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safetensors is a brand new, easy, quick, and secure file format for storing tensors. The design of the file format and its unique implementation are being led
by Hugging Face, and it’s getting largely adopted of their common ‘transformers’ framework. The safetensors R package deal is a pure-R implementation, permitting to each learn and write safetensor information.
The preliminary model (0.1.0) of safetensors is now on CRAN.
Motivation
The principle motivation for safetensors within the Python group is safety. As famous
within the official documentation:
The principle rationale for this crate is to take away the necessity to use pickle on PyTorch which is utilized by default.
Pickle is taken into account an unsafe format, because the motion of loading a Pickle file can
set off the execution of arbitrary code. This has by no means been a priority for torch
for R customers, because the Pickle parser that’s included in LibTorch solely helps a subset
of the Pickle format, which doesn’t embrace executing code.
Nonetheless, the file format has extra benefits over different generally used codecs, together with:
-
Help for lazy loading: You’ll be able to select to learn a subset of the tensors saved within the file.
-
Zero copy: Studying the file doesn’t require extra reminiscence than the file itself.
(Technically the present R implementation does makes a single copy, however that may
be optimized out if we actually want it sooner or later). -
Easy: Implementing the file format is straightforward, and doesn’t require complicated dependencies.
Which means that it’s a great format for exchanging tensors between ML frameworks and
between completely different programming languages. For example, you’ll be able to write a safetensors file
in R and cargo it in Python, and vice-versa.
There are extra benefits in comparison with different file codecs widespread on this area, and
you’ll be able to see a comparability desk right here.
Format
The safetensors format is described within the determine under. It’s principally a header file
containing some metadata, adopted by uncooked tensor buffers.
Fundamental utilization
safetensors will be put in from CRAN utilizing:
set up.packages("safetensors")
We will then write any named checklist of torch tensors:
library(torch)
library(safetensors)
<- checklist(
tensors x = torch_randn(10, 10),
y = torch_ones(10, 10)
)
str(tensors)
#> Checklist of two
#> $ x:Float [1:10, 1:10]
#> $ y:Float [1:10, 1:10]
<- tempfile()
tmp safe_save_file(tensors, tmp)
It’s doable to move extra metadata to the saved file by offering a metadata
parameter containing a named checklist.
Studying safetensors information is dealt with by safe_load_file
, and it returns the named
checklist of tensors together with the metadata
attribute containing the parsed file header.
<- safe_load_file(tmp)
tensors str(tensors)
#> Checklist of two
#> $ x:Float [1:10, 1:10]
#> $ y:Float [1:10, 1:10]
#> - attr(*, "metadata")=Checklist of two
#> ..$ x:Checklist of three
#> .. ..$ form : int [1:2] 10 10
#> .. ..$ dtype : chr "F32"
#> .. ..$ data_offsets: int [1:2] 0 400
#> ..$ y:Checklist of three
#> .. ..$ form : int [1:2] 10 10
#> .. ..$ dtype : chr "F32"
#> .. ..$ data_offsets: int [1:2] 400 800
#> - attr(*, "max_offset")= int 929
At present, safetensors solely helps writing torch tensors, however we plan so as to add
assist for writing plain R arrays and tensorflow tensors sooner or later.
Future instructions
The following model of torch will use safetensors
as its serialization format,
which means that when calling torch_save()
on a mannequin, checklist of tensors, or different
varieties of objects supported by torch_save
, you’ll get a sound safetensors file.
That is an enchancment over the earlier implementation as a result of:
-
It’s a lot sooner. Greater than 10x for medium sized fashions. Could possibly be much more for giant information.
This additionally improves the efficiency of parallel dataloaders by ~30%. -
It enhances cross-language and cross-framework compatibility. You’ll be able to practice your mannequin
in R and use it in Python (and vice-versa), or practice your mannequin in tensorflow and run it
with torch.
If you wish to attempt it out, you’ll be able to set up the event model of torch with:
::install_github("mlverse/torch") remotes
Photograph by Nick Fewings on Unsplash
Reuse
Textual content and figures are licensed beneath Inventive Commons Attribution CC BY 4.0. The figures which have been reused from different sources do not fall beneath this license and will be acknowledged by a observe of their caption: “Determine from …”.
Quotation
For attribution, please cite this work as
Falbel (2023, June 15). Posit AI Weblog: safetensors 0.1.0. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2023-06-15-safetensors/
BibTeX quotation
@misc{safetensors, writer = {Falbel, Daniel}, title = {Posit AI Weblog: safetensors 0.1.0}, url = {https://blogs.rstudio.com/tensorflow/posts/2023-06-15-safetensors/}, yr = {2023} }
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