Hello,
is it a forward or a backward (or both) differentiation library?
How does the performnce of your code compare to Julia's autodiff source to source code?
Does it solve from the exp/sin/cos problem? (By differentiating a polynomial approximation you use an approximation one degree lower)
3 comments
[ 6.1 ms ] story [ 6.5 ms ] threadA couple of relevant links for the curious
Github: https://github.com/wsmoses/Enzyme
Paper: https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b...
Project: enzyme.mit.edu
Basically the long story short is that Enzyme has a couple of really interesting contributions:
1) Low-level AD IS possible and can be high performance
2) By working at LLVM we get cross-language and cross-platform AD
3) Working at the LLVM level actually can give more speedups (since it's able to be performed after optimization)
4) We made a plugin for PyTorch/TF that uses Enzyme to import foreign code into those frameworks with ease!
How does the performnce of your code compare to Julia's autodiff source to source code? Does it solve from the exp/sin/cos problem? (By differentiating a polynomial approximation you use an approximation one degree lower)