94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
# Adapted from https://github.com/openai/simple-evals/
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"""
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GPQA: A Graduate-Level Google-Proof Q&A Benchmark
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David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, Samuel R. Bowman
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https://arxiv.org/abs/2311.12022
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"""
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import random
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import re
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from typing import Optional
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import pandas
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from sglang.test import simple_eval_common as common
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from sglang.test.simple_eval_common import (
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ANSWER_PATTERN_MULTICHOICE,
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HTML_JINJA,
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Eval,
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EvalResult,
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MessageList,
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SamplerBase,
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SingleEvalResult,
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format_multichoice_question,
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)
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class GPQAEval(Eval):
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def __init__(
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self,
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filename: str,
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num_examples: Optional[int],
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num_threads: int,
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n_repeats: int = 1,
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):
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df = pandas.read_csv(filename)
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examples = [row.to_dict() for _, row in df.iterrows()]
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rng = random.Random(0)
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if num_examples:
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assert n_repeats == 1, "n_repeats only supported for num_examples"
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examples = rng.sample(examples, num_examples)
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examples = examples * n_repeats
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examples = [
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example | {"permutation": rng.sample(range(4), 4)} for example in examples
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]
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self.examples = examples
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self.n_repeats = n_repeats
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self.num_threads = num_threads
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def __call__(self, sampler: SamplerBase) -> EvalResult:
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def fn(row: dict):
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choices = [
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row["Correct Answer"],
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row["Incorrect Answer 1"],
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row["Incorrect Answer 2"],
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row["Incorrect Answer 3"],
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]
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choices = [choices[i] for i in row["permutation"]]
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correct_index = choices.index(row["Correct Answer"])
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correct_answer = "ABCD"[correct_index]
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choices_dict = dict(
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A=choices[0],
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B=choices[1],
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C=choices[2],
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D=choices[3],
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Question=row["Question"],
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)
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prompt_messages = [
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sampler._pack_message(
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content=format_multichoice_question(choices_dict), role="user"
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)
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]
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response_text = sampler(prompt_messages)
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match = re.search(ANSWER_PATTERN_MULTICHOICE, response_text)
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extracted_answer = match.group(1) if match else None
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score = 1.0 if extracted_answer == correct_answer else 0.0
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html = common.jinja_env.from_string(HTML_JINJA).render(
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prompt_messages=prompt_messages,
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next_message=dict(content=response_text, role="assistant"),
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score=score,
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correct_answer=correct_answer,
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extracted_answer=extracted_answer,
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)
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convo = prompt_messages + [dict(content=response_text, role="assistant")]
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return SingleEvalResult(
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html=html,
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score=score,
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convo=convo,
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metrics={"chars": len(response_text)},
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)
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results = common.map_with_progress(fn, self.examples, self.num_threads)
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return common.aggregate_results(results)
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