Viewing File: /home/ubuntu/combine_ai/combine/lib/python3.10/site-packages/triton/compiler/make_launcher.py

import hashlib
import os
import tempfile

from ..common import _build
from ..common.backend import get_cuda_version_key
from ..common.build import is_hip
from ..runtime.cache import get_cache_manager
from .utils import generate_cu_signature

# ----- stub --------


def make_so_cache_key(version_hash, signature, constants, ids, **kwargs):
    # Get unique key for the compiled code
    signature = {k: 'ptr' if v[0] == '*' else v for k, v in signature.items()}
    key = f"{version_hash}-{''.join(signature.values())}-{constants}-{ids}"
    for kw in kwargs:
        key = f"{key}-{kwargs.get(kw)}"
    key = hashlib.md5(key.encode("utf-8")).hexdigest()
    return key


def make_stub(name, signature, constants, ids, **kwargs):
    # name of files that are cached
    so_cache_key = make_so_cache_key(get_cuda_version_key(), signature, constants, ids, **kwargs)
    so_cache_manager = get_cache_manager(so_cache_key)
    so_name = f"{name}.so"
    # retrieve stub from cache if it exists
    cache_path = so_cache_manager.get_file(so_name)
    if cache_path is None:
        with tempfile.TemporaryDirectory() as tmpdir:
            src = generate_launcher(constants, signature, ids)
            src_path = os.path.join(tmpdir, "main.c")
            with open(src_path, "w") as f:
                f.write(src)
            so = _build(name, src_path, tmpdir)
            with open(so, "rb") as f:
                return so_cache_manager.put(f.read(), so_name, binary=True)
    else:
        return cache_path


# ----- source code generation --------


def ty_to_cpp(ty):
    if ty[0] == '*':
        return "hipDeviceptr_t" if is_hip() else "CUdeviceptr"
    return {
        "i1": "int32_t",
        "i8": "int8_t",
        "i16": "int16_t",
        "i32": "int32_t",
        "i64": "int64_t",
        "u32": "uint32_t",
        "u64": "uint64_t",
        "fp16": "float",
        "bf16": "float",
        "fp32": "float",
        "f32": "float",
        "fp64": "double",
    }[ty]


def generate_launcher(constants, signature, ids):
    # Record the end of regular arguments;
    # subsequent arguments are architecture-specific descriptors, such as tensor descriptors for CUDA.
    signature, desc_start_idx = generate_cu_signature(constants, signature, ids)
    arg_decls = ', '.join(f"{ty_to_cpp(ty)} arg{i}" for i, ty in signature.items())

    def _extracted_type(ty):
        if ty[0] == '*':
            return "PyObject*"
        return {
            'i1': 'int32_t',
            'i32': 'int32_t',
            'i64': 'int64_t',
            'u32': 'uint32_t',
            'u64': 'uint64_t',
            'fp16': 'float',
            'bf16': 'float',
            'fp32': 'float',
            'f32': 'float',
            'fp64': 'double',
        }[ty]

    def format_of(ty):
        return {
            "PyObject*": "O",
            "float": "f",
            "double": "d",
            "long": "l",
            "uint32_t": "I",
            "int32_t": "i",
            "uint64_t": "K",
            "int64_t": "L",
        }[ty]

    format = "iiiiiiiiiKKOOO" + ''.join([format_of(_extracted_type(ty)) for ty in signature.values()])

    # generate glue code
    folded_without_constexprs = [c for c in ids['ids_of_folded_args'] if c not in ids['ids_of_const_exprs']]
    params = [
        i for i in signature.keys()
        if i >= desc_start_idx or (i not in constants and i not in folded_without_constexprs)
    ]
    src = f"""
#include \"cuda.h\"
#include <stdbool.h>
#include <Python.h>
#include <dlfcn.h>

static inline void gpuAssert(CUresult code, const char *file, int line)
{{
   if (code != CUDA_SUCCESS)
   {{
      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);
   }}
}}

#define CUDA_CHECK(ans) {{ gpuAssert((ans), __FILE__, __LINE__); }}

typedef CUresult (*cuLaunchKernelEx_t)(const CUlaunchConfig* config, CUfunction f, void** kernelParams, void** extra);

static cuLaunchKernelEx_t getLaunchKernelExHandle() {{
  // Open the shared library
  void* handle = dlopen("libcuda.so", RTLD_LAZY);
  if (!handle) {{
    PyErr_SetString(PyExc_RuntimeError, "Failed to open libcuda.so");
    return NULL;
  }}
  // Clear any existing error
  dlerror();
  cuLaunchKernelEx_t cuLaunchKernelExHandle = (cuLaunchKernelEx_t)dlsym(handle, "cuLaunchKernelEx");
  // Check for errors
  const char *dlsym_error = dlerror();
  if (dlsym_error) {{
    PyErr_SetString(PyExc_RuntimeError, "Failed to retrieve cuLaunchKernelEx from libcuda.so");
    return NULL;
  }}
  return cuLaunchKernelExHandle;
}}

static void _launch(int gridX, int gridY, int gridZ, int num_warps, int num_ctas, int clusterDimX, int clusterDimY, int clusterDimZ, int shared_memory, CUstream stream, CUfunction function{', ' + arg_decls if len(arg_decls) > 0 else ''}) {{
  void *params[] = {{ {', '.join(f"&arg{i}" for i in params)} }};
  if (gridX*gridY*gridZ > 0) {{
    if (num_ctas == 1) {{
      CUDA_CHECK(cuLaunchKernel(function, gridX, gridY, gridZ, 32*num_warps, 1, 1, shared_memory, stream, params, 0));
    }} else {{
      CUlaunchAttribute launchAttr[2];
      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;
      launchAttr[1].id = CU_LAUNCH_ATTRIBUTE_CLUSTER_SCHEDULING_POLICY_PREFERENCE;
      launchAttr[1].value.clusterSchedulingPolicyPreference = CU_CLUSTER_SCHEDULING_POLICY_SPREAD;
      CUlaunchConfig config;
      config.gridDimX = gridX * clusterDimX;
      config.gridDimY = gridY * clusterDimY;
      config.gridDimZ = gridZ * clusterDimZ;
      config.blockDimX = 32 * num_warps;
      config.blockDimY = 1;
      config.blockDimZ = 1;
      config.sharedMemBytes = shared_memory;
      config.hStream = stream;
      config.attrs = launchAttr;
      config.numAttrs = 2;
      static cuLaunchKernelEx_t cuLaunchKernelExHandle = NULL;
      if (cuLaunchKernelExHandle == NULL) {{
        cuLaunchKernelExHandle = getLaunchKernelExHandle();
      }}
      CUDA_CHECK(cuLaunchKernelExHandle(&config, function, params, 0));
    }}
  }}
}}

typedef struct _DevicePtrInfo {{
    CUdeviceptr dev_ptr;
    bool valid;
}} DevicePtrInfo;

static inline DevicePtrInfo getPointer(PyObject *obj, int idx) {{
  DevicePtrInfo ptr_info;
  ptr_info.dev_ptr = 0;
  ptr_info.valid = true;
  if (PyLong_Check(obj)) {{
    ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(obj);
    return ptr_info;
  }}
  if (obj == Py_None) {{
    // valid nullptr
    return ptr_info;
  }}
  PyObject *ptr = PyObject_GetAttrString(obj, "data_ptr");
  if(ptr){{
    PyObject *empty_tuple = PyTuple_New(0);
    PyObject *ret = PyObject_Call(ptr, empty_tuple, NULL);
    Py_DECREF(empty_tuple);
    Py_DECREF(ptr);
    if (!PyLong_Check(ret)) {{
      PyErr_SetString(PyExc_TypeError, "data_ptr method of Pointer object must return 64-bit int");
      ptr_info.valid = false;
      return ptr_info;
    }}
    ptr_info.dev_ptr = PyLong_AsUnsignedLongLong(ret);
    if(!ptr_info.dev_ptr)
      return ptr_info;
    uint64_t dev_ptr;
    int status = cuPointerGetAttribute(&dev_ptr, CU_POINTER_ATTRIBUTE_DEVICE_POINTER, ptr_info.dev_ptr);
    if (status == CUDA_ERROR_INVALID_VALUE) {{
        PyErr_Format(PyExc_ValueError,
                     "Pointer argument (at %d) cannot be accessed from Triton (cpu tensor?)", idx);
        ptr_info.valid = false;
    }}
    ptr_info.dev_ptr = dev_ptr;
    Py_DECREF(ret);  // Thanks ChatGPT!
    return ptr_info;
  }}
  PyErr_SetString(PyExc_TypeError, "Pointer argument must be either uint64 or have data_ptr method");
  ptr_info.valid = false;
  return ptr_info;
}}

static PyObject* launch(PyObject* self, PyObject* args) {{
  int gridX, gridY, gridZ;
  uint64_t _stream;
  uint64_t _function;
  int num_warps;
  int num_ctas;
  int clusterDimX;
  int clusterDimY;
  int clusterDimZ;
  int shared_memory;
  PyObject *launch_enter_hook = NULL;
  PyObject *launch_exit_hook = NULL;
  PyObject *compiled_kernel = NULL;
  {' '.join([f"{_extracted_type(ty)} _arg{i}; " for i, ty in signature.items()])}
  if(!PyArg_ParseTuple(args, \"{format}\", &gridX, &gridY, &gridZ, &num_warps, &num_ctas, &clusterDimX, &clusterDimY, &clusterDimZ, &shared_memory, &_stream, &_function, &launch_enter_hook, &launch_exit_hook, &compiled_kernel{', ' + ', '.join(f"&_arg{i}" for i, ty in signature.items()) if len(signature) > 0 else ''})) {{
    return NULL;
  }}

  if (launch_enter_hook != Py_None && !PyObject_CallObject(launch_enter_hook, args)) {{
    return NULL;
  }}


  // raise exception asap
  {"; ".join([f"DevicePtrInfo ptr_info{i} = getPointer(_arg{i}, {i}); if (!ptr_info{i}.valid) return NULL;" if ty[0] == "*" else "" for i, ty in signature.items()])};
  Py_BEGIN_ALLOW_THREADS;
  _launch(gridX, gridY, gridZ, num_warps, num_ctas, clusterDimX, clusterDimY, clusterDimZ, shared_memory, (CUstream)_stream, (CUfunction)_function{', ' + ', '.join(f"ptr_info{i}.dev_ptr" if ty[0]=="*" else f"_arg{i}"for i, ty in signature.items()) if len(signature) > 0 else ''});
  Py_END_ALLOW_THREADS;
  if (PyErr_Occurred()) {{
    return NULL;
  }}

  if (launch_exit_hook != Py_None && !PyObject_CallObject(launch_exit_hook, args)) {{
    return NULL;
  }}

  // return None
  Py_INCREF(Py_None);
  return Py_None;
}}

static PyMethodDef ModuleMethods[] = {{
  {{"launch", launch, METH_VARARGS, "Entry point for all kernels with this signature"}},
  {{NULL, NULL, 0, NULL}} // sentinel
}};

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

PyMODINIT_FUNC PyInit___triton_launcher(void) {{
  PyObject *m = PyModule_Create(&ModuleDef);
  if(m == NULL) {{
    return NULL;
  }}
  PyModule_AddFunctions(m, ModuleMethods);
  return m;
}}
"""
    return src