Catniff

Catniff is an experimental tensor ops library and autograd engine made to be Torch-like (its name is a play on "catnip" and "differentiation"). This project is heavily under development currently, so keep in mind that APIs can be completely unstable and backwards-incompatible.

Setup

Install through npm:

npm install catniff

Example

Here is a little demo of a quadratic function:

const { Tensor } = require("catniff");

const x = new Tensor(2, { requiresGrad: true });
const L = x.pow(2).add(x); // x^2 + x

L.backward();

console.log(x.grad.val()); // 5

View all examples in ./examples.

Tensors

Tensors in Catniff can be created by passing in a number or an nD array, and there are built-in methods that can be used to perform tensor arithmetic:

const { Tensor } = require("catniff");

// Tensor init
const A = new Tensor([ 1, 2, 3 ]);
const B = new Tensor(3);

// Tensor addition (.val() returns the raw value rather than the tensor object)
console.log(A.add(B).val());

Autograd

To compute the gradient wrt multiple variables of our mathematical expression, we can simply set requiresGrad to true:

const { Tensor } = require("catniff");

const X = new Tensor(
    [
        [ 0.5, -1.0 ],
        [ 2.0,  0.0 ]
    ],
    { requiresGrad: true }
);

const Y = new Tensor(
    [
        [ 1.0, -2.0 ],
        [ 0.5,  1.5 ]
    ],
    { requiresGrad: true }
);

const D = X.sub(Y);
const E = D.exp();
const F = E.add(1);
const G = F.log();

G.backward();

// X.grad and Y.grad are tensor objects themselves, so we call .val() here to see their raw values
console.log(X.grad.val(), Y.grad.val());

Documentation

Full documentation is available in ./docs/documentation.md.

All available APIs are in ./src/core.ts if you want to dig deeper.

Todos

I'm mostly just learning and playing with this currently, so there are no concrete plans yet, but here is what I currently have in mind:

• Fix whatever is the problem right now (there are a lot of problems right now lol).
• Add more tensor ops.
• Proper documentation.
• GPU acceleration.
• Some general neural net APIs.
• Refactor code.
• Proper tests.
• Option to load more backends.

Copyrights and License

Copyrights © 2025 Nguyen Phu Minh.

This project is licensed under the GPL 3.0 License.