import argparse import getpass import os import os.path as osp from datetime import datetime from mmengine.config import Config from opencompass.partitioners import NaivePartitioner, SizePartitioner from opencompass.runners import DLCRunner, LocalRunner, SlurmRunner from opencompass.utils import LarkReporter, Summarizer, get_logger def parse_args(): parser = argparse.ArgumentParser(description='Run an evaluation task') parser.add_argument('config', help='Train config file path') # add mutually exclusive args `--slurm` `--dlc`, default to local runner luach_method = parser.add_mutually_exclusive_group() luach_method.add_argument('--slurm', action='store_true', default=False, help='Whether to use srun to launch tasks, if ' 'True, `--partition(-p)` must be set. Defaults' ' to False') luach_method.add_argument('--dlc', action='store_true', default=False, help='Whether to use dlc to launch tasks, if ' 'True, `--aliyun-cfg` must be set. Defaults' ' to False') # 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('-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 None', default=None, 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 a task.', type=int, default=2000), parser.add_argument( '--gen-task-coef', help='The dataset cost measurement coefficient for generation tasks', type=int, default=20) parser.add_argument('--max-num-workers', help='Max number of workers to run in parallel.', type=int, default=32) parser.add_argument( '--retry', help='Number of retries if the job failed when using slurm or dlc.', type=int, default=2) # 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) 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 luaching 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='auto', 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 main(): args = parse_args() # initialize logger logger = get_logger(log_level='DEBUG' if args.debug else 'INFO') cfg = Config.fromfile(args.config) 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': dirs = os.listdir(cfg.work_dir) assert len(dirs) > 0, 'No previous results to reuse!' 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) # 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']: # Use SizePartitioner to split into subtasks partitioner = SizePartitioner(osp.join(cfg['work_dir'], 'predictions/'), max_task_size=args.max_partition_size, gen_task_coef=args.gen_task_coef) tasks = partitioner(cfg) # execute the infer subtasks exec_infer_runner(tasks, args, cfg) # evaluate if args.mode in ['all', 'eval']: # Use NaivePartitioner，not split partitioner = NaivePartitioner(osp.join(cfg['work_dir'], 'results/')) tasks = partitioner(cfg) # execute the eval tasks exec_eval_runner(tasks, args, cfg) # visualize if args.mode in ['all', 'eval', 'viz']: summarizer = Summarizer(cfg) summarizer.summarize(time_str=cfg_time_str) def exec_infer_runner(tasks, args, cfg): """execute infer runner according to args.""" if args.slurm: runner = SlurmRunner(dict(type='OpenICLInferTask'), max_num_workers=args.max_num_workers, partition=args.partition, quotatype=args.quotatype, retry=args.retry, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) elif args.dlc: runner = DLCRunner(dict(type='OpenICLInferTask'), max_num_workers=args.max_num_workers, aliyun_cfg=Config.fromfile(args.aliyun_cfg), retry=args.retry, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) else: runner = LocalRunner( task=dict(type='OpenICLInferTask'), # max_num_workers = args.max_num_workers, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) runner(tasks) def exec_eval_runner(tasks, args, cfg): """execute infer runner according to args.""" if args.slurm: runner = SlurmRunner(dict(type='OpenICLEvalTask'), max_num_workers=args.max_num_workers, partition=args.partition, quotatype=args.quotatype, retry=args.retry, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) elif args.dlc: runner = DLCRunner(dict(type='OpenICLEvalTask'), max_num_workers=args.max_num_workers, aliyun_cfg=Config.fromfile(args.aliyun_cfg), retry=args.retry, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) else: runner = LocalRunner( task=dict(type='OpenICLEvalTask'), # max_num_workers = args.max_num_workers, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) runner(tasks) if __name__ == '__main__': main()