fortran

Differentiable Fortran with LFortran and Enzyme

18 days ago

Differentiable Fortran allows backpropagation through existing Fortran, C, or C++ simulation code using LFortran and Enzyme, enabling integration with modern ML frameworks like JAX and PyTorch.
Enzyme applies autodifferentiation at the LLVM IR level, allowing differentiation of any code that compiles to LLVM, avoiding the need to rewrite validated legacy physics code.
LFortran is used instead of Flang because it emits cleaner LLVM IR with plain pointer arithmetic, making it easier for Enzyme to trace memory accesses without opaque runtime calls.
The compilation pipeline involves six steps: converting Fortran to LLVM IR, mild optimization, linking with a C wrapper, synthesizing derivatives via Enzyme, and final optimization into a shared library.