We’re launching Lyria 3.5 in Google Flow Music, with advances across musicality, lyrics, vocals, and creative control
deepmind.google·6d ago
AI Engineering
Hacker News·21d ago·5 min read
Differentiable Fortran with LFortran and Enzyme
TL;DR
Legacy Fortran code often lacks gradient information, hindering its use in modern machine learning. By integrating LFortran, LLVM, and Enzyme, researchers can extract exact gradients from existing Fortran simulations.
✦ Why It Matters
Engineers can integrate legacy Fortran solvers into JAX workflows without rewriting code, enhancing their ML capabilities today.
Key Takeaways
- Explore integrating LFortran with existing Fortran code for ML applications.
- Utilize Enzyme for automatic differentiation in legacy simulations.
- Wrap Fortran solvers as custom primitives in JAX for easy use.
- Leverage validated physics codes without rewriting them for modern ML.
Full Summary
Many established Fortran codes in fields like computational fluid dynamics (CFD) and climate modeling do not provide gradients, which are essential for modern machine learning (ML) frameworks like JAX and PyTorch. Instead of rewriting these codes, researchers have developed a method that combines LFortran, LLVM (a compiler infrastructure), and Enzyme (an automatic differentiation tool) to extract gradients directly from legacy Fortran code.
By targeting a Fortran thermal solver, they demonstrated that it is possible to obtain exact gradients through this integration. The resulting gradients can then be wrapped as a custom JAX primitive, allowing the Fortran solver to be used seamlessly within JAX-based ML pipelines.
This method preserves the integrity of validated legacy code while enabling its application in contemporary ML tasks. The implications are significant, as it opens the door for leveraging decades of reliable physics simulations in new AI-driven contexts.