NiLang.jl (逆lang), is a reversible domain-specific language (DSL) that allow a program to go back to the past.
- Requires Julia version >= 1.3,
NiLang features:
- any program written in NiLang is differentiable,
- a reversible language with abstraction and arrays,
- complex values
- reversible logarithmic number system
The main docs can be found here:
There are also some Pluto-based notebooks:
The strangeness of reversible computing is mainly due to our lack of experience with it.—Henry Baker, 1992
To Start
An example: Compute the norm of a vector
julia> using NiLang julia> @i function f(res, y, x) for i=1:length(x) y += x[i] ^ 2 end res += sqrt(y) end julia> res_out, y_out, x_out = f(0.0, 0.0, [1, 2, 3.0]) (3.7416573867739413, 14.0, [1.0, 2.0, 3.0]) julia> (~f)(res_out, y_out, x_out) # automatically generated inverse program. (0.0, 0.0, [1.0, 2.0, 3.0]) julia> ∂res, ∂y, ∂x = NiLang.AD.gradient(Val(1), f, (0.0, 0.0, [1, 2, 3.0])) # automatic differentiation, `Val(1)` means the first argument of `f` is the loss. (1.0, 0.1336306209562122, [0.2672612419124244, 0.5345224838248488, 0.8017837257372732])
The performance of reversible programming automatic differentiation is much better than most traditional frameworks. Here is why, and how it works,
Check our paper
@misc{Liu2020, title={Differentiate Everything with a Reversible Programming Language}, author={Jin-Guo Liu and Taine Zhao}, year={2020}, eprint={2003.04617}, archivePrefix={arXiv}, primaryClass={cs.PL} }