from __future__ import annotations import builtins import os import time import inspect from typing import Dict, Tuple, List, Optional from .jit import KernelInterface from .errors import OutOfResources, PTXASError from .driver import driver class Autotuner(KernelInterface): def __init__( self, fn, arg_names, configs, key, reset_to_zero, restore_value, pre_hook=None, post_hook=None, prune_configs_by: Optional[Dict] = None, warmup=None, rep=None, use_cuda_graph=False, do_bench=None, ): """ :param prune_configs_by: a dict of functions that are used to prune configs, fields: 'perf_model': performance model used to predicate running time with different configs, returns running time 'top_k': number of configs to bench 'prune_num_stages_by'(optional): a function used to prune num_stages. It takes configs:List[Config] as its input, and returns pruned configs. """ if not configs: self.configs = [ Config({}, num_warps=4, num_stages=3, num_ctas=1, num_buffers_warp_spec=0, num_consumer_groups=0, reg_dec_producer=0, reg_inc_consumer=0) ] else: self.configs = configs self.keys = key self.cache: Dict[Tuple, Config] = {} self.arg_names = arg_names # Reset to zero or restore values self.reset_to_zero = [] if reset_to_zero is not None: self.reset_to_zero = list(reset_to_zero) self.restore_value = [] if restore_value is not None: self.restore_value = list(restore_value) # Hook to reset or restore for required tensors self.pre_hook = lambda kwargs, reset_only=False: 0 self.post_hook = lambda kwargs, exception: 0 self.user_defined_pre_hook = False self.user_defined_post_hook = False if pre_hook: self.pre_hook = pre_hook self.user_defined_pre_hook = True elif (len(self.reset_to_zero) > 0 or len(self.restore_value) > 0): def _pre_hook(kwargs, reset_only=False): for name in self.reset_to_zero: kwargs[name].zero_() if not reset_only: self.restore_copies = {name: kwargs[name].clone() for name in self.restore_value} self.pre_hook = _pre_hook if post_hook: self.post_hook = post_hook self.user_defined_post_hook = True elif len(self.restore_value) > 0: def _post_hook(kwargs, exception): for name in self.restore_value: kwargs[name].copy_(self.restore_copies[name]) self.restore_copies = {} self.post_hook = _post_hook self.perf_model = None self.configs_top_k = 1.0 self.early_config_prune = None if prune_configs_by: self.perf_model = prune_configs_by.get("perf_model", self.perf_model) self.configs_top_k = prune_configs_by.get("top_k", self.configs_top_k) self.early_config_prune = prune_configs_by.get("early_config_prune", self.early_config_prune) self.fn = fn self.base_fn = fn while not inspect.isfunction(self.base_fn): self.base_fn = self.base_fn.fn self.num_warmups = warmup self.num_reps = rep self.use_cuda_graph = use_cuda_graph # If we got explicitly called via the old interface, raise a warning # and proceed with the old behavior. if warmup is not None or rep is not None or use_cuda_graph: import warnings warnings.warn(("warmup, rep, and use_cuda_graph parameters are deprecated. See " "https://github.com/triton-lang/triton/pull/4496 for details."), DeprecationWarning, stacklevel=1) if use_cuda_graph: from ..testing import do_bench_cudagraph self.do_bench = lambda kernel_call, quantiles: do_bench_cudagraph( kernel_call, rep=rep if rep is not None else 100, quantiles=quantiles, ) return import triton.testing self.do_bench = lambda kernel_call, quantiles: triton.testing.do_bench( kernel_call, warmup=warmup if warmup is not None else 25, rep=rep if rep is not None else 100, quantiles=quantiles, ) return if do_bench is None: self.do_bench = driver.active.get_benchmarker() else: self.do_bench = do_bench def _bench(self, *args, config, **meta): from ..compiler.errors import CompileTimeAssertionFailure verbose = os.environ.get("TRITON_PRINT_AUTOTUNING", None) == "1" if verbose: print(f"Autotuning kernel {self.base_fn.__name__} with config {config}") # check for conflicts, i.e. meta-parameters both provided # as kwargs and by the autotuner conflicts = meta.keys() & config.kwargs.keys() if conflicts: raise ValueError(f"Conflicting meta-parameters: {', '.join(conflicts)}." " Make sure that you don't re-define auto-tuned symbols.") # augment meta-parameters with tunable ones current = dict(meta, **config.all_kwargs()) full_nargs = {**self.nargs, **current} def kernel_call(): if config.pre_hook: config.pre_hook(full_nargs) self.pre_hook(full_nargs) try: self.fn.run( *args, **current, ) except Exception as e: try: self.post_hook(full_nargs, exception=e) finally: # Throw exception raised by `self.fn.run` raise self.post_hook(full_nargs, exception=None) try: return self.do_bench(kernel_call, quantiles=(0.5, 0.2, 0.8)) except (OutOfResources, CompileTimeAssertionFailure, PTXASError) as e: if verbose: print(f"Autotuning failed with {e}") return [float("inf"), float("inf"), float("inf")] def run(self, *args, **kwargs): self.nargs = dict(zip(self.arg_names, args)) used_cached_result = True if len(self.configs) > 1: all_args = {**self.nargs, **kwargs} _args = {k: v for (k, v) in all_args.items() if k in self.arg_names} key = [_args[key] for key in self.keys if key in _args] for _, arg in _args.items(): if hasattr(arg, "dtype"): key.append(str(arg.dtype)) key = tuple(key) if key not in self.cache: # prune configs used_cached_result = False pruned_configs = self.prune_configs(kwargs) bench_start = time.time() timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs} bench_end = time.time() self.bench_time = bench_end - bench_start self.cache[key] = builtins.min(timings, key=timings.get) full_nargs = {**self.nargs, **kwargs, **self.cache[key].all_kwargs()} self.pre_hook(full_nargs, reset_only=True) self.configs_timings = timings config = self.cache[key] else: config = self.configs[0] self.best_config = config if os.getenv("TRITON_PRINT_AUTOTUNING", None) == "1" and not used_cached_result: print(f"Triton autotuning for function {self.base_fn.__name__} finished after " f"{self.bench_time:.2f}s; best config selected: {self.best_config};") if config.pre_hook is not None: full_nargs = {**self.nargs, **kwargs, **config.all_kwargs()} config.pre_hook(full_nargs) ret = self.fn.run( *args, **kwargs, **config.all_kwargs(), ) self.nargs = None return ret def prune_configs(self, kwargs: Dict) -> List[Config]: pruned_configs = self.configs if self.early_config_prune: pruned_configs = self.early_config_prune(self.configs, self.nargs, **kwargs) if self.perf_model: top_k = self.configs_top_k if isinstance(top_k, float) and top_k <= 1.0: top_k = int(len(self.configs) * top_k) elif not isinstance(top_k, int): # Slice index must be an integer raise TypeError("Error while pruning configs, top_k must be either 1) a float <= 1.0 or 2) an int") if len(pruned_configs) > top_k: est_timing = { config: self.perf_model( **self.nargs, **kwargs, **config.all_kwargs(), ) for config in pruned_configs } pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k] return pruned_configs def warmup(self, *args, **kwargs): self.nargs = dict(zip(self.arg_names, args)) ret = [] for config in self.prune_configs(kwargs): ret.append(self.fn.warmup( *args, **kwargs, **config.all_kwargs(), )) self.nargs = None return ret class Config: """ An object that represents a possible kernel configuration for the auto-tuner to try. :ivar kwargs: a dictionary of meta-parameters to pass to the kernel as keyword arguments. :type kwargs: dict[Str, Any] :ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if `num_warps=8`, then each kernel instance will be automatically parallelized to cooperatively execute using `8 * 32 = 256` threads. :type num_warps: int :ivar num_stages: the number of stages that the compiler should use when software-pipelining loops. Mostly useful for matrix multiplication workloads on SM80+ GPUs. :type num_ctas: int :ivar num_ctas: number of blocks in a block cluster. SM90+ only. :type maxnreg: Optional[int] :ivar maxnreg: maximum number of registers one thread can use. Corresponds to ptx .maxnreg directive. Not supported on all platforms. :ivar pre_hook: a function that will be called before the kernel is called. Parameters of this function are args. """ def __init__(self, kwargs, num_warps=4, num_stages=3, num_ctas=1, num_buffers_warp_spec=0, num_consumer_groups=0, reg_dec_producer=0, reg_inc_consumer=0, maxnreg=None, pre_hook=None): self.kwargs = kwargs self.num_warps = num_warps self.num_ctas = num_ctas self.num_stages = num_stages self.num_buffers_warp_spec = num_buffers_warp_spec self.num_consumer_groups = num_consumer_groups self.reg_dec_producer = reg_dec_producer self.reg_inc_consumer = reg_inc_consumer self.maxnreg = maxnreg self.pre_hook = pre_hook def all_kwargs(self): return { **self.kwargs, **{ k: v for (k, v) in ( ("num_warps", self.num_warps), ("num_ctas", self.num_ctas), ("num_stages", self.num_stages), ("num_buffers_warp_spec", self.num_buffers_warp_spec), ("num_consumer_groups", self.num_consumer_groups), ("reg_dec_producer", self.reg_dec_producer), ("reg_inc_consumer", self.reg_inc_consumer), ("maxnreg", self.maxnreg), ) if v is not None } } def __str__(self): res = [] for k, v in self.kwargs.items(): res.append(f"{k}: {v}") res.append(f"num_warps: {self.num_warps}") res.append(f"num_ctas: {self.num_ctas}") res.append(f"num_stages: {self.num_stages}") res.append(f"num_buffers_warp_spec: {self.num_buffers_warp_spec}") res.append(f"num_consumer_groups: {self.num_consumer_groups}") res.append(f"reg_dec_producer: {self.reg_dec_producer}") res.append(f"reg_inc_consumer: {self.reg_inc_consumer}") res.append(f"maxnreg: {self.maxnreg}") return ", ".join(res) def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, restore_value=None, pre_hook=None, post_hook=None, warmup=None, rep=None, use_cuda_graph=False, do_bench=None): """ Decorator for auto-tuning a :code:`triton.jit`'d function. .. highlight:: python .. code-block:: python @triton.autotune(configs=[ triton.Config(kwargs={'BLOCK_SIZE': 128}, num_warps=4), triton.Config(kwargs={'BLOCK_SIZE': 1024}, num_warps=8), ], key=['x_size'] # the two above configs will be evaluated anytime # the value of x_size changes ) @triton.jit def kernel(x_ptr, x_size, BLOCK_SIZE: tl.constexpr): ... :note: When all the configurations are evaluated, the kernel will run multiple times. This means that whatever value the kernel updates will be updated multiple times. To avoid this undesired behavior, you can use the `reset_to_zero` argument, which resets the value of the provided tensor to `zero` before running any configuration. If the environment variable :code:`TRITON_PRINT_AUTOTUNING` is set to :code:`"1"`, Triton will print a message to stdout after autotuning each kernel, including the time spent autotuning and the best configuration. :param configs: a list of :code:`triton.Config` objects :type configs: list[triton.Config] :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs. :type key: list[str] :param prune_configs_by: a dict of functions that are used to prune configs, fields: 'perf_model': performance model used to predicate running time with different configs, returns running time 'top_k': number of configs to bench 'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It takes configs:List[Config] as its input, and returns pruned configs. :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs. :type reset_to_zero: list[str] :param restore_value: a list of argument names whose value will be restored after evaluating any configs. :type restore_value: list[str] :param pre_hook: a function that will be called before the kernel is called. This overrides the default pre_hook used for 'reset_to_zero' and 'restore_value'. 'kwargs': a dict of all arguments passed to the kernel. 'reset_only': a boolean indicating whether the pre_hook is called to reset the values only, without a corresponding post_hook. :type pre_hook: lambda args, reset_only :param post_hook: a function that will be called after the kernel is called. This overrides the default post_hook used for 'restore_value'. 'kwargs': a dict of all arguments passed to the kernel. 'exception': the exception raised by the kernel in case of a compilation or runtime error. :type post_hook: lambda args, exception :param warmup: warmup time (in ms) to pass to benchmarking (deprecated). :type warmup: int :param rep: repetition time (in ms) to pass to benchmarking (deprecated). :type rep: int :param do_bench: a benchmark function to measure the time of each run. :type do_bench: lambda fn, quantiles """ def decorator(fn): return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, restore_value, pre_hook=pre_hook, post_hook=post_hook, prune_configs_by=prune_configs_by, warmup=warmup, rep=rep, use_cuda_graph=use_cuda_graph, do_bench=do_bench) return decorator class Heuristics(KernelInterface): def __init__(self, fn, arg_names, values) -> None: self.fn = fn self.values = values self.arg_names = arg_names def run(self, *args, **kwargs): for v, heur in self.values.items(): kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs}) return self.fn.run(*args, **kwargs) def heuristics(values): """ Decorator for specifying how the values of certain meta-parameters may be computed. This is useful for cases where auto-tuning is prohibitively expensive, or just not applicable. .. highlight:: python .. code-block:: python # smallest power-of-two >= x_size @triton.heuristics(values={'BLOCK_SIZE': lambda args: triton.next_power_of_2(args['x_size'])}) @triton.jit def kernel(x_ptr, x_size, BLOCK_SIZE: tl.constexpr): ... :param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter. each such function takes a list of positional arguments as input. :type values: dict[str, Callable[[dict[str, Any]], Any]] """ def decorator(fn): return Heuristics(fn, fn.arg_names, values) return decorator