[Feature] Add CMMLU dataset (#91)

* add CMMLU * debug cmmlu * add slurm args `qos` * fix format: space before comment * remove unused variable * change the location of `answer is` --------- Co-authored-by: 李浩楠 <lihaonan@lihaonandeMacBook-Air.local> Co-authored-by: 李浩楠 <haonan.li> Co-authored-by: Leymore <zfz-960727@163.com>
2025-05-30 16:03:24 +08:00 · 2023-07-25 06:14:27 +04:00 · 2023-07-25 06:14:27 +04:00 · e9cdb24ddd
commit e9cdb24ddd
parent 6e885d668b
8 changed files with 298 additions and 0 deletions
--- a/configs/datasets/cmmlu/cmmlu_gen.py
+++ b/configs/datasets/cmmlu/cmmlu_gen.py
@ -0,0 +1,4 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .cmmlu_gen_ffe7c0 import cmmlu_datasets  # noqa: F401, F403
--- a/configs/datasets/cmmlu/cmmlu_gen_ffe7c0.py
+++ b/configs/datasets/cmmlu/cmmlu_gen_ffe7c0.py
@ -0,0 +1,122 @@
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import FixKRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.datasets import CMMLUDataset
+from opencompass.utils.text_postprocessors import first_capital_postprocess
+
+cmmlu_subject_mapping = {
+    'agronomy': '农学',
+    'anatomy': '解剖学',
+    'ancient_chinese': '古汉语',
+    'arts': '艺术学',
+    'astronomy': '天文学',
+    'business_ethics': '商业伦理',
+    'chinese_civil_service_exam': '中国公务员考试',
+    'chinese_driving_rule': '中国驾驶规则',
+    'chinese_food_culture': '中国饮食文化',
+    'chinese_foreign_policy': '中国外交政策',
+    'chinese_history': '中国历史',
+    'chinese_literature': '中国文学',
+    'chinese_teacher_qualification': '中国教师资格',
+    'clinical_knowledge': '临床知识',
+    'college_actuarial_science': '大学精算学',
+    'college_education': '大学教育学',
+    'college_engineering_hydrology': '大学工程水文学',
+    'college_law': '大学法律',
+    'college_mathematics': '大学数学',
+    'college_medical_statistics': '大学医学统计',
+    'college_medicine': '大学医学',
+    'computer_science': '计算机科学',
+    'computer_security': '计算机安全',
+    'conceptual_physics': '概念物理学',
+    'construction_project_management': '建设工程管理',
+    'economics': '经济学',
+    'education': '教育学',
+    'electrical_engineering': '电气工程',
+    'elementary_chinese': '小学语文',
+    'elementary_commonsense': '小学常识',
+    'elementary_information_and_technology': '小学信息技术',
+    'elementary_mathematics': '初等数学',
+    'ethnology': '民族学',
+    'food_science': '食品科学',
+    'genetics': '遗传学',
+    'global_facts': '全球事实',
+    'high_school_biology': '高中生物',
+    'high_school_chemistry': '高中化学',
+    'high_school_geography': '高中地理',
+    'high_school_mathematics': '高中数学',
+    'high_school_physics': '高中物理学',
+    'high_school_politics': '高中政治',
+    'human_sexuality': '人类性行为',
+    'international_law': '国际法学',
+    'journalism': '新闻学',
+    'jurisprudence': '法理学',
+    'legal_and_moral_basis': '法律与道德基础',
+    'logical': '逻辑学',
+    'machine_learning': '机器学习',
+    'management': '管理学',
+    'marketing': '市场营销',
+    'marxist_theory': '马克思主义理论',
+    'modern_chinese': '现代汉语',
+    'nutrition': '营养学',
+    'philosophy': '哲学',
+    'professional_accounting': '专业会计',
+    'professional_law': '专业法学',
+    'professional_medicine': '专业医学',
+    'professional_psychology': '专业心理学',
+    'public_relations': '公共关系',
+    'security_study': '安全研究',
+    'sociology': '社会学',
+    'sports_science': '体育学',
+    'traditional_chinese_medicine': '中医中药',
+    'virology': '病毒学',
+    'world_history': '世界历史',
+    'world_religions': '世界宗教'
+}
+
+
+cmmlu_all_sets = list(cmmlu_subject_mapping.keys())
+
+cmmlu_datasets = []
+for _name in cmmlu_all_sets:
+    _ch_name = cmmlu_subject_mapping[_name]
+    cmmlu_infer_cfg = dict(
+        ice_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                begin="</E>",
+                round=[
+                    dict(
+                        role="HUMAN",
+                        prompt=
+                        f"以下是关于{_ch_name}的单项选择题，请直接给出正确答案的选项。\n题目：{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}"
+                    ),
+                    dict(role="BOT", prompt='答案是: {answer}'),
+                ]),
+            ice_token="</E>",
+        ),
+        retriever=dict(type=FixKRetriever),
+        inferencer=dict(type=GenInferencer, fix_id_list=[0, 1, 2, 3, 4]),
+    )
+
+    cmmlu_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(type=first_capital_postprocess))
+
+    cmmlu_datasets.append(
+        dict(
+            type=CMMLUDataset,
+            path="./data/cmmlu/",
+            name=_name,
+            abbr=f"cmmlu-{_name}",
+            reader_cfg=dict(
+                input_columns=["question", "A", "B", "C", "D"],
+                output_column="answer",
+                train_split="dev",
+                test_split='test'),
+            infer_cfg=cmmlu_infer_cfg,
+            eval_cfg=cmmlu_eval_cfg,
+        ))
+
+del _name, _ch_name
--- a/configs/datasets/cmmlu/cmmlu_ppl.py
+++ b/configs/datasets/cmmlu/cmmlu_ppl.py
@ -0,0 +1,4 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .cmmlu_ppl_fd1f2f import cmmlu_datasets  # noqa: F401, F403
--- a/configs/datasets/cmmlu/cmmlu_ppl_fd1f2f.py
+++ b/configs/datasets/cmmlu/cmmlu_ppl_fd1f2f.py
@ -0,0 +1,122 @@
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import FixKRetriever
+from opencompass.openicl.icl_inferencer import PPLInferencer
+from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.datasets import CMMLUDataset
+from opencompass.utils.text_postprocessors import first_capital_postprocess
+
+cmmlu_subject_mapping = {
+    'agronomy': '农学',
+    'anatomy': '解剖学',
+    'ancient_chinese': '古汉语',
+    'arts': '艺术学',
+    'astronomy': '天文学',
+    'business_ethics': '商业伦理',
+    'chinese_civil_service_exam': '中国公务员考试',
+    'chinese_driving_rule': '中国驾驶规则',
+    'chinese_food_culture': '中国饮食文化',
+    'chinese_foreign_policy': '中国外交政策',
+    'chinese_history': '中国历史',
+    'chinese_literature': '中国文学',
+    'chinese_teacher_qualification': '中国教师资格',
+    'clinical_knowledge': '临床知识',
+    'college_actuarial_science': '大学精算学',
+    'college_education': '大学教育学',
+    'college_engineering_hydrology': '大学工程水文学',
+    'college_law': '大学法律',
+    'college_mathematics': '大学数学',
+    'college_medical_statistics': '大学医学统计',
+    'college_medicine': '大学医学',
+    'computer_science': '计算机科学',
+    'computer_security': '计算机安全',
+    'conceptual_physics': '概念物理学',
+    'construction_project_management': '建设工程管理',
+    'economics': '经济学',
+    'education': '教育学',
+    'electrical_engineering': '电气工程',
+    'elementary_chinese': '小学语文',
+    'elementary_commonsense': '小学常识',
+    'elementary_information_and_technology': '小学信息技术',
+    'elementary_mathematics': '初等数学',
+    'ethnology': '民族学',
+    'food_science': '食品科学',
+    'genetics': '遗传学',
+    'global_facts': '全球事实',
+    'high_school_biology': '高中生物',
+    'high_school_chemistry': '高中化学',
+    'high_school_geography': '高中地理',
+    'high_school_mathematics': '高中数学',
+    'high_school_physics': '高中物理学',
+    'high_school_politics': '高中政治',
+    'human_sexuality': '人类性行为',
+    'international_law': '国际法学',
+    'journalism': '新闻学',
+    'jurisprudence': '法理学',
+    'legal_and_moral_basis': '法律与道德基础',
+    'logical': '逻辑学',
+    'machine_learning': '机器学习',
+    'management': '管理学',
+    'marketing': '市场营销',
+    'marxist_theory': '马克思主义理论',
+    'modern_chinese': '现代汉语',
+    'nutrition': '营养学',
+    'philosophy': '哲学',
+    'professional_accounting': '专业会计',
+    'professional_law': '专业法学',
+    'professional_medicine': '专业医学',
+    'professional_psychology': '专业心理学',
+    'public_relations': '公共关系',
+    'security_study': '安全研究',
+    'sociology': '社会学',
+    'sports_science': '体育学',
+    'traditional_chinese_medicine': '中医中药',
+    'virology': '病毒学',
+    'world_history': '世界历史',
+    'world_religions': '世界宗教'
+}
+
+
+cmmlu_all_sets = list(cmmlu_subject_mapping.keys())
+
+cmmlu_datasets = []
+for _name in cmmlu_all_sets:
+    _ch_name = cmmlu_subject_mapping[_name]
+    cmmlu_infer_cfg = dict(
+        ice_template=dict(
+            type=PromptTemplate,
+            template={
+                answer: dict(
+                    begin="</E>",
+                    round=[
+                        dict(
+                            role="HUMAN",
+                            prompt=f"以下是关于{_ch_name}的单项选择题，请直接给出正确答案的选项。\n题目：{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}"
+                        ),
+                        dict(role="BOT", prompt=f'答案是: {answer}'),
+                    ])
+                for answer in ["A", "B", "C", "D"]
+            },
+            ice_token="</E>",
+        ),
+        retriever=dict(type=FixKRetriever),
+        inferencer=dict(type=PPLInferencer, fix_id_list=[0, 1, 2, 3, 4]),
+    )
+
+    cmmlu_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
+
+    cmmlu_datasets.append(
+        dict(
+            type=CMMLUDataset,
+            path="./data/cmmlu/",
+            name=_name,
+            abbr=f"cmmlu-{_name}",
+            reader_cfg=dict(
+                input_columns=["question", "A", "B", "C", "D"],
+                output_column="answer",
+                train_split="dev",
+                test_split='test'),
+            infer_cfg=cmmlu_infer_cfg,
+            eval_cfg=cmmlu_eval_cfg,
+        ))
+
+del _name, _ch_name
--- a/opencompass/datasets/init.py
+++ b/opencompass/datasets/init.py
@ -11,6 +11,7 @@ from .ceval import *  # noqa: F401, F403
 from .chid import *  # noqa: F401, F403
 from .civilcomments import *  # noqa: F401, F403
 from .cluewsc import *  # noqa: F401, F403
+from .cmmlu import *  # noqa: F401, F403
 from .cmnli import *  # noqa: F401, F403
 from .cmrc import *  # noqa: F401, F403
 from .commonsenseqa import *  # noqa: F401, F403
--- a/opencompass/datasets/cmmlu.py
+++ b/opencompass/datasets/cmmlu.py
@ -0,0 +1,34 @@
+import csv
+import os.path as osp
+
+from datasets import Dataset, DatasetDict
+
+from opencompass.registry import LOAD_DATASET
+
+from .base import BaseDataset
+
+
+@LOAD_DATASET.register_module()
+class CMMLUDataset(BaseDataset):
+
+    @staticmethod
+    def load(path: str, name: str):
+        dataset = DatasetDict()
+        for split in ['dev', 'test']:
+            raw_data = []
+            filename = osp.join(path, split, f'{name}.csv')
+            with open(filename, encoding='utf-8') as f:
+                reader = csv.reader(f)
+                _ = next(reader)  # skip the header
+                for row in reader:
+                    assert len(row) == 7
+                    raw_data.append({
+                        'question': row[1],
+                        'A': row[2],
+                        'B': row[3],
+                        'C': row[4],
+                        'D': row[5],
+                        'answer': row[6],
+                    })
+            dataset[split] = Dataset.from_list(raw_data)
+        return dataset
--- a/opencompass/runners/slurm.py
+++ b/opencompass/runners/slurm.py
@ -28,6 +28,7 @@ class SlurmRunner(BaseRunner):
        retry (int): Number of retries if the job failed. Defaults to 2.
        partition (str): Slurm partition name. Defaults to None.
        quotatype (str): Slurm quota type. Defaults to None.
+        qos (str): Slurm quality of service. Defaults to None.
        debug (bool): Whether to run in debug mode. Defaults to False.
        lark_bot_url (str): Lark bot url. Defaults to None.
    """
@ -38,6 +39,7 @@ class SlurmRunner(BaseRunner):
                 retry: int = 2,
                 partition: str = None,
                 quotatype: str = None,
+                 qos: str = None,
                 debug: bool = False,
                 lark_bot_url: str = None):
        super().__init__(task=task, debug=debug, lark_bot_url=lark_bot_url)
@ -45,6 +47,7 @@ class SlurmRunner(BaseRunner):
        self.retry = retry
        self.partition = partition
        self.quotatype = quotatype
+        self.qos = qos

    def launch(self, tasks: List[Dict[str, Any]]) -> List[Tuple[str, int]]:
        """Launch multiple tasks.
@ -97,6 +100,8 @@ class SlurmRunner(BaseRunner):
            tmpl += f' -p {self.partition}'
        if self.quotatype:
            tmpl += f' --quotatype={self.quotatype}'
+        if self.qos:
+            tmpl += f' --qos={self.qos}'
        if num_gpus > 0:
            tmpl += f' --gres=gpu:{num_gpus}'
        tmpl += f" -N1 -J '{task_name[:512]}'" + ' {task_cmd}'
--- a/run.py
+++ b/run.py
@ -129,6 +129,10 @@ def parse_slurm_args(slurm_parser):
                              help='Slurm quota type',
                              default=None,
                              type=str)
+    slurm_parser.add_argument('--qos',
+                              help='Slurm quality of service',
+                              default=None,
+                              type=str)


 def parse_dlc_args(dlc_parser):
@ -286,6 +290,7 @@ def exec_infer_runner(tasks, args, cfg):
                             max_num_workers=args.max_num_workers,
                             partition=args.partition,
                             quotatype=args.quotatype,
+                             qos=args.qos,
                             retry=args.retry,
                             debug=args.debug,
                             lark_bot_url=cfg['lark_bot_url'])
@ -311,6 +316,7 @@ def exec_eval_runner(tasks, args, cfg):
                             max_num_workers=args.max_num_workers,
                             partition=args.partition,
                             quotatype=args.quotatype,
+                             qos=args.qos,
                             retry=args.retry,
                             debug=args.debug,
                             lark_bot_url=cfg['lark_bot_url'])