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[Feature] support alpacaeval (#809)
* support alpacaeval_v1 * Update opencompass/summarizers/subjective/__init__.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * Update opencompass/summarizers/subjective/alpacaeval_v1.py Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com> * fix conflict * support alpacaeval v2 * support alpacav2 --------- Co-authored-by: Songyang Zhang <tonysy@users.noreply.github.com>
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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 SubjectiveCmpDataset
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from mmengine.config import read_base
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subjective_reader_cfg = dict(
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input_columns=['question'],
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output_column='judge',
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)
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subjective_all_sets = [
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"alpaca_eval",
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]
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subjective_datasets = []
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gpt4_prompt = """
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I want you to create a leaderboard of different of large-language models. To do so, I will give you the instructions (prompts) given to the models, and the responses of two models. Please rank the models based on which responses would be preferred by humans. All inputs and outputs should be python dictionaries.
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Here is the prompt:
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{
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"instruction": "{question}"
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}
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Here are the outputs of the models:
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[
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{
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"model": "model_1",
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"answer": "{prediction}"
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},
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{
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"model": "model_2",
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"answer": "{prediction2}"
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}
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]
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Now please rank the models by the quality of their answers, so that the model with rank 1 has the best output. Then return a list of the model names and ranks, i.e., produce the following output:
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[
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{"model": <model-name>, "rank": <model-rank>},
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{"model": <model-name>, "rank": <model-rank>}
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]
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Your response must be a valid Python dictionary and should contain nothing else because we will directly execute it in Python. Please provide the ranking that the majority of humans would give.
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"""
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for _name in subjective_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_out_len=4096),
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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='random',
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin=[
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dict(
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role='SYSTEM',
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fallback_role='HUMAN',
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prompt="You are a helpful assistant, that ranks models by the quality of their answers.")
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],
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round=[
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dict(
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role='HUMAN',
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prompt = gpt4_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=SubjectiveCmpDataset,
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path="./data/subjective/",
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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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100
configs/datasets/subjective/alpaca_eval/alpacav2_judgeby_gpt4.py
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100
configs/datasets/subjective/alpaca_eval/alpacav2_judgeby_gpt4.py
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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 SubjectiveCmpDataset
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from mmengine.config import read_base
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subjective_reader_cfg = dict(
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input_columns=['question'],
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output_column='judge',
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)
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subjective_all_sets = [
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"alpaca_eval",
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]
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subjective_datasets = []
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gpt4_prompt = """
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I require a leaderboard for various large language models. I'll provide you with prompts given to these models and their corresponding outputs. Your task is to assess these responses, and select the model that produces the best output from a human perspective.
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## Instruction
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{
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"instruction": "{question}",
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}
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## Model Outputs
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Here are the unordered outputs from the models. Each output is associated with a specific model, identified by a unique model identifier.
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{
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{
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"model_identifier": "m",
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"output": "{prediction}"
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},
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{
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"model_identifier": "M",
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"output": "{prediction2}"
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}
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}
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## Task
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Evaluate the models based on the quality and relevance of their outputs, and select the model that generated the best output. Answer by providing the model identifier of the best model. We will use your output as the name of the best model, so make sure your output only contains one of the following model identifiers and nothing else (no quotes, no spaces, no new lines, ...): m or M.
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## Best Model Identifier
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"""
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for _name in subjective_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_out_len=4096),
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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='random',
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin=[
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dict(
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role='SYSTEM',
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fallback_role='HUMAN',
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prompt="You are a highly efficient assistant, who evaluates and selects the best large language model (LLMs) based on the quality of their responses to a given instruction. This process will be used to create a leaderboard reflecting the most accurate and human-preferred answers.")
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],
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round=[
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dict(
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role='HUMAN',
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prompt = gpt4_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=SubjectiveCmpDataset,
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path="./data/subjective/",
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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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82
configs/eval_subjective_alpacaeval.py
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82
configs/eval_subjective_alpacaeval.py
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from mmengine.config import read_base
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with read_base():
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from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat
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from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat
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from .models.chatglm.hf_chatglm3_6b import models as hf_chatglm3_6b
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from .models.baichuan.hf_baichuan2_7b_chat import models as hf_baichuan2_7b
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from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b
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from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b
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from .datasets.subjective.alpaca_eval.alpacav1_judgeby_gpt4 import subjective_datasets as alpacav1
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from .datasets.subjective.alpaca_eval.alpacav2_judgeby_gpt4 import subjective_datasets as alpacav2
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datasets = [*alpacav2]
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from opencompass.models import HuggingFaceCausalLM, HuggingFace, HuggingFaceChatGLM3
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from opencompass.models.openai_api import OpenAI, 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 AlpacaSummarizer
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models = [*hf_qwen_7b_chat, *hf_chatglm3_6b]
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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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reserved_roles=[
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dict(role='SYSTEM', api_role='SYSTEM'),
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],
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)
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llmeval',
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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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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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eval = dict(
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partitioner=dict(
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type=SubjectiveSizePartitioner,
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max_task_size=1000,
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mode='m2n',
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base_models = [*hf_chatglm3_6b],
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compare_models = [*hf_qwen_7b_chat]
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),
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runner=dict(
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type=SlurmSequentialRunner,
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partition='llmeval',
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quotatype='auto',
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max_num_workers=256,
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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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work_dir = 'outputs/alpaca/'
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summarizer = dict(
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type=AlpacaSummarizer, judge_type='v2'
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)
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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 .alpacaeval import AlpacaSummarizer
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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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175
opencompass/summarizers/subjective/alpacaeval.py
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175
opencompass/summarizers/subjective/alpacaeval.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_alpacav1(completion: str):
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r"""Parse a completion that contains a list of dictionary and returns the rank of the model1.
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Examples
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--------
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>>> ranking_parser("[{'model': 'model_1', 'rank': 1}, {'model': 'model_2', 'rank': 2}]")
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1
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>>> ranking_parser("[{'model': 'model_1', 'rank': 2}, {'model': 'model_2', 'rank': 1}]")
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2
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>>> ranking_parser("[{'model': 'model_1', 'rank': 3}, {'model': 'model_2', 'rank': 1}]")
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None
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"""
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try:
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if isinstance(completion, str):
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completion = re.findall(r'\[.*?\]', completion)[0]
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ordered_completions = ast.literal_eval(completion)
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else:
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ordered_completions = completion
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rank = [c for c in ordered_completions
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if c['model'] == 'model_1'][0]['rank']
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if rank in [1, 2]:
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return {'rank': rank}
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else:
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return None
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except Exception as e:
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return None
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def post_process_alpacav2(completion: str):
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r"""Parse a completion that contains 'm' or 'M' and returns the rank of the model1.
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Examples
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--------
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>>> ranking_parser("m")
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1
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>>> ranking_parser("M")
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2
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>>> ranking_parser("s")
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None
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"""
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try:
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if completion[0] == 'm':
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return {'rank': 1}
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elif completion[0] == 'M':
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return {'rank': 2}
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else:
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return None
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except Exception as e:
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return None
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class AlpacaSummarizer:
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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='v2') -> 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 ['v1', 'v2']
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self.judge_map = {
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'v1': post_process_alpacav1,
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'v2': post_process_alpacav2
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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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"""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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for model_pair in unique_combinations:
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model1, model2, judge_model = model_pair[0]['abbr'], model_pair[1][
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'abbr'], self.judge_abbr
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subdir = model1 + '_' + model2 + '_judged-by--' + self.judge_abbr
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subdir_path = os.path.join(results_folder, subdir)
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if os.path.isdir(subdir_path):
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fout = osp.join(output_dir,
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'judged-by--' + judge_model + '-report.csv')
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for dataset in dataset_cfgs:
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judged_answers, references = get_judgeanswer_and_reference(
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dataset, subdir_path, self.judge_function)
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win_model1, win_model2, categories = defaultdict(
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float), defaultdict(float), defaultdict(float)
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model1, model2 = references[0]['answer1'], references[0][
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'answer2']
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for prediction, reference in zip(judged_answers,
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references):
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categories['total'] += 1
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categories[reference['capability']] += 1
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if prediction['rank'] == 1:
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if reference['answer1'] == model1:
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win_model1[reference['capability']] += 1
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win_model1['total'] += 1
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else:
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win_model2[reference['capability']] += 1
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win_model2['total'] += 1
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else:
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if reference['answer1'] == model1:
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win_model2[reference['capability']] += 1
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win_model2['total'] += 1
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else:
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win_model1[reference['capability']] += 1
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win_model1['total'] += 1
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for capability in categories:
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if capability not in win_model1:
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win_model1[capability] = 0.0
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else:
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win_model1[capability] = round(
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(win_model1[capability] /
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categories[capability]) * 100, 2)
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if capability not in win_model2:
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win_model2[capability] = 0.0
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else:
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win_model2[capability] = round(
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(win_model2[capability] /
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categories[capability]) * 100, 2)
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scores = {
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'win_' + model1: win_model1,
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'win_' + model2: win_model2
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}
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rows = list(scores.keys())
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columns = list(scores[rows[0]].keys())
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columns.insert(0, columns.pop(columns.index('total')))
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with open(fout, 'a+', newline='') as csvfile:
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writer = csv.writer(csvfile)
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writer.writerow([model1 + '_vs_' + model2] + columns)
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for row in rows:
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writer.writerow(
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[row] +
|
||||
[scores[row][column] for column in columns])
|
||||
else:
|
||||
print(subdir_path + ' is not exist! please check!')
|
||||
with open(fout, 'r') as f:
|
||||
x = from_csv(f)
|
||||
print(x)
|
||||
Loading…
Reference in New Issue
Block a user