support dataset repeat and g-pass compute for each evaluator

opencompass/configs/datasets/livemathbench/livemathbench_gen_9befbf.py

@@ -9,7 +9,7 @@ livemathbench_dataset = dict(
     type=LiveMathBenchDataset,
     path='',
     k=16,
-    replication=3,
+    repeat=3,
     dataset_splits=['CNMO', 'CCEE', 'AMC', 'WLPMC'],
     dataset_languages=['cn', 'en'],
     cot=True,
@@ -43,7 +43,7 @@ livemathbench_dataset = dict(
             extract_url=[],
             extract_model_name='',
             k=[4, 8, 16],
-            replication=3,
+            repeat=3,
             thresholds=[0.0, 0.25, 0.5, 0.75, 1.0]
         )
     )

opencompass/configs/datasets/livemathbench/livemathbench_greedy_gen_9befbf.py

@@ -9,7 +9,7 @@ livemathbench_dataset = dict(
     type=LiveMathBenchDataset,
     path='',
     k=1,
-    replication=1,
+    repeat=1,
     dataset_splits=['CNMO', 'CCEE', 'AMC', 'WLPMC'],
     dataset_languages=['cn', 'en'],
     cot=True,
@@ -43,7 +43,7 @@ livemathbench_dataset = dict(
             extract_url=[],
             extract_model_name='',
             k=[1],
-            replication=1,
+            repeat=1,
             thresholds=[0.0]
         )
     )

opencompass/datasets/base.py

@@ -1,5 +1,6 @@
 from abc import abstractstaticmethod
-from typing import Dict, Optional, Union
+from typing import Dict, Optional, Union, List
+from copy import deepcopy
 
 from datasets import Dataset, DatasetDict
 
@@ -8,8 +9,38 @@ from opencompass.openicl import DatasetReader
 
 class BaseDataset:
 
-    def __init__(self, reader_cfg: Optional[Dict] = {}, **kwargs):
+    def __init__(self, 
-        self.dataset = self.load(**kwargs)
+                 reader_cfg: Optional[Dict] = {}, 
+                 k: Union[int, List[int]] = 1, 
+                 repeat: int = 1, 
+                 **kwargs):
+        abbr = kwargs.pop('abbr', 'dataset')
+        dataset = self.load(**kwargs)
+        # maybe duplicate
+        n = (max(k) if isinstance(k, List) else k) * repeat
+        if isinstance(dataset, Dataset):
+            examples = []
+            for idx, example in enumerate(dataset):
+                if 'subdivision' not in example:
+                    example['subdivision'] = abbr
+                if 'idx' not in example:
+                    example['idx'] = idx
+                examples.append(example)
+            examples = sum([deepcopy(examples) for _ in range(n)], [])
+            self.dataset = Dataset.from_list(examples)
+        else:
+            self.dataset = DatasetDict()
+            for key in dataset:
+                examples = []
+                for idx, example in enumerate(dataset[key]):
+                    if 'subdivision' not in example:
+                        example['subdivision'] = f'{abbr}_{key}'
+                    if 'idx' not in example:
+                        example['idx'] = idx
+                    examples.append(example)
+                print(abbr, key, len(examples))
+                examples = sum([deepcopy(examples) for _ in range(n)], [])
+                self.dataset[key] = Dataset.from_list(examples)
         self._init_reader(**reader_cfg)
 
     def _init_reader(self, **kwargs):

opencompass/datasets/livemathbench/livemathbench.py

@@ -2,7 +2,6 @@ import os
 import warnings
 from collections import OrderedDict
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from copy import deepcopy
 from functools import partial
 from itertools import product
 from typing import Any, Callable, Dict, List, Union
@@ -31,8 +30,6 @@ class LiveMathBenchDataset(BaseDataset):
 
     @staticmethod
     def load(path: str,
-             k: Union[int, List[int]],
-             replication: int,
              dataset_splits: List[str] = [
                  'CNMO',
                  'CCEE',
@@ -104,11 +101,7 @@ class LiveMathBenchDataset(BaseDataset):
                                   ('' if 'options' not in example else
                                    ' '.join(example['options']))),
                 })
-                max_k = k if isinstance(k, int) else max(k)
+                dataset.append(example)
-                for idx in range(max_k * replication):
-                    duplicated_example = deepcopy(example)
-                    duplicated_example.update({'replication_idx': idx})
-                    dataset.append(duplicated_example)
 
         return Dataset.from_list(dataset)
 
@@ -127,9 +120,9 @@ class LiveMathBenchEvaluator(GPassKEvaluator):
                  extract_url=[],
                  extract_model_name='',
                  k: Union[int, List[int]] = 16,
-                 replication: int = 3,
+                 repeat: int = 3,
                  thresholds: List[float] = [0.0, 0.25, 0.5, 0.75, 1.0]):
-        super().__init__(k, replication, thresholds)
+        super().__init__(k, repeat, thresholds)
 
         if isinstance(url, str):
             url = [url]

opencompass/openicl/icl_evaluator/icl_base_evaluator.py

@@ -1,11 +1,146 @@
 """Base Evaluator."""
+from typing import Union, List, Dict, Any, Iterable
+from collections import OrderedDict
+from copy import deepcopy
+
+import numpy as np
+from scipy.stats import hypergeom
+from datasets import Dataset
+
+
+def compute_pass_at_k(n, c, k):
+    if n - c < k:
+        return 1.0
+    return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
+
+def _compute_g_pass_at_k(n, c, k, m):
+    if m > min(c, k) or k > n or c < 0 or n <= 0 or m < 0:
+        return 0.0
+    return hypergeom.sf(m - 1, n, c, k)
+
+def compute_g_pass_at_k(n, c, k, t):
+    m = max(int(np.ceil(k * t)), 1)
+    return _compute_g_pass_at_k(n, c, k, m)
+
+def compute_mg_pass_at_k(n, c, k):
+    l, r = int(np.ceil(k * 0.5)), k
+
+    mg_pass_at_k = 0.0
+    for i in range(l + 1, r + 1):
+        mg_pass_at_k += _compute_g_pass_at_k(n, c, k, i)
+    mg_pass_at_k = 2 * mg_pass_at_k / k
+
+    return mg_pass_at_k
 
 
 class BaseEvaluator:
-
     def __init__(self) -> None:
         pass
 
+    def group(self, n: int, details: List[Dict[str, Any]], test_set: Dataset) -> Dict[str, Any]:
+        example2replications = {}
+        for detail, example in zip(details, test_set):
+            example_abbr = f"{example['subdivision']}_{example['idx']}"
+            if example_abbr not in example2replications:
+                example2replications[example_abbr] = []
+            example.update({'detail': detail})
+            example2replications[example_abbr].append(example)
+        for _, replications in example2replications.items():
+            assert len(replications) == n, print(len(replications), n)
+        return example2replications
+
+    def reduce(self, details: List[Dict[str, Any]]) -> Dict[str, Any]:
+        g_passk_details = OrderedDict()
+        all_subdivisions = set([detail['example_abbr'].split('_')[0] for detail in details])
+        all_metrics = list(details[0].keys())
+        
+        for subdivision in sorted(list(all_subdivisions)):
+            for metric in all_metrics:
+                if metric in ['predictions', 'example_abbr']:
+                    continue
+                g_passk_details[f'{subdivision}/{metric}'] = 100 * np.mean([
+                    detail[metric]
+                    for detail in details
+                    if detail['example_abbr'].split('_')[0] == subdivision
+                ])
+
+        for metric in all_metrics:
+            if metric in ['predictions', 'example_abbr']:
+                continue
+            g_passk_details[metric] = 100. * np.mean([detail[metric] for detail in details])
+        return g_passk_details
+
+    def evaluate(self, k: Union[int, List[int]], 
+                 repeat: int, test_set: Dataset,  **score_kwargs):
+        n = (max(k) if isinstance(k, List) else k) * repeat
+        print(len(score_kwargs['predictions']))
+        real_size = len(test_set) // n
+        all_details = []
+        all_results = []
+        for i in range(n):
+            results = self.score(**{
+                key: value[i * real_size: (i + 1) * real_size] if isinstance(value, Iterable) else value
+                for key, value in score_kwargs.items()
+            })
+            details = results.pop('details', None)
+            if details is not None:
+                if isinstance(details, Dict):
+                    details = list(details.values())
+                all_details.extend(details)
+            all_results.append(results)
+        
+        eval_results = {}
+        for single_results in all_results:
+            for key in single_results:
+                if key not in eval_results:
+                    eval_results[key] = []
+                eval_results[key].append(single_results[key])
+        for key in deepcopy(eval_results):
+            if isinstance(eval_results[key][0], float) or isinstance(eval_results[key][0], int):
+                if n > 1:
+                    eval_results[key + f' ({n // repeat}x{repeat}={n} runs average)'] = np.mean(eval_results[key])
+                    eval_results.pop(key)
+                else:
+                    eval_results[key] = np.mean(eval_results[key])
+            else:
+                eval_results[key] = eval_results[key][0]
+
+        grouped_examples = self.group(n, all_details, test_set)
+        if len(all_details) != 0:
+            eval_details = []
+            for example_abbr, examples in grouped_examples.items():
+                detail = {
+                    'predictions': [],
+                    'example_abbr': example_abbr
+                }
+
+                c = 0
+                can_calculate = False
+                for example in examples:
+                    detail['predictions'].append(example['detail'])
+                    # only compute G-Pass@k when details have correct labels
+                    if example['detail'].get('correct', None) is not None:
+                        can_calculate = True
+                        c += int(example['detail']['correct'])
+                    elif example['detail'].get('is_correct', None) is not None:
+                        can_calculate = True
+                        c += int(example['detail']['is_correct'])
+
+                if can_calculate:
+                    thresholds = [0.0, 0.25, 0.5, 0.75, 1.0]
+                    for _k in ([k] if isinstance(k, int) else k):
+                        for threshold in thresholds:
+                            detail[f'G-Pass@{_k}_{threshold}'] = compute_g_pass_at_k(
+                                n=n, c=c, k=_k, t=threshold)
+                        detail[f'mG-Pass@{_k}'] = compute_mg_pass_at_k(n=n, c=c, k=_k)
+
+                eval_details.append(detail)
+
+            eval_results.update(self.reduce(eval_details))
+            eval_results['details'] = eval_details
+        
+        return eval_results
+
     def score(self):
         raise NotImplementedError("Method hasn't been implemented yet")
 

opencompass/openicl/icl_evaluator/icl_gpassk_evaluator.py

@@ -57,10 +57,10 @@ class GPassKEvaluator(BaseEvaluator):
         integers (e.g., `[4, 8, 16]` computes G-Pass@4,
         G-Pass@8, and G-Pass@16).
 
-        replication (int): Controls the number of generations
+        repeat (int): Controls the number of generations
         used to estimate G-Pass@k. The total number of
         generations is determined by multiplying the
-        maximum of `k` with `replication`. This parameter
+        maximum of `k` with `repeat`. This parameter
         should be a single integer.
 
         thresholds (list of float): A list of floating-point
@@ -71,7 +71,7 @@ class GPassKEvaluator(BaseEvaluator):
     def __init__(
             self,
             k: Union[int, List[int]] = 16,
-            replication: int = 3,
+            repeat: int = 3,
             thresholds: List[float] = [0.0, 0.25, 0.5, 0.75, 1.0]) -> None:
         super().__init__()
 
@@ -79,8 +79,8 @@ class GPassKEvaluator(BaseEvaluator):
             k = [k]
 
         self.k = k
-        self.replication = replication
+        self.repeat = repeat
-        self.n = max(k) * replication
+        self.n = max(k) * repeat
         self.thresholds = thresholds
 
     @property

opencompass/openicl/icl_inferencer/icl_gen_inferencer.py

@@ -104,6 +104,9 @@ class GenInferencer(BaseInferencer):
             max_seq_len=self.max_seq_len,
             ice_template=ice_template,
             prompt_template=prompt_template)
+        
+        
+        print(len(prompt_list))
 
         # 3.1 Fetch and zip prompt & gold answer if output column exists
         ds_reader = retriever.dataset_reader

opencompass/tasks/openicl_eval.py

@@ -215,7 +215,9 @@ class OpenICLEvalTask(BaseTask):
                 k: preds[k]
                 for k in signature(icl_evaluator.score).parameters
             }
-            result = icl_evaluator.score(**preds)
+            k = self.dataset_cfg.get('k', 1)
+            repeat = self.dataset_cfg.get('repeat', 1)
+            result = icl_evaluator.evaluate(k, repeat, test_set, **preds)
 
             # Get model postprocess result
             model_details = None
@@ -223,7 +225,7 @@ class OpenICLEvalTask(BaseTask):
             if 'model_postprocessor' in self.eval_cfg:
                 model_preds = copy.deepcopy(preds)
                 model_preds['predictions'] = model_pred_strs
-                model_result = icl_evaluator.score(**model_preds)
+                model_result = icl_evaluator.evaluate(k, repeat, test_set, **model_preds)
                 for key in model_result:
                     if key == 'details':
                         model_details = model_result[key]

opencompass/utils/build.py

@@ -9,7 +9,6 @@ def build_dataset_from_cfg(dataset_cfg: ConfigDict):
     dataset_cfg = copy.deepcopy(dataset_cfg)
     dataset_cfg.pop('infer_cfg', None)
     dataset_cfg.pop('eval_cfg', None)
-    dataset_cfg.pop('abbr', None)
     return LOAD_DATASET.build(dataset_cfg)