Author here — I work on Tesseract at Pasteur Labs, and I wrote this up because the "what if this was possible" was bugging me for way too long :)
I was surprised by how well this worked, the LFortran + Enzyme stack seems to be a very clean way to get gradients through Fortran code via LLVM IR transformations. Pretty cool to see a 220-line Fortran heat solver turn into ~6,900-line reverse pass automatically if I dare say so.
Would be awesome to see this applied to a real scientific codebase, and I hope that the demo is enough to convince people that it’s worth trying.
Very interesting stuff. How would I get GPU offload working? I have a rather complex scientific code I'm working on with JAX. Most of it can be expressed well with JAX's programming model, but the last 10% really sucks. It's still worth it so I don't have to mess around with offload onto whatever XPU flavor of the week. But going to C++ would really make my life easier, as long as I could use e.g. Kokkos.
Very interesting. Does LFortran have the same internal array layout as the standard C runtime ?
A shared layout and a shared calling convention would be very nice.
Sorry about my naive question. Haven't touched Fortran directly in three decades I think.
EDIT: thanks for your reply. For some reason it has been flagged dead. So am responding here. You can mail dang hn at ycombinator dot co m about the flagging. He is very nice.
Really nice work. I love Enzyme, and used it in my project about differentiable atomic descriptors. Idea was that I can quickly gobble up existing C++ and fortran codes alike for atomic descriptors and create a encompassing library what differentiate against hyper-params as well! But at time Enzyme was very early ~0.0.50 version or so. In our observations also Enzyme was fast enough that performance wise it matched the analytical gradients (when embedded inside entire pipeline)  .
Author here — I work on Tesseract at Pasteur Labs, and I wrote this up because the "what if this was possible" was bugging me for way too long :)
I was surprised by how well this worked, the LFortran + Enzyme stack seems to be a very clean way to get gradients through Fortran code via LLVM IR transformations. Pretty cool to see a 220-line Fortran heat solver turn into ~6,900-line reverse pass automatically if I dare say so.
Would be awesome to see this applied to a real scientific codebase, and I hope that the demo is enough to convince people that it’s worth trying.
Very interesting stuff. How would I get GPU offload working? I have a rather complex scientific code I'm working on with JAX. Most of it can be expressed well with JAX's programming model, but the last 10% really sucks. It's still worth it so I don't have to mess around with offload onto whatever XPU flavor of the week. But going to C++ would really make my life easier, as long as I could use e.g. Kokkos.
Very interesting. Does LFortran have the same internal array layout as the standard C runtime ?
A shared layout and a shared calling convention would be very nice.
Sorry about my naive question. Haven't touched Fortran directly in three decades I think.
EDIT: thanks for your reply. For some reason it has been flagged dead. So am responding here. You can mail dang hn at ycombinator dot co m about the flagging. He is very nice.
When you say you 'wrote this up', you mean you had an AI write (at least) chunks of it.
Really nice work. I love Enzyme, and used it in my project about differentiable atomic descriptors. Idea was that I can quickly gobble up existing C++ and fortran codes alike for atomic descriptors and create a encompassing library what differentiate against hyper-params as well! But at time Enzyme was very early ~0.0.50 version or so. In our observations also Enzyme was fast enough that performance wise it matched the analytical gradients (when embedded inside entire pipeline)  .