Description of the project: Enzyme performs automatic differentiation (in the calculus sense) of LLVM programs. This enables users to use Enzyme to perform various algorithms such as back-propagation in ML or scientific simulation on existing code for any language that lowers to LLVM.
The support for an increasing number of LLVM Versions (7-main), AD modes (Reverse, Forward, Forward-Vector, Reverse-Vector, Jacobian), and libraries (BLAS, OpenMP, MPI, CUDA, ROCm, …) leads to a steadily increasing code base. In order to limit complexity and help new contributors we would like to express our core logic using LLVM Tablegen. The applicant is free to decide how to best map the program transformation abstractions within Enzyme to Tablegen
- A working tablegen rule generation system within Enzyme
- Moving several existing rules to the new autogenerated system (e.g. LLVM instructions, LLVM intrinsics, BLAS calls, MPI calls, …)
Desirable skills: Good knowledge of C++, calculus, and LLVM and/or Clang, and/or MLIR internals. Experience with Tablegen, Enzyme or automatic differentiation would be nice, but can also be learned in the project.
Project type: Large