Re:Foundations for Efficient and Expressive Differentiable P...
by tiarkrompf on Sunday December 23, 2018 @06:23PM
(#18749101)
Thanks! Yes, it wasn't obvious at all when we started looking at AD either.
Lantern supports a good deal of PyTorch (via Snek, our Python front-end similar to AutoGraph) and can also read ONNX. Full feature parity is not our main goal--so far, supported features have been driven mostly by what is required for certain interesting models.
Re:Foundations for Efficient and Expressive Differentiable P...
by p1esk on Monday December 24, 2018 @05:41PM
(#18754681)
How does this effort compare to Myia https://github.com/mila-udem/myia ?
Like all really good ideas, this one seems "obvious" in hindsight. I mean that is a compliment: It would have never occurred to me that transforming code into continuation-passing-style code would allow for automatic differentiation through all dynamic control-flow structures, by leveraging the function-call stack, thus eliminating the need for some kind of "tape" data structure, e.g., as in PyTorch.
My question is about the ongoing work to provide a JIT compiler for Python code. Do you expect it will provide full support for the entire PyTorch and/or Tensorflow APIs?