clifra 1.0.0

A PyTorch framework for Clifford geometric algebra deep learning

clifra

Layout-first Clifford algebra tools for PyTorch.

Clifra exposes one planner-owned algebra host. Full-lane tensors and compact grade layouts share the same algebra, while planning builds static executors for products, metrics, exponentials, actions, layers, and analysis utilities.

Install

uv sync
uv sync --extra dev

Docs:

uv sync --group docs
uv run --group docs mkdocs serve

Minimal Use

import torch

from clifra import make_algebra

algebra = make_algebra(3, 0, device="cpu")
vectors = algebra.layout((1,))
products = algebra.plan_product(
    op="gp",
    left_layout=vectors,
    right_layout=vectors,
    output_layout=algebra.layout((0, 2)),
)

left = torch.randn(8, vectors.dim)
right = torch.randn(8, vectors.dim)
out = products(left, right)

Checks

uv run pytest tests/ -m unit -q --tb=short
uv run pytest tests/ -m "not slow" -q --tb=short
uv run ruff check .
uv run --group docs mkdocs build

Docs

The docs are intentionally small: API pages come from live docstrings, and docs/core-design.md keeps the process snippets.

License

Apache License 2.0. See LICENSE and NOTICE.

Citation

@software{kim2026clifra,
  author  = {Kim, Eunkyum},
  title   = {clifra: Clifford Algebra Layers for PyTorch},
  url     = {https://github.com/Concode0/clifra},
  version = {1.0.0},
  year    = {2026},
  doi     = {10.5281/zenodo.18939519},
  license = {Apache-2.0}
}