Hi,
I have a likelihood function in which if I have a data point which is, say, 68, I must then calculate 68 derivatives. I have hard-coded a decent amount of derivatives already. Because of the nature of the likelihood function and how I must use a ridiculous order of derivatives, I need an arbitrary floating point library for which I use mpmath. I’ve tried optimizing my code by dynamically switching the precision, using parallel python or gnu parallel and HPCs, etc. I’m debating on whether or not I should migrate to C++ to get the calculation even quicker. Anyway, my question, does pytorch enable the use of arbitrary floating point numbers in its automatic differentiation? I’d like to experiment with the idea of staying in python, automatic differentiation, and employing GPUs to speed up my own workflow and optimize computations. Also, does pytorch’s JIT allow for mpmath or another arbitrary floating precision module? For reference, I don’t think Numba does allow arb floating precision numbers.
Thanks,
Zach