d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
125 lines
3.7 KiB
Rust
125 lines
3.7 KiB
Rust
//! Pipeline benchmarks for RuvLLM
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//!
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//! Benchmarks the complete request-to-response pipeline.
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use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
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use ruvllm::{Config, Request, RuvLLM};
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use tokio::runtime::Runtime;
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fn benchmark_query(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = Config::builder()
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.embedding_dim(128)
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.router_hidden_dim(32)
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.learning_enabled(false)
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.build()
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.unwrap();
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let llm = rt.block_on(RuvLLM::new(config)).unwrap();
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c.bench_function("query_simple", |b| {
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b.to_async(&rt)
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.iter(|| async { black_box(llm.query("What is Rust?").await.unwrap()) })
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});
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}
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fn benchmark_query_lengths(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = Config::builder()
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.embedding_dim(128)
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.router_hidden_dim(32)
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.learning_enabled(false)
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.build()
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.unwrap();
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let llm = rt.block_on(RuvLLM::new(config)).unwrap();
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let queries = vec![
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("short", "Hi"),
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("medium", "What is machine learning and how does it work?"),
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("long", "Please explain in detail how neural networks process information, including concepts like forward propagation, backpropagation, gradient descent, and the role of activation functions in learning complex patterns from data."),
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];
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let mut group = c.benchmark_group("query_by_length");
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for (name, query) in queries {
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group.bench_with_input(BenchmarkId::from_parameter(name), &query, |b, query| {
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b.to_async(&rt)
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.iter(|| async { black_box(llm.query(*query).await.unwrap()) })
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});
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}
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group.finish();
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}
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fn benchmark_concurrent_queries(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = Config::builder()
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.embedding_dim(128)
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.router_hidden_dim(32)
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.learning_enabled(false)
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.build()
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.unwrap();
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let llm = std::sync::Arc::new(rt.block_on(RuvLLM::new(config)).unwrap());
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let mut group = c.benchmark_group("concurrent_queries");
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for concurrency in [1, 2, 4, 8] {
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group.bench_with_input(
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BenchmarkId::from_parameter(concurrency),
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&concurrency,
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|b, &concurrency| {
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b.to_async(&rt).iter(|| async {
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let mut handles = Vec::new();
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for _ in 0..concurrency {
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let llm_clone = llm.clone();
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handles.push(tokio::spawn(async move {
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llm_clone.query("Test query").await.unwrap()
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}));
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}
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for handle in handles {
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black_box(handle.await.unwrap());
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}
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})
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},
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);
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}
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group.finish();
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}
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fn benchmark_session(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = Config::builder()
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.embedding_dim(128)
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.router_hidden_dim(32)
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.learning_enabled(false)
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.build()
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.unwrap();
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let llm = rt.block_on(RuvLLM::new(config)).unwrap();
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c.bench_function("session_multi_turn", |b| {
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b.to_async(&rt).iter(|| async {
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let session = llm.new_session();
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black_box(llm.query_session(&session, "First question").await.unwrap());
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black_box(llm.query_session(&session, "Follow up").await.unwrap());
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black_box(
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llm.query_session(&session, "Another follow up")
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.await
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.unwrap(),
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);
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})
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});
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}
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criterion_group!(
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benches,
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benchmark_query,
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benchmark_query_lengths,
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benchmark_concurrent_queries,
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benchmark_session,
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);
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criterion_main!(benches);
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