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OpenCompass/examples/eval_corebench_2409_chat_objective.py
Linchen Xiao a6193b4c02
[Refactor] Code refactoarization (#1831) 
* Update

* fix lint

* update

* fix lint
2025-01-20 19:17:38 +08:00

209 lines
8.5 KiB
Python
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import os.path as osp
from mmengine.config import read_base
from opencompass.partitioners import NaivePartitioner, NumWorkerPartitioner
from opencompass.runners import LocalRunner
from opencompass.tasks import OpenICLEvalTask, OpenICLInferTask
#######################################################################
# PART 0 Essential Configs #
#######################################################################
with read_base():
# Datasets Part
## Core Set
# ## Examination
# ## Reasoning
from opencompass.configs.datasets.bbh.bbh_gen_4a31fa import bbh_datasets
from opencompass.configs.datasets.cmmlu.cmmlu_0shot_cot_gen_305931 import \
cmmlu_datasets
from opencompass.configs.datasets.drop.drop_openai_simple_evals_gen_3857b0 import \
drop_datasets
# ## Scientific
from opencompass.configs.datasets.gpqa.gpqa_openai_simple_evals_gen_5aeece import \
gpqa_datasets
from opencompass.configs.datasets.gsm8k.gsm8k_0shot_v2_gen_a58960 import \
gsm8k_datasets
from opencompass.configs.datasets.hellaswag.hellaswag_10shot_gen_e42710 import \
hellaswag_datasets
# ## Coding
from opencompass.configs.datasets.humaneval.humaneval_gen_8e312c import \
humaneval_datasets
# TODO: Add LiveCodeBench
# ## Instruction Following
from opencompass.configs.datasets.IFEval.IFEval_gen_3321a3 import \
ifeval_datasets
# ## Math
from opencompass.configs.datasets.math.math_0shot_gen_393424 import \
math_datasets
from opencompass.configs.datasets.MathBench.mathbench_2024_gen_50a320 import \
mathbench_datasets
from opencompass.configs.datasets.mbpp.sanitized_mbpp_mdblock_gen_a447ff import \
sanitized_mbpp_datasets
from opencompass.configs.datasets.mmlu.mmlu_openai_simple_evals_gen_b618ea import \
mmlu_datasets
from opencompass.configs.datasets.mmlu_pro.mmlu_pro_0shot_cot_gen_08c1de import \
mmlu_pro_datasets
from opencompass.configs.summarizers.groups.bbh import bbh_summary_groups
from opencompass.configs.summarizers.groups.cmmlu import \
cmmlu_summary_groups
# Summarizer
from opencompass.configs.summarizers.groups.mmlu import mmlu_summary_groups
from opencompass.configs.summarizers.groups.mmlu_pro import \
mmlu_pro_summary_groups
# Model List
# from opencompass.configs.models.qwen.lmdeploy_qwen2_1_5b_instruct import models as lmdeploy_qwen2_1_5b_instruct_model
# from opencompass.configs.models.hf_internlm.lmdeploy_internlm2_5_7b_chat import models as hf_internlm2_5_7b_chat_model
# from opencompass.configs.models.openbmb.hf_minicpm_2b_sft_bf16 import models as hf_minicpm_2b_sft_bf16_model
# from opencompass.configs.models.yi.hf_yi_1_5_6b_chat import models as hf_yi_1_5_6b_chat_model
# from opencompass.configs.models.gemma.hf_gemma_2b_it import models as hf_gemma_2b_it_model
# from opencompass.configs.models.yi.hf_yi_1_5_34b_chat import models as hf_yi_1_5_34b_chat_model
#######################################################################
# PART 1 Datasets List #
#######################################################################
# datasets list for evaluation
datasets = sum((v for k, v in locals().items() if k.endswith('_datasets')), [])
#######################################################################
# PART 2 Datset Summarizer #
#######################################################################
# with read_base():
core_summary_groups = [
{
'name':
'core_average',
'subsets': [['mmlu', 'accuracy'], ['mmlu_pro', 'accuracy'],
['cmmlu', 'accuracy'], ['bbh', 'score'],
['math', 'accuracy'],
['openai_humaneval', 'humaneval_pass@1'],
['GPQA_diamond', 'accuracy'],
['IFEval', 'Prompt-level-strict-accuracy'],
['drop', 'accuracy'], ['sanitized_mbpp', 'score'],
['gsm8k', 'accuracy'], ['hellaswag', 'accuracy'],
['mathbench-t (average)', 'naive_average']],
},
]
summarizer = dict(
dataset_abbrs=[
['core_average', 'naive_average'],
['mmlu', 'accuracy'],
['mmlu_pro', 'accuracy'],
['cmmlu', 'accuracy'],
['bbh', 'score'],
['math', 'accuracy'],
['openai_humaneval', 'humaneval_pass@1'],
['GPQA_diamond', 'accuracy'],
['IFEval', 'Prompt-level-strict-accuracy'],
['drop', 'accuracy'],
['sanitized_mbpp', 'score'],
['gsm8k', 'accuracy'],
['hellaswag', 'accuracy'],
'mathbench-a (average)',
'mathbench-t (average)'
'',
['mmlu', 'accuracy'],
['mmlu-stem', 'accuracy'],
['mmlu-social-science', 'accuracy'],
['mmlu-humanities', 'accuracy'],
['mmlu-other', 'accuracy'],
'',
['mmlu_pro', 'accuracy'],
['mmlu_pro_math', 'accuracy'],
['mmlu_pro_physics', 'accuracy'],
['mmlu_pro_chemistry', 'accuracy'],
['mmlu_pro_law', 'accuracy'],
['mmlu_pro_engineering', 'accuracy'],
['mmlu_pro_other', 'accuracy'],
['mmlu_pro_economics', 'accuracy'],
['mmlu_pro_health', 'accuracy'],
['mmlu_pro_psychology', 'accuracy'],
['mmlu_pro_business', 'accuracy'],
['mmlu_pro_biology', 'accuracy'],
['mmlu_pro_philosophy', 'accuracy'],
['mmlu_pro_computer_science', 'accuracy'],
['mmlu_pro_history', 'accuracy'],
'',
['cmmlu', 'accuracy'],
['cmmlu-stem', 'accuracy'],
['cmmlu-social-science', 'accuracy'],
['cmmlu-humanities', 'accuracy'],
['cmmlu-other', 'accuracy'],
['cmmlu-china-specific', 'accuracy'],
'',
['bbh', 'extract_rate'],
['math', 'extract_rate'],
# ['openai_humaneval', 'extract_rate'],
['GPQA_diamond', 'extract_rate'],
# ['IFEval', 'extract_rate'],
'',
['mmlu', 'extract_rate'],
['mmlu-stem', 'extract_rate'],
['mmlu-social-science', 'extract_rate'],
['mmlu-humanities', 'extract_rate'],
['mmlu-other', 'extract_rate'],
'',
['mmlu_pro', 'extract_rate'],
['mmlu_pro_math', 'extract_rate'],
['mmlu_pro_physics', 'extract_rate'],
['mmlu_pro_chemistry', 'extract_rate'],
['mmlu_pro_law', 'extract_rate'],
['mmlu_pro_engineering', 'extract_rate'],
['mmlu_pro_other', 'extract_rate'],
['mmlu_pro_economics', 'extract_rate'],
['mmlu_pro_health', 'extract_rate'],
['mmlu_pro_psychology', 'extract_rate'],
['mmlu_pro_business', 'extract_rate'],
['mmlu_pro_biology', 'extract_rate'],
['mmlu_pro_philosophy', 'extract_rate'],
['mmlu_pro_computer_science', 'extract_rate'],
['mmlu_pro_history', 'extract_rate'],
'',
['cmmlu', 'extract_rate'],
['cmmlu-stem', 'extract_rate'],
['cmmlu-social-science', 'extract_rate'],
['cmmlu-humanities', 'extract_rate'],
['cmmlu-other', 'extract_rate'],
['cmmlu-china-specific', 'extract_rate'],
],
summary_groups=sum(
[v for k, v in locals().items() if k.endswith('_summary_groups')], []),
)
#######################################################################
# PART 3 Models List #
#######################################################################
models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
#######################################################################
# PART 4 Inference/Evaluation Configuaration #
#######################################################################
# Local Runner
infer = dict(
partitioner=dict(type=NumWorkerPartitioner, num_worker=8),
runner=dict(
type=LocalRunner,
max_num_workers=16,
retry=0, # Modify if needed
task=dict(type=OpenICLInferTask)),
)
# eval with local runner
eval = dict(
partitioner=dict(type=NaivePartitioner, n=10),
runner=dict(type=LocalRunner,
max_num_workers=16,
task=dict(type=OpenICLEvalTask)),
)
#######################################################################
# PART 5 Utils Configuaration #
#######################################################################
base_exp_dir = 'outputs/corebench_2409_objective/'
work_dir = osp.join(base_exp_dir, 'chat_objective')
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