Repository: open-compass/opencompass
Issue: [Bug] model attribute - max_out_len do not take effect
Root cause: Documentation Deficiency

### 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-12.1',
'GCC': 'gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0',
'GPU 0,1,2,3,4,5,6,7': 'NVIDIA A100-SXM4-80GB',
'MMEngine': '0.10.4',
'MUSA available': False,
'NVCC': 'Cuda compilation tools, release 12.1, V12.1.105',
'OpenCV': '4.10.0',
'PyTorch': '2.3.0+cu121',
'PyTorch compiling details': 'PyTorch built with:\n'
' - GCC 9.3\n'
' - C++ Version: 201703\n'
' - Intel(R) oneAPI Math Kernel Library Version '
'2023.1-Product Build 20230303 for Intel(R) 64 '
'architecture applications\n'
' - Intel(R) MKL-DNN v3.3.6 (Git Hash '
'86e6af5974177e513fd3fee58425e1063e7f1361)\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 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=2.3.0, '
'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': '0.18.0+cu121',
'numpy_random_seed': 2147483648,
'opencompass': '0.2.6+a62c613',
'sys.platform': 'linux'}

### 重现问题 - 代码/配置示例


from mmengine.config import read_base

from opencompass.partitioners import SizePartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLInferTask

with read_base():
    from .datasets.mmlu.mmlu_gen_4d595a import mmlu_datasets
    from .datasets.cmmlu.cmmlu_gen_c13365 import cmmlu_datasets
    from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
    from .datasets.GaokaoBench.GaokaoBench_no_subjective_gen_4c31db import GaokaoBench_datasets
    from .datasets.triviaqa.triviaqa_wiki_1shot_gen_bc5f21 import triviaqa_datasets
    # from .datasets.nq.nq_open_1shot_gen_2e45e5 import nq_datasets
    from .datasets.race.race_gen_69ee4f import race_datasets
    from .datasets.winogrande.winogrande_5shot_gen_b36770 import winogrande_datasets
    from .datasets.hellaswag.hellaswag_10shot_gen_e42710 import hellaswag_datasets
    from .datasets.bbh.bbh_gen_2879b0 import bbh_datasets
    from .datasets.gsm8k.gsm8k_gen_1d7fe4 import gsm8k_datasets
    from .datasets.math.math_0shot_gen_393424 import math_datasets
    # from .datasets.TheoremQA.TheoremQA_5shot_gen_6f0af8 import TheoremQA_datasets
    from .datasets.humaneval.humaneval_gen_8e312c import humaneval_datasets
    from .datasets.mbpp.sanitized_mbpp_gen_830460 import sanitized_mbpp_datasets
    # from .datasets.gpqa.gpqa_gen_4baadb import gpqa_datasets
    # from .datasets.IFEval.IFEval_gen_3321a3 import ifeval_datasets
    from .summarizers.chat_OC15 import summarizer

datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])

from opencompass.models import HuggingFaceBaseModel

models = [
dict(
type=HuggingFaceBaseModel,
# 以下参数为 HuggingFaceCausalLM 的初始化参数
path='llama-7B',
tokenizer_path='llama-7B',
tokenizer_kwargs=dict(padding_side='left', truncation_side='left'),
max_seq_len=2048,
batch_padding=False,
# 以下参数为各类模型都有的参数，非 HuggingFaceCausalLM 的初始化参数
abbr='llama7B', # 模型简称，用于结果展示
max_out_len=100, # 最长生成 token 数
batch_size=8, # 批次大小
run_cfg=dict(num_gpus=2), # 运行配置，用于指定资源需求
)
]

infer = dict(
partitioner=dict(type=SizePartitioner),
runner=dict(
type=LocalRunner,
max_num_workers=8,
task=dict(type=OpenICLInferTask)),
)

### 重现问题 - 命令或脚本

python ./run.py <configfile>

### 重现问题 - 错误信息

Even though I have set the attribute "max_out_len", it's output token number still far exceeds 100. 
I want to ask if this attribute do not take effect or not? 

### 其他信息

_No response_

If `max_out_len` is specified in the dataset config, the out given `max_out_len` will be covered, like:
```
    ...
    inferencer=dict(type=GenInferencer, max_out_len=512)) # reset max_out_len to 512
    ...
 ```
Because even for identical models, varying the `max_out_len` parameter can yield distinct outcomes, so it is fixed in the dataset config.

Thanks. But the interface is hard to notice if I haven't read the source code of opencompass. 
The previous similar situation I encountered is command line parameter `-a vllm` and the "type" attribute in model class of configuration file. In this case, the inference engine will also be overlapped and cause unpredictable behaviour. 
I believe this kind of conflicts should be avoided by some method.