xautodl/exps/algos/BOHB.py

368 lines
13 KiB
Python

##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
###################################################################
# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
# required to install hpbandster ##################################
# pip install hpbandster ##################################
###################################################################
# bash ./scripts-search/algos/BOHB.sh -1 ##################
###################################################################
import os, sys, time, random, argparse
from copy import deepcopy
from pathlib import Path
import torch
from xautodl.config_utils import load_config
from xautodl.datasets import get_datasets, SearchDataset
from xautodl.procedures import prepare_seed, prepare_logger
from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
from xautodl.models import CellStructure, get_search_spaces
from nas_201_api import NASBench201API as API
# 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_configuration_space(max_nodes, search_space):
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 config2structure_func(max_nodes):
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, nas_bench):
assert torch.cuda.is_available(), "CUDA is not available."
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.set_num_threads(xargs.workers)
prepare_seed(xargs.rand_seed)
logger = prepare_logger(args)
if xargs.dataset == "cifar10":
dataname = "cifar10-valid"
else:
dataname = xargs.dataset
if xargs.data_path is not None:
train_data, valid_data, xshape, class_num = get_datasets(
xargs.dataset, xargs.data_path, -1
)
split_Fpath = "configs/nas-benchmark/cifar-split.txt"
cifar_split = load_config(split_Fpath, None, None)
train_split, valid_split = cifar_split.train, cifar_split.valid
logger.log("Load split file from {:}".format(split_Fpath))
config_path = "configs/nas-benchmark/algos/R-EA.config"
config = load_config(
config_path, {"class_num": class_num, "xshape": xshape}, logger
)
# To split data
train_data_v2 = deepcopy(train_data)
train_data_v2.transform = valid_data.transform
valid_data = train_data_v2
search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
# data loader
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
num_workers=xargs.workers,
pin_memory=True,
)
valid_loader = torch.utils.data.DataLoader(
valid_data,
batch_size=config.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
num_workers=xargs.workers,
pin_memory=True,
)
logger.log(
"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
)
)
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
extra_info = {
"config": config,
"train_loader": train_loader,
"valid_loader": valid_loader,
}
else:
config_path = "configs/nas-benchmark/algos/R-EA.config"
config = load_config(config_path, None, logger)
logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
extra_info = {"config": config, "train_loader": None, "valid_loader": None}
# nas dataset load
assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset)
search_space = get_search_spaces("cell", xargs.search_space_name)
cs = get_configuration_space(xargs.max_nodes, search_space)
config2structure = config2structure_func(xargs.max_nodes)
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
# nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
# logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
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("--data_path", type=str, help="Path to dataset")
parser.add_argument(
"--dataset",
type=str,
choices=["cifar10", "cifar100", "ImageNet16-120"],
help="Choose between Cifar10/100 and ImageNet-16.",
)
# channels and number-of-cells
parser.add_argument("--search_space_name", type=str, help="The search space name.")
parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
parser.add_argument("--channel", type=int, help="The number of channels.")
parser.add_argument(
"--num_cells", type=int, help="The number of cells in one stage."
)
parser.add_argument(
"--time_budget",
type=int,
help="The total time cost budge for searching (in seconds).",
)
# BOHB
parser.add_argument(
"--strategy",
default="sampling",
type=str,
nargs="?",
help="optimization strategy for the acquisition function",
)
parser.add_argument(
"--min_bandwidth",
default=0.3,
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=0.33,
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=100,
type=int,
nargs="?",
help="number of iterations for optimization method",
)
# log
parser.add_argument(
"--workers",
type=int,
default=2,
help="number of data loading workers (default: 2)",
)
parser.add_argument(
"--save_dir", type=str, help="Folder to save checkpoints and log."
)
parser.add_argument(
"--arch_nas_dataset",
type=str,
help="The path to load the architecture dataset (tiny-nas-benchmark).",
)
parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
parser.add_argument("--rand_seed", type=int, help="manual seed")
args = parser.parse_args()
# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
nas_bench = None
else:
print(
"{:} build NAS-Benchmark-API from {:}".format(
time_string(), args.arch_nas_dataset
)
)
nas_bench = API(args.arch_nas_dataset)
if args.rand_seed < 0:
save_dir, all_indexes, num, all_times = None, [], 500, []
for i in range(num):
print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
args.rand_seed = random.randint(1, 100000)
save_dir, index, ctime = main(args, nas_bench)
all_indexes.append(index)
all_times.append(ctime)
print("\n average time : {:.3f} s".format(sum(all_times) / len(all_times)))
torch.save(all_indexes, save_dir / "results.pth")
else:
main(args, nas_bench)