准备工作
打开开发机，选择cuda11.7环境，A100选择10%，点击创建，然后进入开发机即可，和之前的操作一样。接下来创建环境，下载必要的依赖包
conda create -n opencompass python=3.10
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
cd ~
conda activate opencompass
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
数据的准备：
cp /share/temp/datasets/OpenCompassData-core-20231110.zip /root/opencompass/
unzip OpenCompassData-core-20231110.zip
查看所有跟 InternLM 及 C-Eval 相关的配置：
python tools/list_configs.py internlm ceval
结果：
+----------------------------------------+----------------------------------------------------------------------+
| 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 |
+--------------------------------+-------------------------------------------------------------------+
选择configs/models/hf_internlm/的hf_internlm2_chat_1_8b.py
使用OpenCompass 评测
使用命令行配置参数法进行评测
将下面代码贴到hf_internlm2_chat_1_8b.py中：
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),
)
]
配置环境变量
#环境变量配置
export MKL_SERVICE_FORCE_INTEL=1
使用命令行评估
python run.py --datasets ceval_gen --models hf_internlm2_chat_1_8b --debug
评估结果
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。
首先在configs文件夹下创建eval_tutorial_demo.py
cd /root/opencompass/configs
touch eval_tutorial_demo.py
将以下代码粘贴到eval_tutorial_demo.py中：
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
测评：
cd /root/opencompass
python run.py configs/eval_tutorial_demo.py --debug
结果：
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
...
