feat: 新增 IFEval 指令遵循评测
- 实现 25 种正则约束 verifier - 将评测脚本从 scripts/tools/ 移至 scripts/eval/
This commit is contained in:
@@ -1,336 +0,0 @@
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"""HumanEval code generation benchmark.
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Generates n completions per problem, extracts function bodies, executes
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against hidden tests, and computes pass@k.
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Usage::
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python scripts/tools/evaluate_humaneval.py --param_path ./params \
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--data_path HumanEval.jsonl.gz --output results.json \
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--num_samples 200 --temperature 0.8 --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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import signal
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import sys
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from math import prod
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from multiprocessing import Process, Queue
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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import torch
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import tqdm
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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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HUMANEVAL_URL = (
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"https://github.com/openai/human-eval/raw/master/data/HumanEval.jsonl.gz"
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)
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_STOP_SEQUENCES = [
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"\nclass ",
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"\ndef ",
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"\n# ",
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"\nif __name__",
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"\nprint(",
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"\n\n\n",
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]
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def _download_humaneval(data_path: str):
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if os.path.exists(data_path):
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return
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import gzip
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import urllib.request
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os.makedirs(os.path.dirname(data_path) or ".", exist_ok=True)
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print(f"Downloading HumanEval from {HUMANEVAL_URL} ...")
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tmp = data_path + ".tmp"
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urllib.request.urlretrieve(HUMANEVAL_URL, tmp)
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with gzip.open(tmp, "rb") as f_in:
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with open(data_path, "wb") as f_out:
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f_out.write(f_in.read())
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os.remove(tmp)
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print(f" saved 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 _extract_function_body(code: str, entry_point: str) -> Optional[str]:
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"""Extract the function body from a completion."""
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pattern = rf"def\s+{re.escape(entry_point)}\b[^:]*:"
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match = re.search(pattern, code)
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if not match:
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# Use the full code as-is if we can't find the function
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return code
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body_start = match.end()
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lines = code[body_start:].split("\n")
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body_lines = []
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started = False
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for line in lines:
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stripped = line.rstrip()
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if not stripped and not started:
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continue
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if not stripped and started:
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body_lines.append("")
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continue
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if not started:
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started = True
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if stripped.lstrip() == stripped and started:
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break
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body_lines.append(stripped)
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body = "\n".join(body_lines)
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if not body.strip():
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return None
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return body
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def _trim_stop_sequences(text: str) -> str:
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for stop in _STOP_SEQUENCES:
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idx = text.find(stop)
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if idx != -1:
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text = text[:idx]
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return text
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def _execute_code(problem: dict, completion: str, timeout: float = 3.0) -> bool:
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"""Run the completion against hidden tests in a subprocess."""
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def _worker(queue, full_code):
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try:
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namespace = {}
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exec(full_code, namespace)
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check = namespace.get("check")
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if check is None:
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queue.put(False)
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return
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check(namespace.get(problem["entry_point"]))
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queue.put(True)
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except Exception:
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queue.put(False)
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full_code = problem["prompt"] + completion + "\n" + problem["test"]
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queue: Queue = Queue()
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proc = Process(target=_worker, args=(queue, full_code))
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proc.start()
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proc.join(timeout)
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if proc.is_alive():
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proc.terminate()
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proc.join()
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return False
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try:
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return queue.get_nowait()
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except Exception:
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return False
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def _pass_at_k(n: int, c: int, k: int) -> float:
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"""Unbiased estimator of pass@k."""
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if n - c < k:
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return 1.0
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return 1.0 - float(prod(1.0 - k / np.arange(n - c + 1, n + 1)))
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def _deduplicate(completions: List[str]) -> List[str]:
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seen = set()
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unique = []
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for c in completions:
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if c not in seen:
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seen.add(c)
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unique.append(c)
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return unique
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def _generate(
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engine: InferenceEngine,
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prompt: str,
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num_samples: int,
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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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batch_size: int,
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) -> List[str]:
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batches = [prompt] * min(batch_size, num_samples)
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completions = []
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remaining = num_samples
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while remaining > 0:
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current = min(batch_size, remaining)
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batch_prompts = batches[:current]
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outputs = engine.generate(
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prompt=batch_prompts,
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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(outputs, str):
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outputs = [outputs]
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completions.extend(outputs)
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remaining -= current
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return _deduplicate(completions)
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def evaluate(
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engine: InferenceEngine,
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problems: List[dict],
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num_samples: int,
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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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batch_size: int,
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k_values: Tuple[int, ...] = (1, 10, 100),
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) -> Dict:
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results = {}
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all_pass_at_k = {k: [] for k in k_values}
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for problem in tqdm.tqdm(problems, desc="HumanEval", unit="problem"):
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task_id = problem["task_id"]
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prompt = problem["prompt"]
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entry_point = problem["entry_point"]
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raw_completions = _generate(
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engine,
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prompt,
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num_samples,
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max_tokens,
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temperature,
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top_p,
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top_k,
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batch_size,
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)
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completions = []
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for raw in raw_completions:
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trimmed = _trim_stop_sequences(raw)
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body = _extract_function_body(trimmed, entry_point)
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if body:
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completions.append(body)
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passed = 0
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for comp in completions:
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if _execute_code(problem, comp):
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passed += 1
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n = len(completions)
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c = passed
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result = {"task_id": task_id, "n": n, "passed": c}
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for k in k_values:
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result[f"pass@{k}"] = round(_pass_at_k(n, c, k), 4)
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all_pass_at_k[k].append(_pass_at_k(n, c, k))
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results[task_id] = result
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summary = {}
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for k in k_values:
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vals = all_pass_at_k[k]
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summary[f"pass@{k}"] = round(float(np.mean(vals)), 4)
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results["_summary"] = summary
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return results
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def main():
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parser = argparse.ArgumentParser(description="HumanEval 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="./humaneval/HumanEval.jsonl",
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help="HumanEval 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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"--num_samples",
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type=int,
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default=200,
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help="Completions per problem",
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)
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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", type=float, default=0.8, 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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"--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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"--problems",
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type=int,
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nargs="+",
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default=None,
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help="Specific problem indices (0-based)",
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)
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args = parser.parse_args()
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_download_humaneval(args.data_path)
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problems = _load_problems(args.data_path)
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if args.problems:
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problems = [problems[i] for i in args.problems if i < len(problems)]
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model = AutoModel.from_pretrained(args.param_path)
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tokenizer = AutoTokenizer.from_pretrained(args.param_path)
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model.to(device="cuda", dtype=torch.bfloat16)
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engine = InferenceEngine(
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model=model,
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tokenizer=tokenizer,
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max_batch_size=args.batch_size,
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)
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results = evaluate(
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engine=engine,
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problems=problems,
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num_samples=args.num_samples,
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max_tokens=args.max_tokens,
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temperature=args.temperature,
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top_p=args.top_p,
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top_k=args.top_k,
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batch_size=args.batch_size,
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k_values=(1, 10, 100),
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)
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summary = results.pop("_summary")
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print(f"\n{'=' * 60}")
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for k, v in summary.items():
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print(f" {k}: {v:.2%}")
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print(f"{'=' * 60}")
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if args.output:
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results["_summary"] = summary
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with open(args.output, "w", encoding="utf-8") as f:
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json.dump(results, f, indent=2, ensure_ascii=False)
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print(f"Results saved to {args.output}")
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engine.shutdown()
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if __name__ == "__main__":
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main()
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@@ -1,319 +0,0 @@
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"""MMLU evaluation via log-likelihood ranking."""
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import argparse
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import csv
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import json
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import os
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import shutil
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import tarfile
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import requests
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import torch
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import torch.nn.functional as F
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import tqdm
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from astrai.model import AutoModel
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from astrai.tokenize import AutoTokenizer
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MMLU_URL = "https://people.eecs.berkeley.edu/~hendrycks/data.tar"
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MMLU_SUBJECTS = [
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"abstract_algebra",
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"anatomy",
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"astronomy",
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"business_ethics",
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"clinical_knowledge",
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"college_biology",
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"college_chemistry",
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"college_computer_science",
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"college_mathematics",
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"college_medicine",
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"college_physics",
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"computer_security",
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"conceptual_physics",
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"econometrics",
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"electrical_engineering",
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"elementary_mathematics",
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"formal_logic",
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"global_facts",
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"high_school_biology",
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"high_school_chemistry",
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"high_school_computer_science",
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"high_school_european_history",
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"high_school_geography",
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"high_school_government_and_politics",
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"high_school_macroeconomics",
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"high_school_mathematics",
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"high_school_microeconomics",
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"high_school_physics",
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"high_school_psychology",
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"high_school_statistics",
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"high_school_us_history",
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"high_school_world_history",
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"human_aging",
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"human_sexuality",
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"international_law",
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"jurisprudence",
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"logical_fallacies",
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"machine_learning",
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"management",
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"marketing",
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"medical_genetics",
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"miscellaneous",
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"moral_disputes",
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"moral_scenarios",
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"nutrition",
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"philosophy",
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"prehistory",
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"professional_accounting",
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"professional_law",
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"professional_medicine",
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"professional_psychology",
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"public_relations",
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"security_studies",
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"sociology",
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"us_foreign_policy",
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"virology",
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"world_religions",
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]
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def _download_and_extract(url: str, data_dir: str):
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tar_path = os.path.join(data_dir, "data.tar")
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os.makedirs(data_dir, exist_ok=True)
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print(f"Downloading MMLU data from {url}...")
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resp = requests.get(url, stream=True, timeout=300)
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resp.raise_for_status()
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total = int(resp.headers.get("content-length", 0))
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with tqdm.tqdm(total=total, unit="B", unit_scale=True, desc=" Download") as bar:
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with open(tar_path, "wb") as f:
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for chunk in resp.iter_content(chunk_size=8192):
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f.write(chunk)
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bar.update(len(chunk))
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print("Extracting...")
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with tarfile.open(tar_path, "r") as tf:
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tf.extractall(data_dir)
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os.remove(tar_path)
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def download_mmlu(data_dir: str):
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_download_and_extract(MMLU_URL, data_dir)
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src = os.path.join(data_dir, "data")
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if os.path.exists(src):
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for item in os.listdir(src):
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src_item = os.path.join(src, item)
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dst_item = os.path.join(data_dir, item)
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if os.path.exists(dst_item):
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if os.path.isdir(dst_item):
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shutil.rmtree(dst_item)
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else:
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os.remove(dst_item)
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os.rename(src_item, dst_item)
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os.rmdir(src)
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print(f"MMLU data saved to {data_dir}")
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def _strip_prefix(text: str, prefix: str) -> str:
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if text.startswith(prefix):
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return text[len(prefix) :].strip()
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return text
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def load_csv(path: str) -> list[dict]:
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data = []
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with open(path, "r", encoding="utf-8") as f:
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for row in csv.reader(f):
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if len(row) < 6:
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continue
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if row[0].strip().lower() == "question":
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continue
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data.append(
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{
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"question": row[0].strip(),
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"A": _strip_prefix(row[1].strip(), "A)"),
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"B": _strip_prefix(row[2].strip(), "B)"),
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"C": _strip_prefix(row[3].strip(), "C)"),
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"D": _strip_prefix(row[4].strip(), "D)"),
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"answer": row[5].strip(),
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}
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)
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return data
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def build_prompt(
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question: str, choices: dict, subject: str, n_shot: int, dev_data: list[dict]
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) -> str:
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prompt = ""
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if n_shot > 0 and dev_data:
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prompt = f"The following are multiple choice questions (with answers) about {subject}.\n\n"
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for item in dev_data[:n_shot]:
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prompt += f"Question: {item['question']}\n"
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for k in ("A", "B", "C", "D"):
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prompt += f"{k}. {item[k]}\n"
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prompt += f"Answer: {item['answer']}\n\n"
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prompt += f"Question: {question}\n"
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for k in ("A", "B", "C", "D"):
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prompt += f"{k}. {choices[k]}\n"
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prompt += "Answer:"
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return prompt
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def apply_chat(
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tokenizer, raw_prompt: str, n_shot: int, dev_data: list[dict] | None
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) -> str:
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"""Wrap raw MMLU prompt in the model's chat template format.
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For few-shot, prepend example Q&A pairs as a second user/assistant exchange.
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"""
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messages = []
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if n_shot > 0 and dev_data:
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for item in dev_data[:n_shot]:
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q = f"Question: {item['question']}\n"
|
||||
for k in ("A", "B", "C", "D"):
|
||||
q += f"{k}. {item[k]}\n"
|
||||
q += "Answer:"
|
||||
messages.append({"role": "user", "content": q})
|
||||
messages.append({"role": "assistant", "content": item["answer"]})
|
||||
messages.append({"role": "user", "content": raw_prompt})
|
||||
return tokenizer.apply_chat_template(
|
||||
messages, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
|
||||
|
||||
def choice_logprob(
|
||||
model, tokenizer, context_ids: list[int], choice_letter: str, device: str
|
||||
) -> float:
|
||||
choice_text = choice_letter
|
||||
choice_ids = tokenizer.encode(choice_text, add_special_tokens=False)
|
||||
input_ids = context_ids + choice_ids
|
||||
max_len = model.config.max_len
|
||||
if len(input_ids) > max_len:
|
||||
overflow = len(input_ids) - max_len
|
||||
input_ids = input_ids[overflow:]
|
||||
ctx_len = len(input_ids) - len(choice_ids)
|
||||
else:
|
||||
ctx_len = len(context_ids)
|
||||
|
||||
input_tensor = torch.tensor([input_ids], device=device, dtype=torch.long)
|
||||
with torch.inference_mode():
|
||||
logits = model(input_tensor)["logits"][0]
|
||||
|
||||
score = 0.0
|
||||
for i, tid in enumerate(choice_ids):
|
||||
pos = ctx_len - 1 + i
|
||||
if pos >= len(logits):
|
||||
break
|
||||
score += F.log_softmax(logits[pos], dim=-1)[tid].item()
|
||||
return score
|
||||
|
||||
|
||||
def evaluate_subject(
|
||||
model,
|
||||
tokenizer,
|
||||
subject: str,
|
||||
test_data: list[dict],
|
||||
dev_data: list[dict] | None,
|
||||
device: str,
|
||||
n_shot: int,
|
||||
) -> tuple[float, int, int]:
|
||||
correct = 0
|
||||
total = 0
|
||||
for item in tqdm.tqdm(test_data, desc=f"{subject:40s}", leave=False):
|
||||
raw_prompt = build_prompt(
|
||||
item["question"], item, subject, n_shot, dev_data or []
|
||||
)
|
||||
context = apply_chat(tokenizer, raw_prompt, n_shot, dev_data or [])
|
||||
context_ids = tokenizer.encode(context)
|
||||
scores = {
|
||||
c: choice_logprob(model, tokenizer, context_ids, c, device)
|
||||
for c in ("A", "B", "C", "D")
|
||||
}
|
||||
if max(scores, key=scores.get) == item["answer"]:
|
||||
correct += 1
|
||||
total += 1
|
||||
return correct / total, correct, total
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="MMLU evaluation")
|
||||
parser.add_argument(
|
||||
"--param_path", type=str, default="./params", help="Model directory"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--data_dir", type=str, default="./mmlu_data", help="MMLU data directory"
|
||||
)
|
||||
parser.add_argument("--download", action="store_true", help="Download MMLU data")
|
||||
parser.add_argument(
|
||||
"--n_shot", type=int, default=5, help="Few-shot examples (0 for zero-shot)"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--subjects", type=str, nargs="+", help="Specific subjects (default: all)"
|
||||
)
|
||||
parser.add_argument("--output", type=str, help="Output JSON path")
|
||||
parser.add_argument("--split", type=str, default="test", choices=["test", "val"])
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default="cuda" if torch.cuda.is_available() else "cpu",
|
||||
help="Device",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dtype",
|
||||
type=str,
|
||||
default="bfloat16" if torch.cuda.is_available() else "float32",
|
||||
help="Torch dtype",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.download or not os.path.exists(args.data_dir):
|
||||
download_mmlu(args.data_dir)
|
||||
|
||||
model = AutoModel.from_pretrained(args.param_path)
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.param_path)
|
||||
device = args.device
|
||||
dtype = getattr(torch, args.dtype)
|
||||
model.to(device=device, dtype=dtype)
|
||||
model.eval()
|
||||
|
||||
subjects = args.subjects or MMLU_SUBJECTS
|
||||
results = {}
|
||||
total_correct = 0
|
||||
total_questions = 0
|
||||
|
||||
for subject in subjects:
|
||||
dev_path = os.path.join(args.data_dir, "dev", f"{subject}_dev.csv")
|
||||
test_path = os.path.join(
|
||||
args.data_dir, args.split, f"{subject}_{args.split}.csv"
|
||||
)
|
||||
|
||||
if not os.path.exists(test_path):
|
||||
print(f" Skipping {subject}: test file not found")
|
||||
continue
|
||||
|
||||
dev_data = load_csv(dev_path) if os.path.exists(dev_path) else None
|
||||
test_data = load_csv(test_path)
|
||||
|
||||
acc, corr, tot = evaluate_subject(
|
||||
model, tokenizer, subject, test_data, dev_data, device, args.n_shot
|
||||
)
|
||||
results[subject] = {"accuracy": round(acc, 4), "correct": corr, "total": tot}
|
||||
total_correct += corr
|
||||
total_questions += tot
|
||||
print(f" {subject:40s} {acc:.2%} ({corr}/{tot})")
|
||||
|
||||
overall = total_correct / total_questions if total_questions else 0
|
||||
print(f"\n{'=' * 70}")
|
||||
print(f" Overall: {overall:.2%} ({total_correct}/{total_questions})")
|
||||
results["_overall"] = {
|
||||
"accuracy": round(overall, 4),
|
||||
"correct": total_correct,
|
||||
"total": total_questions,
|
||||
}
|
||||
|
||||
if args.output:
|
||||
with open(args.output, "w", encoding="utf-8") as f:
|
||||
json.dump(results, f, indent=2)
|
||||
print(f"Results saved to {args.output}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user