torch-mice 0.4.7

Mixture of Convex Experts (MiCE) for PyTorch

MiCE (Mixture of Convex Examples)

What is MiCE?

MiCE is a lightweight PyTorch library for building convex large language models via a series of novel mechanisms. Instead of softmax routing or hard top-k gating, MiCE seeks to apply fusing, kernels, convex embedding, atlas lookups, intrinsically convex neural networks and convex norms.

Conditioned on the right data, this helps guarantee convexity, interpretability, and efficient compute without exponentials or discrete dispatch.

Why MiCE?

• Efficiency
  No softmax, no log-sum-exp,no MLP, very little to no concavity anywhere in the model.

• Interpretability
  Clear regions of dominance — visualize arg-max and margins over latent parameters in any 2-D slice.

---

Feature Comparison

Feature	MiCE (MoMx)	LogsumEXP MoE	Hard MoE	Standard MLP
Routing	max(mean(…))	LogsumEXP(weights)	top-k mask	none
Convexity	✅ (vector-valued)	✅ (scalar only)	❌	❌
Atlas inversion	optional (invert)	—	—	—
Compute cost	~2.6× MLP	>10× (exp/log)	~k× experts	baseline
Params	~2.6× MLP	high	high	baseline
Gradient smoothness	high (piecewise convex)	smooth	sparse	smooth
Interpretability	high	medium	low	low

---

Installation

pip install torch-mice

import torch
from torch_mice import VectorHull

# Forward-only mode (default):
hull = VectorHull(in_dim=512, petals=8, out_dim=512, invert=False)
y_fwd = hull(x)

# Full atlas mode with exact inversion:
hull_atlas = VectorHull(in_dim=512, petals=8, out_dim=512, invert=True)
y_atlas = hull_atlas(x)

License

Licensed under the Gratis Public License © 2025 Joshuah Rainstar