polyweave 0.1.0

Multiplicative (Sigma-Pi) layers and hypernetworks for weight generation.

PolyWeave

Multiplicative (Sigma-Pi) layers and hypernetworks for weight generation.

PolyWeave is a small, modular PyTorch library for building hypernetworks — networks that generate the weights of another network — with an emphasis on multiplicative (Sigma-Pi) computation. Alongside a vanilla additive teacher, it provides Sigma-Pi layers whose multiplicative branch is a genuine geometric product, together with diagnostics that let you measure when multiplicative interactions are actually used rather than assuming they help.

The library underpins an ongoing series of papers on multiplicative hypernetworks. The first, "When Does the Pi Branch Fire?", introduces the pi-scale recruitment diagnostic; a follow-up studies how linear a transformer's feed-forward block really is by distilling it into a single layer. A proper citation block will be added here once the papers are public.

Status: v0.1.0 — alpha. The API may still shift before a PyPI release. 📖 Documentation: https://Notabot123.github.io/polyweave/

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Key ideas

• Genuine geometric-product pi branch. The multiplicative branch forms a weighted product of the input magnitudes in log space and exponentiates back: pi = exp(pi_scale) · ∏ᵢ |xᵢ| ^ wᵢ, with bounded signed exponents w = max_exponent · tanh(raw) (so factors can amplify or divide), geometric-mean normalisation for scale-freeness, and a clamp for stability. The signed sigma (additive) branch carries sign. This really multiplies — unlike the deprecated tanh(W·signed_log(x)) form, which never exponentiated back to a product.
• Recruitment diagnostics. Two complementary read-outs measure how much the product is used: exponent_abs_mean() reads the weights (how far the product departs from a no-op), and branch_energy(x) reads the activations (the pi branch's share of the output). The historical gate pi_scale_mean() = exp(pi_scale).mean() is also exposed.
• Measurement, not assumption. These diagnostics describe whether a layer uses a product — they are a probe of structure, not a guarantee of accuracy. PolyWeave is built to ask that question honestly across different targets. See the Concepts guide for details.

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Installation

PolyWeave is not yet on PyPI; install from source:

git clone https://github.com/Notabot123/polyweave.git
cd polyweave
pip install -e .                 # core library (torch, matplotlib)
pip install -e ".[experiments]"  # + torchvision, to run the paper experiments
pip install -e ".[distill]"      # + transformers, datasets, for the distillation study
pip install -e ".[dev]"          # + pytest, pytest-cov, to run the test suite
pip install -e ".[docs]"         # + mkdocs-material, mkdocstrings, to build the docs

Once published, installation will simply be pip install polyweave.

Requires Python ≥ 3.9 and PyTorch ≥ 2.0.

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Quickstart

The layers are drop-in nn.Modules. Each exposes the pi_scale_mean diagnostic.

import torch
from polyweave import ConvSigmaPi2d, SigmaPiLinear

# A channels-preserving Sigma-Pi conv block (additive + signed-log multiplicative).
block = ConvSigmaPi2d(channels=32, kernel_size=3)
y = block(torch.randn(8, 32, 28, 28))
print(block.pi_scale_mean())   # how strongly the pi branch is recruited

# A Sigma-Pi fully-connected layer.
fc = SigmaPiLinear(in_features=128, out_features=64)
out = fc(torch.randn(8, 128))
print(fc.pi_scale_mean())

Building a weight-generating teacher, training it, and generating + installing weights into a student is covered by the hypernets, training, targets, and evaluation modules — see the experiment scripts below for end-to-end examples.

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Reproducing the paper experiments

The headline result is a cross-experiment "recruitment ordering" chart. Run the full multi-seed suite (downloads CIFAR-10 automatically on first run):

python -m polyweave.experiments.multiseed

This trains the FC, conv1, and attention-Q/K teachers across seeds, writes the aggregated numbers to plots/multiseed_results.json, and renders plots/polyweave_pi_ordering.{pdf,png}. It also saves the seed-42 conv1 models to models/seed42/conv1_models.pt, which the analysis scripts below reuse without retraining:

python -m polyweave.experiments.ensemble          --seed 42   # ensemble diversity
python -m polyweave.experiments.student_occlusion --seed 42   # occlusion sensitivity

Note: trained model payloads and downloaded datasets are not committed to the repo (they are large and fully regenerable). Run multiseed first to produce them.

Individual experiments can also be run on their own — see polyweave/experiments/ (cifar_fc.py, cifar_conv1.py, synthetic_attention.py).

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Project layout

polyweave/
  ops/             pure functions (signed-log, ...)
  layers/          nn.Module blocks: ConvSigmaPi2d, SigmaPiLinear, PolyLinear
  targets/         pack / unpack / install generated weights for a target layer
  prototypes/      compact support-set representations (statistical + learnable)
  students/        networks whose weights a teacher generates (CNN, transformer)
  hypernets/       full weight-generating teachers
  training/        generic teacher-training loop + checkpoint I/O
  evaluation/      zero-shot / recovery evaluation, ensembling
  interpretability/ occlusion sensitivity and related probes
  metrics.py       diagnostics (pi-scale, ensemble disagreement)
  viz/             publication-quality plotting (PDF, colourblind-safe palette)
  experiments/     runnable scripts reproducing the paper

One-off experiment drivers, plotting scripts, and paper drafts live under research/ (kept for provenance, excluded from the installed package). The shipped library is everything under polyweave/.

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Tests

pytest

The suite guards behavioural sanity (shapes, gradients, invariants), not bit-exact reproduction of training runs.

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License

Apache License 2.0 © 2026 Stuart Whipp. See LICENSE and NOTICE.