#include "cuda.h"
#include <dlfcn.h>
#include <stdbool.h>
#define PY_SSIZE_T_CLEAN
#include <Python.h>
#include <stdatomic.h>

// Raises a Python exception and returns false if code is not CUDA_SUCCESS.
static bool gpuAssert(CUresult code, const char *file, int line) {
  if (code == CUDA_SUCCESS)
    return true;

  const char *prefix = "Triton Error [CUDA]: ";
  const char *str;
  cuGetErrorString(code, &str);
  char err[1024] = {0};
  strcat(err, prefix);
  strcat(err, str);
  PyGILState_STATE gil_state;
  gil_state = PyGILState_Ensure();
  PyErr_SetString(PyExc_RuntimeError, err);
  PyGILState_Release(gil_state);
  return false;
}

// To be used only *outside* a Py_{BEGIN,END}_ALLOW_THREADS block.
#define CUDA_CHECK_AND_RETURN_NULL(ans)                                        \
  do {                                                                         \
    if (!gpuAssert((ans), __FILE__, __LINE__))                                 \
      return NULL;                                                             \
  } while (0)

// To be used inside a Py_{BEGIN,END}_ALLOW_THREADS block.
#define CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(ans)                          \
  do {                                                                         \
    if (!gpuAssert((ans), __FILE__, __LINE__)) {                               \
      PyEval_RestoreThread(_save);                                             \
      return NULL;                                                             \
    }                                                                          \
  } while (0)

// Used to check if functions exist in old CUDA driver versions.
#define INITIALIZE_FUNCTION_POINTER_IF_NULL(funcPointer, initializerFunction)  \
  do {                                                                         \
    if ((funcPointer) == NULL) {                                               \
      (funcPointer) = (initializerFunction)();                                 \
      if ((funcPointer) == NULL) {                                             \
        return NULL;                                                           \
      }                                                                        \
    }                                                                          \
  } while (0)

static PyObject *getDeviceProperties(PyObject *self, PyObject *args) {
  int device_id;
  if (!PyArg_ParseTuple(args, "i", &device_id))
    return NULL;
  // Get device handle
  CUdevice device;
  cuDeviceGet(&device, device_id);

  // create a struct to hold device properties
  int max_shared_mem;
  int max_num_regs;
  int multiprocessor_count;
  int warp_size;
  int sm_clock_rate;
  int mem_clock_rate;
  int mem_bus_width;
  CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute(
      &max_shared_mem, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN,
      device));
  CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute(
      &max_num_regs, CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK, device));
  CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute(
      &multiprocessor_count, CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, device));
  CUDA_CHECK_AND_RETURN_NULL(
      cuDeviceGetAttribute(&warp_size, CU_DEVICE_ATTRIBUTE_WARP_SIZE, device));
  CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute(
      &sm_clock_rate, CU_DEVICE_ATTRIBUTE_CLOCK_RATE, device));
  CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute(
      &mem_clock_rate, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, device));
  CUDA_CHECK_AND_RETURN_NULL(cuDeviceGetAttribute(
      &mem_bus_width, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, device));

  return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i, s:i, s:i}", "max_shared_mem",
                       max_shared_mem, "max_num_regs", max_num_regs,
                       "multiprocessor_count", multiprocessor_count, "warpSize",
                       warp_size, "sm_clock_rate", sm_clock_rate,
                       "mem_clock_rate", mem_clock_rate, "mem_bus_width",
                       mem_bus_width);
}

static PyObject *loadBinary(PyObject *self, PyObject *args) {
  const char *name;
  const char *data;
  Py_ssize_t data_size;
  int shared;
  int device;
  if (!PyArg_ParseTuple(args, "ss#ii", &name, &data, &data_size, &shared,
                        &device)) {
    return NULL;
  }
  CUfunction fun;
  CUmodule mod;
  int32_t n_regs = 0;
  int32_t n_spills = 0;
  // create driver handles
  CUcontext pctx = 0;

  Py_BEGIN_ALLOW_THREADS;
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuCtxGetCurrent(&pctx));
  if (!pctx) {
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
        cuDevicePrimaryCtxRetain(&pctx, device));
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuCtxSetCurrent(pctx));
  }

  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuModuleLoadData(&mod, data));
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
      cuModuleGetFunction(&fun, mod, name));
  // get allocated registers and spilled registers from the function
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
      cuFuncGetAttribute(&n_regs, CU_FUNC_ATTRIBUTE_NUM_REGS, fun));
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
      cuFuncGetAttribute(&n_spills, CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES, fun));
  n_spills /= 4;
  // set dynamic shared memory if necessary
  int shared_optin;
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuDeviceGetAttribute(
      &shared_optin, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN,
      device));
  if (shared > 49152 && shared_optin > 49152) {
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
        cuFuncSetCacheConfig(fun, CU_FUNC_CACHE_PREFER_SHARED));
    int shared_total, shared_static;
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuDeviceGetAttribute(
        &shared_total, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_MULTIPROCESSOR,
        device));
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuFuncGetAttribute(
        &shared_static, CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES, fun));
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
        cuFuncSetAttribute(fun, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES,
                           shared_optin - shared_static));
  }
  Py_END_ALLOW_THREADS;

  if (PyErr_Occurred()) {
    return NULL;
  }
  return Py_BuildValue("(KKii)", (uint64_t)mod, (uint64_t)fun, n_regs,
                       n_spills);
}

typedef CUresult (*cuOccupancyMaxActiveClusters_t)(
    int *numClusters, CUfunction func, const CUlaunchConfig *config);

typedef CUresult (*cuTensorMapEncodeTiled_t)(
    CUtensorMap *tensorMap, CUtensorMapDataType tensorDataType,
    cuuint32_t tensorRank, void *globalAddress, const cuuint64_t *globalDim,
    const cuuint64_t *globalStrides, const cuuint32_t *boxDim,
    const cuuint32_t *elementStrides, CUtensorMapInterleave interleave,
    CUtensorMapSwizzle swizzle, CUtensorMapL2promotion l2Promotion,
    CUtensorMapFloatOOBfill oobFill);

#define defineGetFunctionHandle(name, symbolName)                              \
  static symbolName##_t name() {                                               \
    /* Open the shared library */                                              \
    void *libHandle = dlopen("libcuda.so.1", RTLD_LAZY);                       \
    if (!libHandle) {                                                          \
      PyErr_SetString(PyExc_RuntimeError, "Failed to open libcuda.so.1");      \
      return NULL;                                                             \
    }                                                                          \
    /* Clear any existing error */                                             \
    dlerror();                                                                 \
    symbolName##_t funcHandle = (symbolName##_t)dlsym(libHandle, #symbolName); \
    /* Check for errors */                                                     \
    const char *err = dlerror();                                               \
    if (err) {                                                                 \
      PyErr_SetString(PyExc_RuntimeError,                                      \
                      "Failed to retrieve " #symbolName " from libcuda.so.1"); \
      dlclose(libHandle);                                                      \
      return NULL;                                                             \
    }                                                                          \
    return funcHandle;                                                         \
  }

defineGetFunctionHandle(getCuOccupancyMaxActiveClustersHandle,
                        cuOccupancyMaxActiveClusters);

defineGetFunctionHandle(getCuTensorMapEncodeTiledHandle,
                        cuTensorMapEncodeTiled);

static PyObject *occupancyMaxActiveClusters(PyObject *self, PyObject *args) {
  int clusterDimX = -1, clusterDimY = -1, clusterDimZ = -1,
      maxActiveClusters = -1;
  int shared = 0;
  CUfunction func;

  if (!PyArg_ParseTuple(args, "Kiiii", &func, &shared, &clusterDimX,
                        &clusterDimY, &clusterDimZ)) {
    return NULL;
  }

  // Let each SM have one block
  int maxActiveBlocks = 1;
  Py_BEGIN_ALLOW_THREADS;
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuFuncSetAttribute(
      func, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared));
  Py_END_ALLOW_THREADS;

  CUlaunchAttribute launchAttr[1];
  launchAttr[0].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_DIMENSION;
  launchAttr[0].value.clusterDim.x = clusterDimX;
  launchAttr[0].value.clusterDim.y = clusterDimY;
  launchAttr[0].value.clusterDim.z = clusterDimZ;
  CUlaunchConfig config;
  config.gridDimX = clusterDimX;
  config.gridDimY = maxActiveBlocks * clusterDimY;
  config.gridDimZ = clusterDimZ;
  config.blockDimX = 128;
  config.blockDimY = 1;
  config.blockDimZ = 1;
  config.sharedMemBytes = shared;
  config.hStream = 0;
  config.numAttrs = 1;
  config.attrs = launchAttr;

  static cuOccupancyMaxActiveClusters_t cuOccupancyMaxActiveClusters = NULL;
  INITIALIZE_FUNCTION_POINTER_IF_NULL(cuOccupancyMaxActiveClusters,
                                      getCuOccupancyMaxActiveClustersHandle);

  Py_BEGIN_ALLOW_THREADS;
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuFuncSetAttribute(
      func, CU_FUNC_ATTRIBUTE_NON_PORTABLE_CLUSTER_SIZE_ALLOWED, 1));
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
      cuOccupancyMaxActiveClusters(&maxActiveClusters, func, &config));
  Py_END_ALLOW_THREADS;
  return PyLong_FromLong(maxActiveClusters);
}

static PyObject *setPrintfFifoSize(PyObject *self, PyObject *args) {
  long size;
  if (!PyArg_ParseTuple(args, "l", &size)) {
    return NULL;
  }
  if (size < 0) {
    PyErr_SetString(PyExc_ValueError, "fifo size must be non-negative");
    return NULL;
  }

  Py_BEGIN_ALLOW_THREADS;

  // Ensure we have an active context.
  CUcontext ctx = NULL;
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuCtxGetCurrent(&ctx));
  if (!ctx) {
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
        cuDevicePrimaryCtxRetain(&ctx, /*device=*/0));
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(cuCtxSetCurrent(ctx));
  }

  // We can't set the fifo size after running a kernel that calls printf.  This
  // is true even if the set() call is a nop and the new size is the same as the
  // old size.
  //
  // This is unfriendly, so check if the old size matches the new size, and skip
  // the set() call if so.
  size_t oldSize = 0;
  CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
      cuCtxGetLimit(&oldSize, CU_LIMIT_PRINTF_FIFO_SIZE));
  if (oldSize != size) {
    CUDA_CHECK_AND_RETURN_NULL_ALLOW_THREADS(
        cuCtxSetLimit(CU_LIMIT_PRINTF_FIFO_SIZE, size));
  }

  Py_END_ALLOW_THREADS;
  return Py_None;
}

// Simple helper to experiment creating TMA descriptors on the host.
// This is a useful to test TMA operations independently.
static PyObject *fill1DTMADescriptor(PyObject *self, PyObject *args) {
  unsigned long long global_address;
  uint64_t dim;
  uint32_t tensorDim;
  int elementSize;
  Py_buffer desc_buffer;
  if (!PyArg_ParseTuple(args, "KKiiy*", &global_address, &dim, &tensorDim,
                        &elementSize, &desc_buffer)) {
    return NULL;
  }
  char *desc = (char *)desc_buffer.buf;
  uint64_t dims[1] = {dim};
  uint64_t globalStrides[1] = {dim * elementSize};
  uint32_t boxDim[1] = {tensorDim};
  uint32_t elementStrides[1] = {1};
  CUtensorMapDataType type;
  switch (elementSize) {
  case 1:
    type = CU_TENSOR_MAP_DATA_TYPE_UINT8;
    break;
  case 2:
    type = CU_TENSOR_MAP_DATA_TYPE_UINT16;
    break;
  case 4:
    type = CU_TENSOR_MAP_DATA_TYPE_UINT32;
    break;
  default:
    PyErr_SetString(PyExc_ValueError, "elementSize must be 1, 2, or 4");
  }
  assert((elementSize * tensorDim) >= 32 && "block size too small.");
  int rank = 1;
  static cuTensorMapEncodeTiled_t cuTensorMapEncodeTiled = NULL;
  INITIALIZE_FUNCTION_POINTER_IF_NULL(cuTensorMapEncodeTiled,
                                      getCuTensorMapEncodeTiledHandle);
  CUresult result = cuTensorMapEncodeTiled(
      (CUtensorMap *)desc, type, rank, (void *)global_address, dims,
      globalStrides, boxDim, elementStrides, CU_TENSOR_MAP_INTERLEAVE_NONE,
      CU_TENSOR_MAP_SWIZZLE_NONE, CU_TENSOR_MAP_L2_PROMOTION_NONE,
      CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE);
  assert(result == CUDA_SUCCESS);
  return Py_None;
}

// Simple helper to experiment creating TMA descriptors on the host.
// This is a useful to test TMA operations independently.
static PyObject *fill2DTMADescriptor(PyObject *self, PyObject *args) {
  unsigned long long global_address;
  uint64_t dims[2];
  uint32_t tensorDims[2];
  int elementSize;
  Py_buffer desc_buffer;
  if (!PyArg_ParseTuple(args, "KKKiiiy*", &global_address, &dims[1], &dims[0],
                        &tensorDims[1], &tensorDims[0], &elementSize,
                        &desc_buffer)) {
    return NULL;
  }
  char *desc = (char *)desc_buffer.buf;
  uint64_t globalStrides[2] = {dims[0] * elementSize,
                               dims[0] * dims[1] * elementSize};
  uint32_t elementStrides[2] = {1, 1};
  CUtensorMapDataType type;
  switch (elementSize) {
  case 1:
    type = CU_TENSOR_MAP_DATA_TYPE_UINT8;
    break;
  case 2:
    type = CU_TENSOR_MAP_DATA_TYPE_UINT16;
    break;
  case 4:
    type = CU_TENSOR_MAP_DATA_TYPE_UINT32;
    break;
  default:
    PyErr_SetString(PyExc_ValueError, "elementSize must be 1, 2, or 4");
  }
  int rank = 2;
  // Swizzling should be picked in codegen but since we need to set it on the
  // descriptor we rely on a convention between this function and codegen.
  CUtensorMapSwizzle swizzle = CU_TENSOR_MAP_SWIZZLE_128B;
  uint32_t contigDimSizeInByte = elementSize * tensorDims[0];
  if (contigDimSizeInByte >= 128) {
    swizzle = CU_TENSOR_MAP_SWIZZLE_128B;
  } else if (contigDimSizeInByte >= 64) {
    swizzle = CU_TENSOR_MAP_SWIZZLE_64B;
  } else if (contigDimSizeInByte >= 32) {
    swizzle = CU_TENSOR_MAP_SWIZZLE_32B;
  } else {
    assert(false && "block size too small.");
  }
  // The bounding box inner dimension must be less than or equal to the swizzle
  // size.
  // https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__TENSOR__MEMORY.html#group__CUDA__TENSOR__MEMORY_1ga7c7d2aaac9e49294304e755e6f341d7
  // We clamp the block size and the codegen will emit multiple copy operations.
  if (contigDimSizeInByte > 128) {
    tensorDims[0] = 128 / elementSize;
  }
  static cuTensorMapEncodeTiled_t cuTensorMapEncodeTiled = NULL;
  INITIALIZE_FUNCTION_POINTER_IF_NULL(cuTensorMapEncodeTiled,
                                      getCuTensorMapEncodeTiledHandle);
  CUresult result = cuTensorMapEncodeTiled(
      (CUtensorMap *)desc, type, rank, (void *)global_address, dims,
      globalStrides, tensorDims, elementStrides, CU_TENSOR_MAP_INTERLEAVE_NONE,
      swizzle, CU_TENSOR_MAP_L2_PROMOTION_L2_128B,
      CU_TENSOR_MAP_FLOAT_OOB_FILL_NONE);
  assert(result == CUDA_SUCCESS);
  return Py_None;
}

static PyMethodDef ModuleMethods[] = {
    {"load_binary", loadBinary, METH_VARARGS,
     "Load provided cubin into CUDA driver"},
    {"get_device_properties", getDeviceProperties, METH_VARARGS,
     "Get the properties for a given device"},
    {"cuOccupancyMaxActiveClusters", occupancyMaxActiveClusters, METH_VARARGS,
     "Python interface for cuOccupancyMaxActiveClusters function"},
    {"set_printf_fifo_size", setPrintfFifoSize, METH_VARARGS,
     "Python interface for cuCtxSetLimit(CU_LIMIT_PRINTF_FIFO_SIZE, x), which "
     "controls how many bytes can be streamed from kernels before data starts "
     "being dropped.  This inherits all the limitations of this call; in "
     "particular it's an error to change this value after launching any kernel "
     "that calls printf()."},
    {"fill_1d_tma_descriptor", fill1DTMADescriptor, METH_VARARGS, "doc"},
    {"fill_2d_tma_descriptor", fill2DTMADescriptor, METH_VARARGS, "doc"},

    {NULL, NULL, 0, NULL} // sentinel
};

static struct PyModuleDef ModuleDef = {PyModuleDef_HEAD_INIT, "cuda_utils",
                                       NULL, // documentation
                                       -1,   // size
                                       ModuleMethods};

PyMODINIT_FUNC PyInit_cuda_utils(void) {
  PyObject *m = PyModule_Create(&ModuleDef);
  if (m == NULL) {
    return NULL;
  }

  PyModule_AddFunctions(m, ModuleMethods);

  return m;
}