import argparse import copy import fnmatch import os.path as osp import random import time from collections import Counter from inspect import signature from typing import Optional import mmengine from mmengine.config import Config, ConfigDict from mmengine.utils import mkdir_or_exist from opencompass.openicl.icl_evaluator.lm_evaluator import LMEvaluator from opencompass.registry import (ICL_EVALUATORS, MODELS, TASKS, 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, task_abbr_from_cfg) from opencompass.utils.types import get_type_from_cfg @TASKS.register_module(force=(__name__ == '__main__')) # A hack for script run class OpenICLEvalTask(BaseTask): """OpenICL 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 = 'OpenICLEval' 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', {}) 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): for model_cfg, dataset_cfgs in zip(self.model_cfgs, self.dataset_cfgs): for dataset_cfg in dataset_cfgs: self.model_cfg = model_cfg self.dataset_cfg = dataset_cfg # Load Dataset self.eval_cfg = self.dataset_cfg.get('eval_cfg') self.output_column = dataset_cfg['reader_cfg']['output_column'] # overwrite postprocessor if the model has specified one ds_abbr = dataset_abbr_from_cfg(self.dataset_cfg) model_postprocessors = self.model_cfg.get( 'pred_postprocessor', {}) for pattern in model_postprocessors.keys(): if fnmatch.fnmatch(ds_abbr, pattern): self.eval_cfg[ 'pred_postprocessor'] = model_postprocessors[ pattern] # noqa break out_path = get_infer_output_path( self.model_cfg, self.dataset_cfg, osp.join(self.work_dir, 'results')) if osp.exists(out_path): continue self._score() def _score(self): test_set = build_dataset_from_cfg(self.dataset_cfg).test # Postprocess dataset if necessary if 'dataset_postprocessor' in self.eval_cfg: proc = self.eval_cfg['dataset_postprocessor']['type'] if isinstance(proc, str): proc = TEXT_POSTPROCESSORS.get(proc) def postprocess(sample): s = sample[self.output_column] sample[self.output_column] = proc(s) return sample test_set = test_set.map(postprocess) # Load predictions filename = get_infer_output_path( self.model_cfg, self.dataset_cfg, osp.join(self.work_dir, 'predictions')) # in case the prediction is partial root, ext = osp.splitext(filename) partial_filename = root + '_0' + ext # Get sc_size if use Self-Consistency sc_size = self.eval_cfg.get('sc_size') # Get out_path out_path = get_infer_output_path(self.model_cfg, self.dataset_cfg, osp.join(self.work_dir, 'results')) if not osp.exists(osp.realpath(filename)) and not osp.exists( osp.realpath(partial_filename)): result = {'error': 'No predictions found.'} else: if osp.exists(osp.realpath(filename)): preds = mmengine.load(filename) preds = [preds[str(i)] for i in range(len(preds))] else: filename = partial_filename preds = [] i = 1 while osp.exists(osp.realpath(filename)): sub_preds = mmengine.load(filename) preds.extend( [sub_preds[str(i)] for i in range(len(sub_preds))]) filename = root + f'_{i}' + ext i += 1 preds = {k: [pred.get(k) for pred in preds] for k in preds[0]} pred_strs = preds.pop('prediction') if ('pred_role' in self.eval_cfg and 'meta_template' in self.model_cfg and not MODELS.get(self.model_cfg['type']).is_api): # Create a prompt template for role config parsing from opencompass.models.base import LMTemplateParser parser = LMTemplateParser(self.model_cfg['meta_template']) role = parser.roles[self.eval_cfg['pred_role']] if sc_size is not None: for pred in pred_strs: if not isinstance(pred, list): raise TypeError( 'The prediction for Self-Consistency' 'must be list.') pred_strs.append([ self._extract_role_pred(sc_pred, role.get('begin', None), role.get('end', None)) for sc_pred in pred ]) else: pred_strs = [ self._extract_role_pred(pred, role.get('begin', None), role.get('end', None)) for pred in pred_strs ] # Postprocess predictions if necessary if 'pred_postprocessor' in self.eval_cfg: kwargs = self.eval_cfg['pred_postprocessor'] proc = kwargs.pop('type') if isinstance(proc, str): proc = TEXT_POSTPROCESSORS.get(proc) if sc_size is not None: pred_strs = [[proc(s, **kwargs) for s in preds] for preds in pred_strs] else: pred_strs = [proc(s, **kwargs) for s in pred_strs] # Get majority voting predictions if use self-consistency if sc_size is not None: pred_strs = [ Counter(s).most_common(1)[0][0] for s in pred_strs ] if get_type_from_cfg(self.eval_cfg['evaluator']) == LMEvaluator: if not self.judge_cfg: raise ValueError('Using LMEvaluator in dataset, but ' 'missing "eval.runner.task.judge_cfg" ' 'as the judge configuration.') self.eval_cfg['evaluator']['judge_cfg'] = self.judge_cfg self.eval_cfg['evaluator']['dataset_cfg'] = self.dataset_cfg self.eval_cfg['evaluator']['output_path'] = out_path icl_evaluator = ICL_EVALUATORS.build(self.eval_cfg['evaluator']) preds['predictions'] = pred_strs preds['references'] = (test_set[self.output_column] if self.output_column else None) preds = { k: preds[k] for k in signature(icl_evaluator.score).parameters } result = icl_evaluator.score(**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 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 = OpenICLEvalTask(cfg) inferencer.run() end_time = time.time() get_logger().info(f'time elapsed: {end_time - start_time:.2f}s')