mlx_dart 0.1.0

MLX tensor operations and neural network primitives for Dart — the Dart equivalent of mlx-swift, for Apple silicon (macOS / iOS).

mlx_dart

A Dart port of mlx-swift — MLX tensor operations and neural network primitives for Dart on Apple silicon.

MLX is an array framework for machine learning on Apple silicon. mlx_dart brings the same API surface to Dart, wrapping the MLX C bindings via FFI.

Installation

Add to your pubspec.yaml:

dependencies:
  mlx_dart:
    git:
      url: https://github.com/jamiewest/mlx_dart.git

The package requires libmlxc.dylib (or libmlx.dylib) to be present on the library search path at runtime. Build it from ml-explore/mlx-c.

Quick Start

import 'package:mlx_dart/mlx_dart.dart';

void main() {
  final ctx = MLXContext.gpu();

  final a = MLXArray.fromFloats(ctx, [1, 2, 3, 4], shape: [2, 2]);
  final b = MLXArray.ones(ctx, [2, 2]);
  final c = a.matmul(b);
  c.eval();

  print(c.toFloat32List()); // [3.0, 3.0, 7.0, 7.0]

  c.dispose();
  b.dispose();
  a.dispose();
  ctx.dispose();
}

API Status

The table below tracks progress against mlx-swift modules.

MLX Core (Source/MLX in mlx-swift)

Feature	mlx-swift	mlx_dart
MLXContext (device + stream)	Device / Stream	MLXContext
MLXDtype	DType	MLXDtype
Array construction	MLXArray.init(...)	MLXArray.zeros/ones/arange/fromFloats/fromInts/bool_/int_/float_
Properties (ndim, shape, size, dtype, nbytes)	yes	yes
eval() / evalAll	yes	yes
Data extraction (toFloat32List, toInt32List, itemFloat, itemInt)	yes	yes
toString()	yes	yes
Arithmetic operators (+, -, *, /)	yes	yes
Shape ops (reshape, expandDims, squeeze, flatten, transpose, T, swapAxes)	yes	yes
Slicing (slice, sliceUpdate, take)	yes	yes
Reductions (sum, mean, max, argmax, softmax, cumsum, sort, argsort)	yes	yes
Element-wise math (exp, log, sqrt, rsqrt, abs, sigmoid, tanh, sin, cos, square, erf, floor, ceil, round, clip, pad)	yes	yes
Comparisons (greaterEqual, less, logicalAnd)	yes	partial
Type casting (astype)	yes	yes
matmul	yes	yes
concatenate, stack	yes	yes
where	yes	yes
conv2d	yes	yes
repeat, split	yes	yes
Fast NN primitives (rmsNorm, layerNorm, rope, scaledDotProductAttention)	MLXFast	yes (on MLXArray)
Causal mask utility	createCausalMask	createCausalMask
Bilinear resize	custom	bilinearResize
Sampling (argmaxSample, categoricalSample)	various	yes
Missing ops (min, prod, std, var, log1p, log2, logSumExp, maximum, minimum, negative, notEqual, isNaN, isInf, sign, sinh, cosh, tan, atan, acos, asin, expm1, erfInverse, tril, triu, trace, top, roll, einsum, inner, outer, kron, tensordot, tiled, nanToNum, degrees, radians)	yes	not yet
eye, full, linspace factory ops	yes	not yet
conv1d, conv3d, transposed convolutions	yes	not yet
Grad transforms (grad, vjp, jvp, valueAndGrad)	yes	not yet
vmap	yes	not yet
compile	yes	not yet
MLXCustomFunction	yes	not yet
Memory / wired memory	yes	not yet
State / global stream management	yes	not yet
IO (safetensors, GGUF)	yes	not yet

MLXRandom (Source/MLXRandom)

Feature	mlx-swift	mlx_dart
seed, key, split	yes	not yet
uniform	yes	not yet
normal, multivariateNormal	yes	not yet
randInt	yes	not yet
bernoulli	yes	not yet
truncatedNormal	yes	not yet
gumbel	yes	not yet
categorical	partial (on MLXArray)	categoricalSample
laplace	yes	not yet

MLXNN (Source/MLXNN)

Infrastructure

Feature	mlx-swift	mlx_dart
Module base class	yes	Module
Parameter tracking / parameters()	yes	ParametersMixin
loadWeights	yes	yes
sanitize	yes	yes
WeightLoader	—	WeightLoader
Sequential container	yes	not yet
UnaryLayer protocol	yes	not yet
Value-and-grad helpers	ValueAndGrad	not yet

Layers

Layer	mlx-swift	mlx_dart
Linear	yes	yes
Embedding	yes	yes
RMSNorm	yes	yes
LayerNorm	yes	yes
Conv2d	yes	yes
GroupNorm	yes	not yet
BatchNorm	yes	not yet
InstanceNorm	yes	not yet
Conv1d	yes	not yet
Conv3d	yes	not yet
ConvTransposed1d/2d/3d	yes	not yet
Dropout, Dropout2d, Dropout3d	yes	not yet
MaxPool1d/2d/3d	yes	not yet
AvgPool1d/2d/3d	yes	not yet
MultiHeadAttention	yes	not yet
Transformer (encoder + decoder)	yes	not yet
SinusoidalPositionalEncoding	yes	not yet
RNN, GRU, LSTM	yes	not yet
Upsample	yes	not yet
Quantized layers	yes	not yet
KV cache (Cache, RotatingKVCache)	yes	not yet

Activations

Functional activations available on MLXArray: relu(), gelu(), silu(), sigmoid(), tanh(), softmax().

Activation	mlx-swift	mlx_dart
relu, gelu, silu, sigmoid, tanh	yes	yes (on MLXArray)
leakyRelu	yes	not yet
elu, celu	yes	not yet
relu6, reluSquared	yes	not yet
logSoftmax	yes	not yet
softPlus / softsign	yes	not yet
softshrink	yes	not yet
Activation layer classes (ReLU, GELU, etc.)	yes	not yet

Losses

Loss	mlx-swift	mlx_dart
crossEntropy	yes	not yet
binaryCrossEntropy	yes	not yet
l1Loss, mseLoss	yes	not yet
nllLoss, klDivLoss	yes	not yet
smoothL1Loss, huberLoss	yes	not yet
hingeLoss, tripletLoss	yes	not yet
logCoshLoss, cosineSimilarityLoss	yes	not yet

MLXOptimizers (Source/MLXOptimizers)

Optimizer	mlx-swift	mlx_dart
SGD	yes	not yet
Adam, AdamW, Adamax	yes	not yet
RMSprop, AdaGrad, AdaDelta	yes	not yet
Lion, Adafactor	yes	not yet

MLXLinalg (Source/MLXLinalg)

Feature	mlx-swift	mlx_dart
norm	yes	not yet
svd, qr, lu	yes	not yet
inv, triInv, cholesky, choleskyInv	yes	not yet
solve, solveTriangular	yes	not yet
cross	yes	not yet

MLXFFT (Source/MLXFFT)

Feature	mlx-swift	mlx_dart
fft, ifft, fft2, ifft2, fftn, ifftn	yes	not yet
rfft, irfft, rfft2, irfft2	yes	not yet

Architecture

mlx_dart
├── lib/src/
│   ├── context.dart   — MLXContext, MLXException
│   ├── array.dart     — MLXArray, MLXDtype, free ops
│   ├── module.dart    — Module, ParametersMixin, WeightLoader
│   └── layers.dart    — Linear, Embedding, RMSNorm, LayerNorm, Conv2d
└── pubspec.yaml

Key design decisions mirroring mlx-swift:

• Explicit context — MLXContext wraps a device + stream. All arrays belong to a context (mlx-swift uses an implicit global default, Dart makes it explicit).
• Manual disposal — MLXArray.dispose() must be called when an array is no longer needed (no GC finaliser). Consider try/finally patterns.
• Functional style — operations return new arrays rather than mutating in place, matching mlx-swift.

Contributing

Contributions expanding coverage of the mlx-swift API surface are welcome. Good first targets:

1. Additional MLXArray ops (missing element-wise math, reductions, factory ops)
2. MLXRandom module
3. Sequential container and remaining MLXNN layers
4. MLXOptimizers
5. Grad / vmap transforms

License

Apache 2.0 — see LICENSE.