288 lines
12 KiB
Python
288 lines
12 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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# bash ./scripts-search/algos/BOHB.sh -1 ##################
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###################################################################
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import os, sys, time, random, argparse
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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 nas_201_api import NASBench201API as API
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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_configuration_space(max_nodes, search_space):
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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 config2structure_func(max_nodes):
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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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class MyWorker(Worker):
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def __init__(self, *args, convert_func=None, dataname=None, nas_bench=None, time_budget=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.convert_func = convert_func
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self._dataname = dataname
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self._nas_bench = nas_bench
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self.time_budget = time_budget
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self.seen_archs = []
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self.sim_cost_time = 0
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self.real_cost_time = 0
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self.is_end = False
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def get_the_best(self):
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assert len(self.seen_archs) > 0
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best_index, best_acc = -1, None
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for arch_index in self.seen_archs:
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info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp="200", is_random=True)
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vacc = info["valid-accuracy"]
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if best_acc is None or best_acc < vacc:
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best_acc = vacc
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best_index = arch_index
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assert best_index != -1
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return best_index
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def compute(self, config, budget, **kwargs):
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start_time = time.time()
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structure = self.convert_func(config)
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arch_index = self._nas_bench.query_index_by_arch(structure)
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info = self._nas_bench.get_more_info(arch_index, self._dataname, None, hp="200", is_random=True)
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cur_time = info["train-all-time"] + info["valid-per-time"]
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cur_vacc = info["valid-accuracy"]
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self.real_cost_time += time.time() - start_time
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if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
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self.sim_cost_time += cur_time
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self.seen_archs.append(arch_index)
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return {
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"loss": 100 - float(cur_vacc),
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"info": {
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"seen-arch": len(self.seen_archs),
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"sim-test-time": self.sim_cost_time,
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"current-arch": arch_index,
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},
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}
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else:
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self.is_end = True
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return {
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"loss": 100,
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"info": {"seen-arch": len(self.seen_archs), "sim-test-time": self.sim_cost_time, "current-arch": None},
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}
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def main(xargs, nas_bench):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(xargs.workers)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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if xargs.dataset == "cifar10":
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dataname = "cifar10-valid"
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else:
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dataname = xargs.dataset
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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split_Fpath = "configs/nas-benchmark/cifar-split.txt"
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log("Load split file from {:}".format(split_Fpath))
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(config_path, {"class_num": class_num, "xshape": xshape}, logger)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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logger.log(
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"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
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)
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)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {"config": config, "train_loader": train_loader, "valid_loader": valid_loader}
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else:
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(config_path, None, logger)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {"config": config, "train_loader": None, "valid_loader": None}
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# nas dataset load
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assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset)
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search_space = get_search_spaces("cell", xargs.search_space_name)
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cs = get_configuration_space(xargs.max_nodes, search_space)
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config2structure = config2structure_func(xargs.max_nodes)
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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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# nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
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# logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
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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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dataname=dataname,
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nas_bench=nas_bench,
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time_budget=xargs.time_budget,
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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=12,
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max_budget=200,
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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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real_cost_time = time.time() - start_time
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id2config = results.get_id2config_mapping()
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incumbent = results.get_incumbent_id()
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logger.log("Best found configuration: {:} within {:.3f} s".format(id2config[incumbent]["config"], real_cost_time))
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best_arch = config2structure(id2config[incumbent]["config"])
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info = nas_bench.query_by_arch(best_arch, "200")
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if info is None:
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logger.log("Did not find this architecture : {:}.".format(best_arch))
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else:
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logger.log("{:}".format(info))
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logger.log("-" * 100)
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logger.log("workers : {:.1f}s with {:} archs".format(workers[0].time_budget, len(workers[0].seen_archs)))
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch(best_arch), real_cost_time
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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("--data_path", type=str, help="Path to dataset")
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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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# channels and number-of-cells
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parser.add_argument("--search_space_name", type=str, help="The search space name.")
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parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
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parser.add_argument("--channel", type=int, help="The number of channels.")
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parser.add_argument("--num_cells", type=int, help="The number of cells in one stage.")
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parser.add_argument("--time_budget", type=int, help="The total time cost budge for searching (in seconds).")
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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=100, 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("--workers", type=int, default=2, help="number of data loading workers (default: 2)")
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parser.add_argument("--save_dir", type=str, help="Folder to save checkpoints and log.")
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parser.add_argument(
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"--arch_nas_dataset", type=str, help="The path to load the architecture dataset (tiny-nas-benchmark)."
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)
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parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
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parser.add_argument("--rand_seed", type=int, help="manual seed")
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args = parser.parse_args()
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# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
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nas_bench = None
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else:
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print("{:} build NAS-Benchmark-API from {:}".format(time_string(), args.arch_nas_dataset))
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nas_bench = API(args.arch_nas_dataset)
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if args.rand_seed < 0:
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save_dir, all_indexes, num, all_times = None, [], 500, []
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for i in range(num):
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print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
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args.rand_seed = random.randint(1, 100000)
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save_dir, index, ctime = main(args, nas_bench)
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all_indexes.append(index)
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all_times.append(ctime)
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print("\n average time : {:.3f} s".format(sum(all_times) / len(all_times)))
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torch.save(all_indexes, save_dir / "results.pth")
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else:
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main(args, nas_bench)
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