mlx-arsenal 0.2.1

Reusable mid-level building blocks for MLX — the missing layer between mlx.nn and full model implementations

mlx-arsenal

Low-level operations and reusable building blocks missing from MLX core — the toolbox you want when porting PyTorch models to Apple Silicon.

Install

pip install mlx-arsenal

Or directly from source:

pip install git+https://github.com/dgrauet/mlx-arsenal.git

Modules

Module	Components	Replaces (PyTorch)
mlx_arsenal.spatial	interpolate_nearest, interpolate_3d, avg_pool1d, replicate_pad, upsample_nearest/bilinear, pixel_shuffle/unshuffle, patchify/unpatchify, PatchEmbed2d/3d	F.interpolate, F.avg_pool1d, F.pad(mode="replicate"), F.pixel_shuffle
mlx_arsenal.layout	to_channels_last/first, channels_last ctx manager, convert_conv_weights, load_safetensors	NCHW ↔ NHWC conversion, weight transposition
mlx_arsenal.conv	weight_norm, WeightNorm	nn.utils.weight_norm
mlx_arsenal.attention	causal_mask, sliding_window_mask	Attention mask creation
mlx_arsenal.norm	PixelNorm, ScaleNorm	Custom normalization layers
mlx_arsenal.encoding	FourierEmbedder	Sinusoidal positional encoding
mlx_arsenal.diffusion	get_timestep_embedding, TimestepEmbedding, get_sampling_sigmas, dynamic_shift_schedule, FlowMatchEulerDiscreteScheduler, euler_step, classifier_free_guidance	Flow-matching diffusion primitives
mlx_arsenal.moe	MoEGate, MoELayer	Top-k mixture-of-experts dispatch
mlx_arsenal.rasterize	rasterize_triangles, interpolate	Differentiable triangle rasterization with Metal z-buffer
mlx_arsenal.tiling	tiled_process, temporal_slice_process	Memory-efficient large tensor processing

Quick start

from mlx_arsenal.spatial import interpolate_nearest, avg_pool1d, replicate_pad
from mlx_arsenal.layout import to_channels_last, convert_conv_weights
from mlx_arsenal.attention import causal_mask

# Resize a video tensor (B, D, H, W, C)
x_resized = interpolate_nearest(x, size=(8, 32, 32))

# Temporal pooling
pooled = avg_pool1d(temporal_features, kernel_size=2)

# Pad with edge replication (like F.pad mode="replicate")
padded = replicate_pad(x, [(0,0), (2,0), (1,1), (1,1), (0,0)])

# Convert PyTorch conv weights to MLX channels-last layout
mlx_weights = convert_conv_weights(pytorch_weights)

# Causal attention mask for autoregressive decoding
mask = causal_mask(seq_len=128, offset=kv_cache_len)

Requirements

• Python >= 3.10
• MLX >= 0.27.0
• Apple Silicon Mac

Development

pip install -e ".[dev]"
pytest tests/

License

Apache 2.0