Posit AI Weblog: safetensors 0.1.0

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 fashionable ‘transformers’ framework. The safetensors R package deal is a pure-R implementation, permitting to each learn and write safetensor recordsdata.

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, for the reason that 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 further 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 advanced dependencies.
  Which means that it’s a superb format for exchanging tensors between ML frameworks and
  between totally different programming languages. As an illustration, you’ll be able to write a safetensors file
  in R and cargo it in Python, and vice-versa.

There are further benefits in comparison with different file codecs frequent on this house, and
you’ll be able to see a comparability desk here.

Format

The safetensors format is described within the determine beneath. It’s principally a header file
containing some metadata, adopted by uncooked tensor buffers.

Fundamental utilization

safetensors will be put in from CRAN utilizing:

install.packages("safetensors")

library(torch)
library(safetensors)

tensors <- list(
  x = torch_randn(10, 10),
  y = torch_ones(10, 10)
)

str(tensors)
#> List of 2
#>  $ x:Float [1:10, 1:10]
#>  $ y:Float [1:10, 1:10]

tmp <- tempfile()
safe_save_file(tensors, tmp)

tensors <- safe_load_file(tmp)
str(tensors)
#> List of 2
#>  $ x:Float [1:10, 1:10]
#>  $ y:Float [1:10, 1:10]
#>  - attr(*, "metadata")=List of 2
#>   ..$ x:List of 3
#>   .. ..$ shape       : int [1:2] 10 10
#>   .. ..$ dtype       : chr "F32"
#>   .. ..$ data_offsets: int [1:2] 0 400
#>   ..$ y:List of 3
#>   .. ..$ shape       : int [1:2] 10 10
#>   .. ..$ dtype       : chr "F32"
#>   .. ..$ data_offsets: int [1:2] 400 800
#>  - attr(*, "max_offset")= int 929

remotes::install_github("mlverse/torch")

Photo by Nick Fewings on Unsplash

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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,
  creator = {Falbel, Daniel},
  title = {Posit AI Weblog: safetensors 0.1.0},
  url = {https://blogs.rstudio.com/tensorflow/posts/2023-06-15-safetensors/},
  12 months = {2023}
}