# mypy: allow-untyped-defs import logging import os from typing import Any, List from torch._inductor.metrics import get_metric_table, is_metric_table_enabled from .. import config from ..codecache import PyCodeCache, TritonFuture from ..runtime.runtime_utils import do_bench_gpu from ..utils import cache_on_self from ..virtualized import V from .common import TensorArg log = logging.getLogger(__name__) def get_kernel_argdefs(kernel): arg_defs, _, _, _ = kernel.args.python_argdefs() return arg_defs def _get_all_args(args_list, arg_types_list=None): all_args = max(args_list, key=len)[:] arg_types = max(arg_types_list, key=len)[:] if arg_types_list is not None else None for args in args_list: assert set(args).issubset(set(all_args)), f"{args} v.s. {all_args}" return all_args, arg_types def get_all_kernel_argdefs(kernels): """ The logic here must match with `get_all_call_args`, except no need to get arg_types here """ argdefs_list = [get_kernel_argdefs(kernel) for kernel in kernels] return _get_all_args(argdefs_list)[0] def get_all_call_args(call_args_list, arg_types_list): """ Passed in the call_args for each subkernel and return the call_args for the combined multi-kernel. Note an algorithm as follows does not always work: ``` all_call_args: Dict[ Any, None ] = {} # use a dict rather than set to maintain insertion order for call_args in call_args_list: all_call_args.update({arg: None for arg in call_args}) all_call_args = list(all_call_args.keys()) ``` It will fail if any kernel has the same argument passed in multiple times. Check test_pass_same_arg_multi_times in test_multi_kernel.py Instead, we pick the longest call args and assert that other call args are a subset of it. """ return _get_all_args(call_args_list, arg_types_list) def get_numel_argdefs(kernel): numel_argdefs = [] for tree in kernel.range_trees: if tree.prefix != "r" or kernel.inside_reduction: numel_argdefs.append(f"{tree.prefix}numel") return numel_argdefs class MultiKernelState: """ Maintain state of multi-kernel compilation so we don't define duplicated multi-kernel for the same set of sub-kernels. V.graph.wrapper_code has a reference to MultiKernelState instance. """ def __init__(self): self.subkernel_to_kernel_name = {} def define_kernel(self, kernels): """ Previously we name the multi kernel as "multi_kernel_{kernel_names[0]}". This has some minor issue. E.g. for persistent reduction https://gist.github.com/shunting314/39e7c00ff8bb2055942ed5a3255d61ca , there are 2 flavors of non-persistent reduction: https://gist.github.com/shunting314/056d43d35907e87efb883970b35c17d4 and https://gist.github.com/shunting314/02ee753b65c513c54e695626afe682bd The only different is cache eviction policy. We should name the multi-kernel differently in these 2 cases. """ kernel_names = tuple(k.kernel_name for k in kernels) if kernel_names in self.subkernel_to_kernel_name: return self.subkernel_to_kernel_name[kernel_names] # name the multi kernel based on the first kernel multi_kernel_name = f"multi_kernel_{len(self.subkernel_to_kernel_name)}" self.subkernel_to_kernel_name[kernel_names] = multi_kernel_name if V.graph.cpp_wrapper: # we should not generate any python code for multi-kernel during # the second pass of cpp-wrapper. return multi_kernel_name wrapper = V.graph.wrapper_code kernel_call_def_code = "\n".join( [ f""" def call{idx}(need_clone_args=False): args = [{', '.join(get_kernel_argdefs(kernels[idx]))}] if need_clone_args: args, _ = multi_kernel_call.kernels[{idx}].clone_args(*args) multi_kernel_call.kernels[{idx}].run(*args, {', '.join(get_numel_argdefs(kernels[idx]))}, grid=grid, stream=stream) """.format( idx ).strip( "\n" ) for idx in range(len(kernels)) ] ) # add subkernel src code hashes to the multi-kernel source code so changing a # subkernel implementation will result in a different py file for # multi-kernel. This makes cache implementation straightforward since # we can decide cache file name based on multi-kernel py file name # directly. # # Without the hash added for subkernels, the cache file may be shared by # different subkernels which is incorrect. subkernel_hashes = "\n".join( f"# subkernel{i} code hash: {kernel.code_hash}" for i, kernel in enumerate(kernels) ) src_code = f""" {subkernel_hashes} def run(multi_kernel_call, {', '.join(get_all_kernel_argdefs(kernels))}, {', '.join(get_numel_argdefs(kernels[0]))}, grid, stream): {kernel_call_def_code} multi_kernel_call.run_with_argless_kernels([call0, call1]) """ # noqa: B950 line too long wrapper.header.splice( f""" {multi_kernel_name} = async_compile.multi_kernel({multi_kernel_name!r}, [ {", ".join(kernel_names)}, ], ''' """ ) wrapper.header.splice(src_code) wrapper.header.splice( """ ''' ) """ ) return multi_kernel_name class MultiKernel: """ This class maintains the compile time state for multi kernels. Assume we do codegen for a MultiKernel encapsulating kernel1 and kernel2. The generated definition for the multi-kernel will looks like: ``` multi_kernel_kernel1 = MultiKernelCall([kernel1, kernel2], multi_kernel_definition_code) ``` Here is an concrete example: https://gist.github.com/shunting314/d9f3fb6bc6cee3dbae005825ca196d39 """ def __init__(self, kernels): assert len(kernels) >= 2 self.kernels = kernels self.kernel_name = V.graph.wrapper_code.multi_kernel_state.define_kernel( kernels ) # need this since some code in inductor check if the kernel object has an args # attribute to decide if it's a non-null kernel. self.args = object() def call_kernel(self, kernel_name): """ Collect the union of arguments from all subkernels as the arguments for the multi-kernel. """ assert kernel_name == self.kernel_name call_args_list, arg_types = zip( *[kernel.get_call_args() for kernel in self.kernels] ) call_args_list = list(call_args_list) arg_types_list = list(arg_types) all_call_args, arg_types = get_all_call_args(call_args_list, arg_types_list) grid: List[Any] = [] if V.graph.cpp_wrapper: # for the second pass of cpp-wrapper codegen, we should call # the fast kernel directly picked_kernel = MultiKernelCall.lookup_choice(kernel_name) kernel_name = self.kernels[picked_kernel].kernel_name final_call_args = call_args_list[picked_kernel] arg_types = arg_types_list[picked_kernel] else: final_call_args = all_call_args # numels for all subkernels should be the same. Use kernels[0] here self.kernels[0].add_numel_to_call_args_and_grid( kernel_name, final_call_args, arg_types, grid ) grid = V.graph.wrapper_code.generate_default_grid(kernel_name, grid) current_device = V.graph.scheduler.get_current_device_or_throw() V.graph.wrapper_code.generate_kernel_call( kernel_name, final_call_args, grid, current_device.index, arg_types=arg_types, ) def codegen_nan_check(self): wrapper = V.graph.wrapper_code seen = set() for k in self.kernels: _, call_args, precompile_args, _ = k.args.python_argdefs() for arg, precompile_arg in zip(call_args, precompile_args): if arg in seen: continue seen.add(arg) if isinstance(precompile_arg, TensorArg): line = f"assert not {arg}.isnan().any().item()" wrapper.writeline(line) line = f"assert not {arg}.isinf().any().item()" wrapper.writeline(line) @property def removed_buffers(self): return set.intersection(*[k.removed_buffers for k in self.kernels]) @property def inplaced_to_remove(self): return set.intersection(*[k.inplaced_to_remove for k in self.kernels]) @property @cache_on_self def inplace_update_buffers(self): """ Make sure all kernels have the same inplace update mappings. """ for k in self.kernels[1:]: assert k.inplace_update_buffers == self.kernels[0].inplace_update_buffers return self.kernels[0].inplace_update_buffers def warn_mix_layout(self, kernel_name: str): pass class MultiKernelCall: """ This class is called at run time to actually run the kernel """ def __init__(self, multi_kernel_name, kernels, src_code): assert len(kernels) >= 2 self._kernels = kernels self.multi_kernel_name = multi_kernel_name self._run = PyCodeCache.load(src_code).run self.disable_cache = os.environ.get( "TORCHINDUCTOR_DISABLE_MULTI_KERNEL_CACHE" ) == "1" or is_metric_table_enabled("persistent_red_perf") self.picked_kernel = None if config.triton.multi_kernel > 1: # manually force a subkernel to ease perf testing picked_by_config = config.triton.multi_kernel - 2 assert picked_by_config < len(self._kernels) self.picked_kernel = picked_by_config elif not self.disable_cache: self.load_cache() self._recorded = False def cache_file_path(self): py_file_path = self._run.__globals__["__file__"] return os.path.splitext(py_file_path)[0] + ".picked_kernel" def load_cache(self): assert self.picked_kernel is None path = self.cache_file_path() if os.path.exists(path): with open(path) as fd: self.picked_kernel = int(fd.read()) assert self.picked_kernel >= 0 and self.picked_kernel < len( self._kernels ) log.debug( "Load picked kernel %d from cache file %s", self.picked_kernel, path ) def store_cache(self): assert self.picked_kernel is not None path = self.cache_file_path() with open(path, "w") as fd: fd.write(str(self.picked_kernel)) log.debug("Store picked kernel %d to cache file %s", self.picked_kernel, path) @property def kernels(self): """ Read results from future. This should be called after parallel compilation is done. In case you call this before compilation is done, it may slow down the parallel compilation. """ for i, kernel in enumerate(self._kernels): if isinstance(kernel, TritonFuture): self._kernels[i] = kernel.result() return self._kernels def run(self, *args, **kwargs): self._run(self, *args, **kwargs) @staticmethod def benchmark_sub_kernels(kernel_calls): """ Benchmark all the sub kernels and return the execution time (in milliseconds) for each of time. Unit test may mock this method to force a specific kernel to be picked. """ return [ do_bench_gpu(lambda: kernel_call(True), rep=40, fast_flush=True) for kernel_call in kernel_calls ] # record_choice and lookup_choice are helper functions for cpp-wrapper # codegen. The first pass use record_choice to keep the choice and # the second pass do lookup by calling lookup_choice. # # An alternative that reused the multi-kernel cache does not work well # since during codegen of the second pass, it's very hard to know the # path for the cache file. Also reading the cache file need do some IO # which can be slower. @staticmethod def record_choice(multi_kernel_name, choice): """ Record the multi-kernel choice for cpp-wrapper first pass codegen for the second pass. We should do nothing if this function is not called during codegen. """ from torch._inductor.graph import GraphLowering if not isinstance(V.graph, GraphLowering): return if not V.graph.record_multi_kernel_choice: return V.graph.multi_kernel_to_choice[multi_kernel_name] = choice @staticmethod def lookup_choice(multi_kernel_name): # this should always been done during cpp-wrapper codegen assert V.graph.record_multi_kernel_choice # there should be no miss return V.graph.multi_kernel_to_choice[multi_kernel_name] def run_with_argless_kernels(self, kernel_calls): if self.picked_kernel is None: timings = self.benchmark_sub_kernels(kernel_calls) self.picked_kernel = timings.index(min(timings)) k0 = self.kernels[0] log.debug( "pick %dth sub-kernel in %s. Size hints %s. Reduction hint %s. Timings %s", self.picked_kernel, [k.inductor_meta.get("kernel_name") for k in self.kernels], k0.size_hints, k0.inductor_meta.get("reduction_hint"), timings, ) def get_kernel_path(k): return k.fn.fn.__code__.co_filename get_metric_table("persistent_red_perf").add_row( lambda: { "kernel1_name": get_kernel_path(self.kernels[0]), "kernel2_name": get_kernel_path(self.kernels[1]), "kernel1_latency": timings[0], "kernel2_latency": timings[1], "size_hints": k0.size_hints, "reduction_hint": k0.inductor_meta.get("reduction_hint"), "speedup": timings[1] / timings[0], } ) if not self.disable_cache: self.store_cache() if not self._recorded: self._recorded = True self.record_choice(self.multi_kernel_name, self.picked_kernel) kernel_calls[self.picked_kernel]()