File size: 5,422 Bytes
c9a5147 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | """Qwen3Guard-Gen をバッチ推論するバックエンド（第3パス用）。
vLLM（GPU、既定）と transformers（左パディングの一括生成）を選べる。出力は
``Safety: Safe|Unsafe|Controversial`` と ``Categories: ...`` を含む短い文字列で、
``parse`` が安全性ラベルとカテゴリ名の一覧に分解する。
テスト用に ``FakeBackend`` を用意する（``marker`` を含む本文を Unsafe と判定する）。
"""
from __future__ import annotations
import re
from typing import Any
DEFAULT_MODEL = "Qwen/Qwen3Guard-Gen-0.6B"
SAFETY_RE = re.compile(r"Safety:\s*(Safe|Unsafe|Controversial)", re.I)
CATEGORY_NAMES = (
"Violent", "Non-violent Illegal Acts", "Sexual Content or Sexual Acts", "PII", "Suicide & Self-Harm",
"Unethical Acts", "Politically Sensitive Topics", "Copyright Violation", "Jailbreak",
)
CATEGORY_RE = re.compile("|".join(re.escape(name) for name in CATEGORY_NAMES), re.I)
CANONICAL = {name.lower(): name for name in CATEGORY_NAMES}
def parse(raw: str) -> tuple[str | None, list[str]]:
"""モデル出力から (安全性ラベル, カテゴリ一覧) を取り出す。取れなければ (None, [])。"""
match = SAFETY_RE.search(raw or "")
categories = sorted({CANONICAL[m.lower()] for m in CATEGORY_RE.findall(raw or "")})
return (match.group(1).title() if match else None), categories
class VllmBackend:
def __init__(self, model: str = DEFAULT_MODEL, max_chars: int = 2000, gpu_memory_utilization: float = 0.85, max_model_len: int = 4096) -> None:
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
self.tokenizer = AutoTokenizer.from_pretrained(model)
self.llm = LLM(model=model, gpu_memory_utilization=gpu_memory_utilization, max_model_len=max_model_len, enable_prefix_caching=True)
self.params = SamplingParams(max_tokens=48, temperature=0.0)
self.model = model
self.max_chars = max_chars
def classify(self, texts: list[str]) -> list[str]:
prompts = [self.tokenizer.apply_chat_template([{"role": "user", "content": t[: self.max_chars]}], tokenize=False) for t in texts]
outputs = self.llm.generate(prompts, self.params, use_tqdm=False)
return [o.outputs[0].text for o in outputs]
class TransformersBackend:
def __init__(self, model: str = DEFAULT_MODEL, max_chars: int = 2000, device: str = "auto", batch_size: int = 8) -> None:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
self.torch = torch
self.tokenizer = AutoTokenizer.from_pretrained(model)
self.tokenizer.padding_side = "left"
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
kwargs: dict[str, Any] = {"device_map": "auto"} if device == "auto" else {}
try:
self.model = AutoModelForCausalLM.from_pretrained(model, dtype="auto", **kwargs)
except TypeError:
self.model = AutoModelForCausalLM.from_pretrained(model, torch_dtype="auto", **kwargs)
if device != "auto":
self.model = self.model.to(device)
self.model.eval()
self.max_chars = max_chars
self.batch_size = batch_size
def classify(self, texts: list[str]) -> list[str]:
results: list[str] = []
for start in range(0, len(texts), self.batch_size):
chunk = texts[start : start + self.batch_size]
prompts = [self.tokenizer.apply_chat_template([{"role": "user", "content": t[: self.max_chars]}], tokenize=False) for t in chunk]
inputs = self.tokenizer(prompts, return_tensors="pt", padding=True).to(self.model.device)
with self.torch.no_grad():
generated = self.model.generate(**inputs, max_new_tokens=48, do_sample=False, pad_token_id=self.tokenizer.pad_token_id)
new_tokens = generated[:, inputs["input_ids"].shape[1]:]
results.extend(self.tokenizer.batch_decode(new_tokens, skip_special_tokens=True))
return results
class FakeBackend:
"""テスト用。``marker`` を含む本文を Unsafe（``category``）、``controversial_marker`` を含む本文を Controversial とする。"""
def __init__(self, marker: str = "UNSAFE_MARKER", category: str = "Sexual Content or Sexual Acts", controversial_marker: str = "CONTRO_MARKER") -> None:
self.marker = marker
self.category = category
self.controversial_marker = controversial_marker
self.calls = 0
def classify(self, texts: list[str]) -> list[str]:
self.calls += 1
out = []
for text in texts:
if self.marker in text:
out.append(f"Safety: Unsafe\nCategories: {self.category}")
elif self.controversial_marker in text:
out.append("Safety: Controversial\nCategories: Politically Sensitive Topics")
else:
out.append("Safety: Safe\nCategories: None")
return out
def make_backend(name: str, model: str = DEFAULT_MODEL, max_chars: int = 2000, device: str = "auto") -> Any:
if name == "vllm":
return VllmBackend(model, max_chars)
if name == "transformers":
return TransformersBackend(model, max_chars, device)
if name == "fake":
return FakeBackend()
raise ValueError(f"unknown backend: {name}")
|