ViperEkura
cb8dcb97ea
- 删除所有 def 函数 -> None 返回值类型标注 - FSDPExecutor 参数从 **kwargs 拆为显式声明，None 值自动过滤 - 新增 MmapStorage (bin) 存储后端，基于 numpy.memmap 零拷贝加载 - 新增 save_bin/load_bin/json_to_bin 工具函数 - detect_format 支持 bin 格式自动检测
96 lines
2.5 KiB
Python
96 lines
2.5 KiB
Python
"""
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AutoModel base class for model loading and saving.
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"""
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from contextlib import contextmanager
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from pathlib import Path
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from typing import Self, Union
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import torch.nn as nn
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from astrai.config.model_config import BaseModelConfig, ConfigFactory
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from astrai.factory import BaseFactory
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from astrai.serialization import load_model_config, load_model_weights, save_model
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@contextmanager
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def _disable_random_init(enable: bool = True):
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if not enable:
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yield
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return
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names = (
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"xavier_normal_",
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"xavier_uniform_",
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"kaiming_normal_",
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"kaiming_uniform_",
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"zeros_",
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"ones_",
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"constant_",
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"normal_",
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"uniform_",
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)
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orig = {n: getattr(nn.init, n) for n in names if hasattr(nn.init, n)}
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for n in orig:
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setattr(nn.init, n, lambda *a, **kw: None)
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try:
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yield
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finally:
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for n, fn in orig.items():
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setattr(nn.init, n, fn)
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class AutoModel(BaseFactory["AutoModel"], nn.Module):
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"""
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Autoregressive language model base class.
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Provides model loading/saving, registration, and generation.
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"""
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def __init__(self, config: BaseModelConfig):
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super().__init__()
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self.config = config
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@classmethod
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def from_pretrained(
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cls,
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path: Union[str, Path],
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disable_random_init: bool = True,
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strict: bool = True,
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) -> nn.Module:
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model_path = Path(path)
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config_path = model_path / "config.json"
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if not config_path.exists():
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raise FileNotFoundError(f"Config file not found: {config_path}")
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raw = load_model_config(str(model_path))
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config = ConfigFactory.load(raw)
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model_type = config.model_type or "autoregressive_lm"
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actual_cls = AutoModel.get_component_class(model_type)
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with _disable_random_init(enable=disable_random_init):
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model = actual_cls(config)
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weights_path = model_path / "model.safetensors"
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if weights_path.exists():
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state_dict = load_model_weights(str(model_path))
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model.load_state_dict(state_dict, strict=strict)
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return model
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def save_pretrained(
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self,
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save_directory: Union[str, Path],
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):
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save_model(
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config=self.config.to_dict(),
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state_dict=self.state_dict(),
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save_directory=str(save_directory),
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)
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def to(self, *args, **kwargs) -> Self:
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"""Move model to device/dtype."""
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return super().to(*args, **kwargs)
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