Repository: open-compass/opencompass
Issue: [Bug] 单机多卡评测最后一张卡一直卡住
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 9.4.0-1ubuntu1~20.04.1) 9.4.0',
 'GPU 0,1,2,3,4,5,6,7': 'Tesla V100-SXM2-32GB',
 'MMEngine': '0.10.4',
 'MUSA available': False,
 'NVCC': 'Cuda compilation tools, release 11.8, V11.8.89',
 'OpenCV': '4.10.0',
 'PyTorch': '2.4.1+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 90.1  (built against CUDA 12.4)\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=2.4.1, '
                              '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': '0.19.1+cu121',
 'lmdeploy': "not installed:No module named 'lmdeploy'",
 'numpy_random_seed': 2147483648,
 'opencompass': '0.3.1+fb6a0df',
 'sys.platform': 'linux',
 'transformers': '4.40.2'}


### 重现问题 - 代码/配置示例

```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
# 原始vicuna7b
python run.py --datasets gsm8k_gen   \
     --hf-path /home/mnt/zhaohongbo/.cache/huggingface/hub/models--lmsys--vicuna-7b-v1.5/snapshots/3321f76e3f527bd14065daf69dad9344000a201d \
     --w './work_dirs/vicuna7b-gsm8k_gen'  \
     --hf-num-gpus 8
```

### 重现问题 - 命令或脚本

```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
# 原始vicuna7b
python run.py --datasets gsm8k_gen   \
     --hf-path /home/mnt/zhaohongbo/.cache/huggingface/hub/models--lmsys--vicuna-7b-v1.5/snapshots/3321f76e3f527bd14065daf69dad9344000a201d \
     --w './work_dirs/vicuna7b-gsm8k_gen'  \
     --hf-num-gpus 8
```

### 重现问题 - 错误信息

![image](https://github.com/user-attachments/assets/df17a263-6a4e-4dd4-b070-cac28ef0995f)
gpustat看第7张卡利用率一直是0
并且，杀掉程序的时候发现一直卡在lock这里
![image](https://github.com/user-attachments/assets/eb215b90-4f01-487b-a86e-10cdf63b6068)


### 其他信息

请问，我启动多卡的程序是否正确？如果使用 --debug
```
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
# 原始vicuna7b
python run.py --datasets gsm8k_gen   \
     --hf-path /home/mnt/zhaohongbo/.cache/huggingface/hub/models--lmsys--vicuna-7b-v1.5/snapshots/3321f76e3f527bd14065daf69dad9344000a201d \
     --w './work_dirs/vicuna7b-gsm8k_gen'  \
     --debug
```
的方式是可以正常运行的，模型没有问题，数据集没有问题

For a 7B model, you can use --max-num-worker for data parrallel.      --hf-num-gpus 8 means loading one model on 8 gpus with tensor parralle.

非常感谢及时的回复！问题已经解决，另外想请教一下，**有类似于torchrun方式的多机多卡启动方式吗？谢谢**

Actually, we use torchrun for data parrallel. For multi-node multi-gpu, we use slurm, dlc(aliyun) or volc(火山云). You can find more information in runners. Can you provide more information or background for multi-node multi-gpu with torchrun.

我使用的是公司自己搭建的多机多卡调度配置，比如使用torchrun的时候，命令如下
```
torchrun --nproc_per_node 8 \
        --nnodes=$WORLD_SIZE \
        --node_rank=$RANK \
        --master_addr=$MASTER_ADDR \
        --master_port=$MASTER_PORT \
        main.py
```
集群会自动分配$WORLD_SIZE等变量，在多个节点上自动启动