MLX Development Guide

Environment Setup

Use uv for Python environment and package management:

# Install MLX
uv add mlx

# Run MLX scripts
uv run python train.py

# Run with specific dependencies
uv run --with mlx python script.py

Critical Rules

1. Lazy Evaluation - Always Evaluate at Loop Boundaries

Operations build a graph; nothing computes until mx.eval():

# CORRECT: Evaluate at iteration boundaries
for batch in dataset:
    loss, grads = value_and_grad_fn(model, batch)
    optimizer.update(model, grads)
    mx.eval(loss, model.parameters())  # ALL computation here

# WRONG: Evaluating too frequently
for _ in range(100):
    a = a + b
    mx.eval(a)  # Massive overhead!

Implicit eval triggers: print(a), a.item(), np.array(a), if a > 0:.

2. Array Indexing Differs from NumPy

# Lists must be mx.array
a[[0, 1]]              # ValueError!
a[mx.array([0, 1])]    # Works

# Slice indices must be Python ints
i = mx.array(2)
x[i:i+2]               # ValueError!
x[i.item():i.item()+2] # Works (forces eval)

# Slices create COPIES, not views (opposite of NumPy)
b = a[:]
b[2] = 0  # a is unchanged!

# Boolean mask READS not supported
a[mask]  # Not supported - use mx.where()

# No bounds checking - out-of-bounds returns garbage

For accumulating updates, use at[] syntax:

a = a.at[idx].add(1)  # Properly accumulates at duplicate indices

See references/array-indexing.md for complete patterns.

3. Neural Networks: NHWC Format and call

# Conv2d uses NHWC (not NCHW like PyTorch)
x_mlx = mx.array(x_torch.numpy().transpose(0, 2, 3, 1))

# Override __call__, not forward()
class MyModel(nn.Module):
    def __call__(self, x):  # NOT forward()
        return self.layer(x)

# No dtype in constructors - use set_dtype()
layer = nn.Linear(10, 10)
layer.set_dtype(mx.bfloat16)

See references/neural-networks.md for layer equivalents.

4. Data Types: float64 is CPU-Only

a = mx.array([1.0], dtype=mx.float64)
mx.exp(a, stream=mx.gpu)  # RuntimeError!

# Solutions:
mx.exp(a, stream=mx.cpu)
mx.exp(a.astype(mx.float32))

# bfloat16 from external sources gets misinterpreted
from ml_dtypes import bfloat16
x = np.array(1., dtype=bfloat16)
mx.array(x)  # Returns complex64!
mx.array(x.astype(np.float32), dtype=mx.bfloat16)  # Correct

See references/dtypes.md for full type support table.

5. Compilation: Capture All Mutable State

from functools import partial

state = [model.state, optimizer.state, mx.random.state]  # Include random!

@partial(mx.compile, inputs=state, outputs=state)
def train_step(x, y):
    loss, grads = nn.value_and_grad(model, loss_fn)(model, x, y)
    optimizer.update(model, grads)
    return loss

# No print() in compiled functions - crashes during tracing
# String decoding triggers recompilation - decode outside loop

See references/compilation.md for recompilation triggers.

Quick Reference Tables

Dtype Support

Type	GPU	Notes
float32	Yes	Default float
float16	Yes
bfloat16	Yes	M3+ recommended
float64	CPU only	GPU throws!
int8-64, uint8-64	Yes
complex64	Partial	No matmul

PyTorch -> MLX Equivalents

PyTorch	MLX
`tensor.to('cuda')`	Not needed (unified memory)
`nn.forward()`	`nn.__call__()`
NCHW format	NHWC format
`torch.gather()`	`mx.take_along_axis()`
`torch.scatter_add_()`	`arr.at[idx].add()`

Not Available in MLX

np.nonzero() - restructure algorithm
np.unique() - pre-sort or use dicts
arr[bool_mask] read - use mx.where()
np.linalg.det(), np.linalg.lstsq()

Performance Notes

Transformers: MLX typically 2-3x faster than PyTorch MPS
Convolutions: 10-150x SLOWER than PyTorch MPS (known limitation)
LLM inference: Excellent, especially quantized
Use float16/bfloat16 for 2x memory bandwidth
Use 4-bit quantization for LLMs (4x bandwidth)

Idiomatic Training Example

import mlx.core as mx
import mlx.nn as nn
import mlx.optimizers as optim
from functools import partial

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = [nn.Linear(784, 256), nn.Linear(256, 10)]

    def __call__(self, x):
        for layer in self.layers[:-1]:
            x = mx.maximum(layer(x), 0)
        return self.layers[-1](x)

def loss_fn(model, x, y):
    return nn.losses.cross_entropy(model(x), y, reduction="mean")

model = Model()
optimizer = optim.AdamW(learning_rate=1e-3)

state = [model.state, optimizer.state, mx.random.state]

@partial(mx.compile, inputs=state, outputs=state)
def train_step(x, y):
    loss, grads = nn.value_and_grad(model, loss_fn)(model, x, y)
    optimizer.update(model, grads)
    return loss

for epoch in range(num_epochs):
    for x_batch, y_batch in dataloader:
        loss = train_step(x_batch, y_batch)
        mx.eval(state)
    print(f"Epoch {epoch}: {loss.item():.4f}")

mlx-dev