import argparse import copy import fnmatch import os.path as osp import random import time from typing import List, Optional, Union import mmengine from mmengine.config import Config, ConfigDict from mmengine.utils import mkdir_or_exist from opencompass.registry import ICL_EVALUATORS, MODELS, TEXT_POSTPROCESSORS from opencompass.tasks.base import BaseTask from opencompass.utils import (build_dataset_from_cfg, dataset_abbr_from_cfg, get_infer_output_path, get_logger, model_abbr_from_cfg, task_abbr_from_cfg) class SubjectiveEvalTask(BaseTask): """Subjective Evaluation Task. This task is used to evaluate the metric between predictions and references. Args: cfg (ConfigDict): The configuration of the entire evaluation task. """ name_prefix = 'SubjectiveEval' log_subdir = 'logs/eval' output_subdir = 'results' def __init__(self, cfg: ConfigDict): super().__init__(cfg) self.logger = get_logger() judge_cfg = cfg.eval.runner.task.get('judge_cfg', {}) if type(judge_cfg) != ConfigDict: print('*' * 100) print('Due to different Judge model needs different summarizer and' " prompts, we don't support multi judge model evaluation at " 'one time, please do not use list to set your judge cfg, jus' 't use a dict or list[0] should be fine. If you want to eval' 'uation multi judge model in one script, we suggest you to u' 'se a bash or bat script to start multi configs evaluation!') print('*' * 100) assert type(judge_cfg) == ConfigDict run_cfg = judge_cfg.get('run_cfg', {}) self.num_gpus = run_cfg.get('num_gpus', 0) self.num_procs = run_cfg.get('num_procs', 1) self.judge_cfg = copy.deepcopy(judge_cfg) def get_command(self, cfg_path, template): """Get the command template for the task. Args: cfg_path (str): The path to the config file of the task. template (str): The template which have '{task_cmd}' to format the command. """ script_path = __file__ if self.num_gpus > 0: port = random.randint(12000, 32000) command = (f'torchrun --master_port={port} ' f'--nproc_per_node {self.num_procs} ' f'{script_path} {cfg_path}') else: command = f'python {script_path} {cfg_path}' return template.format(task_cmd=command) def run(self): # model_cfg can be a list of model configs for model_cfg, dataset_cfgs in zip(self.model_cfgs, self.dataset_cfgs): for dataset_cfg in dataset_cfgs: # Load Dataset eval_cfg = dataset_cfg.get('eval_cfg') output_column = dataset_cfg['reader_cfg']['output_column'] if type(model_cfg) == ConfigDict: model_cfg = (model_cfg, ) model_cfg += ({ 'abbr': 'judged-by--' + model_abbr_from_cfg(self.judge_cfg) }, ) out_path = get_infer_output_path( model_cfg, dataset_cfg, osp.join(self.work_dir, 'results')) if osp.exists(out_path): continue self._score(model_cfg, dataset_cfg, eval_cfg, output_column) def _load_model_pred(self, model_cfg: Union[ConfigDict, List[ConfigDict]], dataset_cfg: ConfigDict, eval_cfg: ConfigDict) -> Union[None, List[str]]: if isinstance(model_cfg, (tuple, list)): return [ self._load_model_pred(m, dataset_cfg, eval_cfg) for m in model_cfg ] # Load predictions filename = get_infer_output_path( model_cfg, dataset_cfg, osp.join(self.work_dir, 'predictions')) # in case the prediction is partial root, ext = osp.splitext(filename) partial_filename = root + '_0' + ext pred_strs = None if not osp.exists(osp.realpath(filename)) and not osp.exists( osp.realpath(partial_filename)): return {'error': 'No predictions found.'} else: if osp.exists(osp.realpath(filename)): preds = mmengine.load(filename) pred_strs = [ preds[str(i)]['prediction'] for i in range(len(preds)) ] else: filename = partial_filename pred_strs = [] i = 1 while osp.exists(osp.realpath(filename)): preds = mmengine.load(filename) filename = root + f'_{i}' + ext i += 1 pred_strs += [ preds[str(i)]['prediction'] for i in range(len(preds)) ] if ('pred_role' in eval_cfg and 'meta_template' in model_cfg and not MODELS.get(model_cfg['type']).is_api): # Create a prompt template for role config parsing from opencompass.models.base import LMTemplateParser parser = LMTemplateParser(model_cfg['meta_template']) role = parser.roles[eval_cfg['pred_role']] pred_strs = [ self._extract_role_pred(pred, role.get('begin', None), role.get('end', None)) for pred in pred_strs ] # Postprocess predictions if necessary ds_abbr = dataset_abbr_from_cfg(dataset_cfg) model_postprocessors = model_cfg.get('pred_postprocessor', {}) pred_postprocessor = None for pattern in model_postprocessors.keys(): if fnmatch.fnmatch(ds_abbr, pattern): pred_postprocessor = model_postprocessors[pattern] break if 'pred_postprocessor' in eval_cfg or pred_postprocessor: kwargs = pred_postprocessor or eval_cfg['pred_postprocessor'] proc = TEXT_POSTPROCESSORS.get(kwargs.pop('type')) pred_strs = [proc(s, **kwargs) for s in pred_strs] return { 'model_name': model_abbr_from_cfg(model_cfg), 'model_preds': pred_strs } def _score(self, model_cfg, dataset_cfg, eval_cfg, output_column): test_set = build_dataset_from_cfg(dataset_cfg).test # Postprocess dataset if necessary if 'dataset_postprocessor' in eval_cfg: proc = TEXT_POSTPROCESSORS.get( eval_cfg['dataset_postprocessor']['type']) def postprocess(sample): s = sample[output_column] sample[output_column] = proc(s) return sample test_set = test_set.map(postprocess) # Get out_path out_path = get_infer_output_path(model_cfg, dataset_cfg, osp.join(self.work_dir, 'results')) new_model_cfg = [] for m_cfg in model_cfg: if len(m_cfg) > 1: new_model_cfg.append(m_cfg) if len(new_model_cfg) == 1: new_model_cfg = new_model_cfg[0] model_preds = self._load_model_pred(new_model_cfg, dataset_cfg, eval_cfg) if not self.judge_cfg: raise ValueError('missing "eval.runner.task.judge_cfg"') eval_cfg['evaluator']['judge_cfg'] = self.judge_cfg eval_cfg['evaluator']['dataset_cfg'] = dataset_cfg eval_cfg['evaluator']['output_path'] = out_path icl_evaluator = ICL_EVALUATORS.build(eval_cfg['evaluator']) references = (test_set[output_column] if output_column else None) if 'error' not in model_preds: result = icl_evaluator.score(predictions=model_preds, references=references) else: result = model_preds if 'error' in result: self.logger.error( f'Task {task_abbr_from_cfg(self.cfg)}: {result["error"]}') return else: self.logger.info( f'Task {task_abbr_from_cfg(self.cfg)}') #: {result}') # Save result mkdir_or_exist(osp.split(out_path)[0]) mmengine.dump(result, open(out_path, 'w', encoding='utf-8'), file_format='json', ensure_ascii=False, indent=4) def _extract_role_pred(self, s: str, begin_str: Optional[str], end_str: Optional[str]) -> str: """Extract the role prediction from the full prediction string. The role prediction may be the substring between the begin and end string. Args: s (str): Full prediction string. begin_str (str): The beginning string of the role end_str (str): The ending string of the role. Returns: str: The extracted role prediction. """ start = 0 end = len(s) if begin_str: begin_idx = s.find(begin_str) if begin_idx != -1: start = begin_idx + len(begin_str) if end_str: # TODO: Support calling tokenizer for the accurate eos token # and avoid such hardcode end_idx = s.find(end_str[:1], start) if end_idx != -1: end = end_idx return s[start:end] def get_output_paths(self, file_extension: str = 'json') -> List[str]: """Get the paths to the output files. Every file should exist if the task succeeds. Args: file_extension (str): The file extension of the output files. Default: 'json'. """ output_paths = [] for model, datasets in zip(self.model_cfgs, self.dataset_cfgs): for dataset in datasets: if type(model) == ConfigDict: model = (model, ) model += ({ 'abbr': 'judged-by--' + model_abbr_from_cfg(self.judge_cfg) }, ) output_paths.append( get_infer_output_path( model, dataset, osp.join(self.work_dir, self.output_subdir), file_extension)) return output_paths def parse_args(): parser = argparse.ArgumentParser(description='Score Calculator') parser.add_argument('config', help='Config file path') args = parser.parse_args() return args if __name__ == '__main__': args = parse_args() cfg = Config.fromfile(args.config) start_time = time.time() inferencer = SubjectiveEvalTask(cfg) inferencer.run() end_time = time.time() get_logger().info(f'time elapsed: {end_time - start_time:.2f}s')