Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

crates/sona/benches/sona_bench.rs

@@ -0,0 +1,98 @@
+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use ruvector_sona::{SonaConfig, SonaEngine};
+
+fn trajectory_benchmark(c: &mut Criterion) {
+    let mut group = c.benchmark_group("trajectory");
+
+    for dim in [64, 128, 256, 512].iter() {
+        let engine = SonaEngine::with_config(SonaConfig {
+            hidden_dim: *dim,
+            embedding_dim: *dim,
+            ..Default::default()
+        });
+
+        group.bench_with_input(BenchmarkId::new("single", dim), dim, |b, &dim| {
+            b.iter(|| {
+                let mut builder = engine.begin_trajectory(vec![0.1; dim]);
+                builder.add_step(vec![0.5; dim], vec![], 0.8);
+                builder.add_step(vec![0.6; dim], vec![], 0.9);
+                engine.end_trajectory(builder, black_box(0.85));
+            });
+        });
+    }
+
+    group.finish();
+}
+
+fn lora_application_benchmark(c: &mut Criterion) {
+    let mut group = c.benchmark_group("lora");
+
+    for dim in [64, 128, 256, 512].iter() {
+        let engine = SonaEngine::with_config(SonaConfig {
+            hidden_dim: *dim,
+            embedding_dim: *dim,
+            ..Default::default()
+        });
+
+        // Warmup with some trajectories
+        for _ in 0..10 {
+            let mut builder = engine.begin_trajectory(vec![0.1; *dim]);
+            builder.add_step(vec![0.5; *dim], vec![], 0.8);
+            engine.end_trajectory(builder, 0.85);
+        }
+        engine.flush();
+
+        group.bench_with_input(BenchmarkId::new("micro", dim), dim, |b, &dim| {
+            let input = vec![1.0; dim];
+            let mut output = vec![0.0; dim];
+            b.iter(|| {
+                engine.apply_micro_lora(black_box(&input), black_box(&mut output));
+            });
+        });
+
+        group.bench_with_input(BenchmarkId::new("base", dim), dim, |b, &dim| {
+            let input = vec![1.0; dim];
+            let mut output = vec![0.0; dim];
+            b.iter(|| {
+                engine.apply_base_lora(0, black_box(&input), black_box(&mut output));
+            });
+        });
+    }
+
+    group.finish();
+}
+
+fn background_learning_benchmark(c: &mut Criterion) {
+    let mut group = c.benchmark_group("learning");
+    group.sample_size(10); // Fewer samples for expensive operation
+
+    let engine = SonaEngine::with_config(SonaConfig {
+        hidden_dim: 256,
+        embedding_dim: 256,
+        ..Default::default()
+    });
+
+    // Prepare 100 trajectories
+    for _ in 0..100 {
+        let mut builder = engine.begin_trajectory(vec![0.1; 256]);
+        builder.add_step(vec![0.5; 256], vec![], 0.8);
+        builder.add_step(vec![0.6; 256], vec![], 0.9);
+        engine.end_trajectory(builder, 0.85);
+    }
+
+    group.bench_function("force_learn", |b| {
+        b.iter(|| {
+            black_box(engine.force_learn());
+        });
+    });
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    trajectory_benchmark,
+    lora_application_benchmark,
+    background_learning_benchmark
+);
+criterion_main!(benches);