mirror of
https://github.com/open-compass/opencompass.git
synced 2025-05-30 16:03:24 +08:00
181 lines
6.4 KiB
Python
181 lines
6.4 KiB
Python
from mmengine.config import read_base
|
||
|
||
with read_base():
|
||
# from .datasets.subjective.multiround.mtbench_single_judge_diff_temp import subjective_datasets
|
||
from .datasets.subjective.wildbench.wildbench_pair_judge import subjective_datasets
|
||
from .models.openai.gpt_4 import models as gpt4_models
|
||
from .models.hf_llama.hf_llama2_70b_chat import models as llama2_models
|
||
# from .models.gemma.hf_gemma_2b_it import models
|
||
# from .models.hf_llama.hf_llama3_70b_instruct import models as llama3_model
|
||
# # from .models.hf_internlm.hf_internlm2_chat_7b import models
|
||
# from .models.yi.hf_yi_1_5_34b_chat import models as yi_model
|
||
# from .models.qwen.hf_qwen1_5_72b_chat import models as qwen_model
|
||
|
||
from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
|
||
from opencompass.partitioners import NaivePartitioner, SizePartitioner
|
||
from opencompass.partitioners.sub_naive import SubjectiveNaivePartitioner
|
||
from opencompass.partitioners.sub_size import SubjectiveSizePartitioner
|
||
from opencompass.runners import LocalRunner
|
||
from opencompass.runners import SlurmSequentialRunner
|
||
from opencompass.tasks import OpenICLInferTask
|
||
from opencompass.tasks.subjective_eval import SubjectiveEvalTask
|
||
from opencompass.summarizers import WildBenchPairSummarizer
|
||
from opencompass.models.claude_api.claude_api import Claude
|
||
from opencompass.models import HuggingFacewithChatTemplate
|
||
|
||
|
||
models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
|
||
|
||
api_meta_template = dict(
|
||
round=[
|
||
dict(role='SYSTEM', api_role='SYSTEM'),
|
||
dict(role='HUMAN', api_role='HUMAN'),
|
||
dict(role='BOT', api_role='BOT', generate=True),
|
||
]
|
||
)
|
||
|
||
# _meta_template = dict(
|
||
# round=[
|
||
# dict(role='HUMAN', begin='\n<|im_start|>user\n', end='<|im_end|>'),
|
||
# dict(role='BOT', begin='\n<|im_start|>assistant\n', end='<|im_end|>', generate=True),
|
||
# ],
|
||
# )
|
||
# -------------Inference Stage ----------------------------------------
|
||
# For subjective evaluation, we often set do sample for models
|
||
|
||
models = [
|
||
dict(
|
||
type=HuggingFacewithChatTemplate,
|
||
abbr='llama-3-8b-instruct-hf',
|
||
path='meta-llama/Meta-Llama-3-8B-Instruct',
|
||
max_out_len=4096,
|
||
batch_size=8,
|
||
run_cfg=dict(num_gpus=1),
|
||
stop_words=['<|end_of_text|>', '<|eot_id|>'],
|
||
),
|
||
dict(
|
||
type=HuggingFacewithChatTemplate,
|
||
abbr='yi-1.5-6b-chat-hf',
|
||
path='01-ai/Yi-1.5-6B-Chat',
|
||
max_out_len=4096,
|
||
batch_size=8,
|
||
run_cfg=dict(num_gpus=1),
|
||
),
|
||
dict(
|
||
type=HuggingFacewithChatTemplate,
|
||
abbr='qwen1.5-7b-chat-hf',
|
||
path='Qwen/Qwen1.5-7B-Chat',
|
||
max_out_len=4096,
|
||
batch_size=8,
|
||
run_cfg=dict(num_gpus=1),
|
||
),
|
||
# dict(
|
||
# type=HuggingFacewithChatTemplate,
|
||
# abbr='llama-3-70b-instruct-hf',
|
||
# path='meta-llama/Meta-Llama-3-70B-Instruct',
|
||
# max_out_len=4096,
|
||
# batch_size=8,
|
||
# run_cfg=dict(num_gpus=4),
|
||
# stop_words=['<|end_of_text|>', '<|eot_id|>'],
|
||
# ),
|
||
# dict(
|
||
# type=HuggingFacewithChatTemplate,
|
||
# abbr='yi-1.5-34b-chat-hf',
|
||
# path='01-ai/Yi-1.5-34B-Chat',
|
||
# max_out_len=4096,
|
||
# batch_size=8,
|
||
# run_cfg=dict(num_gpus=2),
|
||
# ),
|
||
# dict(
|
||
# type=HuggingFacewithChatTemplate,
|
||
# abbr='qwen1.5-72b-chat-hf',
|
||
# path='Qwen/Qwen1.5-72B-Chat',
|
||
# max_out_len=4096,
|
||
# batch_size=8,
|
||
# run_cfg=dict(num_gpus=8),
|
||
# )
|
||
]
|
||
|
||
datasets = [*subjective_datasets]
|
||
|
||
# -------------Evalation Stage ----------------------------------------
|
||
|
||
## ------------- JudgeLLM Configuration
|
||
judge_models = [dict(
|
||
abbr='GPT4-Turbo',
|
||
type=OpenAI,
|
||
path='gpt-4-0613', # To compare with the official leaderboard, please use gpt4-0613
|
||
key='xxxx', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
|
||
meta_template=api_meta_template,
|
||
query_per_second=16,
|
||
max_out_len=2048,
|
||
max_seq_len=2048,
|
||
batch_size=8,
|
||
temperature=0,
|
||
)]
|
||
|
||
gpt4 = dict(
|
||
abbr='gpt4-turbo',
|
||
type=OpenAI,
|
||
path='gpt-4-0409-preview',
|
||
key='', # The key will be obtained from $OPENAI_API_KEY, but you can write down your key here as well
|
||
meta_template=api_meta_template,
|
||
query_per_second=1,
|
||
max_out_len=2048,
|
||
max_seq_len=4096,
|
||
batch_size=4,
|
||
retry=20,
|
||
temperature=1,
|
||
) # Re-inference gpt4's predictions or you can choose to use the pre-commited gpt4's predictions
|
||
|
||
claude = dict(abbr='HaiKu',
|
||
type=Claude,
|
||
path='claude-2',
|
||
key='YOUR_CLAUDE_KEY',
|
||
query_per_second=1,
|
||
max_out_len=2048, max_seq_len=2048, batch_size=2,
|
||
)
|
||
## single evaluation
|
||
# eval = dict(
|
||
# partitioner=dict(type=SubjectiveSizePartitioner, strategy='split', max_task_size=10000, mode='singlescore', models=models, judge_models=judge_models),
|
||
# runner=dict(type=LocalRunner, max_num_workers=32, task=dict(type=SubjectiveEvalTask)),
|
||
# )
|
||
infer = dict(
|
||
partitioner=dict(type=SizePartitioner, max_task_size=1000, strategy='split'),
|
||
runner=dict(
|
||
type=SlurmSequentialRunner,
|
||
max_num_workers=64,
|
||
quotatype='reserved',
|
||
partition='llmeval',
|
||
task=dict(type=OpenICLInferTask)),
|
||
)
|
||
|
||
eval = dict(
|
||
partitioner=dict(
|
||
type=SubjectiveNaivePartitioner,
|
||
mode='m2n', # m个模型 与 n个模型进行对战
|
||
infer_order='random',
|
||
# 在m2n模式下，需要指定base_models和compare_models，将会对base_models和compare_models生成对应的两两pair（去重且不会与自身进行比较）
|
||
base_models = [*llama2_models, gpt4, claude], # 用于对比的基线模型
|
||
compare_models = models, # 待评测模型
|
||
judge_models=judge_models
|
||
),
|
||
runner=dict(
|
||
type=LocalRunner,
|
||
# partition='llmeval',
|
||
# quotatype='auto',
|
||
max_num_workers=3,
|
||
task=dict(
|
||
type=SubjectiveEvalTask
|
||
)),
|
||
given_pred = [{'abbr':'gpt4-turbo', 'path':'./data/WildBench/gpt4'},
|
||
{'abbr': 'llama-2-70b-chat-hf', 'path':'./data/WildBench/llama2-70b'},
|
||
{'abbr': 'HaiKu', 'path':'./data/WildBench/claude'},
|
||
{'abbr': 'llama-2-70b-chat-turbomind', 'path':'./data/WildBench/llama2-70b'},
|
||
{'abbr': 'llama-2-70b-chat-vllm', 'path':'./data/WildBench/llama2-70b'}]
|
||
)
|
||
|
||
summarizer = dict(type=WildBenchPairSummarizer)
|
||
|
||
work_dir = 'outputs/wildbench/'
|