本文将进行使用 OpenCompass 来评测 InternLM2 1.8B实践
课程内容
在 OpenCompass 中评估一个模型通常包括以下几个阶段：配置 -> 推理 -> 评估 -> 可视化。
- 配置：这是整个工作流的起点。您需要配置整个评估过程，选择要评估的模型和数据集。此外，还可以选择评估策略、计算后端等，并定义显示结果的方式。
- 推理与评估：在这个阶段，OpenCompass 将会开始对模型和数据集进行并行推理和评估。推理阶段主要是让模型从数据集产生输出，而评估阶段则是衡量这些输出与标准答案的匹配程度。这两个过程会被拆分为多个同时运行的“任务”以提高效率。
- 可视化：评估完成后，OpenCompass 将结果整理成易读的表格，并将其保存为 CSV 和 TXT 文件。
接下来，我们将展示 OpenCompass 的基础用法，分别用命令行方式和配置文件的方式评测InternLM2-Chat-1.8B，展示书生浦语在 C-Eval 基准任务上的评估。更多评测技巧请查看 Quick Start文档 。
闯关任务
首先创建环境
conda create -n opencompass python=3.10
conda activate opencompass
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
# 注意：一定要先 cd /root
cd /root
git clone -b 0.2.4 https://github.com/open-compass/opencompass
cd opencompass
pip install -e .
apt-get update
apt-get install cmake
pip install -r requirements.txt
pip install protobuf
准备评测数据集
解压评测数据集到 /root/opencompass/data/ 处。（注意： 上方在git clone opencompass 时一定要将 opencompass clone 到 /root 路径下）
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
配置InternLM和ceval 相关的配置文件
列出所有跟 InternLM 及 C-Eval 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 | configs/models/hf_internlm/hf_internlm2_chat_1_8b.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_1cd8bf | configs/datasets/ceval/ceval_ppl_1cd8bf.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 |
| configuration_internlm | configs/datasets/cdme/internlm2-chat-7b/configuration_internlm.py |
| modeling_internlm2 | configs/datasets/cdme/internlm2-chat-7b/modeling_internlm2.py |
| tokenization_internlm | configs/datasets/cdme/internlm2-chat-7b/tokenization_internlm.py |
+--------------------------------+-------------------------------------------------------------------+
启动评测 (10% A100 8GB 资源)
使用命令行配置参数法进行评测
打开 opencompass文件夹下configs/models/hf_internlm/的hf_internlm2_chat_1_8b.py ,贴入以下代码
from opencompass.models import HuggingFaceCausalLM
models = [
dict(
type=HuggingFaceCausalLM,
abbr='internlm2-1.8b-hf',
path="/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b",
tokenizer_path='/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b',
model_kwargs=dict(
trust_remote_code=True,
device_map='auto',
),
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
use_fast=False,
trust_remote_code=True,
),
max_out_len=100,
min_out_len=1,
max_seq_len=2048,
batch_size=8,
run_cfg=dict(num_gpus=1, num_procs=1),
)
]
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 InternLM2-Chat-1.8B 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。
#环境变量配置
export MKL_SERVICE_FORCE_INTEL=1
#或
export MKL_THREADING_LAYER=GNU
<!---->
python run.py --datasets ceval_gen --models hf_internlm2_chat_1_8b --debug
命令解析
python run.py
--datasets ceval_gen \ # 数据集准备
--models hf_internlm2_chat_1_8b \ # 模型准备
--debug
屏幕上显示：
[2024-08-09 16:48:07,016] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...
评测完成后，评测结果位于: /root/opencompass/outputs/default/20240812_224546/summary/summary_20240812_224546.txt
dataset version metric mode internlm2-1.8b-hf
---------------------------------------------- --------- ------------- ------ -----------------------
20240812_224546
tabulate format
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
dataset version metric mode internlm2-1.8b-hf
---------------------------------------------- --------- -------- ------ -------------------
ceval-computer_network db9ce2 accuracy gen 47.37
ceval-operating_system 1c2571 accuracy gen 47.37
ceval-computer_architecture a74dad accuracy gen 23.81
ceval-college_programming 4ca32a accuracy gen 27.03
ceval-college_physics 963fa8 accuracy gen 42.11
ceval-college_chemistry e78857 accuracy gen 37.5
ceval-advanced_mathematics ce03e2 accuracy gen 26.32
ceval-probability_and_statistics 65e812 accuracy gen 22.22
ceval-discrete_mathematics e894ae accuracy gen 25
ceval-electrical_engineer ae42b9 accuracy gen 27.03
ceval-metrology_engineer ee34ea accuracy gen 54.17
ceval-high_school_mathematics 1dc5bf accuracy gen 22.22
ceval-high_school_physics adf25f accuracy gen 42.11
ceval-high_school_chemistry 2ed27f accuracy gen 52.63
ceval-high_school_biology 8e2b9a accuracy gen 26.32
ceval-middle_school_mathematics bee8d5 accuracy gen 36.84
ceval-middle_school_biology 86817c accuracy gen 80.95
ceval-middle_school_physics 8accf6 accuracy gen 47.37
ceval-middle_school_chemistry 167a15 accuracy gen 80
ceval-veterinary_medicine b4e08d accuracy gen 43.48
ceval-college_economics f3f4e6 accuracy gen 32.73
ceval-business_administration c1614e accuracy gen 39.39
ceval-marxism cf874c accuracy gen 68.42
ceval-mao_zedong_thought 51c7a4 accuracy gen 70.83
ceval-education_science 591fee accuracy gen 55.17
ceval-teacher_qualification 4e4ced accuracy gen 59.09
ceval-high_school_politics 5c0de2 accuracy gen 57.89
ceval-high_school_geography 865461 accuracy gen 47.37
ceval-middle_school_politics 5be3e7 accuracy gen 76.19
ceval-middle_school_geography 8a63be accuracy gen 75
ceval-modern_chinese_history fc01af accuracy gen 52.17
ceval-ideological_and_moral_cultivation a2aa4a accuracy gen 73.68
ceval-logic f5b022 accuracy gen 31.82
ceval-law a110a1 accuracy gen 29.17
ceval-chinese_language_and_literature 0f8b68 accuracy gen 47.83
ceval-art_studies 2a1300 accuracy gen 42.42
ceval-professional_tour_guide 4e673e accuracy gen 51.72
ceval-legal_professional ce8787 accuracy gen 34.78
ceval-high_school_chinese 315705 accuracy gen 36.84
ceval-high_school_history 7eb30a accuracy gen 65
ceval-middle_school_history 48ab4a accuracy gen 86.36
ceval-civil_servant 87d061 accuracy gen 42.55
ceval-sports_science 70f27b accuracy gen 52.63
ceval-plant_protection 8941f9 accuracy gen 40.91
ceval-basic_medicine c409d6 accuracy gen 68.42
ceval-clinical_medicine 49e82d accuracy gen 36.36
ceval-urban_and_rural_planner 95b885 accuracy gen 52.17
ceval-accountant 002837 accuracy gen 36.73
ceval-fire_engineer bc23f5 accuracy gen 38.71
ceval-environmental_impact_assessment_engineer c64e2d accuracy gen 51.61
ceval-tax_accountant 3a5e3c accuracy gen 36.73
ceval-physician 6e277d accuracy gen 42.86
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使用配置文件修改参数法进行评测
除了通过命令行配置实验外，OpenCompass 还允许用户在配置文件中编写实验的完整配置，并通过 run.py 直接运行它。配置文件是以 Python 格式组织的，并且必须包括 datasets 和 models 字段。本次测试配置在 configs文件夹 中。此配置通过 继承机制 引入所需的数据集和模型配置，并以所需格式组合 datasets 和 models 字段。
运行以下代码，在configs文件夹下创建eval_tutorial_demo.py
cd /root/opencompass/configs
touch eval_tutorial_demo.py
打开eval_tutorial_demo.py 贴入以下代码
from mmengine.config import read_base
with read_base():
from .datasets.ceval.ceval_gen import ceval_datasets
from .models.hf_internlm.hf_internlm2_chat_1_8b import models as hf_internlm2_chat_1_8b_models
datasets = ceval_datasets
models = hf_internlm2_chat_1_8b_models
因此，运行任务时，我们只需将配置文件的路径传递给 run.py：
cd /root/opencompass
python run.py configs/eval_tutorial_demo.py --debug
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