Update

HVU_QA/generate_question.py

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+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import re
+import shutil
+import sys
+import tempfile
+import threading
+import warnings
+from pathlib import Path
+from typing import Any
+
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+if os.environ.get("PYTORCH_CUDA_ALLOC_CONF") == "expandable_segments:True":
+    os.environ.pop("PYTORCH_CUDA_ALLOC_CONF", None)
+os.environ.setdefault("TRANSFORMERS_NO_ADVISORY_WARNINGS", "1")
+warnings.filterwarnings("ignore", message=r"You are using the default legacy behaviour.*")
+warnings.filterwarnings("ignore", message=r"Special tokens have been added.*")
+
+
+def raise_missing_dependency_error(exc: ModuleNotFoundError) -> None:
+    raise SystemExit(f"Thiếu thư viện Python: {exc.name}") from exc
+
+
+try:
+    import torch
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+    from transformers import logging as transformers_logging
+    from transformers.utils import is_torch_available
+except ModuleNotFoundError as exc:
+    raise_missing_dependency_error(exc)
+
+transformers_logging.set_verbosity_error()
+
+if not is_torch_available():
+    raise SystemExit("Transformers không nhận PyTorch trong môi trường hiện tại.")
+
+
+APP_TITLE = "Hệ thống sinh câu hỏi"
+TASK_PREFIX = "sinh câu hỏi"
+QUESTION_LIMIT = 100
+TOKENIZER_FILES = (
+    "config.json",
+    "special_tokens_map.json",
+    "spiece.model",
+    "tokenizer.json",
+    "tokenizer_config.json",
+    "added_tokens.json",
+)
+GENERATION_PASSES = (
+    (0.9, 0.95, None, 1, 4),
+    (1.0, 0.97, 16, 1, 5),
+    (1.08, 0.99, 8, 2, 6),
+)
+
+
+def normalize_text(text: Any) -> str:
+    return " ".join(str(text or "").split())
+
+
+def unique_text(items: list[str]) -> list[str]:
+    seen: set[str] = set()
+    output: list[str] = []
+    for item in items:
+        value = normalize_text(item)
+        key = value.lower()
+        if key and key not in seen:
+            seen.add(key)
+            output.append(value)
+    return output
+
+
+def parse_question_count(value: Any, default: int = 5) -> int:
+    try:
+        parsed = int(value)
+    except (TypeError, ValueError):
+        parsed = default
+    return max(1, min(parsed, QUESTION_LIMIT))
+
+
+def format_questions(items: list[str]) -> str:
+    if not items:
+        return "Không sinh được câu hỏi phù hợp."
+    return "\n".join(f"{index}. {item}" for index, item in enumerate(items, 1))
+
+
+def as_bool(value: str | None, default: bool = False) -> bool:
+    if value is None:
+        return default
+    return value.strip().lower() in {"1", "true", "yes", "y", "on"}
+
+
+def resolve_model_dir(model_dir: str | Path, prefer_nested_model: bool = True) -> Path:
+    model_root = Path(model_dir).expanduser().resolve()
+    nested_candidates = [model_root / "best-model", model_root / "final-model"]
+    candidates = [*nested_candidates, model_root] if prefer_nested_model else [model_root, *nested_candidates]
+    for candidate in candidates:
+        if candidate.is_dir() and (candidate / "config.json").exists():
+            return candidate
+    raise FileNotFoundError(f"Không tìm thấy thư mục mô hình hợp lệ: {model_root}")
+
+
+def path_needs_ascii_mirror(path: Path) -> bool:
+    try:
+        str(path).encode("ascii")
+    except UnicodeEncodeError:
+        return True
+    return False
+
+
+def resolve_tokenizer_dir(model_dir: Path) -> Path:
+    if not path_needs_ascii_mirror(model_dir):
+        return model_dir
+
+    digest = hashlib.sha1(str(model_dir).encode("utf-8")).hexdigest()[:16]
+    cache_base = Path(os.getenv("LOCALAPPDATA") or tempfile.gettempdir())
+    tokenizer_dir = cache_base / "HVU_QA" / "tokenizer_cache" / digest
+    tokenizer_dir.mkdir(parents=True, exist_ok=True)
+
+    copied = False
+    for filename in TOKENIZER_FILES:
+        source = model_dir / filename
+        if not source.exists():
+            continue
+        destination = tokenizer_dir / filename
+        if destination.exists() and destination.stat().st_size == source.stat().st_size:
+            continue
+        shutil.copy2(source, destination)
+        copied = True
+
+    if copied:
+        marker = tokenizer_dir / "source_model_dir.txt"
+        marker.write_text(str(model_dir), encoding="utf-8")
+
+    return tokenizer_dir
+
+
+def parse_dtype(value: str) -> torch.dtype:
+    normalized = value.strip().lower()
+    mapping = {
+        "float16": torch.float16,
+        "fp16": torch.float16,
+        "float32": torch.float32,
+        "fp32": torch.float32,
+        "bfloat16": torch.bfloat16,
+        "bf16": torch.bfloat16,
+    }
+    if normalized not in mapping:
+        raise ValueError(f"Không hỗ trợ gpu_dtype={value}")
+    return mapping[normalized]
+
+
+class QuestionGenerator:
+    def __init__(
+        self,
+        model_dir: str | Path = "t5-viet-qg-finetuned",
+        task_prefix: str = TASK_PREFIX,
+        max_source_length: int = 512,
+        max_new_tokens: int = 64,
+        device: str = "auto",
+        cpu_threads: int | None = None,
+        gpu_dtype: str = "auto",
+        prefer_nested_model: bool = True,
+    ) -> None:
+        self.model_root = Path(model_dir).expanduser().resolve()
+        self.model_dir = resolve_model_dir(model_dir, prefer_nested_model=prefer_nested_model)
+        self.task_prefix = task_prefix
+        self.max_source_length = max_source_length
+        self.max_new_tokens = max_new_tokens
+        self.requested_device = device
+        self.cpu_threads = cpu_threads
+        self.gpu_dtype = gpu_dtype
+        self.prefer_nested_model = prefer_nested_model
+        self.device: torch.device | None = None
+        self.dtype: torch.dtype | None = None
+        self.tokenizer = None
+        self.model = None
+        self._load_lock = threading.Lock()
+
+    def _resolve_device(self) -> torch.device:
+        requested = self.requested_device.lower()
+        if requested == "cpu":
+            return torch.device("cpu")
+        if requested == "cuda":
+            if not torch.cuda.is_available():
+                raise RuntimeError("Bạn đã chọn device=cuda nhưng máy hiện tại không có CUDA.")
+            return torch.device("cuda")
+        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    def _resolve_dtype(self) -> torch.dtype:
+        if self.device is None or self.device.type != "cuda":
+            return torch.float32
+        if self.gpu_dtype == "auto":
+            if hasattr(torch.cuda, "is_bf16_supported") and torch.cuda.is_bf16_supported():
+                return torch.bfloat16
+            return torch.float16
+        return parse_dtype(self.gpu_dtype)
+
+    def _configure_runtime(self) -> None:
+        if self.device is None:
+            return
+        if self.device.type == "cpu":
+            if self.cpu_threads:
+                torch.set_num_threads(max(1, int(self.cpu_threads)))
+                if hasattr(torch, "set_num_interop_threads"):
+                    torch.set_num_interop_threads(max(1, min(int(self.cpu_threads), 4)))
+            return
+
+        if hasattr(torch.backends, "cuda") and hasattr(torch.backends.cuda, "matmul"):
+            torch.backends.cuda.matmul.allow_tf32 = True
+        if hasattr(torch.backends, "cudnn"):
+            torch.backends.cudnn.allow_tf32 = True
+            torch.backends.cudnn.benchmark = True
+
+    def _load_tokenizer(self):
+        use_fast = as_bool(os.getenv("HVU_USE_FAST_TOKENIZER"), default=False)
+        tokenizer_dir = resolve_tokenizer_dir(self.model_dir)
+        try:
+            return AutoTokenizer.from_pretrained(str(tokenizer_dir), use_fast=use_fast)
+        except Exception:
+            if use_fast:
+                return AutoTokenizer.from_pretrained(str(tokenizer_dir), use_fast=False)
+            if (tokenizer_dir / "tokenizer.json").exists():
+                try:
+                    return AutoTokenizer.from_pretrained(str(tokenizer_dir), use_fast=True)
+                except Exception:
+                    pass
+            return AutoTokenizer.from_pretrained(str(tokenizer_dir), use_fast=False)
+
+    def load(self) -> None:
+        if self.model is not None and self.tokenizer is not None:
+            return
+
+        with self._load_lock:
+            if self.model is not None and self.tokenizer is not None:
+                return
+
+            self.device = self._resolve_device()
+            self.dtype = self._resolve_dtype()
+            self._configure_runtime()
+
+            model_kwargs: dict[str, Any] = {}
+            if self.device.type == "cuda":
+                model_kwargs["torch_dtype"] = self.dtype
+                model_kwargs["low_cpu_mem_usage"] = True
+
+            self.tokenizer = self._load_tokenizer()
+            self.model = AutoModelForSeq2SeqLM.from_pretrained(str(self.model_dir), **model_kwargs)
+            self.model.to(self.device)
+            self.model.eval()
+
+    def metadata(self) -> dict[str, Any]:
+        active_device = self.device.type if self.device is not None else None
+        predicted_device = "cuda" if torch.cuda.is_available() and self.requested_device != "cpu" else "cpu"
+        return {
+            "title": APP_TITLE,
+            "model_root": str(self.model_root),
+            "model_dir": str(self.model_dir),
+            "requested_device": self.requested_device,
+            "active_device": active_device,
+            "predicted_device": predicted_device,
+            "loaded": self.model is not None,
+            "gpu_available": torch.cuda.is_available(),
+            "gpu_dtype": None if self.dtype is None else str(self.dtype).replace("torch.", ""),
+            "cpu_threads": torch.get_num_threads(),
+        }
+
+    def _candidate_answers(self, text: str, limit: int) -> list[str]:
+        text = normalize_text(text)
+        if not text:
+            return []
+
+        candidates: list[str] = []
+        split_pattern = r"(?<=[.!?])\s+|\n+"
+        for sentence in [normalize_text(part) for part in re.split(split_pattern, text) if normalize_text(part)]:
+            if 3 <= len(sentence.split()) <= 30:
+                candidates.append(sentence)
+            for clause in (normalize_text(part) for part in re.split(r"\s*[,;:]\s*", sentence)):
+                if 3 <= len(clause.split()) <= 20:
+                    candidates.append(clause)
+
+        if not candidates:
+            words = text.split()
+            candidates = [" ".join(words[: min(12, len(words))])] if words else [text]
+
+        ranked = sorted(unique_text(candidates), key=lambda item: (abs(len(item.split()) - 10), len(item)))
+        return ranked[:limit]
+
+    def _build_prompt(self, context: str, answer: str) -> str:
+        return f"{self.task_prefix}:\nngữ cảnh: {context}\nđáp án: {answer}"
+
+    @torch.inference_mode()
+    def _sample(self, context: str, answer: str, count: int, temperature: float, top_p: float) -> list[str]:
+        if self.tokenizer is None or self.model is None or self.device is None:
+            raise RuntimeError("Model chưa được load.")
+
+        inputs = self.tokenizer(
+            self._build_prompt(context, answer),
+            return_tensors="pt",
+            truncation=True,
+            max_length=self.max_source_length,
+        ).to(self.device)
+        outputs = self.model.generate(
+            **inputs,
+            max_new_tokens=self.max_new_tokens,
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            num_return_sequences=count,
+            no_repeat_ngram_size=3,
+            repetition_penalty=1.1,
+        )
+        questions: list[str] = []
+        for token_ids in outputs:
+            question = normalize_text(self.tokenizer.decode(token_ids, skip_special_tokens=True))
+            if question:
+                questions.append(question if question.endswith("?") else f"{question}?")
+        return [question for question in unique_text(questions) if len(question.split()) >= 3]
+
+    @torch.inference_mode()
+    def _beam_search(self, context: str, answer: str, count: int) -> list[str]:
+        if self.tokenizer is None or self.model is None or self.device is None:
+            raise RuntimeError("Model chưa được load.")
+
+        inputs = self.tokenizer(
+            self._build_prompt(context, answer),
+            return_tensors="pt",
+            truncation=True,
+            max_length=self.max_source_length,
+        ).to(self.device)
+        outputs = self.model.generate(
+            **inputs,
+            max_new_tokens=self.max_new_tokens,
+            num_beams=max(4, count),
+            num_return_sequences=min(count, 4),
+            early_stopping=True,
+            no_repeat_ngram_size=3,
+            repetition_penalty=1.1,
+        )
+        questions: list[str] = []
+        for token_ids in outputs:
+            question = normalize_text(self.tokenizer.decode(token_ids, skip_special_tokens=True))
+            if question:
+                questions.append(question if question.endswith("?") else f"{question}?")
+        return [question for question in unique_text(questions) if len(question.split()) >= 3]
+
+    def generate(self, text: str, count: int = 5) -> list[str]:
+        self.load()
+        context = normalize_text(text)
+        if not context:
+            raise ValueError("Vui lòng nhập đoạn văn.")
+
+        count = parse_question_count(count)
+        pool = unique_text(
+            self._candidate_answers(context, max(32, count * 5)) + [context[:180], context[:280], context]
+        )
+        output: list[str] = []
+        seen: set[str] = set()
+
+        for temperature, top_p, limit, rounds, floor in GENERATION_PASSES:
+            answers = pool[:limit] if limit else pool
+            for _ in range(rounds):
+                for answer in answers:
+                    remaining = count - len(output)
+                    if remaining <= 0:
+                        return output[:count]
+                    sample_count = min(8, max(floor, remaining * 2))
+                    for question in self._sample(context, answer, sample_count, temperature, top_p):
+                        key = question.lower()
+                        if key not in seen:
+                            seen.add(key)
+                            output.append(question)
+                            if len(output) >= count:
+                                return output[:count]
+
+        for answer in pool[: min(8, len(pool))]:
+            remaining = count - len(output)
+            if remaining <= 0:
+                break
+            for question in self._beam_search(context, answer, remaining):
+                key = question.lower()
+                if key not in seen:
+                    seen.add(key)
+                    output.append(question)
+                    if len(output) >= count:
+                        break
+
+        return output[:count]
+
+
+def read_input_text(args: argparse.Namespace) -> str:
+    if args.text:
+        return args.text
+    if args.input_file:
+        return Path(args.input_file).read_text(encoding="utf-8")
+    if sys.stdin.isatty():
+        return input("Nhập đoạn văn cần sinh câu hỏi:\n").strip()
+    return sys.stdin.read().strip()
+
+
+def read_question_count(args: argparse.Namespace) -> int:
+    if args.num_questions is not None:
+        return parse_question_count(args.num_questions)
+    if args.text or args.input_file or not sys.stdin.isatty():
+        return parse_question_count(None)
+
+    while True:
+        raw_value = input(f"Nhập số lượng câu hỏi cần sinh (1-{QUESTION_LIMIT}): ").strip()
+        if not raw_value:
+            print("Vui lòng nhập số lượng câu hỏi cần sinh.")
+            continue
+        try:
+            parsed = int(raw_value)
+        except ValueError:
+            print("Vui lòng nhập một số nguyên.")
+            continue
+        if 1 <= parsed <= QUESTION_LIMIT:
+            return parsed
+        print(f"Số lượng câu hỏi phải nằm trong khoảng 1 đến {QUESTION_LIMIT}.")
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_dir", default="t5-viet-qg-finetuned")
+    parser.add_argument("--task_prefix", default=TASK_PREFIX)
+    parser.add_argument("--max_source_length", type=int, default=512)
+    parser.add_argument("--max_new_tokens", type=int, default=64)
+    parser.add_argument("--num_questions", type=int, default=None)
+    parser.add_argument("--device", choices=["auto", "cpu", "cuda"], default="auto")
+    parser.add_argument("--cpu_threads", type=int, default=None)
+    parser.add_argument("--gpu_dtype", default="auto")
+    parser.add_argument("--text", default=None)
+    parser.add_argument("--input_file", default=None)
+    parser.add_argument("--output_format", choices=["text", "json"], default="text")
+    return parser
+
+
+def main() -> None:
+    if hasattr(sys.stdout, "reconfigure"):
+        sys.stdout.reconfigure(encoding="utf-8")
+    if hasattr(sys.stderr, "reconfigure"):
+        sys.stderr.reconfigure(encoding="utf-8")
+    args = build_parser().parse_args()
+    generator = QuestionGenerator(
+        model_dir=args.model_dir,
+        task_prefix=args.task_prefix,
+        max_source_length=args.max_source_length,
+        max_new_tokens=args.max_new_tokens,
+        device=args.device,
+        cpu_threads=args.cpu_threads,
+        gpu_dtype=args.gpu_dtype,
+        prefer_nested_model=True,
+    )
+    text = read_input_text(args)
+    question_count = read_question_count(args)
+    questions = generator.generate(text, question_count)
+    payload = {
+        "text": normalize_text(text),
+        "questions": questions,
+        "formatted": format_questions(questions),
+        "meta": generator.metadata(),
+    }
+    if args.output_format == "json":
+        print(json.dumps(payload, ensure_ascii=False, indent=2))
+        return
+    print(payload["formatted"])
+
+
+if __name__ == "__main__":
+    main()