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from pathlib import Path
from typing import Any, Optional, Union
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from style_bert_vits2.logging import logger
def load_safetensors(
checkpoint_path: Union[str, Path],
model: torch.nn.Module,
for_infer: bool = False,
) -> tuple[torch.nn.Module, Optional[int]]:
"""
指定されたパスから safetensors モデルを読み込み、モデルとイテレーションを返す。
Args:
checkpoint_path (Union[str, Path]): モデルのチェックポイントファイルのパス
model (torch.nn.Module): 読み込む対象のモデル
for_infer (bool): 推論用に読み込むかどうかのフラグ
Returns:
tuple[torch.nn.Module, Optional[int]]: 読み込まれたモデルとイテレーション回数（存在する場合）
"""
tensors: dict[str, Any] = {}
iteration: Optional[int] = None
with safe_open(str(checkpoint_path), framework="pt", device="cpu") as f: # type: ignore
for key in f.keys():
if key == "iteration":
iteration = f.get_tensor(key).item()
tensors[key] = f.get_tensor(key)
if hasattr(model, "module"):
result = model.module.load_state_dict(tensors, strict=False)
else:
result = model.load_state_dict(tensors, strict=False)
for key in result.missing_keys:
if key.startswith("enc_q") and for_infer:
continue
logger.warning(f"Missing key: {key}")
for key in result.unexpected_keys:
if key == "iteration":
continue
logger.warning(f"Unexpected key: {key}")
if iteration is None:
logger.info(f"Loaded '{checkpoint_path}'")
else:
logger.info(f"Loaded '{checkpoint_path}' (iteration {iteration})")
return model, iteration
def save_safetensors(
model: torch.nn.Module,
iteration: int,
checkpoint_path: Union[str, Path],
is_half: bool = False,
for_infer: bool = False,
) -> None:
"""
モデルを safetensors 形式で保存する。
Args:
model (torch.nn.Module): 保存するモデル
iteration (int): イテレーション回数
checkpoint_path (Union[str, Path]): 保存先のパス
is_half (bool): モデルを半精度で保存するかどうかのフラグ
for_infer (bool): 推論用に保存するかどうかのフラグ
"""
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
keys = []
for k in state_dict:
if "enc_q" in k and for_infer:
continue
keys.append(k)
new_dict = (
{k: state_dict[k].half() for k in keys}
if is_half
else {k: state_dict[k] for k in keys}
)
new_dict["iteration"] = torch.LongTensor([iteration])
logger.info(f"Saved safetensors to {checkpoint_path}")
save_file(new_dict, checkpoint_path)
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