readme.md

本文将进行使用 OpenCompass 来评测 InternLM2 1.8B实践

概览

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 https://opencompass.readthedocs.io/zh-cn/latest/get_started/quick_start.html 文档。

环境配置

创建开发机和 conda 环境

在创建开发机界面选择镜像为 Cuda11.7-conda，并选择 GPU 为10% A100。

安装——面向GPU的环境安装

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

数据准备

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

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

如果一切正常，您应该看到屏幕上显示：

[2024-08-09 16:48:07,016] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...

评测完成后，将会看到：

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      13.51                                                                                 
ceval-college_physics                           963fa8     accuracy       gen      42.11                                                                                 
ceval-college_chemistry                         e78857     accuracy       gen      33.33                                                                                 
ceval-advanced_mathematics                      ce03e2     accuracy       gen      10.53                                                                                 
...          

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

如果一切正常，您应该看到屏幕上显示：

[2024-08-09 16:48:07,016] [opencompass.openicl.icl_inferencer.icl_gen_inferencer] [INFO] Starting inference process...

评测完成后，将会看到：

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      13.51                                                                                 
ceval-college_physics                           963fa8     accuracy       gen      42.11                                                                                 
ceval-college_chemistry                         e78857     accuracy       gen      33.33                                                                                 
ceval-advanced_mathematics                      ce03e2     accuracy       gen      10.53                                                                                 
...      

结语

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。更多评测技巧欢迎查看 https://opencompass.readthedocs.io/zh-cn/latest/get_started/quick_start.html 文档~我们下节课再见！

作业

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