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import os
from typing import Union, List
from lm_eval.api.task import ConfigurableTask
from lm_eval.api.instance import Instance
# from lm_eval.api.registry import register_task
from lm_eval.api.metrics import mean
from src.backend.envs import DEVICE
import pandas as pd
from src.backend.tasks.measurement_task_utils import measure_system_metrics
import json
from typing import (
Any,
Dict,
List,
Optional,
Union,
)
from datasets import Dataset
import re
from src.backend.tasks.arena_hard.arena_utils import (
load_questions,
load_questions,
load_model_answers,
make_config,
)
from src.backend.tasks.arena_hard.arena_judgment import (
judgment,
get_battles_from_scores,
compute_mle_elo,
predict_win_rate,
get_win_rate_column
)
def load_questions(question_file: str):
"""Load questions from a file."""
questions = []
with open(question_file, "r") as ques_file:
for line in ques_file:
if line:
questions.append(json.loads(line))
return questions
def download_wrapper(func):
def download(self, *args, **kwargs):
print("Using Arena Hard, No need to download")
return download
original_download = ConfigurableTask.download
ConfigurableTask.download = download_wrapper(original_download)
# @register_task("selfcheckgpt")
@measure_system_metrics
class ArenaHard(ConfigurableTask):
VERSION = 0.0
OUTPUT_TYPE = "generate_until"
data_path = os.path.join(os.path.dirname(__file__), 'question.jsonl')
judge_config_path = os.path.join(os.path.dirname(__file__), "configs/judge_config.yaml")
configs = make_config(judge_config_path)
model_ans_dir = os.path.join(os.path.dirname(__file__), "model_answer")
model_answers = load_model_answers(model_ans_dir)
data = load_questions(data_path)
def __init__(self):
super().__init__(config={"metadata": {"version": self.VERSION}})
# these end tokens are hard coded because of the current limitaion of the llm-eval.
# self.generation_kwargs = {"until": ["\n\n", "<unk>", "<|im_end|>", "</s>", "<|endoftext|>"], "max_length": 512}
self.generation_kwargs = {"until": ["</s>", "<|im_end|>"], "max_gen_toks": 4096}
# self.generation_kwargs_sampling_number = 5 # the number of sampling for self-consistence
# self.generation_kwargs_sampling = {
# "temperature": 0.99,
# "do_sample": True,
# "until": ["<im_end>", "<im_end>"],
# "max_length": 1024,
# }
def transform_data(self, data):
transformed_data = []
for i in range(len(data)):
if self.configs["baseline"]:
baseline_answer = self.model_answers[self.configs["baseline_model"]][data[i]["question_id"]]
else:
baseline_answer = None
transformed_item = {
"question_id": data[i]["question_id"],
"content": data[i]["turns"][0]["content"], # Assuming you want the first turn's content
"model_answer": baseline_answer
}
transformed_data.append(transformed_item)
return transformed_data
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def validation_docs(self):
self.dataset = self.transform_data(self.data)
self.dataset = Dataset.from_dict({"question_id": [item["question_id"] for item in self.dataset],
"content": [item["content"] for item in self.dataset],
"model_answer": [item["model_answer"] for item in self.dataset]})
return self.dataset
def doc_to_text(self, doc):
sentence = doc["content"]
doc_text = f"{sentence}\n"
return doc_text
def doc_to_target(self, doc):
q_id = doc["question_id"]
return q_id
def construct_requests(self, doc: dict, ctx: str, **kwargs) -> Union[List[Instance], Instance]:
arguments = (ctx, self.generation_kwargs)
request_list = [
Instance(request_type="generate_until", doc=doc, arguments=arguments, idx=0, **kwargs),
]
# sampling_arguments = (ctx, self.generation_kwargs_sampling)
# request_list.extend(
# [
# Instance(request_type="generate_until", doc=doc, arguments=sampling_arguments, idx=idx, **kwargs)
# for idx in range(1, self.generation_kwargs_sampling_number + 1)
# ]
# )
return request_list
def process_results(self, doc, results):
response_temperature_0 = results[0]
# other_responses = results[1:]
api_config_path = os.path.join(os.path.dirname(__file__), "configs/api_config.yaml")
endpoint_list = make_config(api_config_path)
if self.configs["regex_pattern"]:
pattern = re.compile(self.configs["regex_pattern"])
ref_answer_dir = os.path.join(os.path.dirname(__file__), "reference_answer")
ref_answers = None
if self.configs["reference"]:
ref_answers = load_model_answers(ref_answer_dir)
ref_answers = [ref_answers[model] for model in self.configs["ref_model"]]
# output_files = {}
# models = ["custom_model"]
# output_dir = f"{os.path.join(os.path.dirname(__file__))}/model_judgments/{self.configs['judge_model']}"
# for model in models:
# output_files[model] = os.path.join(
# output_dir,
# f"{model}.jsonl",
# )
# for output_file in output_files.values():
# os.makedirs(os.path.dirname(output_file), exist_ok=True)
endpoint_info = endpoint_list[self.configs["judge_model"]]
question = doc
kwargs = {}
kwargs["question"] = question
kwargs["answer"] = response_temperature_0
if ref_answers:
kwargs["reference"] = [ref_answer[doc["question_id"]] for ref_answer in ref_answers]
assert len(kwargs["reference"]) == len(self.configs["ref_model"])
else:
kwargs["reference"] = None
if self.configs["baseline"]:
kwargs["baseline_answer"] = doc["model_answer"]
else:
kwargs["baseline_answer"] = None
kwargs["configs"] = self.configs
kwargs["endpoint_dict"] = endpoint_info
# kwargs["output_file"] = output_files["custom_model"]
kwargs["regex_pattern"] = pattern
scores = judgment(**kwargs)
return {"score": scores}
def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
##TODO implement the aggregation function to calculate elo for score
def get_win_rate(score_list):
battles = get_battles_from_scores(score_list)
bootstrap_online_elo = compute_mle_elo(battles)
stats = pd.DataFrame()
stats["results"] = None
stats["results"] = stats['results'].astype('object')
for i, model in enumerate(bootstrap_online_elo.index):
stats.at[i, "model"] = model
stats.at[i, "score"] = bootstrap_online_elo[model]
stats.sort_values(by="model", inplace=True)
stats["score"] = get_win_rate_column(stats, "score", "gpt-4-0314").tolist()
return stats["score"][1]
return {k: get_win_rate for k in ["score"]}
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {k: True for k in ["score"]}
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