A WIP Cassette-based automatic differentiation package for the Julia language.
This package is a prototype, and generally will not be usable by normal users until a stable version of Cassette is released (hopefully in the Julia 1.x timeframe).
Capstan takes the stance that users should only ever have to think about the differentiability of their algorithms, not their code.
Planned features include:
- forward-mode and reverse-mode operation
- mixed-mode fused broadcast optimizations
- works even with Julia code containing concrete dispatch/structural type constraints
- complex differentiation
- works both on GPU and CPU
- user-extensible scalar and tensor derivative definitions
- API for custom perturbation seeding
- configurable dynamic and static execution modes
- nested/higher-order differentiation
- tape-level sparsity optimizations
- dependency analysis (e.g. simplifying multivariable chain rule application/compiling away needless additions)