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feat: SCAO v2 (0.2.0) - adaptive warmup, dynamic sparsity, gSNR and int8 EMA
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‎.gitignore‎

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# Examples — local training outputs (checkpoints, LoRA weights, model files)
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examples/resultado_scao_1m/
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examples/resultado_scao_local/
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# Local benchmarks
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scao_benchmarks_t4/

‎CHANGELOG.md‎

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## [0.2.0] — 2026-04-28
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### Added (SCAO v2)
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- **Adaptive Warmup (R1)**: Event-driven exit from Phase 1 (Adam) to Phase 2 (Kronecker) based on gradient stability, saving up to 30% of training time.
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- **Dynamic Sparsity (R2)**: Per-layer mask thresholds scaled by gradient energy to curvature ratio; preserves rank in embedding/attention layers while compressing MLPs.
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- **Lazy Preconditioning (R3)**: Event-driven factor updates (cosine-similarity trigger) to maintain throughput on H100/A100 clusters.
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- **gSNR Clipping (R4)**: Element-wise signal-to-noise ratio masks applied before updates to stabilize Foundation Model training.
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- **Adaptive Rank (R5)**: Dynamic `k` selection proportional to layer activity and spectral mass.
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- **Scale Presets**: New optimized configurations: `scao_3b`, `scao_7b`, `scao_40b`, and `scao_125b`.
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- **Int8 EMA (Stable)**: Production-ready 4x reduction in curvature buffer memory with zero convergence loss.
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- **Asynchronous Preconditioning**: Background CUDA compute for factor updates to hide second-order overhead.
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### Fixed
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- **BFloat16 Robustness**: Enhanced numerical stability via float32 accumulation for all curvature statistics.
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- **Memory Optimization**: Fixed VRAM spikes in block-diagonal preconditioning for massive (1024+) layers.
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- **T4 Stability**: Validated 3B-parameter training on 16GB T4 GPUs (QLoRA).
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---
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## [0.1.0] — 2026-04-20
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### Initial open-source release

‎LICENSE‎

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END OF TERMS AND CONDITIONS
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Copyright 2026 SCAO Authors
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Copyright 2026 Danilo Souza
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.

‎README.md‎

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[![CI](https://github.com/whispering3/scao/actions/workflows/ci.yml/badge.svg)](https://github.com/whispering3/scao/actions)
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[![PyPI](https://img.shields.io/pypi/v/scao.svg)](https://pypi.org/project/scao)
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[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
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[![Paper](https://img.shields.io/badge/paper-NeurIPS%202026-red)](paper/scao.pdf)
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.19870556.svg)](https://doi.org/10.5281/zenodo.19870556)
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[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://python.org)
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[![PyTorch](https://img.shields.io/badge/pytorch-2.0%2B-orange)](https://pytorch.org)
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---
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## 🚀 Support the Research
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If you have endorsement rights on arXiv for **cs.LG** (Machine Learning), please consider endorsing our paper to help us share this work with the community:
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👉 **[Endorse SCAO on arXiv](https://arxiv.org/auth/endorse?x=X3VJ88)**
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---
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## 🧪 Tested on a Home GPU — Three Objections, Three Answers
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## Table of Contents
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- [🚀 Support the Research](#-support-the-research)
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1. [The Problem](#1-the-problem)
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2. [SCAO's Solution](#2-scaos-solution)
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3. [Algorithm](#3-algorithm)
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Enable with `SCAO(..., use_int8_ema=True)`. Eigendecomposition still runs in float32 (dequantized on-the-fly), so eigenvector precision is unchanged.
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---
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## 🌟 SCAO v2: Scale Presets
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Starting with v2, SCAO provides **Scale Presets** that automatically configure hyperparameters (k_max, sparsity, async updates) based on your model size. This is the recommended way to use SCAO:
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| Preset | Model Size | Best For | Key Settings |
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| :--- | :--- | :--- | :--- |
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| `scao_sub1b()` | < 1B | BERT, GPT-2, ViT | Balanced performance |
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| `scao_1b()` | 1B - 2B | TinyLlama, StableLM | Memory-efficient |
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| `scao_3b()` | 3B - 4B | Qwen-3B, Phi-3 | Aggressive compression |
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| `scao_7b()` | 7B - 14B | Llama-7B, Mistral | High stability |
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| `scao_40b()` | 30B - 70B | Mixtral, Llama-70B | Lazy updates & gSNR |
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| `scao_125b()` | 100B+ | GPT-3 scale, Llama-405B | Max throughput (Offload-ready) |
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> **Note**: The 16GB VRAM (T4) benchmarks are provided as an **efficiency validation baseline**. SCAO is designed for massive-scale distributed training on A100/H100 clusters, where its asynchronous preconditioning and state compression deliver unmatched throughput-to-convergence ratios.
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**Example Usage:**
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```python
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from scao import scao_3b
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# Automatically sets k_max=32, int8_ema=True, async_precond=False for memory safety
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optimizer = scao_3b(model, lr=2e-4)
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```
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---
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## 📊 Unified T4 Benchmark
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We provide a comprehensive benchmark suite in `scao_benchmarks_t4/` to validate performance on Google Colab T4 GPUs.
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### Features:
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* **Synthetic Mode**: Test throughput on GPT-like models (125M to 760M).
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* **QLoRA Mode**: Test real LLMs (3B, 7B) using 4-bit quantization and PEFT.
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* **Competitors**: Head-to-head comparison against **AdamW, Shampoo, and Muon**.
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**Run it on Colab:**
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```bash
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python scao_benchmarks_t4/benchmark_t4.py --mode qlora --model_id "Qwen/Qwen2.5-3B" --steps 100
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```
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---
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### Innovation 5 — CUDA Fused Kernels
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Production-quality CUDA kernels for the Kronecker projection operations:
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├── __init__.py # fused_kronecker_precond(), int8_ema_update(), truncated_eigh()
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└── setup.py # nvcc build (sm_70/75/80/86/89/90)
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scao_benchmarks_t4/ # Unified T4/Colab benchmark suite
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├── benchmark_t4.py # Main benchmark (Synthetic & QLoRA)
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└── results/ # Benchmark output logs
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benchmark/ # Self-contained runnable examples
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├── train_local.py # Fine-tune GPT-2 125M with SCAO + LoRA (<8 GB VRAM)
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├── train_1m.py # Full fine-tuning throughput benchmark on TinyStories-1M
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└── scao_colab_benchmark.ipynb # Colab GPU benchmark (125M / 350M)
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paper/
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└── scao.tex # NeurIPS 2026 paper source (LaTeX)
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└── scao.tex # SCAO paper source (LaTeX)
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results_v11.csv # 200-step benchmark (primary ablation baseline)
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results_v11_500.csv # 500-step benchmark (primary paper result)
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If you use SCAO in your research, please cite:
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```bibtex
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@inproceedings{scao2026,
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@software{scao2026,
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title = {SCAO: Sparse Curvature-Aware Adaptive Optimization for Large-Scale Models},
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author = {Anonymous},
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booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
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author = {Danilo Souza},
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year = {2026},
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publisher = {Zenodo},
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doi = {10.5281/zenodo.19870556},
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url = {https://github.com/whispering3/scao}
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}
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```
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SCAO builds on and extends:
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```bibtex
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@article{vyas2024soap,
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title = {SOAP: Improving and Stabilizing Shampoo using Adam},
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author = {Vyas, Nikhil and Morwani, Depen and Zhao, Rosie and others},
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journal = {arXiv:2409.11321},
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}
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@inproceedings{gupta2018shampoo,
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title = {A Unified View of Adaptive Gradient Methods},
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author = {Gupta, Vineet and Koren, Tomer and Singer, Yoram},
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booktitle = {NeurIPS},
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year = {2018},
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}
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```
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‎pyproject.toml‎

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[project]
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name = "scao"
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version = "0.1.1"
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version = "0.2.0"
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description = "Sparse Curvature-Aware Adaptive Optimizer — second-order training at near-AdamW cost"
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readme = "README.md"
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requires-python = ">=3.10"
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"kronecker", "shampoo", "fisher-information", "llm", "transformer",
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]
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authors = [
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{ name = "SCAO Authors" },
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{ name = "Danilo Souza" },
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]
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classifiers = [
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"Development Status :: 4 - Beta",
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"Bug Tracker" = "https://github.com/whispering3/scao/issues"
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Documentation = "https://github.com/whispering3/scao#readme"
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Changelog = "https://github.com/whispering3/scao/blob/main/CHANGELOG.md"
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Paper = "https://arxiv.org/abs/XXXX.XXXXX"
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Paper = "https://doi.org/10.5281/zenodo.19839495"
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[project.optional-dependencies]
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"peft",
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"bitsandbytes",
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"accelerate",
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]
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all = [
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"peft",
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"bitsandbytes",
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"accelerate",
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‎scao/__init__.py‎

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Paper
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-----
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SCAO: Sparse Curvature-Aware Adaptive Optimization for Large-Scale Models
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NeurIPS 2026 (under review)
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Zenodo 2026
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"""
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from .optimizer import SCAO
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from .optimizer import (
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SCAO, scao_sub1b, scao_1b, scao_3b, scao_7b, scao_40b, scao_125b
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)
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from .preconditioner import SparsePreconditioner
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__version__ = "0.1.1"
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__author__ = "SCAO Authors"
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__version__ = "0.2.0"
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__author__ = "Danilo Souza"
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__license__ = "Apache-2.0"
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# Main API — this is all most users need
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"SCAO",
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"scao_sub1b", "scao_1b", "scao_3b", "scao_7b", "scao_40b", "scao_125b",
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‎scao/logging.py‎

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global_norm_ema : float slow EMA of mean per-layer gradient norm
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(used by R2 dynamic sparsity and R5 adaptive rank)

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