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https://github.com/open-compass/opencompass.git
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267401bded
* feat: add custom summarizer in CLI run mode * feat: search local config by match_cfg_file
318 lines
14 KiB
Python
318 lines
14 KiB
Python
import argparse
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import getpass
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import os
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import os.path as osp
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from datetime import datetime
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from mmengine.config import Config, DictAction
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from opencompass.partitioners import MultimodalNaivePartitioner
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from opencompass.registry import PARTITIONERS, RUNNERS
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from opencompass.runners import SlurmRunner
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from opencompass.utils import LarkReporter, Summarizer, get_logger
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from opencompass.utils.run import (exec_mm_infer_runner, fill_eval_cfg,
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fill_infer_cfg, get_config_from_arg)
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def parse_args():
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parser = argparse.ArgumentParser(description='Run an evaluation task')
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parser.add_argument('config', nargs='?', help='Train config file path')
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# add mutually exclusive args `--slurm` `--dlc`, defaults to local runner
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# if "infer" or "eval" not specified
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launch_method = parser.add_mutually_exclusive_group()
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launch_method.add_argument('--slurm',
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action='store_true',
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default=False,
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help='Whether to force tasks to run with srun. '
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'If True, `--partition(-p)` must be set. '
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'Defaults to False')
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launch_method.add_argument('--dlc',
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action='store_true',
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default=False,
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help='Whether to force tasks to run on dlc. If '
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'True, `--aliyun-cfg` must be set. Defaults'
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' to False')
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# multi-modal support
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parser.add_argument('--mm-eval',
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help='Whether or not enable multimodal evaluation',
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action='store_true',
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default=False)
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# Add shortcut parameters (models, datasets and summarizer)
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parser.add_argument('--models', nargs='+', help='', default=None)
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parser.add_argument('--datasets', nargs='+', help='', default=None)
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parser.add_argument('--summarizer', help='', default=None)
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# add general args
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parser.add_argument('--debug',
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help='Debug mode, in which scheduler will run tasks '
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'in the single process, and output will not be '
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'redirected to files',
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action='store_true',
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default=False)
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parser.add_argument('--dry-run',
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help='Dry run mode, in which the scheduler will not '
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'actually run the tasks, but only print the commands '
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'to run',
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action='store_true',
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default=False)
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parser.add_argument('-m',
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'--mode',
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help='Running mode. You can choose "infer" if you '
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'only want the inference results, or "eval" if you '
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'already have the results and want to evaluate them, '
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'or "viz" if you want to visualize the results.',
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choices=['all', 'infer', 'eval', 'viz'],
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default='all',
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type=str)
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parser.add_argument('-r',
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'--reuse',
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nargs='?',
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type=str,
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const='latest',
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help='Reuse previous outputs & results, and run any '
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'missing jobs presented in the config. If its '
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'argument is not specified, the latest results in '
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'the work_dir will be reused. The argument should '
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'also be a specific timestamp, e.g. 20230516_144254'),
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parser.add_argument('-w',
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'--work-dir',
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help='Work path, all the outputs will be '
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'saved in this path, including the slurm logs, '
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'the evaluation results, the summary results, etc.'
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'If not specified, the work_dir will be set to '
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'./outputs/default.',
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default=None,
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type=str)
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parser.add_argument('-l',
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'--lark',
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help='Report the running status to lark bot',
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action='store_true',
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default=False)
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parser.add_argument('--max-partition-size',
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help='The maximum size of an infer task. Only '
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'effective when "infer" is missing from the config.',
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type=int,
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default=40000),
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parser.add_argument(
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'--gen-task-coef',
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help='The dataset cost measurement coefficient for generation tasks, '
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'Only effective when "infer" is missing from the config.',
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type=int,
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default=20)
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parser.add_argument('--max-num-workers',
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help='Max number of workers to run in parallel. '
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'Will be overrideen by the "max_num_workers" argument '
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'in the config.',
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type=int,
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default=32)
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parser.add_argument('--max-workers-per-gpu',
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help='Max task to run in parallel on one GPU. '
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'It will only be used in the local runner.',
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type=int,
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default=1)
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parser.add_argument(
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'--retry',
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help='Number of retries if the job failed when using slurm or dlc. '
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'Will be overrideen by the "retry" argument in the config.',
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type=int,
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default=2)
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# set srun args
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slurm_parser = parser.add_argument_group('slurm_args')
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parse_slurm_args(slurm_parser)
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# set dlc args
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dlc_parser = parser.add_argument_group('dlc_args')
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parse_dlc_args(dlc_parser)
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# set hf args
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hf_parser = parser.add_argument_group('hf_args')
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parse_hf_args(hf_parser)
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args = parser.parse_args()
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if args.slurm:
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assert args.partition is not None, (
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'--partition(-p) must be set if you want to use slurm')
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if args.dlc:
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assert os.path.exists(args.aliyun_cfg), (
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'When launching tasks using dlc, it needs to be configured '
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'in "~/.aliyun.cfg", or use "--aliyun-cfg $ALiYun-CFG_Path"'
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' to specify a new path.')
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return args
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def parse_slurm_args(slurm_parser):
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"""These args are all for slurm launch."""
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slurm_parser.add_argument('-p',
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'--partition',
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help='Slurm partition name',
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default=None,
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type=str)
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slurm_parser.add_argument('-q',
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'--quotatype',
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help='Slurm quota type',
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default=None,
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type=str)
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slurm_parser.add_argument('--qos',
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help='Slurm quality of service',
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default=None,
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type=str)
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def parse_dlc_args(dlc_parser):
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"""These args are all for dlc launch."""
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dlc_parser.add_argument('--aliyun-cfg',
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help='The config path for aliyun config',
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default='~/.aliyun.cfg',
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type=str)
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def parse_hf_args(hf_parser):
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"""These args are all for the quick construction of HuggingFace models."""
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hf_parser.add_argument('--hf-path', type=str)
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hf_parser.add_argument('--peft-path', type=str)
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hf_parser.add_argument('--tokenizer-path', type=str)
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hf_parser.add_argument('--model-kwargs', nargs='+', action=DictAction)
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hf_parser.add_argument('--tokenizer-kwargs', nargs='+', action=DictAction)
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hf_parser.add_argument('--max-out-len', type=int)
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hf_parser.add_argument('--max-seq-len', type=int)
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hf_parser.add_argument('--no-batch-padding',
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action='store_true',
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default=False)
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hf_parser.add_argument('--batch-size', type=int)
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hf_parser.add_argument('--num-gpus', type=int)
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hf_parser.add_argument('--pad-token-id', type=int)
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def main():
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args = parse_args()
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if args.dry_run:
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args.debug = True
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# initialize logger
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logger = get_logger(log_level='DEBUG' if args.debug else 'INFO')
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cfg = get_config_from_arg(args)
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if args.work_dir is not None:
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cfg['work_dir'] = args.work_dir
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else:
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cfg.setdefault('work_dir', './outputs/default/')
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# cfg_time_str defaults to the current time
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cfg_time_str = dir_time_str = datetime.now().strftime('%Y%m%d_%H%M%S')
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if args.reuse:
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if args.reuse == 'latest':
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if not os.path.exists(cfg.work_dir) or not os.listdir(
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cfg.work_dir):
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logger.warning('No previous results to reuse!')
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else:
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dirs = os.listdir(cfg.work_dir)
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dir_time_str = sorted(dirs)[-1]
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else:
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dir_time_str = args.reuse
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logger.info(f'Reusing experiements from {dir_time_str}')
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elif args.mode in ['eval', 'viz']:
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raise ValueError('You must specify -r or --reuse when running in eval '
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'or viz mode!')
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# update "actual" work_dir
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cfg['work_dir'] = osp.join(cfg.work_dir, dir_time_str)
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os.makedirs(osp.join(cfg.work_dir, 'configs'), exist_ok=True)
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# dump config
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output_config_path = osp.join(cfg.work_dir, 'configs',
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f'{cfg_time_str}.py')
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cfg.dump(output_config_path)
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# Config is intentally reloaded here to avoid initialized
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# types cannot be serialized
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cfg = Config.fromfile(output_config_path, format_python_code=False)
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# report to lark bot if specify --lark
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if not args.lark:
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cfg['lark_bot_url'] = None
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elif cfg.get('lark_bot_url', None):
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content = f'{getpass.getuser()}\'s task has been launched!'
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LarkReporter(cfg['lark_bot_url']).post(content)
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if args.mode in ['all', 'infer']:
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# When user have specified --slurm or --dlc, or have not set
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# "infer" in config, we will provide a default configuration
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# for infer
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if (args.dlc or args.slurm) and cfg.get('infer', None):
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logger.warning('You have set "infer" in the config, but '
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'also specified --slurm or --dlc. '
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'The "infer" configuration will be overridden by '
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'your runtime arguments.')
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# Check whether run multimodal evaluation
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if args.mm_eval:
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partitioner = MultimodalNaivePartitioner(
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osp.join(cfg['work_dir'], 'predictions/'))
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tasks = partitioner(cfg)
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exec_mm_infer_runner(tasks, args, cfg)
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return
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if args.dlc or args.slurm or cfg.get('infer', None) is None:
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fill_infer_cfg(cfg, args)
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if args.partition is not None:
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if RUNNERS.get(cfg.infer.runner.type) == SlurmRunner:
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cfg.infer.runner.partition = args.partition
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cfg.infer.runner.quotatype = args.quotatype
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else:
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logger.warning('SlurmRunner is not used, so the partition '
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'argument is ignored.')
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if args.debug:
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cfg.infer.runner.debug = True
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if args.lark:
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cfg.infer.runner.lark_bot_url = cfg['lark_bot_url']
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cfg.infer.partitioner['out_dir'] = osp.join(cfg['work_dir'],
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'predictions/')
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partitioner = PARTITIONERS.build(cfg.infer.partitioner)
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tasks = partitioner(cfg)
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if args.dry_run:
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return
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runner = RUNNERS.build(cfg.infer.runner)
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# Add extra attack config if exists
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if hasattr(cfg, 'attack'):
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for task in tasks:
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cfg.attack.dataset = task.datasets[0][0].abbr
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task.attack = cfg.attack
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runner(tasks)
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# evaluate
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if args.mode in ['all', 'eval']:
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# When user have specified --slurm or --dlc, or have not set
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# "eval" in config, we will provide a default configuration
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# for eval
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if (args.dlc or args.slurm) and cfg.get('eval', None):
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logger.warning('You have set "eval" in the config, but '
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'also specified --slurm or --dlc. '
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'The "eval" configuration will be overridden by '
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'your runtime arguments.')
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if args.dlc or args.slurm or cfg.get('eval', None) is None:
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fill_eval_cfg(cfg, args)
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if args.partition is not None:
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if RUNNERS.get(cfg.infer.runner.type) == SlurmRunner:
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cfg.eval.runner.partition = args.partition
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cfg.eval.runner.quotatype = args.quotatype
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else:
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logger.warning('SlurmRunner is not used, so the partition '
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'argument is ignored.')
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if args.debug:
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cfg.eval.runner.debug = True
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if args.lark:
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cfg.eval.runner.lark_bot_url = cfg['lark_bot_url']
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cfg.eval.partitioner['out_dir'] = osp.join(cfg['work_dir'], 'results/')
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partitioner = PARTITIONERS.build(cfg.eval.partitioner)
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tasks = partitioner(cfg)
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if args.dry_run:
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return
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runner = RUNNERS.build(cfg.eval.runner)
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runner(tasks)
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# visualize
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if args.mode in ['all', 'eval', 'viz']:
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summarizer = Summarizer(cfg)
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summarizer.summarize(time_str=cfg_time_str)
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if __name__ == '__main__':
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main()
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