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gradhep / relaxed

Provides differentiable versions of common HEP operations and objectives.

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Backend independence a la pyhf

phinate opened this issue · comments

commented

pyhf has this awesome feature that it's inherently backend-independent thanks to the implementation of the pyhf.tensor submodule. I've ripped that straight out of pyhf, and used two of its functions for implementations of a differentiable histogram and cut.

This needs to be cleaned up to only use things that are actually used in practice, and additional support for fixed point differentiation needs to be added for backends other than jax. The code taken from neos also needs to be adapted to follow the protocol in tensor.

commented

Closing for now, as a jax-only solution seems like a viable way forward since gradients can be more easily transferred between frameworks due to recent work :)