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Safetensors documentation

Torch API

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Torch API

safetensors.torch.load_file

< >

( filename: Union device: Union = 'cpu' ) → Dict[str, torch.Tensor]

Parameters

  • filename (str, or os.PathLike) — The name of the file which contains the tensors
  • device (Union[str, int], optional, defaults to cpu) — The device where the tensors need to be located after load. available options are all regular torch device locations.

Returns

Dict[str, torch.Tensor]

dictionary that contains name as key, value as torch.Tensor

Loads a safetensors file into torch format.

Example:

from safetensors.torch import load_file

file_path = "./my_folder/bert.safetensors"
loaded = load_file(file_path)

safetensors.torch.load

< >

( data: bytes ) → Dict[str, torch.Tensor]

Parameters

  • data (bytes) — The content of a safetensors file

Returns

Dict[str, torch.Tensor]

dictionary that contains name as key, value as torch.Tensor on cpu

Loads a safetensors file into torch format from pure bytes.

Example:

from safetensors.torch import load

file_path = "./my_folder/bert.safetensors"
with open(file_path, "rb") as f:
    data = f.read()

loaded = load(data)

safetensors.torch.save_file

< >

( tensors: Dict filename: Union metadata: Optional = None ) → None

Parameters

  • tensors (Dict[str, torch.Tensor]) — The incoming tensors. Tensors need to be contiguous and dense.
  • filename (str, or os.PathLike)) — The filename we’re saving into.
  • metadata (Dict[str, str], optional, defaults to None) — Optional text only metadata you might want to save in your header. For instance it can be useful to specify more about the underlying tensors. This is purely informative and does not affect tensor loading.

Returns

None

Saves a dictionary of tensors into raw bytes in safetensors format.

Example:

from safetensors.torch import save_file
import torch

tensors = {"embedding": torch.zeros((512, 1024)), "attention": torch.zeros((256, 256))}
save_file(tensors, "model.safetensors")

safetensors.torch.save

< >

( tensors: Dict metadata: Optional = None ) → bytes

Parameters

  • tensors (Dict[str, torch.Tensor]) — The incoming tensors. Tensors need to be contiguous and dense.
  • metadata (Dict[str, str], optional, defaults to None) — Optional text only metadata you might want to save in your header. For instance it can be useful to specify more about the underlying tensors. This is purely informative and does not affect tensor loading.

Returns

bytes

The raw bytes representing the format

Saves a dictionary of tensors into raw bytes in safetensors format.

Example:

from safetensors.torch import save
import torch

tensors = {"embedding": torch.zeros((512, 1024)), "attention": torch.zeros((256, 256))}
byte_data = save(tensors)

safetensors.torch.load_model

< >

( model: Module filename: Union strict: bool = True device: Union = 'cpu' ) → `(missing, unexpected)

Parameters

  • model (torch.nn.Module) — The model to load onto.
  • filename (str, or os.PathLike) — The filename location to load the file from.
  • strict (bool, optional, defaults to True) — Whether to fail if you’re missing keys or having unexpected ones. When false, the function simply returns missing and unexpected names.
  • device (Union[str, int], optional, defaults to cpu) — The device where the tensors need to be located after load. available options are all regular torch device locations.

Returns

`(missing, unexpected)

(List[str], List[str]) missingare names in the model which were not modified during loadingunexpected` are names that are on the file, but weren’t used during the load.

Loads a given filename onto a torch model. This method exists specifically to avoid tensor sharing issues which are not allowed in safetensors. More information on tensor sharing

safetensors.torch.save_model

< >

( model: Module filename: str metadata: Optional = None force_contiguous: bool = True )

Parameters

  • model (torch.nn.Module) — The model to save on disk.
  • filename (str) — The filename location to save the file
  • metadata (Dict[str, str], optional) — Extra information to save along with the file. Some metadata will be added for each dropped tensors. This information will not be enough to recover the entire shared structure but might help understanding things
  • force_contiguous (boolean, optional, defaults to True) — Forcing the state_dict to be saved as contiguous tensors. This has no effect on the correctness of the model, but it could potentially change performance if the layout of the tensor was chosen specifically for that reason.

Saves a given torch model to specified filename. This method exists specifically to avoid tensor sharing issues which are not allowed in safetensors. More information on tensor sharing

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