ViperEkura
2565755e45
- Replace GitHub/berkeley direct downloads with HF datasets API - MMLU: cais/mmlu (all config), map val->validation split, write per-subject CSV - HumanEval: openai/openai_humaneval - IFEval: google/IFEval - Enables HF_ENDPOINT mirror for faster downloads in CN
603 lines
18 KiB
Python
603 lines
18 KiB
Python
"""IFEval instruction-following evaluation benchmark.
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Evaluates model responses against regex-based constraint verifiers.
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Supports all IFEval constraint types except language detection.
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Usage::
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python scripts/eval/evaluate_ifeval.py --param_path ./params \
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--data_path ifeval.jsonl --output results.json \
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--temperature 0.1 --max_tokens 512
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"""
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import argparse
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import json
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import os
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import re
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from typing import Callable, Dict, List, Optional
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import torch
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import tqdm
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from datasets import load_dataset
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from astrai.inference import InferenceEngine
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from astrai.model import AutoModel
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from astrai.tokenize import AutoTokenizer
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IFEVAL_HF_DATASET = "google/IFEval"
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CONSTRAINT_VERIFIERS: Dict[str, Callable[[str, dict], bool]] = {}
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def register(instruction_id: str):
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def decorator(fn):
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CONSTRAINT_VERIFIERS[instruction_id] = fn
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return fn
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return decorator
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@register("keywords:existence")
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def check_keyword_existence(response: str, kwargs: dict) -> bool:
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for kw in kwargs["keywords"]:
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if not re.search(re.escape(kw), response, re.IGNORECASE):
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return False
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return True
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@register("keywords:frequency")
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def check_keyword_frequency(response: str, kwargs: dict) -> bool:
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keyword = kwargs["keyword"]
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frequency = kwargs.get("frequency", 1)
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relation = kwargs.get("relation", "at least")
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count = len(re.findall(re.escape(keyword), response, re.IGNORECASE))
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if relation == "less than":
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return count < frequency
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return count >= frequency
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@register("keywords:forbidden_words")
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def check_forbidden_words(response: str, kwargs: dict) -> bool:
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for word in kwargs["forbidden_words"]:
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if re.search(r"\b" + re.escape(word) + r"\b", response, re.IGNORECASE):
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return False
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return True
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@register("keywords:letter_frequency")
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def check_letter_frequency(response: str, kwargs: dict) -> bool:
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letter = kwargs["letter"].lower()
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frequency = kwargs.get("let_frequency", 1)
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relation = kwargs.get("let_relation", "at least")
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count = response.lower().count(letter)
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if relation == "less than":
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return count < frequency
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return count >= frequency
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@register("detectable_content:number_placeholders")
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def check_placeholders(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_placeholders", 1)
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placeholders = re.findall(r"\[.*?\]", response)
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return len(placeholders) >= num
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@register("detectable_content:postscript")
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def check_postscript(response: str, kwargs: dict) -> bool:
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marker = kwargs.get("postscript_marker", "P.S.")
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response_lower = response.lower()
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if marker == "P.P.S":
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return bool(re.search(r"p\.\s?p\.\s?s", response_lower))
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elif marker == "P.S.":
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return bool(re.search(r"p\.\s?s\.", response_lower))
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else:
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return bool(re.search(re.escape(marker.lower()), response_lower))
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@register("detectable_format:number_bullet_lists")
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def check_bullet_lists(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_bullets", 1)
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bullets = re.findall(r"^\s*\*[^\*].*$", response, re.MULTILINE)
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dashes = re.findall(r"^\s*-.*$", response, re.MULTILINE)
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return len(bullets) + len(dashes) == num
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@register("detectable_format:number_highlighted_sections")
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def check_highlighted_sections(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_highlights", 1)
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highlights = re.findall(r"\*[^\n\*]+\*", response)
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count = 0
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for h in highlights:
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if h.strip("*").strip():
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count += 1
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return count >= num
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@register("detectable_format:multiple_sections")
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def check_multiple_sections(response: str, kwargs: dict) -> bool:
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splitter = kwargs.get("section_spliter", "Section")
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num = kwargs.get("num_sections", 1)
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pattern = r"\s?" + re.escape(splitter) + r"\s?\d+\s?"
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sections = re.split(pattern, response)
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return len(sections) - 1 >= num
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@register("detectable_format:title")
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def check_title(response: str, kwargs: dict) -> bool:
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titles = re.findall(r"<<[^>\n]+>>", response)
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for title in titles:
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if title.strip("<>").strip():
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return True
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return False
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@register("detectable_format:json_format")
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def check_json_format(response: str, kwargs: dict) -> bool:
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value = response.strip()
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for prefix in ("```json", "```Json", "```JSON", "```"):
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if value.lower().startswith(prefix.lower()):
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value = value[len(prefix) :].strip()
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if value.endswith("```"):
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value = value[:-3].strip()
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try:
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json.loads(value)
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return True
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except (ValueError, json.JSONDecodeError):
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return False
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@register("detectable_format:general_punctuation")
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def check_general_punctuation(response: str, kwargs: dict) -> bool:
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punctuation_blacklist = kwargs.get("punctuation_blacklist", [])
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for punct in punctuation_blacklist:
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if punct in response:
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return False
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return True
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@register("detectable_format:number_highlighted_words")
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def check_highlighted_words(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_highlights", 1)
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highlights = re.findall(r"\*[^\s\*][^\*]*[^\s\*]\*", response)
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return len(highlights) >= num
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@register("startend:end_checker")
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def check_end_checker(response: str, kwargs: dict) -> bool:
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end_phrase = kwargs["end_phrase"]
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return (
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response.strip()
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.rstrip('"')
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.rstrip()
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.lower()
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.endswith(end_phrase.strip().lower())
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)
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@register("startend:quotation")
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def check_quotation(response: str, kwargs: dict) -> bool:
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value = response.strip()
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return value.startswith('"') and value.endswith('"')
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@register("startend:start_checker")
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def check_start_checker(response: str, kwargs: dict) -> bool:
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starter = kwargs["starter"]
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return bool(re.search(r"^\s*" + re.escape(starter), response, re.MULTILINE))
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@register("change_case:english_capital")
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def check_english_capital(response: str, kwargs: dict) -> bool:
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return response.isupper()
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@register("change_case:english_lowercase")
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def check_english_lowercase(response: str, kwargs: dict) -> bool:
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return response.islower()
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@register("change_case:capital_word_frequency")
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def check_capital_word_frequency(response: str, kwargs: dict) -> bool:
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frequency = kwargs.get("capital_frequency", 1)
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relation = kwargs.get("capital_relation", "at least")
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capital_words = re.findall(r"\b[A-Z]{2,}\b", response)
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count = len(capital_words)
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if relation == "less than":
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return count < frequency
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return count >= frequency
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@register("punctuation:no_comma")
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def check_no_comma(response: str, kwargs: dict) -> bool:
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return "," not in response
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def count_words(text: str) -> int:
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return len(re.findall(r"\b\w+\b", text))
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def count_sentences(text: str) -> int:
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text = text.strip()
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if not text:
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return 0
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sentences = re.split(r"(?<=[.!?])\s+", text)
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return len([s for s in sentences if s.strip()])
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@register("length_constraints:number_words")
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def check_number_words(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_words", 100)
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relation = kwargs.get("relation", "at least")
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cnt = count_words(response)
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if relation == "less than":
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return cnt < num
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return cnt >= num
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@register("length_constraints:number_sentences")
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def check_number_sentences(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_sentences", 5)
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relation = kwargs.get("relation", "at least")
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cnt = count_sentences(response)
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if relation == "less than":
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return cnt < num
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return cnt >= num
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@register("length_constraints:number_paragraphs")
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def check_number_paragraphs(response: str, kwargs: dict) -> bool:
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num = kwargs.get("num_paragraphs", 1)
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if "***" in response:
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paragraphs = re.split(r"\s?\*\*\*\s?", response)
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else:
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paragraphs = re.split(r"\n\n+", response)
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actual = len([p for p in paragraphs if p.strip()])
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return actual == num
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@register("length_constraints:nth_paragraph_first_word")
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def check_nth_paragraph_first_word(response: str, kwargs: dict) -> bool:
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num_paragraphs = kwargs.get("num_paragraphs", 1)
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nth = kwargs.get("nth_paragraph", 1)
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first_word = kwargs.get("first_word", "").lower()
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paragraphs = re.split(r"\n\n+", response)
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paragraphs = [p.strip() for p in paragraphs if p.strip()]
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if len(paragraphs) != num_paragraphs:
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return False
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if nth > len(paragraphs):
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return False
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target = paragraphs[nth - 1]
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words = target.split()
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if not words:
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return False
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word = words[0].strip().lstrip("'\"").rstrip(".,!?:;\"'")
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return word.lower() == first_word
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@register("length_constraints:nth_word_checker")
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def check_nth_word(response: str, kwargs: dict) -> bool:
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nth = kwargs.get("nth_word", 1)
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target = kwargs.get("target_word", "").lower()
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words = re.findall(r"\b\w+\b", response)
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if nth > len(words):
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return False
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return words[nth - 1].lower() == target
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@register("combination:repeat_prompt")
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def check_repeat_prompt(response: str, kwargs: dict) -> bool:
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prompt = kwargs["prompt_to_repeat"]
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return response.strip().lower().startswith(prompt.strip().lower())
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@register("combination:two_responses")
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def check_two_responses(response: str, kwargs: dict) -> bool:
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parts = response.split("******")
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valid = [p for p in parts if p.strip()]
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if len(valid) != 2:
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return False
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return valid[0].strip() != valid[1].strip()
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def download_ifeval(data_path: str):
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if os.path.exists(data_path):
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return
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os.makedirs(os.path.dirname(data_path) or ".", exist_ok=True)
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print(f"Downloading IFEval from HuggingFace ({IFEVAL_HF_DATASET}) ...")
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ds = load_dataset(IFEVAL_HF_DATASET, split="train")
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with open(data_path, "w", encoding="utf-8") as f:
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for item in ds:
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f.write(json.dumps(item, ensure_ascii=False) + "\n")
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print(f" saved {len(ds)} items to {data_path}")
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def load_problems(data_path: str) -> List[dict]:
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problems = []
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with open(data_path, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if line:
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problems.append(json.loads(line))
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return problems
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def verify_response(response: str, instruction_id: str, kwargs: dict) -> Optional[bool]:
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verifier = CONSTRAINT_VERIFIERS.get(instruction_id)
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if verifier is None:
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return None
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try:
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return verifier(response, kwargs)
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except Exception:
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return False
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def generate_one(
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engine: InferenceEngine,
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tokenizer: AutoTokenizer,
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prompt: str,
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max_tokens: int,
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temperature: float,
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top_p: float,
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top_k: int,
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) -> str:
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formatted = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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tokenize=False,
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add_generation_prompt=True,
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)
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output = engine.generate(
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prompt=formatted,
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stream=False,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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)
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if isinstance(output, list):
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return output[0]
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return output
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def evaluate(
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engine: InferenceEngine,
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tokenizer: AutoTokenizer,
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problems: List[dict],
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max_tokens: int,
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temperature: float,
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top_p: float,
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top_k: int,
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num_samples: int = 1,
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) -> Dict:
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results = {}
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constraint_stats: Dict[str, Dict[str, int]] = {}
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total_constraints = 0
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total_passed = 0
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for problem in tqdm.tqdm(problems, desc="IFEval", unit="problem"):
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key = problem["key"]
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prompt = problem["prompt"]
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instruction_ids = problem["instruction_id_list"]
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kwargs_list = problem["kwargs"]
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samples = []
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for _ in range(num_samples):
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response = generate_one(
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engine, tokenizer, prompt, max_tokens, temperature, top_p, top_k
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)
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samples.append(response)
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constraint_results = []
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passed = 0
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verified = 0
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for idx, instruction_id in enumerate(instruction_ids):
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kwargs = kwargs_list[idx] if idx < len(kwargs_list) else {}
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best_pass = False
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for response in samples:
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result = verify_response(response, instruction_id, kwargs)
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if result is None:
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continue
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if result:
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best_pass = True
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break
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verifier_exists = instruction_id in CONSTRAINT_VERIFIERS
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if verifier_exists:
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verified += 1
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if best_pass:
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passed += 1
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constraint_results.append(
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{
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"instruction_id": instruction_id,
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"passed": best_pass,
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"supported": verifier_exists,
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"kwargs": kwargs,
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}
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)
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if verifier_exists:
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if instruction_id not in constraint_stats:
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constraint_stats[instruction_id] = {
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"total": 0,
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"passed": 0,
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}
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constraint_stats[instruction_id]["total"] += 1
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if best_pass:
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constraint_stats[instruction_id]["passed"] += 1
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total_constraints += verified
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total_passed += passed
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accuracy = passed / verified if verified > 0 else None
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results[str(key)] = {
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"key": key,
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"prompt": prompt,
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"response": samples[0],
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"num_samples": num_samples,
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"num_constraints": len(instruction_ids),
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"num_verified": verified,
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"num_passed": passed,
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"accuracy": round(accuracy, 4) if accuracy is not None else None,
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"constraints": constraint_results,
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}
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overall_accuracy = (
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round(total_passed / total_constraints, 4) if total_constraints > 0 else 0.0
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)
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type_summary = {}
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for inst_id, stats in sorted(constraint_stats.items()):
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type_summary[inst_id] = {
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"total": stats["total"],
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"passed": stats["passed"],
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"accuracy": round(stats["passed"] / stats["total"], 4)
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if stats["total"] > 0
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else 0.0,
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}
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unsupported_count = sum(
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1
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for p in problems
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for iid in p["instruction_id_list"]
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if iid not in CONSTRAINT_VERIFIERS
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)
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results["_summary"] = {
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"total_problems": len(problems),
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"total_constraints": total_constraints,
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"total_passed": total_passed,
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"overall_accuracy": overall_accuracy,
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"unsupported_constraints": unsupported_count,
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"supported_types": sorted(CONSTRAINT_VERIFIERS.keys()),
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"per_type_accuracy": type_summary,
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}
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return results
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def main():
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parser = argparse.ArgumentParser(description="IFEval benchmark")
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parser.add_argument(
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"--param_path", type=str, default="./params", help="Model directory"
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)
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parser.add_argument(
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"--data_path",
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type=str,
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default="./ifeval/input_data.jsonl",
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help="IFEval JSONL file (auto-download if missing)",
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)
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parser.add_argument("--output", type=str, default=None, help="Output JSON path")
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parser.add_argument(
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"--max_tokens", type=int, default=512, help="Max generation tokens"
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)
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parser.add_argument(
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"--temperature",
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type=float,
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default=0.1,
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help="Sampling temperature",
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)
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parser.add_argument("--top_p", type=float, default=0.95, help="Top-p sampling")
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parser.add_argument("--top_k", type=int, default=50, help="Top-k sampling")
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parser.add_argument(
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"--num_samples",
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type=int,
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default=1,
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help="Number of samples per problem (best-of-n scoring)",
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)
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parser.add_argument(
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"--batch_size", type=int, default=1, help="Inference batch size"
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)
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parser.add_argument(
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"--limit",
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type=int,
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default=None,
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help="Limit to first N problems (for quick testing)",
|
|
)
|
|
parser.add_argument(
|
|
"--dump_responses",
|
|
type=str,
|
|
default=None,
|
|
help="Path to dump raw model responses (JSONL)",
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
download_ifeval(args.data_path)
|
|
problems = load_problems(args.data_path)
|
|
if args.limit:
|
|
problems = problems[: args.limit]
|
|
|
|
print(f"Loaded {len(problems)} problems")
|
|
print(f"Supported constraint types: {len(CONSTRAINT_VERIFIERS)}")
|
|
|
|
model = AutoModel.from_pretrained(args.param_path)
|
|
tokenizer = AutoTokenizer.from_pretrained(args.param_path)
|
|
model.to(device="cuda", dtype=torch.bfloat16)
|
|
model.eval()
|
|
|
|
engine = InferenceEngine(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
max_batch_size=args.batch_size,
|
|
)
|
|
|
|
results = evaluate(
|
|
engine=engine,
|
|
tokenizer=tokenizer,
|
|
problems=problems,
|
|
max_tokens=args.max_tokens,
|
|
temperature=args.temperature,
|
|
top_p=args.top_p,
|
|
top_k=args.top_k,
|
|
num_samples=args.num_samples,
|
|
)
|
|
|
|
summary = results.pop("_summary")
|
|
print(f"\n{'=' * 60}")
|
|
print(f" Problems: {summary['total_problems']}")
|
|
print(f" Constraints: {summary['total_constraints']}")
|
|
print(f" Passed: {summary['total_passed']}")
|
|
print(f" Accuracy: {summary['overall_accuracy']:.2%}")
|
|
print(f" Unsupported: {summary['unsupported_constraints']}")
|
|
print(f"{'=' * 60}")
|
|
|
|
print("\nPer-type accuracy:")
|
|
for inst_id, stats in sorted(summary["per_type_accuracy"].items()):
|
|
print(
|
|
f" {inst_id:50s} {stats['accuracy']:.2%} "
|
|
f"({stats['passed']}/{stats['total']})"
|
|
)
|
|
|
|
if args.output:
|
|
results["_summary"] = summary
|
|
with open(args.output, "w", encoding="utf-8") as f:
|
|
json.dump(results, f, indent=2, ensure_ascii=False)
|
|
print(f"\nResults saved to {args.output}")
|
|
|
|
if args.dump_responses:
|
|
with open(args.dump_responses, "w", encoding="utf-8") as f:
|
|
for k, v in results.items():
|
|
if k.startswith("_"):
|
|
continue
|
|
f.write(
|
|
json.dumps(
|
|
{
|
|
"key": v["key"],
|
|
"prompt": v["prompt"],
|
|
"response": v["response"],
|
|
},
|
|
ensure_ascii=False,
|
|
)
|
|
+ "\n"
|
|
)
|
|
print(f"Responses dumped to {args.dump_responses}")
|
|
|
|
engine.shutdown()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|