from __future__ import annotations from functools import cached_property import inspect from typing import Any, Callable, Dict, Final, List, Optional, Tuple, Union import torch from triton.compiler import CompiledKernel from triton.runtime import JITFunction import tvm_ffi from .utils import type_canonicalize class TVMFFIJITFunction(object): def __init__(self, fn: JITFunction, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.fn: Final[JITFunction] = fn self.signature: Optional[List[str]] = None self.ctypes: Optional[List[Optional[str]]] = None self.kernel: Optional[bytes] = None self.num_warps: Optional[int] = None @tvm_ffi.register_global_func(self.fullname) def _( grid: Union[ Callable[[Dict[str, Any]], Tuple[int, int, int]], Tuple[int, int, int] ], _: int, num_warps: Optional[int], num_stages: Optional[int], *args, **kwargs, ): args: List[Any] = map(self.canonicalize, args) kwargs: Dict[str, Any] = { k: self.canonicalize(v) for k, v in kwargs.items() } if num_warps is not None: kwargs["num_warps"] = num_warps if num_stages is not None: kwargs["num_stages"] = num_stages kernel: CompiledKernel = self.fn[grid](*args, **kwargs) self.num_warps, _, _ = kernel.packed_metadata self.signature = [*inspect.signature(self.fn.fn).parameters.keys()] self.ctypes = [type_canonicalize(v) for v in kernel.src.signature.values()] self.kernel = kernel.kernel return kernel def __getitem__( self, grid: Union[ Callable[[Dict[str, Any]], Tuple[int, int, int]], Tuple[int, int, int] ], ): return self.fn[grid] @property def cache_hash(self) -> int: return self.ctypes_hash ^ self.kernel_hash @property def ctypes_hash(self) -> int: return hash(tuple(self.ctypes) if self.ctypes is not None else None) @property def kernel_hash(self) -> int: return hash(self.kernel) @cached_property def fnname(self) -> str: return self.fn.fn.__name__ @cached_property def fullname(self) -> str: return f"triton.{self.name}" @cached_property def name(self) -> str: return f"{self.fnname}_{hash(self.fn.fn)}" @staticmethod def canonicalize(val: Any) -> Any: if hasattr(val, "__dlpack__"): return torch.from_dlpack(val) else: return val def jit(fn: JITFunction) -> TVMFFIJITFunction: return TVMFFIJITFunction(fn)