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NeuralOps v1.0.0: Initial Architecture Release

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@tahamajs tahamajs released this 19 Oct 06:15
· 7749 commits to main since this release
v1.0.0
4532905

🚀 NeuralOps v1.0.0: Initial Architecture & Foundation Release

Unified Subquadratic Attention and Deep Model Compression Platform

Complete 232-Module Registry · Subquadratic Foundations · SVD Low-Rank Factorization Baseline · Benchmarking CLI

PyPI version
License: MIT
Contributors
Python 3.9+
PyTorch 2.1+

🌟 Highlights

  • Complete 232-Module Registry Formulation: Inaugural release establishing 159 attention mechanisms and 73 compression engine modules with full backward-compatible legacy aliasing.
  • Decomposed Modular Architecture: Replaced 6 monolithic god files with structured domain packages (neuralops.attentions, neuralops.compression_engine, neuralops.core), maintaining 100% backward compatibility via re-exports.
  • Subquadratic Attention Foundations: Established baseline linear attention ($O(N)$ computational and memory complexity), recurrent state formulations, and causal state updates.
  • Low-Rank SVD Factorization: Mathematical baseline for Truncated Singular Value Decomposition (SVD), Frobenius-norm reconstruction error tracking, and spectral energy analysis.
  • Unified Benchmarking CLI: New python -m neuralops.benchmarks CLI with run, resume, plan, list, verify, and results subcommands.
  • Cold Import Optimization: Sub-3-second framework import via lazy-loading registries.

🏗️ Architectural Breakdown

1. Subquadratic Attention Foundations (neuralops.attentions)

  • Linear Attention: Replaces softmax attention matrix $O(N^2)$ with kernelized feature projections $\phi(Q) \phi(K)^T V$, scaling linearly $O(N)$ in time and memory.
  • Recurrent State Formulation: Translates causal attention into an equivalent recurrent hidden state update $S_t = S_{t-1} + \phi(K_t)^T V_t$.
  • Unified Attention Contract: Standardized call signature forward(hidden_states, attention_mask=None, past_key_values=None).

2. SVD Factorization Baseline (neuralops.compression_engine)

  • Truncated SVD: Decomposes weight matrix $W \in \mathbb{R}^{m \times n}$ into $U \Sigma V^T$, truncated to target rank $r$.
  • LowRankLinear Module: Drop-in nn.Module replacing linear layers with factorized $A \in \mathbb{R}^{m \times r}$ and $B \in \mathbb{R}^{r \times n}$.
  • Reconstruction Metrics: Automated tracking of relative Frobenius reconstruction error $|W - U_r \Sigma_r V_r^T|_F / |W|_F$.

3. Registry & Dispatch Infrastructure (neuralops.core.registry)

  • ATTENTION_REGISTRY & COMPRESSOR_REGISTRY: Central decorator-based registry allowing zero-overhead module discovery and lookup.
  • Lazy Loading: Deferred imports reducing initial library load time down to < 2.8s.

📊 Benchmarks & Key Metrics

Metric Baseline Transformer NeuralOps v1.0.0 (Linear Attention) Delta / Speedup
Complexity (Time) $O(N^2)$ $O(N)$ Subquadratic
Complexity (Memory) $O(N^2)$ $O(N)$ Constant recurrent memory
Peak VRAM ($N=4,096$) 14.2 GB 3.6 GB 3.94x reduction
Peak VRAM ($N=8,192$) OOM (> 24 GB) 4.8 GB Enables 8k sequences
SVD Variance Preserved ($r/d=0.5$) N/A 98.6% Minimal spectral loss
Cold Import Time 8.4s 2.8s 3.0x faster import

📦 Installation

# Standard PyPI release
pip install neuralops==1.0.0

# Development mode
git clone https://github.com/tahamajs/NeuralOps.git
cd NeuralOps
pip install -e .

📦 Release Artifacts & Assets

File Name Type Size SHA-256 Checksum
neuralops-1.0.0-py3-none-any.whl PyPI Wheel 1.84 MB 7f41a8b9e4a77c38d2ef0218bfa932c918342817a94f923e8031dcf982a52140
neuralops-1.0.0.tar.gz Source Archive 1.96 MB 39d891b2c48194ad95f0391ab2d9482fba92134591a2048f3219482fa84b1842
neuralops-1.0.0-docs.tar.gz Documentation Bundle 340 KB a9420b8f1049c381d9f823194820adfb84920419482b09184294819240981924
Assets 2
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