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PROJECT LOOMY

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Loomy(luminamoon) is the official reference implementation and central monorepo of PROJECT LOOMY, providing low-precision numerical optimization, multimodal dataset streaming, and formal verification suites developed by Silver Moon (@cloverxion) and Lumina Moon (@cloverx-id) contributors.

Built with a modular architecture under the PROJECT LOOMY specification, luminamoon connects specialized components for CPU/GPU hardware execution, low-latency streaming, and verified numerical stability:

System Architecture

flowchart TD
    Shards["Dataset Sources (HF / PyTorch / Shards)"] --> DataLoader["LuminaMoon Data Engine (SIMD + StringTensor + XXH3)"]
    DataLoader --> TrainLoop["Training Loop (Batch Stream)"]
    DataLoader --> Checkpoint["128B Ping-Pong Checkpoint & WAL"]
    TrainLoop --> Checkpoint
    TrainLoop --> Dispatcher["LuminaV Hardware Dispatcher"]
    Eval["LSO-Eval Suite (Z3 SMT & Parity Gate)"] -.-> Dispatcher
    Dispatcher --> Triton["Triton AOT/JIT GPU (CUDA / ROCm)"]
    Dispatcher --> Vulkan["Vulkan 1.3/1.4 Compute (UMA / SPIR-V)"]
    Dispatcher --> SIMD["Rayon CPU SIMD (AVX-512 / AVX2)"]
    Dispatcher --> Scalar["Reference Scalar Core (Fallback)"]

Projects & Components

Project / Component Path Status Description Documentation
LuminaV Optimizer optimizer/ Active (Spec v1.5.3) Canonical LuminaV v1.5.3 optimizer engine with CPU SIMD (AVX-512/AVX2), Vulkan UMA, and Triton AOT/JIT kernels. Optimizer Guide
LuminaMoon Data Engine data/ Active (v1.0.6.post1) Modular preprocessing engine: AVX-512/AVX2/NEON SIMD text normalization, dual-layout StringTensor (CPU StringView & GPU contiguous buffers), heuristic Shannon quality filtering, MinHash/XXH3-64 deduplication, PII masking, and 128B Ping-Pong crash recovery. Data Engine Guide
LSO-Eval Suite eval/ Active Formal verification suite: Z3 SMT solver invariants, 2D trajectory benchmarks, and numerical parity audits. Eval Guide
Changelogs changelogs/ Active Dedicated version histories separating Software Package releases (v1.0.0–v1.0.6.post1) and LuminaV Algorithm Specifications (v1.0.0–v1.5.3). Changelogs
Documentation docs/ Active Centralized documentation portal, developer internal blueprints (docs/optimize/, docs/dataloader/), and ecosystem version audits. Docs Portal
Python Package python/luminamoon/ Active (v1.0.6.post1) Unified free-threaded (abi3t) Python namespace exposing LuminaV Optimizer, DataStreamLoader, and PyTorch/NumPy/HuggingFace interop. API Reference

Quickstart

Installation

Install the compiled package wheel from PyPI:

pip install luminamoon

Or build from source using Maturin and the Rust 2024 toolchain:

git clone https://github.com/cloverx-id/luminamoon.git
cd luminamoon
pip install maturin
maturin develop --release

Basic Usage (LuminaV Optimizer)

import numpy as np
from luminamoon import LuminaV

# Initialize parameters
weights = np.random.randn(64, 10).astype(np.float32)
biases = np.zeros(10, dtype=np.float32)

# Instantiate LuminaV optimizer (uses calibrated default hyperparameters)
optimizer = LuminaV([weights, biases], lr=8e-4, weight_decay=0.08, buffer=2)

# Execute optimization step with explicit gradients
grad_w = np.random.randn(64, 10).astype(np.float32)
grad_b = np.random.randn(10, dtype=np.float32)
optimizer.step(grads=[grad_w, grad_b])

For PyTorch deep learning workflows, see the PyTorch Integration Guide.

LuminaV Hyperparameter Quick Reference

All default values below are exposed directly in IDE autocompletion and LuminaV.__init__ docstrings:

Parameter Type Default Description
lr float 8e-4 (0.0008) Learning rate (\(\eta\)).
betas Tuple[float, float] (0.9, 0.999) Exponential moving average factors \((\beta_1, \beta_2)\) for first moment and second moment / temporal defect.
eps float 1e-8 Numerical stability constant (\(\epsilon\)) in the adaptive denominator.
weight_decay float 0.08 (8e-2) Decoupled weight decay coefficient.
tau float 0.8 Soft-projection temperature threshold (\(\tau\)) for denominator damping.
lam float 0.3 Temporal defect sensitivity weight (\(\lambda\)) in Single-Buffer TD-CLM (buffer=1).
cautious bool True Enables sign-aligned cautious update masking and lagged EMA scaling.
cautious_clamp_min float 0.5 Lower bound in (0.0, 1.0] for cautious alignment scaling.
buffer int \| str 2 State topology: 2 ("dual", "clm") for Dual-Buffer Convex Lookahead Momentum (CLM), or 1 ("single", "td-clm") for Single-Buffer Triangle-Defect Convex Lookahead Momentum (TD-CLM).
centered_defect bool False Centers innovation defect \((g - m)\) across both Dual-Buffer CLM (buffer=2) and Single-Buffer TD-CLM (buffer=1) modes.
stochastic_rounding bool True Applies stateless 32-bit MurmurHash3 bit-avalanche stochastic rounding when writing to bfloat16 / float16 weights.
bound bool True Enables adaptive trust-region update bounding.
bound_type str \| int "coordinate" Trust-region geometry: "coordinate" (element-wise clip, zero collective sync) or "radial" (global norm ratio).
bound_ratio float 0.05 Maximum update magnitude relative to parameter scale.
master_weights str \| bool "none" Mixed-precision master weight mode for bfloat16/float16: "none" (False), "semi", or "full" (True).
execution str \| int "auto" Hardware backend: "auto", "triton", "vulkan", "simd", or "scalar".
fused_single_pass bool True Fuses parameter update and scalar reduction statistics into a single kernel pass.
ema_decay str \| float "auto" EMA decay for lagged cautious/radial scalars ("auto" resolves to 0.80 for buffer=2 and 0.95 for buffer=1).
fsdp_sync bool False Synchronizes global reduction scratch buffers across torch.distributed FSDP shards.
seed int 1337 Deterministic PRNG seed for stateless stochastic rounding.
version Optional[str] "1.5.3" Locks the active LuminaV mathematical specification version.

Software vs. Algorithm Versioning (v1.0.6.post1 / Spec v1.5.3)

luminamoon maintains two orthogonal version tracks (documented in changelogs/): - Software Package Version (1.0.6.post1): Tracks the unified Rust + Python wheel distribution (luminamoon.__version__ and luminamoon.data.get_version()). See changelogs/SOFTWARE_CHANGELOG.md. - LuminaV Algorithm Specification (1.5.3): Tracks the canonical mathematical update rules (LuminaV.ALGORITHM_SPEC and LuminaV.getVersion()). See changelogs/LUMINAV_ALGORITHM_CHANGELOG.md.

All version resolution operates 100% offline using local compatibility profiles:

import luminamoon
from luminamoon import LuminaV
import luminamoon.data as lmd

# 1. Introspect Software Package Version vs. Algorithm Specification Version
print(luminamoon.__version__)    # "1.0.6.post1" (Software distribution)
print(lmd.get_version())         # "1.0.6.post1" (Data Engine module)
print(LuminaV.ALGORITHM_SPEC)    # "1.5.3" (Canonical mathematical specification)
print(LuminaV.getVersion())      # "1.5.3"

# 2. Lock or select a specific algorithm version per-instance or globally
opt = LuminaV([weights, biases], lr=8e-4, version="1.5.3")
opt.setVersion("1.5.3")          # Instance-level version lock
LuminaV.setVersion("1.5.3")      # Class-level default

# 3. Canonical specification lock (Note: Specifications prior to v1.5.0 are deprecated)
# LuminaV v1.5.3 TD-CLM is the canonical mathematical specification across CPU/GPU runtimes.
opt_canonical = LuminaV([weights, biases], version="1.5.3")

Basic Usage (LuminaMoon Data Engine: Fused Preprocessing & Zero-Copy Pipeline)

from luminamoon.data import FusedPipeline, StringTensor, create_resumable_collate, ResumablePipeline

# 1. Ultra-fast fused single-pass preprocessing (>400k records/s) in Rust
pipeline = FusedPipeline(
    trim_whitespace=True,
    normalize_newlines=True,  # AVX-512 / AVX2 / NEON SIMD
    min_chars=10,
    min_entropy=2.0,
)
string_tensor = pipeline(["  sample 1\r\n", "sample 2 with proper prose text"])

# 2. Zero-copy export to Apache Arrow, Polars, and GPU-ready PyTorch buffers
polars_series = string_tensor.to_polars()
arrow_array = string_tensor.to_arrow()
offsets_tensor, payload_tensor = string_tensor.to_torch_gpu(device="cuda")

# 3. Stream dataset with async prefetching & 128B Ping-Pong crash-recovery
pipe = ResumablePipeline(
    source=raw_data,
    fused_pipeline=pipeline,
    batch_size=64,
    prefetch_batches=4,
    checkpoint_path="./cache/checkpoint.meta",
)
for batch in pipe:
    # Training step...
    pipe.commit()  # Atomically persists Dual-Slot A/B checkpoint and deduplication WAL

For detailed architecture, Shannon entropy heuristics, MinHash deduplication, and PyTorch collate_fn adapters, see the Data Engine Guide.

Verification & CI/CD

All pull requests and releases are validated across Linux, Windows, and macOS:

# Enforce zero-warning static analysis across the Rust workspace
cargo clippy --workspace --all-targets -- -D warnings

# Run Rust workspace unit and differential parity tests (Optimizer + DataLoader)
cargo test --workspace

# Run Python static type check
npx pyright python/luminamoon

# Run standalone NumPy, PyTorch, and DataLoader verification suites
python optimizer/tests/test_standalone_numpy.py
python optimizer/tests/test_end_to_end_training.py
python -B tests/test_data_modular.py
python -B tests/test_gpu_vs_cpu_strings.py

# Run LSO-Eval formal verification suite
python -m eval

Developer Resources

For contributors and systems engineers building on or extending PROJECT LOOMY (luminamoon): - Changelogs (Software & Algorithm): changelogs/ - Optimizer Modular Documentation: docs/optimize/ - Data Engine Modular Documentation: data/ - Optimizer Library Audits: docs/library_audit/ - Data Standalone Library Audits: data/docs/library_audit/ - Data Engine Architecture & User Guide: data/README.md - User Documentation: docs/en/

Authors & Maintenance

PROJECT LOOMY (luminamoon) is engineered and maintained by: - Silver Moon (@cloverxion) - Lumina Moon (@cloverx-id) contributors

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.