ai-content-maker/.venv/Lib/site-packages/safetensors/tensorflow.py

138 lines
3.8 KiB
Python

import os
from typing import Dict, Optional, Union
import numpy as np
import tensorflow as tf
from safetensors import numpy, safe_open
def save(tensors: Dict[str, tf.Tensor], metadata: Optional[Dict[str, str]] = None) -> bytes:
"""
Saves a dictionary of tensors into raw bytes in safetensors format.
Args:
tensors (`Dict[str, tf.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
Example:
```python
from safetensors.tensorflow import save
import tensorflow as tf
tensors = {"embedding": tf.zeros((512, 1024)), "attention": tf.zeros((256, 256))}
byte_data = save(tensors)
```
"""
np_tensors = _tf2np(tensors)
return numpy.save(np_tensors, metadata=metadata)
def save_file(
tensors: Dict[str, tf.Tensor],
filename: Union[str, os.PathLike],
metadata: Optional[Dict[str, str]] = None,
) -> None:
"""
Saves a dictionary of tensors into raw bytes in safetensors format.
Args:
tensors (`Dict[str, tf.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`
Example:
```python
from safetensors.tensorflow import save_file
import tensorflow as tf
tensors = {"embedding": tf.zeros((512, 1024)), "attention": tf.zeros((256, 256))}
save_file(tensors, "model.safetensors")
```
"""
np_tensors = _tf2np(tensors)
return numpy.save_file(np_tensors, filename, metadata=metadata)
def load(data: bytes) -> Dict[str, tf.Tensor]:
"""
Loads a safetensors file into tensorflow format from pure bytes.
Args:
data (`bytes`):
The content of a safetensors file
Returns:
`Dict[str, tf.Tensor]`: dictionary that contains name as key, value as `tf.Tensor` on cpu
Example:
```python
from safetensors.tensorflow import load
file_path = "./my_folder/bert.safetensors"
with open(file_path, "rb") as f:
data = f.read()
loaded = load(data)
```
"""
flat = numpy.load(data)
return _np2tf(flat)
def load_file(filename: Union[str, os.PathLike]) -> Dict[str, tf.Tensor]:
"""
Loads a safetensors file into tensorflow format.
Args:
filename (`str`, or `os.PathLike`)):
The name of the file which contains the tensors
Returns:
`Dict[str, tf.Tensor]`: dictionary that contains name as key, value as `tf.Tensor`
Example:
```python
from safetensors.tensorflow import load_file
file_path = "./my_folder/bert.safetensors"
loaded = load_file(file_path)
```
"""
result = {}
with safe_open(filename, framework="tf") as f:
for k in f.keys():
result[k] = f.get_tensor(k)
return result
def _np2tf(numpy_dict: Dict[str, np.ndarray]) -> Dict[str, tf.Tensor]:
for k, v in numpy_dict.items():
numpy_dict[k] = tf.convert_to_tensor(v)
return numpy_dict
def _tf2np(tf_dict: Dict[str, tf.Tensor]) -> Dict[str, np.array]:
for k, v in tf_dict.items():
tf_dict[k] = v.numpy()
return tf_dict