mhc-mlx 0.5.4

High-performance MLX implementation of Manifold-Constrained Hyper-Connections (mHC)

mhc-mlx

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

mHC improves training stability and performance in deep architectures by constraining residual connections to the Birkhoff polytope (doubly stochastic matrices). This library provides optimized Metal kernels that achieve significant speedups over standard baseline implementations.

Original Paper: mHC: Manifold-Constrained Hyper-Connections (DeepSeek-AI)

Installation

pip install mhc-mlx

Compatibility

• Hardware: Apple Silicon (M1, M2, M3, M4).
• Software: macOS, MLX >= 0.30.0.
• Fallback: Automatically falls back to a compiled pure-MLX path on other platforms.

Quick Start (30-second Demo)

import mlx.core as mx
import mlx.nn as nn
from mhc_mlx import MHCRewire

# 1. Take any standard MLX layer
layer = nn.Linear(2048, 2048)

# 2. Wrap it with mHC stability (automatically uses optimized Metal kernels)
model = MHCRewire(layer, dims=2048, n=32)

# 3. Run forward pass
x = mx.random.normal((1, 2048))
y = model(x)
mx.eval(y)

# 4. Run backward pass (fully vectorized)
loss_fn = lambda m, x: mx.sum(m(x))
grads = mx.grad(loss_fn)(model, x)
mx.eval(grads)

print(f"Output shape: {y.shape}") # (1, 2048)

Note: You can also use from mlx_mhc import MHCRewire for a community-friendly alias.

Performance

mhc-mlx utilizes fused Metal kernels to minimize memory bandwidth bottlenecks. We benchmarked on an Apple M4 Pro (macOS 15.6).

Comparative Benchmarks

Comparison with a standard MLX implementation of mHC ($C=512$):

Metric	mhc-mlx	Baseline Impl	Speedup
Inference Latency ($B=1$)	392 us	1120 us	2.86x
Training Throughput ($B=32$)	105 us	866 us	8.25x

Why It's Faster

Approach	Architecture	Impact
Baseline	Multiple kernel launches	High memory overhead, low GPU occupancy
mhc-mlx	Fused Metal Kernels	Minimal memory round-trips, maximal bandwidth

Reproduce Benchmarks

Run the standardized benchmark suite on your own hardware:

mhc-mlx-bench --mode latency --C 512,2048,4096

Key Optimizations

• "Zero-Cost" Weight Folding: MHCRewire folds scaling directly into nn.Linear weights where possible.
• Quantized Layer Support: Seamlessly wraps nn.QuantizedLinear (4-bit/8-bit).
• Fully Fused Kernel: Single-pass kernel for Aggregate + RMS + Mix + Add.
• Adaptive Dispatch: Runtime heuristic selects the fastest kernel strategy for your workload.

Diagnostics

If you encounter issues, run the diagnostic utility:

mhc-mlx-info

License

MIT