import argparse import copy import fnmatch import math import os.path as osp import statistics import time from collections import Counter from inspect import signature from shutil import which from typing import List, Optional import mmengine from mmengine.config import Config, ConfigDict from mmengine.utils import mkdir_or_exist 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) def extract_role_pred(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, start) if end_idx != -1: end = end_idx return s[start:end] @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. """ name_prefix = 'OpenICLEval' log_subdir = 'logs/eval' output_subdir = 'results' def __init__(self, cfg: ConfigDict): super().__init__(cfg) self.logger = get_logger() self.num_gpus = max( c.get('eval_cfg', {}).get('num_gpus', 0) for c in sum(self.dataset_cfgs, [])) self.dump_details = cfg.get('eval', {}).get('runner', {}).get( 'task', {}).get('dump_details', False) def get_command(self, cfg_path, template): script_path = __file__ python = 'python3' if which('python3') else 'python' 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') 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 pred_dicts = copy.deepcopy(preds) preds = {k: [pred.get(k) for pred in preds] for k in preds[0]} pred_strs = preds.pop('prediction', None) pred_list_flag = pred_strs is not None and isinstance( pred_strs[0], list) 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: assert pred_list_flag, ( 'The prediction for Self-Consistency' 'must be list.') if pred_list_flag: pred_strs = [[ extract_role_pred(_pred, role.get('begin', None), role.get('end', None)) for _pred in pred ] for pred in pred_strs] else: pred_strs = [ 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 pred_list_flag: 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 ] icl_evaluator = ICL_EVALUATORS.build(self.eval_cfg['evaluator']) # need results dir to save other files out_path = get_infer_output_path( self.model_cfg, self.dataset_cfg, osp.join(self.work_dir, 'results')) icl_evaluator._out_dir = osp.splitext(out_path)[ 0] # strip extension preds['predictions'] = pred_strs preds['references'] = (test_set[self.output_column] if self.output_column else None) preds['test_set'] = test_set preds = { k: preds[k] for k in signature(icl_evaluator.score).parameters } result = icl_evaluator.score(**preds) if self.dump_details: details = result.get('details', None) try: result['details'] = self.format_details( pred_strs, test_set[self.output_column], details, pred_dicts) result['type'] = result['details'].pop('type', None) if 'PPL' in str( self.dataset_cfg.infer_cfg.inferencer.type): result['correct_bpb'], result['incorrect_bpb'] = \ self.calculate_bpb(pred_dicts) except Exception as e: self.logger.warning(f'Skip dumping details due to: {e}.') else: result.pop('details', None) if 'error' in result: self.logger.error( f'Task {task_abbr_from_cfg(self.cfg)}: {result["error"]}') return else: result_wo_details = { i: result[i] for i in result if i != 'details' } self.logger.info( f'Task {task_abbr_from_cfg(self.cfg)}: {result_wo_details}') # Save result out_path = get_infer_output_path(self.model_cfg, self.dataset_cfg, osp.join(self.work_dir, 'results')) mkdir_or_exist(osp.split(out_path)[0]) mmengine.dump(result, out_path, ensure_ascii=False, indent=4) def format_details(self, predictions, references, details, pred_dicts): """This function is responsible for formatting prediction details. Args: predictions (list): The prediction list. references (list): The reference list. details (list): Contains the 'pred' 'answer' and 'correct' for each sample. Such as `[{'pred': '光荣和ωforce', 'answers': ['光荣和ω-force', '光荣和ωforce'], 'correct': True}]` pred_dicts (list): Contains a list of samples with the original prompts. Such as `[{'origin_prompt': '根据文章回答问题。你的答案应该尽可能3》…………', 'prediction': ' 光荣和ω-force\n', 'gold': ['光荣和ω-force']}]` Returns: list: The formatted prediction details. """ results = {} for i in range(len(predictions)): ppl_flag = False result = {} origin_prediction = copy.deepcopy(pred_dicts[i]) origin_prediction.pop('in-context examples', None) origin_prediction.pop('prediction', None) keys = copy.deepcopy(list(origin_prediction.keys())) for key in keys: if key.startswith('label:'): ppl_flag = True origin_prediction[key].pop('testing input', None) new_key = key.replace('label: ', '') origin_prediction[new_key] = origin_prediction.pop(key) if ppl_flag: results['type'] = 'PPL' result['origin_prediction'] = origin_prediction result['predictions'] = str(predictions[i]) result['references'] = str(references[i]) result['correct'] = str(predictions[i]) == str(references[i]) elif details is not None: results['type'] = 'GEN' result['prompt'] = origin_prediction['origin_prompt'] result['origin_prediction'] = pred_dicts[i]['prediction'] result['predictions'] = details[i]['pred'] result['references'] = details[i]['answer'] result['correct'] = details[i]['correct'] else: results['type'] = 'GEN' result['prompt'] = origin_prediction['origin_prompt'] result['origin_prediction'] = pred_dicts[i]['prediction'] result['predictions'] = str(predictions[i]) result['references'] = str(references[i]) results[str(i)] = result return results def calculate_bpb(self, pred_dicts: List): """This function is used to calculate the BPB (Bits Per Byte) for the data. The correct BPB is obtained directly from the values in the 'predictions' file. The incorrect BPB is the average of the remaining BPB values for each sample under different labels after subtracting the correct BPB. The calculation of BPB (Bits Per Byte) is similar to PPL, with the difference that it computes the additional bits needed on average, in terms of character length, to encode the true sequence based on the predictions. This calculation involves applying a weighting factor based on the ratio of words to characters. Args: pred_dicts (list): Contains a list of samples with each options and BPB scores. Returns: dict: Contains correct and incorrect bpb. """ incorrect_bpb_list = [] bpb_list = [] for pred_dict in pred_dicts: preds = { key: value for key, value in pred_dict.items() if key.startswith('label: ') } values = [] for item in preds.items(): values.append(item[1]) bpbs = [value['BPB'] for value in values] incorrect_bpb_list.append( (sum(bpbs) - min(bpbs)) / (len(bpbs) - 1)) bpb_list.append(min(bpbs)) def filters(origins): targets = [target for target in origins if not math.isnan(target)] return targets mean_incorrect = statistics.mean(filters(incorrect_bpb_list)) mean_correct = statistics.mean(filters(bpb_list)) return 100 * mean_correct, 100 * mean_incorrect 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')