d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
122 lines
4.2 KiB
JavaScript
122 lines
4.2 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Benchmark: Sequential vs Parallel ONNX Embeddings
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*/
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import { cpus } from 'os';
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import { ParallelEmbedder } from './parallel-embedder.mjs';
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import { createEmbedder } from './loader.js';
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console.log('🧪 Parallel vs Sequential ONNX Embeddings Benchmark\n');
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console.log(`CPU Cores: ${cpus().length}`);
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console.log('='.repeat(60));
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// Test data - various batch sizes
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const testTexts = [
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"Machine learning is transforming technology",
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"Deep learning uses neural networks",
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"Natural language processing understands text",
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"Computer vision analyzes images",
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"Reinforcement learning learns from rewards",
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"Generative AI creates new content",
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"Vector databases enable semantic search",
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"Embeddings capture semantic meaning",
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"Transformers revolutionized NLP",
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"BERT is a popular language model",
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"GPT generates human-like text",
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"RAG combines retrieval and generation",
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];
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async function benchmarkSequential(embedder, texts, iterations = 3) {
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const times = [];
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for (let i = 0; i < iterations; i++) {
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const start = performance.now();
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for (const text of texts) {
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embedder.embedOne(text);
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}
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times.push(performance.now() - start);
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}
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return times.reduce((a, b) => a + b) / times.length;
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}
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async function benchmarkParallel(embedder, texts, iterations = 3) {
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const times = [];
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for (let i = 0; i < iterations; i++) {
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const start = performance.now();
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await embedder.embedBatch(texts);
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times.push(performance.now() - start);
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}
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return times.reduce((a, b) => a + b) / times.length;
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}
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async function main() {
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try {
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// Initialize sequential embedder
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console.log('\n📦 Loading model for sequential test...');
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const seqEmbedder = await createEmbedder();
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console.log('✅ Sequential embedder ready\n');
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// Warm up
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seqEmbedder.embedOne("warmup");
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// Initialize parallel embedder
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console.log('📦 Initializing parallel embedder...');
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const parEmbedder = new ParallelEmbedder({ numWorkers: Math.min(4, cpus().length) });
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await parEmbedder.init();
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// Benchmark different batch sizes
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for (const batchSize of [4, 8, 12]) {
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const texts = testTexts.slice(0, batchSize);
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console.log(`\n${'='.repeat(60)}`);
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console.log(`📊 Batch Size: ${batchSize} texts`);
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console.log('='.repeat(60));
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// Sequential benchmark
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console.log('\n⏱️ Sequential (single-threaded)...');
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const seqTime = await benchmarkSequential(seqEmbedder, texts);
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console.log(` Time: ${seqTime.toFixed(1)}ms`);
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console.log(` Per text: ${(seqTime / batchSize).toFixed(1)}ms`);
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// Parallel benchmark
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console.log('\n⏱️ Parallel (worker threads)...');
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const parTime = await benchmarkParallel(parEmbedder, texts);
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console.log(` Time: ${parTime.toFixed(1)}ms`);
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console.log(` Per text: ${(parTime / batchSize).toFixed(1)}ms`);
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// Speedup
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const speedup = seqTime / parTime;
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const icon = speedup > 1.2 ? '🚀' : speedup > 1 ? '✅' : '⚠️';
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console.log(`\n${icon} Speedup: ${speedup.toFixed(2)}x`);
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}
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// Verify correctness
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console.log(`\n${'='.repeat(60)}`);
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console.log('🔍 Verifying correctness...');
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console.log('='.repeat(60));
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const testText = "Vector databases are awesome";
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const seqEmb = seqEmbedder.embedOne(testText);
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const parEmb = await parEmbedder.embedOne(testText);
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// Compare embeddings
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let diff = 0;
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for (let i = 0; i < seqEmb.length; i++) {
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diff += Math.abs(seqEmb[i] - parEmb[i]);
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}
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const avgDiff = diff / seqEmb.length;
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console.log(`\nEmbedding difference: ${avgDiff.toExponential(4)}`);
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console.log(avgDiff < 1e-6 ? '✅ Embeddings match!' : '⚠️ Embeddings differ');
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// Cleanup
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await parEmbedder.shutdown();
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console.log('\n✅ Benchmark complete!');
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} catch (error) {
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console.error('❌ Error:', error.message);
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console.error(error.stack);
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process.exit(1);
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}
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}
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main();
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