# Copyright 2018 ARM Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import random from itertools import groupby, chain from future.moves.itertools import zip_longest from wa.framework.configuration.core import (MetaConfiguration, RunConfiguration, JobGenerator, settings) from wa.framework.configuration.parsers import ConfigParser from wa.framework.configuration.plugin_cache import PluginCache from wa.framework.exception import NotFoundError from wa.framework.job import Job from wa.utils import log class CombinedConfig(object): @staticmethod def from_pod(pod): instance = CombinedConfig() instance.settings = MetaConfiguration.from_pod(pod.get('settings', {})) instance.run_config = RunConfiguration.from_pod(pod.get('run_config', {})) return instance def __init__(self, settings=None, run_config=None): # pylint: disable=redefined-outer-name self.settings = settings self.run_config = run_config def to_pod(self): return {'settings': self.settings.to_pod(), 'run_config': self.run_config.to_pod()} class ConfigManager(object): """ Represents run-time state of WA. Mostly used as a container for loaded configuration and discovered plugins. This exists outside of any command or run and is associated with the running instance of wA itself. """ @property def enabled_instruments(self): return self.jobs_config.enabled_instruments @property def enabled_processors(self): return self.jobs_config.enabled_processors @property def job_specs(self): if not self._jobs_generated: msg = 'Attempting to access job specs before '\ 'jobs have been generated' raise RuntimeError(msg) return [j.spec for j in self._jobs] @property def jobs(self): if not self._jobs_generated: msg = 'Attempting to access jobs before '\ 'they have been generated' raise RuntimeError(msg) return self._jobs def __init__(self, settings=settings): # pylint: disable=redefined-outer-name self.settings = settings self.run_config = RunConfiguration() self.plugin_cache = PluginCache() self.jobs_config = JobGenerator(self.plugin_cache) self.loaded_config_sources = [] self._config_parser = ConfigParser() self._jobs = [] self._jobs_generated = False self.agenda = None def load_config_file(self, filepath): self._config_parser.load_from_path(self, filepath) self.loaded_config_sources.append(filepath) def load_config(self, values, source): self._config_parser.load(self, values, source) self.loaded_config_sources.append(source) def get_plugin(self, name=None, kind=None, *args, **kwargs): return self.plugin_cache.get_plugin(name, kind, *args, **kwargs) def get_instruments(self, target): instruments = [] for name in self.enabled_instruments: try: instruments.append(self.get_plugin(name, kind='instrument', target=target)) except NotFoundError: msg = 'Instrument "{}" not found' raise NotFoundError(msg.format(name)) return instruments def get_processors(self): processors = [] for name in self.enabled_processors: try: proc = self.plugin_cache.get_plugin(name, kind='output_processor') except NotFoundError: msg = 'Output Processor "{}" not found' raise NotFoundError(msg.format(name)) processors.append(proc) return processors def get_config(self): return CombinedConfig(self.settings, self.run_config) def finalize(self): if not self.agenda: msg = 'Attempting to finalize config before agenda has been set' raise RuntimeError(msg) self.run_config.merge_device_config(self.plugin_cache) return self.get_config() def generate_jobs(self, context): job_specs = self.jobs_config.generate_job_specs(context.tm) exec_order = self.run_config.execution_order log.indent() for spec, i in permute_iterations(job_specs, exec_order): job = Job(spec, i, context) job.load(context.tm.target) self._jobs.append(job) context.run_state.add_job(job) log.dedent() self._jobs_generated = True def permute_by_workload(specs): """ This is that "classic" implementation that executes all iterations of a workload spec before proceeding onto the next spec. """ for spec in specs: for i in range(1, spec.iterations + 1): yield (spec, i) def permute_by_iteration(specs): """ Runs the first iteration for all benchmarks first, before proceeding to the next iteration, i.e. A1, B1, C1, A2, B2, C2... instead of A1, A1, B1, B2, C1, C2... If multiple sections where specified in the agenda, this will run all sections for the first global spec first, followed by all sections for the second spec, etc. e.g. given sections X and Y, and global specs A and B, with 2 iterations, this will run X.A1, Y.A1, X.B1, Y.B1, X.A2, Y.A2, X.B2, Y.B2 """ groups = [list(g) for _, g in groupby(specs, lambda s: s.workload_id)] all_tuples = [] for spec in chain(*groups): all_tuples.append([(spec, i + 1) for i in range(spec.iterations)]) for t in chain(*list(map(list, zip_longest(*all_tuples)))): if t is not None: yield t def permute_by_section(specs): """ Runs the first iteration for all benchmarks first, before proceeding to the next iteration, i.e. A1, B1, C1, A2, B2, C2... instead of A1, A1, B1, B2, C1, C2... If multiple sections where specified in the agenda, this will run all specs for the first section followed by all specs for the seciod section, etc. e.g. given sections X and Y, and global specs A and B, with 2 iterations, this will run X.A1, X.B1, Y.A1, Y.B1, X.A2, X.B2, Y.A2, Y.B2 """ groups = [list(g) for _, g in groupby(specs, lambda s: s.section_id)] all_tuples = [] for spec in chain(*groups): all_tuples.append([(spec, i + 1) for i in range(spec.iterations)]) for t in chain(*list(map(list, zip_longest(*all_tuples)))): if t is not None: yield t def permute_randomly(specs): """ This will generate a random permutation of specs/iteration tuples. """ result = [] for spec in specs: for i in range(1, spec.iterations + 1): result.append((spec, i)) random.shuffle(result) for t in result: yield t permute_map = { 'by_iteration': permute_by_iteration, 'by_workload': permute_by_workload, 'by_section': permute_by_section, 'random': permute_randomly, } def permute_iterations(specs, exec_order): if exec_order not in permute_map: msg = 'Unknown execution order "{}"; must be in: {}' raise ValueError(msg.format(exec_order, list(permute_map.keys()))) return permute_map[exec_order](specs)