jsgrad

jsgrad is a dependency-free ML library in Typescript for model inference and training with support to WebGPU and other runtimes.

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jsgrad - A Modern ML Library for JavaScript and TypeScript

jsgrad is a rewrite of tinygrad in TypeScript. JS ecosystem is very large, but it didn't have a good ML library for model inference and training. Since tinygrad doesn't use any external python libraries, has potential to be the fastest way to run models, is quite simple compared to others and supports many runtimes, I decided to rewrite it in TS to get the same experience in browser and in deno/node/bun.

Why you should use jsgrad?

0 dependencies
will be fast (not yet)
Multiple runtime backends (WebGPU, WASM, CLANG, + others coming soon)
Clean, modern API inspired by tinygrad's elegant design
Works in browser and in Deno (Node and Bun support coming soon)

See MNIST inference and training example on jsgrad.org

Usage

There are multiple ways to use jsgrad:

Hosted esm script in JS (minimal Llama HTLM example)

import { MNIST, Tensor } from 'https://esm.sh/jsr/@jsgrad/jsgrad'

const mnist = await new MNIST().load()
console.log(await mnist.call(Tensor.ones([1, 1, 28, 28])).tolist())

Install package from jsr.io

# with deno
deno add jsr:@jsgrad/jsgrad
# with npm
npx jsr add @jsgrad/jsgrad
# with yarn
yarn dlx jsr add @jsgrad/jsgrad
# with pnpm
pnpm dlx jsr add @jsgrad/jsgrad
# with bun
bunx jsr add @jsgrad/jsgrad

and then import with

import { MNIST, Tensor } from '@jsgrad/jsgrad'

const mnist = await new MNIST().load()
console.log(await mnist.call(Tensor.ones([1, 1, 28, 28])).tolist())

Goal - The easiest and fastest way to run and train models in JS/TS.

Soon everything should work like this in browser and server with no install step, while still being fast:

Uses the fastest local runtime for Llama

const llama = await new Llama({ model: '3.1-3B' }).load()
const res = await llama.run({ prompt: 'Hello how are you?' })

Offload the computation to CLOUD

const llama = await new Llama({ model: '3.1-3B', device: 'CLOUD', host: process.env.CLOUD_HOST }).load()
const res = await llama.run({ prompt: 'Hello how are you?' })

Whisper

const whisper = await new Whisper({ model: 'large-v2' }).load()
const listening = whisper.startListening()
// after some time
const text = await listening.stop()

Text to speech

const tts = await new TTS()
const audio = await tts.run({ text: 'Hello how are you?' })
audio.play()

Training new models:

class MNIST extends Model {
  layers: Layer[] = [
    new nn.Conv2d(1, 32, 5),
    Tensor.relu,
    new nn.Conv2d(32, 32, 5),
    Tensor.relu,
    new nn.BatchNorm(32),
    Tensor.max_pool2d,
    new nn.Conv2d(32, 64, 3),
    Tensor.relu,
    new nn.Conv2d(64, 64, 3),
    Tensor.relu,
    new nn.BatchNorm(64),
    Tensor.max_pool2d,
    (x) => x.flatten(1),
    new nn.Linear(576, 10),
  ]
}

const [X_train, Y_train, X_test, Y_test] = await mnist()

const model = new MNIST()
const opt = Adam(get_parameters(model))

const train_step = async (): Promise<Tensor> => {
  Tensor.training = true
  opt.zero_grad()
  const samples = Tensor.randint([BS], undefined, X_train.shape[0])
  const loss = model.call(X_train.get(samples)).sparse_categorical_crossentropy(Y_train.get(samples)).backward()
  await opt.step()
  Tensor.training = false
  return loss
}

const get_test_acc = (): Tensor => model.call(X_test).argmax(1).eq(Y_test).mean().mul(100)

let test_acc = NaN
const t = new Tqdm(range(get_number_env('STEPS', 70)))
for await (const i of t) {
  const loss = await (await train_step()).item()
  if (i % 10 === 9) test_acc = await get_test_acc().item()
  t.set_description(`loss: ${loss.toFixed(2)}, test_accuracy: ${test_acc.toFixed(2)}`)
}
await model.save('./mnist.safetensors')

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