from __future__ import annotations, division import ast import functools import hashlib import inspect import os import subprocess import textwrap from collections import defaultdict, namedtuple from typing import (Callable, Generic, Iterable, List, Optional, TypeVar, Union, cast, overload) from ..common.backend import get_backend, path_to_ptxas TRITON_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) TRITON_VERSION = "2.1.0" def get_cuda_stream(idx=None): if idx is None: idx = get_current_device() try: from torch._C import _cuda_getCurrentRawStream return _cuda_getCurrentRawStream(idx) except ImportError: import torch return torch.cuda.current_stream(idx).cuda_stream def get_current_device(): import torch return torch.cuda.current_device() def set_current_device(idx): import torch torch.cuda.set_device(idx) def get_device_capability(idx): import torch return torch.cuda.get_device_capability(idx) T = TypeVar('T') # ----------------------------------------------------------------------------- # Dependencies Finder # ----------------------------------------------------------------------------- class DependenciesFinder(ast.NodeVisitor): """ This AST visitor is used to find dependencies of a JITFunction. This can be used to invalidate a JITFunction's hash when its source code -- or that of its dependencies -- changes. """ def __init__(self, globals, src) -> None: super().__init__() self.ret = hashlib.md5(src.encode("utf-8")).hexdigest() self.globals = globals def visit_Name(self, node): return self.globals.get(node.id, None) def visit_Attribute(self, node): lhs = self.visit(node.value) while isinstance(lhs, ast.Attribute): lhs = self.visit(lhs.value) if lhs is None or (getattr(lhs, "__name__", "") == "triton" or getattr(lhs, "__name__", "").endswith(".triton")): return None return getattr(lhs, node.attr) def visit_Call(self, node): func = self.visit(node.func) if func is None: return if inspect.isbuiltin(func): return if func.__module__ and (func.__module__.startswith('triton.') or '.triton.' in func.__module__): return assert isinstance(func, JITFunction), f"Function \"{func.__name__}\" is being called from a Triton function but is not a Triton function itself. Decorate it with @triton.jit to fix this" if func.hash is None: tree = ast.parse(func.src) finder = DependenciesFinder(func.__globals__, func.src) finder.visit(tree) func.hash = finder.ret noinline = str(getattr(func, 'noinline', False)) self.ret = (self.ret + func.hash + noinline).encode("utf-8") self.ret = hashlib.md5(self.ret).hexdigest() # ----------------------------------------------------------------------------- # JITFunction # ----------------------------------------------------------------------------- @functools.lru_cache() def version_key(): import pkgutil contents = [] # frontend with open(__file__, "rb") as f: contents += [hashlib.md5(f.read()).hexdigest()] # compiler compiler_path = os.path.join(TRITON_PATH, 'compiler') for lib in pkgutil.iter_modules([compiler_path]): with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f: contents += [hashlib.md5(f.read()).hexdigest()] # backend with open(os.path.join(TRITON_PATH, "_C/libtriton.so"), "rb") as f: contents += [hashlib.md5(f.read()).hexdigest()] # language language_path = os.path.join(TRITON_PATH, 'language') for lib in pkgutil.iter_modules([language_path]): with open(lib.module_finder.find_spec(lib.name).origin, "rb") as f: contents += [hashlib.md5(f.read()).hexdigest()] # ptxas version ptxas = path_to_ptxas()[0] ptxas_version = hashlib.md5(subprocess.check_output([ptxas, "--version"])).hexdigest() return '-'.join(TRITON_VERSION) + '-' + ptxas_version + '-' + '-'.join(contents) class KernelInterface(Generic[T]): run: T def __getitem__(self, grid) -> T: """ A JIT function is launched with: fn[grid](*args, **kwargs). Hence JITFunction.__getitem__ returns a callable proxy that memorizes the grid. """ return cast(T, functools.partial(cast(Callable, self.run), grid=grid)) class JITFunction(KernelInterface[T]): # Hook for inspecting compiled functions and modules cache_hook = None divisibility = 16 @staticmethod def _key_of(arg): if hasattr(arg, "dtype"): return arg.dtype elif isinstance(arg, bool): return "i1" elif isinstance(arg, int): if -2**31 <= arg and arg <= 2**31 - 1: return "i32" elif 2**63 <= arg and arg <= 2**64 - 1: return "u64" else: return "i64" elif isinstance(arg, float): return 'fp32' elif arg is None: return None else: raise TypeError(f'Unsupported type {type(arg)} for {arg}') @staticmethod def _device_of(arg): if hasattr(arg, "device"): if hasattr(arg.device, 'type'): return arg.device.type return '' @staticmethod def _pinned_memory_of(arg): if hasattr(arg, "is_pinned"): if isinstance(arg.is_pinned, Callable): return arg.is_pinned() return False @staticmethod def _spec_of(arg): if hasattr(arg, "data_ptr"): return (arg.data_ptr() % JITFunction.divisibility == 0) elif isinstance(arg, int): return (arg % 16 == 0, arg == 1) return (arg is None, ) def _get_config(self, *args): def is_divisible_by_16(x): if hasattr(x, "data_ptr"): return x.data_ptr() % JITFunction.divisibility == 0 elif isinstance(x, int): return x % JITFunction.divisibility == 0 if x is None: return True return False divisible_by_16 = {i for i, arg in enumerate(args) if is_divisible_by_16(arg) and i not in self.do_not_specialize} equal_to_1 = {i for i, arg in enumerate(args) if not isinstance(arg, bool) and isinstance(arg, int) and arg == 1 and i not in self.do_not_specialize} return namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"])(tuple(divisible_by_16), tuple(equal_to_1)) # return _triton.code_gen.instance_descriptor(divisible_by_16, equal_to_1) @staticmethod def _type_of(key): # None are nullptr -- implicitly converted to *i8 if key is None: return '*i8' dtype_str = str(key).split(".")[-1] tys = { "bool": "i1", "float8e4": "fp8e4", "float8e5": "fp8e5", "float8e4b15": "fp8e4b15", "float16": "fp16", "bfloat16": "bf16", "float32": "fp32", "float64": "fp64", "int8": "i8", "int16": "i16", "int32": "i32", "int64": "i64", "uint8": "u8", "uint16": "u16", "uint32": "u32", "uint64": "u64", } # reinterpret can create triton type for v in list(tys.values()): tys[v] = v return key if isinstance(key, str) else f"*{tys[dtype_str]}" def _make_signature(self, sig_key): signature = ",".join([self._type_of(k) for i, k in enumerate(sig_key)]) return signature def _make_constants(self, constexpr_key): constants = dict(zip(self.constexprs, constexpr_key)) return constants def _call_hook(self, key, signature, device, constants, num_warps, num_stages, extern_libs, configs): if JITFunction.cache_hook is None: return False name = self.fn.__name__ module = self.fn.__module__ arg_reprs = ', '.join([f'{name}: {ty}' for name, ty in zip(self.arg_names, key[1])]) repr = f"{name}[num_warps={num_warps}, num_stages={num_stages}]({arg_reprs})" key = str(key) class LegacyCompiler: def __init__(self, module, name): self.module = module self.name = name pass kwargs = dict(signature=signature, device=device, constants=constants, num_warps=num_warps, num_stages=num_stages, extern_libs=extern_libs, configs=configs) return JITFunction.cache_hook(key=key, repr=repr, fn=LegacyCompiler(module, name), compile={"key": key, **kwargs}, is_manual_warmup=False, already_compiled=False) def _get_arg_specialization_key(self, arg) -> str: arg_annotation = self.__annotations__.get(arg, '') if arg_annotation == '': return f'({arg}.data_ptr() % {JITFunction.divisibility} == 0) if hasattr({arg}, "data_ptr") \ else ({arg} % {JITFunction.divisibility} == 0, {arg} == 1) if isinstance({arg}, int) \ else (False,)' elif 'Tensor' in arg_annotation: return f'({arg}.data_ptr() % {JITFunction.divisibility} == 0)' elif arg_annotation == 'int': return f'({arg} % {JITFunction.divisibility} == 0, {arg} == 1)' else: return '(False,)' def _get_arg_sig_key(self, arg) -> str: arg_annotation = self.__annotations__.get(arg, '') if 'Tensor' in arg_annotation: return f'{arg}.dtype' elif arg_annotation == 'bool': return "i1" elif arg_annotation == 'float': return 'fp32' else: return f'_key_of({arg})' def _conclude_device_type(self, device_types: List[str], pinned_memory_flags: List[bool]) -> str: device_types = [device_type for device_type in device_types if device_type != ''] # Return cuda if one of the input tensors is cuda if 'cuda' in device_types: import torch return 'hip' if torch.version.hip else 'cuda' is_cpu = all(device_type == 'cpu' for device_type in device_types) is_pinned_memory = any(pinned_memory_flag for pinned_memory_flag in pinned_memory_flags) # Return cuda if all the input tensors are cpu while the memory is pinned if is_cpu and is_pinned_memory: return 'cuda' return device_types[0] if len(device_types) > 0 else 'cuda' def _make_launcher(self): regular_args = [f'{arg}' for i, arg in enumerate(self.arg_names) if i not in self.constexprs] constexpr_args = [f'{arg}' for i, arg in enumerate(self.arg_names) if i in self.constexprs] args = ', '.join(regular_args) # cache key for regular argument type sig_keys = ', '.join([self._get_arg_sig_key(arg) for arg in regular_args]) device_types = '[' + ', '.join([f'_device_of({arg})' for arg in regular_args]) + ']' pinned_memory_flags = '[' + ', '.join([f'_pinned_memory_of({arg})' for arg in regular_args]) + ']' # cache key for constexpr argument values constexpr_keys = ', '.join(constexpr_args) # cache key for argument specialization specializations = [] for i, arg in enumerate(regular_args): if i in self.do_not_specialize: continue specializations += [self._get_arg_specialization_key(arg)] spec_keys = ', '.join(specializations) grid_args = ','.join([f'"{arg}": {arg}' for arg in self.arg_names]) args_signature = ', '.join(name if dflt == inspect._empty else f'{name} = {dflt}' for name, dflt in zip(self.arg_names, self.arg_defaults)) src = f""" def {self.fn.__name__}({args_signature}, grid=None, num_warps=4, num_stages=3, extern_libs=None, stream=None, warmup=False, device=None, device_type=None): from ..compiler import compile, CompiledKernel sig_key = {sig_keys}, constexpr_key = {f'{constexpr_keys},' if len(constexpr_keys) > 0 else ()} spec_key = {f'{spec_keys},' if len(spec_keys) > 0 else ()} key = (version_key, sig_key, constexpr_key, spec_key, num_warps, num_stages, self.debug) if not extern_libs is None: key = (key, tuple(extern_libs.items())) assert num_warps > 0 and (num_warps & (num_warps - 1)) == 0, "num_warps must be a power of 2" assert grid is not None if callable(grid): grid = grid({{{grid_args}}}) grid_size = len(grid) grid_0 = grid[0] grid_1 = grid[1] if grid_size > 1 else 1 grid_2 = grid[2] if grid_size > 2 else 1 if device_type is None: device_types = [_device_type for _device_type in {device_types} if _device_type != ''] device_type = self._conclude_device_type(device_types, {pinned_memory_flags}) device_backend = None if device_type not in ['cuda', 'hip']: device_backend = get_backend(device_type) if device_backend is None: raise ValueError('Cannot find backend for ' + device_type) if device is None: if device_type in ['cuda', 'hip']: device = get_current_device() set_current_device(device) else: device = device_backend.get_current_device() device_backend.set_current_device(device) if stream is None and not warmup: if device_type in ['cuda', 'hip']: stream = get_cuda_stream(device) else: stream = device_backend.get_stream() bin = cache[device].get(key, None) if bin is not None: if not warmup: bin.c_wrapper(grid_0, grid_1, grid_2, bin.num_warps, bin.shared, stream, bin.cu_function, CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, bin, {args}) return bin # kernel not cached -- compile else: # build dict of constant values args = [{args}] all_args = {', '.join([f'{arg}' for arg in self.arg_names])}, configs = self._get_config(*all_args), constants = self._make_constants(constexpr_key) constants.update({{i: None for i, arg in enumerate(all_args) if arg is None}}) constants.update({{i: 1 for i in configs[0].equal_to_1}}) # build kernel signature -- doesn't include specialized arguments signature = {{ i: self._type_of(_key_of(arg)) for i, arg in enumerate(all_args) if i not in self.constexprs }} # build stub signature -- includes arguments that are specialized for i, arg in constants.items(): if callable(arg): raise TypeError(f"Callable constexpr at index {{i}} is not supported") if not self._call_hook(key, signature, device, constants, num_warps, num_stages, extern_libs, configs): bin = compile(self, signature=signature, device=device, constants=constants, num_warps=num_warps, num_stages=num_stages, extern_libs=extern_libs, configs=configs, debug=self.debug, device_type=device_type) if not warmup: bin.c_wrapper(grid_0, grid_1, grid_2, bin.num_warps, bin.shared, stream, bin.cu_function, CompiledKernel.launch_enter_hook, CompiledKernel.launch_exit_hook, bin, *args) self.cache[device][key] = bin return bin return None """ scope = {"version_key": version_key(), "get_cuda_stream": get_cuda_stream, "self": self, "_spec_of": self._spec_of, "_key_of": self._key_of, "_device_of": self._device_of, "_pinned_memory_of": self._pinned_memory_of, "cache": self.cache, "__spec__": __spec__, "get_backend": get_backend, "get_current_device": get_current_device, "set_current_device": set_current_device} exec(src, scope) return scope[self.fn.__name__] def __init__(self, fn, version=None, do_not_specialize=None, debug=None, noinline=None): self.fn = fn self.module = fn.__module__ self.version = version # function signature information signature = inspect.signature(fn) self.arg_names = [v.name for v in signature.parameters.values()] self.arg_defaults = [v.default for v in signature.parameters.values()] self.has_defaults = any(v != inspect._empty for v in self.arg_defaults) # specialization hints self.do_not_specialize = [] if do_not_specialize is None else do_not_specialize self.do_not_specialize = {self.arg_names.index(arg) if isinstance(arg, str) else arg for arg in self.do_not_specialize} # function source code (without decorators) self.src = textwrap.dedent(inspect.getsource(fn)) self.src = self.src[self.src.find("def"):] # cache of just-in-time compiled kernels self.cache = defaultdict(dict) self.hash = None # JITFunction can be instantiated as kernel # when called with a grid using __getitem__ self.kernel_decorators = [] self.kernel = None self.debug = True if os.environ.get("TRITON_DEBUG", "0") == "1" else debug self.noinline = noinline # annotations normalize_ty = lambda ty: ty.__name__ if isinstance(ty, type) else ty self.__annotations__ = {name: normalize_ty(ty) for name, ty in fn.__annotations__.items()} # index of constexprs self.constexprs = [self.arg_names.index(name) for name, ty in self.__annotations__.items() if 'constexpr' in ty] # launcher self.run = self._make_launcher() # re-use docs of wrapped function self.__doc__ = fn.__doc__ self.__name__ = fn.__name__ self.__globals__ = fn.__globals__ self.__module__ = fn.__module__ @property def cache_key(self): # TODO : hash should be attribute of `self` if self.hash is None: dependencies_finder = DependenciesFinder(globals=self.__globals__, src=self.src) dependencies_finder.visit(self.parse()) self.hash = dependencies_finder.ret + version_key() return self.hash def warmup(self, *args, **kwargs): return self.run(*map(MockTensor.wrap_dtype, args), **kwargs, warmup=True) # we do not parse `src` in the constructor because # the user might want to monkey-patch self.src dynamically. # Our unit tests do this, for example. def parse(self): tree = ast.parse(self.src) assert isinstance(tree, ast.Module) assert len(tree.body) == 1 assert isinstance(tree.body[0], ast.FunctionDef) return tree def __call__(self, *args, **kwargs): raise RuntimeError("Cannot call @triton.jit'd outside of the scope of a kernel") def __setattr__(self, name, value): # - when kernel decorators change, cached kernel # needs to be cleared if name == 'kernel_decorators': self.kernel = None super(JITFunction, self).__setattr__(name, value) # - when `.src` attribute is set, cache path needs # to be reinitialized if name == 'src': self.hash = None def __repr__(self): return f"JITFunction({self.module}:{self.fn.__name__})" # ----------------------------------------------------------------------------- # `jit` decorator # ----------------------------------------------------------------------------- @overload def jit(fn: T) -> JITFunction[T]: ... @overload def jit( *, version=None, do_not_specialize: Optional[Iterable[int]] = None, debug: Optional[bool] = None, noinline: Optional[bool] = None, ) -> Callable[[T], JITFunction[T]]: ... def jit( fn: Optional[T] = None, *, version=None, do_not_specialize: Optional[Iterable[int]] = None, debug: Optional[bool] = None, noinline: Optional[bool] = None, interpret: Optional[bool] = None, ) -> Union[JITFunction[T], Callable[[T], JITFunction[T]]]: """ Decorator for JIT-compiling a function using the Triton compiler. :note: When a jit'd function is called, arguments are implicitly converted to pointers if they have a :code:`.data_ptr()` method and a `.dtype` attribute. :note: This function will be compiled and run on the GPU. It will only have access to: * python primitives, * builtins within the triton package, * arguments to this function, * other jit'd functions :param fn: the function to be jit-compiled :type fn: Callable """ def decorator(fn: T) -> JITFunction[T]: assert callable(fn) if interpret: from ..interpreter.interpreter import GridSelector return GridSelector(fn) else: return JITFunction( fn, version=version, do_not_specialize=do_not_specialize, debug=debug, noinline=noinline, ) if fn is not None: return decorator(fn) else: return decorator # ----------------------------------------------------------------------------- # Utilities for mocking tensors # ----------------------------------------------------------------------------- class MockTensor: """ Can be used in place of real tensors when calling: kernel.warmup(MockTensor(torch.float32), ...) """ @staticmethod def wrap_dtype(arg): if arg.__class__.__name__ == "dtype" and\ arg.__module__ == "torch": return MockTensor(arg) return arg def __init__(self, dtype): self.dtype = dtype @staticmethod def data_ptr(): return 0 # optimistically assumes multiple of 16 class TensorWrapper: def __init__(self, base, dtype): self.dtype = dtype self.base = base self.is_cuda = base.is_cuda self.device = base.device self.shape = self.base.shape def data_ptr(self): return self.base.data_ptr() def stride(self, i): return self.base.stride(i) def __str__(self) -> str: return f'TensorWrapper[{self.dtype}]({self.base})' def reinterpret(tensor, dtype): if isinstance(tensor, TensorWrapper): if dtype == tensor.base.dtype: # Reinterpreting to the original interpretation; return the base. return tensor.base else: # Reinterpreting a wrapped tensor to a different type. return TensorWrapper(tensor.base, dtype) elif hasattr(tensor, "data_ptr"): # A new wrapper is needed around an unwrapped tensor. return TensorWrapper(tensor, dtype) else: raise TypeError(f'Cannot reinterpret a {type(tensor)}.')