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 from opencompass.runners import SlurmRunner from opencompass.utils import LarkReporter, Summarizer, 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 and datasets) parser.add_argument('--models', nargs='+', help='', default=None) parser.add_argument('--datasets', nargs='+', 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('-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) # 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) 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) hf_parser.add_argument('--tokenizer-kwargs', nargs='+', action=DictAction) 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 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) 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.partition is not None: if RUNNERS.get(cfg.infer.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 = Summarizer(cfg) summarizer.summarize(time_str=cfg_time_str) if __name__ == '__main__': main()