Repository: open-compass/opencompass
Issue: [Bug] 从0.2.6换到0.3.5，同一个模型的性能下降特别多，请问应该如何排查原因
Root cause: Configuration Error

### 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 (GCC) 9.4.0',
 'GPU 0,1,2,3,4,5,6,7': 'NVIDIA L20',
 'MMEngine': '0.10.4',
 'MUSA available': False,
 'NVCC': 'Cuda compilation tools, release 12.4, V12.4.131',
 'OpenCV': '4.10.0',
 'PyTorch': '2.4.0+cu121',
 '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 v3.4.2 (Git Hash '
                              '1137e04ec0b5251ca2b4400a4fd3c667ce843d67)\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 12.1\n'
                              '  - NVCC architecture flags: '
                              '-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 90.2  (built against CUDA 12.5)\n'
                              '    - Built with CuDNN 90.1\n'
                              '  - Magma 2.6.1\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, CUDA_VERSION=12.1, '
                              'CUDNN_VERSION=9.1.0, '
                              'CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, '
                              'CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 '
                              '-fabi-version=11 -fvisibility-inlines-hidden '
                              '-DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO '
                              '-DLIBKINETO_NOROCTRACER -DUSE_FBGEMM '
                              '-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 -Wno-unused-parameter '
                              '-Wno-unused-function -Wno-unused-result '
                              '-Wno-strict-overflow -Wno-strict-aliasing '
                              '-Wno-stringop-overflow -Wsuggest-override '
                              '-Wno-psabi -Wno-error=pedantic '
                              '-Wno-error=old-style-cast -Wno-missing-braces '
                              '-fdiagnostics-color=always -faligned-new '
                              '-Wno-unused-but-set-variable '
                              '-Wno-maybe-uninitialized -fno-math-errno '
                              '-fno-trapping-math -Werror=format '
                              '-Wno-stringop-overflow, LAPACK_INFO=mkl, '
                              'PERF_WITH_AVX=1, PERF_WITH_AVX2=1, '
                              'PERF_WITH_AVX512=1, TORCH_VERSION=2.4.0, '
                              'USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=1, '
                              'USE_EXCEPTION_PTR=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, \n',
 'Python': '3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0]',
 'TorchVision': '0.19.0+cu121',
 'numpy_random_seed': 2147483648,
 'opencompass': '0.2.6+',
 'sys.platform': 'linux'}
```

### Reproduces the problem - code/configuration sample

1

### Reproduces the problem - command or script

1

### Reproduces the problem - error message

1

### Other information

请问有什么排查原因的方法吗

Please provide more information about which model and which dataset config.

> Please provide more information about which model and which dataset config.

模型是我自己训练的，数据集是mmlu。我也有仔细查看v0.3.1的更新介绍，但是没发现与mmlu有关的改动。
通过比较不同版本的结果。我发现v0.3.0之前是同一个结果，从v0.3.1后性能都会下降，并且0.3.1的测试速度快了很多。
进一步检查模型输出结果，发现0.3.0之前的结果是一致的，0.3.1后的结果是一致的。测试时，--max-out-len一直都是2，也就是说只会输出选择的答案。mmlu的评测逻辑应该直接比较选择结果，所以我感觉也不存在评测代码会影响结果这个原因。

如果评测代码没变，有可能是在推理速度上是做了什么改变，导致模型推理的结果发生了一些变化？

找到问题了，在[这一行](https://github.com/open-compass/opencompass/blob/main/opencompass/utils/run.py#L306)。
这个[PR](https://github.com/open-compass/opencompass/pull/1430)改变了config判断的逻辑，导致我的模型会进入到这个if语句下面。然后，在[这一行](https://github.com/open-compass/opencompass/blob/4a20e1176d1caa3e4476d0c429612c419bb369e7/opencompass/utils/run.py#L306)即使我在命令行将--batch-size设置为别的数字，只要使用了vllm，还是会设置成16)

想问一下为什么batchsize一定是16？

![image](https://github.com/user-attachments/assets/8bc37d52-ad28-4a8d-8197-60c24980e2a4)
左边是0.3.1后的config，右边是是0.3.1之前的config。似乎不光命令行的batch_size会失效，generation_kwargs、max_seq_len等也会失效