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
407 lines
11 KiB
Python
407 lines
11 KiB
Python
"""HumanEval benchmark — functional pipeline design.
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Pipeline:
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load -> generate -> extract -> test -> score -> report
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Each stage is a pure function (except GPU/CPU-bound I/O stages).
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Config is a single dataclass; side effects are isolated at pipeline boundaries.
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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 subprocess
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import sys
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from dataclasses import dataclass
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from math import prod
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from typing import Dict, Iterator, List, Optional, Sequence, 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 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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# ---------------------------------------------------------------------------
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# Config
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# ---------------------------------------------------------------------------
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HUMANEVAL_HF_DATASET = "openai/openai_humaneval"
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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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@dataclass
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class EvalConfig:
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param_path: str = "./params"
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data_path: str = "./humaneval/HumanEval.jsonl"
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output: Optional[str] = None
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test_only: Optional[str] = None
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generate_only: bool = False
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num_samples: int = 200
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max_tokens: int = 512
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temperature: float = 0.8
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top_p: float = 0.95
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top_k: int = 50
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batch_size: int = 32
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test_timeout: float = 3.0
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test_workers: int = 8
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k_values: Tuple[int, ...] = (1, 10, 100)
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problem_indices: Optional[List[int]] = None
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def download(path: str):
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if os.path.exists(path):
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return
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os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
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print(f"Downloading HumanEval from HuggingFace ({HUMANEVAL_HF_DATASET}) ...")
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ds = load_dataset(HUMANEVAL_HF_DATASET, split="test")
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with open(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)} problems to {path}")
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def load_jsonl(path: str) -> List[dict]:
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rows = []
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with open(path, 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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rows.append(json.loads(line))
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return rows
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def save_json(path: str, data):
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with open(path, "w", encoding="utf-8") as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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def create_engine(param_path: str, batch_size: int) -> InferenceEngine:
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model = AutoModel.from_pretrained(param_path)
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tokenizer = AutoTokenizer.from_pretrained(param_path)
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model.to(device="cuda", dtype=torch.bfloat16)
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return InferenceEngine(
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model=model,
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tokenizer=tokenizer,
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max_batch_size=batch_size,
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)
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def trim_stop(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 extract_body(code: str, entry_point: str) -> Optional[str]:
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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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return code
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lines = code[match.end() :].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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return body if body.strip() else None
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def deduplicate(seq: Sequence[str]) -> List[str]:
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seen = set()
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return [x for x in seq if not (x in seen or seen.add(x))]
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def generate_batch(
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engine: InferenceEngine,
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prompt: str,
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n: int,
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batch_size: 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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) -> List[str]:
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completions = []
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remaining = n
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while remaining > 0:
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current = min(batch_size, remaining)
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outputs = engine.generate(
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prompt=[prompt] * current,
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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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completions.extend(outputs if isinstance(outputs, list) else [outputs])
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remaining -= current
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return deduplicate(completions)
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def extract_completions(
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raw: Sequence[str],
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entry_point: str,
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) -> List[str]:
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bodies = []
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for r in raw:
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t = trim_stop(r)
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body = extract_body(t, entry_point)
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if body:
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bodies.append(body)
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return bodies
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def generate_all(
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engine: InferenceEngine,
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problems: Sequence[dict],
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cfg: EvalConfig,
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) -> List[dict]:
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results = []
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for problem in tqdm.tqdm(problems, desc="Generating", unit="problem"):
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raw = generate_batch(
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engine,
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problem["prompt"],
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cfg.num_samples,
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cfg.batch_size,
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cfg.max_tokens,
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cfg.temperature,
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cfg.top_p,
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cfg.top_k,
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)
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bodies = extract_completions(raw, problem["entry_point"])
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results.append(
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dict(
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task_id=problem["task_id"],
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entry_point=problem["entry_point"],
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prompt=problem["prompt"],
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test=problem["test"],
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completions=bodies,
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)
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)
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return results
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def execute_one(args: tuple) -> bool:
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full_code, entry_point, timeout = args
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try:
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r = subprocess.run(
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[sys.executable, "-c", full_code],
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capture_output=True,
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timeout=timeout,
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)
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return r.returncode == 0
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except subprocess.TimeoutExpired:
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return False
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except Exception:
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return False
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def test_one(item: dict, cfg: EvalConfig, pool=None) -> Tuple[str, int, int]:
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from concurrent.futures import ProcessPoolExecutor
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task_id = item["task_id"]
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completions = item["completions"]
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codes = [
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(
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item["prompt"] + c + "\n" + item["test"],
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item["entry_point"],
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cfg.test_timeout,
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)
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for c in completions
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]
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n = len(codes)
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def _run(p):
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return sum(1 for ok in p.map(execute_one, codes) if ok)
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if pool is not None:
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passed = _run(pool)
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else:
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with ProcessPoolExecutor(max_workers=cfg.test_workers) as p:
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passed = _run(p)
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return task_id, n, passed
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def test_all(
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items: Sequence[dict],
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cfg: EvalConfig,
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) -> Iterator[Tuple[str, int, int]]:
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from concurrent.futures import ProcessPoolExecutor
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pool = ProcessPoolExecutor(max_workers=cfg.test_workers)
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try:
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for item in tqdm.tqdm(items, desc="Testing", unit="problem"):
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yield test_one(item, cfg, pool)
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finally:
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pool.shutdown(wait=True)
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def pass_at_k(n: int, c: int, k: int) -> float:
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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 score_results(
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results: Iterator[Tuple[str, int, int]],
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k_values: Tuple[int, ...],
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) -> Dict:
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"""Score pass@k for each problem.
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k values are filtered per-problem: if a problem has n < k samples
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(e.g. after deduplication), pass@k is not computed for that problem.
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The summary averages only over problems where the k was computed.
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"""
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scores = {k: [] for k in k_values}
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output = {}
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for task_id, n, passed in results:
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entry = {"task_id": task_id, "n": n, "passed": passed}
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for k in k_values:
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if k <= n:
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pk = round(pass_at_k(n, passed, k), 4)
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entry[f"pass@{k}"] = pk
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scores[k].append(pk)
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else:
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entry[f"pass@{k}"] = None
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output[task_id] = entry
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summary = {}
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for k in k_values:
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vals = scores[k]
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if vals:
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summary[f"pass@{k}"] = round(float(np.mean(vals)), 4)
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else:
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summary[f"pass@{k}"] = None
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output["_summary"] = summary
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return output
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def run_pipeline(cfg: EvalConfig) -> Dict:
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if cfg.test_only:
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with open(cfg.test_only, encoding="utf-8") as f:
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generated = json.load(f)
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else:
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download(cfg.data_path)
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problems = load_jsonl(cfg.data_path)
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if cfg.problem_indices:
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problems = [problems[i] for i in cfg.problem_indices if i < len(problems)]
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engine = create_engine(cfg.param_path, cfg.batch_size)
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try:
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generated = generate_all(engine, problems, cfg)
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finally:
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engine.shutdown()
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if cfg.output:
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mid = cfg.output.replace(".json", "_completions.json")
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save_json(mid, generated)
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print(f"Completions saved to {mid}")
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if cfg.generate_only:
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return {}
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results = test_all(generated, cfg)
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scored = score_results(results, cfg.k_values)
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return scored
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def parse_args(argv: Optional[List[str]] = None) -> EvalConfig:
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p = argparse.ArgumentParser(description="HumanEval benchmark")
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p.add_argument("--param_path", type=str, default="./params")
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p.add_argument("--data_path", type=str, default="./humaneval/HumanEval.jsonl")
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p.add_argument("--output", type=str, default=None)
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p.add_argument(
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"--test_only",
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type=str,
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default=None,
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help="Skip generation, test existing completions JSON",
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)
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p.add_argument(
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"--generate_only", action="store_true", help="Only generate, skip testing"
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)
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p.add_argument("--num_samples", type=int, default=200)
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p.add_argument("--max_tokens", type=int, default=512)
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p.add_argument("--temperature", type=float, default=0.8)
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p.add_argument("--top_p", type=float, default=0.95)
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p.add_argument("--top_k", type=int, default=50)
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p.add_argument("--batch_size", type=int, default=32)
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p.add_argument("--test_workers", type=int, default=8)
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p.add_argument("--test_timeout", type=float, default=3.0)
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p.add_argument("--problems", type=int, nargs="+", default=None)
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args = p.parse_args(argv)
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return EvalConfig(
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param_path=args.param_path,
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data_path=args.data_path,
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output=args.output,
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test_only=args.test_only,
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generate_only=args.generate_only,
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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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test_workers=args.test_workers,
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test_timeout=args.test_timeout,
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problem_indices=args.problems,
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)
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def report(scored: Dict):
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summary = scored.pop("_summary", {})
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print(f"\n{'=' * 60}")
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for k, v in summary.items():
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if v is not None:
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print(f" {k}: {v:.2%}")
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else:
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print(f" {k}: N/A")
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print(f"{'=' * 60}")
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scored["_summary"] = summary
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def main():
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cfg = parse_args()
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scored = run_pipeline(cfg)
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report(scored)
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if cfg.output:
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save_json(cfg.output, scored)
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print(f"Results saved to {cfg.output}")
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if __name__ == "__main__":
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main()
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