ruvector-gnn-wasm

RuVector GNN WASM

WebAssembly bindings for RuVector Graph Neural Network operations.

Features

• GNN Layer Operations: Multi-head attention, GRU updates, layer normalization
• Tensor Compression: Adaptive compression based on access frequency
• Differentiable Search: Soft attention-based similarity search
• Hierarchical Forward: Multi-layer GNN processing

Installation

npm install ruvector-gnn-wasm

Usage

Initialize

import init, {
  JsRuvectorLayer,
  JsTensorCompress,
  differentiableSearch,
  SearchConfig
} from 'ruvector-gnn-wasm';

await init();

GNN Layer

// Create a GNN layer
const layer = new JsRuvectorLayer(
  4,    // input dimension
  8,    // hidden dimension
  2,    // number of attention heads
  0.1   // dropout rate
);

// Forward pass
const nodeEmbedding = new Float32Array([1.0, 2.0, 3.0, 4.0]);
const neighbors = [
  new Float32Array([0.5, 1.0, 1.5, 2.0]),
  new Float32Array([2.0, 3.0, 4.0, 5.0])
];
const edgeWeights = new Float32Array([0.3, 0.7]);

const output = layer.forward(nodeEmbedding, neighbors, edgeWeights);
console.log('Output dimension:', layer.outputDim);

Tensor Compression

const compressor = new JsTensorCompress();

// Compress based on access frequency
const embedding = new Float32Array(128).fill(0.5);
const compressed = compressor.compress(embedding, 0.5); // 50% access frequency

// Decompress
const decompressed = compressor.decompress(compressed);

// Or specify compression level explicitly
const compressedPQ8 = compressor.compressWithLevel(embedding, "pq8");

// Get compression ratio
const ratio = compressor.getCompressionRatio(0.5); // Returns ~2.0 for half precision

Compression Levels

Access frequency determines compression:

• f > 0.8: Full precision (no compression) - hot data
• f > 0.4: Half precision (2x compression) - warm data
• f > 0.1: 8-bit PQ (4x compression) - cool data
• f > 0.01: 4-bit PQ (8x compression) - cold data
• f <= 0.01: Binary (32x compression) - archive data

Differentiable Search

const query = new Float32Array([1.0, 0.0, 0.0]);
const candidates = [
  new Float32Array([1.0, 0.0, 0.0]),  // Perfect match
  new Float32Array([0.9, 0.1, 0.0]),  // Close match
  new Float32Array([0.0, 1.0, 0.0])   // Orthogonal
];

const config = new SearchConfig(2, 1.0); // k=2, temperature=1.0
const result = differentiableSearch(query, candidates, config);

console.log('Top indices:', result.indices);
console.log('Weights:', result.weights);

API Reference

JsRuvectorLayer

class JsRuvectorLayer {
  constructor(
    inputDim: number,
    hiddenDim: number,
    heads: number,
    dropout: number
  );

  forward(
    nodeEmbedding: Float32Array,
    neighborEmbeddings: Float32Array[],
    edgeWeights: Float32Array
  ): Float32Array;

  readonly outputDim: number;
}

JsTensorCompress

class JsTensorCompress {
  constructor();

  compress(embedding: Float32Array, accessFreq: number): object;
  compressWithLevel(embedding: Float32Array, level: string): object;
  decompress(compressed: object): Float32Array;
  getCompressionRatio(accessFreq: number): number;
}

Compression levels: "none", "half", "pq8", "pq4", "binary"

differentiableSearch

function differentiableSearch(
  query: Float32Array,
  candidateEmbeddings: Float32Array[],
  config: SearchConfig
): { indices: number[], weights: number[] };

SearchConfig

class SearchConfig {
  constructor(k: number, temperature: number);
  k: number;          // Number of results
  temperature: number; // Softmax temperature (lower = sharper)
}

cosineSimilarity

function cosineSimilarity(a: Float32Array, b: Float32Array): number;

Building from Source

# Install wasm-pack
curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh

# Build for Node.js
wasm-pack build --target nodejs

# Build for browser
wasm-pack build --target web

# Build for bundler (webpack, etc.)
wasm-pack build --target bundler

Performance

• GNN layers use efficient attention mechanisms
• Compression reduces memory usage by 2-32x
• All operations are optimized for WASM
• No garbage collection during forward passes

License

MIT