Documentation

Adding a mixer — the extension point that matters most: a 1-D sequence model becomes an N-D one by satisfying one shape contract. Worked example, conformance report, and the mistakes to expect.
Adding an nd_method — changing how the axes are handled rather than what happens along one, including the two bugs the conformance suite found in this project's own example strategy.
CUDA verification checklist — every CUDA claim as one runnable file, and what it reported on an RTX 5090: 13 passed, 0 failed, plus the one gap that a Blackwell card could not close. Fifteen minutes on a free Colab T4 to re-run.
Hugging Face model card — the source for Celsia/torch-dimensions, where the benchmark checkpoints and agreement runs are published. Edited here, uploaded as that repo's README.md.

Evaluation and Device Comparison

One sheet, three devices, sixteen models, two benchmarks — regenerate it with python benchmarks/figure.py. Every panel reads artifact directories that already exist, so the figure cannot drift from the numbers it draws.

device	hardware	torch
CUDA	NVIDIA RTX 5090 (Blackwell, sm_120)	2.12.1+cu130
MPS	Apple Mac Studio, M1 Ultra (Metal)	2.13.0
CPU	Apple M1 Ultra (arm64)	2.13.0

All sixteen models on all three devices, in both benchmarks. The throughput panel is the surprising one: Mamba on CPU runs at 0.1 steps/s against 24 on CUDA — 455× — because the upstream reference scans are Python loops over sequence length, while the RTX 5090 loses to the M1 Ultra's CPU on the small S4 models, where launch overhead dominates.

CPU and MPS are the same machine and the same torch build, so the difference between them is the device alone; CPU-vs-CUDA crosses machines and versions. Worst float32 output difference across every pair is 3.11e-06, thirteen of sixteen models at or below 1e-06, and 2.2e-16 in float64.

Two corrections were needed before any of that was measurable, and both generalise beyond this project: CUDA does not run float32 by default (cudnn.allow_tf32 ships True — TF32 is 10 mantissa bits), and a fixed seed does not give identical S4 weights across platforms (torch.linalg.eigh fixes eigenvectors only up to a phase). Full detail in ../BENCH-README.md and ../AGREEMENT.md.

Elsewhere in the repo: DESIGN.md (the architecture), PLAN.md (what is built and what is next), RESULTS.md (reproductions), BENCHMARKS.md (measured costs), DEBUG.md (every bug found, and what caught it), VIEWER.md (the GUI).