update: traducción NLLB y compose
This commit is contained in:
jlimolina
parent
8109dbf274
commit
da4c59a0e1
5 changed files with 593 additions and 21 deletions
472
translation_worker.py
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472
translation_worker.py
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import os
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import time
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import logging
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import contextlib
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from typing import List, Optional
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import psycopg2
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import psycopg2.extras
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import torch
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from langdetect import detect, DetectorFactory
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DetectorFactory.seed = 0 # resultados reproducibles
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
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LOG = logging.getLogger(__name__)
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# ---------- Config DB ----------
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DB_CONFIG = {
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"host": os.environ.get("DB_HOST", "localhost"),
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"port": int(os.environ.get("DB_PORT", 5432)),
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"dbname": os.environ.get("DB_NAME", "rss"),
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"user": os.environ.get("DB_USER", "rss"),
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"password": os.environ.get("DB_PASS", "x"),
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}
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# ---------- Helpers ENV (con retrocompatibilidad) ----------
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def _env_list(name: str, *fallbacks: str, default: str = "es") -> List[str]:
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raw = None
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for key in (name, *fallbacks):
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raw = os.environ.get(key)
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if raw:
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break
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raw = raw if raw is not None else default
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return [s.strip() for s in raw.split(",") if s and s.strip()]
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def _env_int(name: str, *fallbacks: str, default: int = 8) -> int:
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for key in (name, *fallbacks):
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val = os.environ.get(key)
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if val:
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try:
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return int(val)
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except ValueError:
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pass
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return default
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def _env_float(name: str, *fallbacks: str, default: float = 5.0) -> float:
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for key in (name, *fallbacks):
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val = os.environ.get(key)
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if val:
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try:
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return float(val)
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except ValueError:
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pass
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return default
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def _env_str(name: str, *fallbacks: str, default: Optional[str] = None) -> Optional[str]:
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for key in (name, *fallbacks):
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val = os.environ.get(key)
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if val:
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return val
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return default
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TARGET_LANGS = _env_list("TARGET_LANGS", "TRANSLATE_TO", default="es")
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BATCH_SIZE = _env_int("BATCH", "TRANSLATOR_BATCH", "TRANSLATE_BATCH", default=8)
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ENQUEUE_MAX = _env_int("ENQUEUE", "TRANSLATOR_ENQUEUE", "TRANSLATE_ENQUEUE", default=200)
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SLEEP_IDLE = _env_float("SLEEP_IDLE", "TRANSLATOR_SLEEP_IDLE", "TRANSLATE_SLEEP_IDLE", default=5.0)
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DEVICE_CFG = (_env_str("DEVICE", default="auto") or "auto").lower() # 'cpu' | 'cuda' | 'auto'
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# Límites de tokens (ajusta si ves OOM)
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MAX_SRC_TOKENS = _env_int("MAX_SRC_TOKENS", default=384)
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MAX_NEW_TOKENS = _env_int("MAX_NEW_TOKENS", default=192)
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# ---- Beams: por defecto 2 para títulos y 1 para cuerpo; respeta NUM_BEAMS si sólo se define ese ----
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def _beams_from_env():
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nb_global = os.environ.get("NUM_BEAMS")
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has_title = os.environ.get("NUM_BEAMS_TITLE") is not None
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has_body = os.environ.get("NUM_BEAMS_BODY") is not None
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if nb_global and not has_title and not has_body:
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try:
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v = max(1, int(nb_global))
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return v, v
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except ValueError:
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pass
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# por defecto: 2 (título), 1 (cuerpo)
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return _env_int("NUM_BEAMS_TITLE", default=2), _env_int("NUM_BEAMS_BODY", default=1)
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NUM_BEAMS_TITLE, NUM_BEAMS_BODY = _beams_from_env()
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# Modelo por defecto: NLLB 600M (cámbialo por facebook/nllb-200-1.3B si quieres el 1.3B)
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UNIVERSAL_MODEL = _env_str("UNIVERSAL_MODEL", default="facebook/nllb-200-distilled-600M")
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# ---------- Mapeo idiomas a códigos NLLB ----------
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NLLB_LANG = {
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# básicos
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"es": "spa_Latn", "en": "eng_Latn", "fr": "fra_Latn", "de": "deu_Latn", "it": "ita_Latn", "pt": "por_Latn",
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# nórdicos
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"nl": "nld_Latn", "sv": "swe_Latn", "da": "dan_Latn", "fi": "fin_Latn",
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# noruego
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"no": "nob_Latn", "nb": "nob_Latn", "nn": "nno_Latn",
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# CEE
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"pl": "pol_Latn", "cs": "ces_Latn", "sk": "slk_Latn", "sl": "slv_Latn",
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"hu": "hun_Latn", "ro": "ron_Latn", "bg": "bul_Cyrl", "el": "ell_Grek",
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"ru": "rus_Cyrl", "uk": "ukr_Cyrl", "hr": "hrv_Latn", "sr": "srp_Cyrl", "bs": "bos_Latn",
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# ME/Asia
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"tr": "tur_Latn", "ar": "arb_Arab", "fa": "pes_Arab", "he": "heb_Hebr",
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"zh": "zho_Hans", "ja": "jpn_Jpan", "ko": "kor_Hang",
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# SEA
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"vi": "vie_Latn", "th": "tha_Thai", "id": "ind_Latn", "ms": "zsm_Latn",
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# variantes
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"pt-br": "por_Latn", "pt-pt": "por_Latn",
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}
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def map_to_nllb(code: Optional[str]) -> Optional[str]:
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if not code:
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return None
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code = code.strip().lower()
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if code in NLLB_LANG:
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return NLLB_LANG[code]
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return f"{code}_Latn"
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def normalize_lang(code: Optional[str], default: Optional[str] = None) -> Optional[str]:
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if not code:
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return default
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code = code.strip().lower()
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return code if code else default
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# ---------- DB ----------
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def get_conn():
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return psycopg2.connect(**DB_CONFIG)
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def ensure_indexes(conn):
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with conn.cursor() as cur:
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cur.execute("""
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CREATE INDEX IF NOT EXISTS traducciones_lang_to_status_idx
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ON traducciones (lang_to, status);
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CREATE INDEX IF NOT EXISTS traducciones_status_idx
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ON traducciones (status);
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""")
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conn.commit()
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def ensure_pending(conn, lang_to: str, enqueue_limit: int):
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with conn.cursor() as cur:
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cur.execute("""
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INSERT INTO traducciones (noticia_id, lang_from, lang_to, status)
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SELECT sub.id, NULL, %s, 'pending'
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FROM (
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SELECT n.id
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FROM noticias n
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LEFT JOIN traducciones t
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ON t.noticia_id = n.id AND t.lang_to = %s
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WHERE t.id IS NULL
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ORDER BY n.fecha DESC NULLS LAST, n.id
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LIMIT %s
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) AS sub;
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""", (lang_to, lang_to, enqueue_limit))
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conn.commit()
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def fetch_pending_batch(conn, lang_to: str, batch_size: int):
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with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
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cur.execute("""
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SELECT t.id AS tr_id, t.noticia_id, t.lang_from, t.lang_to,
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n.titulo, n.resumen
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FROM traducciones t
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JOIN noticias n ON n.id = t.noticia_id
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WHERE t.lang_to = %s AND t.status = 'pending'
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ORDER BY t.id
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LIMIT %s;
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""", (lang_to, batch_size))
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rows = cur.fetchall()
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if rows:
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ids = [r["tr_id"] for r in rows]
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with conn.cursor() as cur:
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cur.execute("UPDATE traducciones SET status='processing' WHERE id = ANY(%s)", (ids,))
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conn.commit()
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return rows
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def mark_done(conn, tr_id: int, title_tr: str, body_tr: str, lang_from: Optional[str]):
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with conn.cursor() as cur:
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cur.execute("""
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UPDATE traducciones
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SET titulo_trad=%s, resumen_trad=%s,
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lang_from = COALESCE(lang_from, %s),
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status='done', error=NULL
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WHERE id=%s;
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""", (title_tr, body_tr, lang_from, tr_id))
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conn.commit()
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def mark_error(conn, tr_id: int, msg: str):
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with conn.cursor() as cur:
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cur.execute("UPDATE traducciones SET status='error', error=%s WHERE id=%s;", (msg[:1500], tr_id))
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conn.commit()
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def detect_lang(text1: str, text2: str) -> Optional[str]:
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txt = (text1 or "").strip() or (text2 or "").strip()
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if not txt:
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return None
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try:
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return detect(txt)
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except Exception:
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return None
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# ---------- Modelo único y manejo de CUDA (NLLB) ----------
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_TOKENIZER: Optional[AutoTokenizer] = None
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_MODEL: Optional[AutoModelForSeq2SeqLM] = None
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_DEVICE: Optional[torch.device] = None
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_CUDA_FAILS: int = 0
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_CUDA_DISABLED: bool = False
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def _resolve_device() -> torch.device:
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global _CUDA_DISABLED
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if _CUDA_DISABLED:
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return torch.device("cpu")
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if DEVICE_CFG == "cpu":
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return torch.device("cpu")
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if DEVICE_CFG == "cuda":
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return torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# auto
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return torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def _is_cuda_mem_error(exc: Exception) -> bool:
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s = str(exc)
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return ("CUDA out of memory" in s) or ("CUDACachingAllocator" in s) or ("expandable_segment" in s)
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def _free_cuda():
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if torch.cuda.is_available():
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try:
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except Exception:
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pass
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def _load_model_on(device: torch.device):
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"""Carga (o recarga) el modelo/tokenizer en el dispositivo indicado."""
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global _TOKENIZER, _MODEL, _DEVICE
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dtype = torch.float16 if device.type == "cuda" else torch.float32
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LOG.info("Cargando modelo universal %s (device=%s, dtype=%s)", UNIVERSAL_MODEL, device, dtype)
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tok = AutoTokenizer.from_pretrained(UNIVERSAL_MODEL)
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mdl = AutoModelForSeq2SeqLM.from_pretrained(
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UNIVERSAL_MODEL,
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torch_dtype=dtype,
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low_cpu_mem_usage=True
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)
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# use_cache=False reduce picos de VRAM en generación
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try:
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mdl.config.use_cache = False
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except Exception:
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pass
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mdl.to(device)
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mdl.eval()
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_TOKENIZER, _MODEL, _DEVICE = tok, mdl, device
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def get_universal_components():
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"""Devuelve (tokenizer, model, device). Carga en GPU si está disponible y estable."""
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global _TOKENIZER, _MODEL, _DEVICE, _CUDA_FAILS, _CUDA_DISABLED
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if _MODEL is not None and _DEVICE is not None:
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return _TOKENIZER, _MODEL, _DEVICE
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dev = _resolve_device()
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try:
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_load_model_on(dev)
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return _TOKENIZER, _MODEL, _DEVICE
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except Exception as e:
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LOG.warning("Fallo cargando modelo en %s: %s", dev, e)
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if dev.type == "cuda" and _is_cuda_mem_error(e):
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_CUDA_FAILS += 1
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_CUDA_DISABLED = True
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_free_cuda()
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LOG.warning("Deshabilitando CUDA y reintentando en CPU (fallos CUDA=%d)", _CUDA_FAILS)
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_load_model_on(torch.device("cpu"))
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return _TOKENIZER, _MODEL, _DEVICE
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_load_model_on(torch.device("cpu"))
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return _TOKENIZER, _MODEL, _DEVICE
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# ---------- Utilidades ----------
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def _token_chunks(tokenizer, text: str, max_tokens: int) -> List[str]:
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if not text:
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return []
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ids = tokenizer.encode(text, add_special_tokens=False)
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if len(ids) <= max_tokens:
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return [text]
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chunks = []
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for i in range(0, len(ids), max_tokens):
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sub = ids[i:i+max_tokens]
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piece = tokenizer.decode(sub, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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if piece.strip():
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chunks.append(piece.strip())
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return chunks
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def _norm(s: str) -> str:
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import re
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return re.sub(r"\W+", "", (s or "").lower()).strip()
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def _forced_bos_id(tokenizer: AutoTokenizer, model: AutoModelForSeq2SeqLM, tgt_code: str) -> int:
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"""
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Resuelve el id del token de idioma destino para NLLB de forma robusta,
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funcionando aunque falte `lang_code_to_id` en el tokenizer.
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"""
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# 1) tokenizer.lang_code_to_id (si existe)
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try:
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mapping = getattr(tokenizer, "lang_code_to_id", None)
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if isinstance(mapping, dict):
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tid = mapping.get(tgt_code)
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if isinstance(tid, int):
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return tid
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except Exception:
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pass
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# 2) model.config.lang_code_to_id (si existe)
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try:
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mapping = getattr(getattr(model, "config", None), "lang_code_to_id", None)
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if isinstance(mapping, dict):
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tid = mapping.get(tgt_code)
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if isinstance(tid, int):
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return tid
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except Exception:
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pass
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# 3) convert_tokens_to_ids (algunos builds registran el código como token especial)
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try:
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tid = tokenizer.convert_tokens_to_ids(tgt_code)
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if isinstance(tid, int) and tid not in (-1, getattr(tokenizer, "unk_token_id", -1)):
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return tid
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except Exception:
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pass
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# 4) additional_special_tokens/_ids (buscar el código tal cual)
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try:
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ats = getattr(tokenizer, "additional_special_tokens", None)
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ats_ids = getattr(tokenizer, "additional_special_tokens_ids", None)
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if isinstance(ats, list) and isinstance(ats_ids, list) and tgt_code in ats:
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idx = ats.index(tgt_code)
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if 0 <= idx < len(ats_ids) and isinstance(ats_ids[idx], int):
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return ats_ids[idx]
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except Exception:
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pass
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# 5) último recurso: usa eos/bos para no romper generate()
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LOG.warning("No pude resolver lang code id para '%s'. Uso fallback (eos/bos).", tgt_code)
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return getattr(tokenizer, "eos_token_id", None) or getattr(tokenizer, "bos_token_id", None) or 0
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@torch.inference_mode()
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def translate_text(src_lang: str, tgt_lang: str, text: str, num_beams: int = 1, _tries: int = 0) -> str:
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if not text or not text.strip():
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return ""
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tok, mdl, device = get_universal_components()
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src_code = map_to_nllb(src_lang) or "eng_Latn"
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tgt_code = map_to_nllb(tgt_lang) or "spa_Latn"
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# Configura idioma origen (si la prop existe)
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try:
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tok.src_lang = src_code
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except Exception:
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pass
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forced_bos = _forced_bos_id(tok, mdl, tgt_code)
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parts = _token_chunks(tok, text, MAX_SRC_TOKENS)
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outs: List[str] = []
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try:
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autocast_ctx = torch.amp.autocast("cuda", dtype=torch.float16) if device.type == "cuda" else contextlib.nullcontext()
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for p in parts:
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enc = tok(p, return_tensors="pt", truncation=True, max_length=MAX_SRC_TOKENS)
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enc = {k: v.to(device) for k, v in enc.items()}
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gen_kwargs = dict(
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forced_bos_token_id=forced_bos,
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max_new_tokens=MAX_NEW_TOKENS,
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num_beams=max(1, int(num_beams)),
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do_sample=False,
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use_cache=False, # ↓ memoria
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)
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# Evita el warning cuando num_beams = 1
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if int(num_beams) > 1:
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gen_kwargs["early_stopping"] = True
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with autocast_ctx:
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generated = mdl.generate(**enc, **gen_kwargs)
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out = tok.batch_decode(generated, skip_special_tokens=True)[0].strip()
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outs.append(out)
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del enc, generated
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if device.type == "cuda":
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_free_cuda()
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return "\n".join([o for o in outs if o]).strip()
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except Exception as e:
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if device.type == "cuda" and _is_cuda_mem_error(e) and _tries < 2:
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LOG.warning("CUDA OOM/allocator: intento de recuperación %d. Detalle: %s", _tries + 1, e)
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# desactiva CUDA y relanza en CPU
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global _MODEL, _DEVICE, _CUDA_DISABLED
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_CUDA_DISABLED = True
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try:
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if _MODEL is not None:
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del _MODEL
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except Exception:
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pass
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_free_cuda()
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_MODEL = None
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_DEVICE = None
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time.sleep(1.0)
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return translate_text(src_lang, tgt_lang, text, num_beams=num_beams, _tries=_tries + 1)
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raise
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def process_batch(conn, rows):
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for r in rows:
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tr_id = r["tr_id"]
|
||||
lang_to = normalize_lang(r["lang_to"], "es") or "es"
|
||||
lang_from = normalize_lang(r["lang_from"]) or detect_lang(r["titulo"] or "", r["resumen"] or "") or "en"
|
||||
|
||||
title = (r["titulo"] or "").strip()
|
||||
body = (r["resumen"] or "").strip()
|
||||
|
||||
# Si ya está en el mismo idioma, copia tal cual
|
||||
if (map_to_nllb(lang_from) or "eng_Latn") == (map_to_nllb(lang_to) or "spa_Latn"):
|
||||
mark_done(conn, tr_id, title, body, lang_from)
|
||||
continue
|
||||
|
||||
try:
|
||||
# Beams distintos: mejor calidad en títulos con coste de VRAM controlado
|
||||
title_tr = translate_text(lang_from, lang_to, title, num_beams=NUM_BEAMS_TITLE) if title else ""
|
||||
body_tr = translate_text(lang_from, lang_to, body, num_beams=NUM_BEAMS_BODY) if body else ""
|
||||
|
||||
# Si la "traducción" es igual al original, déjala vacía
|
||||
if _norm(title_tr) == _norm(title):
|
||||
title_tr = ""
|
||||
if _norm(body_tr) == _norm(body):
|
||||
body_tr = ""
|
||||
|
||||
mark_done(conn, tr_id, title_tr, body_tr, lang_from)
|
||||
except Exception as e:
|
||||
LOG.exception("Error traduciendo fila")
|
||||
mark_error(conn, tr_id, str(e))
|
||||
|
||||
def main():
|
||||
LOG.info(
|
||||
"Arrancando worker de traducción (NLLB). TARGET_LANGS=%s, BATCH=%s, ENQUEUE=%s, DEVICE=%s, BEAMS(title/body)=%s/%s",
|
||||
TARGET_LANGS, BATCH_SIZE, ENQUEUE_MAX, DEVICE_CFG, NUM_BEAMS_TITLE, NUM_BEAMS_BODY
|
||||
)
|
||||
# Pre-carga el modelo una vez para reservar memoria de forma limpia
|
||||
get_universal_components()
|
||||
|
||||
while True:
|
||||
any_work = False
|
||||
with get_conn() as conn:
|
||||
ensure_indexes(conn)
|
||||
for lt in TARGET_LANGS:
|
||||
lt = normalize_lang(lt, "es") or "es"
|
||||
ensure_pending(conn, lt, ENQUEUE_MAX)
|
||||
while True:
|
||||
rows = fetch_pending_batch(conn, lt, BATCH_SIZE)
|
||||
if not rows:
|
||||
break
|
||||
any_work = True
|
||||
LOG.info("[%s] Procesando %d elementos…", lt, len(rows))
|
||||
process_batch(conn, rows)
|
||||
if not any_work:
|
||||
time.sleep(SLEEP_IDLE)
|
||||
|
||||
if __name__ == "__main__":
|
||||
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
|
||||
main()
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue