# mypy: allow-untyped-defs from __future__ import annotations import builtins import copy import functools import hashlib import inspect import logging import math import operator import os import os.path import re import sys import threading import time from typing import Any, Dict, List, Optional, Set, Tuple import torch from .autotune_cache import AutotuneCache from .benchmarking import benchmarker from .coordinate_descent_tuner import CoordescTuner from .hints import ( _NUM_THREADS_PER_WARP, AutotuneHint, DeviceProperties, HeuristicType, ReductionHint, TileHint, TRITON_MAX_BLOCK, ) from .runtime_utils import ( cache_dir, ceildiv, conditional_product, create_bandwidth_info_str, dynamo_timed, get_first_attr, get_max_y_grid, get_num_bytes, next_power_of_2, triton_config_to_hashable, validate_triton_config, ) try: import triton except ImportError: triton = None if triton is not None: from triton import Config from triton.compiler import CompiledKernel from triton.runtime.autotuner import OutOfResources from triton.runtime.jit import KernelInterface try: from triton.compiler.compiler import ASTSource except ImportError: ASTSource = None try: from triton.backends.compiler import GPUTarget except ImportError: GPUTarget = None else: Config = object KernelInterface = object OutOfResources = object ASTSource = None GPUTarget = None try: autograd_profiler = torch.autograd.profiler except AttributeError: # Compile workers only have a mock version of torch class autograd_profiler: # type: ignore[no-redef] _is_profiler_enabled = False log = logging.getLogger(__name__) def autotune_hints_to_configs( hints: Set[AutotuneHint], size_hints, block_size: int ) -> List[Config]: """ AutotuneHints can be attached to the metadata of triton kernels for providing suggestions about what to try for autotuning. One reason to do this is if there are some configs that are only useful in specific scenarios, in which case we can avoid wasting compile time on autotuning unless we know we are in one of those scenarios. Based on those hints, this function will generate a list of additional autotuning configs to try. """ xyz_options: Tuple[Tuple[int, Optional[int], Optional[int]], ...] configs = [] for hint in hints: if hint == AutotuneHint.ELEMENTS_PER_WARP_32: if len(size_hints) == 1: xyz_options = ((block_size // 4, None, None),) elif len(size_hints) == 2: xyz_options = ((block_size // 4, 1, None), (1, block_size // 4, None)) elif len(size_hints) == 3: xyz_options = ( (block_size // 4, 1, 1), (1, block_size // 4, 1), (1, 1, block_size // 4), ) for xyz in xyz_options: configs.append( triton_config( size_hints, *xyz, num_elements_per_warp=32, ) ) return configs def disable_pointwise_autotuning(inductor_meta): # Autotuning can give different benchmarking results from run to run, and # therefore we disable autotuning when use_deterministic flag is on. if inductor_meta.get("are_deterministic_algorithms_enabled"): return True return not inductor_meta.get("autotune_pointwise", True) def _dump_launch_params(args, kwargs, launcher, kernel_name): call_args = [] call_kwargs = {} for arg in args: if isinstance(arg, (int, bool)): call_args.append(str(arg)) else: call_args.append("T") for k, v in kwargs.items(): if isinstance(arg, (int, bool)): call_kwargs[k] = v else: call_kwargs[k] = v for k, v in launcher.config.kwargs.items(): call_kwargs[k] = v call_kwargs["num_warps"] = launcher.config.num_warps call_kwargs["num_stages"] = launcher.config.num_stages args_str = "" args_str += ", ".join(call_args) for k, v in call_kwargs.items(): args_str += f", {k}={v}" abs_path = os.path.abspath(sys.argv[0]) with open(f"{abs_path}.launch_params", "a") as f: f.write(f"{kernel_name} | {args_str}\n") class CachingAutotuner(KernelInterface): """ Simplified version of Triton autotuner that has no invalidation key and caches the best config to disk to improve cold start times. Unlike the main triton Autotuner, this version can precompile all configs, and does not rely on the Triton JIT. """ def __init__( self, fn, triton_meta, # passed directly to triton configs, save_cache_hook, mutated_arg_names: List[str], # see [Note: clone mutated buffers] heuristic_type, size_hints=None, inductor_meta=None, # metadata not relevant to triton custom_kernel=False, # whether the kernel is inductor-generated or custom filename: Optional[str] = None, ): super().__init__() assert len(configs) > 0, "Non-empty TritonConfig list required for compiling" # makes sure there are no pre-hooks on any of the triton configs for cfg in configs: validate_triton_config(cfg) self.fn = fn self.device_props: DeviceProperties = triton_meta["device"] self.triton_meta = { **triton_meta, "device": self.device_props.index, "device_type": self.device_props.type, } self.inductor_meta = {} if inductor_meta is None else inductor_meta self.save_cache_hook = save_cache_hook self.mutated_arg_names = mutated_arg_names self.configs = configs self.heuristic_type = heuristic_type self.custom_kernel = custom_kernel self.cuda_kernel_saved = False if log.isEnabledFor(logging.DEBUG): log.debug( "CachingAutotuner gets %d configs for %s", len(self.configs), self.fn.__name__, ) for c in self.configs: log.debug(c) self.launchers = [] # type: ignore[var-annotated] self.lock = threading.Lock() if os.getenv("TRITON_CACHE_DIR") is None: os.environ["TRITON_CACHE_DIR"] = os.path.join( cache_dir(), "triton", str(self.triton_meta.get("device", 0)), ) log.debug("Triton cache dir: %s", os.environ["TRITON_CACHE_DIR"]) self.size_hints = size_hints self.coordesc_tuner = CoordescTuner( is_mm=False, name=self.fn.__name__, size_hints=size_hints, inductor_meta=self.inductor_meta, ) self.filename = filename self.precompile_time_taken_ns = 0 self.autotune_time_taken_ns = 0 def precompile(self, warm_cache_only=False): with self.lock: if self.launchers: return self.launchers = [] compiled_binaries = [] if not self.configs: raise RuntimeError("No triton configs are available") for c in self.configs: try: compiled_binary, launcher = self._precompile_config( c, warm_cache_only ) except OutOfResources as e: if len(self.configs) == 1: # There are no valid Triton configs raise e # Skip the config if we run out of resource continue self.launchers.append(launcher) compiled_binaries.append(compiled_binary) if len(self.launchers) == 0: raise RuntimeError( "No valid triton configs. Report a fatal compilation error" ) seen_configs = set(self.configs) device_prop = self.device_props if ( self.inductor_meta.get("dynamic_scale_rblock", True) and self.heuristic_type == HeuristicType.REDUCTION and self.size_hints is not None # Disable for AMDGPU/Intel as Triton is not ready to return n_regs for a compiled_binary. and device_prop.type == "cuda" and device_prop.major and device_prop.major >= 8 ): assert device_prop.regs_per_multiprocessor assert device_prop.max_threads_per_multi_processor assert device_prop.multi_processor_count for triton_config, compiled_binary in zip( self.configs, compiled_binaries ): assert len(self.size_hints) == 2 xblock = triton_config.kwargs.get("XBLOCK", 1) rblock = triton_config.kwargs["RBLOCK"] total_block = (self.size_hints[0] + xblock - 1) // xblock nreg = getattr(compiled_binary, "n_regs", None) if nreg is None: continue # make sure rblock is not too small if rblock <= 64: continue # each SM of A100 has 65536 32-bit registers. To maximize # the theoretical occupancy, we need run 2048 threads on each # SM. So each thread should use no more than 65536 / 2048 # = 32 registers. In cases where occupancy matters, and each # thread uses too many registers, reduce RBLOCK to reduce # the register usage. # For kernel https://gist.github.com/shunting314/e4cccc031fe30d378b9b23c08c238cbd # from PLBartForCausalLM, latency improve from # 7.795ms to 4.883ms. # if ( nreg <= device_prop.regs_per_multiprocessor // device_prop.max_threads_per_multi_processor ): continue nreg_per_warp = nreg * 32 nreg_per_block = nreg_per_warp * triton_config.num_warps # Previously we set max_blocks_per_sm to 'max_threads_per_multi_processo / (32 * num_warps)' # The formula below is a tighter upper bound since we have the assumption that # nreg > device_prop.regs_per_multiprocessor // device_prop.max_threads_per_multi_processor # due to the if condition above and: # regs_per_multiprocessor / nreg_per_block # = regs_per_multiprocessor / (nreg * 32 * num_warps) # < regs_per_multiprocessor / ((regs_per_multiprocessor / max_threads_per_multi_processor) * 32 * num_warps) # = max_threads_per_multi_processor / (32 * num_warps) # Using a tigher upper bound can reveal more optimization opportunities. max_blocks_per_sm = max( device_prop.regs_per_multiprocessor // nreg_per_block, 1 ) if ( total_block <= max_blocks_per_sm * device_prop.multi_processor_count ): # no need to improve occupancy continue new_config = copy.deepcopy(triton_config) new_config.kwargs["RBLOCK"] = rblock // 2 if new_config in seen_configs: continue seen_configs.add(new_config) log.debug( "Dynamically scale down RBLOCK from TritonConfig(%s) and get a new TritonConfig(%s)", triton_config, new_config, ) self.launchers.append( self._precompile_config(new_config, warm_cache_only)[1] ) self.configs = None def get_device_interface(self): # this code cannot run in compile workers, because it imports from torch from torch._dynamo.device_interface import get_interface_for_device return get_interface_for_device(self.device_props.type.replace("hip", "cuda")) def _precompile_config(self, cfg: Config, warm_cache_only: bool): """Ahead of time compile a given autotuner config.""" compile_meta = copy.deepcopy(self.triton_meta) for k, v in cfg.kwargs.items(): if self.device_props.type == "hip": if k == "matrix_instr_nonkdim": compile_meta["matrix_instr_nonkdim"] = v continue if k == "waves_per_eu": compile_meta["waves_per_eu"] = v continue compile_meta["constants"][self.fn.arg_names.index(k)] = v compile_meta["num_warps"] = cfg.num_warps compile_meta["num_stages"] = cfg.num_stages compile_meta["debug"] = self.inductor_meta.get( "assert_indirect_indexing", True ) and not self.inductor_meta.get("is_hip", False) # device type will be "hip" rather than "cuda" here compile_meta["device_type"] = self.device_props.type compile_meta["cc"] = self.device_props.cc if ASTSource: compile_args = ( ASTSource( self.fn, compile_meta["signature"], compile_meta["constants"], compile_meta["configs"][0], ), ) cc_str = str(compile_meta["cc"]) if "gfx10" in cc_str or "gfx11" in cc_str: rocm_warp_size = 32 else: rocm_warp_size = 64 if GPUTarget: target = GPUTarget( compile_meta["device_type"], compile_meta["cc"], rocm_warp_size if torch.version.hip else 32, ) else: target = ( (compile_meta["device_type"], compile_meta["cc"]) if not torch.version.hip else [ compile_meta["device_type"], compile_meta["cc"], rocm_warp_size, ] ) options = { "num_warps": compile_meta["num_warps"], "num_stages": compile_meta["num_stages"], "debug": compile_meta["debug"], } if self.device_props.type == "hip": if "waves_per_eu" in compile_meta: options["waves_per_eu"] = compile_meta["waves_per_eu"] if "matrix_instr_nonkdim" in compile_meta: options["matrix_instr_nonkdim"] = compile_meta[ "matrix_instr_nonkdim" ] compile_kwargs = { "target": target, "options": options, } else: compile_args = (self.fn,) compile_kwargs = compile_meta if warm_cache_only: return ( triton.compile(*compile_args, **compile_kwargs), None, ) # importing from torch is safe now that precompile has returned from torch._dynamo.device_interface import DeviceGuard device_interface = self.get_device_interface() # load binary to the correct device with DeviceGuard(device_interface, compile_meta["device"]): # type: ignore[attr-defined] # need to initialize context device_interface.synchronize(device_interface.current_device()) try: binary = triton.compile(*compile_args, **compile_kwargs) except Exception: log.exception( "Triton compilation failed: %s\n%s\nmetadata: %s", self.inductor_meta.get("kernel_name", "triton_"), self.fn.src, compile_meta, ) raise binary._init_handles() call_args = [ arg for i, arg in enumerate(self.fn.arg_names) if i not in self.fn.constexprs ] def_args = [name for name in self.fn.arg_names if name not in cfg.kwargs] binary_shared = ( binary.shared if hasattr(binary, "shared") else binary.metadata.shared ) scope = { "grid_meta": cfg.kwargs, "bin": binary, "launch_enter_hook": CompiledKernel.launch_enter_hook, "launch_exit_hook": CompiledKernel.launch_exit_hook, "metadata": binary.packed_metadata if hasattr(binary, "packed_metadata") else binary.metadata, "shared": binary_shared, } scope["num_warps"] = ( binary.num_warps if hasattr(binary, "num_warps") else binary.metadata.num_warps ) scope["cta_args"] = ( (binary.num_ctas, *get_first_attr(binary, "cluster_dims", "clusterDims")) if hasattr(binary, "num_ctas") else ( (binary.metadata.num_ctas, *binary.metadata.cluster_dims) if hasattr(binary, "metadata") else () ) ) scope["function"] = get_first_attr(binary, "function", "cu_function") def get_launch_args_without_kernel_launch_metadata( grid, grid_0, grid_1, grid_2, stream, function, metadata, bin, launch_enter_hook, launch_exit_hook, num_warps, shared, cta_args, args, ): """ Construct launch args before CompiledKernel.launch_metadata is added. """ return ( grid_0, grid_1, grid_2, num_warps, *cta_args, shared, stream, function, launch_enter_hook, launch_exit_hook, metadata, ) # Getting the kernel launch args is extremely perf-sensitive. Evaluating # `bin.launch_metadata` is relatively expensive, and returns None unless a # `launch_enter_hook` is installed. So if we don't have that hook installed, # we want to burn None in to the launch args with zero overhead. # See https://github.com/pytorch/pytorch/issues/123597 if binary.launch_enter_hook: def get_launch_args_with_kernel_launch_metadata( grid, grid_0, grid_1, grid_2, stream, function, metadata, bin, launch_enter_hook, launch_exit_hook, num_warps, shared, cta_args, args, ): """ Construct launch args after CompiledKernel.launch_metadata is added by https://github.com/openai/triton/pull/3492 . """ return ( grid_0, grid_1, grid_2, stream, function, metadata, bin.launch_metadata(grid, stream, *args), launch_enter_hook, launch_exit_hook, ) else: def get_launch_args_with_kernel_launch_metadata( grid, grid_0, grid_1, grid_2, stream, function, metadata, bin, launch_enter_hook, launch_exit_hook, num_warps, shared, cta_args, args, ): """ Construct launch args after CompiledKernel.launch_metadata is added by https://github.com/openai/triton/pull/3492 . """ return ( grid_0, grid_1, grid_2, stream, function, metadata, None, launch_enter_hook, launch_exit_hook, ) scope["get_launch_args"] = ( get_launch_args_with_kernel_launch_metadata if hasattr(binary, "launch_metadata") else get_launch_args_without_kernel_launch_metadata ) scope["runner"] = get_first_attr(binary, "run", "c_wrapper") exec( f""" def launcher({', '.join(def_args)}, grid, stream): if callable(grid): grid_0, grid_1, grid_2 = grid(grid_meta) else: grid_0, grid_1, grid_2 = grid args = {', '.join(call_args)}, launch_args = get_launch_args( grid, grid_0, grid_1, grid_2, stream, function, metadata, bin, launch_enter_hook, launch_exit_hook, num_warps, shared, cta_args, args ) runner(*launch_args, *args) return bin """.lstrip(), scope, ) launcher = scope["launcher"] launcher.config = cfg launcher.n_regs = getattr(binary, "n_regs", None) launcher.n_spills = getattr(binary, "n_spills", None) launcher.shared = binary_shared launcher.store_cubin = self.inductor_meta.get("store_cubin", False) # store this global variable to avoid the high overhead of reading it when calling run if launcher.store_cubin: launcher.fn = self.fn launcher.bin = binary return binary, launcher def bench(self, launcher, *args, grid, with_profiler=False, **kwargs): """Measure the performance of a given launcher""" # we don't skip configs wiht spilled registers when auto-tuning custom # (user-written) Triton kernels, as (i) we don't have any knowledge or # control over the kernel code; (ii) there is empirical evidence that # for some (complicated) custom Triton kernels, a register-spilling # config may yield the best latency. if not self.custom_kernel and launcher.n_spills > self.inductor_meta.get( "spill_threshold", 16 ): log.debug( "Skip config %s because of register spilling: %d", launcher.config, launcher.n_spills, ) return float("inf") device_interface = self.get_device_interface() stream = device_interface.get_raw_stream(device_interface.current_device()) def kernel_call(): cloned_args, cloned_kwargs = self.clone_args(*args, **kwargs) launcher( *cloned_args, **cloned_kwargs, grid=grid, stream=stream, ) if with_profiler: from torch._inductor.utils import do_bench_using_profiling return do_bench_using_profiling(kernel_call, warmup=10, rep=40) return benchmarker.benchmark_gpu(kernel_call, rep=40, fast_flush=True) def clone_args(self, *args, **kwargs) -> Tuple[List[Any], Dict[str, Any]]: from ..compile_fx import clone_preserve_strides # [Note: clone mutated buffers] # clone inplace buffers to avoid autotune contaminating them if # the kernel does in-place stores. avoid cloning other buffers because # it leads to increase memory use cloned_args = [] for i, arg in enumerate(args): if self.fn.arg_names[i] in self.mutated_arg_names: assert isinstance(arg, torch.Tensor) cloned_args.append(clone_preserve_strides(arg)) else: cloned_args.append(arg) cloned_kwargs: Dict[str, Any] = {} for name, arg in kwargs.items(): if name in self.mutated_arg_names: assert isinstance(arg, torch.Tensor) cloned_kwargs[name] = clone_preserve_strides(arg) else: cloned_kwargs[name] = arg return cloned_args, cloned_kwargs def benchmark_all_configs(self, *args, **kwargs): with dynamo_timed("CachingAutotuner.benchmark_all_configs"): timings = { launcher: self.bench(launcher, *args, **kwargs) for launcher in self.launchers } for k, v in timings.items(): self.coordesc_tuner.cache_benchmark_result(k.config, v) if log.isEnabledFor(logging.DEBUG): log.debug("Benchmark all input configs for %s, get:", self.fn.__name__) for k, v in timings.items(): log.debug( "%s: %f, nreg %d, nspill %d, #shared-mem %s", k.config, v, k.n_regs, k.n_spills, k.shared, ) return timings def autotune_to_one_config(self, *args, **kwargs): """Do the actual autotuning""" start_time = time.time_ns() timings = self.benchmark_all_configs(*args, **kwargs) benchmark_time_taken_ns = time.time_ns() - start_time self.launchers = [builtins.min(timings, key=timings.get)] self.autotune_time_taken_ns = ( self.precompile_time_taken_ns + benchmark_time_taken_ns ) if self.save_cache_hook: self.save_cache_hook(self.launchers[0].config, self.autotune_time_taken_ns) def save_gpu_kernel(self, grid, stream, launcher): if callable(grid): grid_x, grid_y, grid_z = grid(launcher.config.kwargs) else: grid_x, grid_y, grid_z = grid key = self.inductor_meta.get("kernel_name", None) # unique kernel name assert key is not None, "kernel_name can not be None" params = { "mangled_name": launcher.bin.metadata.name if hasattr(launcher.bin.metadata, "name") else launcher.bin.metadata["name"], "grid_x": grid_x, "grid_y": grid_y, "grid_z": grid_z, "x_block": launcher.config.kwargs.get("XBLOCK", 1), "y_block": launcher.config.kwargs.get("YBLOCK", None), "z_block": launcher.config.kwargs.get("ZBLOCK", None), "num_warps": launcher.bin.num_warps if hasattr(launcher.bin, "num_warps") else launcher.bin.metadata.num_warps, "shared_mem": launcher.bin.shared if hasattr(launcher.bin, "shared") else launcher.bin.metadata.shared, "stream": stream, # User defined triton kernels will have arbitrary kwarg names "meta": launcher.config.kwargs, } from torch._inductor.codecache import CudaKernelParamCache bin_type = {"hip": "hsaco", "xpu": "spv"}.get(self.device_props.type, "cubin") binary = launcher.bin.asm[bin_type] CudaKernelParamCache.set(key, params, binary, bin_type) self.cuda_kernel_saved = True def coordinate_descent_tuning(self, launcher, *args, **kwargs): """ Coordinate descent tuning can be run with or without max-autotune. The only difference between these two is the starting config for coordinate_descent tuning. E.g., assuming regular autotune only get one config C1; while max-autotune get 4 configs C1, C2, C3, C4 and max-autotune figure out C3 is the best. Then if coordinate desecnt tuning is run with max-autotune disabled, it will start from C1; while if coordinate descent tuning is run with max-autotune enabled, it will start from C3. """ if ( self.heuristic_type == HeuristicType.TEMPLATE or self.heuristic_type == HeuristicType.USER_AUTOTUNE ): # skip triton template return launcher config2launcher = {launcher.config: launcher} def benchmark_one_config(config): with self.lock: _, launcher = self._precompile_config(config, False) config2launcher[config] = launcher out = self.bench(launcher, *args, **kwargs) log.debug( "COORDESC: %s: %f, nreg %d, nspill %d, #shared-mem %d", launcher.config, out, launcher.n_regs, launcher.n_spills, launcher.shared, ) return out assert not ( self.heuristic_type == HeuristicType.PERSISTENT_REDUCTION and "RBLOCK" in launcher.config.kwargs ), "Coordinate descent tuner relies on the assumption that persistent reduction's triton config does not have RBLOCK" start_time = time.time_ns() best_config = self.coordesc_tuner.autotune( benchmark_one_config, launcher.config, None ) coordesc_time_taken_ns = time.time_ns() - start_time best_config.found_by_coordesc = True if self.save_cache_hook: self.save_cache_hook( best_config, self.autotune_time_taken_ns + coordesc_time_taken_ns, found_by_coordesc=True, ) return config2launcher.get(best_config) def run(self, *args, grid, stream, **kwargs): if len(self.launchers) != 1: if len(self.launchers) == 0: start_time = time.time_ns() self.precompile() self.precompile_time_taken_ns = time.time_ns() - start_time if len(self.launchers) > 1: self.autotune_to_one_config(*args, grid=grid, **kwargs) if not getattr( self.launchers[0].config, "found_by_coordesc", False ) and self.inductor_meta.get("coordinate_descent_tuning", False): self.launchers = [ self.coordinate_descent_tuning( self.launchers[0], *args, grid=grid, **kwargs ) ] (launcher,) = self.launchers if launcher.store_cubin: self.save_gpu_kernel(grid, stream, launcher) if os.environ.get("TORCHINDUCTOR_DUMP_LAUNCH_PARAMS", 0) == "1": _dump_launch_params(args, kwargs, launcher, self.fn.__name__) # it is faster than entering and exiting a context manager, even if the context # manager is a nullcontext. if autograd_profiler._is_profiler_enabled: # grid can be a tuple of ints or a string. if isinstance(grid, tuple): grid_info = str(grid) else: grid_info = getattr(grid, "grid_fn_str", "") with torch._C._profiler._RecordFunctionFast( self.inductor_meta.get("kernel_name", "triton kernel"), args, { "kernel_file": "" if self.filename is None else self.filename, "kernel_backend": "triton", "grid": grid_info, "stream": stream, }, ): return launcher( *args, **kwargs, grid=grid, stream=stream, ) else: return launcher( *args, **kwargs, grid=grid, stream=stream, ) def _find_names(obj): import gc import inspect frame = inspect.currentframe() while frame is not None: frame.f_locals frame = frame.f_back obj_names = [] for referrer in gc.get_referrers(obj): if isinstance(referrer, dict): for k, v in referrer.items(): if v is obj: obj_names.append(k) return obj_names collected_calls: List[Any] = [] def start_graph(): collected_calls.clear() def end_graph(output_file): if len(collected_calls) == 0: return overall_time = sum(call[0] for call in collected_calls) overall_gb = sum(call[1] for call in collected_calls) cur_file = inspect.stack()[1].filename summary_str = ( f"SUMMARY ({cur_file})\n" f"{overall_time:.2f}ms \t {overall_gb:.2f} GB\t {overall_gb/(overall_time/1e3):.2f}GB/s" ) print(summary_str) print() if output_file is not None: # sort perf numbers in descending order, i.e. placing the # most runtime-heavy kernels at the top of the list sorted_calls = sorted(collected_calls, key=lambda c: float(c[0]), reverse=True) try: with open(output_file, "a") as file: log.debug("Save profile bandwidth results to %s", output_file) file.write("====================\n") file.write(f"TRITON KERNELS BANDWIDTH INFO ({cur_file})\n") for ms, num_gb, gb_per_s, kernel_name in sorted_calls: # also display the runtime percentage for each kernel percentage = f"{ms/overall_time*100:.2f}%" suffix = f" \t {percentage} \t {kernel_name}" bw_info_str = create_bandwidth_info_str( ms, num_gb, gb_per_s, suffix=suffix, color=False, ) file.write(bw_info_str + "\n") file.write(f"{summary_str}\n\n") except Exception as e: log.warning( "failed to write profile bandwidth result into %s: %s", output_file, e, ) class DebugAutotuner(CachingAutotuner): def __init__(self, *args, regex_filter="", with_profiler=False, **kwargs): self.regex_filter = regex_filter self.with_profiler = with_profiler super().__init__(*args, **kwargs) self.cached = None def run(self, *args, grid, stream): possible_names = _find_names(self) kernel_name = f"{max(possible_names, key=len)}" if not re.match(self.regex_filter, kernel_name): return super().run(*args, grid=grid, stream=stream) (launcher,) = self.launchers if self.cached is None: ms = self.bench( launcher, *args, grid=grid, with_profiler=self.with_profiler ) num_in_out_ptrs = len( [ arg_name for arg_name in self.fn.arg_names if arg_name.startswith("in_out_ptr") ] ) num_gb = self.inductor_meta.get("kernel_num_gb", None) if num_gb is None: num_gb = get_num_bytes(*args, num_in_out_args=num_in_out_ptrs) / 1e9 gb_per_s = num_gb / (ms / 1e3) self.cached = ms, num_gb, gb_per_s, kernel_name collected_calls.append((ms, num_gb, gb_per_s, kernel_name)) print( create_bandwidth_info_str( ms, num_gb, gb_per_s, suffix=f" \t {kernel_name}" ) ) def hash_configs(configs: List[Config]): """ Hash used to check for changes in configurations """ hasher = hashlib.sha256() for cfg in configs: hasher.update( f"{sorted(cfg.kwargs.items())} {cfg.num_warps} {cfg.num_stages}\n".encode() ) return hasher.hexdigest() def cached_autotune( size_hints: Optional[List[int]], configs: List[Config], triton_meta, heuristic_type, filename=None, inductor_meta=None, custom_kernel=False, ): """ A copy of triton.autotune that calls our subclass. Our subclass has additional debugging, error handling, and on-disk caching. """ configs = unique_configs(configs) assert len(configs) == 1 or filename inductor_meta = {} if inductor_meta is None else inductor_meta disabled = inductor_meta.get("force_disable_caches", False) # on disk caching logic and/or remote caching autotune_cache = None if ( not disabled and filename is not None and (len(configs) > 1 or inductor_meta.get("coordinate_descent_tuning")) ): configs_hash = hash_configs(configs) autotune_cache = AutotuneCache.create(inductor_meta, filename, configs_hash) if autotune_cache: if best_config := autotune_cache.read_best(inductor_meta, configs): configs = [best_config] else: if disabled: log.debug("autotune caching is disabled by config.force_disable_caches") mutated_arg_names = inductor_meta.pop("mutated_arg_names", ()) def decorator(fn): # Remove XBLOCK from config if it's not a function argument. # This way, coordinate descent tuning will not try to tune it. # # Context: When TritonKernel.no_x_dim is True, we hardcode XBLOCK to 1. import inspect if "XBLOCK" not in inspect.signature(fn.fn).parameters: for tconfig in configs: if "XBLOCK" in tconfig.kwargs: assert tconfig.kwargs["XBLOCK"] == 1 tconfig.kwargs.pop("XBLOCK") if inductor_meta.get("profile_bandwidth"): return DebugAutotuner( fn, triton_meta=triton_meta, inductor_meta=inductor_meta, regex_filter=inductor_meta["profile_bandwidth_regex"], with_profiler=inductor_meta[ "profile_bandwidth_with_do_bench_using_profiling" ], configs=configs, save_cache_hook=autotune_cache and autotune_cache.save, mutated_arg_names=mutated_arg_names, heuristic_type=heuristic_type, size_hints=size_hints, custom_kernel=custom_kernel, filename=filename, ) return CachingAutotuner( fn, triton_meta=triton_meta, inductor_meta=inductor_meta, configs=configs, save_cache_hook=autotune_cache and autotune_cache.save, mutated_arg_names=mutated_arg_names, heuristic_type=heuristic_type, size_hints=size_hints, custom_kernel=custom_kernel, filename=filename, ) return decorator def unique_configs(configs: List[Config]): """Remove duplicate configurations""" seen = set() pruned_configs = [] for cfg in configs: key = triton_config_to_hashable(cfg) if key not in seen: seen.add(key) pruned_configs.append(cfg) return pruned_configs def check_config(cfg, *, xnumel=None, ynumel=None, znumel=None): for numel, label in zip((xnumel, ynumel, znumel), "XYZ"): if numel is None: continue block = cfg[f"{label}BLOCK"] if numel == 1: assert block == 1, ( f"TritonKernel.indexing assumes numel == 1 => BLOCK == 1" f" but {label.lower()}numel=={numel} and {label}BLOCK={block} (cfg={cfg})." ) max_block = TRITON_MAX_BLOCK[label] max_block_str = f'config.triton.max_block["{label}"]' assert max_block % block == 0, ( f"TritonKernel.indexing assumes {label}BLOCK divides {max_block_str}" f" but {label}BLOCK={block} and {max_block_str}={max_block} (cfg={cfg})." ) def _num_warps(num_warps, max_num_warps=8, min_num_warps=2, register_intensive=False): # On AMD GPU each warp has 64 lanes which is double the size on NV GPU, # therefore using half the number of warps here correspondingly. if torch.version.hip: max_num_warps = (max_num_warps + 1) // 2 min_num_warps = (min_num_warps + 1) // 2 # persistent reduction is register intensive if register_intensive: max_num_warps = max_num_warps // 2 return next_power_of_2(min(max(num_warps, min_num_warps), max_num_warps)) def _check_max_grid_x(size_hints, x, num_warps): # Check if maxGridSize is exceeded - if so then must scale XBLOCK further max_grid_x = 2147483647 warp_size = ( 64 if torch.version.hip else 32 ) # TODO: query warp size once #129663 is merged num_blocks = (size_hints[0] + x - 1) // x while (num_blocks * num_warps * warp_size) > max_grid_x and x < size_hints[0]: x *= 2 # Scale up XBLOCK if grid exceeds limits num_blocks = num_blocks // 2 if x >= max_grid_x: raise AssertionError( "Reduction config exceeds cudaDeviceProp maxGridSize. Please raise a pytorch issue" ) return x, num_blocks def triton_config( size_hints, x, y=None, z=None, num_stages=1, num_elements_per_warp=256, min_elem_per_thread=0, ) -> Config: """ Construct a pointwise triton config with some adjustment heuristics based on size_hints. Size_hints is a tuple of numels in each tile dimension and will be rounded up to the nearest power of 2. num_elements_per_warp is a suggestion for controlling how many warps the triton config should contain. e.g.: if x=16, y=8, z=4 then num_elements = 16*8*4 = 512. Then if we set num_elements_per_warp=128, we'll launch 512 (elem) / 128 (elem/warp) = 4 warps. Note that it's just a suggestion, and sometimes other adjustment heuristics will override the num_elements_per_warp. min_elem_per_thread controls the minimum number of elements processed by each thread. It's always enforced. """ # Ideally we want to read this from some device config # for a 2d size_hints [a, b], a should be mapped to YBLOCK rather than XBLOCK size_hints = list(reversed(size_hints)) maxGridSize = [2147483647, 65535, 65535] target = conditional_product(x, y, z) if conditional_product(*size_hints) < target: target //= 8 # shrink sizes to size hints x = min(x, size_hints[0]) if y: y = min(y, size_hints[1]) if z: z = min(z, size_hints[2]) # if we are below original block size, scale up where we can; # or if the calculated grid size is larger than the limit, we bump up the corresponding dimension while x < min(size_hints[0], TRITON_MAX_BLOCK["X"]) and ( x * maxGridSize[0] < size_hints[0] or conditional_product(x, y, z) < target ): x *= 2 while ( y and y < min(size_hints[1], TRITON_MAX_BLOCK["Y"]) and ( y * maxGridSize[1] < size_hints[1] or conditional_product(x, y, z) < target ) ): y *= 2 while ( z and z < min(size_hints[2], TRITON_MAX_BLOCK["Z"]) and ( z * maxGridSize[2] < size_hints[2] or conditional_product(x, y, z) < target ) ): z *= 2 num_warps = _num_warps( conditional_product(x, y, z) // num_elements_per_warp, min_num_warps=1 ) # we are going to arrive at 2 warps only if bs was too small due to # numel being too small. However to workaround some ptx bugs we still # want at least 4 warps if there's enough elements per thread # given that this is a rare situation, don't expect this to affect perf # in general # see https://github.com/pytorch/pytorch/pull/97950 if conditional_product(x, y, z) >= 128 and not torch.version.hip: num_warps = max(num_warps, 4) xnumel = size_hints[0] ynumel = size_hints[1] if y else None znumel = size_hints[2] if z else None # Increase x to satisfy min_elem_per_thread requirements. block_size = max( conditional_product(x, y, z), min_elem_per_thread * _NUM_THREADS_PER_WARP * num_warps, ) x *= math.ceil(block_size / conditional_product(x, y, z)) x, _num_blocks = _check_max_grid_x(size_hints, x, num_warps) cfg = {"XBLOCK": x} if y: cfg["YBLOCK"] = y if z: cfg["ZBLOCK"] = z assert x <= TRITON_MAX_BLOCK["X"], f"increase TRITON_MAX_BLOCK['X'] to {x}" check_config(cfg, xnumel=xnumel, ynumel=ynumel, znumel=znumel) return Config(cfg, num_warps=num_warps, num_stages=num_stages) def triton_config_reduction( size_hints, x, r, num_stages=1, num_warps=None, register_intensive=False ) -> Config: """ Construct a reduction triton config with some adjustment heuristics based on size_hints. Size_hints is a tuple of numels in each tile dimension and will be rounded up to the nearest power of 2. """ target = conditional_product(x, r) if conditional_product(*size_hints) < target: target //= 8 # shrink sizes to size hints x = min(x, size_hints[0]) r = min(r, size_hints[1]) # if we are below original block size, scale up where we can while x < size_hints[0] and conditional_product(x, r) < target: x *= 2 while r < size_hints[1] and conditional_product(x, r) < target: r *= 2 if num_warps is None: num_warps = conditional_product(x, r) // 128 num_warps = _num_warps( num_warps, max_num_warps=16, register_intensive=register_intensive ) x, _num_blocks = _check_max_grid_x(size_hints, x, num_warps) while conditional_product(x, r) > target: if r == 1: break r = r // 2 cfg = {"XBLOCK": x, "RBLOCK": r} check_config(cfg, xnumel=size_hints[0]) assert x <= TRITON_MAX_BLOCK["X"], f"increase TRITON_MAX_BLOCK['X'] to {x}" assert r <= TRITON_MAX_BLOCK["R"], f"increase TRITON_MAX_BLOCK['r'] to {r}" return Config(cfg, num_warps=num_warps, num_stages=num_stages) def triton_config_tiled_reduction(size_hints, x, y, r, num_stages=1): """ Construct a tile reduction triton config with some adjustment heuristics based on size_hints. Size_hints is a tuple of numels in each tile dimension and will be rounded up to the nearest power of 2. """ target = conditional_product(x, y, r) if conditional_product(*size_hints) < target: target //= 8 # shrink sizes to size hints x = min(x, size_hints[0]) y = min(y, size_hints[1]) r = min(r, size_hints[2]) # if we are below original block size, scale up where we can while x < size_hints[0] and conditional_product(x, y, r) < target: x *= 2 while r < size_hints[2] and conditional_product(x, y, r) < target: r *= 2 while y < size_hints[1] and conditional_product(x, y, r) < target: y *= 2 cfg = {"XBLOCK": x, "YBLOCK": y, "RBLOCK": r} num_warps = _num_warps(conditional_product(x, y, r) // 256, min_num_warps=1) check_config(cfg, xnumel=size_hints[0], ynumel=size_hints[1]) assert r <= TRITON_MAX_BLOCK["R"], f"increase TRITON_MAX_BLOCK['r'] to {r}" return Config(cfg, num_warps=num_warps, num_stages=num_stages) def pointwise( size_hints, triton_meta, tile_hint=None, filename=None, min_elem_per_thread=0, inductor_meta=None, ): """ Construct @triton.heuristics() based on size_hints. """ inductor_meta = {} if inductor_meta is None else inductor_meta assert not inductor_meta.get("no_x_dim") numel = functools.reduce(operator.mul, size_hints) bs = max(256, min(numel // 128, 1024)) hinted_configs = autotune_hints_to_configs( inductor_meta.get("autotune_hints", set()), size_hints, bs ) triton_config_with_settings = functools.partial( triton_config, min_elem_per_thread=min_elem_per_thread ) if len(size_hints) == 1: if disable_pointwise_autotuning(inductor_meta) and not ( inductor_meta.get("max_autotune") or inductor_meta.get("max_autotune_pointwise") ): return cached_autotune( size_hints, [triton_config_with_settings(size_hints, bs)], triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.POINTWISE, filename=filename, ) else: return cached_autotune( size_hints, [ triton_config_with_settings( size_hints, bs, num_elements_per_warp=256 ), triton_config_with_settings( size_hints, bs // 2, num_elements_per_warp=64 ), *hinted_configs, ], triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.POINTWISE, filename=filename, ) if len(size_hints) == 2: if ( disable_pointwise_autotuning(inductor_meta) or tile_hint == TileHint.SQUARE ) and not ( inductor_meta.get("max_autotune") or inductor_meta.get("max_autotune_pointwise") ): return cached_autotune( size_hints, [triton_config_with_settings(size_hints, 32, 32)], triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.POINTWISE, filename=filename, ) return cached_autotune( size_hints, [ triton_config_with_settings(size_hints, 32, 32), triton_config_with_settings(size_hints, 64, 64), # ~8% better for fp16 triton_config_with_settings(size_hints, 256, 16), triton_config_with_settings(size_hints, 16, 256), triton_config_with_settings(size_hints, bs, 1), triton_config_with_settings(size_hints, 1, bs), *hinted_configs, ], triton_meta=triton_meta, inductor_meta=inductor_meta, filename=filename, heuristic_type=HeuristicType.POINTWISE, ) if len(size_hints) == 3: if disable_pointwise_autotuning(inductor_meta): return cached_autotune( size_hints, [triton_config_with_settings(size_hints, 16, 16, 16)], triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.POINTWISE, filename=filename, ) return cached_autotune( size_hints, [ triton_config_with_settings(size_hints, 16, 16, 16), triton_config_with_settings(size_hints, 64, 8, 8), triton_config_with_settings(size_hints, 8, 64, 8), triton_config_with_settings(size_hints, 8, 8, 64), triton_config_with_settings(size_hints, bs, 1, 1), triton_config_with_settings(size_hints, 1, bs, 1), triton_config_with_settings(size_hints, 1, 1, bs), *hinted_configs, ], triton_meta=triton_meta, inductor_meta=inductor_meta, filename=filename, heuristic_type=HeuristicType.POINTWISE, ) raise NotImplementedError(f"size_hints: {size_hints}") def _reduction_configs( *, size_hints: List[int], inductor_meta: Dict[str, Any] ) -> List[Config]: reduction_hint = inductor_meta.get("reduction_hint", None) assert len(size_hints) == 2 rnumel = size_hints[-1] register_intensive = False MAX_RBLOCK = 2048 if ( size_hints[0] >= 1024 and inductor_meta.get("num_load", 0) + inductor_meta.get("num_reduction", 0) >= 10 ): # A heuristics to reduce RBLOCK if a kernel potentially need many registers. # Consider load and reduction since load need move data into registers and # reduction needs an accumulator. # # The magic numbers are a bit arbitrary. # # We cannot rely on dynamically scaling down RBLOCK later, since sometimes # triton makes it to use less registers with worse perf. Check: # https://github.com/pytorch/pytorch/issues/126463 # # The heuristic is a very simple one since registers can be reused. But # hopefully it can be a good enough indicator. MAX_RBLOCK = 1024 register_intensive = True contiguous_config = triton_config_reduction( size_hints, 1, (rnumel if 256 <= rnumel < MAX_RBLOCK else MAX_RBLOCK), register_intensive=register_intensive, ) outer_config = triton_config_reduction( size_hints, 64, 8, register_intensive=register_intensive ) tiny_config = triton_config_reduction( size_hints, 2 * (256 // rnumel) if rnumel <= 256 else 1, min(rnumel, MAX_RBLOCK), register_intensive=register_intensive, ) if inductor_meta.get("max_autotune") or inductor_meta.get("max_autotune_pointwise"): pass # skip all these cases elif reduction_hint == ReductionHint.INNER: return [contiguous_config] elif reduction_hint == ReductionHint.OUTER: return [outer_config] elif reduction_hint == ReductionHint.OUTER_TINY: return [tiny_config] if disable_pointwise_autotuning(inductor_meta): return [triton_config_reduction(size_hints, 32, 128)] return [ contiguous_config, outer_config, tiny_config, triton_config_reduction(size_hints, 64, 64), triton_config_reduction(size_hints, 8, 512), # halve the XBLOCK/RBLOCK compared to outer_config # TODO: this may only be beneficial when each iteration of the reduction # is quite heavy. E.g. https://gist.github.com/shunting314/189a8ef69f90db9d614a823385147a72 triton_config_reduction(size_hints, 64, 4, num_warps=8), ] def reduction( size_hints, reduction_hint=False, triton_meta=None, filename=None, inductor_meta=None, ): """args to @triton.heuristics()""" inductor_meta = {} if inductor_meta is None else inductor_meta inductor_meta["reduction_hint"] = reduction_hint if inductor_meta.get("no_x_dim"): size_hints = [1, *size_hints[1:]] assert triton_meta is not None rnumel = size_hints[-1] if len(size_hints) != 2: raise NotImplementedError(f"size_hints: {size_hints}") configs = _reduction_configs(size_hints=size_hints, inductor_meta=inductor_meta) return cached_autotune( size_hints, configs=configs, triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.REDUCTION, filename=filename, ) def persistent_reduction( size_hints, reduction_hint=False, triton_meta=None, filename=None, inductor_meta=None, ): inductor_meta = {} if inductor_meta is None else inductor_meta inductor_meta["reduction_hint"] = reduction_hint if inductor_meta.get("no_x_dim"): size_hints = [1, *size_hints[1:]] xnumel, rnumel = size_hints configs = [ triton_config_reduction(size_hints, xblock, rnumel, register_intensive=True) for xblock in (1, 8, 32, 128) if xblock == 1 or (rnumel * xblock <= 4096 and xblock <= xnumel) ] # TODO(jansel): we should be able to improve these heuristics if reduction_hint == ReductionHint.INNER and rnumel >= 256: configs = configs[:1] elif reduction_hint == ReductionHint.OUTER: configs = configs[-1:] elif reduction_hint == ReductionHint.OUTER_TINY: configs = [ triton_config_reduction( size_hints, 2 * (256 // rnumel) if rnumel <= 256 else 1, rnumel ) ] for c in configs: # we don't need RBLOCK for persistent reduction c.kwargs.pop("RBLOCK") if disable_pointwise_autotuning(inductor_meta): configs = configs[:1] return cached_autotune( size_hints, configs, triton_meta=triton_meta, inductor_meta=inductor_meta, filename=filename, heuristic_type=HeuristicType.PERSISTENT_REDUCTION, ) def split_scan( size_hints, reduction_hint=False, triton_meta=None, filename=None, inductor_meta=None, ): """Heuristic for TritonSplitScanKernel""" inductor_meta = {} if inductor_meta is None else inductor_meta inductor_meta["reduction_hint"] = reduction_hint if inductor_meta.get("no_x_dim"): size_hints = [1, *size_hints[1:]] assert triton_meta is not None if len(size_hints) != 2: raise NotImplementedError(f"size_hints: {size_hints}") configs = _reduction_configs(size_hints=size_hints, inductor_meta=inductor_meta) # Fixup configs to enforce the minimum RBLOCK size min_rblock = inductor_meta.get("min_split_scan_rblock", 256) for cfg in configs: if cfg.kwargs["RBLOCK"] < min_rblock: cfg.kwargs["RBLOCK"] = min_rblock return cached_autotune( size_hints, configs=configs, triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.SPLIT_SCAN, filename=filename, ) def template(num_stages, num_warps, triton_meta, filename=None, inductor_meta=None): """ Compile a triton template """ return cached_autotune( None, [triton.Config({}, num_stages=num_stages, num_warps=num_warps)], triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.TEMPLATE, filename=filename, ) def user_autotune( configs, triton_meta, filename=None, inductor_meta=None, custom_kernel=False ): """ Compile a user defined triton kernel """ defaults = inspect.signature(triton.Config).parameters default_num_stages = defaults["num_stages"].default default_num_warps = defaults["num_warps"].default if len(configs) == 0: configs = [ triton.Config( {}, num_stages=default_num_stages, num_warps=default_num_warps ) ] else: configs = [ triton.Config( c.get("kwargs", {}), num_stages=c.get("num_stages", default_num_stages), num_warps=c.get("num_warps", default_num_warps), ) for c in configs ] return cached_autotune( None, configs, triton_meta=triton_meta, heuristic_type=HeuristicType.USER_AUTOTUNE, filename=filename, inductor_meta=inductor_meta, custom_kernel=custom_kernel, ) def foreach(triton_meta, num_warps, filename=None, inductor_meta=None): """ Compile a triton foreach kernel """ return cached_autotune( None, [triton.Config({}, num_stages=1, num_warps=num_warps)], triton_meta=triton_meta, inductor_meta=inductor_meta, heuristic_type=HeuristicType.TEMPLATE, filename=filename, ) def grid(*numels): """Helper function to compute triton grids""" if len(numels) == 1: xnumel, ynumel, znumel = numels[0], None, None elif len(numels) == 2: xnumel, ynumel, znumel = numels[1], numels[0], None elif len(numels) == 3: xnumel, ynumel, znumel = numels[2], numels[1], numels[0] else: raise AssertionError(f"invalid size for numels {len(numels)}") def get_grid_dim(numel, block): if numel is None: return 1 if block is None: return numel return ceildiv(numel, block) def grid_fn(meta): x_grid = get_grid_dim(xnumel, meta.get("XBLOCK", 1)) y_grid = get_grid_dim(ynumel, meta.get("YBLOCK", None)) max_y_grid = get_max_y_grid() if znumel is None: div = ceildiv(y_grid, max_y_grid) y_grid = ceildiv(y_grid, div) z_grid = div else: z_grid = get_grid_dim(znumel, meta.get("ZBLOCK", None)) torch._check( y_grid <= max_y_grid, lambda: f"Generated y grid beyond 2^16 ({y_grid}) not supported with z dimension present. File issue", ) return ( x_grid, y_grid, z_grid, ) setattr(grid_fn, "grid_fn_str", f"grid{numels}") # noqa: B010 return grid_fn def split_scan_grid(xnumel, rnumel): def grid_fn(meta): assert meta.get("XBLOCK", 1) == 1 return (ceildiv(rnumel, meta.get("RBLOCK", 1)), xnumel, 1) grid_fn_str = f"split_scan_grid({xnumel}, {rnumel})" setattr(grid_fn, "grid_fn_str", grid_fn_str) # noqa: B010 return grid_fn def grid_combo_kernels( *numels, num_kernels, min_blocks, is_sequential, default_meta=None ): """min_blocks is the minimal size of the grid x dimension""" if not is_sequential: # round robin dispatch numels_agg = list(numels) for i in range(len(numels_agg)): if isinstance(numels_agg[i], (list, tuple)): numels_agg[i] = max(max(numels_agg[i]), 0) # noqa: PLW3301 kernel_grid_fn = grid(*numels_agg) if isinstance(numels[-1], (list, tuple)): min_blocks_d = max(-min(numels[-1]), 0) * num_kernels else: min_blocks_d = None if min_blocks is None: assert min_blocks_d is not None min_blocks = min_blocks_d else: assert ( min_blocks_d is None or min_blocks == min_blocks_d ), f"inconsistent min_blocks {min_blocks} vs x grid {numels[-1]}" else: # sequential dispatch seq_numels = list(numels) # x numels are not used here, just a place holder seq_numels[-1] = 1024 for i in range(len(seq_numels) - 1): if isinstance(seq_numels[i], (list, tuple)): seq_numels[i] = max(seq_numels[i]) kernel_grid_fn = grid(*seq_numels) def get_grid_dim(numel, block): if numel is None: return 1 if block is None: return numel return ceildiv(numel, block) def grid_fn(meta): assert min_blocks is not None, "min_blocks must be a number" cuda_grid = list(kernel_grid_fn(meta)) cuda_grid[0] = max(num_kernels * cuda_grid[0], min_blocks) return tuple(cuda_grid) def seq_grid_fn(meta): cuda_grid = list(kernel_grid_fn(meta)) # x <= 0 means this kernel's x grid is not tunable (x_no_dim is true) x_grid = sum( [ -x if x <= 0 else get_grid_dim(x, meta.get("XBLOCK", 1)) for x in numels[-1] ] ) cuda_grid[0] = x_grid return tuple(cuda_grid) def grid_fn_default_meta(meta): return grid_fn(default_meta) def seq_grid_fn_default_meta(meta): return seq_grid_fn(default_meta) if default_meta is None: return grid_fn if not is_sequential else seq_grid_fn else: return grid_fn_default_meta if not is_sequential else seq_grid_fn_default_meta