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open-compass
[Bug] hf_chatglm3_6b评测AFQMC数据集时，自测结果与官方不一致。且自测结果不稳定。
先决条件
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-12.3',
'GCC': 'x86_64-linux-gnu-gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0',
'GPU 0,1,2,3': 'NVIDIA L40S',
'MMEngine': '0.10.4',
'MUSA available': False,
'NVCC': 'Cuda compilation tools, release 12.3, V12.3.52',
'OpenCV': '4.9.0',
'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: 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=2.2.2, '
'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': '3.10.14 (main, Apr 6 2024, 18:45:05) [GCC 9.4.0]',
'TorchVision': '0.17.2+cu121',
'numpy_random_seed': 2147483648,
'opencompass': '0.2.4+19d7e63',
'sys.platform': 'linux'}
重现问题 - 代码/配置示例
# hf_chatglm3_6b.py
from opencompass.models import HuggingFaceChatGLM3
api_meta_template = dict(
round=[
dict(role='HUMAN', api_role='HUMAN'),
dict(role='BOT', api_role='BOT', generate=True),
]
)
chatglm3_6b=dict(
type=HuggingFaceChatGLM3,
abbr='chatglm3-6b-hf',
#path='THUDM/chatglm3-6b',
path='/export/custom_models/THUDM/chatglm3-6b',
#tokenizer_path='THUDM/chatglm3-6b',
tokenizer_path='/export/custom_models/THUDM/chatglm3-6b',
model_kwargs=dict(
device_map='auto',
trust_remote_code=True,
),
tokenizer_kwargs=dict(
padding_side='left',
truncation_side='left',
trust_remote_code=True,
),
meta_template=api_meta_template,
max_out_len=100,
max_seq_len=4096,
batch_size=1,
run_cfg=dict(num_gpus=1, num_procs=1)
)
models = [
chatglm3_6b
]
## CLUE_afqmc_gen.py
from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import AFQMCDataset_V2
from opencompass.utils.text_postprocessors import first_capital_postprocess
afqmc_reader_cfg = dict(
input_columns=["sentence1", "sentence2"],
output_column="label",
test_split="train")
afqmc_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role="HUMAN",
prompt=
"语句一：“{sentence1}”\n语句二：“{sentence2}”\n语句一与语句二是关于蚂蚁金融产品的疑问，两者所询问的内容是否完全一致？\nA. 不完全一致\nB. 完全一致\n请从“A”，“B”中进行选择。\n答：",
),
]),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer),
)
afqmc_eval_cfg = dict(
evaluator=dict(type=AccEvaluator),
pred_role="BOT",
pred_postprocessor=dict(type=first_capital_postprocess),
)
afqmc_datasets = [
dict(
abbr="afqmc-dev",
type=AFQMCDataset_V2,
path="./data/CLUE/AFQMC/dev.json",
reader_cfg=afqmc_reader_cfg,
infer_cfg=afqmc_infer_cfg,
eval_cfg=afqmc_eval_cfg,
),
]
重现问题 - 命令或脚本
python run.py --models hf_chatglm3_6b --datasets CLUE_afqmc_gen
重现问题 - 错误信息
我用hf_chatglm3_6b模型评测CLUE_afqmc_gen数据集时，官方给出的结果是63.9。我自测的结果在57.0~60.0之间波动。
其他信息
【改动】我只把模型加载地址改为了本地加载，而不是从互联网下载。
【几个观察】
1.执行几次相同的命令后，在outputs/default/的不同时间戳目录下发现配置文件是一样的，除了workdir。
2.在在outputs/default/的不同时间戳目录下对同一问题的预测结果有差异。
