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105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
from mmengine.config import read_base
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with read_base():
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from opencompass.configs.datasets.subjective.alignbench.alignbench_judgeby_critiquellm import alignbench_datasets
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from opencompass.configs.datasets.subjective.alpaca_eval.alpacav2_judgeby_gpt4 import alpacav2_datasets
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from opencompass.configs.datasets.subjective.compassarena.compassarena_compare import compassarena_datasets
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from opencompass.configs.datasets.subjective.arena_hard.arena_hard_compare import arenahard_datasets
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from opencompass.configs.datasets.subjective.compassbench.compassbench_compare import compassbench_datasets
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from opencompass.configs.datasets.subjective.fofo.fofo_judge import fofo_datasets
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from opencompass.configs.datasets.subjective.wildbench.wildbench_pair_judge import wildbench_datasets
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from opencompass.configs.datasets.subjective.multiround.mtbench_single_judge_diff_temp import mtbench_datasets
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from opencompass.configs.datasets.subjective.multiround.mtbench101_judge import mtbench101_datasets
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from opencompass.models import (HuggingFace, HuggingFaceCausalLM,
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HuggingFaceChatGLM3, OpenAI)
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from opencompass.partitioners import NaivePartitioner, SizePartitioner
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from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
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from opencompass.partitioners.sub_num_worker import \
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SubjectiveNumWorkerPartitioner
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from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
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from opencompass.runners import LocalRunner, SlurmSequentialRunner
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from opencompass.summarizers import SubjectiveSummarizer
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from opencompass.tasks import OpenICLInferTask
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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api_meta_template = dict(round=[
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dict(role='HUMAN', api_role='HUMAN'),
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dict(role='BOT', api_role='BOT', generate=True),
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])
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# -------------Inference Stage ----------------------------------------
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# For subjective evaluation, we often set do sample for models
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models = [
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dict(
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type=HuggingFaceChatGLM3,
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abbr='chatglm3-6b-hf',
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path='THUDM/chatglm3-6b',
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tokenizer_path='THUDM/chatglm3-6b',
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model_kwargs=dict(
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device_map='auto',
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trust_remote_code=True,
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),
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tokenizer_kwargs=dict(
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padding_side='left',
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truncation_side='left',
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trust_remote_code=True,
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),
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generation_kwargs=dict(
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do_sample=
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True, #For subjective evaluation, we suggest you do set do_sample when running model inference!
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),
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meta_template=api_meta_template,
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max_out_len=2048,
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max_seq_len=4096,
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batch_size=8,
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run_cfg=dict(num_gpus=1, num_procs=1),
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)
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]
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datasets = [
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*alignbench_datasets, *alpacav2_datasets, *arenahard_datasets,
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*compassarena_datasets, *compassbench_datasets, *fofo_datasets,
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*mtbench_datasets, *mtbench101_datasets, *wildbench_datasets
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] # add datasets you want
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(type=LocalRunner,
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max_num_workers=16,
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task=dict(type=OpenICLInferTask)),
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)
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# -------------Evalation Stage ----------------------------------------
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## ------------- JudgeLLM Configuration
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judge_models = [
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dict(
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abbr='GPT4-Turbo',
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type=OpenAI,
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path='gpt-4-1106-preview',
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key=
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'xxxx', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
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meta_template=api_meta_template,
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query_per_second=16,
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max_out_len=2048,
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max_seq_len=2048,
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batch_size=8,
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temperature=0,
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)
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]
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## ------------- Evaluation Configuration
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eval = dict(
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partitioner=dict(
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type=SubjectiveNaivePartitioner,
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models=models,
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judge_models=judge_models,
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),
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runner=dict(type=LocalRunner,
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max_num_workers=16,
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task=dict(type=SubjectiveEvalTask)),
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)
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summarizer = dict(type=SubjectiveSummarizer, function='subjective')
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work_dir = 'outputs/subjective/'
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