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[Feature] Add CompassArena (#828)
* add compass arena * add compass_arena * add compass arena * Update opencompass/summarizers/subjective/compass_arena.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update opencompass/summarizers/subjective/__init__.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update opencompass/datasets/subjective/compass_arena.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update opencompass/datasets/subjective/__init__.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update configs/eval_subjective_compassarena.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update configs/datasets/subjective/compassarena/compassarena_compare.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update configs/eval_subjective_compassarena.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update configs/datasets/subjective/compassarena/compassarena_compare.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * fix check position bias --------- Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com>
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configs/datasets/subjective/compassarena/compassarena_compare.py
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160
configs/datasets/subjective/compassarena/compassarena_compare.py
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@ -0,0 +1,160 @@
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from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.openicl.icl_evaluator import LMEvaluator
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from opencompass.datasets import CompassArenaDataset
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subjective_reader_cfg = dict(
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input_columns=['question', 'ref'],
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output_column='judge',
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)
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data_path ="data/subjective/"
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subjective_datasets = []
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base_prompt = """
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[回答1开始]
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{prediction}
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[回答1结束]
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[回答2开始]
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{prediction2}
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[回答2结束]
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根据评分要求，在以下 3 个选项中做出选择:
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A. 回答1更好
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B. 回答2更好
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C. 回答1、2平局
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并提供你的解释原因。
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如果你认为回答1更好，你的输出应形如：
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选择：A
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原因：blahblah blahblah\n
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如果你认为回答2更好，你的输出应形如：
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选择：B
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原因：blahblah blahblah\n
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如果你认为回答1、2打成平手，你的输出应形如：
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选择：C
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原因：blahblah blahblah\n
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"""
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knowledge_prompt = """
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请根据提供的 评分要求，用户问题，参考答案 以及 相应的两个回答（回答1，回答2），判断两个回答中哪一个更好。
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评分要求（重要性依次递减）:
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1. 更好的回答能与参考答案吻合或表明参考答案的意思。
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2. 在都准确答对问题的前提下，更好的回答能对知识点进行额外补充，且补充的知识准确无误。
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3. 更好的回答更加符合与人类对话的习惯，包括语气、情调等。
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[用户问题]
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{question}
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[参考答案]
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{ref}
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""" + base_prompt
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language_prompt = """
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请根据提供的 评分要求，用户问题 以及 相应的两个回答（回答1，回答2），判断两个回答中哪一个更好。
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评分要求（重要性依次递减）:
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1. 在有明确的参考答案的情况下，越贴近参考答案或表明了参考答案的意思的回答越好。
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2. 更好的回答在语言表达上更流畅，更加符合与人类对话的习惯，包括语气、情调等
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3. 在都准确答对问题的前提下，更好的回答能进行额外补充，且补充的内容准确无误。
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[用户问题]
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{question}
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[参考答案]
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{ref}
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""" + base_prompt
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math_prompt = """
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请根据提供的 评分要求，用户问题，参考答案 以及 相应的两个回答（回答1，回答2），判断两个回答中哪一个更好。
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评分要求（重要性依次递减）:
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1. 更好的回答的答案能和参考答案一致。
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2. 若两个回答的答案都与参考答案不一致，则更好的回答的推理过程应更加合理。
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3. 更好的回答更加符合与人类对话的习惯，包括语气、情调等。
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[用户问题]
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{question}
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[参考答案]
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{ref}
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""" + base_prompt
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reason_prompt = math_prompt
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qa_prompt = """
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请根据提供的 评分要求，用户问题 以及 相应的两个回答（回答1，回答2），判断两个回答中哪一个更好。
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评分要求（重要性依次递减）:
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1. 好的回答必须首先具有事实正确性，即除了想象的内容外，所引用或阐述的各种信息都是真实正确的
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2. 好的回答必须具有逻辑连贯性，围绕一个中心进行回答，且前后连贯，逻辑没有问题
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3. 在都准确答对问题的前提下，更好的回答能进行额外补充，且补充的内容准确无误
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[用户问题]
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{question}
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""" + base_prompt
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creation_prompt = """
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请根据提供的 评分要求，用户问题 以及 相应的两个回答（回答1，回答2），判断两个回答中哪一个更好。
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评分要求（重要性依次递减）:
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1. 好的回答必须首先符合用户问题里的各种需求，不能跑题
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2. 好的回答必须具有逻辑连贯性，围绕一个中心进行回答
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3. 好的回答必须具有创造性的词语和表达丰富度
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[用户问题]
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{question}
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""" + base_prompt
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subjective_all_sets = ["knowledge", "language", "math", "reason", "qa", "creationv2_zh"]
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prompt_all_sets = [knowledge_prompt, language_prompt, math_prompt, reason_prompt, qa_prompt, creation_prompt]
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for _name,_prompt in zip(subjective_all_sets, prompt_all_sets):
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subjective_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt="{question}"
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),
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]),
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_seq_len=4096, max_out_len=2048),
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)
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subjective_eval_cfg = dict(
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evaluator=dict(
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type=LMEvaluator,
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infer_order='double',
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt = _prompt
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),
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]),
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),
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),
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pred_role="BOT",
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)
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subjective_datasets.append(
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dict(
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abbr=f"{_name}",
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type=CompassArenaDataset,
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path=data_path,
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name=_name,
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reader_cfg=subjective_reader_cfg,
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infer_cfg=subjective_infer_cfg,
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eval_cfg=subjective_eval_cfg
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))
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95
configs/eval_subjective_compassarena.py
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95
configs/eval_subjective_compassarena.py
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@ -0,0 +1,95 @@
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from os import getenv as gv
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from opencompass.models import HuggingFaceCausalLM
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from mmengine.config import read_base
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with read_base():
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from .models.chatglm.hf_chatglm3_6b_32k import models as chatglm3_6b_32k_model
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from .models.yi.hf_yi_6b_chat import models as yi_6b_chat_model
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from .datasets.subjective.compassarena.compassarena_compare import subjective_datasets
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3, OpenAI
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from opencompass.models.openai_api import OpenAIAllesAPIN
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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 CompassArenaSummarizer
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infer = dict(
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#partitioner=dict(type=NaivePartitioner),
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partitioner=dict(type=SizePartitioner, max_task_size=10000),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llm_dev2',
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quotatype='auto',
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max_num_workers=256,
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task=dict(type=OpenICLInferTask)),
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)
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api_meta_template = dict(
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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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)
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gpt4 = dict(
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abbr='gpt4-turbo',
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type=OpenAI, path='gpt-4-1106-preview',
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key='', # 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=1,
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max_out_len=2048,
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max_seq_len=4096,
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batch_size=4,
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retry=20,
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temperature = 1
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)
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models = [*chatglm3_6b_32k_model, *yi_6b_chat_model]
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datasets = [*subjective_datasets]
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work_dir = 'outputs/compass_arena/'
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# -------------Inferen Stage ----------------------------------------
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judge_model = dict(
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abbr='GPT4-Turbo',
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type=OpenAI, path='gpt-4-1106-preview',
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key='', # 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=1,
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max_out_len=1024,
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max_seq_len=4096,
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batch_size=2,
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retry=20,
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temperature = 0
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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=SubjectiveSizePartitioner,
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strategy='split',
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max_task_size=10000,
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mode='m2n',
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base_models = [gpt4],
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compare_models = [*chatglm3_6b_32k_model, *yi_6b_chat_model, ]
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),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llm_dev2',
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quotatype='auto',
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max_num_workers=32,
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task=dict(
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type=SubjectiveEvalTask,
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judge_cfg=judge_model
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)),
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)
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summarizer = dict(
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type=CompassArenaSummarizer
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)
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@ -1,4 +1,5 @@
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from .alignbench import AlignmentBenchDataset # noqa: F401, F403
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from .compass_arena import CompassArenaDataset # noqa: F401, F403
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from .corev2 import Corev2Dataset # noqa: F401, F403
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from .creationbench import CreationBenchDataset # noqa: F401, F403
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from .information_retrival import IRDataset # noqa: F401, F403
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28
opencompass/datasets/subjective/compass_arena.py
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28
opencompass/datasets/subjective/compass_arena.py
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from datasets import Dataset
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from opencompass.registry import LOAD_DATASET
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from .subjective_cmp import SubjectiveCmpDataset
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@LOAD_DATASET.register_module()
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class CompassArenaDataset(SubjectiveCmpDataset):
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def load(
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self,
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path: str,
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name: str,
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):
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dataset = list(super().load(path, name))
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creation_dataset = []
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for data in dataset:
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if 'reference' in data['others']:
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if data['others']['reference'] is not None:
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data['ref'] = data['others']['reference']
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else:
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data['ref'] = '满足用户需求，言之有理即可'
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else:
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data['ref'] = '满足用户需求，言之有理即可'
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creation_dataset.append(data)
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dataset = Dataset.from_list(creation_dataset)
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return dataset
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@ -26,7 +26,8 @@ class SubjectiveCmpDataset(BaseDataset):
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'capability': capability,
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'others': others,
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'judge': {
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'capability': capability
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'capability': capability,
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'question': question
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}
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})
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dataset = Dataset.from_list(raw_data)
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@ -1,5 +1,6 @@
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# flake8: noqa: F401, E501
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from .alignmentbench import AlignmentBenchSummarizer
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from .compass_arena import CompassArenaSummarizer
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from .corev2 import Corev2Summarizer
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from .creationbench import CreationBenchSummarizer
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from .information_retrival import IRSummarizer
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174
opencompass/summarizers/subjective/compass_arena.py
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174
opencompass/summarizers/subjective/compass_arena.py
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# flake8: noqa: E501
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import ast
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import csv
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import os
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import os.path as osp
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import re
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from collections import defaultdict
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from datetime import datetime
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from itertools import product
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import mmengine
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from mmengine import ConfigDict
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from prettytable import from_csv
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from opencompass.partitioners.sub_naive import remove_duplicate_pairs
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from opencompass.utils import dataset_abbr_from_cfg, model_abbr_from_cfg
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from .utils import get_judgeanswer_and_reference, get_outdir
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def post_process_compass_arena(s):
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if result := re.findall('(?:选择：|Choice: )([ABC])', s):
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return result[0]
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else:
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return None
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def check_position_bias(judged_answers, references, banned_choice=['C']):
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"""Check position bias for judgellm's judgement.
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Args:
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judged_answers: The successfully extracted judgement.
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references: The references contains original question, which is used to located the same question for different position judgement.
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"""
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position_bias_flag = 0
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position_bias_dict = {}
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for judge, ref in zip(judged_answers, references):
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question = ref['others']['question']
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question_hash = hash(question)
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if question_hash not in position_bias_dict:
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position_bias_dict[question_hash] = {
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'question': question,
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'judge': judge
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}
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else:
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first_judge = position_bias_dict[question_hash]['judge']
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if judge == first_judge and first_judge not in banned_choice and judge not in banned_choice:
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# If second choice is same with first choice, there has position bias.
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position_bias_flag += 1
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return position_bias_flag
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class CompassArenaSummarizer:
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"""Do the subjectivity analyze based on evaluation results.
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Args:
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config (ConfigDict): The configuration object of the evaluation task.
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It's expected to be filled out at runtime.
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"""
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def __init__(self, config: ConfigDict, judge_type='general') -> None:
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self.tasks = []
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self.cfg = config
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self.base_models = self.cfg['eval']['partitioner']['base_models']
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self.compare_models = self.cfg['eval']['partitioner']['compare_models']
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self.judge_abbr = model_abbr_from_cfg(self.cfg['judge_model'])
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self.judge_type = judge_type
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assert self.judge_type in ['general']
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self.judge_map = {
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'general': post_process_compass_arena,
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}
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self.judge_function = self.judge_map[self.judge_type]
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def summarize(self,
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time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S'),
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check_pos_bias=True):
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"""Summarize the subjectivity analysis based on evaluation results.
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Args:
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time_str (str): Timestamp for file naming.
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Returns:
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pd.DataFrame: The summary results.
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"""
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dataset_cfgs = self.cfg['datasets']
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output_dir, results_folder = get_outdir(self.cfg, time_str)
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model_combinations = list(
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product(self.base_models, self.compare_models))
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unique_combinations = remove_duplicate_pairs(
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[combo for combo in model_combinations if combo[0] != combo[1]])
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fout_list = []
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for model_pair in unique_combinations:
|
||||
model1, model2, judge_model = model_pair[0]['abbr'], model_pair[1][
|
||||
'abbr'], self.judge_abbr
|
||||
subdir = model1 + '_' + model2 + '_judged-by--' + self.judge_abbr
|
||||
subdir_path = os.path.join(results_folder, subdir)
|
||||
if os.path.isdir(subdir_path):
|
||||
for dataset in dataset_cfgs:
|
||||
dataset_abbr = dataset_abbr_from_cfg(dataset)
|
||||
fout = osp.join(
|
||||
output_dir, 'judged-by--' + judge_model + '-' +
|
||||
dataset_abbr + '-report.csv')
|
||||
fout_list.append(fout)
|
||||
judged_answers, references = get_judgeanswer_and_reference(
|
||||
dataset,
|
||||
subdir_path,
|
||||
self.judge_function,
|
||||
)
|
||||
if check_pos_bias:
|
||||
bias_num = check_position_bias(judged_answers,
|
||||
references)
|
||||
else:
|
||||
bias_num = 0
|
||||
win_model1, win_model2, categories = defaultdict(
|
||||
float), defaultdict(float), defaultdict(float)
|
||||
model1, model2 = references[0]['answer1'], references[0][
|
||||
'answer2']
|
||||
for prediction, reference in zip(judged_answers,
|
||||
references):
|
||||
if dataset_abbr == 'zhihu_hot_0113':
|
||||
reference['capability'] = 'QA'
|
||||
categories['total'] += 1
|
||||
categories[reference['capability']] += 1
|
||||
if prediction == 'A':
|
||||
if reference['answer1'] == model1:
|
||||
win_model1[reference['capability']] += 1
|
||||
win_model1['total'] += 1
|
||||
else:
|
||||
win_model2[reference['capability']] += 1
|
||||
win_model2['total'] += 1
|
||||
elif prediction == 'B':
|
||||
if reference['answer1'] == model1:
|
||||
win_model2[reference['capability']] += 1
|
||||
win_model2['total'] += 1
|
||||
else:
|
||||
win_model1[reference['capability']] += 1
|
||||
win_model1['total'] += 1
|
||||
for capability in categories:
|
||||
if capability not in win_model1:
|
||||
win_model1[capability] = 0.0
|
||||
else:
|
||||
win_model1[capability] = round(
|
||||
(win_model1[capability] /
|
||||
categories[capability]) * 100, 2)
|
||||
if capability not in win_model2:
|
||||
win_model2[capability] = 0.0
|
||||
else:
|
||||
win_model2[capability] = round(
|
||||
(win_model2[capability] /
|
||||
categories[capability]) * 100, 2)
|
||||
win_model1['position_bias'] = bias_num
|
||||
win_model2['position_bias'] = bias_num
|
||||
scores = {
|
||||
'win_' + model1: win_model1,
|
||||
'win_' + model2: win_model2
|
||||
}
|
||||
rows = list(scores.keys())
|
||||
columns = list(scores[rows[0]].keys())
|
||||
columns.insert(0, columns.pop(columns.index('total')))
|
||||
columns.insert(1,
|
||||
columns.pop(columns.index('position_bias')))
|
||||
with open(fout, 'a+', newline='') as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
writer.writerow([model1 + '_vs_' + model2] + columns)
|
||||
for row in rows:
|
||||
writer.writerow(
|
||||
[row] +
|
||||
[scores[row][column] for column in columns])
|
||||
else:
|
||||
print(subdir_path + ' is not exist! please check!')
|
||||
for fout in fout_list:
|
||||
with open(fout, 'r') as f:
|
||||
x = from_csv(f)
|
||||
print(x)
|
||||
Loading…
Reference in New Issue
Block a user