Automatic differentiation library written in pure Vim script.

vim-autograd provides a foundation for automatic differentiation through the Define-by-Run style algorithm such as Chainer or PyTorch. Since it is written completely in pure Vim script, there are no dependencies.

This library allows us to create next-generation plugins with numerical computation of multidimensional arrays or deep learning using the gradient descent method.

If you are using vim-plug, can install as follows.

Plug 'pit-ray/vim-autograd'

If you want to use the more efficient Vim9 script, please install the experimental vim9 branch implementation.

Plug 'pit-ray/vim-autograd', {'branch': 'vim9'}

A computational graph is constructed by applying the provided differentiable functions to a Tensor object, and the gradient is calculated by backpropagating from the output.

function! s:f(x) abort  " y = x^5 - 2x^3 let y = autograd#sub(a:x.p(5), a:x.p(3).m(2)) return y endfunction function! s:example() abort let x = autograd#tensor(2.0) let y = s:f(x) call y.backward() echo x.grad.data endfunction call s:example()

Output

[56.0] 

The computational graph is automatically generated like the below.

• Basic differentiation and computational graph visualization
• Higher-order differentiation using double-backprop
• Classification using deep learning

• https://zenn.dev/pitray/articles/482e89ddff329c

• oreilly-japan/deep-learning-from-scratch-3
• chainer/chainer
• pytorch/pytorch
• numpy/numpy
• mattn/vim-brain

This library is provided by MIT License.

• pit-ray