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136 lines
4.5 KiB
Python
136 lines
4.5 KiB
Python
from mmengine.config import read_base
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with read_base():
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# from .datasets.subjective.multiround.mtbench_single_judge_diff_temp import subjective_datasets
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from .datasets.subjective.wildbench.wildbench_single_judge import subjective_datasets
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# from .models.gemma.hf_gemma_2b_it import models as gemma_2b_models
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# from .models.hf_llama.hf_llama3_70b_instruct import models as llama3_model
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# # from .models.hf_internlm.hf_internlm2_chat_7b import models
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# from .models.yi.hf_yi_1_5_34b_chat import models as yi_model
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# from .models.qwen.hf_qwen1_5_72b_chat import models as qwen_model
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, 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_size import SubjectiveSizePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.runners import SlurmSequentialRunner
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from opencompass.tasks import OpenICLInferTask
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from opencompass.tasks.subjective_eval import SubjectiveEvalTask
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from opencompass.summarizers import WildBenchSingleSummarizer
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from opencompass.models import HuggingFacewithChatTemplate
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# models = sum([v for k, v in locals().items() if k.endswith("_model")], [])
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api_meta_template = dict(
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round=[
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dict(role='SYSTEM', api_role='SYSTEM'),
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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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)
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# _meta_template = dict(
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# round=[
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# dict(role='HUMAN', begin='\n<|im_start|>user\n', end='<|im_end|>'),
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# dict(role='BOT', begin='\n<|im_start|>assistant\n', end='<|im_end|>', generate=True),
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# ],
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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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# set max_out_len to 4096.
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models = [
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='llama-3-8b-instruct-hf',
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path='meta-llama/Meta-Llama-3-8B-Instruct',
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max_out_len=4096,
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batch_size=8,
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run_cfg=dict(num_gpus=1),
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stop_words=['<|end_of_text|>', '<|eot_id|>'],
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),
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='yi-1.5-6b-chat-hf',
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path='01-ai/Yi-1.5-6B-Chat',
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max_out_len=4096,
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batch_size=8,
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run_cfg=dict(num_gpus=1),
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),
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dict(
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type=HuggingFacewithChatTemplate,
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abbr='qwen1.5-7b-chat-hf',
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path='Qwen/Qwen1.5-7B-Chat',
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max_out_len=4096,
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batch_size=8,
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run_cfg=dict(num_gpus=1),
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),
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# dict(
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# type=HuggingFacewithChatTemplate,
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# abbr='llama-3-70b-instruct-hf',
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# path='meta-llama/Meta-Llama-3-70B-Instruct',
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# max_out_len=4096,
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# batch_size=8,
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# run_cfg=dict(num_gpus=4),
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# stop_words=['<|end_of_text|>', '<|eot_id|>'],
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# ),
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# dict(
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# type=HuggingFacewithChatTemplate,
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# abbr='yi-1.5-34b-chat-hf',
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# path='01-ai/Yi-1.5-34B-Chat',
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# max_out_len=4096,
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# batch_size=8,
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# run_cfg=dict(num_gpus=2),
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# ),
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# dict(
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# type=HuggingFacewithChatTemplate,
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# abbr='qwen1.5-72b-chat-hf',
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# path='Qwen/Qwen1.5-72B-Chat',
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# max_out_len=4096,
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# batch_size=8,
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# run_cfg=dict(num_gpus=4),
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# )
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]
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datasets = [*subjective_datasets]
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# -------------Evalation Stage ----------------------------------------
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## ------------- JudgeLLM Configuration
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judge_models = [dict(
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abbr='GPT4-Turbo',
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type=OpenAI,
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path='gpt-4-0613', # To compare with the official leaderboard, please use gpt4-0613
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key='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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infer = dict(
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partitioner=dict(type=SizePartitioner, max_task_size=1000, strategy='split'),
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runner=dict(
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type=SlurmSequentialRunner,
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max_num_workers=64,
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quotatype='reserved',
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partition='llmeval',
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task=dict(type=OpenICLInferTask)),
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)
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## single evaluation
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eval = dict(
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partitioner=dict(type=SubjectiveSizePartitioner, strategy='split', max_task_size=10000, mode='singlescore', models=models, judge_models=judge_models),
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runner=dict(type=LocalRunner,
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max_num_workers=2,
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task=dict(type=SubjectiveEvalTask)),
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)
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summarizer = dict(type=WildBenchSingleSummarizer)
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work_dir = 'outputs/wildbench/'
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