Repository: open-compass/opencompass
Issue: meta-llama/Meta-Llama-3-8B-Instruct evaluated results is not consistent with hugging face's official  results
Root cause: Algorithmic 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

python -c "import opencompass.utils;import pprint;pprint.pprint(dict(opencompass.utils.collect_env()))"
{'CUDA available': True,
 'CUDA_HOME': '/usr/local/cuda',
 'GCC': 'gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0',
 'GPU 0,1,2,3': 'NVIDIA GeForce RTX 4090',
 'MMEngine': '0.10.4',
 'MUSA available': False,
 'NVCC': 'Cuda compilation tools, release 12.4, V12.4.131',
 'OpenCV': '4.10.0',
 'PyTorch': '2.2.2+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 v3.3.2 (Git Hash '
                              '2dc95a2ad0841e29db8b22fbccaf3e5da7992b01)\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 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 8.9.2\n'
                              '  - Magma 2.6.1\n'
                              '  - Build settings: BLAS_INFO=mkl, '
                              'BUILD_TYPE=Release, CUDA_VERSION=12.1, '
                              'CUDNN_VERSION=8.9.2, '
                              '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_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 -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=2.2.2, '
                              '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, '
                              'USE_ROCM_KERNEL_ASSERT=OFF, \n',
 'Python': '3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]',
 'TorchVision': '0.17.2+cu121',
 'numpy_random_seed': 2147483648,
 'opencompass': '0.2.5+',
 'sys.platform': 'linux'}

### 重现问题 - 代码/配置示例

meta-llama/Meta-Llama-3-8B-Instruct evaluated results is not consistent with hugging face's official  results:  especially in GSM-8K_gen \winogrande_gen :


the script as follows:





CUDA_VISIBLE_DEVICES=1 python run.py --datasets winogrande_gen  --hf-type chat --hf-path /home/ubuntu/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/snapshots/c4a54320a52ed5f88b7a2f84496903ea4ff07b45/  --hf-num-gpus 1   --max-seq-len 2048 --max-out-len 256 -a  lmdeploy -r    --dump-eval-details  --model-kwargs device_map='auto' trust_remote_code=True temperature=0.01 do_sample=True



CUDA_VISIBLE_DEVICES=1,2 python run.py --datasets gsm8k_gen  --hf-type chat --hf-path /home/ubuntu/.cache/huggingface/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/snapshots/c4a54320a52ed5f88b7a2f84496903ea4ff07b45/  --hf-num-gpus 2   --max-seq-len 2048 --max-out-len 256 -a  lmdeploy -r    --dump-eval-details  --model-kwargs device_map='auto' trust_remote_code=True 


The results:

winogrande_gen:    Meta-Llama-3-8B-Instruct,    63.69

GSM8K:  meta-llama--Meta-Llama-3-8B-Instruct   79.00

Hugging face results:


winogrande:    Meta-Llama-3-8B-Instruct,    68

GSM8K:  meta-llama--Meta-Llama-3-8B-Instruct   74








### 重现问题 - 命令或脚本

As code upside this

### 重现问题 - 错误信息

nothing

### 其他信息

_No response_

同求最新官方结果：

model： llama3  qwen2 glm4
datasets： mmlu ceval

同样希望官方给出qwen2, glm4使用opencompass的评测脚本和结果，自己用opencompass评测qwen2-7b-chat分数和qwen2官方宣传的分数低很多。

qwen2 huggingface model 评测结果有问题
![image](https://github.com/user-attachments/assets/2ca04925-78e8-4829-8878-a4c7eac8b773)


还没解决吗

> qwen2 huggingface model 评测结果有问题 ![image](https://private-user-images.githubusercontent.com/30854283/349072092-2ca04925-78e8-4829-8878-a4c7eac8b773.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.8l1u3TFWPmn2JiZK5KgKMd91nVfPjZCGtppJ8yhpOks)

我得到的也是这个结果，请问您解决了吗