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* docs: Add comprehensive GNN v2 implementation plans Add 22 detailed planning documents for 19 advanced GNN features: Tier 1 (Immediate - 3-6 months): - GNN-Guided HNSW Routing (+25% QPS) - Incremental Graph Learning/ATLAS (10-100x faster updates) - Neuro-Symbolic Query Execution (hybrid neural + logical) Tier 2 (Medium-Term - 6-12 months): - Hyperbolic Embeddings (Poincaré ball model) - Degree-Aware Adaptive Precision (2-4x memory reduction) - Continuous-Time Dynamic GNN (concept drift detection) Tier 3 (Research - 12+ months): - Graph Condensation (10-100x smaller graphs) - Native Sparse Attention (8-15x GPU speedup) - Quantum-Inspired Attention (long-range dependencies) Novel Innovations (10 experimental features): - Gravitational Embedding Fields, Causal Attention Networks - Topology-Aware Gradient Routing, Embedding Crystallization - Semantic Holography, Entangled Subspace Attention - Predictive Prefetch Attention, Morphological Attention - Adversarial Robustness Layer, Consensus Attention Includes comprehensive regression prevention strategy with: - Feature flag system for safe rollout - Performance baseline (186 tests + 6 search_v2 tests) - Automated rollback mechanisms Related to #38 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(micro-hnsw-wasm): Add neuromorphic HNSW v2.3 with SNN integration ## New Crate: micro-hnsw-wasm v2.3.0 - Published to crates.io: https://crates.io/crates/micro-hnsw-wasm - 11.8KB WASM binary with 58 exported functions - Neuromorphic vector search combining HNSW + Spiking Neural Networks ### Core Features - HNSW graph-based approximate nearest neighbor search - Multi-distance metrics: L2, Cosine, Dot product - GNN extensions: typed nodes, edge weights, neighbor aggregation - Multi-core sharding: 256 cores × 32 vectors = 8K total ### Spiking Neural Network (SNN) - LIF (Leaky Integrate-and-Fire) neurons with membrane dynamics - STDP (Spike-Timing Dependent Plasticity) learning - Spike propagation through graph topology - HNSW→SNN bridge for similarity-driven neural activation ### Novel Neuromorphic Features (v2.3) - Spike-Timing Vector Encoding (rate-to-time conversion) - Homeostatic Plasticity (self-stabilizing thresholds) - Oscillatory Resonance (40Hz gamma synchronization) - Winner-Take-All Circuits (competitive selection) - Dendritic Computation (nonlinear branch integration) - Temporal Pattern Recognition (spike history matching) - Combined Neuromorphic Search pipeline ### Performance Optimizations - 5.5x faster SNN tick (2,726ns → 499ns) - 18% faster STDP learning - Pre-computed reciprocal constants - Division elimination in hot paths ### Documentation & Organization - Reorganized docs into subdirectories (gnn/, implementation/, publishing/, status/) - Added comprehensive README with badges, SEO, citations - Added benchmark.js and test_wasm.js test suites - Added DEEP_REVIEW.md with performance analysis - Added Verilog RTL for ASIC synthesis 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
184 lines
6.2 KiB
JavaScript
184 lines
6.2 KiB
JavaScript
/**
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* Integration test for @ruvector/attention NAPI package
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* Tests all attention mechanisms from published npm package
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*/
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import { test, describe } from 'node:test';
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import assert from 'node:assert';
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// Import from published NAPI package
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import {
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scaledDotAttention,
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MultiHeadAttention,
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HyperbolicAttention,
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LinearAttention,
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FlashAttention,
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LocalGlobalAttention,
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MoEAttention
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} from '@ruvector/attention';
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describe('NAPI Attention Package Tests', () => {
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test('Scaled Dot-Product Attention', () => {
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const dim = 64;
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const query = new Float32Array(dim).fill(0.5);
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const keys = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = scaledDotAttention(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ Scaled dot-product attention works correctly');
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});
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test('Multi-Head Attention', () => {
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const dim = 64;
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const numHeads = 8;
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const mha = new MultiHeadAttention(dim, numHeads);
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assert.strictEqual(mha.dim, dim, 'Dimension should match');
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assert.strictEqual(mha.numHeads, numHeads, 'Number of heads should match');
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const query = new Float32Array(dim).fill(0.5);
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const keys = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = mha.compute(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ Multi-head attention works correctly');
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});
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test('Hyperbolic Attention', () => {
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const dim = 64;
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const curvature = 1.0;
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const hyperbolic = new HyperbolicAttention(dim, curvature);
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assert.strictEqual(hyperbolic.curvature, curvature, 'Curvature should match');
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const query = new Float32Array(dim).fill(0.1);
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const keys = [
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new Float32Array(dim).map(() => Math.random() * 0.1),
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new Float32Array(dim).map(() => Math.random() * 0.1)
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = hyperbolic.compute(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ Hyperbolic attention works correctly');
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});
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test('Linear Attention (Performer-style)', () => {
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const dim = 64;
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const numFeatures = 128;
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const linear = new LinearAttention(dim, numFeatures);
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const query = new Float32Array(dim).fill(0.5);
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const keys = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = linear.compute(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ Linear attention works correctly');
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});
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test('Flash Attention', () => {
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const dim = 64;
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const blockSize = 16;
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const flash = new FlashAttention(dim, blockSize);
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const query = new Float32Array(dim).fill(0.5);
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const keys = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = flash.compute(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ Flash attention works correctly');
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});
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test('Local-Global Attention', () => {
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const dim = 64;
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const localWindow = 4;
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const globalTokens = 2;
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const localGlobal = new LocalGlobalAttention(dim, localWindow, globalTokens);
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const query = new Float32Array(dim).fill(0.5);
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const keys = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = localGlobal.compute(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ Local-global attention works correctly');
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});
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test('Mixture of Experts (MoE) Attention', () => {
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const dim = 64;
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const numExperts = 4;
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const topK = 2;
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const moe = new MoEAttention(dim, numExperts, topK);
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const query = new Float32Array(dim).fill(0.5);
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const keys = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const values = [
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new Float32Array(dim).map(() => Math.random()),
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new Float32Array(dim).map(() => Math.random())
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];
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const result = moe.compute(query, keys, values);
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assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
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assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
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console.log(' ✓ MoE attention works correctly');
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});
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});
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console.log('\n✅ All NAPI attention tests passed!\n');
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