torch v0.11.0 is now on CRAN! This weblog put up highlights a number of the adjustments included
on this launch. However you may at all times discover the complete changelog
on the torch web site.

Improved loading of state dicts

For a very long time it has been potential to make use of torch from R to load state dicts (i.e. 
mannequin weights) educated with PyTorch utilizing the load_state_dict() operate.
Nonetheless, it was frequent to get the error:

Error in cpp_load_state_dict(path) :  isGenericDict() INTERNAL ASSERT FAILED at

This occurred as a result of when saving the state_dict from Python, it wasn’t actually
a dictionary, however an ordered dictionary. Weights in PyTorch are serialized as Pickle recordsdata – a Python-specific format much like our RDS. To load them in C++, with no Python runtime,
LibTorch implements a pickle reader that’s capable of learn solely a subset of the
file format, and this subset didn’t embody ordered dicts.

This launch provides assist for studying the ordered dictionaries, so that you received’t see
this error any longer.

In addition to that, studying theses recordsdata requires half of the height reminiscence utilization, and in
consequence additionally is far sooner. Listed here are the timings for studying a 3B parameter
mannequin (StableLM-3B) with v0.10.0:

  x <- torch::load_state_dict("~/Downloads/pytorch_model-00001-of-00002.bin")
  y <- torch::load_state_dict("~/Downloads/pytorch_model-00002-of-00002.bin")
   consumer  system elapsed 
662.300  26.859 713.484 

and with v0.11.0

   consumer  system elapsed 
  0.022   3.016   4.016 

Which means that we went from minutes to just some seconds.

Utilizing JIT operations

Probably the most frequent methods of extending LibTorch/PyTorch is by implementing JIT
operations. This permits builders to write down customized, optimized code in C++ and
use it instantly in PyTorch, with full assist for JIT tracing and scripting.
See our ‘Torch outside the box’
weblog put up if you wish to be taught extra about it.

Utilizing JIT operators in R used to require package deal builders to implement C++/Rcpp
for every operator in the event that they needed to have the ability to name them from R instantly.
This launch added assist for calling JIT operators with out requiring authors to
implement the wrappers.

The one seen change is that we now have a brand new image within the torch namespace, known as
jit_ops. Let’s load torchvisionlib, a torch extension that registers many alternative
JIT operations. Simply loading the package deal with library(torchvisionlib) will make
its operators out there for torch to make use of – it is because the mechanism that registers
the operators acts when the package deal DLL (or shared library) is loaded.

For example, let’s use the read_file operator that effectively reads a file
right into a uncooked (bytes) torch tensor.

... [the output was truncated (use n=-1 to disable)]
[ CPUByteType{325862} ]

We’ve made it so autocomplete works properly, such which you could interactively discover the out there
operators utilizing jit_ops$ and urgent to set off RStudio’s autocomplete.

Different small enhancements

This launch additionally provides many small enhancements that make torch extra intuitive:

  • Now you can specify the tensor dtype utilizing a string, eg: torch_randn(3, dtype = "float64"). (Beforehand you needed to specify the dtype utilizing a torch operate, equivalent to torch_float64()).

    torch_randn(3, dtype = "float64")
    [ CPUDoubleType{3} ]
  • Now you can use with_device() and local_device() to quickly modify the gadget
    on which tensors are created. Earlier than, you had to make use of gadget in every tensor
    creation operate name. This permits for initializing a module on a selected gadget:

    with_device(gadget="mps", {
      linear <- nn_linear(10, 1)
    torch_device(sort='mps', index=0)
  • It’s now potential to quickly modify the torch seed, which makes creating
    reproducible applications simpler.

    with_torch_manual_seed(seed = 1, {
    [ CPUFloatType{1} ]

Thanks to all contributors to the torch ecosystem. This work wouldn’t be potential with out
all of the useful points opened, PRs you created, and your arduous work.

If you’re new to torch and need to be taught extra, we extremely suggest the recently announced ebook ‘Deep Studying and Scientific Computing with R torch’.

If you wish to begin contributing to torch, be happy to achieve out on GitHub and see our contributing guide.

The total changelog for this launch could be discovered here.

Photograph by Ian Schneider on Unsplash


Textual content and figures are licensed beneath Artistic Commons Attribution CC BY 4.0. The figures which were reused from different sources do not fall beneath this license and could be acknowledged by a word of their caption: “Determine from …”.


For attribution, please cite this work as

Falbel (2023, June 7). Posit AI Weblog: torch 0.11.0. Retrieved from

BibTeX quotation

  creator = {Falbel, Daniel},
  title = {Posit AI Weblog: torch 0.11.0},
  url = {},
  12 months = {2023}

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