mod.rs•15.8 kB
pub mod performance;
pub mod real_time;
pub mod memory;
pub mod quality;
pub use performance::{PerformanceBenchmark, BenchmarkConfig, BenchmarkResult};
pub use real_time::{RealTimeBenchmark, ThroughputBenchmark, LatencyBenchmark};
pub use memory::{MemoryBenchmark, MemoryProfile, AllocationTracker};
pub use quality::{QualityBenchmark, SimilarityMetrics, EmbeddingQualityScore};
use crate::embedding::{CodeEmbeddingModel, EmbeddingError};
use crate::languages::{CodeLanguage, CodeInput};
use crate::optimizer::{EmbeddingOptimizer, OptimizationConfig};
use std::time::{Duration, Instant};
use std::collections::HashMap;
use serde::{Serialize, Deserialize};
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComprehensiveBenchmarkSuite {
pub performance_benchmarks: Vec<PerformanceBenchmarkConfig>,
pub real_time_benchmarks: Vec<RealTimeBenchmarkConfig>,
pub memory_benchmarks: Vec<MemoryBenchmarkConfig>,
pub quality_benchmarks: Vec<QualityBenchmarkConfig>,
pub system_config: SystemConfig,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceBenchmarkConfig {
pub name: String,
pub test_cases: Vec<TestCase>,
pub iterations: usize,
pub warmup_iterations: usize,
pub languages: Vec<CodeLanguage>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealTimeBenchmarkConfig {
pub name: String,
pub target_latency_ms: f64,
pub concurrent_requests: usize,
pub duration_seconds: u64,
pub code_sizes: Vec<CodeSize>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryBenchmarkConfig {
pub name: String,
pub cache_sizes: Vec<usize>,
pub batch_sizes: Vec<usize>,
pub memory_limit_mb: usize,
pub gc_behavior: GcBehavior,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityBenchmarkConfig {
pub name: String,
pub similarity_threshold: f32,
pub code_variants: Vec<CodeVariant>,
pub comparison_models: Vec<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TestCase {
pub name: String,
pub code_content: String,
pub language: CodeLanguage,
pub expected_features: Option<ExpectedFeatures>,
pub size_category: CodeSize,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CodeSize {
Small, // < 100 lines
Medium, // 100-1000 lines
Large, // 1000-10000 lines
XLarge, // > 10000 lines
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CodeVariant {
SameLogicDifferentNames,
RefactoredCode,
CommentChanges,
WhitespaceChanges,
LanguageTranslation,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GcBehavior {
Aggressive,
Conservative,
Adaptive,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExpectedFeatures {
pub function_count: usize,
pub complexity_score: f32,
pub import_count: usize,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemConfig {
pub device: String,
pub model_config: String,
pub optimization_level: OptimizationLevel,
pub cache_enabled: bool,
pub parallel_processing: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizationLevel {
None,
Basic,
Aggressive,
Custom(OptimizationConfig),
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSuiteResult {
pub timestamp: std::time::SystemTime,
pub system_info: SystemInfo,
pub performance_results: Vec<PerformanceBenchmarkResult>,
pub real_time_results: Vec<RealTimeBenchmarkResult>,
pub memory_results: Vec<MemoryBenchmarkResult>,
pub quality_results: Vec<QualityBenchmarkResult>,
pub summary: BenchmarkSummary,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SystemInfo {
pub cpu_cores: usize,
pub ram_gb: usize,
pub gpu_info: Option<String>,
pub os: String,
pub rust_version: String,
pub candle_version: String,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceBenchmarkResult {
pub name: String,
pub avg_latency_ms: f64,
pub p95_latency_ms: f64,
pub p99_latency_ms: f64,
pub throughput_ops_per_sec: f64,
pub memory_peak_mb: f64,
pub cache_hit_rate: f64,
pub error_rate: f64,
pub language_breakdown: HashMap<CodeLanguage, LanguagePerformance>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealTimeBenchmarkResult {
pub name: String,
pub achieved_latency_ms: f64,
pub latency_variance: f64,
pub dropped_requests: usize,
pub successful_requests: usize,
pub concurrent_capacity: usize,
pub resource_utilization: ResourceUtilization,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryBenchmarkResult {
pub name: String,
pub peak_memory_mb: f64,
pub avg_memory_mb: f64,
pub memory_efficiency: f64,
pub gc_pressure: GcPressure,
pub cache_efficiency: CacheEfficiency,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityBenchmarkResult {
pub name: String,
pub avg_similarity_score: f32,
pub quality_consistency: f32,
pub false_positive_rate: f32,
pub false_negative_rate: f32,
pub semantic_preservation: f32,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LanguagePerformance {
pub avg_latency_ms: f64,
pub throughput_ops_per_sec: f64,
pub embedding_quality: f32,
pub parse_success_rate: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUtilization {
pub cpu_usage_percent: f64,
pub memory_usage_mb: f64,
pub gpu_usage_percent: Option<f64>,
pub io_operations: u64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GcPressure {
pub collections_per_sec: f64,
pub avg_collection_time_ms: f64,
pub memory_freed_mb: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheEfficiency {
pub hit_rate: f64,
pub eviction_rate: f64,
pub memory_utilization: f64,
pub avg_lookup_time_ms: f64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSummary {
pub overall_score: f64,
pub performance_grade: Grade,
pub real_time_grade: Grade,
pub memory_grade: Grade,
pub quality_grade: Grade,
pub recommendations: Vec<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum Grade {
Excellent, // A
Good, // B
Average, // C
Poor, // D
Failing, // F
}
pub struct BenchmarkRunner {
embedding_model: CodeEmbeddingModel,
optimizer: Option<EmbeddingOptimizer>,
system_monitor: SystemMonitor,
}
impl BenchmarkRunner {
pub fn new(embedding_model: CodeEmbeddingModel) -> Self {
Self {
embedding_model,
optimizer: None,
system_monitor: SystemMonitor::new(),
}
}
pub fn with_optimizer(mut self, optimizer: EmbeddingOptimizer) -> Self {
self.optimizer = Some(optimizer);
self
}
pub async fn run_comprehensive_benchmark(&mut self, suite: &ComprehensiveBenchmarkSuite) -> Result<BenchmarkSuiteResult, EmbeddingError> {
let start_time = Instant::now();
let system_info = self.collect_system_info().await;
println!("🚀 Starting comprehensive benchmark suite...");
println!("System: {} cores, {}GB RAM", system_info.cpu_cores, system_info.ram_gb);
// Run performance benchmarks
let mut performance_results = Vec::new();
for config in &suite.performance_benchmarks {
println!("📊 Running performance benchmark: {}", config.name);
let result = self.run_performance_benchmark(config).await?;
performance_results.push(result);
}
// Run real-time benchmarks
let mut real_time_results = Vec::new();
for config in &suite.real_time_benchmarks {
println!("⚡ Running real-time benchmark: {}", config.name);
let result = self.run_real_time_benchmark(config).await?;
real_time_results.push(result);
}
// Run memory benchmarks
let mut memory_results = Vec::new();
for config in &suite.memory_benchmarks {
println!("🧠 Running memory benchmark: {}", config.name);
let result = self.run_memory_benchmark(config).await?;
memory_results.push(result);
}
// Run quality benchmarks
let mut quality_results = Vec::new();
for config in &suite.quality_benchmarks {
println!("✨ Running quality benchmark: {}", config.name);
let result = self.run_quality_benchmark(config).await?;
quality_results.push(result);
}
let summary = self.compute_summary(&performance_results, &real_time_results, &memory_results, &quality_results);
let total_duration = start_time.elapsed();
println!("✅ Benchmark suite completed in {:.2}s", total_duration.as_secs_f64());
println!("📈 Overall Score: {:.1}/100", summary.overall_score);
Ok(BenchmarkSuiteResult {
timestamp: std::time::SystemTime::now(),
system_info,
performance_results,
real_time_results,
memory_results,
quality_results,
summary,
})
}
async fn run_performance_benchmark(&mut self, config: &PerformanceBenchmarkConfig) -> Result<PerformanceBenchmarkResult, EmbeddingError> {
let benchmark = PerformanceBenchmark::new(config.clone());
benchmark.run(&mut self.embedding_model).await
}
async fn run_real_time_benchmark(&mut self, config: &RealTimeBenchmarkConfig) -> Result<RealTimeBenchmarkResult, EmbeddingError> {
let benchmark = RealTimeBenchmark::new(config.clone());
benchmark.run(&mut self.embedding_model).await
}
async fn run_memory_benchmark(&mut self, config: &MemoryBenchmarkConfig) -> Result<MemoryBenchmarkResult, EmbeddingError> {
let benchmark = MemoryBenchmark::new(config.clone());
benchmark.run(&mut self.embedding_model).await
}
async fn run_quality_benchmark(&mut self, config: &QualityBenchmarkConfig) -> Result<QualityBenchmarkResult, EmbeddingError> {
let benchmark = QualityBenchmark::new(config.clone());
benchmark.run(&mut self.embedding_model).await
}
async fn collect_system_info(&self) -> SystemInfo {
SystemInfo {
cpu_cores: num_cpus::get(),
ram_gb: 16, // Would get actual RAM in real implementation
gpu_info: None, // Would detect GPU in real implementation
os: std::env::consts::OS.to_string(),
rust_version: env!("CARGO_PKG_VERSION").to_string(),
candle_version: "0.4.0".to_string(), // Would get actual version
}
}
fn compute_summary(&self,
performance_results: &[PerformanceBenchmarkResult],
real_time_results: &[RealTimeBenchmarkResult],
memory_results: &[MemoryBenchmarkResult],
quality_results: &[QualityBenchmarkResult]) -> BenchmarkSummary {
// Compute weighted scores
let perf_score = self.compute_performance_score(performance_results);
let rt_score = self.compute_real_time_score(real_time_results);
let mem_score = self.compute_memory_score(memory_results);
let qual_score = self.compute_quality_score(quality_results);
let overall_score = (perf_score * 0.3) + (rt_score * 0.3) + (mem_score * 0.2) + (qual_score * 0.2);
let recommendations = self.generate_recommendations(performance_results, real_time_results, memory_results, quality_results);
BenchmarkSummary {
overall_score,
performance_grade: Self::score_to_grade(perf_score),
real_time_grade: Self::score_to_grade(rt_score),
memory_grade: Self::score_to_grade(mem_score),
quality_grade: Self::score_to_grade(qual_score),
recommendations,
}
}
fn compute_performance_score(&self, results: &[PerformanceBenchmarkResult]) -> f64 {
if results.is_empty() { return 0.0; }
let avg_throughput: f64 = results.iter().map(|r| r.throughput_ops_per_sec).sum::<f64>() / results.len() as f64;
let avg_latency: f64 = results.iter().map(|r| r.avg_latency_ms).sum::<f64>() / results.len() as f64;
// Higher throughput is better, lower latency is better
let throughput_score = (avg_throughput / 1000.0).min(100.0); // Normalize to ~100 scale
let latency_score = (200.0 - avg_latency).max(0.0); // 200ms baseline
(throughput_score + latency_score) / 2.0
}
fn compute_real_time_score(&self, results: &[RealTimeBenchmarkResult]) -> f64 {
if results.is_empty() { return 0.0; }
let avg_latency: f64 = results.iter().map(|r| r.achieved_latency_ms).sum::<f64>() / results.len() as f64;
let success_rate: f64 = results.iter().map(|r| {
let total = r.successful_requests + r.dropped_requests;
if total > 0 { r.successful_requests as f64 / total as f64 } else { 0.0 }
}).sum::<f64>() / results.len() as f64;
let latency_score = (100.0 - avg_latency).max(0.0);
let success_score = success_rate * 100.0;
(latency_score + success_score) / 2.0
}
fn compute_memory_score(&self, results: &[MemoryBenchmarkResult]) -> f64 {
if results.is_empty() { return 0.0; }
let avg_efficiency: f64 = results.iter().map(|r| r.memory_efficiency).sum::<f64>() / results.len() as f64;
let avg_cache_hit_rate: f64 = results.iter().map(|r| r.cache_efficiency.hit_rate).sum::<f64>() / results.len() as f64;
(avg_efficiency * 100.0 * 0.6) + (avg_cache_hit_rate * 100.0 * 0.4)
}
fn compute_quality_score(&self, results: &[QualityBenchmarkResult]) -> f64 {
if results.is_empty() { return 0.0; }
let avg_similarity: f64 = results.iter().map(|r| r.avg_similarity_score as f64).sum::<f64>() / results.len() as f64;
let avg_consistency: f64 = results.iter().map(|r| r.quality_consistency as f64).sum::<f64>() / results.len() as f64;
(avg_similarity * 100.0 * 0.7) + (avg_consistency * 100.0 * 0.3)
}
fn score_to_grade(score: f64) -> Grade {
match score as i32 {
90..=100 => Grade::Excellent,
80..=89 => Grade::Good,
70..=79 => Grade::Average,
60..=69 => Grade::Poor,
_ => Grade::Failing,
}
}
fn generate_recommendations(&self,
_performance_results: &[PerformanceBenchmarkResult],
_real_time_results: &[RealTimeBenchmarkResult],
_memory_results: &[MemoryBenchmarkResult],
_quality_results: &[QualityBenchmarkResult]) -> Vec<String> {
vec![
"Consider enabling quantization for better memory efficiency".to_string(),
"Increase cache size for better performance".to_string(),
"Use batch processing for higher throughput".to_string(),
]
}
}
pub struct SystemMonitor {
start_time: Instant,
peak_memory: usize,
}
impl SystemMonitor {
pub fn new() -> Self {
Self {
start_time: Instant::now(),
peak_memory: 0,
}
}
pub fn record_memory_usage(&mut self, usage: usize) {
self.peak_memory = self.peak_memory.max(usage);
}
pub fn get_runtime_seconds(&self) -> f64 {
self.start_time.elapsed().as_secs_f64()
}
pub fn get_peak_memory_mb(&self) -> f64 {
self.peak_memory as f64 / 1024.0 / 1024.0
}
}
impl Default for SystemMonitor {
fn default() -> Self {
Self::new()
}
}