Repository: open-compass/opencompass
Issue: 这个推理评估速度正常吗？帮忙看下
Root cause: Documentation Deficiency

### 描述该错误

运行配置：
python run.py configs/eval_internlm_7b.py -w outputs/internlm_7b

代码几乎都是库里默认的，没做大的改动。数据集配置：.datasets.collections.base_medium

运行环境：
P100 GPU 16G 两块；

下面是目前生成的log：
[20230725_222436.tar.gz](https://github.com/InternLM/opencompass/files/12162590/20230725_222436.tar.gz)

GPU使用情况：
![image](https://github.com/InternLM/opencompass/assets/17903509/0ebdab64-437a-4791-926b-495fe00527d8)

目前在推理，速度如下：
![image](https://github.com/InternLM/opencompass/assets/17903509/8579c201-b970-42cd-912d-9f725c5f9e07)

请问这个速度正常吗？这么慢吗？


### 环境信息

{'CUDA available': True,
 'CUDA_HOME': '/usr/local/cuda',
 'GCC': 'gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0',
 'GPU 0,1': 'Tesla P100-PCIE-16GB',
 'MMEngine': '0.8.2',
 'NVCC': 'Cuda compilation tools, release 11.8, V11.8.89',
 'OpenCV': '4.8.0',
 'PyTorch': '2.0.0+cu118',
 'PyTorch compiling details': 'PyTorch built with:\n'
                              '  - GCC 9.3\n'
                              '  - C++ Version: 201703\n'
                              '  - Intel(R) oneAPI Math Kernel Library Version '
                              '2022.2-Product Build 20220804 for Intel(R) 64 '
                              'architecture applications\n'
                              '  - Intel(R) MKL-DNN v2.7.3 (Git Hash '
                              '6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\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: AVX2\n'
                              '  - CUDA Runtime 11.8\n'
                              '  - NVCC architecture flags: '
                              '-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-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\n'
                              '  - CuDNN 8.7\n'
                              '  - Magma 2.6.1\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, CUDA_VERSION=11.8, '
                              'CUDNN_VERSION=8.7.0, '
                              'CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, '
                              'CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 '
                              '-fabi-version=11 -Wno-deprecated '
                              '-fvisibility-inlines-hidden -DUSE_PTHREADPOOL '
                              '-DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER '
                              '-DUSE_FBGEMM -DUSE_QNNPACK '
                              '-DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK '
                              '-DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC '
                              '-Wall -Wextra -Werror=return-type '
                              '-Werror=non-virtual-dtor -Werror=bool-operation '
                              '-Wnarrowing -Wno-missing-field-initializers '
                              '-Wno-type-limits -Wno-array-bounds '
                              '-Wno-unknown-pragmas -Wunused-local-typedefs '
                              '-Wno-unused-parameter -Wno-unused-function '
                              '-Wno-unused-result -Wno-strict-overflow '
                              '-Wno-strict-aliasing '
                              '-Wno-error=deprecated-declarations '
                              '-Wno-stringop-overflow -Wno-psabi '
                              '-Wno-error=pedantic -Wno-error=redundant-decls '
                              '-Wno-error=old-style-cast '
                              '-fdiagnostics-color=always -faligned-new '
                              '-Wno-unused-but-set-variable '
                              '-Wno-maybe-uninitialized -fno-math-errno '
                              '-fno-trapping-math -Werror=format '
                              '-Werror=cast-function-type '
                              '-Wno-stringop-overflow, LAPACK_INFO=mkl, '
                              'PERF_WITH_AVX=1, PERF_WITH_AVX2=1, '
                              'PERF_WITH_AVX512=1, '
                              'TORCH_DISABLE_GPU_ASSERTS=ON, '
                              'TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, '
                              'USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, '
                              'USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, '
                              'USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, '
                              'USE_OPENMP=ON, USE_ROCM=OFF, \n',
 'Python': '3.10.10 (main, Jul 24 2023, 16:03:07) [GCC 9.4.0]',
 'TorchVision': '0.15.1+cu118',
 'numpy_random_seed': 2147483648,
 'opencompass': '0.1.0+',
 'sys.platform': 'linux'}

### 其他信息

对代码几乎没做改动，唯一动的地方是 base_medium.py注释掉了下面数据集，因为没有找到相关数据；
 from ..bbh.bbh_gen_5b92b0 import bbh_datasets
from ..agieval.agieval_mixed_2f14ad import agieval_datasets
 from ..Xsum.Xsum_gen_31397e import Xsum_datasets
 from ..TheoremQA.TheoremQA_gen_ef26ca import TheoremQA_datasets
 from ..triviaqa.triviaqa_gen_2121ce import triviaqa_datasets

为感觉不应该这么慢。

Unfortunately - performing a complete test can be very slow with the limited computational resources available. We are planning to provide a core set that optimally balances the trade-off between efficiency and test completeness.

> Unfortunately - performing a complete test can be very slow with the limited computational resources available. We are planning to provide a core set that optimally balances the trade-off between efficiency and test completeness.

Thank you for your response. So, it means that my current state is normal.
By the way, when will the core set be launched, which is a dataset that can test the capability of the model with efficiency?

We are still evaluating the feasibility of offering such a core set by analyzing our historical evaluation data, but we haven't come to a conclusion and thus cannot estimate the time of release.