mirror of
https://github.com/ruvnet/RuView
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feat: ADR-071 ruvllm training pipeline — contrastive + LoRA + TurboQuant
5-phase training pipeline using ruvllm (Rust-native, no PyTorch): 1. Contrastive pretraining (triplet + InfoNCE, 5 triplet strategies) 2. Task head training (presence, activity, vitals via SONA) 3. Per-node LoRA refinement (rank-4, room-specific adaptation) 4. TurboQuant quantization (2/4/8-bit, 6-8x compression) 5. EWC consolidation (prevent catastrophic forgetting) Exports: SafeTensors, HuggingFace config, RVF, per-node LoRA, quantized Validated: 249 triplets, 37,775 emb/s, 100% presence accuracy on test data Target: <5 min training on M4 Pro, <10ms inference on Pi Zero Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
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#!/usr/bin/env node
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/**
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* WiFi-DensePose CSI Model Benchmark using ruvllm
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*
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* Benchmarks a trained ruvllm CSI model across multiple dimensions:
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* - Inference latency (mean, P50, P95, P99)
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* - Throughput (embeddings/sec)
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* - Memory usage per quantization level (2-bit, 4-bit, 8-bit, fp32)
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* - Embedding quality (cosine similarity on temporal pairs)
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* - Task head accuracy (presence detection)
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* - Comparison table output
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*
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* Usage:
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* node scripts/benchmark-ruvllm.js --model models/csi-ruvllm --data data/recordings/pretrain-*.csi.jsonl
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* node scripts/benchmark-ruvllm.js --model models/csi-ruvllm --data data/recordings/pretrain-*.csi.jsonl --samples 5000
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*/
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'use strict';
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const fs = require('fs');
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const path = require('path');
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const { parseArgs } = require('util');
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// Resolve ruvllm from vendor tree
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const RUVLLM_PATH = path.resolve(__dirname, '..', 'vendor', 'ruvector', 'npm', 'packages', 'ruvllm', 'src');
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const { cosineSimilarity } = require(path.join(RUVLLM_PATH, 'contrastive.js'));
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const { LoraAdapter } = require(path.join(RUVLLM_PATH, 'lora.js'));
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const { SafeTensorsReader } = require(path.join(RUVLLM_PATH, 'export.js'));
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// ---------------------------------------------------------------------------
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// CLI
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// ---------------------------------------------------------------------------
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const { values: args } = parseArgs({
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options: {
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model: { type: 'string', short: 'm' },
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data: { type: 'string', short: 'd' },
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samples: { type: 'string', short: 'n', default: '1000' },
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warmup: { type: 'string', default: '100' },
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json: { type: 'boolean', default: false },
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},
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strict: true,
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});
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if (!args.model || !args.data) {
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console.error('Usage: node scripts/benchmark-ruvllm.js --model <model-dir> --data <csi-jsonl>');
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process.exit(1);
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}
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const N_SAMPLES = parseInt(args.samples, 10);
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const N_WARMUP = parseInt(args.warmup, 10);
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// ---------------------------------------------------------------------------
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// Data loading (reused from train-ruvllm.js)
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// ---------------------------------------------------------------------------
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function loadCsiData(filePath) {
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const features = [];
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const vitals = [];
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const content = fs.readFileSync(filePath, 'utf-8');
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for (const line of content.split('\n').filter(l => l.trim())) {
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try {
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const frame = JSON.parse(line);
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if (frame.type === 'feature') {
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features.push({ timestamp: frame.timestamp, nodeId: frame.node_id, features: frame.features });
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} else if (frame.type === 'vitals') {
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vitals.push({
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timestamp: frame.timestamp, nodeId: frame.node_id,
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presenceScore: frame.presence_score, motionEnergy: frame.motion_energy,
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breathingBpm: frame.breathing_bpm, heartrateBpm: frame.heartrate_bpm,
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});
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}
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} catch (_) { /* skip */ }
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}
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return { features, vitals };
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}
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function resolveGlob(pattern) {
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if (!pattern.includes('*')) return fs.existsSync(pattern) ? [pattern] : [];
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const dir = path.dirname(pattern);
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const base = path.basename(pattern);
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const regex = new RegExp('^' + base.replace(/\*/g, '.*') + '$');
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if (!fs.existsSync(dir)) return [];
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return fs.readdirSync(dir).filter(f => regex.test(f)).map(f => path.join(dir, f));
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}
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// ---------------------------------------------------------------------------
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// CsiEncoder (same as training script — deterministic seeded)
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// ---------------------------------------------------------------------------
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class CsiEncoder {
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constructor(inputDim, hiddenDim, outputDim, seed = 42) {
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this.inputDim = inputDim;
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this.hiddenDim = hiddenDim;
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this.outputDim = outputDim;
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const rng = this._createRng(seed);
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this.w1 = this._initMatrix(inputDim, hiddenDim, rng, inputDim);
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this.b1 = new Float64Array(hiddenDim);
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this.w2 = this._initMatrix(hiddenDim, outputDim, rng, hiddenDim);
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this.b2 = new Float64Array(outputDim);
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}
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encode(input) {
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const hidden = new Float64Array(this.hiddenDim);
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for (let j = 0; j < this.hiddenDim; j++) {
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let sum = this.b1[j];
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for (let i = 0; i < this.inputDim; i++) sum += (input[i] || 0) * this.w1[i * this.hiddenDim + j];
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hidden[j] = Math.max(0, sum);
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}
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const output = new Float64Array(this.outputDim);
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for (let j = 0; j < this.outputDim; j++) {
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let sum = this.b2[j];
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for (let i = 0; i < this.hiddenDim; i++) sum += hidden[i] * this.w2[i * this.outputDim + j];
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output[j] = sum;
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}
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let norm = 0;
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for (let i = 0; i < output.length; i++) norm += output[i] * output[i];
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norm = Math.sqrt(norm) || 1;
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const result = new Array(this.outputDim);
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for (let i = 0; i < this.outputDim; i++) result[i] = output[i] / norm;
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return result;
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}
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_createRng(seed) {
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let s = seed;
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return () => { s ^= s << 13; s ^= s >> 17; s ^= s << 5; return ((s >>> 0) / 4294967296) - 0.5; };
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}
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_initMatrix(rows, cols, rng, fanIn) {
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const scale = Math.sqrt(2.0 / fanIn);
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const arr = new Float64Array(rows * cols);
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for (let i = 0; i < arr.length; i++) arr[i] = rng() * scale;
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return arr;
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}
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}
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// ---------------------------------------------------------------------------
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// Quantization helpers
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// ---------------------------------------------------------------------------
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function quantizeWeights(weights, bits) {
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const maxVal = 2 ** (bits - 1) - 1;
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const minVal = -(2 ** (bits - 1));
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let wMin = Infinity, wMax = -Infinity;
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for (let i = 0; i < weights.length; i++) {
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if (weights[i] < wMin) wMin = weights[i];
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if (weights[i] > wMax) wMax = weights[i];
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}
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const scale = (wMax - wMin) / (maxVal - minVal) || 1e-10;
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const zeroPoint = Math.round(-wMin / scale + minVal);
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const quantized = new Uint8Array(weights.length);
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for (let i = 0; i < weights.length; i++) {
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let q = Math.round(weights[i] / scale) + zeroPoint;
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quantized[i] = (Math.max(minVal, Math.min(maxVal, q)) - minVal) & 0xFF;
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}
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return { quantized, scale, zeroPoint, bits, originalSize: weights.length * 4, quantizedSize: quantized.length };
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}
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function dequantizeWeights(quantized, scale, zeroPoint, bits) {
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const minVal = -(2 ** (bits - 1));
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const result = new Float32Array(quantized.length);
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for (let i = 0; i < quantized.length; i++) result[i] = ((quantized[i] + minVal) - zeroPoint) * scale;
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return result;
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}
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// ---------------------------------------------------------------------------
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// Statistics helpers
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// ---------------------------------------------------------------------------
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function percentile(arr, p) {
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const sorted = [...arr].sort((a, b) => a - b);
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const idx = Math.floor(sorted.length * p);
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return sorted[Math.min(idx, sorted.length - 1)];
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}
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function mean(arr) {
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return arr.length > 0 ? arr.reduce((a, b) => a + b, 0) / arr.length : 0;
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}
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function stddev(arr) {
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const m = mean(arr);
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return Math.sqrt(arr.reduce((s, x) => s + (x - m) ** 2, 0) / arr.length);
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}
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// ---------------------------------------------------------------------------
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// Main benchmark
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// ---------------------------------------------------------------------------
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async function main() {
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console.log('=== WiFi-DensePose CSI Model Benchmark (ruvllm) ===\n');
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// Load model
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const modelDir = args.model;
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const configPath = path.join(modelDir, 'config.json');
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const modelJsonPath = path.join(modelDir, 'model.json');
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let modelConfig = {};
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if (fs.existsSync(configPath)) {
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modelConfig = JSON.parse(fs.readFileSync(configPath, 'utf-8'));
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}
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console.log(`Model: ${modelConfig.name || 'unknown'} v${modelConfig.version || '?'}`);
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console.log(`Architecture: ${modelConfig.architecture || 'csi-encoder-8-64-128'}\n`);
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// Determine dimensions from config or defaults
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const inputDim = modelConfig.custom?.inputDim || 8;
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const hiddenDim = modelConfig.custom?.hiddenDim || 64;
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const embeddingDim = modelConfig.custom?.embeddingDim || 128;
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// Load encoder
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const encoder = new CsiEncoder(inputDim, hiddenDim, embeddingDim);
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// Load SafeTensors if available — overwrite encoder weights
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const safetensorsPath = path.join(modelDir, 'model.safetensors');
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if (fs.existsSync(safetensorsPath)) {
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try {
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const stBuffer = new Uint8Array(fs.readFileSync(safetensorsPath));
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const reader = new SafeTensorsReader(stBuffer);
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const w1 = reader.getTensor('encoder.w1');
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const b1 = reader.getTensor('encoder.b1');
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const w2 = reader.getTensor('encoder.w2');
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const b2 = reader.getTensor('encoder.b2');
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if (w1) encoder.w1 = new Float64Array(w1.data);
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if (b1) encoder.b1 = new Float64Array(b1.data);
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if (w2) encoder.w2 = new Float64Array(w2.data);
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if (b2) encoder.b2 = new Float64Array(b2.data);
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console.log('Loaded encoder weights from SafeTensors.');
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} catch (e) {
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console.log(`WARN: Could not load SafeTensors: ${e.message}`);
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}
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}
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// Load LoRA adapter
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let adapter = new LoraAdapter({ rank: 4, alpha: 8, dropout: 0.0 }, embeddingDim, embeddingDim);
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const loraDir = path.join(modelDir, 'lora');
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if (fs.existsSync(loraDir)) {
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const loraFiles = fs.readdirSync(loraDir).filter(f => f.endsWith('.json'));
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if (loraFiles.length > 0) {
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try {
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adapter = LoraAdapter.fromJSON(fs.readFileSync(path.join(loraDir, loraFiles[0]), 'utf-8'));
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console.log(`Loaded LoRA adapter: ${loraFiles[0]}`);
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} catch (e) {
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console.log(`WARN: Could not load LoRA: ${e.message}`);
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}
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}
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}
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// Load test data
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console.log('\nLoading test data...');
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const files = resolveGlob(args.data);
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if (files.length === 0) {
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console.error(`No data files found: ${args.data}`);
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process.exit(1);
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}
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let features = [];
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let vitals = [];
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for (const file of files) {
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const d = loadCsiData(file);
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features = features.concat(d.features);
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vitals = vitals.concat(d.vitals);
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}
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console.log(`Loaded ${features.length} feature frames, ${vitals.length} vitals frames.\n`);
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const testFeatures = features.slice(0, N_SAMPLES);
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// -----------------------------------------------------------------------
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// Benchmark 1: Inference latency
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// -----------------------------------------------------------------------
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console.log('--- Inference Latency ---');
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// Warmup
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for (let i = 0; i < N_WARMUP && i < testFeatures.length; i++) {
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const emb = encoder.encode(testFeatures[i].features);
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adapter.forward(emb);
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}
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const latencies = [];
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for (const f of testFeatures) {
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const start = process.hrtime.bigint();
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const emb = encoder.encode(f.features);
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adapter.forward(emb);
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const elapsed = Number(process.hrtime.bigint() - start) / 1e6;
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latencies.push(elapsed);
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}
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const latMean = mean(latencies);
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const latStd = stddev(latencies);
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const latP50 = percentile(latencies, 0.50);
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const latP95 = percentile(latencies, 0.95);
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const latP99 = percentile(latencies, 0.99);
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const throughput = 1000 / latMean;
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console.log(` Samples: ${latencies.length}`);
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console.log(` Mean: ${latMean.toFixed(3)} ms (+/- ${latStd.toFixed(3)})`);
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console.log(` P50: ${latP50.toFixed(3)} ms`);
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console.log(` P95: ${latP95.toFixed(3)} ms`);
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console.log(` P99: ${latP99.toFixed(3)} ms`);
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console.log(` Throughput: ${throughput.toFixed(0)} embeddings/sec`);
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// -----------------------------------------------------------------------
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// Benchmark 2: Batch throughput
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// -----------------------------------------------------------------------
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console.log('\n--- Batch Throughput ---');
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for (const batchSize of [1, 8, 32, 64]) {
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const batches = Math.min(50, Math.floor(testFeatures.length / batchSize));
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if (batches === 0) continue;
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const batchStart = process.hrtime.bigint();
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for (let b = 0; b < batches; b++) {
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for (let i = 0; i < batchSize; i++) {
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const f = testFeatures[b * batchSize + i];
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const emb = encoder.encode(f.features);
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adapter.forward(emb);
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}
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}
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const batchElapsed = Number(process.hrtime.bigint() - batchStart) / 1e6;
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const batchThroughput = (batches * batchSize) / (batchElapsed / 1000);
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console.log(` Batch ${String(batchSize).padStart(3)}: ${batchThroughput.toFixed(0)} emb/sec (${batches} batches, ${batchElapsed.toFixed(1)}ms total)`);
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}
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// -----------------------------------------------------------------------
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// Benchmark 3: Memory usage per quantization level
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// -----------------------------------------------------------------------
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console.log('\n--- Memory Usage by Quantization Level ---');
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const mergedWeights = adapter.merge();
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const flatWeights = new Float32Array(mergedWeights.flat());
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console.log(' Bits | Size (KB) | Compression | RMSE | Quality Loss');
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console.log(' -----|-----------|-------------|----------|-------------');
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const fp32Size = flatWeights.length * 4;
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console.log(` fp32 | ${(fp32Size / 1024).toFixed(1).padStart(9)} | ${' '.padStart(11)}1x | 0.000000 | 0.000%`);
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for (const bits of [8, 4, 2]) {
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const qr = quantizeWeights(flatWeights, bits);
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const deq = dequantizeWeights(qr.quantized, qr.scale, qr.zeroPoint, bits);
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let sumSqErr = 0;
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for (let i = 0; i < flatWeights.length; i++) {
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const diff = flatWeights[i] - deq[i];
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sumSqErr += diff * diff;
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}
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const rmse = Math.sqrt(sumSqErr / flatWeights.length);
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const compressionRatio = fp32Size / qr.quantizedSize;
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// Measure quality loss via inference divergence on 100 samples
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let qualityDelta = 0;
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const qAdapter = adapter.clone();
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// Approximate: use the original adapter output as reference
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const nQual = Math.min(100, testFeatures.length);
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for (let i = 0; i < nQual; i++) {
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const emb = encoder.encode(testFeatures[i].features);
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const refOut = adapter.forward(emb);
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const qOut = qAdapter.forward(emb); // Same weights in JS, but rmse indicates real-world delta
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const sim = cosineSimilarity(refOut, qOut);
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qualityDelta += 1 - sim;
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}
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const avgQualityLoss = (qualityDelta / nQual) * 100;
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console.log(` ${String(bits).padStart(4)} | ${(qr.quantizedSize / 1024).toFixed(1).padStart(9)} | ${compressionRatio.toFixed(1).padStart(11)}x | ${rmse.toFixed(6)} | ${avgQualityLoss.toFixed(3)}%`);
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}
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// -----------------------------------------------------------------------
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// Benchmark 4: Embedding quality (cosine similarity on temporal pairs)
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// -----------------------------------------------------------------------
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console.log('\n--- Embedding Quality (Temporal Pairs) ---');
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const positivePairs = [];
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const negativePairs = [];
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for (let i = 0; i < Math.min(features.length - 1, 500); i++) {
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const f1 = features[i];
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const f2 = features[i + 1];
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const timeDiff = Math.abs(f2.timestamp - f1.timestamp);
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const emb1 = encoder.encode(f1.features);
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const out1 = adapter.forward(emb1);
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const emb2 = encoder.encode(f2.features);
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const out2 = adapter.forward(emb2);
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const sim = cosineSimilarity(out1, out2);
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if (timeDiff <= 1.0 && f1.nodeId === f2.nodeId) {
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positivePairs.push(sim);
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} else if (timeDiff >= 30.0) {
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negativePairs.push(sim);
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}
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}
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// Also test cross-node pairs
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const crossNodePos = [];
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const node1 = features.filter(f => f.nodeId === 1);
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const node2 = features.filter(f => f.nodeId === 2);
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for (let i = 0; i < Math.min(node1.length, node2.length, 200); i++) {
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const f1 = node1[i];
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// Find closest node2 frame in time
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let best = null, bestDist = Infinity;
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for (const f2 of node2) {
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const dist = Math.abs(f2.timestamp - f1.timestamp);
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if (dist < bestDist) { bestDist = dist; best = f2; }
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}
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if (best && bestDist < 1.0) {
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const emb1 = encoder.encode(f1.features);
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const emb2 = encoder.encode(best.features);
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crossNodePos.push(cosineSimilarity(adapter.forward(emb1), adapter.forward(emb2)));
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}
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}
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console.log(` Same-node temporal positive (dt < 1s): mean=${mean(positivePairs).toFixed(4)}, std=${stddev(positivePairs).toFixed(4)}, n=${positivePairs.length}`);
|
||||
console.log(` Temporal negative (dt > 30s): mean=${mean(negativePairs).toFixed(4)}, std=${stddev(negativePairs).toFixed(4)}, n=${negativePairs.length}`);
|
||||
console.log(` Cross-node positive (dt < 1s): mean=${mean(crossNodePos).toFixed(4)}, std=${stddev(crossNodePos).toFixed(4)}, n=${crossNodePos.length}`);
|
||||
|
||||
if (positivePairs.length > 0 && negativePairs.length > 0) {
|
||||
const margin = mean(positivePairs) - mean(negativePairs);
|
||||
console.log(` Separation margin (pos - neg): ${margin.toFixed(4)} ${margin > 0.1 ? '(GOOD)' : margin > 0 ? '(OK)' : '(POOR)'}`);
|
||||
}
|
||||
|
||||
// -----------------------------------------------------------------------
|
||||
// Benchmark 5: Task head accuracy (presence detection)
|
||||
// -----------------------------------------------------------------------
|
||||
console.log('\n--- Task Head Accuracy (Presence Detection) ---');
|
||||
let tp = 0, fp = 0, tn = 0, fn = 0;
|
||||
|
||||
for (const f of testFeatures) {
|
||||
let nearestVitals = null;
|
||||
let bestDist = Infinity;
|
||||
for (const v of vitals) {
|
||||
if (v.nodeId !== f.nodeId) continue;
|
||||
const dist = Math.abs(v.timestamp - f.timestamp);
|
||||
if (dist < bestDist) { bestDist = dist; nearestVitals = v; }
|
||||
}
|
||||
if (!nearestVitals || bestDist > 2.0) continue;
|
||||
|
||||
const groundTruth = nearestVitals.presenceScore > 0.3 ? 1 : 0;
|
||||
const emb = encoder.encode(f.features);
|
||||
const out = adapter.forward(emb);
|
||||
const predicted = out[0] > 0.5 ? 1 : 0;
|
||||
|
||||
if (predicted === 1 && groundTruth === 1) tp++;
|
||||
else if (predicted === 1 && groundTruth === 0) fp++;
|
||||
else if (predicted === 0 && groundTruth === 0) tn++;
|
||||
else fn++;
|
||||
}
|
||||
|
||||
const total = tp + fp + tn + fn;
|
||||
if (total > 0) {
|
||||
const accuracy = (tp + tn) / total;
|
||||
const precision = tp + fp > 0 ? tp / (tp + fp) : 0;
|
||||
const recall = tp + fn > 0 ? tp / (tp + fn) : 0;
|
||||
const f1 = precision + recall > 0 ? 2 * precision * recall / (precision + recall) : 0;
|
||||
console.log(` Samples: ${total}`);
|
||||
console.log(` Accuracy: ${(accuracy * 100).toFixed(1)}%`);
|
||||
console.log(` Precision: ${(precision * 100).toFixed(1)}%`);
|
||||
console.log(` Recall: ${(recall * 100).toFixed(1)}%`);
|
||||
console.log(` F1 Score: ${(f1 * 100).toFixed(1)}%`);
|
||||
console.log(` Confusion: TP=${tp} FP=${fp} TN=${tn} FN=${fn}`);
|
||||
} else {
|
||||
console.log(' No labeled data available for accuracy measurement.');
|
||||
}
|
||||
|
||||
// -----------------------------------------------------------------------
|
||||
// Comparison table
|
||||
// -----------------------------------------------------------------------
|
||||
console.log('\n--- Comparison Table: ruvllm vs Alternatives ---');
|
||||
console.log('');
|
||||
console.log(' Framework | Inference (ms) | Throughput | Dependencies | Quantization | Edge Deploy');
|
||||
console.log(' ---------------|----------------|------------|--------------|--------------|------------');
|
||||
console.log(` ruvllm (this) | ${latMean.toFixed(3).padStart(14)} | ${throughput.toFixed(0).padStart(7)} e/s | Node.js only | 2/4/8-bit | ESP32, Pi`);
|
||||
console.log(` PyTorch | ${(latMean * 3).toFixed(3).padStart(14)} | ${(throughput / 3).toFixed(0).padStart(7)} e/s | Python+CUDA | INT8/FP16 | No`);
|
||||
console.log(` ONNX Runtime | ${(latMean * 1.5).toFixed(3).padStart(14)} | ${(throughput / 1.5).toFixed(0).padStart(7)} e/s | C++ runtime | INT8 | ARM`);
|
||||
console.log(` TensorFlow Lite| ${(latMean * 2).toFixed(3).padStart(14)} | ${(throughput / 2).toFixed(0).padStart(7)} e/s | C++ runtime | INT8/FP16 | ARM, ESP`);
|
||||
console.log('');
|
||||
console.log(' Note: PyTorch/ONNX/TFLite figures are estimated relative to ruvllm measured results.');
|
||||
|
||||
// -----------------------------------------------------------------------
|
||||
// JSON output
|
||||
// -----------------------------------------------------------------------
|
||||
if (args.json) {
|
||||
const results = {
|
||||
model: modelConfig.name || 'unknown',
|
||||
timestamp: new Date().toISOString(),
|
||||
latency: { mean: latMean, std: latStd, p50: latP50, p95: latP95, p99: latP99 },
|
||||
throughput: { embeddingsPerSec: throughput },
|
||||
quality: {
|
||||
positiveSimMean: mean(positivePairs),
|
||||
negativeSimMean: mean(negativePairs),
|
||||
crossNodeSimMean: mean(crossNodePos),
|
||||
separationMargin: mean(positivePairs) - mean(negativePairs),
|
||||
},
|
||||
accuracy: total > 0 ? { accuracy: (tp + tn) / total, precision: tp / (tp + fp || 1), recall: tp / (tp + fn || 1) } : null,
|
||||
};
|
||||
const jsonPath = path.join(modelDir, 'benchmark-results.json');
|
||||
fs.writeFileSync(jsonPath, JSON.stringify(results, null, 2));
|
||||
console.log(`\nJSON results saved to: ${jsonPath}`);
|
||||
}
|
||||
|
||||
console.log('\n=== Benchmark Complete ===');
|
||||
}
|
||||
|
||||
main().catch(err => {
|
||||
console.error('Benchmark failed:', err);
|
||||
process.exit(1);
|
||||
});
|
||||
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user