################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 # ################################################################### # BOHB: Robust and Efficient Hyperparameter Optimization at Scale # # required to install hpbandster ################################## # pip install hpbandster ################################## ################################################################### # python exps/algos-v2/bohb.py --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 ################################################################### import os, sys, time, random, argparse from copy import deepcopy from pathlib import Path import torch lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) from config_utils import load_config from datasets import get_datasets, SearchDataset from procedures import prepare_seed, prepare_logger from log_utils import AverageMeter, time_string, convert_secs2time from nas_201_api import NASBench201API as API from models import CellStructure, get_search_spaces # BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018 import ConfigSpace from hpbandster.optimizers.bohb import BOHB import hpbandster.core.nameserver as hpns from hpbandster.core.worker import Worker def get_topology_config_space(search_space, max_nodes=4): cs = ConfigSpace.ConfigurationSpace() #edge2index = {} for i in range(1, max_nodes): for j in range(i): node_str = '{:}<-{:}'.format(i, j) cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space)) return cs def get_size_config_space(search_space): cs = ConfigSpace.ConfigurationSpace() import pdb; pdb.set_trace() #edge2index = {} for i in range(1, max_nodes): for j in range(i): node_str = '{:}<-{:}'.format(i, j) cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space)) return cs def config2topology_func(max_nodes=4): def config2structure(config): genotypes = [] for i in range(1, max_nodes): xlist = [] for j in range(i): node_str = '{:}<-{:}'.format(i, j) op_name = config[node_str] xlist.append((op_name, j)) genotypes.append( tuple(xlist) ) return CellStructure( genotypes ) return config2structure class MyWorker(Worker): def __init__(self, *args, convert_func=None, dataname=None, nas_bench=None, time_budget=None, **kwargs): super().__init__(*args, **kwargs) self.convert_func = convert_func self._dataname = dataname self._nas_bench = nas_bench self.time_budget = time_budget self.seen_archs = [] self.sim_cost_time = 0 self.real_cost_time = 0 self.is_end = False def get_the_best(self): assert len(self.seen_archs) > 0 best_index, best_acc = -1, None for arch_index in self.seen_archs: info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp='200', is_random=True) vacc = info['valid-accuracy'] if best_acc is None or best_acc < vacc: best_acc = vacc best_index = arch_index assert best_index != -1 return best_index def compute(self, config, budget, **kwargs): start_time = time.time() structure = self.convert_func( config ) arch_index = self._nas_bench.query_index_by_arch( structure ) info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp='200', is_random=True) cur_time = info['train-all-time'] + info['valid-per-time'] cur_vacc = info['valid-accuracy'] self.real_cost_time += (time.time() - start_time) if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end: self.sim_cost_time += cur_time self.seen_archs.append( arch_index ) return ({'loss': 100 - float(cur_vacc), 'info': {'seen-arch' : len(self.seen_archs), 'sim-test-time' : self.sim_cost_time, 'current-arch' : arch_index} }) else: self.is_end = True return ({'loss': 100, 'info': {'seen-arch' : len(self.seen_archs), 'sim-test-time' : self.sim_cost_time, 'current-arch' : None} }) def main(xargs, api): torch.set_num_threads(4) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) logger.log('{:} use api : {:}'.format(time_string(), api)) search_space = get_search_spaces(xargs.search_space, 'nas-bench-301') if xargs.search_space == 'tss': cs = get_topology_config_space(xargs.max_nodes, search_space) config2structure = config2topology_func(xargs.max_nodes) else: cs = get_size_config_space(xargs.max_nodes, search_space) import pdb; pdb.set_trace() hb_run_id = '0' NS = hpns.NameServer(run_id=hb_run_id, host='localhost', port=0) ns_host, ns_port = NS.start() num_workers = 1 workers = [] for i in range(num_workers): w = MyWorker(nameserver=ns_host, nameserver_port=ns_port, convert_func=config2structure, dataname=dataname, nas_bench=nas_bench, time_budget=xargs.time_budget, run_id=hb_run_id, id=i) w.run(background=True) workers.append(w) start_time = time.time() bohb = BOHB(configspace=cs, run_id=hb_run_id, eta=3, min_budget=12, max_budget=200, nameserver=ns_host, nameserver_port=ns_port, num_samples=xargs.num_samples, random_fraction=xargs.random_fraction, bandwidth_factor=xargs.bandwidth_factor, ping_interval=10, min_bandwidth=xargs.min_bandwidth) results = bohb.run(xargs.n_iters, min_n_workers=num_workers) bohb.shutdown(shutdown_workers=True) NS.shutdown() real_cost_time = time.time() - start_time id2config = results.get_id2config_mapping() incumbent = results.get_incumbent_id() logger.log('Best found configuration: {:} within {:.3f} s'.format(id2config[incumbent]['config'], real_cost_time)) best_arch = config2structure( id2config[incumbent]['config'] ) info = nas_bench.query_by_arch(best_arch, '200') if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch)) else : logger.log('{:}'.format(info)) logger.log('-'*100) logger.log('workers : {:.1f}s with {:} archs'.format(workers[0].time_budget, len(workers[0].seen_archs))) logger.close() return logger.log_dir, nas_bench.query_index_by_arch( best_arch ), real_cost_time if __name__ == '__main__': parser = argparse.ArgumentParser("BOHB: Robust and Efficient Hyperparameter Optimization at Scale") parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.') # general arg parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.') parser.add_argument('--time_budget', type=int, default=20000, help='The total time cost budge for searching (in seconds).') parser.add_argument('--loops_if_rand', type=int, default=500, help='The total runs for evaluation.') # BOHB parser.add_argument('--strategy', default="sampling", type=str, nargs='?', help='optimization strategy for the acquisition function') parser.add_argument('--min_bandwidth', default=.3, type=float, nargs='?', help='minimum bandwidth for KDE') parser.add_argument('--num_samples', default=64, type=int, nargs='?', help='number of samples for the acquisition function') parser.add_argument('--random_fraction', default=.33, type=float, nargs='?', help='fraction of random configurations') parser.add_argument('--bandwidth_factor', default=3, type=int, nargs='?', help='factor multiplied to the bandwidth') parser.add_argument('--n_iters', default=300, type=int, nargs='?', help='number of iterations for optimization method') # log parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.') parser.add_argument('--rand_seed', type=int, help='manual seed') args = parser.parse_args() if args.search_space == 'tss': api = NASBench201API(verbose=False) elif args.search_space == 'sss': api = NASBench301API(verbose=False) else: raise ValueError('Invalid search space : {:}'.format(args.search_space)) args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, 'BOHB') print('save-dir : {:}'.format(args.save_dir)) if args.rand_seed < 0: save_dir, all_info = None, collections.OrderedDict() for i in range(args.loops_if_rand): print ('{:} : {:03d}/{:03d}'.format(time_string(), i, args.loops_if_rand)) args.rand_seed = random.randint(1, 100000) save_dir, all_archs, all_total_times = main(args, api) all_info[i] = {'all_archs': all_archs, 'all_total_times': all_total_times} save_path = save_dir / 'results.pth' print('save into {:}'.format(save_path)) torch.save(all_info, save_path) else: main(args, api)