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Enzyme: Cross-language Automatic differentiation for LLVM IR
- achernya 6y agoIt looks like the links to the code have some junk characters at the end. It should be https://github.com/wsmoses/Enzyme https://github.com/wsmoses/Enzyme.
- wsmoses 6y agoHi 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)