mlx-mhc 0.3.0

MLX implementation of Manifold-Constrained Hyper-Connections (mHC) for Apple Silicon

mlx-mhc

First MLX implementation of DeepSeek's Manifold-Constrained Hyper-Connections (mHC) for Apple Silicon.

Based on: arXiv:2512.24880

Installation

pip install mlx-mhc

Quick Start

import mlx.core as mx
import mlx_mhc as mhc

# Sinkhorn-Knopp projection to doubly stochastic matrix
matrix = mx.random.normal((8, 8))
doubly_stochastic = mhc.sinkhorn_knopp(matrix)

# Manifold Hyper-Connection module
connection = mhc.ManifoldHyperConnection(dims=512, expansion=2)
output = connection(x, layer_output)

What is mHC?

mHC (Manifold-Constrained Hyper-Connections) improves training stability for large language models by constraining residual connection mixing matrices to the Birkhoff polytope (doubly stochastic matrices).

Key benefits:

• Prevents gradient explosion in deep networks
• Maintains identity mapping property
• 2.1% improvement on benchmarks with only 6.7% overhead

API

sinkhorn_knopp(matrix, max_iterations=100, epsilon=1e-6, log_space=True)

Project a matrix onto the Birkhoff polytope (set of doubly stochastic matrices).

ManifoldHyperConnection(dims, expansion=2, sinkhorn_iterations=10)

MLX module implementing mHC for transformer residual connections.

License

MIT