Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
312
crates/ruvector-cli/tests/gnn_performance_test.rs
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312
crates/ruvector-cli/tests/gnn_performance_test.rs
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//! GNN Performance Optimization Tests
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//!
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//! Verifies that the GNN caching layer achieves the expected performance improvements:
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//! - Layer caching: ~250-500x faster (5-10ms vs ~2.5s)
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//! - Query caching: Instant results for repeated queries
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//! - Batch operations: Amortized overhead
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//!
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//! NOTE: These tests use relaxed thresholds for debug builds.
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//! Run with `cargo test --release` for production performance numbers.
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use std::time::Instant;
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// Import from the crate being tested
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mod gnn_cache_tests {
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use ruvector_gnn::layer::RuvectorLayer;
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use std::time::Instant;
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// Debug builds are ~10-20x slower than release
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#[cfg(debug_assertions)]
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const LATENCY_MULTIPLIER: f64 = 20.0;
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#[cfg(not(debug_assertions))]
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const LATENCY_MULTIPLIER: f64 = 1.0;
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/// Test that GNN layer creation has acceptable latency
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#[test]
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fn test_layer_creation_latency() {
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let start = Instant::now();
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let _layer = RuvectorLayer::new(128, 256, 4, 0.1).unwrap();
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let elapsed = start.elapsed();
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// Layer creation: 100ms in release, ~2000ms in debug
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let threshold_ms = 100.0 * LATENCY_MULTIPLIER;
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assert!(
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elapsed.as_millis() < threshold_ms as u128,
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"Layer creation took {}ms, expected <{}ms (debug={})",
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elapsed.as_millis(),
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threshold_ms,
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cfg!(debug_assertions)
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);
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println!(
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"Layer creation latency: {:.3}ms (threshold: {:.0}ms)",
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elapsed.as_secs_f64() * 1000.0,
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threshold_ms
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);
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}
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/// Test that forward pass has acceptable latency
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#[test]
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fn test_forward_pass_latency() {
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let layer = RuvectorLayer::new(128, 256, 4, 0.1).unwrap();
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let node = vec![0.5f32; 128];
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let neighbors = vec![vec![0.3f32; 128], vec![0.7f32; 128]];
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let weights = vec![0.5f32, 0.5f32];
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// Warm up
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let _ = layer.forward(&node, &neighbors, &weights);
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// Measure
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let start = Instant::now();
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let iterations = 100;
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for _ in 0..iterations {
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let _ = layer.forward(&node, &neighbors, &weights);
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}
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let elapsed = start.elapsed();
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let avg_ms = elapsed.as_secs_f64() * 1000.0 / iterations as f64;
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// Forward pass: 5ms in release, ~100ms in debug
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let threshold_ms = 5.0 * LATENCY_MULTIPLIER;
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assert!(
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avg_ms < threshold_ms,
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"Average forward pass took {:.3}ms, expected <{:.0}ms",
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avg_ms,
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threshold_ms
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);
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println!(
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"Average forward pass latency: {:.3}ms ({} iterations, threshold: {:.0}ms)",
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avg_ms, iterations, threshold_ms
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);
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}
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/// Test batch operations performance
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#[test]
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fn test_batch_operations_performance() {
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let layer = RuvectorLayer::new(64, 128, 2, 0.1).unwrap();
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// Create batch of operations
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let batch_size = 100;
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let nodes: Vec<Vec<f32>> = (0..batch_size).map(|_| vec![0.5f32; 64]).collect();
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let neighbors: Vec<Vec<Vec<f32>>> = (0..batch_size)
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.map(|_| vec![vec![0.3f32; 64], vec![0.7f32; 64]])
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.collect();
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let weights: Vec<Vec<f32>> = (0..batch_size).map(|_| vec![0.5f32, 0.5f32]).collect();
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// Warm up
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let _ = layer.forward(&nodes[0], &neighbors[0], &weights[0]);
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// Measure batch
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let start = Instant::now();
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for i in 0..batch_size {
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let _ = layer.forward(&nodes[i], &neighbors[i], &weights[i]);
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}
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let elapsed = start.elapsed();
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let total_ms = elapsed.as_secs_f64() * 1000.0;
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let avg_ms = total_ms / batch_size as f64;
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// Batch: 500ms in release, ~10s in debug
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let threshold_ms = 500.0 * LATENCY_MULTIPLIER;
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println!(
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"Batch of {} operations: total={:.3}ms, avg={:.3}ms/op (threshold: {:.0}ms)",
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batch_size, total_ms, avg_ms, threshold_ms
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);
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assert!(
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total_ms < threshold_ms,
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"Batch took {:.3}ms, expected <{:.0}ms",
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total_ms,
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threshold_ms
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);
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}
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/// Test different layer sizes
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#[test]
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fn test_layer_size_scaling() {
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let sizes = [
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(64, 128, 2), // Small
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(128, 256, 4), // Medium
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(384, 768, 8), // Base (BERT-like)
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(768, 1024, 16), // Large
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];
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println!("\nLayer size scaling test:");
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println!(
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"{:>10} {:>10} {:>8} {:>12} {:>12}",
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"Input", "Hidden", "Heads", "Create(ms)", "Forward(ms)"
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);
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for (input, hidden, heads) in sizes {
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// Measure creation
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let start = Instant::now();
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let layer = RuvectorLayer::new(input, hidden, heads, 0.1).unwrap();
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let create_ms = start.elapsed().as_secs_f64() * 1000.0;
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// Measure forward
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let node = vec![0.5f32; input];
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let neighbors = vec![vec![0.3f32; input], vec![0.7f32; input]];
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let weights = vec![0.5f32, 0.5f32];
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// Warm up
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let _ = layer.forward(&node, &neighbors, &weights);
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let start = Instant::now();
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let iterations = 10;
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for _ in 0..iterations {
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let _ = layer.forward(&node, &neighbors, &weights);
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}
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let forward_ms = start.elapsed().as_secs_f64() * 1000.0 / iterations as f64;
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println!(
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"{:>10} {:>10} {:>8} {:>12.3} {:>12.3}",
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input, hidden, heads, create_ms, forward_ms
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);
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}
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}
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}
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/// Integration tests for the GNN cache system
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#[cfg(test)]
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mod gnn_cache_integration {
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use std::time::Instant;
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// Debug builds are ~10-20x slower than release
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#[cfg(debug_assertions)]
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const LATENCY_MULTIPLIER: f64 = 20.0;
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#[cfg(not(debug_assertions))]
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const LATENCY_MULTIPLIER: f64 = 1.0;
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/// Simulate the before/after scenario
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#[test]
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fn test_caching_benefit_simulation() {
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// Simulate "before" scenario: each operation pays full init cost
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// In reality this would be ~2.5s, but we use a smaller value for testing
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let simulated_init_cost_ms = 50.0; // Represents the ~2.5s in real scenario
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// Simulate "after" scenario: only first operation pays init cost
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let operations = 10;
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let forward_cost_ms = 2.0; // Actual forward pass cost
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// Before: each operation = init + forward
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let before_total = operations as f64 * (simulated_init_cost_ms + forward_cost_ms);
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// After: first op = init + forward, rest = forward only
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let after_total = simulated_init_cost_ms + (operations as f64 * forward_cost_ms);
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let speedup = before_total / after_total;
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println!("\nCaching benefit simulation:");
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println!("Operations: {}", operations);
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println!("Before (no cache): {:.1}ms total", before_total);
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println!("After (with cache): {:.1}ms total", after_total);
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println!("Speedup: {:.1}x", speedup);
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// Verify significant speedup
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assert!(
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speedup > 5.0,
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"Expected at least 5x speedup, got {:.1}x",
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speedup
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);
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}
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/// Test actual repeated operations benefit
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#[test]
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fn test_repeated_operations_speedup() {
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use ruvector_gnn::layer::RuvectorLayer;
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// First: measure time including layer creation
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let start_cold = Instant::now();
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let layer = RuvectorLayer::new(128, 256, 4, 0.1).unwrap();
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let node = vec![0.5f32; 128];
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let neighbors = vec![vec![0.3f32; 128], vec![0.7f32; 128]];
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let weights = vec![0.5f32, 0.5f32];
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let _ = layer.forward(&node, &neighbors, &weights);
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let cold_time = start_cold.elapsed();
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// Then: measure time for subsequent operations (layer already created)
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let iterations = 50;
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let start_warm = Instant::now();
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for _ in 0..iterations {
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let _ = layer.forward(&node, &neighbors, &weights);
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}
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let warm_time = start_warm.elapsed();
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let avg_warm_ms = warm_time.as_secs_f64() * 1000.0 / iterations as f64;
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// Warm threshold: 5ms in release, ~100ms in debug
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let warm_threshold_ms = 5.0 * LATENCY_MULTIPLIER;
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println!("\nRepeated operations test:");
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println!(
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"Cold start (create + forward): {:.3}ms",
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cold_time.as_secs_f64() * 1000.0
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);
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println!(
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"Warm average ({} iterations): {:.3}ms/op (threshold: {:.0}ms)",
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iterations, avg_warm_ms, warm_threshold_ms
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);
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println!("Warm total: {:.3}ms", warm_time.as_secs_f64() * 1000.0);
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// Warm operations should be significantly faster per-op
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assert!(
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avg_warm_ms < warm_threshold_ms,
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"Warm operations too slow: {:.3}ms (threshold: {:.0}ms)",
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avg_warm_ms,
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warm_threshold_ms
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);
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}
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/// Test that caching demonstrates clear benefit
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#[test]
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fn test_caching_demonstrates_benefit() {
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use ruvector_gnn::layer::RuvectorLayer;
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// Create layer once
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let start = Instant::now();
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let layer = RuvectorLayer::new(64, 128, 2, 0.1).unwrap();
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let creation_time = start.elapsed();
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let node = vec![0.5f32; 64];
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let neighbors = vec![vec![0.3f32; 64]];
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let weights = vec![1.0f32];
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// Warm up
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let _ = layer.forward(&node, &neighbors, &weights);
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// Measure forward passes
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let iterations = 20;
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let start = Instant::now();
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for _ in 0..iterations {
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let _ = layer.forward(&node, &neighbors, &weights);
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}
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let forward_time = start.elapsed();
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let creation_ms = creation_time.as_secs_f64() * 1000.0;
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let total_forward_ms = forward_time.as_secs_f64() * 1000.0;
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let avg_forward_ms = total_forward_ms / iterations as f64;
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println!("\nCaching benefit demonstration:");
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println!("Layer creation: {:.3}ms (one-time cost)", creation_ms);
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println!(
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"Forward passes: {:.3}ms total for {} ops",
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total_forward_ms, iterations
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);
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println!("Average forward: {:.3}ms/op", avg_forward_ms);
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// The key insight: creation cost is paid once, forward is repeated
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// If we had to recreate the layer each time, total would be:
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let without_caching = iterations as f64 * (creation_ms + avg_forward_ms);
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let with_caching = creation_ms + total_forward_ms;
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let benefit_ratio = without_caching / with_caching;
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println!("Without caching: {:.3}ms", without_caching);
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println!("With caching: {:.3}ms", with_caching);
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println!("Caching benefit: {:.1}x faster", benefit_ratio);
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// Caching should provide at least 2x benefit
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assert!(
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benefit_ratio > 2.0,
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"Caching should provide at least 2x benefit, got {:.1}x",
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benefit_ratio
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);
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}
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}
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