uses of this type within ATen are memory bound, including the
+/// element-wise kernels, and the half intrinsics aren't efficient on all GPUs.
+/// If you are writing a compute bound kernel, you can use the CUDA half
+/// intrinsics directly on the Half type from device code.
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/bit_cast.h>
+#include <torch/headeronly/util/floating_point_utils.h>
+
+#if defined(__cplusplus)
+#include <cmath>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#include <cstdint>
+#include <cstring>
+#include <ostream>
+
+#ifdef __CUDACC__
+#include <cuda_fp16.h>
+#endif
+
+#ifdef __HIPCC__
+#include <hip/hip_fp16.h>
+#endif
+
+#if defined(CL_SYCL_LANGUAGE_VERSION)
+#include <CL/sycl.hpp> // for SYCL 1.2.1
+#elif defined(SYCL_LANGUAGE_VERSION)
+#include <sycl/sycl.hpp> // for SYCL 2020
+#endif
+
+#if (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+#include <torch/headeronly/cpu/vec/vec_half.h>
+#endif
+
+#if defined(__aarch64__) && !defined(__CUDACC__)
+#include <arm_neon.h>
+#endif
+
+#if defined(__GNUC__) || defined(__clang__)
+#if defined(__x86_64__) || defined(_M_X64) || defined(__i386) || \
+    defined(_M_IX86)
+#if defined(__F16C__) &&                               \
+    !(defined(__CUDA_ARCH__) || defined(__CUDACC__) || \
+      defined(__HIP_DEVICE_COMPILE__))
+#define C10_X86_F16 1
+#include <immintrin.h> // import conversion ops from f16cintrin.h
+#endif // defined(__F16C__) && !(defined(__CUDA_ARCH__) || defined(__CUDACC__)
+       // || defined(__HIP_DEVICE_COMPILE__))
+#endif // __x86_64__ || _M_X64 || __i386 || _M_IX86
+#endif // __GNUC__ || __clang__
+
+namespace c10 {
+
+struct alignas(2) Half {
+  unsigned short x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  // HIP wants __host__ __device__ tag, CUDA does not
+#if defined(USE_ROCM)
+  C10_HOST_DEVICE Half() = default;
+#else
+  Half() = default;
+#endif
+
+  constexpr C10_HOST_DEVICE Half(unsigned short bits, from_bits_t /*unused*/)
+      : x(bits) {}
+#if defined(__aarch64__) && !defined(__CUDACC__)
+  inline Half(float16_t value);
+  inline operator float16_t() const;
+#else
+  inline C10_HOST_DEVICE Half(float value);
+  inline C10_HOST_DEVICE operator float() const;
+#endif
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+  inline C10_HOST_DEVICE Half(const __half& value);
+  inline C10_HOST_DEVICE operator __half() const;
+#endif
+#ifdef SYCL_LANGUAGE_VERSION
+  inline C10_HOST_DEVICE Half(const sycl::half& value);
+  inline C10_HOST_DEVICE operator sycl::half() const;
+#endif
+};
+
+inline std::ostream& operator<<(std::ostream& out, const Half& value) {
+  out << (float)value;
+  return out;
+}
+
+namespace detail {
+/*
+ * Convert a 16-bit floating-point number in IEEE half-precision format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format.
+ *
+ * @note The implementation relies on IEEE-like (no assumption about rounding
+ * mode and no operations on denormals) floating-point operations and bitcasts
+ * between integer and floating-point variables.
+ */
+C10_HOST_DEVICE inline float fp16_ieee_to_fp32_value(uint16_t h) {
+#ifdef C10_X86_F16
+  return _cvtsh_ss(h);
+#else
+  /*
+   * Extend the half-precision floating-point number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+-----+------------+-------------------+
+   *      | S |EEEEE|MM MMMM MMMM|0000 0000 0000 0000|
+   *      +---+-----+------------+-------------------+
+   * Bits  31  26-30    16-25            0-15
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)h << 16;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the high bits
+   * of the 32-bit word:
+   *
+   *      +-----+------------+---------------------+
+   *      |EEEEE|MM MMMM MMMM|0 0000 0000 0000 0000|
+   *      +-----+------------+---------------------+
+   * Bits  27-31    17-26            0-16
+   */
+  const uint32_t two_w = w + w;
+
+  /*
+   * Shift mantissa and exponent into bits 23-28 and bits 13-22 so they become
+   * mantissa and exponent of a single-precision floating-point number:
+   *
+   *       S|Exponent |          Mantissa
+   *      +-+---+-----+------------+----------------+
+   *      |0|000|EEEEE|MM MMMM MMMM|0 0000 0000 0000|
+   *      +-+---+-----+------------+----------------+
+   * Bits   | 23-31   |           0-22
+   *
+   * Next, there are some adjustments to the exponent:
+   * - The exponent needs to be corrected by the difference in exponent bias
+   * between single-precision and half-precision formats (0x7F - 0xF = 0x70)
+   * - Inf and NaN values in the inputs should become Inf and NaN values after
+   * conversion to the single-precision number. Therefore, if the biased
+   * exponent of the half-precision input was 0x1F (max possible value), the
+   * biased exponent of the single-precision output must be 0xFF (max possible
+   * value). We do this correction in two steps:
+   *   - First, we adjust the exponent by (0xFF - 0x1F) = 0xE0 (see exp_offset
+   * below) rather than by 0x70 suggested by the difference in the exponent bias
+   * (see above).
+   *   - Then we multiply the single-precision result of exponent adjustment by
+   * 2**(-112) to reverse the effect of exponent adjustment by 0xE0 less the
+   * necessary exponent adjustment by 0x70 due to difference in exponent bias.
+   *     The floating-point multiplication hardware would ensure than Inf and
+   * NaN would retain their value on at least partially IEEE754-compliant
+   * implementations.
+   *
+   * Note that the above operations do not handle denormal inputs (where biased
+   * exponent == 0). However, they also do not operate on denormal inputs, and
+   * do not produce denormal results.
+   */
+  constexpr uint32_t exp_offset = UINT32_C(0xE0) << 23;
+  // const float exp_scale = 0x1.0p-112f;
+  constexpr uint32_t scale_bits = (uint32_t)15 << 23;
+  float exp_scale_val = 0;
+#if defined(_MSC_VER) && defined(__clang__)
+  __builtin_memcpy(&exp_scale_val, &scale_bits, sizeof(exp_scale_val));
+#else
+  std::memcpy(&exp_scale_val, &scale_bits, sizeof(exp_scale_val));
+#endif
+
+  const float exp_scale = exp_scale_val;
+  const float normalized_value =
+      fp32_from_bits((two_w >> 4) + exp_offset) * exp_scale;
+
+  /*
+   * Convert denormalized half-precision inputs into single-precision results
+   * (always normalized). Zero inputs are also handled here.
+   *
+   * In a denormalized number the biased exponent is zero, and mantissa has
+   * on-zero bits. First, we shift mantissa into bits 0-9 of the 32-bit word.
+   *
+   *                  zeros           |  mantissa
+   *      +---------------------------+------------+
+   *      |0000 0000 0000 0000 0000 00|MM MMMM MMMM|
+   *      +---------------------------+------------+
+   * Bits             10-31                0-9
+   *
+   * Now, remember that denormalized half-precision numbers are represented as:
+   *    FP16 = mantissa * 2**(-24).
+   * The trick is to construct a normalized single-precision number with the
+   * same mantissa and thehalf-precision input and with an exponent which would
+   * scale the corresponding mantissa bits to 2**(-24). A normalized
+   * single-precision floating-point number is represented as: FP32 = (1 +
+   * mantissa * 2**(-23)) * 2**(exponent - 127) Therefore, when the biased
+   * exponent is 126, a unit change in the mantissa of the input denormalized
+   * half-precision number causes a change of the constructed single-precision
+   * number by 2**(-24), i.e. the same amount.
+   *
+   * The last step is to adjust the bias of the constructed single-precision
+   * number. When the input half-precision number is zero, the constructed
+   * single-precision number has the value of FP32 = 1 * 2**(126 - 127) =
+   * 2**(-1) = 0.5 Therefore, we need to subtract 0.5 from the constructed
+   * single-precision number to get the numerical equivalent of the input
+   * half-precision number.
+   */
+  constexpr uint32_t magic_mask = UINT32_C(126) << 23;
+  constexpr float magic_bias = 0.5f;
+  const float denormalized_value =
+      fp32_from_bits((two_w >> 17) | magic_mask) - magic_bias;
+
+  /*
+   * - Choose either results of conversion of input as a normalized number, or
+   * as a denormalized number, depending on the input exponent. The variable
+   * two_w contains input exponent in bits 27-31, therefore if its smaller than
+   * 2**27, the input is either a denormal number, or zero.
+   * - Combine the result of conversion of exponent and mantissa with the sign
+   * of the input number.
+   */
+  constexpr uint32_t denormalized_cutoff = UINT32_C(1) << 27;
+  const uint32_t result = sign |
+      (two_w < denormalized_cutoff ? fp32_to_bits(denormalized_value)
+                                   : fp32_to_bits(normalized_value));
+  return fp32_from_bits(result);
+#endif // C10_X86_F16
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 16-bit floating-point number in IEEE half-precision format, in bit
+ * representation.
+ *
+ * @note The implementation relies on IEEE-like (no assumption about rounding
+ * mode and no operations on denormals) floating-point operations and bitcasts
+ * between integer and floating-point variables.
+ */
+inline uint16_t fp16_ieee_from_fp32_value(float f) {
+#ifdef C10_X86_F16
+  return _cvtss_sh(f, _MM_FROUND_TO_NEAREST_INT);
+#else
+  // const float scale_to_inf = 0x1.0p+112f;
+  // const float scale_to_zero = 0x1.0p-110f;
+  constexpr uint32_t scale_to_inf_bits = (uint32_t)239 << 23;
+  constexpr uint32_t scale_to_zero_bits = (uint32_t)17 << 23;
+  float scale_to_inf_val = 0, scale_to_zero_val = 0;
+  std::memcpy(&scale_to_inf_val, &scale_to_inf_bits, sizeof(scale_to_inf_val));
+  std::memcpy(
+      &scale_to_zero_val, &scale_to_zero_bits, sizeof(scale_to_zero_val));
+  const float scale_to_inf = scale_to_inf_val;
+  const float scale_to_zero = scale_to_zero_val;
+
+#if defined(_MSC_VER) && _MSC_VER == 1916
+  float base = ((signbit(f) != 0 ? -f : f) * scale_to_inf) * scale_to_zero;
+#else
+  float base = (fabsf(f) * scale_to_inf) * scale_to_zero;
+#endif
+
+  const uint32_t w = fp32_to_bits(f);
+  const uint32_t shl1_w = w + w;
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  uint32_t bias = shl1_w & UINT32_C(0xFF000000);
+  if (bias < UINT32_C(0x71000000)) {
+    bias = UINT32_C(0x71000000);
+  }
+
+  base = fp32_from_bits((bias >> 1) + UINT32_C(0x07800000)) + base;
+  const uint32_t bits = fp32_to_bits(base);
+  const uint32_t exp_bits = (bits >> 13) & UINT32_C(0x00007C00);
+  const uint32_t mantissa_bits = bits & UINT32_C(0x00000FFF);
+  const uint32_t nonsign = exp_bits + mantissa_bits;
+  return static_cast<uint16_t>(
+      (sign >> 16) |
+      (shl1_w > UINT32_C(0xFF000000) ? UINT16_C(0x7E00) : nonsign));
+#endif // C10_X86_F16
+}
+
+/*
+ * Convert a 16-bit floating-point number in IEEE half-precision format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline uint32_t fp16_ieee_to_fp32_bits(uint16_t h) {
+  /*
+   * Extend the half-precision floating-point number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+-----+------------+-------------------+
+   *      | S |EEEEE|MM MMMM MMMM|0000 0000 0000 0000|
+   *      +---+-----+------------+-------------------+
+   * Bits  31  26-30    16-25            0-15
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)h << 16;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the bits 0-30
+   * of the 32-bit word:
+   *
+   *      +---+-----+------------+-------------------+
+   *      | 0 |EEEEE|MM MMMM MMMM|0000 0000 0000 0000|
+   *      +---+-----+------------+-------------------+
+   * Bits  30  27-31     17-26            0-16
+   */
+  const uint32_t nonsign = w & UINT32_C(0x7FFFFFFF);
+  /*
+   * Renorm shift is the number of bits to shift mantissa left to make the
+   * half-precision number normalized. If the initial number is normalized, some
+   * of its high 6 bits (sign == 0 and 5-bit exponent) equals one. In this case
+   * renorm_shift == 0. If the number is denormalize, renorm_shift > 0. Note
+   * that if we shift denormalized nonsign by renorm_shift, the unit bit of
+   * mantissa will shift into exponent, turning the biased exponent into 1, and
+   * making mantissa normalized (i.e. without leading 1).
+   */
+#ifdef _MSC_VER
+  unsigned long nonsign_bsr;
+  _BitScanReverse(&nonsign_bsr, (unsigned long)nonsign);
+  uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+  uint32_t renorm_shift = __builtin_clz(nonsign);
+#endif
+  renorm_shift = renorm_shift > 5 ? renorm_shift - 5 : 0;
+  /*
+   * Iff half-precision number has exponent of 15, the addition overflows
+   * it into bit 31, and the subsequent shift turns the high 9 bits
+   * into 1. Thus inf_nan_mask == 0x7F800000 if the half-precision number
+   * had exponent of 15 (i.e. was NaN or infinity) 0x00000000 otherwise
+   */
+  const int32_t inf_nan_mask =
+      ((int32_t)(nonsign + 0x04000000) >> 8) & INT32_C(0x7F800000);
+  /*
+   * Iff nonsign is 0, it overflows into 0xFFFFFFFF, turning bit 31
+   * into 1. Otherwise, bit 31 remains 0. The signed shift right by 31
+   * broadcasts bit 31 into all bits of the zero_mask. Thus zero_mask ==
+   * 0xFFFFFFFF if the half-precision number was zero (+0.0h or -0.0h)
+   * 0x00000000 otherwise
+   */
+  const int32_t zero_mask = (int32_t)(nonsign - 1) >> 31;
+  /*
+   * 1. Shift nonsign left by renorm_shift to normalize it (if the input
+   * was denormal)
+   * 2. Shift nonsign right by 3 so the exponent (5 bits originally)
+   * becomes an 8-bit field and 10-bit mantissa shifts into the 10 high
+   * bits of the 23-bit mantissa of IEEE single-precision number.
+   * 3. Add 0x70 to the exponent (starting at bit 23) to compensate the
+   * different in exponent bias (0x7F for single-precision number less 0xF
+   * for half-precision number).
+   * 4. Subtract renorm_shift from the exponent (starting at bit 23) to
+   * account for renormalization. As renorm_shift is less than 0x70, this
+   * can be combined with step 3.
+   * 5. Binary OR with inf_nan_mask to turn the exponent into 0xFF if the
+   * input was NaN or infinity.
+   * 6. Binary ANDNOT with zero_mask to turn the mantissa and exponent
+   * into zero if the input was zero.
+   * 7. Combine with the sign of the input number.
+   */
+  return sign |
+      ((((nonsign << renorm_shift >> 3) + ((0x70 - renorm_shift) << 23)) |
+        inf_nan_mask) &
+       ~zero_mask);
+}
+
+#ifdef C10_X86_F16
+#undef C10_X86_F16
+#endif // C10_X86_F16
+
+#if defined(__aarch64__) && !defined(__CUDACC__)
+inline float16_t fp16_from_bits(uint16_t h) {
+  return c10::bit_cast<float16_t>(h);
+}
+
+inline uint16_t fp16_to_bits(float16_t f) {
+  return c10::bit_cast<uint16_t>(f);
+}
+
+// According to https://godbolt.org/z/frExdbsWG it would translate to single
+// fcvt s0, h0
+inline float native_fp16_to_fp32_value(uint16_t h) {
+  return static_cast<float>(fp16_from_bits(h));
+}
+
+inline uint16_t native_fp16_from_fp32_value(float f) {
+  return fp16_to_bits(static_cast<float16_t>(f));
+}
+#endif
+
+} // namespace detail
+
+//---------- below is copied from c10/util/Half-inl.h ----------------//
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+#if defined(__aarch64__) && !defined(__CUDACC__)
+/// Constructors
+inline Half::Half(float16_t value) : x(detail::fp16_to_bits(value)) {}
+inline Half::operator float16_t() const {
+  return detail::fp16_from_bits(x);
+}
+#else
+
+inline C10_HOST_DEVICE Half::Half(float value)
+    :
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+      x(__half_as_short(__float2half(value)))
+#elif defined(__SYCL_DEVICE_ONLY__)
+      x(c10::bit_cast<uint16_t>(sycl::half(value)))
+#elif (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+      x(at::vec::float2half_scalar(value))
+#else
+      x(detail::fp16_ieee_from_fp32_value(value))
+#endif
+{
+}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Half::operator float() const {
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+  return __half2float(*reinterpret_cast<const __half*>(&x));
+#elif defined(__SYCL_DEVICE_ONLY__)
+  return float(c10::bit_cast<sycl::half>(x));
+#elif (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+  return at::vec::half2float_scalar(x);
+#elif defined(__aarch64__) && !defined(__CUDACC__)
+  return detail::native_fp16_to_fp32_value(x);
+#else
+  return detail::fp16_ieee_to_fp32_value(x);
+#endif
+}
+
+#endif /* !defined(__aarch64__) || defined(__CUDACC__) \
+        */
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+inline C10_HOST_DEVICE Half::Half(const __half& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE Half::operator __half() const {
+  return *reinterpret_cast<const __half*>(&x);
+}
+#endif
+
+#ifdef SYCL_LANGUAGE_VERSION
+inline C10_HOST_DEVICE Half::Half(const sycl::half& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE Half::operator sycl::half() const {
+  return *reinterpret_cast<const sycl::half*>(&x);
+}
+#endif
+
+// CUDA intrinsics
+
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350)) || \
+    (defined(__clang__) && defined(__CUDA__))
+inline __device__ Half __ldg(const Half* ptr) {
+  return __ldg(reinterpret_cast<const __half*>(ptr));
+}
+#endif
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Half operator+(const Half& a, const Half& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator-(const Half& a, const Half& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator*(const Half& a, const Half& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator/(const Half& a, const Half& b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator-(const Half& a) {
+#if (defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530) || \
+    defined(__HIP_DEVICE_COMPILE__)
+  return __hneg(a);
+#elif defined(__SYCL_DEVICE_ONLY__)
+  return -c10::bit_cast<sycl::half>(a);
+#else
+  return -static_cast<float>(a);
+#endif
+}
+
+inline C10_HOST_DEVICE Half& operator+=(Half& a, const Half& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Half& operator-=(Half& a, const Half& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Half& operator*=(Half& a, const Half& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Half& operator/=(Half& a, const Half& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Half a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Half a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Half a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Half a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Half b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Half b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Half b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Half b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Half& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Half& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Half& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Half& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Half a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Half a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Half a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Half a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Half b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Half b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Half b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Half b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Half operator+(Half a, int b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a + static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator-(Half a, int b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a - static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator*(Half a, int b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a * static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator/(Half a, int b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a / static_cast<Half>(b);
+}
+
+inline C10_HOST_DEVICE Half operator+(int a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) + b;
+}
+inline C10_HOST_DEVICE Half operator-(int a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) - b;
+}
+inline C10_HOST_DEVICE Half operator*(int a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) * b;
+}
+inline C10_HOST_DEVICE Half operator/(int a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Half operator+(Half a, int64_t b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a + static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator-(Half a, int64_t b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a - static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator*(Half a, int64_t b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a * static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator/(Half a, int64_t b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return a / static_cast<Half>(b);
+}
+
+inline C10_HOST_DEVICE Half operator+(int64_t a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) + b;
+}
+inline C10_HOST_DEVICE Half operator-(int64_t a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) - b;
+}
+inline C10_HOST_DEVICE Half operator*(int64_t a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) * b;
+}
+inline C10_HOST_DEVICE Half operator/(int64_t a, Half b) {
+  // NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
+  return static_cast<Half>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Half to float.
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+
+using c10::Half;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+using c10::operator<<;
+
+namespace detail {
+#if defined(__aarch64__) && !defined(__CUDACC__)
+using c10::detail::fp16_from_bits;
+using c10::detail::fp16_to_bits;
+using c10::detail::native_fp16_from_fp32_value;
+using c10::detail::native_fp16_to_fp32_value;
+#endif
+
+using c10::detail::fp16_ieee_from_fp32_value;
+using c10::detail::fp16_ieee_to_fp32_bits;
+using c10::detail::fp16_ieee_to_fp32_value;
+} // namespace detail
+
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Half> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = true;
+  static constexpr auto has_denorm = numeric_limits<float>::has_denorm;
+  static constexpr auto has_denorm_loss =
+      numeric_limits<float>::has_denorm_loss;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = true;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 11;
+  static constexpr int digits10 = 3;
+  static constexpr int max_digits10 = 5;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -13;
+  static constexpr int min_exponent10 = -4;
+  static constexpr int max_exponent = 16;
+  static constexpr int max_exponent10 = 4;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+  static constexpr c10::Half min() {
+    return c10::Half(0x0400, c10::Half::from_bits());
+  }
+  static constexpr c10::Half lowest() {
+    return c10::Half(0xFBFF, c10::Half::from_bits());
+  }
+  static constexpr c10::Half max() {
+    return c10::Half(0x7BFF, c10::Half::from_bits());
+  }
+  static constexpr c10::Half epsilon() {
+    return c10::Half(0x1400, c10::Half::from_bits());
+  }
+  static constexpr c10::Half round_error() {
+    return c10::Half(0x3800, c10::Half::from_bits());
+  }
+  static constexpr c10::Half infinity() {
+    return c10::Half(0x7C00, c10::Half::from_bits());
+  }
+  static constexpr c10::Half quiet_NaN() {
+    return c10::Half(0x7E00, c10::Half::from_bits());
+  }
+  static constexpr c10::Half signaling_NaN() {
+    return c10::Half(0x7D00, c10::Half::from_bits());
+  }
+  static constexpr c10::Half denorm_min() {
+    return c10::Half(0x0001, c10::Half::from_bits());
+  }
+};
+
+} // namespace std

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/HeaderOnlyArrayRef.h

@@ -0,0 +1,248 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Exception.h>
+
+#include <algorithm>
+#include <array>
+#include <cstddef>
+#include <functional>
+#include <initializer_list>
+#include <iterator>
+#include <type_traits>
+#include <vector>
+
+namespace c10 {
+
+/// HeaderOnlyArrayRef - A subset of ArrayRef that is implemented only
+/// in headers. This will be a base class from which ArrayRef inherits, so that
+/// we can keep much of the implementation shared.
+///
+/// [HeaderOnlyArrayRef vs ArrayRef note]
+/// As HeaderOnlyArrayRef is a subset of ArrayRef, it has slightly less
+/// functionality than ArrayRef. We document the minor differences below:
+/// 1. ArrayRef has an extra convenience constructor for SmallVector.
+/// 2. ArrayRef uses TORCH_CHECK. HeaderOnlyArrayRef uses header-only
+///    STD_TORCH_CHECK, which will output a std::runtime_error vs a
+///    c10::Error. Consequently, you should use ArrayRef when possible
+///    and HeaderOnlyArrayRef only when necessary to support headeronly code.
+/// In all other aspects, HeaderOnlyArrayRef is identical to ArrayRef, with the
+/// positive benefit of being header-only and thus independent of libtorch.so.
+template <typename T>
+class HeaderOnlyArrayRef {
+ public:
+  using iterator = const T*;
+  using const_iterator = const T*;
+  using size_type = size_t;
+  using value_type = T;
+
+  using reverse_iterator = std::reverse_iterator<iterator>;
+
+ protected:
+  /// The start of the array, in an external buffer.
+  const T* Data;
+
+  /// The number of elements.
+  size_type Length;
+
+ public:
+  /// @name Constructors
+  /// @{
+
+  /// Construct an empty HeaderOnlyArrayRef.
+  /* implicit */ constexpr HeaderOnlyArrayRef() : Data(nullptr), Length(0) {}
+
+  /// Construct a HeaderOnlyArrayRef from a single element.
+  // TODO Make this explicit
+  constexpr HeaderOnlyArrayRef(const T& OneElt) : Data(&OneElt), Length(1) {}
+
+  /// Construct a HeaderOnlyArrayRef from a pointer and length.
+  constexpr HeaderOnlyArrayRef(const T* data, size_t length)
+      : Data(data), Length(length) {}
+
+  /// Construct a HeaderOnlyArrayRef from a range.
+  constexpr HeaderOnlyArrayRef(const T* begin, const T* end)
+      : Data(begin), Length(end - begin) {}
+
+  template <
+      typename Container,
+      typename U = decltype(std::declval<Container>().data()),
+      typename = std::enable_if_t<
+          (std::is_same_v<U, T*> || std::is_same_v<U, T const*>)>>
+  /* implicit */ HeaderOnlyArrayRef(const Container& container)
+      : Data(container.data()), Length(container.size()) {}
+
+  /// Construct a HeaderOnlyArrayRef from a std::vector.
+  // The enable_if stuff here makes sure that this isn't used for
+  // std::vector<bool>, because ArrayRef can't work on a std::vector<bool>
+  // bitfield.
+  template <typename A>
+  /* implicit */ HeaderOnlyArrayRef(const std::vector<T, A>& Vec)
+      : Data(Vec.data()), Length(Vec.size()) {
+    static_assert(
+        !std::is_same_v<T, bool>,
+        "HeaderOnlyArrayRef<bool> cannot be constructed from a std::vector<bool> bitfield.");
+  }
+
+  /// Construct a HeaderOnlyArrayRef from a std::array
+  template <size_t N>
+  /* implicit */ constexpr HeaderOnlyArrayRef(const std::array<T, N>& Arr)
+      : Data(Arr.data()), Length(N) {}
+
+  /// Construct a HeaderOnlyArrayRef from a C array.
+  template <size_t N>
+  // NOLINTNEXTLINE(*c-arrays*)
+  /* implicit */ constexpr HeaderOnlyArrayRef(const T (&Arr)[N])
+      : Data(Arr), Length(N) {}
+
+  /// Construct a HeaderOnlyArrayRef from a std::initializer_list.
+  /* implicit */ constexpr HeaderOnlyArrayRef(
+      const std::initializer_list<T>& Vec)
+      : Data(
+            std::begin(Vec) == std::end(Vec) ? static_cast<T*>(nullptr)
+                                             : std::begin(Vec)),
+        Length(Vec.size()) {}
+
+  /// @}
+  /// @name Simple Operations
+  /// @{
+
+  constexpr iterator begin() const {
+    return this->Data;
+  }
+  constexpr iterator end() const {
+    return this->Data + this->Length;
+  }
+
+  // These are actually the same as iterator, since ArrayRef only
+  // gives you const iterators.
+  constexpr const_iterator cbegin() const {
+    return this->Data;
+  }
+  constexpr const_iterator cend() const {
+    return this->Data + this->Length;
+  }
+
+  constexpr reverse_iterator rbegin() const {
+    return reverse_iterator(end());
+  }
+  constexpr reverse_iterator rend() const {
+    return reverse_iterator(begin());
+  }
+
+  /// Check if all elements in the array satisfy the given expression
+  constexpr bool allMatch(const std::function<bool(const T&)>& pred) const {
+    return std::all_of(cbegin(), cend(), pred);
+  }
+
+  /// empty - Check if the array is empty.
+  constexpr bool empty() const {
+    return this->Length == 0;
+  }
+
+  constexpr const T* data() const {
+    return this->Data;
+  }
+
+  /// size - Get the array size.
+  constexpr size_t size() const {
+    return this->Length;
+  }
+
+  /// front - Get the first element.
+  constexpr const T& front() const {
+    STD_TORCH_CHECK(
+        !this->empty(),
+        "HeaderOnlyArrayRef: attempted to access front() of empty list");
+    return this->Data[0];
+  }
+
+  /// back - Get the last element.
+  constexpr const T& back() const {
+    STD_TORCH_CHECK(
+        !this->empty(),
+        "HeaderOnlyArrayRef: attempted to access back() of empty list");
+    return this->Data[this->Length - 1];
+  }
+
+  /// equals - Check for element-wise equality.
+  constexpr bool equals(HeaderOnlyArrayRef RHS) const {
+    return this->Length == RHS.Length &&
+        std::equal(begin(), end(), RHS.begin());
+  }
+
+  /// slice(n, m) - Take M elements of the array starting at element N
+  constexpr HeaderOnlyArrayRef<T> slice(size_t N, size_t M) const {
+    STD_TORCH_CHECK(
+        N + M <= this->size(),
+        "HeaderOnlyArrayRef: invalid slice, N = ",
+        N,
+        "; M = ",
+        M,
+        "; size = ",
+        this->size());
+    return HeaderOnlyArrayRef<T>(this->data() + N, M);
+  }
+
+  /// slice(n) - Chop off the first N elements of the array.
+  constexpr HeaderOnlyArrayRef<T> slice(size_t N) const {
+    STD_TORCH_CHECK(
+        N <= this->size(),
+        "HeaderOnlyArrayRef: invalid slice, N = ",
+        N,
+        "; size = ",
+        this->size());
+    return slice(N, this->size() - N);
+  }
+
+  /// @}
+  /// @name Operator Overloads
+  /// @{
+  constexpr const T& operator[](size_t Index) const {
+    return this->Data[Index];
+  }
+
+  /// Vector compatibility
+  constexpr const T& at(size_t Index) const {
+    STD_TORCH_CHECK(
+        Index < this->Length,
+        "HeaderOnlyArrayRef: invalid index Index = ",
+        Index,
+        "; Length = ",
+        this->Length);
+    return this->Data[Index];
+  }
+
+  /// Disallow accidental assignment from a temporary.
+  ///
+  /// The declaration here is extra complicated so that "arrayRef = {}"
+  /// continues to select the move assignment operator.
+  template <typename U>
+  std::enable_if_t<std::is_same_v<U, T>, HeaderOnlyArrayRef<T>>& operator=(
+      // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+      U&& Temporary) = delete;
+
+  /// Disallow accidental assignment from a temporary.
+  ///
+  /// The declaration here is extra complicated so that "arrayRef = {}"
+  /// continues to select the move assignment operator.
+  template <typename U>
+  std::enable_if_t<std::is_same_v<U, T>, HeaderOnlyArrayRef<T>>& operator=(
+      std::initializer_list<U>) = delete;
+
+  /// @}
+  /// @name Expensive Operations
+  /// @{
+  std::vector<T> vec() const {
+    return std::vector<T>(this->Data, this->Data + this->Length);
+  }
+
+  /// @}
+};
+
+} // namespace c10
+
+namespace torch::headeronly {
+using c10::HeaderOnlyArrayRef;
+using IntHeaderOnlyArrayRef = HeaderOnlyArrayRef<int64_t>;
+} // namespace torch::headeronly

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/Metaprogramming.h

@@ -0,0 +1,237 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/TypeList.h>
+#include <type_traits>
+
+namespace c10::guts {
+
+/**
+ * Access information about result type or arguments from a function type.
+ * Example:
+ * using A = function_traits<int (float, double)>::return_type // A == int
+ * using A = function_traits<int (float, double)>::parameter_types::tuple_type
+ * // A == tuple<float, double>
+ */
+template <class Func>
+struct function_traits {
+  static_assert(
+      !std::is_same_v<Func, Func>,
+      "In function_traits<Func>, Func must be a plain function type.");
+};
+template <class Result, class... Args>
+struct function_traits<Result(Args...)> {
+  using func_type = Result(Args...);
+  using return_type = Result;
+  using parameter_types = typelist::typelist<Args...>;
+  static constexpr auto number_of_parameters = sizeof...(Args);
+};
+
+/**
+ * infer_function_traits: creates a `function_traits` type for a simple
+ * function (pointer) or functor (lambda/struct). Currently does not support
+ * class methods.
+ */
+
+template <typename Functor>
+struct infer_function_traits {
+  using type = function_traits<
+      c10::guts::detail::strip_class_t<decltype(&Functor::operator())>>;
+};
+
+template <typename Result, typename... Args>
+struct infer_function_traits<Result (*)(Args...)> {
+  using type = function_traits<Result(Args...)>;
+};
+
+template <typename Result, typename... Args>
+struct infer_function_traits<Result(Args...)> {
+  using type = function_traits<Result(Args...)>;
+};
+
+template <typename T>
+using infer_function_traits_t = typename infer_function_traits<T>::type;
+
+/**
+ * make_function_traits: creates a `function_traits` type given a Return type
+ * and a typelist of Argument types
+ *
+ * Example:
+ * bool f(int, int);
+ *
+ * infer_function_traits_t<f> == make_function_traits_t<bool,
+ * typelist::typelist<int, int>>
+ */
+template <typename Result, typename ArgList>
+struct make_function_traits {
+  static_assert(
+      false_t<ArgList>::value,
+      "In guts::make_function_traits<Result, TypeList>, the ArgList argument must be typelist<...>.");
+};
+
+template <typename Result, typename... Args>
+struct make_function_traits<Result, typelist::typelist<Args...>> {
+  using type = function_traits<Result(Args...)>;
+};
+
+template <typename Result, typename ArgList>
+using make_function_traits_t =
+    typename make_function_traits<Result, ArgList>::type;
+
+/**
+ * make_offset_index_sequence<Start, N>
+ * Like make_index_sequence<N>, but starting from Start instead of 0.
+ *
+ * Example:
+ *  make_offset_index_sequence<10, 3> == std::index_sequence<10, 11, 12>
+ */
+template <size_t Start, size_t N, size_t... Is>
+struct make_offset_index_sequence_impl
+    : make_offset_index_sequence_impl<Start, N - 1, Start + N - 1, Is...> {
+  static_assert(
+      static_cast<int>(Start) >= 0,
+      "make_offset_index_sequence: Start < 0");
+  static_assert(static_cast<int>(N) >= 0, "make_offset_index_sequence: N < 0");
+};
+
+template <size_t Start, size_t... Is>
+struct make_offset_index_sequence_impl<Start, 0, Is...> {
+  typedef std::index_sequence<Is...> type;
+};
+
+template <size_t Start, size_t N>
+using make_offset_index_sequence =
+    typename make_offset_index_sequence_impl<Start, N>::type;
+
+/**
+ * Use tuple_elements to extract a position-indexed subset of elements
+ * from the argument tuple into a result tuple.
+ *
+ * Example:
+ *  std::tuple<int, const char*, double> t = std::make_tuple(0, "HEY", 2.0);
+ *  std::tuple<int, double> result = tuple_elements(t, std::index_sequence<0,
+ * 2>());
+ */
+template <class Tuple, size_t... Is>
+constexpr auto tuple_elements(Tuple t, std::index_sequence<Is...> /*unused*/) {
+  return std::tuple<std::tuple_element_t<Is, Tuple>...>(std::get<Is>(t)...);
+}
+
+/**
+ * Use tuple_take to extract the first or last n elements from the argument
+ * tuple into a result tuple.
+ *
+ * Example:
+ *  std::tuple<int, const char*, double> t = std::make_tuple(0, "HEY", 2.0);
+ *  std::tuple<int, const char*> first_two = tuple_take<decltype(t), 2>(t);
+ *  std::tuple<const char*, double> last_two = tuple_take<decltype(t), -2>(t);
+ */
+template <class Tuple, int N, class Enable = void>
+struct TupleTake {};
+
+template <class Tuple, int N>
+struct TupleTake<Tuple, N, std::enable_if_t<N >= 0, void>> {
+  static auto call(Tuple t) {
+    constexpr size_t size = std::tuple_size<Tuple>();
+    static_assert(N <= size, "tuple_take: N > size");
+    return tuple_elements(t, std::make_index_sequence<N>{});
+  }
+};
+
+template <class Tuple, int N>
+    struct TupleTake < Tuple,
+    N, std::enable_if_t<N<0, void>> {
+  static auto call(Tuple t) {
+    constexpr size_t size = std::tuple_size<Tuple>();
+    static_assert(-N <= size, "tuple_take: -N > size");
+    return tuple_elements(t, make_offset_index_sequence<size + N, -N>{});
+  }
+};
+
+template <class Tuple, int N>
+auto tuple_take(Tuple t) {
+  return TupleTake<Tuple, N>::call(t);
+}
+
+/**
+ * Use tuple_slice to extract a contiguous subtuple from the argument.
+ *
+ * Example:
+ *  std::tuple<int, const char*, double, bool> t = std::make_tuple(0,
+ * "HEY", 2.0, false); std::tuple<int, const char*> middle_two =
+ * tuple_slice<decltype(t), 1, 2>(t);
+ */
+template <class Tuple, size_t Start, size_t N>
+constexpr auto tuple_slice(Tuple t) {
+  constexpr size_t size = std::tuple_size<Tuple>();
+  static_assert(Start + N <= size, "tuple_slice: Start + N > size");
+  return tuple_elements(t, make_offset_index_sequence<Start, N>{});
+}
+
+/**
+ * Use tuple_map to run a mapping function over a tuple to get a new tuple.
+ *
+ * Example 1:
+ *   auto result = tuple_map(std::tuple<int32_t, int32_t, int32_t>(3, 4, 5), []
+ * (int32_t a) -> int16_t {return a+1;});
+ *   // result == std::tuple<int16_t, int16_t, int16_t>(4, 5, 6)
+ *
+ * Example 2:
+ *   struct Mapper {
+ *     std::string operator()(int32_t a) const {
+ *       return std::to_string(a);
+ *     }
+ *     int64_t operator()(const std::string& a) const {
+ *        return atoi(a.c_str());
+ *     }
+ *   };
+ *   auto result = tuple_map(std::tuple<int32_t, std::string>(3, "4"),
+ * Mapper());
+ *   // result == std::tuple<std::string, int64_t>("3", 4)
+ *
+ * Example 3:
+ *   struct A final {
+ *    int32_t func() {
+ *      return 5;
+ *    }
+ *  };
+ *  struct B final {
+ *    std::string func() {
+ *      return "5";
+ *    }
+ *  };
+ *  auto result = tuple_map(std::make_tuple(A(), B()), [] (auto a) { return
+ * a.func(); });
+ *  // result == std::tuple<int32_t, std::string>(5, "5");
+ */
+namespace detail {
+template <class Mapper, class... Args, size_t... Indices>
+auto tuple_map(
+    // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
+    std::tuple<Args...>&& tuple,
+    const Mapper& mapper,
+    std::index_sequence<Indices...> /*unused*/) {
+  return std::tuple<decltype(mapper(std::forward<Args>(std::get<Indices>(
+      tuple))))...>(mapper(std::forward<Args>(std::get<Indices>(tuple)))...);
+}
+} // namespace detail
+
+template <class Mapper, class... Args>
+auto tuple_map(std::tuple<Args...>&& tuple, const Mapper& mapper) {
+  return detail::tuple_map(
+      std::move(tuple), mapper, std::index_sequence_for<Args...>());
+}
+
+} // namespace c10::guts
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly, guts)
+
+using c10::guts::function_traits;
+using c10::guts::infer_function_traits_t;
+using c10::guts::make_function_traits_t;
+using c10::guts::tuple_elements;
+using c10::guts::tuple_map;
+using c10::guts::tuple_slice;
+using c10::guts::tuple_take;
+
+HIDDEN_NAMESPACE_END(torch, headeronly, guts)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/TypeList.h

@@ -0,0 +1,548 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/TypeTraits.h>
+#include <algorithm>
+#include <cstddef>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+namespace c10::guts {
+
+template <class... T>
+struct false_t : std::false_type {};
+template <template <class> class... T>
+struct false_higher_t : std::false_type {};
+
+namespace typelist {
+
+/**
+ * Type holding a list of types for compile time type computations
+ */
+template <class... Items>
+struct typelist final {
+ public:
+  typelist() = delete; // not for instantiation
+};
+
+/**
+ * Returns the number of types in a typelist
+ * Example:
+ *   3  ==  size<typelist<int, int, double>>::value
+ */
+template <class TypeList>
+struct size final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::size<T>, T must be typelist<...>.");
+};
+template <class... Types>
+struct size<typelist<Types...>> final {
+  static constexpr size_t value = sizeof...(Types);
+};
+
+/**
+ * Transforms a list of types into a tuple holding these types.
+ * Example:
+ *   std::tuple<int, string>  ==  to_tuple_t<typelist<int, string>>
+ */
+template <class TypeList>
+struct to_tuple final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::to_tuple<T>, T must be typelist<...>.");
+};
+template <class... Types>
+struct to_tuple<typelist<Types...>> final {
+  using type = std::tuple<Types...>;
+};
+template <class TypeList>
+using to_tuple_t = typename to_tuple<TypeList>::type;
+
+/**
+ * Creates a typelist containing the types of a given tuple.
+ * Example:
+ *   typelist<int, string>  ==  from_tuple_t<std::tuple<int, string>>
+ */
+template <class Tuple>
+struct from_tuple final {
+  static_assert(
+      false_t<Tuple>::value,
+      "In typelist::from_tuple<T>, T must be std::tuple<...>.");
+};
+template <class... Types>
+struct from_tuple<std::tuple<Types...>> final {
+  using type = typelist<Types...>;
+};
+template <class Tuple>
+using from_tuple_t = typename from_tuple<Tuple>::type;
+
+/**
+ * Concatenates multiple type lists.
+ * Example:
+ *   typelist<int, string, int>  ==  concat_t<typelist<int, string>,
+ * typelist<int>>
+ */
+template <class... TypeLists>
+struct concat final {
+  static_assert(
+      false_t<TypeLists...>::value,
+      "In typelist::concat<T1, ...>, the T arguments each must be typelist<...>.");
+};
+template <class... Head1Types, class... Head2Types, class... TailLists>
+struct concat<typelist<Head1Types...>, typelist<Head2Types...>, TailLists...>
+    final {
+  using type =
+      typename concat<typelist<Head1Types..., Head2Types...>, TailLists...>::
+          type;
+};
+template <class... HeadTypes>
+struct concat<typelist<HeadTypes...>> final {
+  using type = typelist<HeadTypes...>;
+};
+template <>
+struct concat<> final {
+  using type = typelist<>;
+};
+template <class... TypeLists>
+using concat_t = typename concat<TypeLists...>::type;
+
+/**
+ * Filters the types in a type list by a type trait.
+ * Examples:
+ *   typelist<int&, const string&&>  ==  filter_t<std::is_reference,
+ * typelist<void, string, int&, bool, const string&&, int>>
+ */
+template <template <class> class Condition, class TypeList>
+struct filter final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::filter<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition, class Head, class... Tail>
+struct filter<Condition, typelist<Head, Tail...>> final {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::filter<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+  using type = std::conditional_t<
+      Condition<Head>::value,
+      concat_t<
+          typelist<Head>,
+          typename filter<Condition, typelist<Tail...>>::type>,
+      typename filter<Condition, typelist<Tail...>>::type>;
+};
+template <template <class> class Condition>
+struct filter<Condition, typelist<>> final {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::filter<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+  using type = typelist<>;
+};
+template <template <class> class Condition, class TypeList>
+using filter_t = typename filter<Condition, TypeList>::type;
+
+/**
+ * Counts how many types in the list fulfill a type trait
+ * Examples:
+ *   2  ==  count_if<std::is_reference, typelist<void, string, int&, bool, const
+ * string&&, int>>
+ */
+template <template <class> class Condition, class TypeList>
+struct count_if final {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::count_if<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::count_if<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+  // TODO Direct implementation might be faster
+  static constexpr size_t value = size<filter_t<Condition, TypeList>>::value;
+};
+
+/**
+ * Checks if a typelist contains a certain type.
+ * Examples:
+ *  contains<typelist<int, string>, string> == true_type
+ *  contains<typelist<int, string>, double> == false_type
+ */
+namespace detail {
+template <class TypeList, class Type, class Enable = void>
+struct contains {};
+template <class Type>
+struct contains<typelist<>, Type, void> : std::false_type {};
+template <class Type, class Head, class... Tail>
+struct contains<
+    typelist<Head, Tail...>,
+    Type,
+    std::enable_if_t<std::is_same_v<Head, Type>>> : std::true_type {};
+template <class Type, class Head, class... Tail>
+struct contains<
+    typelist<Head, Tail...>,
+    Type,
+    std::enable_if_t<!std::is_same_v<Head, Type>>>
+    : contains<typelist<Tail...>, Type> {};
+} // namespace detail
+template <class TypeList, class Type>
+using contains = typename detail::contains<TypeList, Type>::type;
+
+/**
+ * Returns true iff the type trait is true for all types in the type list
+ * Examples:
+ *   true   ==  all<std::is_reference, typelist<int&, const float&&, const
+ * MyClass&>>::value false  ==  all<std::is_reference, typelist<int&, const
+ * float&&, MyClass>>::value
+ */
+template <template <class> class Condition, class TypeList>
+struct all {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::all<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition, class... Types>
+struct all<Condition, typelist<Types...>>
+    : std::conjunction<Condition<Types>...> {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::all<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+};
+
+/**
+ * Returns true iff the type trait is true for any type in the type list
+ * Examples:
+ *   true   ==  true_for_any_type<std::is_reference, typelist<int, const
+ * float&&, const MyClass>>::value false  ==
+ * true_for_any_type<std::is_reference, typelist<int, const float,
+ * MyClass>>::value
+ */
+template <template <class> class Condition, class TypeList>
+struct true_for_any_type final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::true_for_any_type<Condition, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition, class... Types>
+struct true_for_any_type<Condition, typelist<Types...>> final
+    : std::disjunction<Condition<Types>...> {
+  static_assert(
+      is_type_condition<Condition>::value,
+      "In typelist::true_for_any_type<Condition, TypeList>, the Condition argument must be a condition type trait, i.e. have a static constexpr bool ::value member.");
+};
+
+/**
+ * Maps types of a type list using a type trait
+ * Example:
+ *  typelist<int&, double&, string&>  ==  map_t<std::add_lvalue_reference_t,
+ * typelist<int, double, string>>
+ */
+template <template <class> class Mapper, class TypeList>
+struct map final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::map<Mapper, TypeList>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Mapper, class... Types>
+struct map<Mapper, typelist<Types...>> final {
+  using type = typelist<Mapper<Types>...>;
+};
+template <template <class> class Mapper, class TypeList>
+using map_t = typename map<Mapper, TypeList>::type;
+
+/**
+ * Returns the first element of a type list.
+ * Example:
+ *   int  ==  head_t<typelist<int, string>>
+ */
+template <class TypeList>
+struct head final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::head<T>, the T argument must be typelist<...>.");
+};
+template <class Head, class... Tail>
+struct head<typelist<Head, Tail...>> final {
+  using type = Head;
+};
+template <class TypeList>
+using head_t = typename head<TypeList>::type;
+
+/**
+ * Returns the first element of a type list, or the specified default if the
+ * type list is empty. Example: int  ==  head_t<bool, typelist<int, string>>
+ *   bool  ==  head_t<bool, typelist<>>
+ */
+template <class Default, class TypeList>
+struct head_with_default final {
+  using type = Default;
+};
+template <class Default, class Head, class... Tail>
+struct head_with_default<Default, typelist<Head, Tail...>> final {
+  using type = Head;
+};
+template <class Default, class TypeList>
+using head_with_default_t = typename head_with_default<Default, TypeList>::type;
+
+/**
+ * Returns the N-th element of a type list.
+ * Example:
+ * int == element_t<1, typelist<float, int, char>>
+ */
+
+/// Base template.
+template <size_t Index, class TypeList>
+struct element final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::element<T>, the T argument must be typelist<...>.");
+};
+
+/// Successful case, we have reached the zero index and can "return" the head
+/// type.
+template <class Head, class... Tail>
+struct element<0, typelist<Head, Tail...>> {
+  using type = Head;
+};
+
+/// Error case, we have an index but ran out of types! It will only be selected
+/// if `Ts...` is actually empty!
+template <size_t Index, class... Ts>
+struct element<Index, typelist<Ts...>> {
+  static_assert(
+      Index < sizeof...(Ts),
+      "Index is out of bounds in typelist::element");
+};
+
+/// Shave off types until we hit the <0, Head, Tail...> or <Index> case.
+template <size_t Index, class Head, class... Tail>
+struct element<Index, typelist<Head, Tail...>>
+    : element<Index - 1, typelist<Tail...>> {};
+
+/// Convenience alias.
+template <size_t Index, class TypeList>
+using element_t = typename element<Index, TypeList>::type;
+
+/**
+ * Returns the last element of a type list.
+ * Example:
+ *   int  ==  last_t<typelist<int, string>>
+ */
+template <class TypeList>
+struct last final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::last<T>, the T argument must be typelist<...>.");
+};
+template <class Head, class... Tail>
+struct last<typelist<Head, Tail...>> final {
+  using type = typename last<typelist<Tail...>>::type;
+};
+template <class Head>
+struct last<typelist<Head>> final {
+  using type = Head;
+};
+template <class TypeList>
+using last_t = typename last<TypeList>::type;
+static_assert(std::is_same_v<int, last_t<typelist<double, float, int>>>);
+
+/**
+ * Take/drop a number of arguments from a typelist.
+ * Example:
+ *   typelist<int, string> == take_t<typelist<int, string, bool>, 2>
+ *   typelist<bool> == drop_t<typelist<int, string, bool>, 2>
+ */
+namespace detail {
+template <class TypeList, size_t offset, class IndexSequence>
+struct take_elements final {};
+
+template <class TypeList, size_t offset, size_t... Indices>
+struct take_elements<TypeList, offset, std::index_sequence<Indices...>> final {
+  using type = typelist<typename element<offset + Indices, TypeList>::type...>;
+};
+} // namespace detail
+
+template <class TypeList, size_t num>
+struct take final {
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::take<T, num>, the T argument must be typelist<...>.");
+  static_assert(
+      num <= size<TypeList>::value,
+      "Tried to typelist::take more elements than there are in the list");
+  using type = typename detail::
+      take_elements<TypeList, 0, std::make_index_sequence<num>>::type;
+};
+template <class TypeList, size_t num>
+using take_t = typename take<TypeList, num>::type;
+
+template <class TypeList, size_t num>
+struct drop final {
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::drop<T, num>, the T argument must be typelist<...>.");
+  static_assert(
+      num <= size<TypeList>::value,
+      "Tried to typelist::drop more elements than there are in the list");
+  using type = typename detail::take_elements<
+      TypeList,
+      num,
+      std::make_index_sequence<size<TypeList>::value - num>>::type;
+};
+template <class TypeList, size_t num>
+using drop_t = typename drop<TypeList, num>::type;
+
+/**
+ * Like drop, but returns an empty list rather than an assertion error if `num`
+ * is larger than the size of the TypeList.
+ * Example:
+ *   typelist<> == drop_if_nonempty_t<typelist<string, bool>, 2>
+ *   typelist<> == drop_if_nonempty_t<typelist<int, string, bool>, 3>
+ */
+template <class TypeList, size_t num>
+struct drop_if_nonempty final {
+  static_assert(
+      is_instantiation_of<typelist, TypeList>::value,
+      "In typelist::drop<T, num>, the T argument must be typelist<...>.");
+  using type = typename detail::take_elements<
+      TypeList,
+      std::min(num, size<TypeList>::value),
+      std::make_index_sequence<
+          size<TypeList>::value - std::min(num, size<TypeList>::value)>>::type;
+};
+template <class TypeList, size_t num>
+using drop_if_nonempty_t = typename drop_if_nonempty<TypeList, num>::type;
+
+/**
+ * Reverses a typelist.
+ * Example:
+ *   typelist<int, string>  == reverse_t<typelist<string, int>>
+ */
+template <class TypeList>
+struct reverse final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::reverse<T>, the T argument must be typelist<...>.");
+};
+template <class Head, class... Tail>
+struct reverse<typelist<Head, Tail...>> final {
+  using type =
+      concat_t<typename reverse<typelist<Tail...>>::type, typelist<Head>>;
+};
+template <>
+struct reverse<typelist<>> final {
+  using type = typelist<>;
+};
+template <class TypeList>
+using reverse_t = typename reverse<TypeList>::type;
+
+/**
+ * Find the index of the first type in a typelist fulfilling a type trait
+ * condition. Example:
+ *
+ * 2 == find_if<typelist<char, int, char&, int&>, std::is_reference>::value
+ */
+template <class TypeList, template <class> class Condition, class Enable = void>
+struct find_if final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::find_if<TypeList, Condition>, the TypeList argument must be typelist<...>.");
+};
+template <template <class> class Condition>
+struct find_if<typelist<>, Condition, void> final {
+  static_assert(
+      false_higher_t<Condition>::value,
+      "In typelist::find_if<Type/List, Condition>, didn't find any type fulfilling the Condition.");
+};
+template <class Head, class... Tail, template <class> class Condition>
+struct find_if<
+    typelist<Head, Tail...>,
+    Condition,
+    std::enable_if_t<Condition<Head>::value>>
+    final {
+  static constexpr size_t value = 0;
+};
+template <class Head, class... Tail, template <class> class Condition>
+struct find_if<
+    typelist<Head, Tail...>,
+    Condition,
+    std::enable_if_t<!Condition<Head>::value>>
+    final {
+  static constexpr size_t value =
+      1 + find_if<typelist<Tail...>, Condition>::value;
+};
+
+/**
+ * Maps a list of types into a list of values.
+ * Examples:
+ *   // Example 1
+ *   auto sizes =
+ *     map_types_to_values<typelist<int64_t, bool, uint32_t>>(
+ *       [] (auto t) { return sizeof(decltype(t)::type); }
+ *     );
+ *   //  sizes  ==  std::tuple<size_t, size_t, size_t>{8, 1, 4}
+ *
+ *   // Example 2
+ *   auto shared_ptrs =
+ *     map_types_to_values<typelist<int, double>>(
+ *       [] (auto t) { return make_shared<typename decltype(t)::type>(); }
+ *     );
+ *   // shared_ptrs == std::tuple<shared_ptr<int>, shared_ptr<double>>()
+ */
+namespace detail {
+template <class T>
+struct type_ final {
+  using type = T;
+};
+template <class TypeList>
+struct map_types_to_values final {
+  static_assert(
+      false_t<TypeList>::value,
+      "In typelist::map_types_to_values<T>, the T argument must be typelist<...>.");
+};
+template <class... Types>
+struct map_types_to_values<typelist<Types...>> final {
+  template <class Func>
+  static auto call(Func&& func) {
+    return std::tuple{std::forward<Func>(func)(type_<Types>())...};
+  }
+};
+} // namespace detail
+
+template <class TypeList, class Func>
+auto map_types_to_values(Func&& func) {
+  return detail::map_types_to_values<TypeList>::call(std::forward<Func>(func));
+}
+
+} // namespace typelist
+} // namespace c10::guts
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+
+namespace guts {
+using c10::guts::false_t;
+
+namespace typelist {
+using c10::guts::typelist::all;
+using c10::guts::typelist::concat_t;
+using c10::guts::typelist::contains;
+using c10::guts::typelist::count_if;
+using c10::guts::typelist::drop_if_nonempty_t;
+using c10::guts::typelist::drop_t;
+using c10::guts::typelist::filter_t;
+using c10::guts::typelist::find_if;
+using c10::guts::typelist::from_tuple_t;
+using c10::guts::typelist::head_t;
+using c10::guts::typelist::head_with_default_t;
+using c10::guts::typelist::map_t;
+using c10::guts::typelist::map_types_to_values;
+using c10::guts::typelist::reverse_t;
+using c10::guts::typelist::size;
+using c10::guts::typelist::take_t;
+using c10::guts::typelist::to_tuple_t;
+using c10::guts::typelist::true_for_any_type;
+using c10::guts::typelist::typelist;
+
+} // namespace typelist
+
+} // namespace guts
+
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/TypeSafeSignMath.h

@@ -0,0 +1,148 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <limits>
+#include <type_traits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wstring-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wstring-conversion")
+#endif
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+/// Returns false since we cannot have x < 0 if x is unsigned.
+template <typename T>
+inline constexpr bool is_negative(
+    const T& /*x*/,
+    std::true_type /*is_unsigned*/) {
+  return false;
+}
+
+/// Returns true if a signed variable x < 0
+template <typename T>
+inline constexpr bool is_negative(const T& x, std::false_type /*is_unsigned*/) {
+  return x < T(0);
+}
+
+/// Returns true if x < 0
+/// NOTE: Will fail on an unsigned custom type
+///       For the most part it's possible to fix this if
+///       the custom type has a constexpr constructor.
+///       However, notably, c10::Half does not :-(
+template <typename T>
+inline constexpr bool is_negative(const T& x) {
+  return is_negative(x, std::is_unsigned<T>());
+}
+
+/// Returns the sign of an unsigned variable x as 0, 1
+template <typename T>
+inline constexpr int signum(const T& x, std::true_type /*is_unsigned*/) {
+  return T(0) < x;
+}
+
+/// Returns the sign of a signed variable x as -1, 0, 1
+template <typename T>
+inline constexpr int signum(const T& x, std::false_type /*is_unsigned*/) {
+  return (T(0) < x) - (x < T(0));
+}
+
+/// Returns the sign of x as -1, 0, 1
+/// NOTE: Will fail on an unsigned custom type
+///       For the most part it's possible to fix this if
+///       the custom type has a constexpr constructor.
+///       However, notably, c10::Half does not :-(
+template <typename T>
+inline constexpr int signum(const T& x) {
+  return signum(x, std::is_unsigned<T>());
+}
+
+/// Returns true if a and b are not both negative
+template <typename T, typename U>
+inline constexpr bool signs_differ(const T& a, const U& b) {
+  return is_negative(a) != is_negative(b);
+}
+
+// Suppress sign compare warning when compiling with GCC
+// as later does not account for short-circuit rule before
+// raising the warning, see https://godbolt.org/z/Tr3Msnz99
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wsign-compare"
+#endif
+
+/// Returns true if x is greater than the greatest value of the type Limit
+template <typename Limit, typename T>
+inline constexpr bool greater_than_max(const T& x) {
+  constexpr bool can_overflow =
+      std::numeric_limits<T>::digits > std::numeric_limits<Limit>::digits;
+  return can_overflow && x > std::numeric_limits<Limit>::max();
+}
+
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif
+
+/// Returns true if x < lowest(Limit). Standard comparison
+template <typename Limit, typename T>
+inline constexpr bool less_than_lowest(
+    const T& x,
+    std::false_type /*limit_is_unsigned*/,
+    std::false_type /*x_is_unsigned*/) {
+  return x < std::numeric_limits<Limit>::lowest();
+}
+
+/// Returns false since all the limit is signed and therefore includes
+/// negative values but x cannot be negative because it is unsigned
+template <typename Limit, typename T>
+inline constexpr bool less_than_lowest(
+    const T& /*x*/,
+    std::false_type /*limit_is_unsigned*/,
+    std::true_type /*x_is_unsigned*/) {
+  return false;
+}
+
+/// Returns true if x < 0, where 0 is constructed from T.
+/// Limit is not signed, so its lower value is zero
+template <typename Limit, typename T>
+inline constexpr bool less_than_lowest(
+    const T& x,
+    std::true_type /*limit_is_unsigned*/,
+    std::false_type /*x_is_unsigned*/) {
+  return x < T(0);
+}
+
+/// Returns false sign both types are unsigned
+template <typename Limit, typename T>
+inline constexpr bool less_than_lowest(
+    const T& /*x*/,
+    std::true_type /*limit_is_unsigned*/,
+    std::true_type /*x_is_unsigned*/) {
+  return false;
+}
+
+/// Returns true if x is less than the lowest value of type T
+/// NOTE: Will fail on an unsigned custom type
+///       For the most part it's possible to fix this if
+///       the custom type has a constexpr constructor.
+///       However, notably, c10::Half does not :
+template <typename Limit, typename T>
+inline constexpr bool less_than_lowest(const T& x) {
+  return less_than_lowest<Limit>(
+      x, std::is_unsigned<Limit>(), std::is_unsigned<T>());
+}
+
+} // namespace c10
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::greater_than_max;
+using c10::is_negative;
+using c10::less_than_lowest;
+using c10::signs_differ;
+using c10::signum;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/TypeTraits.h

@@ -0,0 +1,164 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+
+#include <functional>
+#include <type_traits>
+
+namespace c10::guts {
+
+/**
+ * is_equality_comparable<T> is true_type iff the equality operator is defined
+ * for T.
+ */
+template <class T, class Enable = void>
+struct is_equality_comparable : std::false_type {};
+template <class T>
+struct is_equality_comparable<
+    T,
+    std::void_t<decltype(std::declval<T&>() == std::declval<T&>())>>
+    : std::true_type {};
+template <class T>
+using is_equality_comparable_t = typename is_equality_comparable<T>::type;
+
+/**
+ * is_hashable<T> is true_type iff std::hash is defined for T
+ */
+template <class T, class Enable = void>
+struct is_hashable : std::false_type {};
+template <class T>
+struct is_hashable<T, std::void_t<decltype(std::hash<T>()(std::declval<T&>()))>>
+    : std::true_type {};
+template <class T>
+using is_hashable_t = typename is_hashable<T>::type;
+
+/**
+ * is_function_type<T> is true_type iff T is a plain function type (i.e.
+ * "Result(Args...)")
+ */
+template <class T>
+struct is_function_type : std::false_type {};
+template <class Result, class... Args>
+struct is_function_type<Result(Args...)> : std::true_type {};
+template <class T>
+using is_function_type_t = typename is_function_type<T>::type;
+
+/**
+ * is_instantiation_of<T, I> is true_type iff I is a template instantiation of T
+ * (e.g. vector<int> is an instantiation of vector) Example:
+ *    is_instantiation_of_t<vector, vector<int>> // true
+ *    is_instantiation_of_t<pair, pair<int, string>> // true
+ *    is_instantiation_of_t<vector, pair<int, string>> // false
+ */
+template <template <class...> class Template, class T>
+struct is_instantiation_of : std::false_type {};
+template <template <class...> class Template, class... Args>
+struct is_instantiation_of<Template, Template<Args...>> : std::true_type {};
+template <template <class...> class Template, class T>
+using is_instantiation_of_t = typename is_instantiation_of<Template, T>::type;
+
+namespace detail {
+/**
+ * strip_class: helper to remove the class type from pointers to `operator()`.
+ */
+
+template <typename T>
+struct strip_class {};
+template <typename Class, typename Result, typename... Args>
+struct strip_class<Result (Class::*)(Args...)> {
+  using type = Result(Args...);
+};
+template <typename Class, typename Result, typename... Args>
+struct strip_class<Result (Class::*)(Args...) const> {
+  using type = Result(Args...);
+};
+template <typename T>
+using strip_class_t = typename strip_class<T>::type;
+} // namespace detail
+
+/**
+ * Evaluates to true_type, iff the given class is a Functor
+ * (i.e. has a call operator with some set of arguments)
+ */
+
+template <class Functor, class Enable = void>
+struct is_functor : std::false_type {};
+template <class Functor>
+struct is_functor<
+    Functor,
+    std::enable_if_t<is_function_type<
+        detail::strip_class_t<decltype(&Functor::operator())>>::value>>
+    : std::true_type {};
+
+/**
+ * lambda_is_stateless<T> is true iff the lambda type T is stateless
+ * (i.e. does not have a closure).
+ * Example:
+ *  auto stateless_lambda = [] (int a) {return a;};
+ *  lambda_is_stateless<decltype(stateless_lambda)> // true
+ *  auto stateful_lambda = [&] (int a) {return a;};
+ *  lambda_is_stateless<decltype(stateful_lambda)> // false
+ */
+namespace detail {
+template <class LambdaType, class FuncType>
+struct is_stateless_lambda__ final {
+  static_assert(
+      !std::is_same_v<LambdaType, LambdaType>,
+      "Base case shouldn't be hit");
+};
+// implementation idea: According to the C++ standard, stateless lambdas are
+// convertible to function pointers
+template <class LambdaType, class C, class Result, class... Args>
+struct is_stateless_lambda__<LambdaType, Result (C::*)(Args...) const>
+    : std::is_convertible<LambdaType, Result (*)(Args...)> {};
+template <class LambdaType, class C, class Result, class... Args>
+struct is_stateless_lambda__<LambdaType, Result (C::*)(Args...)>
+    : std::is_convertible<LambdaType, Result (*)(Args...)> {};
+
+// case where LambdaType is not even a functor
+template <class LambdaType, class Enable = void>
+struct is_stateless_lambda_ final : std::false_type {};
+// case where LambdaType is a functor
+template <class LambdaType>
+struct is_stateless_lambda_<
+    LambdaType,
+    std::enable_if_t<is_functor<LambdaType>::value>>
+    : is_stateless_lambda__<LambdaType, decltype(&LambdaType::operator())> {};
+} // namespace detail
+template <class T>
+using is_stateless_lambda = detail::is_stateless_lambda_<std::decay_t<T>>;
+
+/**
+ * is_type_condition<C> is true_type iff C<...> is a type trait representing a
+ * condition (i.e. has a constexpr static bool ::value member) Example:
+ *   is_type_condition<std::is_reference>  // true
+ */
+template <template <class> class C, class Enable = void>
+struct is_type_condition : std::false_type {};
+template <template <class> class C>
+struct is_type_condition<
+    C,
+    std::enable_if_t<
+        std::is_same_v<bool, std::remove_cv_t<decltype(C<int>::value)>>>>
+    : std::true_type {};
+
+/**
+ * is_fundamental<T> is true_type iff the lambda type T is a fundamental type
+ * (that is, arithmetic type, void, or nullptr_t). Example: is_fundamental<int>
+ * // true We define it here to resolve a MSVC bug. See
+ * https://github.com/pytorch/pytorch/issues/30932 for details.
+ */
+template <class T>
+struct is_fundamental : std::is_fundamental<T> {};
+} // namespace c10::guts
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly, guts)
+
+using c10::guts::is_equality_comparable;
+using c10::guts::is_function_type;
+using c10::guts::is_hashable;
+using c10::guts::is_instantiation_of;
+using c10::guts::is_stateless_lambda;
+using c10::guts::is_type_condition;
+
+HIDDEN_NAMESPACE_END(torch, headeronly, guts)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/bit_cast.h

@@ -0,0 +1,50 @@
+#pragma once
+
+#include <cstring>
+#include <type_traits>
+
+#include <torch/headeronly/macros/Macros.h>
+
+#if __has_include(<bit>) && (defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L)
+#include <bit>
+#define C10_HAVE_STD_BIT_CAST 1
+#else
+#define C10_HAVE_STD_BIT_CAST 0
+#endif // __has_include(<bit>) && (__cplusplus >= 202002L ||
+       // (defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L))
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+
+#if C10_HAVE_STD_BIT_CAST
+using std::bit_cast;
+#else
+// Implementations of std::bit_cast() from C++ 20.
+//
+// This is a less sketchy version of reinterpret_cast.
+//
+// See https://en.cppreference.com/w/cpp/numeric/bit_cast for more
+// information as well as the source of our implementations.
+template <class To, class From>
+C10_HOST_DEVICE std::enable_if_t<
+    sizeof(To) == sizeof(From) && std::is_trivially_copyable_v<From> &&
+        std::is_trivially_copyable_v<To>,
+    To>
+// constexpr support needs compiler magic
+bit_cast(const From& src) noexcept {
+  static_assert(
+      std::is_trivially_constructible_v<To>,
+      "This implementation additionally requires "
+      "destination type to be trivially constructible");
+
+  To dst;
+  std::memcpy(&dst, &src, sizeof(To));
+  return dst;
+}
+#endif // C10_HAVE_STD_BIT_CAST
+#undef C10_HAVE_STD_BIT_CAST
+
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+namespace c10 {
+using torch::headeronly::bit_cast;
+} // namespace c10

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/bits.h

@@ -0,0 +1,71 @@
+#pragma once
+#include <cstdint>
+
+#include <torch/headeronly/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * bits1x8 is an uninterpreted dtype of a tensor with 1 bit (packed to byte
+ * boundary), without any semantics defined.
+ */
+struct alignas(1) bits1x8 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  bits1x8() = default;
+  C10_HOST_DEVICE explicit bits1x8(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits2x4 is an uninterpreted dtype of a tensor with 2 bits (packed to byte
+ * boundary), without any semantics defined.
+ */
+struct alignas(1) bits2x4 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  bits2x4() = default;
+  C10_HOST_DEVICE explicit bits2x4(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits4x2 is an uninterpreted dtype of a tensor with 4 bits (packed to byte
+ * boundary), without any semantics defined.
+ */
+struct alignas(1) bits4x2 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  bits4x2() = default;
+  C10_HOST_DEVICE explicit bits4x2(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits8 is an uninterpreted dtype of a tensor with 8 bits, without any
+ * semantics defined.
+ */
+struct alignas(1) bits8 {
+  uint8_t val_;
+  bits8() = default;
+  C10_HOST_DEVICE explicit bits8(uint8_t val) : val_(val) {}
+};
+
+/**
+ * bits16 is an uninterpreted dtype of a tensor with 16 bits, without any
+ * semantics defined.
+ */
+struct alignas(2) bits16 {
+  uint16_t val_;
+  bits16() = default;
+  C10_HOST_DEVICE explicit bits16(uint16_t val) : val_(val) {}
+};
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+
+using c10::bits16;
+using c10::bits1x8;
+using c10::bits2x4;
+using c10::bits4x2;
+using c10::bits8;
+
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/complex.h

@@ -0,0 +1,616 @@
+#pragma once
+
+#include <complex>
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/Half.h>
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+#include <thrust/complex.h>
+#endif
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+#if C10_CLANG_HAS_WARNING("-Wfloat-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wfloat-conversion")
+#endif
+
+namespace c10 {
+
+// c10::complex is an implementation of complex numbers that aims
+// to work on all devices supported by PyTorch
+//
+// Most of the APIs duplicates std::complex
+// Reference: https://en.cppreference.com/w/cpp/numeric/complex
+//
+// [NOTE: Complex Operator Unification]
+// Operators currently use a mix of std::complex, thrust::complex, and
+// c10::complex internally. The end state is that all operators will use
+// c10::complex internally.  Until then, there may be some hacks to support all
+// variants.
+//
+//
+// [Note on Constructors]
+//
+// The APIs of constructors are mostly copied from C++ standard:
+//   https://en.cppreference.com/w/cpp/numeric/complex/complex
+//
+// Since C++14, all constructors are constexpr in std::complex
+//
+// There are three types of constructors:
+// - initializing from real and imag:
+//     `constexpr complex( const T& re = T(), const T& im = T() );`
+// - implicitly-declared copy constructor
+// - converting constructors
+//
+// Converting constructors:
+// - std::complex defines converting constructor between float/double/long
+// double,
+//   while we define converting constructor between float/double.
+// - For these converting constructors, upcasting is implicit, downcasting is
+//   explicit.
+// - We also define explicit casting from std::complex/thrust::complex
+//   - Note that the conversion from thrust is not constexpr, because
+//     thrust does not define them as constexpr ????
+//
+//
+// [Operator =]
+//
+// The APIs of operator = are mostly copied from C++ standard:
+//   https://en.cppreference.com/w/cpp/numeric/complex/operator%3D
+//
+// Since C++20, all operator= are constexpr. Although we are not building with
+// C++20, we also obey this behavior.
+//
+// There are three types of assign operator:
+// - Assign a real value from the same scalar type
+//   - In std, this is templated as complex& operator=(const T& x)
+//     with specialization `complex& operator=(T x)` for float/double/long
+//     double Since we only support float and double, on will use `complex&
+//     operator=(T x)`
+// - Copy assignment operator and converting assignment operator
+//   - There is no specialization of converting assignment operators, which type
+//   is
+//     convertible is solely dependent on whether the scalar type is convertible
+//
+// In addition to the standard assignment, we also provide assignment operators
+// with std and thrust
+//
+//
+// [Casting operators]
+//
+// std::complex does not have casting operators. We define casting operators
+// casting to std::complex and thrust::complex
+//
+//
+// [Operator ""]
+//
+// std::complex has custom literals `i`, `if` and `il` defined in namespace
+// `std::literals::complex_literals`. We define our own custom literals in the
+// namespace `c10::complex_literals`. Our custom literals does not follow the
+// same behavior as in std::complex, instead, we define _if, _id to construct
+// float/double complex literals.
+//
+//
+// [real() and imag()]
+//
+// In C++20, there are two overload of these functions, one it to return the
+// real/imag, another is to set real/imag, they are both constexpr. We follow
+// this design.
+//
+//
+// [Operator +=,-=,*=,/=]
+//
+// Since C++20, these operators become constexpr. In our implementation, they
+// are also constexpr.
+//
+// There are two types of such operators: operating with a real number, or
+// operating with another complex number. For the operating with a real number,
+// the generic template form has argument type `const T &`, while the overload
+// for float/double/long double has `T`. We will follow the same type as
+// float/double/long double in std.
+//
+// [Unary operator +-]
+//
+// Since C++20, they are constexpr. We also make them expr
+//
+// [Binary operators +-*/]
+//
+// Each operator has three versions (taking + as example):
+// - complex + complex
+// - complex + real
+// - real + complex
+//
+// [Operator ==, !=]
+//
+// Each operator has three versions (taking == as example):
+// - complex == complex
+// - complex == real
+// - real == complex
+//
+// Some of them are removed on C++20, but we decide to keep them
+//
+// [Operator <<, >>]
+//
+// These are implemented by casting to std::complex
+//
+//
+//
+// TODO(@zasdfgbnm): c10::complex<c10::Half> is not currently supported,
+// because:
+//  - lots of members and functions of c10::Half are not constexpr
+//  - thrust::complex only support float and double
+
+template <typename T>
+struct alignas(sizeof(T) * 2) complex {
+  using value_type = T;
+
+  T real_ = T(0);
+  T imag_ = T(0);
+
+  constexpr complex() = default;
+  C10_HOST_DEVICE constexpr complex(const T& re, const T& im = T())
+      : real_(re), imag_(im) {}
+  template <typename U>
+  explicit constexpr complex(const std::complex<U>& other)
+      : complex(other.real(), other.imag()) {}
+#if defined(__CUDACC__) || defined(__HIPCC__)
+  template <typename U>
+  explicit C10_HOST_DEVICE complex(const thrust::complex<U>& other)
+      : real_(other.real()), imag_(other.imag()) {}
+// NOTE can not be implemented as follow due to ROCm bug:
+//   explicit C10_HOST_DEVICE complex(const thrust::complex<U> &other):
+//   complex(other.real(), other.imag()) {}
+#endif
+
+  // Use SFINAE to specialize casting constructor for c10::complex<float> and
+  // c10::complex<double>
+  template <typename U = T>
+  C10_HOST_DEVICE explicit constexpr complex(
+      const std::enable_if_t<std::is_same_v<U, float>, complex<double>>& other)
+      : real_(other.real_), imag_(other.imag_) {}
+  template <typename U = T>
+  C10_HOST_DEVICE constexpr complex(
+      const std::enable_if_t<std::is_same_v<U, double>, complex<float>>& other)
+      : real_(other.real_), imag_(other.imag_) {}
+
+  constexpr complex<T>& operator=(T re) {
+    real_ = re;
+    imag_ = 0;
+    return *this;
+  }
+
+  constexpr complex<T>& operator+=(T re) {
+    real_ += re;
+    return *this;
+  }
+
+  constexpr complex<T>& operator-=(T re) {
+    real_ -= re;
+    return *this;
+  }
+
+  constexpr complex<T>& operator*=(T re) {
+    real_ *= re;
+    imag_ *= re;
+    return *this;
+  }
+
+  constexpr complex<T>& operator/=(T re) {
+    real_ /= re;
+    imag_ /= re;
+    return *this;
+  }
+
+  template <typename U>
+  constexpr complex<T>& operator=(const complex<U>& rhs) {
+    real_ = rhs.real();
+    imag_ = rhs.imag();
+    return *this;
+  }
+
+  template <typename U>
+  constexpr complex<T>& operator+=(const complex<U>& rhs) {
+    real_ += rhs.real();
+    imag_ += rhs.imag();
+    return *this;
+  }
+
+  template <typename U>
+  constexpr complex<T>& operator-=(const complex<U>& rhs) {
+    real_ -= rhs.real();
+    imag_ -= rhs.imag();
+    return *this;
+  }
+
+  template <typename U>
+  constexpr complex<T>& operator*=(const complex<U>& rhs) {
+    // (a + bi) * (c + di) = (a*c - b*d) + (a * d + b * c) i
+    T a = real_;
+    T b = imag_;
+    U c = rhs.real();
+    U d = rhs.imag();
+    real_ = a * c - b * d;
+    imag_ = a * d + b * c;
+    return *this;
+  }
+
+#ifdef __APPLE__
+#define FORCE_INLINE_APPLE __attribute__((always_inline))
+#else
+#define FORCE_INLINE_APPLE
+#endif
+  template <typename U>
+  constexpr FORCE_INLINE_APPLE complex<T>& operator/=(const complex<U>& rhs)
+      __ubsan_ignore_float_divide_by_zero__ {
+    // (a + bi) / (c + di) = (ac + bd)/(c^2 + d^2) + (bc - ad)/(c^2 + d^2) i
+    // the calculation below follows numpy's complex division
+    T a = real_;
+    T b = imag_;
+    U c = rhs.real();
+    U d = rhs.imag();
+
+#if defined(__GNUC__) && !defined(__clang__)
+    // std::abs is already constexpr by gcc
+    auto abs_c = std::abs(c);
+    auto abs_d = std::abs(d);
+#else
+    auto abs_c = c < 0 ? -c : c;
+    auto abs_d = d < 0 ? -d : d;
+#endif
+
+    if (abs_c >= abs_d) {
+      if (abs_c == U(0) && abs_d == U(0)) {
+        /* divide by zeros should yield a complex inf or nan */
+        real_ = a / abs_c;
+        imag_ = b / abs_d;
+      } else {
+        auto rat = d / c;
+        auto scl = U(1.0) / (c + d * rat);
+        real_ = (a + b * rat) * scl;
+        imag_ = (b - a * rat) * scl;
+      }
+    } else {
+      auto rat = c / d;
+      auto scl = U(1.0) / (d + c * rat);
+      real_ = (a * rat + b) * scl;
+      imag_ = (b * rat - a) * scl;
+    }
+    return *this;
+  }
+#undef FORCE_INLINE_APPLE
+
+  template <typename U>
+  constexpr complex<T>& operator=(const std::complex<U>& rhs) {
+    real_ = rhs.real();
+    imag_ = rhs.imag();
+    return *this;
+  }
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+  template <typename U>
+  C10_HOST_DEVICE complex<T>& operator=(const thrust::complex<U>& rhs) {
+    real_ = rhs.real();
+    imag_ = rhs.imag();
+    return *this;
+  }
+#endif
+
+  template <typename U>
+  explicit constexpr operator std::complex<U>() const {
+    return std::complex<U>(std::complex<T>(real(), imag()));
+  }
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+  template <typename U>
+  C10_HOST_DEVICE explicit operator thrust::complex<U>() const {
+    return static_cast<thrust::complex<U>>(thrust::complex<T>(real(), imag()));
+  }
+#endif
+
+  // consistent with NumPy behavior
+  explicit constexpr operator bool() const {
+    return real() || imag();
+  }
+
+  C10_HOST_DEVICE constexpr T real() const {
+    return real_;
+  }
+  constexpr void real(T value) {
+    real_ = value;
+  }
+  C10_HOST_DEVICE constexpr T imag() const {
+    return imag_;
+  }
+  constexpr void imag(T value) {
+    imag_ = value;
+  }
+};
+
+namespace complex_literals {
+
+constexpr complex<float> operator""_if(long double imag) {
+  return complex<float>(0.0f, static_cast<float>(imag));
+}
+
+constexpr complex<double> operator""_id(long double imag) {
+  return complex<double>(0.0, static_cast<double>(imag));
+}
+
+constexpr complex<float> operator""_if(unsigned long long imag) {
+  return complex<float>(0.0f, static_cast<float>(imag));
+}
+
+constexpr complex<double> operator""_id(unsigned long long imag) {
+  return complex<double>(0.0, static_cast<double>(imag));
+}
+
+} // namespace complex_literals
+
+template <typename T>
+constexpr complex<T> operator+(const complex<T>& val) {
+  return val;
+}
+
+template <typename T>
+constexpr complex<T> operator-(const complex<T>& val) {
+  return complex<T>(-val.real(), -val.imag());
+}
+
+template <typename T>
+constexpr complex<T> operator+(const complex<T>& lhs, const complex<T>& rhs) {
+  complex<T> result = lhs;
+  return result += rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator+(const complex<T>& lhs, const T& rhs) {
+  complex<T> result = lhs;
+  return result += rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator+(const T& lhs, const complex<T>& rhs) {
+  return complex<T>(lhs + rhs.real(), rhs.imag());
+}
+
+template <typename T>
+constexpr complex<T> operator-(const complex<T>& lhs, const complex<T>& rhs) {
+  complex<T> result = lhs;
+  return result -= rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator-(const complex<T>& lhs, const T& rhs) {
+  complex<T> result = lhs;
+  return result -= rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator-(const T& lhs, const complex<T>& rhs) {
+  complex<T> result = -rhs;
+  return result += lhs;
+}
+
+template <typename T>
+constexpr complex<T> operator*(const complex<T>& lhs, const complex<T>& rhs) {
+  complex<T> result = lhs;
+  return result *= rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator*(const complex<T>& lhs, const T& rhs) {
+  complex<T> result = lhs;
+  return result *= rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator*(const T& lhs, const complex<T>& rhs) {
+  complex<T> result = rhs;
+  return result *= lhs;
+}
+
+template <typename T>
+constexpr complex<T> operator/(const complex<T>& lhs, const complex<T>& rhs) {
+  complex<T> result = lhs;
+  return result /= rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator/(const complex<T>& lhs, const T& rhs) {
+  complex<T> result = lhs;
+  return result /= rhs;
+}
+
+template <typename T>
+constexpr complex<T> operator/(const T& lhs, const complex<T>& rhs) {
+  complex<T> result(lhs, T());
+  return result /= rhs;
+}
+
+// Define operators between integral scalars and c10::complex. std::complex does
+// not support this when T is a floating-point number. This is useful because it
+// saves a lot of "static_cast" when operate a complex and an integer. This
+// makes the code both less verbose and potentially more efficient.
+#define COMPLEX_INTEGER_OP_TEMPLATE_CONDITION                 \
+  typename std::enable_if_t<                                  \
+      std::is_floating_point_v<fT> && std::is_integral_v<iT>, \
+      int> = 0
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator+(const c10::complex<fT>& a, const iT& b) {
+  return a + static_cast<fT>(b);
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator+(const iT& a, const c10::complex<fT>& b) {
+  return static_cast<fT>(a) + b;
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator-(const c10::complex<fT>& a, const iT& b) {
+  return a - static_cast<fT>(b);
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator-(const iT& a, const c10::complex<fT>& b) {
+  return static_cast<fT>(a) - b;
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator*(const c10::complex<fT>& a, const iT& b) {
+  return a * static_cast<fT>(b);
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator*(const iT& a, const c10::complex<fT>& b) {
+  return static_cast<fT>(a) * b;
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator/(const c10::complex<fT>& a, const iT& b) {
+  return a / static_cast<fT>(b);
+}
+
+template <typename fT, typename iT, COMPLEX_INTEGER_OP_TEMPLATE_CONDITION>
+constexpr c10::complex<fT> operator/(const iT& a, const c10::complex<fT>& b) {
+  return static_cast<fT>(a) / b;
+}
+
+#undef COMPLEX_INTEGER_OP_TEMPLATE_CONDITION
+
+template <typename T>
+constexpr bool operator==(const complex<T>& lhs, const complex<T>& rhs) {
+  return (lhs.real() == rhs.real()) && (lhs.imag() == rhs.imag());
+}
+
+template <typename T>
+constexpr bool operator==(const complex<T>& lhs, const T& rhs) {
+  return (lhs.real() == rhs) && (lhs.imag() == T());
+}
+
+template <typename T>
+constexpr bool operator==(const T& lhs, const complex<T>& rhs) {
+  return (lhs == rhs.real()) && (T() == rhs.imag());
+}
+
+template <typename T>
+constexpr bool operator!=(const complex<T>& lhs, const complex<T>& rhs) {
+  return !(lhs == rhs);
+}
+
+template <typename T>
+constexpr bool operator!=(const complex<T>& lhs, const T& rhs) {
+  return !(lhs == rhs);
+}
+
+template <typename T>
+constexpr bool operator!=(const T& lhs, const complex<T>& rhs) {
+  return !(lhs == rhs);
+}
+
+template <typename T, typename CharT, typename Traits>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,
+    const complex<T>& x) {
+  return (os << static_cast<std::complex<T>>(x));
+}
+
+template <typename T, typename CharT, typename Traits>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,
+    complex<T>& x) {
+  std::complex<T> tmp;
+  is >> tmp;
+  x = tmp;
+  return is;
+}
+
+template <typename T>
+C10_HOST_DEVICE complex<T> polar(const T& r, const T& theta = T()) {
+#if defined(__CUDACC__) || defined(__HIPCC__)
+  return static_cast<complex<T>>(thrust::polar(r, theta));
+#else
+  // std::polar() requires r >= 0, so spell out the explicit implementation to
+  // avoid a branch.
+  return complex<T>(r * std::cos(theta), r * std::sin(theta));
+#endif
+}
+
+template <>
+struct alignas(4) complex<Half> {
+  Half real_;
+  Half imag_;
+
+  // Constructors
+  complex() = default;
+  // Half constructor is not constexpr so the following constructor can't
+  // be constexpr
+  C10_HOST_DEVICE explicit inline complex(const Half& real, const Half& imag)
+      : real_(real), imag_(imag) {}
+  C10_HOST_DEVICE inline complex(const c10::complex<float>& value)
+      : real_(value.real()), imag_(value.imag()) {}
+
+  // Conversion operator
+  inline C10_HOST_DEVICE operator c10::complex<float>() const {
+    return {real_, imag_};
+  }
+
+  constexpr C10_HOST_DEVICE Half real() const {
+    return real_;
+  }
+  constexpr C10_HOST_DEVICE Half imag() const {
+    return imag_;
+  }
+
+  C10_HOST_DEVICE complex<Half>& operator+=(const complex<Half>& other) {
+    real_ = static_cast<float>(real_) + static_cast<float>(other.real_);
+    imag_ = static_cast<float>(imag_) + static_cast<float>(other.imag_);
+    return *this;
+  }
+
+  C10_HOST_DEVICE complex<Half>& operator-=(const complex<Half>& other) {
+    real_ = static_cast<float>(real_) - static_cast<float>(other.real_);
+    imag_ = static_cast<float>(imag_) - static_cast<float>(other.imag_);
+    return *this;
+  }
+
+  C10_HOST_DEVICE complex<Half>& operator*=(const complex<Half>& other) {
+    auto a = static_cast<float>(real_);
+    auto b = static_cast<float>(imag_);
+    auto c = static_cast<float>(other.real());
+    auto d = static_cast<float>(other.imag());
+    real_ = a * c - b * d;
+    imag_ = a * d + b * c;
+    return *this;
+  }
+};
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::complex;
+using c10::operator+;
+using c10::operator-;
+using c10::operator*;
+using c10::operator/;
+using c10::operator+=;
+using c10::operator-=;
+using c10::operator*=;
+using c10::operator/=;
+using c10::operator==;
+using c10::operator!=;
+using c10::operator<<;
+using c10::operator>>;
+using c10::polar;
+
+namespace complex_literals {
+using c10::complex_literals::operator""_if;
+using c10::complex_literals::operator""_id;
+} // namespace complex_literals
+
+HIDDEN_NAMESPACE_END(torch, headeronly)
+
+C10_CLANG_DIAGNOSTIC_POP()

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/floating_point_utils.h

@@ -0,0 +1,38 @@
+#pragma once
+
+#include <torch/headeronly/macros/Macros.h>
+#include <torch/headeronly/util/bit_cast.h>
+#include <cstdint>
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly, detail)
+
+C10_HOST_DEVICE inline float fp32_from_bits(uint32_t w) {
+#if defined(__OPENCL_VERSION__)
+  return as_float(w);
+#elif defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+  return __uint_as_float((unsigned int)w);
+#elif defined(__INTEL_COMPILER)
+  return _castu32_f32(w);
+#else
+  return torch::headeronly::bit_cast<float>(w);
+#endif
+}
+
+C10_HOST_DEVICE inline uint32_t fp32_to_bits(float f) {
+#if defined(__OPENCL_VERSION__)
+  return as_uint(f);
+#elif defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+  return (uint32_t)__float_as_uint(f);
+#elif defined(__INTEL_COMPILER)
+  return _castf32_u32(f);
+#else
+  return torch::headeronly::bit_cast<uint32_t>(f);
+#endif
+}
+
+HIDDEN_NAMESPACE_END(torch, headeronly, detail)
+
+namespace c10::detail {
+using torch::headeronly::detail::fp32_from_bits;
+using torch::headeronly::detail::fp32_to_bits;
+} // namespace c10::detail

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/qint32.h

@@ -0,0 +1,22 @@
+#pragma once
+#include <cstdint>
+
+#include <torch/headeronly/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * qint32 is for signed 32 bit quantized Tensors
+ */
+struct alignas(4) qint32 {
+  using underlying = int32_t;
+  int32_t val_;
+  qint32() = default;
+  C10_HOST_DEVICE explicit qint32(int32_t val) : val_(val) {}
+};
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::qint32;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/qint8.h

@@ -0,0 +1,24 @@
+#pragma once
+#include <cstdint>
+
+#include <torch/headeronly/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * This is the data type for quantized Tensors. Right now we only have
+ * qint8 which is for 8 bit Tensors, and qint32 for 32 bit int Tensors,
+ * we might have 4 bit, 2 bit or 1 bit data types in the future.
+ */
+struct alignas(1) qint8 {
+  using underlying = int8_t;
+  int8_t val_;
+  qint8() = default;
+  C10_HOST_DEVICE explicit qint8(int8_t val) : val_(val) {}
+};
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::qint8;
+HIDDEN_NAMESPACE_END(torch, headeronly)

miniconda3/envs/ladir/lib/python3.10/site-packages/torch/include/torch/headeronly/util/quint2x4.h

@@ -0,0 +1,23 @@
+#pragma once
+#include <cstdint>
+
+#include <torch/headeronly/macros/Macros.h>
+
+namespace c10 {
+
+/**
+ * quint2x4 is for un-signed 2 bit quantized Tensors that are packed to byte
+ * boundary.
+ */
+struct alignas(1) quint2x4 {
+  using underlying = uint8_t;
+  uint8_t val_;
+  quint2x4() = default;
+  C10_HOST_DEVICE explicit quint2x4(uint8_t val) : val_(val) {}
+};
+
+} // namespace c10
+
+HIDDEN_NAMESPACE_BEGIN(torch, headeronly)
+using c10::quint2x4;
+HIDDEN_NAMESPACE_END(torch, headeronly)