6 ms·
Hi all, author here. A couple of relevant links for the curious Github: https://github.com/wsmoses/Enzyme https://github.com/wsmoses/Enzyme Paper: https://pr
by wsmoses 6y ago
Hi all, author here.
A couple of relevant links for the curious
Github: https://github.com/wsmoses/Enzyme https://github.com/wsmoses/Enzyme
Paper: https://proceedings.neurips.cc/paper/2020/file/9332c513ef44b682e9347822c2e457ac-Paper.pdf 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!
- freemint 6y agoHello, 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)