Repository: open-compass/opencompass
Issue: [Bug] Time cost of multi-gpu vs single-gpu
Root cause: Configuration Error

### 先决条件

- [x] 我已经搜索过 [问题](https://github.com/open-compass/opencompass/issues/) 和 [讨论](https://github.com/open-compass/opencompass/discussions) 但未得到预期的帮助。
- [x] 错误在 [最新版本](https://github.com/open-compass/opencompass) 中尚未被修复。

### 问题类型

我正在使用官方支持的任务/模型/数据集进行评估。

### 环境

{'CUDA available': True,
 'CUDA_HOME': '/usr/local/cuda',
 'GCC': 'gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0',
 'GPU 0': 'NVIDIA A100-SXM4-80GB',
 'MMEngine': '0.10.6',
 'MUSA available': False,
 'NVCC': 'Cuda compilation tools, release 12.1, V12.1.105',
 'OpenCV': '4.11.0',
 'PyTorch': '2.6.0+cu124',
 'PyTorch compiling details': 'PyTorch built with:\n'
                              '  - GCC 9.3\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 v3.5.3 (Git Hash '
                              '66f0cb9eb66affd2da3bf5f8d897376f04aae6af)\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 12.4\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 90.1\n'
                              '  - Magma 2.6.1\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, '
                              'COMMIT_SHA=2236df1770800ffea5697b11b0bb0d910b2e59e1, '
                              'CUDA_VERSION=12.4, 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 -DLIBKINETO_NOXPUPTI=ON '
                              '-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-strict-overflow -Wno-strict-aliasing '
                              '-Wno-stringop-overflow -Wsuggest-override '
                              '-Wno-psabi -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, '
                              'TORCH_VERSION=2.6.0, 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': '3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0]',
 'TorchVision': '0.21.0+cu124',
 'lmdeploy': '0.7.0.post3',
 'numpy_random_seed': 2147483648,
 'opencompass': '0.4.0+3fd8b4e',
 'sys.platform': 'linux',
 'transformers': '4.48.3'}

### 重现问题 - 代码/配置示例

from opencompass.models import TurboMindModelwithChatTemplate

models = [
    dict(
        type=TurboMindModelwithChatTemplate,
        abbr='llama-3_1-8b-instruct-turbomind',
        path='/nas/shared/ma4agi/model/Llama-3.1-8B-Instruct',
        engine_config=dict(max_batch_size=16, tp=1),
        gen_config=dict(top_k=1, temperature=1e-6, top_p=0.9, max_new_tokens=4096),
        max_seq_len=16384,
        max_out_len=4096,
        batch_size=16,
        run_cfg=dict(num_gpus=8),
        stop_words=['
', '
'],
    )
]


### 重现问题 - 命令或脚本

python run.py --models lmdeploy_llama3_1_8b_instruct  --datasets  math_prm800k_500_0shot_cot_gen 


### 重现问题 - 错误信息

![Image](https://github.com/user-attachments/assets/44a8d906-b860-4775-ad38-73c142ef716f)

### 其他信息
**I tried run_cfg=dict(num_gpus=8), and run_cfg=dict(num_gpus=1) and found that they took the same amount of time, what's the problem?**


![Image](https://github.com/user-attachments/assets/9cccc177-8c2b-4e37-9d40-5c8076bf919c)

![Image](https://github.com/user-attachments/assets/496eaca5-f570-4f19-a7fd-cfa90216ca04)

`engine_config=dict(max_batch_size=16, tp=8)` you should set the tp size in `engine_config` in order for LMDeploy to use all your GPUs for ModelParallel. On the other hand, you can set `tp` and `num_gpus ` to 1 and `infer = dict(
    partitioner=dict(type=NumWorkerPartitioner, num_worker=8),
    runner=dict(type=LocalRunner, task=dict(type=OpenICLInferTask)),
)
` in your config file in other to start 8 instances of 1 GPU each for DataParallel.

Thanks ~