import os from typing import List, Union import tabulate from mmengine.config import Config from opencompass.partitioners import NaivePartitioner, SizePartitioner from opencompass.runners import DLCRunner, LocalRunner, SlurmRunner from opencompass.tasks import OpenICLEvalTask, OpenICLInferTask from opencompass.utils import get_logger, match_files def match_cfg_file(workdir: str, pattern: Union[str, List[str]]) -> List[str]: """Match the config file in workdir recursively given the pattern. Additionally, if the pattern itself points to an existing file, it will be directly returned. """ if isinstance(pattern, str): pattern = [pattern] pattern = [p + '.py' if not p.endswith('.py') else p for p in pattern] files = match_files(workdir, pattern, fuzzy=False) if len(files) != len(pattern): nomatched = [] ambiguous = [] err_msg = ('The provided pattern matches 0 or more than one ' 'config. Please verify your pattern and try again. ' 'You may use tools/list_configs.py to list or ' 'locate the configurations.\n') for p in pattern: files = match_files(workdir, p, fuzzy=False) if len(files) == 0: nomatched.append([p[:-3]]) elif len(files) > 1: ambiguous.append([p[:-3], '\n'.join(f[1] for f in files)]) if nomatched: table = [['Not matched patterns'], *nomatched] err_msg += tabulate.tabulate(table, headers='firstrow', tablefmt='psql') if ambiguous: table = [['Ambiguous patterns', 'Matched files'], *ambiguous] err_msg += tabulate.tabulate(table, headers='firstrow', tablefmt='psql') raise ValueError(err_msg) return files def get_config_from_arg(args) -> Config: """Get the config object given args. Only a few argument combinations are accepted (priority from high to low) 1. args.config 2. args.models and args.datasets 3. Huggingface parameter groups and args.datasets """ if args.config: return Config.fromfile(args.config, format_python_code=False) if args.datasets is None: raise ValueError('You must specify "--datasets" if you do not specify ' 'a config file path.') datasets = [] datasets_dir = os.path.join(args.config_dir, 'datasets') for dataset in match_cfg_file(datasets_dir, args.datasets): get_logger().info(f'Loading {dataset[0]}: {dataset[1]}') cfg = Config.fromfile(dataset[1]) for k in cfg.keys(): if k.endswith('_datasets'): datasets += cfg[k] if not args.models and not args.hf_path: raise ValueError('You must specify a config file path, ' 'or specify --models and --datasets, or ' 'specify HuggingFace model parameters and ' '--datasets.') models = [] if args.models: model_dir = os.path.join(args.config_dir, 'models') for model in match_cfg_file(model_dir, args.models): get_logger().info(f'Loading {model[0]}: {model[1]}') cfg = Config.fromfile(model[1]) if 'models' not in cfg: raise ValueError( f'Config file {model[1]} does not contain "models" field') models += cfg['models'] else: from opencompass.models import HuggingFace model = dict(type=f'{HuggingFace.__module__}.{HuggingFace.__name__}', path=args.hf_path, peft_path=args.peft_path, tokenizer_path=args.tokenizer_path, model_kwargs=args.model_kwargs, tokenizer_kwargs=args.tokenizer_kwargs, max_seq_len=args.max_seq_len, max_out_len=args.max_out_len, batch_padding=not args.no_batch_padding, batch_size=args.batch_size, pad_token_id=args.pad_token_id, run_cfg=dict(num_gpus=args.num_gpus)) models.append(model) summarizer = None if args.summarizer: summarizers_dir = os.path.join(args.config_dir, 'summarizers') s = match_cfg_file(summarizers_dir, [args.summarizer])[0] get_logger().info(f'Loading {s[0]}: {s[1]}') cfg = Config.fromfile(s[1]) summarizer = cfg['summarizer'] return Config(dict(models=models, datasets=datasets, summarizer=summarizer), format_python_code=False) def exec_mm_infer_runner(tasks, args, cfg): """execute multimodal infer runner according to args.""" if args.slurm: runner = SlurmRunner(dict(type='MultimodalInferTask'), 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: raise NotImplementedError('Currently, we do not support evaluating \ multimodal models on dlc.') else: runner = LocalRunner(task=dict(type='MultimodalInferTask'), max_num_workers=args.max_num_workers, debug=args.debug, lark_bot_url=cfg['lark_bot_url']) runner(tasks) def get_config_type(obj) -> str: return f'{obj.__module__}.{obj.__name__}' def fill_infer_cfg(cfg, args): new_cfg = dict(infer=dict( partitioner=dict(type=get_config_type(SizePartitioner), max_task_size=args.max_partition_size, gen_task_coef=args.gen_task_coef), runner=dict( max_num_workers=args.max_num_workers, debug=args.debug, task=dict(type=get_config_type(OpenICLInferTask)), lark_bot_url=cfg['lark_bot_url'], )), ) if args.slurm: new_cfg['infer']['runner']['type'] = get_config_type(SlurmRunner) new_cfg['infer']['runner']['partition'] = args.partition new_cfg['infer']['runner']['quotatype'] = args.quotatype new_cfg['infer']['runner']['qos'] = args.qos new_cfg['infer']['runner']['retry'] = args.retry elif args.dlc: new_cfg['infer']['runner']['type'] = get_config_type(DLCRunner) new_cfg['infer']['runner']['aliyun_cfg'] = Config.fromfile( args.aliyun_cfg) new_cfg['infer']['runner']['retry'] = args.retry else: new_cfg['infer']['runner']['type'] = get_config_type(LocalRunner) new_cfg['infer']['runner'][ 'max_workers_per_gpu'] = args.max_workers_per_gpu cfg.merge_from_dict(new_cfg) def fill_eval_cfg(cfg, args): new_cfg = dict( eval=dict(partitioner=dict(type=get_config_type(NaivePartitioner)), runner=dict( max_num_workers=args.max_num_workers, debug=args.debug, task=dict(type=get_config_type(OpenICLEvalTask)), lark_bot_url=cfg['lark_bot_url'], ))) if args.slurm: new_cfg['eval']['runner']['type'] = get_config_type(SlurmRunner) new_cfg['eval']['runner']['partition'] = args.partition new_cfg['eval']['runner']['quotatype'] = args.quotatype new_cfg['eval']['runner']['qos'] = args.qos new_cfg['eval']['runner']['retry'] = args.retry elif args.dlc: new_cfg['eval']['runner']['type'] = get_config_type(DLCRunner) new_cfg['eval']['runner']['aliyun_cfg'] = Config.fromfile( args.aliyun_cfg) new_cfg['eval']['runner']['retry'] = args.retry else: new_cfg['eval']['runner']['type'] = get_config_type(LocalRunner) new_cfg['eval']['runner'][ 'max_workers_per_gpu'] = args.max_workers_per_gpu cfg.merge_from_dict(new_cfg)