#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
#pragma once
#ifdef _WIN32
#include <windows.h>
#include <functional> // std::function
#ifdef USE_MMAP_SELF
#include <errno.h>
#include <fcntl.h>
#include <io.h>
#include <sys/stat.h>

#define PROT_READ 0x1
#define PROT_WRITE 0x2
#define PROT_EXEC 0x4

#define MAP_SHARED 0x01
#define MAP_PRIVATE 0x02
#define MAP_FAILED ((void*)-1)

#define SEEK_SET 0
#define SEEK_CUR 1
#define SEEK_END 2

struct Dl_info {
  char dli_fname[MAX_PATH]; /**< Filename of defining object */
  void* dli_fbase; /**< Load address of that object */
  const char* dli_sname; /**< Name of nearest lower symbol */
  void* dli_saddr; /**< Exact value of nearest symbol */
};
typedef struct Dl_info Dl_info;

int dladdr(const void* addr, Dl_info* info) {
  // only returns filename, FWIW.
  CHAR tpath[MAX_PATH];
  MEMORY_BASIC_INFORMATION mbi;
  char* path;
  char* tmp;
  size_t length;
  int ret = 0;

  if (!info)
    return 0;

  HMODULE hModule;
  if (!GetModuleHandleExA(
          GET_MODULE_HANDLE_EX_FLAG_FROM_ADDRESS |
              GET_MODULE_HANDLE_EX_FLAG_UNCHANGED_REFCOUNT,
          (LPCSTR)addr,
          &hModule) ||
      hModule == NULL)
    return 0;

  ret = GetModuleFileNameA(hModule, (LPSTR)&tpath, MAX_PATH);
  if (!ret)
    return 0;

  path = tpath;

  length = strlen(path);
  if (length >= MAX_PATH) {
    length = MAX_PATH - 1;
    path[MAX_PATH - 1] = '\0';
  }

  tmp = path;
  while (*tmp) {
    if (*tmp == '\\')
      *tmp = '/';
    tmp++;
  }

  memcpy(info->dli_fname, path, length + 1);
  info->dli_fbase = hModule;
  info->dli_sname = NULL;
  info->dli_saddr = NULL;
  return 1;
}

static DWORD get_creation_disposition(int flags) {
  if (flags & O_CREAT) {
    if (flags & O_EXCL)
      return CREATE_NEW;
    if (flags & O_TRUNC)
      return CREATE_ALWAYS;
    return OPEN_ALWAYS;
  }
  if (flags & O_TRUNC)
    return TRUNCATE_EXISTING;
  return OPEN_EXISTING;
}

#define O_ACCMODE 03
#define O_RDONLY 00
#define O_WRONLY 01
#define O_RDWR 02

static DWORD get_access_mode(int flags) {
  switch (flags & O_ACCMODE) {
    case O_RDONLY:
      return GENERIC_READ;
    case O_WRONLY:
      return GENERIC_WRITE;
    case O_RDWR:
      return GENERIC_READ | GENERIC_WRITE;
    default:
      return GENERIC_READ;
  }
}
#ifndef O_DSYNC
#define O_DSYNC 00010000 /* used to be O_SYNC, see below */
#endif

#ifndef O_SYNC
#define __O_SYNC 04000000
#define O_SYNC (__O_SYNC | O_DSYNC)
#endif

int open(char* pathname, int flags) {
  DWORD dwDesiredAccess = get_access_mode(flags);
  DWORD dwCreationDisposition = get_creation_disposition(flags);
  DWORD dwShareMode = FILE_SHARE_READ | FILE_SHARE_WRITE;
  DWORD dwFlagsAndAttributes = FILE_ATTRIBUTE_NORMAL;

  if (flags & O_SYNC) {
    dwFlagsAndAttributes |= FILE_FLAG_WRITE_THROUGH;
  }

  if (flags & O_SEQUENTIAL) {
    dwFlagsAndAttributes |= FILE_FLAG_SEQUENTIAL_SCAN;
  }

  if (flags & O_RANDOM) {
    dwFlagsAndAttributes |= FILE_FLAG_RANDOM_ACCESS;
  }

  HANDLE hFile = CreateFileA(
      pathname,
      dwDesiredAccess,
      dwShareMode,
      NULL,
      dwCreationDisposition,
      dwFlagsAndAttributes,
      NULL);

  if (hFile == INVALID_HANDLE_VALUE) {
    switch (GetLastError()) {
      case ERROR_FILE_NOT_FOUND:
        errno = ENOENT;
        break;
      case ERROR_PATH_NOT_FOUND:
        errno = ENOTDIR;
        break;
      case ERROR_ACCESS_DENIED:
        errno = EACCES;
        break;
      case ERROR_FILE_EXISTS:
        errno = EEXIST;
        break;
      case ERROR_TOO_MANY_OPEN_FILES:
        errno = EMFILE;
        break;
      default:
        errno = EIO;
    }
    return -1;
  }

  int fd = _open_osfhandle((intptr_t)hFile, flags);
  if (fd == -1) {
    CloseHandle(hFile);
    errno = EMFILE;
    return -1;
  }

  if (flags & O_APPEND) {
    lseek(fd, 0, SEEK_END);
  }

  return fd;
}

int close(int fd) {
  return _close(fd);
}

void* mmap(
    void* addr,
    size_t length,
    int prot,
    int flags,
    int fd,
    off_t offset) {
  HANDLE hFile = (HANDLE)_get_osfhandle(fd);
  if (hFile == INVALID_HANDLE_VALUE) {
    errno = EBADF;
    return MAP_FAILED;
  }

  DWORD flProtect;
  if (prot & PROT_WRITE) {
    flProtect = PAGE_READWRITE;
  } else if (prot & PROT_READ) {
    flProtect = PAGE_READONLY;
  } else {
    flProtect = PAGE_NOACCESS;
  }

  flProtect = PAGE_READONLY;

  DWORD dwDesiredAccess = 0;
  if (prot & PROT_READ)
    dwDesiredAccess |= FILE_MAP_READ;
  if (prot & PROT_WRITE)
    dwDesiredAccess |= FILE_MAP_WRITE;
  if (prot & PROT_EXEC)
    dwDesiredAccess |= FILE_MAP_EXECUTE;

  dwDesiredAccess = FILE_MAP_READ;

  SYSTEM_INFO SysInfo;
  GetSystemInfo(&SysInfo);
  DWORD dwSysGran = SysInfo.dwAllocationGranularity;

  DWORD dwFileMapStart = (offset / dwSysGran) * dwSysGran;
  DWORD dwMapViewSize = (offset % dwSysGran) + length;
  DWORD dwFileMapSize = offset + length;
  int iViewDelta = offset - dwFileMapStart;

  HANDLE hMapping =
      CreateFileMapping(hFile, NULL, flProtect, 0, dwFileMapSize, NULL);

  if (!hMapping) {
    DWORD dwErrCode = GetLastError();
    errno = EACCES;
    return MAP_FAILED;
  }

  void* lpMapAddress = MapViewOfFileEx(
      hMapping, dwDesiredAccess, 0, dwFileMapStart, dwMapViewSize, addr);
  if (!lpMapAddress) {
    DWORD dwErrCode = GetLastError();
    errno = EINVAL;
  }

  void* pData = (char*)lpMapAddress + iViewDelta;

  CloseHandle(hMapping);

  if (!lpMapAddress) {
    return MAP_FAILED;
  }

  return pData;
}

int munmap(void* addr, size_t length) {
  if (!UnmapViewOfFile(addr)) {
    errno = EINVAL;
    return -1;
  }
  return 0;
}
#endif // USE_MMAP_SELF
#else // !_WIN32
#include <dlfcn.h>
#include <sys/mman.h>
#include <unistd.h>
#endif // _WIN32

#include <fcntl.h>
#include <optional>
#include <regex>
#include <stdexcept>
#include <unordered_map>
#include <utility>

// WARNING: Be careful when adding new includes here. This header will be used
// in model.so, and should not refer to any aten/c10 headers except the stable
// C ABI defined in torch/csrc/inductor/aoti_torch/c/shim.h. The same rule
// applies to other files under torch/csrc/inductor/aoti_runtime/.
#include <torch/csrc/inductor/aoti_runtime/device_utils.h>
#ifdef USE_MPS
#include <torch/csrc/inductor/aoti_torch/c/shim_mps.h>
#endif // USE_MPS
#ifdef USE_XPU
#include <torch/csrc/inductor/aoti_runtime/utils_xpu.h>
#else
#include <torch/csrc/inductor/aoti_runtime/utils.h>
#endif // USE_XPU
#include <torch/csrc/inductor/aoti_runtime/constant_type.h>

#define AOTI_RUNTIME_CHECK(EXPR, MSG) \
  do {                                \
    bool ok = EXPR;                   \
    if (!ok) {                        \
      throw std::runtime_error(MSG);  \
    }                                 \
  } while (0)

// At codegen time, we write out a binary file called constants.bin.
// We then turn the raw binary to an object file that exposes this
// symbol and link it into the final .so.
// For information on the binary format, see `man objcopy`, under
// the "binary-architecture" flag:
// https://man7.org/linux/man-pages/man1/objcopy.1.html
// todo: use #embed in C++ 23 once available
// The constants are NOT readonly because they may be mutated.
// NOLINTNEXTLINE(*array*)
extern uint8_t _binary_constants_bin_start[];
// NOLINTNEXTLINE(*array*)
extern uint8_t _binary_constants_bin_end[];

#if defined(USE_CUDA) || defined(USE_XPU)
// Compute required blob size with 64-alignment if on GPU.
#define AOTI_CONST_ALIGNMENT 64
#else
// Use 64-alignment (use something >=64)for better performance on CPU.
#define AOTI_CONST_ALIGNMENT 64
#endif

namespace {

using RAIIDataPtr = std::unique_ptr<void, std::function<void(void*)>>;

#ifdef USE_CUDA

// NOLINTNEXTLINE(clang-diagnostic-unneeded-internal-declaration)
RAIIDataPtr RAII_gpuMalloc(size_t num_bytes) {
#ifdef AOT_INDUCTOR_USE_CACHING_ALLOCATOR
  // Use caching allocator for allocating GPU memory
  void* data_ptr = nullptr;
  AOTI_TORCH_ERROR_CODE_CHECK(
      aoti_torch_cuda_caching_allocator_raw_alloc(num_bytes, &data_ptr));
  auto deleter = [](void* ptr) {
    AOTI_TORCH_ERROR_CODE_CHECK(
        aoti_torch_cuda_caching_allocator_raw_delete(ptr));
  };
  return RAIIDataPtr(data_ptr, deleter);
#else
  // Use cudaMalloc directly for allocating GPU memory
  void* data_ptr = nullptr;
  AOTI_RUNTIME_CUDA_CHECK(cudaMalloc((void**)&data_ptr, num_bytes));
  auto deleter = [](void* ptr) { AOTI_RUNTIME_CUDA_CHECK(cudaFree(ptr)); };
  return RAIIDataPtr(data_ptr, deleter);
#endif
}

#elif defined(USE_XPU)

RAIIDataPtr RAII_gpuMalloc(size_t num_bytes) {
  sycl::queue* queue_ptr = nullptr;
  aoti_torch_get_current_sycl_queue((void**)&queue_ptr);
  void* data_ptr = sycl::malloc_device(num_bytes, *queue_ptr);
  auto deleter = [queue_ptr](void* ptr) { sycl::free(ptr, *queue_ptr); };
  return RAIIDataPtr(data_ptr, deleter);
}

#elif defined(USE_MPS)

RAIIDataPtr RAII_gpuMalloc(size_t num_bytes) {
  void* data_ptr = nullptr;
  aoti_torch_mps_malloc(&data_ptr, num_bytes);
  auto deleter = [](void* ptr) { aoti_torch_mps_free(ptr); };
  return RAIIDataPtr(data_ptr, deleter);
}

#endif // USE_CUDA

// NOLINTNEXTLINE(clang-diagnostic-unneeded-internal-declaration)
RAIIDataPtr RAII_cpuMalloc(size_t num_bytes) {
  void* data_ptr = std::malloc(num_bytes);
  if (!data_ptr) {
    throw std::bad_alloc();
  }
  auto deleter = [](void* ptr) { std::free(ptr); };
  return RAIIDataPtr(data_ptr, deleter);
}
} // anonymous namespace

namespace torch::aot_inductor {

using ConstantMap =
    std::unordered_map<std::string, MaybeOwningAtenTensorHandle>;

// valid device strs are: cpu, cuda, cuda:0, cuda:1, ...
// Update the list here if more devices are supported in the future
inline void parse_device_str(
    const std::string& device_str,
    int32_t& device_type,
    int32_t& device_idx) {
  std::regex re("(cpu|cuda|xpu|mps)(:([0-9]+))?");
  std::smatch sm;
  bool matched = std::regex_match(device_str, sm, re);
  AOTI_RUNTIME_CHECK(matched, "Invalid device: " + device_str);

  if (sm[1].str() == "cpu") {
    device_type = aoti_torch_device_type_cpu();
  } else if (sm[1].str() == "cuda") {
    device_type = aoti_torch_device_type_cuda();
#ifdef USE_XPU
  } else if (sm[1].str() == "xpu") {
    device_type = aoti_torch_device_type_xpu();
#endif
#ifdef USE_MPS
  } else if (sm[1].str() == "mps") {
    device_type = aoti_torch_device_type_mps();
#endif
  } else {
    AOTI_RUNTIME_CHECK(false, "Invalid device: " + device_str);
  }

  if (sm[3].matched) {
    device_idx = stoi(sm[3].str());
  } else {
    device_idx = -1;
  }
}

// Defines the base class for AOTInductorModel, which is generated by the
// AOTInductor cpp codegen. Since we do not need dynamic dispatch, we rely
// on curiously recurring template pattern (CRTP) to save some runtime
// v-table overhead. The generated AOTInductorModel is specialized with
// methods such as run_impl.
template <typename Model>
class AOTInductorModelBase {
 public:
  AOTInductorModelBase(
      size_t num_inputs,
      size_t num_outputs,
      size_t num_constants,
      const std::string& device_str,
      std::optional<std::string> cubin_dir,
      bool include_weights = true)
      : inputs_info_(num_inputs),
        outputs_info_(num_outputs),
        constants_info_(num_constants),
        cubin_dir_(std::move(cubin_dir)),
        include_weights(include_weights) {
    parse_device_str(device_str, device_type_, device_idx_);

#ifdef USE_CUDA
    if (device_idx_ == -1) {
      AOTI_RUNTIME_CUDA_CHECK(cudaGetDevice(&device_idx_));
    } else {
      // If device_idx_ is passed in, we need to set the current device to it
      AOTI_RUNTIME_CUDA_CHECK(cudaSetDevice(device_idx_));
    }
#endif // USE_CUDA
#ifdef USE_XPU
    if (device_idx_ == -1) {
      aoti_torch_get_current_xpu_device(&device_idx_);
    } else {
      aoti_torch_set_current_xpu_device(device_idx_);
    }
#endif // USE_XPU
#ifdef USE_MPS
    if (device_idx_ == -1) {
      device_idx_ = 0;
    }
#endif // USE_MPS
  }

  // NOLINTNEXTLINE(modernize-use-equals-default)
  ~AOTInductorModelBase() {
#ifdef USE_CUDA
    if (run_finished_) {
      auto code = cudaEventDestroy(*run_finished_);
      if (code != cudaSuccess) {
        std::cerr << "Failed to destroy CUDA event in AOTInductor model: "
                  << cudaGetErrorString(code) << '\n';
      }
    }
#endif // USE_CUDA
#ifdef USE_XPU
    if (run_finished_) {
      (*run_finished_)->wait_and_throw();
      delete *run_finished_;
    }
#endif // USE_XPU
  }

  AOTInductorModelBase(AOTInductorModelBase&&) = delete;
  AOTInductorModelBase& operator=(AOTInductorModelBase&&) = delete;
  AOTInductorModelBase(const AOTInductorModelBase&) = delete;
  AOTInductorModelBase& operator=(const AOTInductorModelBase&) = delete;

  void run(
      AtenTensorHandle*
          input_handles, // array of input AtenTensorHandle; handles
                         // are stolen; the array itself is borrowed
      AtenTensorHandle*
          output_handles, // array for writing output AtenTensorHandle; handles
                          // will be stolen by the caller; the array itself is
                          // borrowed
      DeviceStreamType stream,
      AOTIProxyExecutorHandle proxy_executor) {
#ifdef USE_CUDA
    if (!run_finished_) {
      cudaEvent_t run_finished = nullptr;
      AOTI_RUNTIME_CUDA_CHECK(cudaEventCreate(&run_finished));
      run_finished_.emplace(run_finished);
    }
#elif defined(USE_XPU)
    if (run_finished_) {
      (*run_finished_)->wait_and_throw();
      delete *run_finished_;
      run_finished_.reset();
    }
#else // !USE_CUDA && !USE_XPU
    run_finished_ = false;
#endif

    auto* model = static_cast<Model*>(this);
    model->run_impl(input_handles, output_handles, stream, proxy_executor);

#ifdef USE_CUDA
    AOTI_RUNTIME_CUDA_CHECK(cudaEventRecord(*run_finished_, stream));
#elif defined(USE_XPU)
    run_finished_ = std::make_optional<sycl::event*>(new sycl::event(
        static_cast<sycl::queue*>(stream)->ext_oneapi_submit_barrier()));
#else // !USE_CUDA && !USE_XPU
    run_finished_ = true;
#endif // USE_CUDA
  }

  // Non-thread-aware variant of run(). Obviously unsafe to use in a threaded
  // environment :)
  void run_single_threaded(
      AtenTensorHandle*
          input_handles, // array of input AtenTensorHandle; handles
                         // are stolen; the array itself is borrowed
      AtenTensorHandle*
          output_handles, // array for writing output AtenTensorHandle; handles
                          // will be stolen by the caller; the array itself is
                          // borrowed
      DeviceStreamType stream,
      AOTIProxyExecutorHandle proxy_executor) {
    // don't bother with any of the run_finished stuff; this is unsafe to call
    // in a threaded context
    auto* model = static_cast<Model*>(this);
    model->run_impl(input_handles, output_handles, stream, proxy_executor);
  }

  std::unordered_map<std::string, AtenTensorHandle> run_const_fold(
      DeviceStreamType stream,
      AOTIProxyExecutorHandle proxy_executor,
      bool initialization = false) {
#ifdef USE_CUDA
    if (!run_finished_) {
      cudaEvent_t run_finished = nullptr;
      AOTI_RUNTIME_CUDA_CHECK(cudaEventCreate(&run_finished));
      run_finished_.emplace(run_finished);
    }
#elif defined(USE_XPU)
    if (run_finished_) {
      (*run_finished_)->wait_and_throw();
      delete *run_finished_;
      run_finished_.reset();
    }
#else // !USE_CUDA && !USE_XPU
    run_finished_ = false;
#endif

    auto* model = static_cast<Model*>(this);
    auto folded_constants =
        model->const_run_impl(stream, proxy_executor, initialization);

#ifdef USE_CUDA
    AOTI_RUNTIME_CUDA_CHECK(cudaEventRecord(*run_finished_, stream));
#elif defined(USE_XPU)
    // sycl::queue* queue_ptr = nullptr;
    // aoti_torch_get_current_sycl_queue((void**)&queue_ptr);
    run_finished_ = std::make_optional<sycl::event*>(new sycl::event(
        static_cast<sycl::queue*>(stream)->ext_oneapi_submit_barrier()));

#else // !USE_CUDA && !USE_XPU
    run_finished_ = true;
#endif // USE_CUDA

    return folded_constants;
  }

  void update_constants_from_blob(const uint8_t* weight_blob_ptr) {
#if defined(USE_MMAP_EXTERNAL)
    user_managed_mmap = const_cast<uint8_t*>(weight_blob_ptr);
    load_constants(true);
#endif
  }

  void load_constants(bool force = false) {
    size_t num_constants = this->num_constants();
    size_t num_folded_constants = this->num_folded_constants();
    constants_map_->reserve(num_constants);

    // A CUDA model can still have constants on CPU,
    // so we need a separate secondary blob for them.
    std::vector<size_t> constants_internal_offset(
        num_constants - num_folded_constants);
    std::vector<size_t> secondary_cpu_constants_internal_offset(
        num_constants - num_folded_constants);
    size_t blob_size = 0;
    size_t secondary_cpu_blob_size = 0;
    compute_constant_blob(
        blob_size,
        constants_internal_offset,
        secondary_cpu_blob_size,
        secondary_cpu_constants_internal_offset);

    if (!force && !include_weights) {
      return;
    }

    // Allocate main blob
    if (blob_size > 0) {
#if defined(USE_CUDA) || defined(USE_XPU) || defined(USE_MPS)
      constant_blob_ = RAII_gpuMalloc(blob_size);
#else
      constant_blob_ = RAII_cpuMalloc(blob_size);
#endif
    }

    // Allocate secondary blob on CPU
    if (secondary_cpu_blob_size > 0) {
      secondary_cpu_constant_blob_ = RAII_cpuMalloc(secondary_cpu_blob_size);
    }

    size_t bytes_read = 0;
    size_t main_blob_idx = 0;
    size_t secondary_cpu_blob_idx = 0;

    for (size_t i = 0; i < num_constants; i++) {
      bool from_folded = this->constant_from_folded(i);
      if (from_folded) {
        continue;
      }
      std::string name = this->constant_name(i);
      size_t data_size = this->constant_data_size(i);
      int32_t const_device_type = this->constant_device_type(i);
      bool device_type_matches = const_device_type == device_type_;
      uint8_t* internal_ptr = nullptr;
      if (data_size != 0) {
        if (device_type_matches) {
          internal_ptr = constant_ptr(
              constants_internal_offset[main_blob_idx],
              bytes_read,
              data_size,
              /* skip_copy = */ false);
        } else {
          auto* secondary_cpu_constants_ptr =
              static_cast<uint8_t*>(secondary_cpu_constant_blob_.get());
          internal_ptr = secondary_cpu_constants_ptr +
              secondary_cpu_constants_internal_offset[secondary_cpu_blob_idx];
          memcpy(internal_ptr, _get_constants_start() + bytes_read, data_size);
        }
      }

      // Always increment blob indices to stay in sync with
      // compute_constant_blob(), even for zero-size constants.
      if (device_type_matches) {
        main_blob_idx++;
      } else {
        secondary_cpu_blob_idx++;
      }

      bytes_read += data_size;

      // Create at::Tensor from copied memory.
      auto dtype = this->constant_dtype(i);
      auto ndim = this->constant_ndim(i);
      auto size = this->constant_shape(i);
      auto stride = this->constant_stride(i);
#ifdef USE_MPS
      auto offset = this->constant_offset(i) +
          (constants_internal_offset[i] / aoti_torch_dtype_element_size(dtype));
#else
      auto offset = this->constant_offset(i);
#endif
      auto layout = this->constant_layout(i);
      auto opaque_metadata_ptr = this->opaque_metadata(i);
      auto opaque_metadata_size = this->opaque_metadata_size(i);

      AtenTensorHandle tensor_handle = nullptr;
      AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_create_tensor_from_blob_v2(
          internal_ptr,
          ndim,
          size,
          stride,
          offset,
          dtype,
          const_device_type,
          device_type_matches ? device_idx_ : 0,
          &tensor_handle,
          layout,
          opaque_metadata_ptr,
          opaque_metadata_size));
      constants_map_->emplace(std::move(name), tensor_handle);
    }
    if (constants_map_) {
      this->update_constants_array_from_map();
    }
  }

  RAIIDataPtr&& release_constant_blob() {
    return std::move(constant_blob_);
  }

  std::shared_ptr<std::vector<ConstantHandle>> get_constants_array() {
    return constants_;
  }

  int32_t get_device_type() const {
    return device_type_;
  }

  int32_t get_device_idx() const {
    return device_idx_;
  }

  uint8_t* constant_ptr(
      size_t constant_offset,
      size_t bytes_read,
      size_t data_size,
      bool skip_copy) {
    auto* constants_ptr = static_cast<uint8_t*>(constant_blob_.get());
    uint8_t* internal_ptr = constants_ptr + constant_offset;
    // TODO: Handle shared storage case.
    if (!skip_copy) {
#ifdef USE_XPU
      sycl::queue* queue_ptr = nullptr;
      aoti_torch_get_current_sycl_queue((void**)&queue_ptr);
      queue_ptr
          ->memcpy(internal_ptr, _get_constants_start() + bytes_read, data_size)
          .wait();
#elif USE_CUDA
      AOTI_RUNTIME_CUDA_CHECK(cudaMemcpy(
          internal_ptr,
          _get_constants_start() + bytes_read,
          data_size,
          cudaMemcpyHostToDevice));
#elif USE_MPS
      aoti_torch_mps_memcpy(
          constants_ptr,
          constant_offset,
          bytes_read,
          data_size,
          _get_constants_start());
      return constants_ptr;
#else
      memcpy(internal_ptr, _get_constants_start() + bytes_read, data_size);
#endif
    }
    return internal_ptr;
  }

  void compute_constant_blob(
      size_t& blob_size,
      std::vector<size_t>& constants_internal_offset,
      size_t& secondary_cpu_blob_size,
      std::vector<size_t>& secondary_cpu_constants_internal_offset) {
    size_t num_constants = this->num_constants();
    blob_size = 0;
    secondary_cpu_blob_size = 0;
    size_t main_idx = 0;
    size_t secondary_idx = 0;

    for (size_t i = 0; i < num_constants; i++) {
      if (this->constant_from_folded(i)) {
        continue;
      }

      size_t data_size = this->constant_data_size(i);
      // ok to use same AOTI_CONST_ALIGNMENT for both main and secondary blobs
      if (data_size % AOTI_CONST_ALIGNMENT) {
        data_size = AOTI_CONST_ALIGNMENT +
            (data_size / AOTI_CONST_ALIGNMENT) * AOTI_CONST_ALIGNMENT;
      }

      if (this->constant_device_type(i) == device_type_) {
        constants_internal_offset[main_idx++] = blob_size;
        blob_size += data_size;
      } else {
        secondary_cpu_constants_internal_offset[secondary_idx++] =
            secondary_cpu_blob_size;
        secondary_cpu_blob_size += data_size;
      }
    }
  }

  size_t num_inputs() const {
    return inputs_info_.size();
  }

  size_t num_outputs() const {
    return outputs_info_.size();
  }

  size_t num_constants() const {
    return constants_info_.size();
  }

  size_t num_folded_constants() const {
    size_t total_consts = this->num_constants();
    size_t folded_consts = 0;
    for (size_t i = 0; i < total_consts; i++) {
      if (this->constant_from_folded(i)) {
        folded_consts++;
      }
    }
    return folded_consts;
  }

  const char* input_name(int64_t idx) const {
    return inputs_info_.at(idx).name;
  }

  const char* output_name(int64_t idx) const {
    return outputs_info_.at(idx).name;
  }

  const char* constant_name(int64_t idx) const {
    return constants_info_.at(idx).name;
  }

  size_t constant_ndim(int64_t idx) {
    return constants_info_.at(idx).shape.size();
  }

  const int64_t* constant_shape(int64_t idx) const {
    return constants_info_.at(idx).shape.data();
  }

  const int64_t* constant_stride(int64_t idx) const {
    return constants_info_.at(idx).stride.data();
  }

  int32_t constant_dtype(int64_t idx) const {
    return constants_info_.at(idx).dtype;
  }

  int32_t constant_layout(int64_t idx) const {
    return constants_info_.at(idx).layout;
  }

  size_t constant_offset(int64_t idx) const {
    return constants_info_.at(idx).offset;
  }

  size_t constant_data_size(int64_t idx) const {
    return constants_info_.at(idx).data_size;
  }

  const char* constant_original_fqn(int64_t idx) const {
    return constants_info_.at(idx).original_fqn;
  }

  const uint8_t* opaque_metadata(int64_t idx) const {
    return constants_info_.at(idx).opaque_metadata.data();
  }

  size_t opaque_metadata_size(int64_t idx) {
    return constants_info_.at(idx).opaque_metadata.size();
  }

  bool constant_from_folded(int64_t idx) const {
    return constants_info_.at(idx).from_folded;
  }

  int32_t constant_type(int64_t idx) const {
    return constants_info_.at(idx).type;
  }

  int32_t constant_device_type(int64_t idx) const {
    return constants_info_.at(idx).device_type;
  }

  const char* get_in_spec() const {
    return in_spec_.c_str();
  }

  const char* get_out_spec() const {
    return out_spec_.c_str();
  }

  uint64_t constant_blob_size() const {
#if defined(USE_MMAP_SELF) || defined(USE_MMAP_EXTERNAL)
    const uint64_t weights_size =
        reinterpret_cast<const uint64_t*>(_binary_constants_bin_start)[0];
    return weights_size;
#else
    throw std::runtime_error{
        "constant blob size is only available for mmap'd weights"};
#endif
  }

  void update_constants_array_from_map() {
    if (!constants_map_) {
      throw std::runtime_error{
          "constants_map_ was not ready when constants_ is trying to be constructed from it!"};
    }
    if (!constants_) {
      constants_ =
          std::make_shared<std::vector<ConstantHandle>>(constants_info_.size());
    } else {
      constants_->resize(constants_info_.size());
    }
    int idx = 0;
    for (const auto& info : constants_info_) {
      const auto it = constants_map_->find(info.name);
      if (it != constants_map_->end()) {
        constants_->at(idx) = ConstantHandle(it->second);
      }
      idx++;
    }
  }

  void update_constants_map(
      std::shared_ptr<ConstantMap> constants_map,
      bool remap_constants_array = true) {
    constants_map_ = std::move(constants_map);
    if (remap_constants_array) {
      update_constants_array_from_map();
    }
  }

  // This function allows us to update the constants_ that is used to look up
  // the corresponding constant tensor during runtime.
  void update_constants_array(
      std::shared_ptr<std::vector<ConstantHandle>> constants_array) {
    constants_ = std::move(constants_array);
  }

  /// Returns true if the model is complete.
  bool is_finished() {
#ifdef USE_CUDA
    if (!run_finished_) {
      throw std::runtime_error{"Model CUDA event was not initialized"};
    }

    auto event_status = cudaEventQuery(*run_finished_);
    if (event_status == cudaSuccess) {
      return true;
    } else if (event_status == cudaErrorNotReady) {
      return false;
    }

    throw std::runtime_error(
        std::string("The model did not finish successfully. Error: ") +
        cudaGetErrorString(cudaGetLastError()));
#elif defined(USE_XPU)
    if (!run_finished_) {
      throw std::runtime_error{"Model XPU event was not initialized"};
    }
    using namespace sycl::info;
    return (*run_finished_)->get_info<event::command_execution_status>() ==
        event_command_status::complete;

#else // !USE_CUDA && !USE_XPU
    return run_finished_;
#endif // USE_CUDA
  }

  /// Synchronizes completion event.
  void wait_for_completion() {
#ifdef USE_CUDA
    if (!run_finished_) {
      throw std::runtime_error{"Model event was not initialized"};
    }

    AOTI_RUNTIME_CUDA_CHECK(cudaEventSynchronize(*run_finished_));
#endif // USE_CUDA
#ifdef USE_XPU
    if (!run_finished_) {
      throw std::runtime_error{"Model event was not initialized"};
    }
    (*run_finished_)->wait_and_throw();
#endif
  }

 protected:
  uint8_t* _get_constants_start() {
#if defined(USE_MMAP_EXTERNAL)
    if (!user_managed_mmap) {
      throw std::runtime_error{
          "Constants are not mmap'd. Use AOTInductorModelUpdateConstantsBlob to initialize the constants first."};
    }
    // Mapped memory for weights
    return user_managed_mmap;
#endif

#ifndef USE_MMAP_SELF
    // NOLINTNEXTLINE(*const-cast*)
    return const_cast<uint8_t*>(_binary_constants_bin_start);
#else
    if (self_mmap) {
      return self_mmap;
    }
    Dl_info dl_info;
    // get pointer to constant which are appended to the binary
    AOTI_RUNTIME_CHECK(
        dladdr(__func__, &dl_info), "Can't find shared library name");
    int fd = open(dl_info.dli_fname, O_RDONLY);
    AOTI_RUNTIME_CHECK(fd >= 0, "Shared library file cannot be opened");
    auto fsize = lseek(fd, 0, SEEK_END);
    auto weights_size =
        reinterpret_cast<const uint64_t*>(_binary_constants_bin_start)[0];
    auto magic_number =
        reinterpret_cast<const uint64_t*>(_binary_constants_bin_start)[1];
    auto weights_offset = fsize - weights_size;
    AOTI_RUNTIME_CHECK(
        (weights_offset & 0x3fff) == 0,
        "weights_offset must be aligned to 16K boundary");
    auto ptr = mmap(
        NULL,
        weights_size,
        PROT_READ | PROT_WRITE,
        MAP_PRIVATE,
        fd,
        weights_offset);
    close(fd);
    AOTI_RUNTIME_CHECK(ptr != MAP_FAILED, "mmap() failed");
    self_mmap = static_cast<uint8_t*>(ptr);
    AOTI_RUNTIME_CHECK(
        reinterpret_cast<uint64_t*>(
            self_mmap + weights_size - sizeof(uint64_t))[0] == magic_number,
        "Weights data seems corrupt");
    return self_mmap;
#endif
  }

  struct ParamInfo {
    const char* name = nullptr;
  };

  struct ConstInfo {
    const char* name = nullptr;
    std::vector<int64_t> shape;
    std::vector<int64_t> stride;
    int32_t dtype{};
    int32_t device_type{};
    int64_t offset{};
    size_t data_size{};
    int32_t layout{};
    std::vector<uint8_t> opaque_metadata;
    int64_t opaque_metadata_size{};
    const char* original_fqn = nullptr;
    bool from_folded{};
    int32_t type{};
  };

  std::vector<ParamInfo> inputs_info_;
  std::vector<ParamInfo> outputs_info_;
  std::vector<ConstInfo> constants_info_;
  std::string in_spec_;
  std::string out_spec_;

  std::shared_ptr<ConstantMap> constants_map_;
  std::shared_ptr<std::vector<ConstantHandle>> constants_;

  // Holds the blob storage for constants' at::Tensor.
  RAIIDataPtr constant_blob_;
  // For mixed-device models, secondary_cpu_constant_blob_ holds CPU constants
  RAIIDataPtr secondary_cpu_constant_blob_;

#if defined(USE_MMAP_SELF)
  // Mapped memory for weights
  uint8_t* self_mmap = NULL;
#endif

#if defined(USE_MMAP_EXTERNAL)
  // Mapped memory for weights
  uint8_t* user_managed_mmap = NULL;
#endif

  // A directory with CUDA binary files, e.g. compiled kernels, etc.
  const std::optional<std::string> cubin_dir_;

  // This is the flag that implies whether the weight is included in the model.
  // If True, we would prepare the weight when loading the model, otherwise the
  // model will be loaded without weights, and need to be provided by the user.
  bool include_weights;

  // Record if the model finishes an inference run so that its owning
  // AOTModelContainer can reuse this instance.
#ifdef USE_CUDA
  std::optional<cudaEvent_t> run_finished_;
#elif defined(USE_XPU)
  std::optional<sycl::event*> run_finished_;
#else // !USE_CUDA
  bool run_finished_{};
#endif

  // Generated model uses this device index to create CUDA guards.
  int32_t device_type_{};
  int32_t device_idx_{};
};

// Codegen-ed classes can derive from this to keep pointers to loaded kernels.
class AOTInductorModelKernelsBase {
 public:
  virtual ~AOTInductorModelKernelsBase() = default;
};

} // namespace torch::aot_inductor

#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)
