File size: 15,644 Bytes
256a159 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 |
import argparse
import getpass
import os
import os.path as osp
from datetime import datetime
from mmengine.config import Config, DictAction
from opencompass.partitioners import MultimodalNaivePartitioner
from opencompass.registry import PARTITIONERS, RUNNERS, build_from_cfg
from opencompass.runners import SlurmRunner
from opencompass.summarizers import DefaultSummarizer
from opencompass.utils import LarkReporter, get_logger
from opencompass.utils.run import (exec_mm_infer_runner, fill_eval_cfg,
fill_infer_cfg, get_config_from_arg)
def parse_args():
parser = argparse.ArgumentParser(description='Run an evaluation task')
parser.add_argument('config', nargs='?', help='Train config file path')
# add mutually exclusive args `--slurm` `--dlc`, defaults to local runner
# if "infer" or "eval" not specified
launch_method = parser.add_mutually_exclusive_group()
launch_method.add_argument('--slurm',
action='store_true',
default=False,
help='Whether to force tasks to run with srun. '
'If True, `--partition(-p)` must be set. '
'Defaults to False')
launch_method.add_argument('--dlc',
action='store_true',
default=False,
help='Whether to force tasks to run on dlc. If '
'True, `--aliyun-cfg` must be set. Defaults'
' to False')
# multi-modal support
parser.add_argument('--mm-eval',
help='Whether or not enable multimodal evaluation',
action='store_true',
default=False)
# Add shortcut parameters (models, datasets and summarizer)
parser.add_argument('--models', nargs='+', help='', default=None)
parser.add_argument('--datasets', nargs='+', help='', default=None)
parser.add_argument('--summarizer', help='', default=None)
# add general args
parser.add_argument('--debug',
help='Debug mode, in which scheduler will run tasks '
'in the single process, and output will not be '
'redirected to files',
action='store_true',
default=False)
parser.add_argument('--dry-run',
help='Dry run mode, in which the scheduler will not '
'actually run the tasks, but only print the commands '
'to run',
action='store_true',
default=False)
parser.add_argument('-m',
'--mode',
help='Running mode. You can choose "infer" if you '
'only want the inference results, or "eval" if you '
'already have the results and want to evaluate them, '
'or "viz" if you want to visualize the results.',
choices=['all', 'infer', 'eval', 'viz'],
default='all',
type=str)
parser.add_argument('-r',
'--reuse',
nargs='?',
type=str,
const='latest',
help='Reuse previous outputs & results, and run any '
'missing jobs presented in the config. If its '
'argument is not specified, the latest results in '
'the work_dir will be reused. The argument should '
'also be a specific timestamp, e.g. 20230516_144254'),
parser.add_argument('-w',
'--work-dir',
help='Work path, all the outputs will be '
'saved in this path, including the slurm logs, '
'the evaluation results, the summary results, etc.'
'If not specified, the work_dir will be set to '
'./outputs/default.',
default=None,
type=str)
parser.add_argument(
'--config-dir',
default='configs',
help='Use the custom config directory instead of config/ to '
'search the configs for datasets, models and summarizers',
type=str)
parser.add_argument('-l',
'--lark',
help='Report the running status to lark bot',
action='store_true',
default=False)
parser.add_argument('--max-partition-size',
help='The maximum size of an infer task. Only '
'effective when "infer" is missing from the config.',
type=int,
default=40000),
parser.add_argument(
'--gen-task-coef',
help='The dataset cost measurement coefficient for generation tasks, '
'Only effective when "infer" is missing from the config.',
type=int,
default=20)
parser.add_argument('--max-num-workers',
help='Max number of workers to run in parallel. '
'Will be overrideen by the "max_num_workers" argument '
'in the config.',
type=int,
default=32)
parser.add_argument('--max-workers-per-gpu',
help='Max task to run in parallel on one GPU. '
'It will only be used in the local runner.',
type=int,
default=1)
parser.add_argument(
'--retry',
help='Number of retries if the job failed when using slurm or dlc. '
'Will be overrideen by the "retry" argument in the config.',
type=int,
default=2)
parser.add_argument(
'--dump-eval-details',
help='Whether to dump the evaluation details, including the '
'correctness of each sample, bpb, etc.',
action='store_true',
)
# set srun args
slurm_parser = parser.add_argument_group('slurm_args')
parse_slurm_args(slurm_parser)
# set dlc args
dlc_parser = parser.add_argument_group('dlc_args')
parse_dlc_args(dlc_parser)
# set hf args
hf_parser = parser.add_argument_group('hf_args')
parse_hf_args(hf_parser)
# set custom dataset args
custom_dataset_parser = parser.add_argument_group('custom_dataset_args')
parse_custom_dataset_args(custom_dataset_parser)
args = parser.parse_args()
if args.slurm:
assert args.partition is not None, (
'--partition(-p) must be set if you want to use slurm')
if args.dlc:
assert os.path.exists(args.aliyun_cfg), (
'When launching tasks using dlc, it needs to be configured '
'in "~/.aliyun.cfg", or use "--aliyun-cfg $ALiYun-CFG_Path"'
' to specify a new path.')
return args
def parse_slurm_args(slurm_parser):
"""These args are all for slurm launch."""
slurm_parser.add_argument('-p',
'--partition',
help='Slurm partition name',
default=None,
type=str)
slurm_parser.add_argument('-q',
'--quotatype',
help='Slurm quota type',
default=None,
type=str)
slurm_parser.add_argument('--qos',
help='Slurm quality of service',
default=None,
type=str)
def parse_dlc_args(dlc_parser):
"""These args are all for dlc launch."""
dlc_parser.add_argument('--aliyun-cfg',
help='The config path for aliyun config',
default='~/.aliyun.cfg',
type=str)
def parse_hf_args(hf_parser):
"""These args are all for the quick construction of HuggingFace models."""
hf_parser.add_argument('--hf-path', type=str)
hf_parser.add_argument('--peft-path', type=str)
hf_parser.add_argument('--tokenizer-path', type=str)
hf_parser.add_argument('--model-kwargs',
nargs='+',
action=DictAction,
default={})
hf_parser.add_argument('--tokenizer-kwargs',
nargs='+',
action=DictAction,
default={})
hf_parser.add_argument('--max-out-len', type=int)
hf_parser.add_argument('--max-seq-len', type=int)
hf_parser.add_argument('--no-batch-padding',
action='store_true',
default=False)
hf_parser.add_argument('--batch-size', type=int)
hf_parser.add_argument('--num-gpus', type=int)
hf_parser.add_argument('--pad-token-id', type=int)
def parse_custom_dataset_args(custom_dataset_parser):
"""These args are all for the quick construction of custom datasets."""
custom_dataset_parser.add_argument('--custom-dataset-path', type=str)
custom_dataset_parser.add_argument('--custom-dataset-meta-path', type=str)
custom_dataset_parser.add_argument('--custom-dataset-data-type',
type=str,
choices=['mcq', 'qa'])
custom_dataset_parser.add_argument('--custom-dataset-infer-method',
type=str,
choices=['gen', 'ppl'])
def main():
args = parse_args()
if args.dry_run:
args.debug = True
# initialize logger
logger = get_logger(log_level='DEBUG' if args.debug else 'INFO')
cfg = get_config_from_arg(args)
if args.work_dir is not None:
cfg['work_dir'] = args.work_dir
else:
cfg.setdefault('work_dir', './outputs/default/')
# cfg_time_str defaults to the current time
cfg_time_str = dir_time_str = datetime.now().strftime('%Y%m%d_%H%M%S')
if args.reuse:
if args.reuse == 'latest':
if not os.path.exists(cfg.work_dir) or not os.listdir(
cfg.work_dir):
logger.warning('No previous results to reuse!')
else:
dirs = os.listdir(cfg.work_dir)
dir_time_str = sorted(dirs)[-1]
else:
dir_time_str = args.reuse
logger.info(f'Reusing experiements from {dir_time_str}')
elif args.mode in ['eval', 'viz']:
raise ValueError('You must specify -r or --reuse when running in eval '
'or viz mode!')
# update "actual" work_dir
cfg['work_dir'] = osp.join(cfg.work_dir, dir_time_str)
os.makedirs(osp.join(cfg.work_dir, 'configs'), exist_ok=True)
# dump config
output_config_path = osp.join(cfg.work_dir, 'configs',
f'{cfg_time_str}.py')
cfg.dump(output_config_path)
# Config is intentally reloaded here to avoid initialized
# types cannot be serialized
cfg = Config.fromfile(output_config_path, format_python_code=False)
# report to lark bot if specify --lark
if not args.lark:
cfg['lark_bot_url'] = None
elif cfg.get('lark_bot_url', None):
content = f'{getpass.getuser()}\'s task has been launched!'
LarkReporter(cfg['lark_bot_url']).post(content)
if args.mode in ['all', 'infer']:
# When user have specified --slurm or --dlc, or have not set
# "infer" in config, we will provide a default configuration
# for infer
if (args.dlc or args.slurm) and cfg.get('infer', None):
logger.warning('You have set "infer" in the config, but '
'also specified --slurm or --dlc. '
'The "infer" configuration will be overridden by '
'your runtime arguments.')
# Check whether run multimodal evaluation
if args.mm_eval:
partitioner = MultimodalNaivePartitioner(
osp.join(cfg['work_dir'], 'predictions/'))
tasks = partitioner(cfg)
exec_mm_infer_runner(tasks, args, cfg)
return
if args.dlc or args.slurm or cfg.get('infer', None) is None:
fill_infer_cfg(cfg, args)
if args.partition is not None:
if RUNNERS.get(cfg.infer.runner.type) == SlurmRunner:
cfg.infer.runner.partition = args.partition
cfg.infer.runner.quotatype = args.quotatype
else:
logger.warning('SlurmRunner is not used, so the partition '
'argument is ignored.')
if args.debug:
cfg.infer.runner.debug = True
if args.lark:
cfg.infer.runner.lark_bot_url = cfg['lark_bot_url']
cfg.infer.partitioner['out_dir'] = osp.join(cfg['work_dir'],
'predictions/')
partitioner = PARTITIONERS.build(cfg.infer.partitioner)
tasks = partitioner(cfg)
if args.dry_run:
return
runner = RUNNERS.build(cfg.infer.runner)
# Add extra attack config if exists
if hasattr(cfg, 'attack'):
for task in tasks:
cfg.attack.dataset = task.datasets[0][0].abbr
task.attack = cfg.attack
runner(tasks)
# evaluate
if args.mode in ['all', 'eval']:
# When user have specified --slurm or --dlc, or have not set
# "eval" in config, we will provide a default configuration
# for eval
if (args.dlc or args.slurm) and cfg.get('eval', None):
logger.warning('You have set "eval" in the config, but '
'also specified --slurm or --dlc. '
'The "eval" configuration will be overridden by '
'your runtime arguments.')
if args.dlc or args.slurm or cfg.get('eval', None) is None:
fill_eval_cfg(cfg, args)
if args.dump_eval_details:
cfg.eval.runner.task.dump_details = True
if args.partition is not None:
if RUNNERS.get(cfg.eval.runner.type) == SlurmRunner:
cfg.eval.runner.partition = args.partition
cfg.eval.runner.quotatype = args.quotatype
else:
logger.warning('SlurmRunner is not used, so the partition '
'argument is ignored.')
if args.debug:
cfg.eval.runner.debug = True
if args.lark:
cfg.eval.runner.lark_bot_url = cfg['lark_bot_url']
cfg.eval.partitioner['out_dir'] = osp.join(cfg['work_dir'], 'results/')
partitioner = PARTITIONERS.build(cfg.eval.partitioner)
tasks = partitioner(cfg)
if args.dry_run:
return
runner = RUNNERS.build(cfg.eval.runner)
runner(tasks)
# visualize
if args.mode in ['all', 'eval', 'viz']:
summarizer_cfg = cfg.get('summarizer', {})
if not summarizer_cfg or summarizer_cfg.get('type', None) is None:
summarizer_cfg['type'] = DefaultSummarizer
summarizer_cfg['config'] = cfg
summarizer = build_from_cfg(summarizer_cfg)
summarizer.summarize(time_str=cfg_time_str)
if __name__ == '__main__':
main()
|