@frost-beta/mlx
v0.1.0
Published
Node-API bindings for MLX
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node-mlx
A machine learning framework for Node.js, based on MLX.
This project is not affiliated with Apple, you can support the development by sponsoring me.
Supported platforms
GPU support:
- Macs with Apple Silicon
CPU support:
- x64 Macs
- x64/arm64 Linux
Examples
- llm.js - Large language models implemented with JavaScript, vision models included.
- not-a-vector-database - Node.js module for storing and searching embeddings.
- sisi - Semantic Image Search CLI.
- clip - Node.js module for the CLIP model.
- train-model-with-js - Train a simple text generation model with JavaScript.
- train-llama3-js - Train a tiny Llama3 model with parquet datasets.
- train-japanese-llama3-js - Train a Japanese language model.
- fine-tune-decoder-js - Fine-tune a decoder-only model.
Usage
import mlx from '@frost-beta/mlx';
const {core: mx, nn} = mlx;
const model = new nn.Sequential(
new nn.Sequential(new nn.Linear(2, 10), nn.relu),
new nn.Sequential(new nn.Linear(10, 10), new nn.ReLU()),
new nn.Linear(10, 1),
mx.sigmoid,
);
const y = model.forward(mx.random.normal([32, 2]));
console.log(y);
APIs
There is currently no documentations for JavaScript APIs, please check the TypeScript definitions for available APIs, and MLX's official website for documentations.
The JavaScript APIs basically duplicate the official Python APIs by converting
the API names from snake_case to camelCase. For example the mx.not_equal
Python API is renamed to mx.notEqual
in JavaScript.
There are a few exceptions due to limitations of JavaScript:
- JavaScript numbers are always floating-point values, so the default dtype
of
mx.array(42)
ismx.float32
instead ofmx.int32
. - The
mx.var
API is renamed tomx.variance
. - Operator overloading does not work, use
mx.add(a, b)
instead ofa + b
. - Indexing via
[]
operator does not work, usearray.item
andarray.itemPut_
methods instead (the_
suffix means inplace operation). delete array
does nothing, you must wait for garbage collection to get the array's memory freed or usemx.dispose
.- The
Module
instances can not be used as functions, theforward
method must be used instead.
Unimplemented features
Some features are not supported yet and will be implemented in future:
- The
distributed
module has not been implemented. - The
mx.custom_function
API has not been implemented. - The custom Metal kernel has not been implemented.
- It is not supported using JavaScript Array as index.
- The function passed to
mx.vmap
must have all parameters beingmx.array
. - The captured
inputs
/outputs
parameters ofmx.compile
has not been implemented. - When creating a
mx.array
from JavaScript Array, the Array must only include primitive values. - The APIs only accept plain parameters, e.g.
mx.uniform(0, 1, [2, 2])
. Named parameter calls likemx.uniform({shape: [2, 2]})
has not been implemented. - The
.npz
tensor format is not supported yet.
JavaScript-only APIs
There are a few new APIs in node-mlx, for solving JavaScript-only problems.
Constructor of mx.array
You can pass following JavaScript types to mx.array
:
number
/boolean
/mx.Complex
Array<T>
new Array(length)
- Creates a 1D array filled with 0.Buffer
- Same withUInt8Array
.Int8Array
/Uint8Array
/Int16Array
/Uint16Array
/Int32Array
/Uint32Array
/Float32Array
mx.array.toTypedArray
While it is possible to use the tolist()
method of mx.array
to convert the
array to JavaScript Array
, a copy of data is invovled.
For 1D arrays whose dtype has a representation in JavaScript
TypedArray
,
you can use the toTypedArray()
method to expose the data to JavaScript without
copying.
const buffer: Uint8Array = mx.array([1, 2, 3, 4], mx.uint8);
mx.asyncEval
The mx.eval
API is synchronous that the main thread would be blocked waiting
for the result, which breaks the assumption of Node.js that nothing should block
in main thread, and results in a hanging process that not responding to
anything, including tries to end the process with Ctrl+C.
The mx.asyncEval
API is the asynchronous version that returns a Promise that
can be awaited for. It is useful when the program runs a large model and you
want to make the app alive to user interactions while doing computations.
const y = model.forward(x);
await mx.asyncEval(y);
mx.tidy
This is the same with tf.tidy
API of TensorFlow.js, which cleans up all intermediate tensors allocated in the
passed functions except for the returned ones.
let result = mx.tidy(() => {
return model.forward(x);
});
In addition, it also works with async functions.
await mx.tidy(async () => { ... });
mx.dispose
This is the same with tf.dispose
API of TensorFlow.js, which cleans up all the tensors found in the object.
mx.dispose({ a: mx.array([1, 2, 3, 4]) });
mx.dispose(mx.array([1]), mx.array([2]));
Complex numbers
There is no built-in complex numbers in JavaScript, and we use objects to represent them:
interface Complex {
re: number;
im: number;
}
You can also use the mx.Complex(real, imag?)
helper to create complex numbers.
Indexing
Slice in JavaScript is represented as object:
interface Slice {
start: number | null;
stop: number | null;
step: number | null;
}
You can also use the mx.Slice(start?, stop?, step?)
helper to create slices.
The JavaScript standard does not allow using ...
as values. To use ellipsis as
index, use string "..."
instead.
When using arrays as indices, make sure a integer dtype is specified because
the default dtype is float32
, for example
a.index(mx.array([ 1, 2, 3 ], mx.uint32))
.
Here are some examples of translating Python indexing code to JavaScript:
Getters
| Python | JavaScript |
|--------------------------------------|----------------------------------------------------|
| array[None]
| array.index(null)
|
| array[Ellipsis, ...]
| array.index('...', '...')
|
| array[1, 2]
| array.index(1, 2)
|
| array[True, False]
| array.index(true, false)
|
| array[1::2]
| array.index(mx.Slice(1, None, 2))
|
| array[mx.array([1, 2])]
| array.index(mx.array([1, 2], mx.int32))
|
| array[..., 0, True, 1::2]
| array.index('...', 0, true, mx.Slice(1, null, 2)
|
Setters
| Python | JavaScript |
|--------------------------------------|--------------------------------------------------------------|
| array[None] = 1
| array.indexPut_(null, 1)
|
| array[Ellipsis, ...] = 1
| array.indexPut_(['...', '...'], 1)
|
| array[1, 2] = 1
| array.indexPut_([1, 2], 1)
|
| array[True, False] = 1
| array.indexPut_([true, false], 1)
|
| array[1::2] = 1
| array.indexPut_(mx.Slice(1, null, 2), 1)
|
| array[mx.array([1, 2])] = 1
| array.indexPut_(mx.array([1, 2], mx.int32), 1)
|
| array[..., 0, True, 1::2] = 1
| array.indexPut_(['...', 0, true, mx.Slice(1, null, 2)], 1)
|
Translating between Python/JavaScript index types
| Python | JavaScript |
|----------------------|------------------------------|
| None
| null
|
| Ellipsis
| "..."
|
| ...
| "..."
|
| 123
| 123
|
| True
| true
|
| False
| false
|
| :
or ::
| mx.Slice()
|
| 1:
or 1::
| mx.Slice(1)
|
| :3
or :3:
| mx.Slice(null, 3)
|
| ::2
| mx.Slice(null, null, 2)
|
| 1:3
| mx.Slice(1, 3)
|
| 1::2
| mx.Slice(1, null, 2)
|
| :3:2
| mx.Slice(null, 3, 2)
|
| 1:3:2
| mx.Slice(1, 3, 2)
|
| mx.array([1, 2])
| mx.array([1, 2], mx.int32)
|
Building
For building on platforms other than Macs with Apple Silicon, you must have blas installed.
# Linux
sudo apt-get install -y libblas-dev liblapack-dev liblapacke-dev
# x64 Mac
brew install openblas
This project is mixed with C++ and TypeScript code, and uses cmake-js to build the native code.
git clone --recursive https://github.com/frost-beta/node-mlx.git
cd node-mlx
npm install
npm run build -p 8
npm run test
Releasing
The prebuilt binaries are uploaded to the GitHub Releases, when installing node-mlx from npm registry, the prebuilt binaries will always be downloaded and there is no fallback for building from source code.
The version string is always 0.0.1-dev
in package.json
, which means local
development, and npm package can only be published via GitHub workflow by
pushing a new tag.
Versioning
Before matching the features and stability of the official Python APIs, this
project will stay on 0.0.x
for npm versions.
Syncing with upstream
The tests and most TypeScript source code were converted from the Python code
of the official MLX project, when updating the deps/mlx
submodule, review
every new commit and make sure changes to Python APIs/tests/implementations are
also reflected in this repo.