[Fix] Fix subjective alignbench (#730)

opencompass/partitioners/sub_naive.py

@@ -8,6 +8,18 @@ from opencompass.registry import PARTITIONERS
 from .naive import NaivePartitioner
 
 
+def remove_duplicate_pairs(model_combinations):
+    combo_dict = {}
+    for i, combo in enumerate(model_combinations):
+        sorted_names = tuple(sorted((combo[0]['abbr'], combo[1]['abbr'])))
+        if sorted_names not in combo_dict:
+            combo_dict[sorted_names] = i
+    new_model_combinations = [
+        model_combinations[i] for i in combo_dict.values()
+    ]
+    return new_model_combinations
+
+
 @PARTITIONERS.register_module()
 class SubjectiveNaivePartitioner(NaivePartitioner):
     """Naive task partitioner for subjective evaluation. Compared to
@@ -35,17 +47,6 @@ class SubjectiveNaivePartitioner(NaivePartitioner):
         self.compare_models = compare_models
         self.model_pairs = model_pairs
 
-    def remove_duplicate_pairs(self, model_combinations):
-        combo_dict = {}
-        for i, combo in enumerate(model_combinations):
-            sorted_names = tuple(sorted((combo[0]['abbr'], combo[1]['abbr'])))
-            if sorted_names not in combo_dict:
-                combo_dict[sorted_names] = i
-        new_model_combinations = [
-            model_combinations[i] for i in combo_dict.values()
-        ]
-        return new_model_combinations
-
     def get_model_combinations(
             self,
             models: List[ConfigDict],
@@ -57,7 +58,7 @@ class SubjectiveNaivePartitioner(NaivePartitioner):
         elif self.mode == 'm2n':
             assert len(base_models) > 0 and len(compare_models) > 0
             model_combinations = list(product(base_models, compare_models))
-            unique_combinations = self.remove_duplicate_pairs([
+            unique_combinations = remove_duplicate_pairs([
                 combo for combo in model_combinations if combo[0] != combo[1]
             ])
             return unique_combinations

opencompass/summarizers/alignmentbench.py

@@ -38,9 +38,7 @@ def post_process(judgment: str):
             dictionary_str = match.group(1)
             kv_pattern = r"'(.*?)': (\d+)"
             matches = re.findall(kv_pattern, dictionary_str)
-
             result_dict = {key: int(value) for key, value in matches}
-
             return result_dict
         else:
             return None
@@ -95,6 +93,7 @@ class AlignmentBenchSummarizer:
         self.eval_model_abbrs = [
             model_abbr_from_cfg(model) for model in self.eval_model_cfgs
         ]
+        self.judge_abbr = self.cfg['judge_model']['abbr']
 
     def summarize(self,
                   time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):
@@ -106,6 +105,7 @@ class AlignmentBenchSummarizer:
         Returns:
             pd.DataFrame: The summary results.
         """
+
         dataset_cfgs = self.cfg['datasets']
         work_dir = self.cfg['work_dir']
         self.work_dir = work_dir
@@ -118,19 +118,48 @@ class AlignmentBenchSummarizer:
         results_folder = osp.join(work_dir, 'results')
 
         fout_flag, fout_flag2 = 0, 0
-        for subdir in os.listdir(results_folder):
-            if subdir not in self.eval_model_abbrs:
-                continue
+        for eval_model_abbr in self.eval_model_abbrs:
+            subdir = eval_model_abbr + '_judged-by--' + self.judge_abbr
             subdir_path = os.path.join(results_folder, subdir)
             if os.path.isdir(subdir_path):
-                model, judge_model = subdir.split('_')
-                fout = osp.join(output_dir, judge_model + 'dimension.csv')
-                fout2 = osp.join(output_dir, judge_model + 'capability.csv')
+                model, judge_model = eval_model_abbr, self.judge_abbr
+                fout = osp.join(output_dir,
+                                'judged-by--' + judge_model + '-dimension.csv')
+                fout2 = osp.join(
+                    output_dir,
+                    'judged-by--' + judge_model + '-capability.csv')
                 for dataset in dataset_cfgs:
                     dataset_abbr = dataset_abbr_from_cfg(dataset)
-                    filepath = os.path.join(subdir_path,
+                    filename = os.path.join(subdir_path,
                                             dataset_abbr + '.json')
-                    result = mmengine.load(filepath)
+                    partial_filename = os.path.join(subdir_path,
+                                                    dataset_abbr + '_0.json')
+                    if osp.exists(osp.realpath(filename)):
+                        result = mmengine.load(filename)
+                    elif osp.exists(osp.realpath(partial_filename)):
+                        filename = partial_filename
+                        result = {}
+                        i = 1
+                        partial_dict_flag = 0
+                        while osp.exists(osp.realpath(filename)):
+                            res = mmengine.load(filename)
+                            for k, v in res.items():
+                                result[partial_dict_flag] = v
+                                partial_dict_flag += 1
+                            filename = os.path.join(
+                                subdir_path,
+                                dataset_abbr + '_' + str(i) + '.json')
+                            i += 1
+                    else:
+                        result = {}
+
+                    if len(result) == 0:
+                        print('*' * 100)
+                        print('There are no results for ' + filename + ' or ' +
+                              partial_filename)
+                        print('*' * 100)
+                        assert len(result > 0)
+
                     judged_answers = []
                     references = []
                     for k, v in result.items():
@@ -144,8 +173,14 @@ class AlignmentBenchSummarizer:
                     print(
                         f'Among {len(result)} judgements, successfully extracted {len(judged_answers)} judgements.'
                     )
+                    if len(judged_answers) == 0:
+                        print('*' * 100)
+                        print(
+                            'There are no extracted judgements, please change your judge model or check your prompt!!!'
+                        )
+                        print('*' * 100)
+                    assert len(judged_answers) > 0
 
-                    # 初始化一个嵌套字典用于存储模型和评分
                     dimension_ratings = defaultdict(int)
                     dimension_counts = defaultdict(int)
                     capability_ratings = defaultdict(int)
@@ -225,6 +260,8 @@ class AlignmentBenchSummarizer:
                             for sub_category in sub_categories:
                                 row.append(scores[model][sub_category])
                         writer.writerow(row)
+            else:
+                print(subdir_path + ' is not exist! please check!')
         with open(fout, 'r') as f:
             x = from_csv(f)
         print(x)

opencompass/summarizers/corev2.py

@@ -5,6 +5,7 @@ import os.path as osp
 import re
 from collections import defaultdict
 from datetime import datetime
+from itertools import product
 
 import mmengine
 from mmengine import ConfigDict
@@ -14,6 +15,7 @@ try:
 except ImportError:
     from_csv = None
 
+from opencompass.partitioners.sub_naive import remove_duplicate_pairs
 from opencompass.utils import dataset_abbr_from_cfg
 
 
@@ -54,6 +56,9 @@ class Corev2Summarizer:
         self.tasks = []
         self.cfg = config
         self.match_method = match_method
+        self.base_models = self.cfg['eval']['partitioner']['base_models']
+        self.compare_models = self.cfg['eval']['partitioner']['compare_models']
+        self.judge_abbr = self.cfg['judge_model']['abbr']
 
     def summarize(self,
                   time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):
@@ -76,25 +81,70 @@ class Corev2Summarizer:
         mmengine.mkdir_or_exist(output_dir)
         results_folder = osp.join(work_dir, 'results')
 
-        for subdir in os.listdir(results_folder):
+        model_combinations = list(
+            product(self.base_models, self.compare_models))
+        unique_combinations = remove_duplicate_pairs(
+            [combo for combo in model_combinations if combo[0] != combo[1]])
+
+        for model_pair in unique_combinations:
+            model1, model2, judge_model = model_pair[0]['abbr'], model_pair[1][
+                'abbr'], self.judge_abbr
+            subdir = model1 + '_' + model2 + '_judged-by--' + self.judge_abbr
             subdir_path = os.path.join(results_folder, subdir)
             if os.path.isdir(subdir_path):
-                model1, model2, judge_model = subdir.split('_')
-                fout = osp.join(output_dir, judge_model + '-report.csv')
+                fout = osp.join(output_dir,
+                                'judged-by--' + judge_model + '-report.csv')
                 for dataset in dataset_cfgs:
                     dataset_abbr = dataset_abbr_from_cfg(dataset)
-                    filepath = os.path.join(subdir_path,
+                    filename = os.path.join(subdir_path,
                                             dataset_abbr + '.json')
-                    result = mmengine.load(filepath)
+                    partial_filename = os.path.join(subdir_path,
+                                                    dataset_abbr + '_0.json')
+                    if osp.exists(osp.realpath(filename)):
+                        result = mmengine.load(filename)
+                    elif osp.exists(osp.realpath(partial_filename)):
+                        filename = partial_filename
+                        result = {}
+                        i = 1
+                        partial_dict_flag = 0
+                        while osp.exists(osp.realpath(filename)):
+                            res = mmengine.load(filename)
+                            for k, v in res.items():
+                                result[partial_dict_flag] = v
+                                partial_dict_flag += 1
+                            filename = os.path.join(
+                                subdir_path,
+                                dataset_abbr + '_' + str(i) + '.json')
+                            i += 1
+                    else:
+                        result = {}
+
+                    if len(result) == 0:
+                        print('*' * 100)
+                        print('There are no results for ' + filename + ' or ' +
+                              partial_filename)
+                        print('*' * 100)
+                        assert len(result > 0)
+
                     judged_answers = []
                     references = []
                     for k, v in result.items():
                         judged_answers.append(
                             call_function(self.match_method, v['prediction']))
                         references.append(v['gold'])
+                    successful_judged_answers = len(
+                        judged_answers) - judged_answers.count(None)
                     print(
-                        f'Among {len(judged_answers)} judgements, successfully extracted {len(judged_answers)-judged_answers.count(None)} judgements.'
+                        f'Among {len(judged_answers)} judgements, successfully extracted {successful_judged_answers} judgements.'
                     )
+                    if successful_judged_answers == 0:
+                        print('*' * 100)
+                        print(
+                            'There are no extracted judgements, please change your judge model or check your prompt!!!'
+                        )
+                        print('*' * 100)
+                    assert successful_judged_answers > 0
+
                     win_both_model1, win_both_model2, half_draw_model1, half_draw_model2, categories = defaultdict(
                         float), defaultdict(float), defaultdict(
                             float), defaultdict(float), defaultdict(float)
@@ -168,6 +218,8 @@ class Corev2Summarizer:
                             writer.writerow(
                                 [row] +
                                 [scores[row][column] for column in columns])
+            else:
+                print(subdir_path + ' is not exist! please check!')
         with open(fout, 'r') as f:
             x = from_csv(f)
         print(x)

opencompass/tasks/subjective_eval.py

@@ -96,8 +96,11 @@ class SubjectiveEvalTask(BaseTask):
         root, ext = osp.splitext(filename)
         partial_filename = root + '_0' + ext
         pred_strs = None
-        if osp.exists(osp.realpath(filename)) or osp.exists(
+
+        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 = [
@@ -172,8 +175,12 @@ class SubjectiveEvalTask(BaseTask):
         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)
-        result = icl_evaluator.score(predictions=model_preds,
-                                     references=references)
+
+        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(