In research both have an effect on ‘time-to-science’.
Especially when extensive research software support is unavailable.
Publication
FTorch is published in JOSS!
Atkinson et al. (2025)
FTorch: a library for coupling PyTorch models to Fortran. Journal of Open Source Software, 10(107), 7602,
DOI: 10.21105/joss.07602
FTorch creates Torch C++ objects for manipulation under the hood. As detailed in the documentation and examples, proper handling is required, like allocate and deallocate to prevent memory leakage.
Previously one had to explicitly delete torch objects to ensure that C++ memory was cleaned up, otherwise leakage could occur.
Now torch_delete is a finalizer, meaning it will be called whenever a tensor, model, or array of tensors goes out of scope.
Continuing to call torch_delete will still work, so old code remains valid.
Previously errors in Torch resulted in opaque error messages that referenced locations in the Torch library at the point of failure.
Now there are checks in Fortran to catch some input errors before they propagate to the C++ and raise them there, and C++ errors are now caught and handled in the CTorch layer to provide more information to the user about the point of failure.
Before: opaque Torch C++ exception
terminate called after throwing
an instance of 'torch::Error'
what(): Expected all tensors to
be on the same device, but found
at least two devices (cuda:0
and cpu)!
After: caught at CTorch layer
[ERROR]: One of the inputs to torch_jit_module_forward is not a Tensor
PyTorch recommends reusing torch.cuda interfaces for HIP — FTorch builds against the CUDA backend, aliasing HIP at the CMake level.
! Same interface, compiled with HIP flagscall torch_model_load(torch_net, 'model.pt', torch_kHIP, device_index=0)call torch_tensor_from_array(in_tensor(1), in_data, torch_kHIP, device_index=0)
Since the early days all FTorch source code existed in src/ftorch.F90. As features grew this became unsustainable so it is now distributed across several files:
Tidied user pages and API docs, added community pages (PRs welcome)
V1.0 Contributors
10 Total, 5 new!
Jack Atkinson, Joe Wallwork - Maintainers
Mikolaj Kowalski, Tom Meltzer - ICCS RSEs
Niccolo Zanotti - PhD and placement student
Jared Frazier, Zhenkun Li, Zoltán Katona - Community Users
Dominic Orchard, Daniel Katz - code and paper review
ML Coupling Workshop
ICCS ran a two-day ML Coupling Workshop in Cambridge, September 2025, bringing together researchers, RSEs, and modelling centres working on hybrid modelling challenges.
Shared recent advances and expertise in coupling ML to large-scale scientific codes
Discussion sessions on key challenges and best practices
Enables tensor expressions in Fortran that build a computation graph
Further expressions can be added - requests/PRs welcome
Online training - Optimizers
torch_optim derived type wrapping PyTorch optimizers
torch_optim%zero_grad — zeroes gradients at start of each step
torch_optim%step — takes one optimizer iteration
Optimizers exposed: SGD, Adam, AdamW
Others can be added - requests/PRs welcome
Testing optimisation of a single tensor against PyTorch:
Online training - Loss
torch_loss_<...> subroutines create loss tensors.
Loss functions exposed: MSE and CrossEntropy
Others can be added - requests/PRs welcome
! Old calculationcall torch_tensor_mean(loss, (output_vec - target_vec) **2)! Now with loss subroutinecall torch_loss_mse(loss, output_vec, target_vec)
Compiler plugin that differentiates LLVM IR, computing gradients of existing code without source-level rewriting. Currently supports C, C++, Julia, Rust. ICCS are working to add Fortran.
6-month (March-Aug) ICCS project led by Joe Wallwork.
Aiming to expose Enzyme through a Flang plugin and demonstrate differentiable Fortran on a UKCA atmospheric chemistry case study.
Eventual goal is to provide a simple tool for differentiable modelling of existing Fortran code.
Thanks to Joe Wallwork, Tom Meltzer, Elliott Kasoar,
Niccolò Zanotti and the rest of the FTorch team.
The ICCS received support from
FTorch has been supported by
References
Atkinson, Jack, Athena Elafrou, Elliott Kasoar, Joseph G. Wallwork, Thomas Meltzer, Simon Clifford, Dominic Orchard, and Chris Edsall. 2025. “FTorch: A Library for Coupling PyTorch Models to Fortran.”Journal of Open Source Software 10 (107): 7602. https://doi.org/10.21105/joss.07602.
Barker, Michelle, Neil P Chue Hong, Daniel S Katz, Anna-Lena Lamprecht, Carlos Martinez-Ortiz, Fotis Psomopoulos, Jennifer Harrow, et al. 2022. “Introducing the FAIR Principles for Research Software.”Scientific Data 9 (1): 622. https://doi.org/10.1038/s41597-022-01710-x.
Wilkinson, Mark D, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, et al. 2016. “The FAIR Guiding Principles for Scientific Data Management and Stewardship.”Scientific Data 3 (1): 1–9. https://doi.org/10.1038/sdata.2016.18.
