ruvector/tests/docker-integration/test-wasm.mjs
rUv 4d5d3bb092 feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40)
* 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>
2025-12-01 22:30:15 -05:00

186 lines
6.4 KiB
JavaScript

/**
* Integration test for ruvector-attention-wasm package
* Tests all attention mechanisms from published npm package
*/
import { test, describe } from 'node:test';
import assert from 'node:assert';
// Import from published WASM package
import init, {
scaled_dot_attention,
WasmMultiHeadAttention,
WasmHyperbolicAttention,
WasmLinearAttention,
WasmFlashAttention,
WasmLocalGlobalAttention,
WasmMoEAttention
} from 'ruvector-attention-wasm';
describe('WASM Attention Package Tests', async () => {
// Initialize WASM before tests
await init();
test('Scaled Dot-Product Attention', () => {
const dim = 64;
const query = new Float32Array(dim).fill(0.5);
const keys = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = scaled_dot_attention(query, keys, values, null);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ Scaled dot-product attention works correctly');
});
test('Multi-Head Attention', () => {
const dim = 64;
const numHeads = 8;
const mha = new WasmMultiHeadAttention(dim, numHeads);
assert.strictEqual(mha.dim, dim, 'Dimension should match');
assert.strictEqual(mha.num_heads, numHeads, 'Number of heads should match');
const query = new Float32Array(dim).fill(0.5);
const keys = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = mha.compute(query, keys, values);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ Multi-head attention works correctly');
});
test('Hyperbolic Attention', () => {
const dim = 64;
const curvature = 1.0;
const hyperbolic = new WasmHyperbolicAttention(dim, curvature);
assert.strictEqual(hyperbolic.curvature, curvature, 'Curvature should match');
const query = new Float32Array(dim).fill(0.1);
const keys = [
Array.from({ length: dim }, () => Math.random() * 0.1),
Array.from({ length: dim }, () => Math.random() * 0.1)
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = hyperbolic.compute(query, keys, values);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ Hyperbolic attention works correctly');
});
test('Linear Attention (Performer-style)', () => {
const dim = 64;
const numFeatures = 128;
const linear = new WasmLinearAttention(dim, numFeatures);
const query = new Float32Array(dim).fill(0.5);
const keys = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = linear.compute(query, keys, values);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ Linear attention works correctly');
});
test('Flash Attention', () => {
const dim = 64;
const blockSize = 16;
const flash = new WasmFlashAttention(dim, blockSize);
const query = new Float32Array(dim).fill(0.5);
const keys = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = flash.compute(query, keys, values);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ Flash attention works correctly');
});
test('Local-Global Attention', () => {
const dim = 64;
const localWindow = 4;
const globalTokens = 2;
const localGlobal = new WasmLocalGlobalAttention(dim, localWindow, globalTokens);
const query = new Float32Array(dim).fill(0.5);
const keys = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = localGlobal.compute(query, keys, values);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ Local-global attention works correctly');
});
test('Mixture of Experts (MoE) Attention', () => {
const dim = 64;
const numExperts = 4;
const topK = 2;
const moe = new WasmMoEAttention(dim, numExperts, topK);
const query = new Float32Array(dim).fill(0.5);
const keys = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const values = [
Array.from({ length: dim }, () => Math.random()),
Array.from({ length: dim }, () => Math.random())
];
const result = moe.compute(query, keys, values);
assert.ok(result instanceof Float32Array, 'Result should be Float32Array');
assert.strictEqual(result.length, dim, `Result dimension should be ${dim}`);
console.log(' ✓ MoE attention works correctly');
});
});
console.log('\n✅ All WASM attention tests passed!\n');