mlx-arsenal
Low-level operations and reusable building blocks missing from MLX core — the toolbox you want when porting PyTorch models to Apple Silicon.
Tip: if you use Claude Code for MLX ports, the
mlx-portingskill teaches Claude to reach formlx-arsenalsubmodules (diffusion,spatial,attention,norm,encoding,moe,tiling, etc.) before hand-rolling ops.
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, spatial_only_mask, temporal_only_mask, sliding_tile_block_mask, sliding_tile_centered_mask, radial_box_mask, radial_gaussian_mask, frame_stride_diagonal_mask, vertical_stripe_mask, classify_heads_from_qk, classify_heads_from_probs, classify, Kind, block_contiguous_permutation, invert_permutation, block_causal_mask, centroid_compensated_attention, probe_residual_correction, select_probe_rows, tile_labels, antidiagonal_block_scores, top_p_block_mask, block_self_similarity, select_tiling, minmax_block_scores, top_k_block_mask |
Attention mask creation (LLM, block-diffusion LLM, sparse video DiT), head-pattern profiler, SVG2 block-contiguous token permutation, sparse-block compensation (SVG-EAR centroids, SparsePR probe repair), dynamic block masks (XAttention, SPADE adaptive tiling) |
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, DDIMScheduler, euler_step, classifier_free_guidance, TeaCacheController, PerLayerAttentionCache, PerHeadAttentionCache, splice_heads, cfg_head_similarity, cfg_skip_mask, CFGSimilarityProfiler, CFGSkipController, WindowResidualController, VerifiedFeatureCache, geometric_threshold, token_stats, threshold_transfer, topk_transfer, factor_transfer, entropy_bound_transfer, transfer_schedule, block_ranges, pooled_qk, qk_drift, HeadMaskCache |
Flow-matching + DDIM diffusion primitives, TeaCache, AST attention caches, ASC cond/uncond skip, WA-RS residual sharing, masked diffusion LLM (dLLM) block-decoding commit rules, head-wise sparse-mask reuse (HEART) |
mlx_arsenal.moe |
MoEGate, MoELayer |
Top-k mixture-of-experts dispatch |
mlx_arsenal.rasterize |
rasterize_triangles, interpolate |
Tile-binned triangle rasterization with exact fixed-point coverage (Metal kernels) |
mlx_arsenal.tiling |
tiled_process, temporal_slice_process |
Memory-efficient large tensor processing |
mlx_arsenal.streaming |
BlockStreamer, BlockLoraSource, LoraFuser |
Low-RAM transformer block streaming from mmap'd safetensors |
mlx_arsenal.modulation |
AdaLNModulation, ScaleShiftTable, modulate, gated_residual |
DiT AdaLN modulation primitives (1 / 2 / 6 / 9-param variants) |
mlx_arsenal.ffn |
FeedForward, GatedFFN, GeGLU, SwiGLU |
Transformer FFN / MLP blocks (vanilla + gated variants) |
mlx_arsenal.loader |
SDOps, SafetensorsStateDictLoader, StateDict, read_safetensors_metadata |
State-dict key remapping chain + safetensors loader |
mlx_arsenal.rope |
rope_frequencies_1d, rope_frequencies_nd, apply_rotary_emb, rotate_half, meshgrid_nd |
Rotary Position Embeddings (N-D, interleaved + half-rotated variants) |
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)
Block streaming (low-RAM transformers)
Run a 20+ GB transformer on a Mac without holding every block resident at once: keep one shared block module, and rebind its weights from memory-mapped safetensors before each block's forward.
from mlx_arsenal.streaming import BlockStreamer
# Build the model with ONE block in transformer_blocks (not num_layers).
model = build_my_transformer(num_layers=1)
load_non_block_weights(model, weights_path)
streamer = BlockStreamer(
weights_path,
block_prefix="transformer.transformer_blocks.",
)
assert streamer.block_count == num_layers # discovered from safetensors
shared_block = model.transformer_blocks[0]
prev_idx = None
for i in range(streamer.block_count):
streamer.bind(shared_block, idx=i, evict_previous=prev_idx)
x = shared_block(x, ...) # use the rebound block
prev_idx = i
For LoRA: pass a lora_fuser callable to BlockStreamer and one or
more BlockLoraSource instances to bind(..., lora_sources=...).
Quantization-aware fusion strategies stay in the caller — arsenal
only handles the discovery + indexing.
Requirements
- Python >= 3.10
- MLX >= 0.32.1
- Apple Silicon Mac
Development
pip install -e ".[dev]"
pytest tests/
# Optional: install the pre-commit hook so ruff runs on every `git commit`.
pip install pre-commit
pre-commit install
License
Apache 2.0
1 maintainer
Metadata
1 maintainer
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