Repository: open-compass/opencompass
Issue: [Bug] 数据集导入报错 ConfigParsingError
Root cause: Documentation Deficiency

### Prerequisite

- [x] I have searched [Issues](https://github.com/open-compass/opencompass/issues/) and [Discussions](https://github.com/open-compass/opencompass/discussions) but cannot get the expected help.
- [x] The bug has not been fixed in the [latest version](https://github.com/open-compass/opencompass).

### Type

I'm evaluating with the officially supported tasks/models/datasets.

### Environment

```
{'CUDA available': True,
 'CUDA_HOME': '/usr/local/cuda',
 'GCC': 'gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0',
 'GPU 0,1,2,3,4,5,6,7': 'NVIDIA H20-3e',
 'MMEngine': '0.10.7',
 'MUSA available': False,
 'NVCC': 'Cuda compilation tools, release 12.9, V12.9.86',
 'OpenCV': '4.11.0',
 'PyTorch': '2.9.1+cu128',
 'PyTorch compiling details': 'PyTorch built with:\n'
                              '  - GCC 13.3\n'
                              '  - C++ Version: 201703\n'
                              '  - Intel(R) oneAPI Math Kernel Library Version '
                              '2024.2-Product Build 20240605 for Intel(R) 64 '
                              'architecture applications\n'
                              '  - Intel(R) MKL-DNN v3.7.1 (Git Hash '
                              '8d263e693366ef8db40acc569cc7d8edf644556d)\n'
                              '  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n'
                              '  - LAPACK is enabled (usually provided by '
                              'MKL)\n'
                              '  - NNPACK is enabled\n'
                              '  - CPU capability usage: AVX512\n'
                              '  - CUDA Runtime 12.8\n'
                              '  - NVCC architecture flags: '
                              '-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_90,code=sm_90;-gencode;arch=compute_100,code=sm_100;-gencode;arch=compute_120,code=sm_120\n'
                              '  - CuDNN 91.0.2  (built against CUDA 12.9)\n'
                              '    - Built with CuDNN 90.8\n'
                              '  - Magma 2.6.1\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, '
                              'COMMIT_SHA=5811a8d7da873dd699ff6687092c225caffcf1bb, '
                              'CUDA_VERSION=12.8, CUDNN_VERSION=9.8.0, '
                              'CXX_COMPILER=/opt/rh/gcc-toolset-13/root/usr/bin/c++, '
                              'CXX_FLAGS= -fvisibility-inlines-hidden '
                              '-DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO '
                              '-DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON '
                              '-DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK '
                              '-DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE '
                              '-O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra '
                              '-Werror=return-type -Werror=non-virtual-dtor '
                              '-Werror=range-loop-construct '
                              '-Werror=bool-operation -Wnarrowing '
                              '-Wno-missing-field-initializers '
                              '-Wno-unknown-pragmas -Wno-unused-parameter '
                              '-Wno-strict-overflow -Wno-strict-aliasing '
                              '-Wno-stringop-overflow -Wsuggest-override '
                              '-Wno-psabi -Wno-error=old-style-cast '
                              '-faligned-new -Wno-maybe-uninitialized '
                              '-fno-math-errno -fno-trapping-math '
                              '-Werror=format -Wno-dangling-reference '
                              '-Wno-error=dangling-reference '
                              '-Wno-stringop-overflow, LAPACK_INFO=mkl, '
                              'PERF_WITH_AVX=1, PERF_WITH_AVX2=1, '
                              'TORCH_VERSION=2.9.1, USE_CUDA=ON, USE_CUDNN=ON, '
                              'USE_CUSPARSELT=1, USE_GFLAGS=OFF, USE_GLOG=OFF, '
                              'USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, '
                              'USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, '
                              'USE_OPENMP=ON, USE_ROCM=OFF, '
                              'USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, '
                              'USE_XPU=OFF, \n',
 'Python': '3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0]',
 'lmdeploy': "not installed:No module named 'lmdeploy'",
 'numpy_random_seed': 2147483648,
 'opencompass': '0.5.1+2be943a',
 'sys.platform': 'linux',
 'transformers': '4.57.1'}
```

### Reproduces the problem - code/configuration sample

配置脚本：
```python
from mmengine.config import read_base

from opencompass.models import OpenAISDK
from opencompass.partitioners import NaivePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask

with read_base():
    from opencompass.configs.datasets.gsm8k.gsm8k_gen_17d0dc import gsm8k_datasets


api_meta_template = dict(
    round=[
        dict(role='HUMAN', api_role='HUMAN'),
        dict(role='BOT', api_role='BOT', generate=True),
    ],
    reserved_roles=[dict(role='SYSTEM', api_role='SYSTEM')],
)

models = [
    dict(
        abbr='GLM-4.6-RL-s4',
        type=OpenAISDK,
        path="GLM-4.6-RL-s4",
        url='http://127.0.0.1:8000/v1',
        key='EMPTY',
        meta_template=api_meta_template,
        query_per_second=1,
        max_out_len=2048,
        max_seq_len=2048,
        batch_size=8),
]

infer = dict(
    partitioner=dict(type=NaivePartitioner),
    runner=dict(type=LocalRunner,
                max_num_workers=4,
                task=dict(type=OpenICLInferTask)),
)
```

### Reproduces the problem - command or script

python run.py configs/my.py

### Reproduces the problem - error message


```
Traceback (most recent call last):
  File "/mnt/ali-sh-1/dataset/redone/xushiyue/opencompass/run.py", line 4, in <module>
    main()
  File "/mnt/ali-sh-1/dataset/redone/xushiyue/opencompass/opencompass/cli/main.py", line 263, in main
    cfg = get_config_from_arg(args)
  File "/mnt/ali-sh-1/dataset/redone/xushiyue/opencompass/opencompass/utils/run.py", line 98, in get_config_from_arg
    config = Config.fromfile(args.config, format_python_code=False)
  File "/root/miniconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/config/config.py", line 494, in fromfile
    raise e
  File "/root/miniconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/config/config.py", line 492, in fromfile
    cfg_dict, imported_names = Config._parse_lazy_import(filename)
  File "/root/miniconda3/envs/opencompass/lib/python3.10/site-packages/mmengine/config/config.py", line 1074, in _parse_lazy_import
    raise ConfigParsingError(
mmengine.config.utils.ConfigParsingError: /root/miniconda3/envs/opencompass/lib/python3.10/site-packages/opencompass/configs/datasets/gsm8k/gsm8k_gen_1d7fe4.py not found! It means that incorrect module is defined in `with read_base(): = from opencompass.configs.datasets.gsm8k.gsm8k_gen_1d7fe4 import ...`, please make sure the base config module is valid and is consistent with the prior import logic
```



### Other information

查issue发现数据导入错误较为普遍，但至今没有有效解决，请求官方帮助

已修复该问题，导入路径应该为`from .opencompass.configs.datasets.gsm8k.gsm8k_gen_17d0dc import gsm8k_datasets`
但依然有疑问，为什么必须配置tokenizer？警告信息如下：
`11/20 13:04:33 - OpenCompass - WARNING - Can not get tokenizer automatically, will use default tokenizer gpt-4 for length calculation.`

通过vllm部署的api调用模型返回的响应中明明包含详细的`usage`信息，如果输入长度超过最大长度也会返回错误信息，完全多此一举，没有必要使用额外的tokenizer来计算长度，这是否一种历史遗留问题？

> 已修复该问题，导入路径应该为`from .opencompass.configs.datasets.gsm8k.gsm8k_gen_17d0dc import gsm8k_datasets` 但仍然有疑问，为什么必须配置tokenizer？警告信息如下： `11/20 13:04:33 - OpenCompass - WARNING - Can not get tokenizer automatically, will use default tokenizer gpt-4 for length calculation.`
> 
> 通过vllm部署的api调用模型返回的响应中明明包含详细的`usage`信息，如果输入长度超过最大长度基因返回错误信息，完全多此一举，不需要使用额外的标记器来计算长度，这是否是一个历史遗传问题？

opencompass对于输入长度的校验确实令人困惑，事实上这部分功能已有vllm等推理框架提供。或许可以分析该判断的动机，并考虑移除该部分逻辑以提升易用性。