224 lines
8.5 KiB
Python
224 lines
8.5 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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###################################################################
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
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# required to install hpbandster ##################################
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# pip install hpbandster ##################################
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###################################################################
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# OMP_NUM_THREADS=4 python exps/NATS-algos/bohb.py --search_space tss --dataset cifar10 --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 --rand_seed 1
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# OMP_NUM_THREADS=4 python exps/NATS-algos/bohb.py --search_space sss --dataset cifar10 --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3 --rand_seed 1
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###################################################################
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import os, sys, time, random, argparse, collections
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from copy import deepcopy
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from pathlib import Path
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import torch
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from config_utils import load_config
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger
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from log_utils import AverageMeter, time_string, convert_secs2time
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from nats_bench import create
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from models import CellStructure, get_search_spaces
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# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
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import ConfigSpace
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from hpbandster.optimizers.bohb import BOHB
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import hpbandster.core.nameserver as hpns
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from hpbandster.core.worker import Worker
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def get_topology_config_space(search_space, max_nodes=4):
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cs = ConfigSpace.ConfigurationSpace()
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# edge2index = {}
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for i in range(1, max_nodes):
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space))
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return cs
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def get_size_config_space(search_space):
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cs = ConfigSpace.ConfigurationSpace()
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for ilayer in range(search_space["numbers"]):
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node_str = "layer-{:}".format(ilayer)
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cs.add_hyperparameter(ConfigSpace.CategoricalHyperparameter(node_str, search_space["candidates"]))
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return cs
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def config2topology_func(max_nodes=4):
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def config2structure(config):
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genotypes = []
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for i in range(1, max_nodes):
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xlist = []
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for j in range(i):
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node_str = "{:}<-{:}".format(i, j)
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op_name = config[node_str]
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xlist.append((op_name, j))
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genotypes.append(tuple(xlist))
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return CellStructure(genotypes)
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return config2structure
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def config2size_func(search_space):
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def config2structure(config):
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channels = []
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for ilayer in range(search_space["numbers"]):
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node_str = "layer-{:}".format(ilayer)
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channels.append(str(config[node_str]))
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return ":".join(channels)
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return config2structure
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class MyWorker(Worker):
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def __init__(self, *args, convert_func=None, dataset=None, api=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.convert_func = convert_func
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self._dataset = dataset
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self._api = api
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self.total_times = []
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self.trajectory = []
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def compute(self, config, budget, **kwargs):
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arch = self.convert_func(config)
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accuracy, latency, time_cost, total_time = self._api.simulate_train_eval(
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arch, self._dataset, iepoch=int(budget) - 1, hp="12"
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)
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self.trajectory.append((accuracy, arch))
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self.total_times.append(total_time)
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return {"loss": 100 - accuracy, "info": self._api.query_index_by_arch(arch)}
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def main(xargs, api):
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torch.set_num_threads(4)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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logger.log("{:} use api : {:}".format(time_string(), api))
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api.reset_time()
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search_space = get_search_spaces(xargs.search_space, "nats-bench")
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if xargs.search_space == "tss":
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cs = get_topology_config_space(search_space)
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config2structure = config2topology_func()
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else:
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cs = get_size_config_space(search_space)
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config2structure = config2size_func(search_space)
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hb_run_id = "0"
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NS = hpns.NameServer(run_id=hb_run_id, host="localhost", port=0)
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ns_host, ns_port = NS.start()
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num_workers = 1
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workers = []
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for i in range(num_workers):
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w = MyWorker(
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nameserver=ns_host,
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nameserver_port=ns_port,
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convert_func=config2structure,
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dataset=xargs.dataset,
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api=api,
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run_id=hb_run_id,
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id=i,
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)
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w.run(background=True)
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workers.append(w)
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start_time = time.time()
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bohb = BOHB(
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configspace=cs,
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run_id=hb_run_id,
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eta=3,
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min_budget=1,
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max_budget=12,
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nameserver=ns_host,
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nameserver_port=ns_port,
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num_samples=xargs.num_samples,
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random_fraction=xargs.random_fraction,
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bandwidth_factor=xargs.bandwidth_factor,
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ping_interval=10,
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min_bandwidth=xargs.min_bandwidth,
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)
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results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
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bohb.shutdown(shutdown_workers=True)
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NS.shutdown()
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# print('There are {:} runs.'.format(len(results.get_all_runs())))
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# workers[0].total_times
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# workers[0].trajectory
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current_best_index = []
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for idx in range(len(workers[0].trajectory)):
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trajectory = workers[0].trajectory[: idx + 1]
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arch = max(trajectory, key=lambda x: x[0])[1]
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current_best_index.append(api.query_index_by_arch(arch))
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best_arch = max(workers[0].trajectory, key=lambda x: x[0])[1]
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logger.log("Best found configuration: {:} within {:.3f} s".format(best_arch, workers[0].total_times[-1]))
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info = api.query_info_str_by_arch(best_arch, "200" if xargs.search_space == "tss" else "90")
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logger.log("{:}".format(info))
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logger.log("-" * 100)
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logger.close()
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return logger.log_dir, current_best_index, workers[0].total_times
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("BOHB: Robust and Efficient Hyperparameter Optimization at Scale")
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parser.add_argument(
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"--dataset",
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type=str,
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choices=["cifar10", "cifar100", "ImageNet16-120"],
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help="Choose between Cifar10/100 and ImageNet-16.",
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)
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# general arg
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parser.add_argument("--search_space", type=str, choices=["tss", "sss"], help="Choose the search space.")
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parser.add_argument(
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"--time_budget", type=int, default=20000, help="The total time cost budge for searching (in seconds)."
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)
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parser.add_argument("--loops_if_rand", type=int, default=500, help="The total runs for evaluation.")
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# BOHB
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parser.add_argument(
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"--strategy", default="sampling", type=str, nargs="?", help="optimization strategy for the acquisition function"
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)
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parser.add_argument("--min_bandwidth", default=0.3, type=float, nargs="?", help="minimum bandwidth for KDE")
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parser.add_argument(
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"--num_samples", default=64, type=int, nargs="?", help="number of samples for the acquisition function"
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)
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parser.add_argument(
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"--random_fraction", default=0.33, type=float, nargs="?", help="fraction of random configurations"
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)
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parser.add_argument("--bandwidth_factor", default=3, type=int, nargs="?", help="factor multiplied to the bandwidth")
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parser.add_argument(
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"--n_iters", default=300, type=int, nargs="?", help="number of iterations for optimization method"
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)
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# log
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parser.add_argument("--save_dir", type=str, default="./output/search", help="Folder to save checkpoints and log.")
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parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")
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args = parser.parse_args()
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api = create(None, args.search_space, fast_mode=False, verbose=False)
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args.save_dir = os.path.join(
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"{:}-{:}".format(args.save_dir, args.search_space), "{:}-T{:}".format(args.dataset, args.time_budget), "BOHB"
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)
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print("save-dir : {:}".format(args.save_dir))
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if args.rand_seed < 0:
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save_dir, all_info = None, collections.OrderedDict()
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for i in range(args.loops_if_rand):
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print("{:} : {:03d}/{:03d}".format(time_string(), i, args.loops_if_rand))
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args.rand_seed = random.randint(1, 100000)
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save_dir, all_archs, all_total_times = main(args, api)
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all_info[i] = {"all_archs": all_archs, "all_total_times": all_total_times}
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save_path = save_dir / "results.pth"
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print("save into {:}".format(save_path))
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torch.save(all_info, save_path)
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else:
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main(args, api)
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