OpenCompass/opencompass/summarizers/default.py

# flake8: noqa
# yapf: disable
import getpass
import math
import os.path as osp
from datetime import datetime
from typing import List, Optional

import mmengine
import tabulate
from mmengine import ConfigDict

from opencompass.utils import (LarkReporter, dataset_abbr_from_cfg,
                               get_infer_output_path, get_logger,
                               model_abbr_from_cfg)
from opencompass.utils.prompt import get_prompt_hash

METRIC_WHITELIST = ['score', 'auc_score', 'accuracy', 'humaneval_pass@1', 'rouge1', 'avg_toxicity_score', 'bleurt_diff', 'matthews_correlation', 'truth']
METRIC_BLACKLIST = ['bp', 'sys_len', 'ref_len']

class DefaultSummarizer:
    """Default summarizer in OpenCompass.

    Args:
        config (ConfigDict): The configuration object of the evaluation task.
            It's expected to be filled out at runtime.
        dataset_abbrs (list[str], optional): Dataset abbreviations to be
            listed in the summary.
        summary_groups (list): The dataset groups whose results need to be
            averaged out. For example, mmlu. Each item it a dict with
            'name' (str) and 'subsets' (list of dataset abbrs), and optionally
            'weights' if weighted average is needed.
        prompt_db: A deprecated field.
    """

    def __init__(self, config: ConfigDict, dataset_abbrs: Optional[List[str]] = None, summary_groups: List = [], prompt_db = None) -> None:
        self.tasks = []
        self.cfg = config
        self.logger = get_logger()
        self.summary_groups = summary_groups
        self.dataset_abbrs = dataset_abbrs
        if prompt_db:
            self.logger.warning('prompt_db is deprecated and no longer used. '
                                'Please remove it from your config.')

        # Enable lark bot if lark_url is presented
        self.lark_reporter = None
        if self.cfg.get('lark_bot_url', None):
            self.lark_reporter = LarkReporter(self.cfg['lark_bot_url'])

    def summarize(
        self,
        output_path: str = None,
        time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):  # noqa

        model_cfgs = self.cfg['models']
        dataset_cfgs = self.cfg['datasets']
        work_dir = self.cfg['work_dir']

        # pick up results
        raw_results = {}
        parsed_results = {}
        dataset_metrics = {}

        model_abbrs = [model_abbr_from_cfg(model) for model in model_cfgs]
        for model in model_cfgs:
            model_abbr = model_abbr_from_cfg(model)
            parsed_results[model_abbr] = {}
            raw_results[model_abbr] = {}
            for dataset in dataset_cfgs:
                dataset_abbr = dataset_abbr_from_cfg(dataset)
                filepath = get_infer_output_path(model, dataset, osp.join(work_dir, 'results'))
                if not osp.exists(filepath):
                    continue
                result = mmengine.load(filepath)
                result.pop('details', None)
                raw_results[model_abbr][dataset_abbr] = result
                if 'error' in result:
                    self.logger.debug(f'error in {model_abbr} {dataset_abbr} {result["error"]}')
                    continue
                else:
                    parsed_results[model_abbr][dataset_abbr] = []
                    dataset_metrics[dataset_abbr] = []
                    for metric, score in result.items():
                        if metric not in METRIC_BLACKLIST and isinstance(score, (int, float)):
                            parsed_results[model_abbr][dataset_abbr].append(score)
                            dataset_metrics[dataset_abbr].append(metric)
                        else:
                            continue
                    if len(parsed_results[model_abbr][dataset_abbr]) == 0:
                        self.logger.warning(f'unknown result format: {result}, continue')
                        del parsed_results[model_abbr][dataset_abbr]
                        del dataset_metrics[dataset_abbr]
                        continue
                    indice = sorted(
                        list(range(len(dataset_metrics[dataset_abbr]))),
                        key=lambda i: (
                            METRIC_WHITELIST.index(dataset_metrics[dataset_abbr][i])
                            if dataset_metrics[dataset_abbr][i] in METRIC_WHITELIST
                            else len(METRIC_WHITELIST)
                        )
                    )
                    parsed_results[model_abbr][dataset_abbr] = [parsed_results[model_abbr][dataset_abbr][i] for i in indice]
                    dataset_metrics[dataset_abbr] = [dataset_metrics[dataset_abbr][i] for i in indice]

        # parse eval mode
        dataset_eval_mode = {}
        for dataset in dataset_cfgs:
            inferencer = dataset.get('infer_cfg', {}).get('inferencer', {}).get('type', '')
            inferencer = inferencer if isinstance(inferencer, str) else inferencer.__name__
            dataset_abbr = dataset_abbr_from_cfg(dataset)
            if 'GenInferencer' in inferencer:
                dataset_eval_mode[dataset_abbr] = 'gen'
            elif 'PPLInferencer' in inferencer:
                dataset_eval_mode[dataset_abbr] = 'ppl'
            else:
                dataset_eval_mode[dataset_abbr] = 'unknown'
                self.logger.warning(f'unknown inferencer: {inferencer} - {dataset_abbr}')

        # calculate group metrics
        summary_groups = self.summary_groups
        for sg in summary_groups:
            for model_abbr in model_abbrs:
                results = {}
                eval_modes = []
                for dataset_abbr in sg['subsets']:
                    if dataset_abbr in parsed_results[model_abbr]:
                        results[dataset_abbr] = parsed_results[model_abbr][dataset_abbr][0]
                        eval_modes.append(dataset_eval_mode.get(dataset_abbr, 'unknown'))
                if len(results) == len(sg['subsets']):
                    if 'std' in sg and sg['std'] == True:
                        avg = sum(results[k] for k in results) / len(results)
                        variance = sum((results[k] - avg)**2 for k in results) / len(results)
                        metric = 'standard_deviation'
                        results[metric] = math.sqrt(variance)
                    else:
                        if 'weights' in sg:
                            numerator = sum(results[k] * sg['weights'][k] for k in sg['weights'])
                            denominator = sum(sg['weights'].values())
                            metric = 'weighted_average'
                        else:
                            numerator = sum(results[k] for k in results)
                            denominator = len(results)
                            metric = 'naive_average'
                        results[metric] = numerator / denominator

                    eval_modes = list(set(eval_modes))
                    eval_mode = eval_modes[0] if len(eval_modes) == 1 else 'mixed'
                    # add to global results
                    raw_results[model_abbr][sg['name']] = results
                    parsed_results[model_abbr][sg['name']] = [results[metric]]

                    dataset_metrics[sg['name']] = [metric]
                    dataset_eval_mode[sg['name']] = eval_mode
                elif len(results) == 0:
                    continue
                else:
                    raw_results[model_abbr][sg['name']] = {'error': 'missing datasets: {}'.format(set(sg['subsets']) - set(results.keys()))}

        prompt_version = {dataset_abbr_from_cfg(d): get_prompt_hash(d)[:6] for d in dataset_cfgs}

        # format table
        summarizer_dataset_abbrs = []
        if self.dataset_abbrs is None:
            for dataset in dataset_cfgs:
                dataset_abbr = dataset_abbr_from_cfg(dataset)
                if dataset_abbr in dataset_metrics:
                    for metric in dataset_metrics[dataset_abbr]:
                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
                else:
                    summarizer_dataset_abbrs.append((dataset_abbr, None))
            for dataset_abbr in dataset_metrics:
                for metric in dataset_metrics[dataset_abbr]:
                    if (dataset_abbr, metric) not in summarizer_dataset_abbrs:
                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
        else:
            for item in self.dataset_abbrs:
                if isinstance(item, str):
                    summarizer_dataset_abbrs.append((item, None))
                elif isinstance(item, (list, tuple)):
                    summarizer_dataset_abbrs.append((item[0], item[1]))

        table = []
        header = ['dataset', 'version', 'metric', 'mode'] + model_abbrs
        table.append(header)
        for dataset_abbr, metric in summarizer_dataset_abbrs:
            if dataset_abbr not in dataset_metrics:
                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(model_abbrs))
                continue
            if metric is None:
                index = 0
                metric = dataset_metrics[dataset_abbr][0]
            elif metric in dataset_metrics[dataset_abbr]:
                index = dataset_metrics[dataset_abbr].index(metric)
            else:
                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(model_abbrs))
                continue

            row = [dataset_abbr, prompt_version.get(dataset_abbr, '-'), metric, dataset_eval_mode.get(dataset_abbr, '-')]
            for model_abbr in model_abbrs:
                if dataset_abbr in parsed_results[model_abbr]:
                    row.append('{:.02f}'.format(parsed_results[model_abbr][dataset_abbr][index]))
                else:
                    row.append('-')
            table.append(row)

        # format raw txt
        raw_dataset_abbrs = []
        for model_abbr in model_abbrs:
            for dataset_abbr in raw_results[model_abbr]:
                if dataset_abbr not in raw_dataset_abbrs:
                    raw_dataset_abbrs.append(dataset_abbr)
        raw_txts = []
        for model_abbr in model_abbrs:
            raw_txts.append('-------------------------------')
            raw_txts.append(f'Model: {model_abbr}')
            for dataset_abbr in raw_dataset_abbrs:
                result = raw_results[model_abbr].get(dataset_abbr, '{}')
                raw_txts.append(f'{dataset_abbr}: {result}')
        raw_txts = '\n'.join(raw_txts)

        # output to screean
        print(tabulate.tabulate(table, headers='firstrow'))

        # output to file
        if output_path is None:
            output_path = osp.join(work_dir, 'summary', f'summary_{time_str}.txt')
            output_csv_path = osp.join(work_dir, 'summary', f'summary_{time_str}.csv')
        else:
            output_csv_path = output_path.replace('.txt', '.csv')

        output_dir = osp.split(output_path)[0]
        mmengine.mkdir_or_exist(output_dir)
        with open(output_path, 'w', encoding='utf-8') as f:
            f.write(time_str + '\n')
            f.write('tabulate format\n')
            f.write('^' * 128 + '\n')
            f.write(tabulate.tabulate(table, headers='firstrow') + '\n')
            f.write('$' * 128 + '\n')
            f.write('\n' + '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n')
            f.write('csv format\n')
            f.write('^' * 128 + '\n')
            f.write('\n'.join([','.join(row) for row in table]) + '\n')
            f.write('$' * 128 + '\n')
            f.write('\n' + '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n')
            f.write('raw format\n')
            f.write('^' * 128 + '\n')
            f.write(raw_txts + '\n')
            f.write('$' * 128 + '\n')
        self.logger.info(f'write summary to {osp.abspath(output_path)}')

        if self.lark_reporter:
            content = f'{getpass.getuser()} 的'
            content += f'详细评测汇总已输出至 {osp.abspath(output_path)}'
            self.lark_reporter.post(content)

        with open(output_csv_path, 'w', encoding='utf-8') as f:
            f.write('\n'.join([','.join(row) for row in table]) + '\n')
        self.logger.info(f'write csv to {osp.abspath(output_csv_path)}')