安装
面向GPU的环境安装
conda create --name opencompass --clone=/root/share/conda_envs/internlm-base
source activate opencompass
git clone https://github.com/open-compass/opencompass
cd opencompass
pip install -e .有部分第三方功能,如代码能力基准测试 Humaneval 以及 Llama格式的模型评测,可能需要额外步骤才能正常运行，如需评测，详细步骤请参考安装指南。
数据准备
# 解压评测数据集到 data/ 处
cp /share/temp/datasets/OpenCompassData-core-20231110.zip /root/opencompass/
unzip OpenCompassData-core-20231110.zip
# 将会在opencompass下看到data文件夹
查看支持的数据集和模型
# 列出所有跟 internlm 及 ceval 相关的配置
python tools/list_configs.py internlm ceval将会看到
+----------------------------------------+----------------------------------------------------------------------+
| Model | Config Path |
|----------------------------------------+----------------------------------------------------------------------|
| hf_internlm2_1_8b | configs/models/hf_internlm/hf_internlm2_1_8b.py |
| hf_internlm2_20b | configs/models/hf_internlm/hf_internlm2_20b.py |
| hf_internlm2_7b | configs/models/hf_internlm/hf_internlm2_7b.py |
| hf_internlm2_base_20b | configs/models/hf_internlm/hf_internlm2_base_20b.py |
| hf_internlm2_base_7b | configs/models/hf_internlm/hf_internlm2_base_7b.py |
| hf_internlm2_chat_1_8b_sft | configs/models/hf_internlm/hf_internlm2_chat_1_8b_sft.py |
| hf_internlm2_chat_20b | configs/models/hf_internlm/hf_internlm2_chat_20b.py |
| hf_internlm2_chat_20b_sft | configs/models/hf_internlm/hf_internlm2_chat_20b_sft.py |
| hf_internlm2_chat_20b_with_system | configs/models/hf_internlm/hf_internlm2_chat_20b_with_system.py |
| hf_internlm2_chat_7b | configs/models/hf_internlm/hf_internlm2_chat_7b.py |
| hf_internlm2_chat_7b_sft | configs/models/hf_internlm/hf_internlm2_chat_7b_sft.py |
| hf_internlm2_chat_7b_with_system | configs/models/hf_internlm/hf_internlm2_chat_7b_with_system.py |
| hf_internlm2_chat_math_20b | configs/models/hf_internlm/hf_internlm2_chat_math_20b.py |
| hf_internlm2_chat_math_20b_with_system | configs/models/hf_internlm/hf_internlm2_chat_math_20b_with_system.py |
| hf_internlm2_chat_math_7b | configs/models/hf_internlm/hf_internlm2_chat_math_7b.py |
| hf_internlm2_chat_math_7b_with_system | configs/models/hf_internlm/hf_internlm2_chat_math_7b_with_system.py |
| hf_internlm_20b | configs/models/hf_internlm/hf_internlm_20b.py |
| hf_internlm_7b | configs/models/hf_internlm/hf_internlm_7b.py |
| hf_internlm_chat_20b | configs/models/hf_internlm/hf_internlm_chat_20b.py |
| hf_internlm_chat_7b | configs/models/hf_internlm/hf_internlm_chat_7b.py |
| hf_internlm_chat_7b_8k | configs/models/hf_internlm/hf_internlm_chat_7b_8k.py |
| hf_internlm_chat_7b_v1_1 | configs/models/hf_internlm/hf_internlm_chat_7b_v1_1.py |
| internlm_7b | configs/models/internlm/internlm_7b.py |
| ms_internlm_chat_7b_8k | configs/models/ms_internlm/ms_internlm_chat_7b_8k.py |
+----------------------------------------+----------------------------------------------------------------------+
+--------------------------------+------------------------------------------------------------------+
| Dataset | Config Path |
|--------------------------------+------------------------------------------------------------------|
| ceval_clean_ppl | configs/datasets/ceval/ceval_clean_ppl.py |
| ceval_contamination_ppl_810ec6 | configs/datasets/contamination/ceval_contamination_ppl_810ec6.py |
| ceval_gen | configs/datasets/ceval/ceval_gen.py |
| ceval_gen_2daf24 | configs/datasets/ceval/ceval_gen_2daf24.py |
| ceval_gen_5f30c7 | configs/datasets/ceval/ceval_gen_5f30c7.py |
| ceval_ppl | configs/datasets/ceval/ceval_ppl.py |
| ceval_ppl_578f8d | configs/datasets/ceval/ceval_ppl_578f8d.py |
| ceval_ppl_93e5ce | configs/datasets/ceval/ceval_ppl_93e5ce.py |
| ceval_zero_shot_gen_bd40ef | configs/datasets/ceval/ceval_zero_shot_gen_bd40ef.py |
+--------------------------------+------------------------------------------------------------------+启动评测
确保按照上述步骤正确安装 OpenCompass 并准备好数据集后，可以通过以下命令评测 InternLM-Chat-7B 模型在 C-Eval 数据集上的性能。由于 OpenCompass 默认并行启动评估过程，我们可以在第一次运行时以 --debug 模式启动评估，并检查是否存在问题。在 --debug 模式下，任务将按顺序执行，并实时打印输出。
python run.py --datasets ceval_gen --hf-path /share/temp/model_repos/internlm-chat-7b/ --tokenizer-path /share/temp/model_repos/internlm-chat-7b/ --tokenizer-kwargs padding_side='left' truncation='left' trust_remote_code=True --model-kwargs trust_remote_code=True device_map='auto' --max-seq-len 2048 --max-out-len 16 --batch-size 4 --num-gpus 1 --debug命令解析
--datasets ceval_gen \
--hf-path /share/temp/model_repos/internlm-chat-7b/ \ # HuggingFace 模型路径
--tokenizer-path /share/temp/model_repos/internlm-chat-7b/ \ # HuggingFace tokenizer 路径（如果与模型路径相同，可以省略）
--tokenizer-kwargs padding_side='left' truncation='left' trust_remote_code=True \ # 构建 tokenizer 的参数
--model-kwargs device_map='auto' trust_remote_code=True \ # 构建模型的参数
--max-seq-len 2048 \ # 模型可以接受的最大序列长度
--max-out-len 16 \ # 生成的最大 token 数
--batch-size 2 \ # 批量大小
--num-gpus 1 # 运行模型所需的 GPU 数量
--debug
结果
dataset version metric mode opencompass.models.huggingface.HuggingFace_model_repos_internlm-chat-7b
---------------------------------------------- --------- ------------- ------ -------------------------------------------------------------------------
ceval-computer_network db9ce2 accuracy gen 31.58
ceval-operating_system 1c2571 accuracy gen 36.84
ceval-computer_architecture a74dad accuracy gen 28.57
ceval-college_programming 4ca32a accuracy gen 32.43
ceval-college_physics 963fa8 accuracy gen 26.32
ceval-college_chemistry e78857 accuracy gen 16.67
ceval-advanced_mathematics ce03e2 accuracy gen 21.05
ceval-probability_and_statistics 65e812 accuracy gen 38.89
ceval-discrete_mathematics e894ae accuracy gen 18.75
ceval-electrical_engineer ae42b9 accuracy gen 35.14
ceval-metrology_engineer ee34ea accuracy gen 50
ceval-high_school_mathematics 1dc5bf accuracy gen 22.22
ceval-high_school_physics adf25f accuracy gen 31.58
ceval-high_school_chemistry 2ed27f accuracy gen 15.79
ceval-high_school_biology 8e2b9a accuracy gen 36.84
ceval-middle_school_mathematics bee8d5 accuracy gen 26.32
ceval-middle_school_biology 86817c accuracy gen 61.9
ceval-middle_school_physics 8accf6 accuracy gen 63.16
ceval-middle_school_chemistry 167a15 accuracy gen 60
ceval-veterinary_medicine b4e08d accuracy gen 47.83
ceval-college_economics f3f4e6 accuracy gen 41.82
ceval-business_administration c1614e accuracy gen 33.33
ceval-marxism cf874c accuracy gen 68.42
ceval-mao_zedong_thought 51c7a4 accuracy gen 70.83
ceval-education_science 591fee accuracy gen 58.62
ceval-teacher_qualification 4e4ced accuracy gen 70.45
ceval-high_school_politics 5c0de2 accuracy gen 26.32
ceval-high_school_geography 865461 accuracy gen 47.37
ceval-middle_school_politics 5be3e7 accuracy gen 52.38
ceval-middle_school_geography 8a63be accuracy gen 58.33
ceval-modern_chinese_history fc01af accuracy gen 73.91
ceval-ideological_and_moral_cultivation a2aa4a accuracy gen 63.16
ceval-logic f5b022 accuracy gen 31.82
ceval-law a110a1 accuracy gen 25
ceval-chinese_language_and_literature 0f8b68 accuracy gen 30.43
ceval-art_studies 2a1300 accuracy gen 60.61
ceval-professional_tour_guide 4e673e accuracy gen 62.07
ceval-legal_professional ce8787 accuracy gen 39.13
ceval-high_school_chinese 315705 accuracy gen 63.16
ceval-high_school_history 7eb30a accuracy gen 70
ceval-middle_school_history 48ab4a accuracy gen 59.09
ceval-civil_servant 87d061 accuracy gen 53.19
ceval-sports_science 70f27b accuracy gen 52.63
ceval-plant_protection 8941f9 accuracy gen 59.09
ceval-basic_medicine c409d6 accuracy gen 47.37
ceval-clinical_medicine 49e82d accuracy gen 40.91
ceval-urban_and_rural_planner 95b885 accuracy gen 45.65
ceval-accountant 002837 accuracy gen 26.53
ceval-fire_engineer bc23f5 accuracy gen 22.58
ceval-environmental_impact_assessment_engineer c64e2d accuracy gen 64.52
ceval-tax_accountant 3a5e3c accuracy gen 34.69
ceval-physician 6e277d accuracy gen 40.82
ceval-stem - naive_average gen 35.09
ceval-social-science - naive_average gen 52.79
ceval-humanities - naive_average gen 52.58
ceval-other - naive_average gen 44.36
ceval-hard - naive_average gen 23.91
ceval - naive_average gen 44.16
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