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US-011: Performance optimization for API request serialization
Added criterion benchmarks and optimized flatten_tool_result_content: - Added criterion dev-dependency and request_building benchmark suite - Optimized flatten_tool_result_content to pre-allocate capacity and avoid intermediate Vec construction (was collecting to Vec then joining) - Made key functions public for benchmarking: translate_message, build_chat_completion_request, flatten_tool_result_content, is_reasoning_model, model_rejects_is_error_field Benchmark results: - flatten_tool_result_content/single_text: ~17ns - translate_message/text_only: ~200ns - build_chat_completion_request/10 messages: ~16.4µs - is_reasoning_model detection: ~26-42ns All 119 unit tests and 29 integration tests pass. cargo clippy passes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Yeachan-Heo
329
rust/crates/api/benches/request_building.rs
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329
rust/crates/api/benches/request_building.rs
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// Benchmarks for API request building performance
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// Benchmarks are exempt from strict linting as they are test/performance code
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#![allow(
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clippy::cognitive_complexity,
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clippy::doc_markdown,
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clippy::explicit_iter_loop,
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clippy::format_in_format_args,
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clippy::missing_docs_in_private_items,
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clippy::must_use_candidate,
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clippy::needless_pass_by_value,
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clippy::clone_on_copy,
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clippy::too_many_lines,
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clippy::uninlined_format_args
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)]
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use api::{
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build_chat_completion_request, flatten_tool_result_content, is_reasoning_model,
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translate_message, InputContentBlock, InputMessage, MessageRequest, OpenAiCompatConfig,
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ToolResultContentBlock,
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};
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use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
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use serde_json::json;
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/// Create a sample message request with various content types
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fn create_sample_request(message_count: usize) -> MessageRequest {
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let mut messages = Vec::with_capacity(message_count);
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for i in 0..message_count {
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match i % 4 {
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0 => messages.push(InputMessage::user_text(format!("Message {}", i))),
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1 => messages.push(InputMessage {
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role: "assistant".to_string(),
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content: vec![
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InputContentBlock::Text {
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text: format!("Assistant response {}", i),
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},
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InputContentBlock::ToolUse {
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id: format!("call_{}", i),
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name: "read_file".to_string(),
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input: json!({"path": format!("/tmp/file{}", i)}),
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},
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],
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}),
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2 => messages.push(InputMessage {
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role: "user".to_string(),
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content: vec![InputContentBlock::ToolResult {
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tool_use_id: format!("call_{}", i - 1),
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content: vec![ToolResultContentBlock::Text {
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text: format!("Tool result content {}", i),
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}],
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is_error: false,
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}],
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}),
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_ => messages.push(InputMessage {
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role: "assistant".to_string(),
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content: vec![InputContentBlock::ToolUse {
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id: format!("call_{}", i),
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name: "write_file".to_string(),
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input: json!({"path": format!("/tmp/out{}", i), "content": "data"}),
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}],
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}),
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}
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}
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MessageRequest {
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model: "gpt-4o".to_string(),
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max_tokens: 1024,
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messages,
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stream: false,
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system: Some("You are a helpful assistant.".to_string()),
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temperature: Some(0.7),
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top_p: None,
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tools: None,
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tool_choice: None,
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frequency_penalty: None,
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presence_penalty: None,
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stop: None,
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reasoning_effort: None,
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}
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}
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/// Benchmark translate_message with various message types
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fn bench_translate_message(c: &mut Criterion) {
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let mut group = c.benchmark_group("translate_message");
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// Text-only message
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let text_message = InputMessage::user_text("Simple text message".to_string());
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group.bench_with_input(
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BenchmarkId::new("text_only", "single"),
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&text_message,
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|b, msg| {
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b.iter(|| translate_message(black_box(msg), black_box("gpt-4o")));
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},
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);
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// Assistant message with tool calls
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let assistant_message = InputMessage {
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role: "assistant".to_string(),
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content: vec![
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InputContentBlock::Text {
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text: "I'll help you with that.".to_string(),
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},
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InputContentBlock::ToolUse {
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id: "call_1".to_string(),
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name: "read_file".to_string(),
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input: json!({"path": "/tmp/test"}),
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},
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InputContentBlock::ToolUse {
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id: "call_2".to_string(),
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name: "write_file".to_string(),
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input: json!({"path": "/tmp/out", "content": "data"}),
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},
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],
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};
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group.bench_with_input(
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BenchmarkId::new("assistant_with_tools", "2_tools"),
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&assistant_message,
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|b, msg| {
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b.iter(|| translate_message(black_box(msg), black_box("gpt-4o")));
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},
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);
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// Tool result message
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let tool_result_message = InputMessage {
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role: "user".to_string(),
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content: vec![InputContentBlock::ToolResult {
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tool_use_id: "call_1".to_string(),
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content: vec![ToolResultContentBlock::Text {
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text: "File contents here".to_string(),
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}],
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is_error: false,
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}],
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};
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group.bench_with_input(
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BenchmarkId::new("tool_result", "single"),
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&tool_result_message,
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|b, msg| {
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b.iter(|| translate_message(black_box(msg), black_box("gpt-4o")));
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},
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);
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// Tool result for kimi model (is_error excluded)
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group.bench_with_input(
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BenchmarkId::new("tool_result_kimi", "kimi-k2.5"),
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&tool_result_message,
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|b, msg| {
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b.iter(|| translate_message(black_box(msg), black_box("kimi-k2.5")));
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},
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);
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// Large content message
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let large_content = "x".repeat(10000);
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let large_message = InputMessage::user_text(large_content);
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group.bench_with_input(
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BenchmarkId::new("large_text", "10kb"),
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&large_message,
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|b, msg| {
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b.iter(|| translate_message(black_box(msg), black_box("gpt-4o")));
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},
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);
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group.finish();
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}
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/// Benchmark build_chat_completion_request with various message counts
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fn bench_build_request(c: &mut Criterion) {
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let mut group = c.benchmark_group("build_chat_completion_request");
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let config = OpenAiCompatConfig::openai();
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for message_count in [10, 50, 100].iter() {
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let request = create_sample_request(*message_count);
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group.bench_with_input(
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BenchmarkId::new("message_count", message_count),
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&request,
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|b, req| {
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b.iter(|| build_chat_completion_request(black_box(req), config.clone()));
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},
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);
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}
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// Benchmark with reasoning model (tuning params stripped)
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let mut reasoning_request = create_sample_request(50);
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reasoning_request.model = "o1-mini".to_string();
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group.bench_with_input(
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BenchmarkId::new("reasoning_model", "o1-mini"),
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&reasoning_request,
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|b, req| {
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b.iter(|| build_chat_completion_request(black_box(req), config.clone()));
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},
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);
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// Benchmark with gpt-5 (max_completion_tokens)
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let mut gpt5_request = create_sample_request(50);
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gpt5_request.model = "gpt-5".to_string();
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group.bench_with_input(
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BenchmarkId::new("gpt5", "gpt-5"),
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&gpt5_request,
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|b, req| {
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b.iter(|| build_chat_completion_request(black_box(req), config.clone()));
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},
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);
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group.finish();
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}
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/// Benchmark flatten_tool_result_content
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fn bench_flatten_tool_result(c: &mut Criterion) {
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let mut group = c.benchmark_group("flatten_tool_result_content");
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// Single text block
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let single_text = vec![ToolResultContentBlock::Text {
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text: "Simple result".to_string(),
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}];
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group.bench_with_input(
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BenchmarkId::new("single_text", "1_block"),
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&single_text,
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|b, content| {
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b.iter(|| flatten_tool_result_content(black_box(content)));
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},
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);
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// Multiple text blocks
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let multi_text: Vec<ToolResultContentBlock> = (0..10)
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.map(|i| ToolResultContentBlock::Text {
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text: format!("Line {}: some content here\n", i),
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})
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.collect();
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group.bench_with_input(
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BenchmarkId::new("multi_text", "10_blocks"),
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&multi_text,
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|b, content| {
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b.iter(|| flatten_tool_result_content(black_box(content)));
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},
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);
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// JSON content blocks
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let json_content: Vec<ToolResultContentBlock> = (0..5)
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.map(|i| ToolResultContentBlock::Json {
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value: json!({"index": i, "data": "test content", "nested": {"key": "value"}}),
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})
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.collect();
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group.bench_with_input(
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BenchmarkId::new("json_content", "5_blocks"),
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&json_content,
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|b, content| {
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b.iter(|| flatten_tool_result_content(black_box(content)));
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},
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);
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// Mixed content
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let mixed_content = vec![
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ToolResultContentBlock::Text {
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text: "Here's the result:".to_string(),
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},
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ToolResultContentBlock::Json {
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value: json!({"status": "success", "count": 42}),
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},
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ToolResultContentBlock::Text {
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text: "Processing complete.".to_string(),
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},
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];
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group.bench_with_input(
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BenchmarkId::new("mixed_content", "text+json"),
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&mixed_content,
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|b, content| {
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b.iter(|| flatten_tool_result_content(black_box(content)));
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},
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);
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// Large content - simulating typical tool output
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let large_content: Vec<ToolResultContentBlock> = (0..50)
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.map(|i| {
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if i % 3 == 0 {
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ToolResultContentBlock::Json {
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value: json!({"line": i, "content": "x".repeat(100)}),
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}
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} else {
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ToolResultContentBlock::Text {
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text: format!("Line {}: {}", i, "some output content here"),
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}
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}
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})
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.collect();
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group.bench_with_input(
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BenchmarkId::new("large_content", "50_blocks"),
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&large_content,
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|b, content| {
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b.iter(|| flatten_tool_result_content(black_box(content)));
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},
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);
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group.finish();
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}
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/// Benchmark is_reasoning_model detection
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fn bench_is_reasoning_model(c: &mut Criterion) {
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let mut group = c.benchmark_group("is_reasoning_model");
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let models = vec![
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("gpt-4o", false),
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("o1-mini", true),
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("o3", true),
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("grok-3", false),
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("grok-3-mini", true),
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("qwen/qwen-qwq-32b", true),
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("qwen/qwen-plus", false),
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];
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for (model, expected) in models {
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group.bench_with_input(
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BenchmarkId::new(model, if expected { "reasoning" } else { "normal" }),
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model,
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|b, m| {
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b.iter(|| is_reasoning_model(black_box(m)));
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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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criterion_group!(
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benches,
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bench_translate_message,
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bench_build_request,
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bench_flatten_tool_result,
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bench_is_reasoning_model
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);
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criterion_main!(benches);
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