egutierrez
6795598215
GLiREL declara proxies/resume_download como required-keyword en _from_pretrained, pero huggingface_hub 1.x dejo de pasarlos en su from_pretrained. Aplicamos un classmethod monkey-patch idempotente que inyecta valores neutros si faltan. Verificado contra glirel==1.2.1 y huggingface_hub==1.13.0 con jackboyla/glirel-large-v0. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
"""Carga (y cachea) un modelo GLiREL en el device deseado."""
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from __future__ import annotations
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from typing import Any
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# Cache global: (model_name, device) -> modelo cargado.
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_MODEL_CACHE: dict[tuple[str, str], Any] = {}
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def _resolve_device(device: str) -> str:
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"""Resuelve `device='auto'` a `cuda` o `cpu` segun disponibilidad."""
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if device != "auto":
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return device
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try:
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import torch
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except ImportError:
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return "cpu"
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return "cuda" if torch.cuda.is_available() else "cpu"
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def glirel_load_model(
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model_name: str = "jackboyla/glirel-large-v0",
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device: str = "auto",
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) -> Any:
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"""Carga un modelo GLiREL con cache por (model_name, device).
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La primera llamada descarga el modelo desde HuggingFace (~500 MB para
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`glirel-large-v0`). Llamadas sucesivas con los mismos parametros
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devuelven la instancia cacheada.
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Args:
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model_name: ID del modelo en HuggingFace Hub.
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device: 'auto' usa CUDA si esta disponible, o 'cpu'/'cuda'/'cuda:N'
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de forma explicita.
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Returns:
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Instancia del modelo GLiREL lista para `predict_relations`.
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Raises:
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ImportError: si la dependencia `glirel` no esta instalada.
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Solucion: `uv pip install glirel` o instalar el extra `nlp`
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del proyecto (`uv pip install -e '.[nlp]'`).
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"""
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resolved_device = _resolve_device(device)
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cache_key = (model_name, resolved_device)
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cached = _MODEL_CACHE.get(cache_key)
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if cached is not None:
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return cached
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try:
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from glirel import GLiREL
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except ImportError as exc:
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raise ImportError(
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"glirel no esta instalado. Instalalo con "
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"`uv pip install glirel` o `uv pip install -e '.[nlp]'`."
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) from exc
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# huggingface_hub 1.x ya no pasa `proxies`/`resume_download` a
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# `_from_pretrained`, pero glirel todavia los declara required-keyword.
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# Wrappeamos el classmethod para inyectar valores neutros si faltan.
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if not getattr(GLiREL, "_fn_registry_kwargs_patched", False):
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_orig = GLiREL._from_pretrained.__func__
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def _patched(cls, **kw):
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kw.setdefault("proxies", None)
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kw.setdefault("resume_download", False)
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return _orig(cls, **kw)
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GLiREL._from_pretrained = classmethod(_patched)
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GLiREL._fn_registry_kwargs_patched = True
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model = GLiREL.from_pretrained(model_name)
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if hasattr(model, "to"):
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model.to(resolved_device)
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_MODEL_CACHE[cache_key] = model
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return model
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