327 lines
11 KiB
Python
327 lines
11 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os
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import argparse
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import pandas as pd
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import torch
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import json
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from tqdm import tqdm
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from bigdl.llm.utils.common.log4Error import invalidInputError
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from evaluators.qwen import QwenEvaluator
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from evaluators.llama import LlamaEvaluator
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from evaluators.chatglm import ChatGLMEvaluator
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TASK_NAME_MAPPING = {
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"computer_network": ["Computer Network", "\u8ba1\u7b97\u673a\u7f51\u7edc", "STEM"],
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"operating_system": ["Operating System", "\u64cd\u4f5c\u7cfb\u7edf", "STEM"],
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"computer_architecture": [
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"Computer Architecture",
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"\u8ba1\u7b97\u673a\u7ec4\u6210",
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"STEM",
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],
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"college_programming": ["College Programming", "\u5927\u5b66\u7f16\u7a0b", "STEM"],
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"college_physics": ["College Physics", "\u5927\u5b66\u7269\u7406", "STEM"],
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"college_chemistry": ["College Chemistry", "\u5927\u5b66\u5316\u5b66", "STEM"],
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"advanced_mathematics": [
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"Advanced Mathematics",
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"\u9ad8\u7b49\u6570\u5b66",
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"STEM",
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],
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"probability_and_statistics": [
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"Probability and Statistics",
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"\u6982\u7387\u7edf\u8ba1",
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"STEM",
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],
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"discrete_mathematics": [
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"Discrete Mathematics",
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"\u79bb\u6563\u6570\u5b66",
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"STEM",
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],
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"electrical_engineer": [
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"Electrical Engineer",
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"\u6ce8\u518c\u7535\u6c14\u5de5\u7a0b\u5e08",
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"STEM",
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],
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"metrology_engineer": [
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"Metrology Engineer",
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"\u6ce8\u518c\u8ba1\u91cf\u5e08",
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"STEM",
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],
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"high_school_mathematics": [
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"High School Mathematics",
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"\u9ad8\u4e2d\u6570\u5b66",
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"STEM",
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],
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"high_school_physics": ["High School Physics", "\u9ad8\u4e2d\u7269\u7406", "STEM"],
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"high_school_chemistry": [
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"High School Chemistry",
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"\u9ad8\u4e2d\u5316\u5b66",
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"STEM",
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],
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"high_school_biology": ["High School Biology", "\u9ad8\u4e2d\u751f\u7269", "STEM"],
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"middle_school_mathematics": [
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"Middle School Mathematics",
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"\u521d\u4e2d\u6570\u5b66",
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"STEM",
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],
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"middle_school_biology": [
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"Middle School Biology",
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"\u521d\u4e2d\u751f\u7269",
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"STEM",
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],
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"middle_school_physics": [
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"Middle School Physics",
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"\u521d\u4e2d\u7269\u7406",
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"STEM",
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],
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"middle_school_chemistry": [
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"Middle School Chemistry",
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"\u521d\u4e2d\u5316\u5b66",
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"STEM",
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],
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"veterinary_medicine": ["Veterinary Medicine", "\u517d\u533b\u5b66", "STEM"],
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"college_economics": [
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"College Economics",
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"\u5927\u5b66\u7ecf\u6d4e\u5b66",
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"Social Science",
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],
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"business_administration": [
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"Business Administration",
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"\u5de5\u5546\u7ba1\u7406",
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"Social Science",
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],
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"marxism": [
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"Marxism",
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"\u9a6c\u514b\u601d\u4e3b\u4e49\u57fa\u672c\u539f\u7406",
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"Social Science",
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],
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"mao_zedong_thought": [
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"Mao Zedong Thought",
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"\u6bdb\u6cfd\u4e1c\u601d\u60f3\u548c\u4e2d\u56fd\u7279\u8272\u793e\u4f1a\u4e3b\u4e49\u7406\u8bba\u4f53\u7cfb\u6982\u8bba",
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"Social Science",
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],
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"education_science": ["Education Science", "\u6559\u80b2\u5b66", "Social Science"],
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"teacher_qualification": [
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"Teacher Qualification",
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"\u6559\u5e08\u8d44\u683c",
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"Social Science",
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],
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"high_school_politics": [
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"High School Politics",
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"\u9ad8\u4e2d\u653f\u6cbb",
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"Social Science",
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],
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"high_school_geography": [
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"High School Geography",
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"\u9ad8\u4e2d\u5730\u7406",
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"Social Science",
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],
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"middle_school_politics": [
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"Middle School Politics",
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"\u521d\u4e2d\u653f\u6cbb",
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"Social Science",
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],
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"middle_school_geography": [
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"Middle School Geography",
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"\u521d\u4e2d\u5730\u7406",
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"Social Science",
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],
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"modern_chinese_history": [
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"Modern Chinese History",
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"\u8fd1\u4ee3\u53f2\u7eb2\u8981",
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"Humanities",
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],
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"ideological_and_moral_cultivation": [
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"Ideological and Moral Cultivation",
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"\u601d\u60f3\u9053\u5fb7\u4fee\u517b\u4e0e\u6cd5\u5f8b\u57fa\u7840",
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"Humanities",
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],
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"logic": ["Logic", "\u903b\u8f91\u5b66", "Humanities"],
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"law": ["Law", "\u6cd5\u5b66", "Humanities"],
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"chinese_language_and_literature": [
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"Chinese Language and Literature",
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"\u4e2d\u56fd\u8bed\u8a00\u6587\u5b66",
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"Humanities",
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],
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"art_studies": ["Art Studies", "\u827a\u672f\u5b66", "Humanities"],
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"professional_tour_guide": [
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"Professional Tour Guide",
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"\u5bfc\u6e38\u8d44\u683c",
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"Humanities",
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],
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"legal_professional": [
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"Legal Professional",
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"\u6cd5\u5f8b\u804c\u4e1a\u8d44\u683c",
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"Humanities",
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],
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"high_school_chinese": [
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"High School Chinese",
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"\u9ad8\u4e2d\u8bed\u6587",
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"Humanities",
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],
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"high_school_history": [
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"High School History",
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"\u9ad8\u4e2d\u5386\u53f2",
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"Humanities",
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],
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"middle_school_history": [
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"Middle School History",
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"\u521d\u4e2d\u5386\u53f2",
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"Humanities",
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],
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"civil_servant": ["Civil Servant", "\u516c\u52a1\u5458", "Other"],
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"sports_science": ["Sports Science", "\u4f53\u80b2\u5b66", "Other"],
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"plant_protection": ["Plant Protection", "\u690d\u7269\u4fdd\u62a4", "Other"],
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"basic_medicine": ["Basic Medicine", "\u57fa\u7840\u533b\u5b66", "Other"],
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"clinical_medicine": ["Clinical Medicine", "\u4e34\u5e8a\u533b\u5b66", "Other"],
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"urban_and_rural_planner": [
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"Urban and Rural Planner",
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"\u6ce8\u518c\u57ce\u4e61\u89c4\u5212\u5e08",
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"Other",
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],
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"accountant": ["Accountant", "\u6ce8\u518c\u4f1a\u8ba1\u5e08", "Other"],
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"fire_engineer": [
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"Fire Engineer",
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"\u6ce8\u518c\u6d88\u9632\u5de5\u7a0b\u5e08",
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"Other",
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],
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"environmental_impact_assessment_engineer": [
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"Environmental Impact Assessment Engineer",
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"\u73af\u5883\u5f71\u54cd\u8bc4\u4ef7\u5de5\u7a0b\u5e08",
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"Other",
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],
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"tax_accountant": ["Tax Accountant", "\u7a0e\u52a1\u5e08", "Other"],
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"physician": ["Physician", "\u533b\u5e08\u8d44\u683c", "Other"],
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}
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hard_list = [
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"advanced_mathematics",
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"discrete_mathematics",
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"probability_and_statistics",
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"college_physics",
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"college_chemistry",
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"high_school_mathematics",
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"high_school_physics",
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"high_school_chemistry",
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]
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choices = ["A", "B", "C", "D"]
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def cal_ceval(res):
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acc_sum_dict = dict()
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acc_norm_sum_dict = dict()
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cnt_dict = dict()
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acc_sum = 0.0
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cnt = 0
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hard_cnt = 0
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hard_acc_sum = 0.0
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for tt in res.keys():
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name = tt.split("-")[-1]
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acc_sum += float(res[tt])
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cnt += 1
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class_ = TASK_NAME_MAPPING[name][2]
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if class_ not in acc_sum_dict:
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acc_sum_dict[class_] = 0.0
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acc_norm_sum_dict[class_] = 0.0
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cnt_dict[class_] = 0.0
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if name in hard_list:
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hard_cnt += 1
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hard_acc_sum += float(res[tt])
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acc_sum_dict[class_] += float(res[tt])
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cnt_dict[class_] += 1
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print("\n\n\n")
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for k in ["STEM", "Social Science", "Humanities", "Other"]:
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if k in cnt_dict:
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print("%s acc: %.2f " % (k, acc_sum_dict[k] / cnt_dict[k]))
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if hard_cnt > 0:
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print("Hard acc:%.2f " % (hard_acc_sum / hard_cnt))
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print("AVERAGE acc:%.2f " % (acc_sum / cnt))
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def main(args, evaluator):
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if args.eval_type == "validation":
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result = {}
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for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
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val_file_path = os.path.join(
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args.eval_data_path, "val", f"{subject_name}_val.csv"
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)
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val_df = pd.read_csv(val_file_path)
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score, _ = evaluator.eval_subject(subject_name, val_df, args.eval_type)
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result[subject_name] = score
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cal_ceval(result)
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elif args.eval_type == "test":
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all_answers = {}
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for subject_name in tqdm(TASK_NAME_MAPPING.keys()):
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test_file_path = os.path.join(
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args.eval_data_path, "test", f"{subject_name}_test.csv"
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)
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test_df = pd.read_csv(test_file_path)
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_, answers = evaluator.eval_subject(subject_name, test_df, args.eval_type)
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all_answers[subject_name] = answers
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json.dump(all_answers, open('submission.json','w'), ensure_ascii=False, indent=4)
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else:
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invalidInputError(False,
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"Invalid eval_type, please use validation or test.")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", type=str, default="meta-llama/Llama-2-7b-chat-hf")
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parser.add_argument("--eval_type", type=str, default="validation")
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parser.add_argument("--device", type=str, default="xpu")
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parser.add_argument("--eval_data_path", type=str, default="data")
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parser.add_argument("--qtype", type=str, default="sym_int4")
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args = parser.parse_args()
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# decide the model family
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model_families = ['llama', 'qwen', 'chatglm']
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model_family = None
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for family in model_families:
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if family in args.model_path.lower():
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model_family = family
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assert model_family is not None, f"Model {args.model_path}'s model family is not implemented"
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if model_family == "llama":
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evaluator = LlamaEvaluator(
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choices=choices,
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model_path=args.model_path,
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device=args.device,
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qtype=args.qtype
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)
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elif model_family == "qwen":
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evaluator = QwenEvaluator(
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choices=choices,
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model_path=args.model_path,
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device=args.device,
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qtype=args.qtype
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)
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elif model_family == "chatglm":
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evaluator = ChatGLMEvaluator(
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choices=choices,
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model_path=args.model_path,
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device=args.device,
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qtype=args.qtype
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)
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else:
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invalidInputError(
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False,
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"Invalid model_family, currently support llama, qwen, and chatglm only.")
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main(args, evaluator=evaluator)
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