//! Advanced CoreML Model Management System
//! 高度なCoreMLモデル管理システム
//!
//! This module provides sophisticated model lifecycle management including
//! automatic optimization, memory management, and performance monitoring.
use crate::error::{RusTorchError, RusTorchResult};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
#[cfg(all(feature = "coreml", target_os = "macos"))]
use objc2_core_ml::*;
/// Model performance metrics for optimization decisions
/// 最適化判断用モデルパフォーマンス指標
#[derive(Debug, Clone)]
pub struct ModelMetrics {
pub execution_count: u64,
pub total_execution_time: Duration,
pub average_execution_time: Duration,
pub memory_usage_bytes: usize,
pub success_rate: f64,
pub last_used: Instant,
pub error_count: u64,
}
impl ModelMetrics {
fn new() -> Self {
Self {
execution_count: 0,
total_execution_time: Duration::new(0, 0),
average_execution_time: Duration::new(0, 0),
memory_usage_bytes: 0,
success_rate: 1.0,
last_used: Instant::now(),
error_count: 0,
}
}
fn update_execution(&mut self, execution_time: Duration, success: bool) {
self.execution_count += 1;
self.last_used = Instant::now();
if success {
self.total_execution_time += execution_time;
self.average_execution_time = self.total_execution_time / self.execution_count as u32;
} else {
self.error_count += 1;
}
self.success_rate =
(self.execution_count - self.error_count) as f64 / self.execution_count as f64;
}
}
/// Advanced model cache with LRU eviction and performance tracking
/// LRU排出とパフォーマンス追跡付き高度なモデルキャッシュ
pub struct AdvancedModelCache {
models: RwLock<HashMap<String, Arc<Mutex<String>>>>, // Simplified to String placeholder
metrics: RwLock<HashMap<String, ModelMetrics>>,
max_cache_size: usize,
max_memory_usage: usize,
current_memory_usage: Arc<Mutex<usize>>,
}
impl AdvancedModelCache {
/// Create new advanced model cache
/// 新しい高度なモデルキャッシュを作成
pub fn new(max_cache_size: usize, max_memory_mb: usize) -> Self {
Self {
models: RwLock::new(HashMap::new()),
metrics: RwLock::new(HashMap::new()),
max_cache_size,
max_memory_usage: max_memory_mb * 1024 * 1024, // Convert MB to bytes
current_memory_usage: Arc::new(Mutex::new(0)),
}
}
/// Get model from cache with metrics update
/// メトリクス更新付きでキャッシュからモデルを取得
pub fn get_model(&self, key: &str) -> Option<Arc<Mutex<String>>> {
let models = self.models.read().unwrap();
if let Some(model) = models.get(key) {
// Update last access time
let mut metrics = self.metrics.write().unwrap();
if let Some(metric) = metrics.get_mut(key) {
metric.last_used = Instant::now();
}
Some(model.clone())
} else {
None
}
}
/// Insert model with automatic cache management
/// 自動キャッシュ管理付きでモデルを挿入
pub fn insert_model(
&self,
key: String,
model: Arc<Mutex<String>>,
estimated_size: usize,
) -> RusTorchResult<()> {
// Check memory limits
{
let current_usage = *self.current_memory_usage.lock().unwrap();
if current_usage + estimated_size > self.max_memory_usage {
self.evict_lru_models(estimated_size)?;
}
}
// Check cache size limits
{
let models = self.models.read().unwrap();
if models.len() >= self.max_cache_size {
drop(models);
self.evict_lru_models(0)?;
}
}
// Insert model and metrics
{
let mut models = self.models.write().unwrap();
models.insert(key.clone(), model);
}
{
let mut metrics = self.metrics.write().unwrap();
let mut new_metrics = ModelMetrics::new();
new_metrics.memory_usage_bytes = estimated_size;
metrics.insert(key, new_metrics);
}
// Update memory usage
{
let mut current_usage = self.current_memory_usage.lock().unwrap();
*current_usage += estimated_size;
}
Ok(())
}
/// Evict least recently used models to free memory
/// メモリ解放のため最も使用頻度の低いモデルを排出
fn evict_lru_models(&self, required_space: usize) -> RusTorchResult<()> {
let mut models_to_evict = Vec::new();
// Find candidates for eviction based on LRU and low success rate
{
let metrics = self.metrics.read().unwrap();
let mut metric_pairs: Vec<(String, ModelMetrics)> = metrics
.iter()
.map(|(k, v)| (k.clone(), v.clone()))
.collect();
// Sort by last used time (oldest first) and success rate (lowest first)
metric_pairs.sort_by(|a, b| {
let time_cmp = a.1.last_used.cmp(&b.1.last_used);
if time_cmp == std::cmp::Ordering::Equal {
a.1.success_rate
.partial_cmp(&b.1.success_rate)
.unwrap_or(std::cmp::Ordering::Equal)
} else {
time_cmp
}
});
let mut freed_space = 0;
for (key, metric) in metric_pairs {
models_to_evict.push(key);
freed_space += metric.memory_usage_bytes;
if freed_space >= required_space && models_to_evict.len() > 0 {
break;
}
}
}
// Remove selected models
for key in &models_to_evict {
self.remove_model(key)?;
}
Ok(())
}
/// Remove model from cache
/// キャッシュからモデルを削除
fn remove_model(&self, key: &str) -> RusTorchResult<()> {
let memory_to_free = {
let metrics = self.metrics.read().unwrap();
metrics.get(key).map(|m| m.memory_usage_bytes).unwrap_or(0)
};
// Remove from cache
{
let mut models = self.models.write().unwrap();
models.remove(key);
}
{
let mut metrics = self.metrics.write().unwrap();
metrics.remove(key);
}
// Update memory usage
{
let mut current_usage = self.current_memory_usage.lock().unwrap();
*current_usage = current_usage.saturating_sub(memory_to_free);
}
Ok(())
}
/// Update model performance metrics
/// モデルパフォーマンス指標を更新
pub fn update_metrics(&self, key: &str, execution_time: Duration, success: bool) {
let mut metrics = self.metrics.write().unwrap();
if let Some(metric) = metrics.get_mut(key) {
metric.update_execution(execution_time, success);
}
}
/// Get cache statistics
/// キャッシュ統計を取得
pub fn get_cache_stats(&self) -> CacheStatistics {
let models_count = self.models.read().unwrap().len();
let current_memory = *self.current_memory_usage.lock().unwrap();
let metrics = self.metrics.read().unwrap();
let total_executions: u64 = metrics.values().map(|m| m.execution_count).sum();
let total_errors: u64 = metrics.values().map(|m| m.error_count).sum();
let avg_success_rate = if !metrics.is_empty() {
metrics.values().map(|m| m.success_rate).sum::<f64>() / metrics.len() as f64
} else {
0.0
};
CacheStatistics {
cached_models: models_count,
memory_usage_bytes: current_memory,
memory_usage_mb: current_memory / (1024 * 1024),
max_memory_mb: self.max_memory_usage / (1024 * 1024),
total_executions,
total_errors,
average_success_rate: avg_success_rate,
cache_hit_rate: self.calculate_hit_rate(),
}
}
/// Calculate cache hit rate
/// キャッシュヒット率を計算
fn calculate_hit_rate(&self) -> f64 {
let metrics = self.metrics.read().unwrap();
if metrics.is_empty() {
return 0.0;
}
let total_hits: u64 = metrics.values().map(|m| m.execution_count).sum();
let models_created = metrics.len() as u64;
if models_created == 0 {
0.0
} else {
(total_hits - models_created) as f64 / total_hits.max(1) as f64
}
}
/// Clear cache completely
/// キャッシュを完全にクリア
pub fn clear(&self) {
let mut models = self.models.write().unwrap();
let mut metrics = self.metrics.write().unwrap();
let mut current_usage = self.current_memory_usage.lock().unwrap();
models.clear();
metrics.clear();
*current_usage = 0;
}
/// Get models that haven't been used recently
/// 最近使用されていないモデルを取得
pub fn get_stale_models(&self, threshold: Duration) -> Vec<String> {
let metrics = self.metrics.read().unwrap();
let now = Instant::now();
metrics
.iter()
.filter(|(_, metric)| now.duration_since(metric.last_used) > threshold)
.map(|(key, _)| key.clone())
.collect()
}
}
/// Cache performance statistics
/// キャッシュパフォーマンス統計
#[derive(Debug, Clone)]
pub struct CacheStatistics {
pub cached_models: usize,
pub memory_usage_bytes: usize,
pub memory_usage_mb: usize,
pub max_memory_mb: usize,
pub total_executions: u64,
pub total_errors: u64,
pub average_success_rate: f64,
pub cache_hit_rate: f64,
}
/// Model optimization strategy
/// モデル最適化戦略
#[derive(Debug, Clone)]
pub enum OptimizationStrategy {
Speed, // Optimize for execution speed
Memory, // Optimize for memory usage
Balanced, // Balance between speed and memory
Accuracy, // Optimize for prediction accuracy
}
/// Advanced CoreML Model Manager
/// 高度なCoreMLモデルマネージャー
pub struct CoreStringManager {
cache: Arc<AdvancedModelCache>,
optimization_strategy: OptimizationStrategy,
model_directory: Option<PathBuf>,
auto_cleanup_enabled: bool,
cleanup_threshold: Duration,
}
impl CoreStringManager {
/// Create new advanced model manager
/// 新しい高度なモデルマネージャーを作成
pub fn new(optimization_strategy: OptimizationStrategy) -> Self {
let cache = Arc::new(AdvancedModelCache::new(
100, // Max 100 models
512, // Max 512MB memory
));
Self {
cache,
optimization_strategy,
model_directory: None,
auto_cleanup_enabled: true,
cleanup_threshold: Duration::from_secs(3600), // 1 hour
}
}
/// Configure model persistence directory
/// モデル永続化ディレクトリを設定
pub fn with_model_directory<P: AsRef<Path>>(mut self, path: P) -> Self {
self.model_directory = Some(path.as_ref().to_path_buf());
self
}
/// Configure auto cleanup settings
/// 自動クリーンアップ設定を構成
pub fn with_auto_cleanup(mut self, enabled: bool, threshold: Duration) -> Self {
self.auto_cleanup_enabled = enabled;
self.cleanup_threshold = threshold;
self
}
/// Get model with automatic optimization
/// 自動最適化付きでモデルを取得
pub fn get_optimized_model(&self, key: &str) -> Option<Arc<Mutex<String>>> {
let model = self.cache.get_model(key);
// Trigger cleanup if auto cleanup is enabled
if self.auto_cleanup_enabled {
let _ = self.cleanup_stale_models();
}
model
}
/// Store model with optimization hints
/// 最適化ヒント付きでモデルを保存
pub fn store_model(&self, key: String, model: Arc<Mutex<String>>) -> RusTorchResult<()> {
// Estimate model size (placeholder implementation)
let estimated_size = self.estimate_model_size(&model);
// Apply optimization strategy
let optimized_model = self.apply_optimization_strategy(model)?;
// Store in cache
self.cache
.insert_model(key.clone(), optimized_model, estimated_size)?;
// Optionally persist to disk
if let Some(ref model_dir) = self.model_directory {
let _ = self.persist_model_to_disk(&key, model_dir);
}
Ok(())
}
/// Apply optimization strategy to model
/// モデルに最適化戦略を適用
fn apply_optimization_strategy(
&self,
model: Arc<Mutex<String>>,
) -> RusTorchResult<Arc<Mutex<String>>> {
match self.optimization_strategy {
OptimizationStrategy::Speed => {
// Optimize for speed - could involve model quantization, etc.
// For now, return as-is
Ok(model)
}
OptimizationStrategy::Memory => {
// Optimize for memory - could involve model compression
// For now, return as-is
Ok(model)
}
OptimizationStrategy::Balanced => {
// Balanced optimization
Ok(model)
}
OptimizationStrategy::Accuracy => {
// Optimize for accuracy - ensure highest precision
Ok(model)
}
}
}
/// Estimate model memory footprint
/// モデルメモリフットプリントを推定
fn estimate_model_size(&self, _model: &Arc<Mutex<String>>) -> usize {
// Placeholder implementation
// In practice, would analyze model architecture and compute actual size
1024 * 1024 // 1MB default estimate
}
/// Persist model to disk for reuse across sessions
/// セッション間での再利用のためモデルをディスクに永続化
fn persist_model_to_disk(&self, key: &str, model_dir: &Path) -> RusTorchResult<()> {
// Create model directory if it doesn't exist
std::fs::create_dir_all(model_dir).map_err(|e| RusTorchError::TensorOp {
message: format!("Failed to create model directory: {}", e),
source: Some(Box::new(e)),
})?;
// Model persistence would be implemented here
// For now, just create a placeholder file
let model_path = model_dir.join(format!("{}.mlmodel", key));
std::fs::write(&model_path, b"placeholder").map_err(|e| RusTorchError::TensorOp {
message: format!("Failed to persist model: {}", e),
source: Some(Box::new(e)),
})?;
Ok(())
}
/// Clean up stale models
/// 古いモデルをクリーンアップ
pub fn cleanup_stale_models(&self) -> RusTorchResult<usize> {
let stale_models = self.cache.get_stale_models(self.cleanup_threshold);
let count = stale_models.len();
for key in stale_models {
let _ = self.cache.remove_model(&key);
}
Ok(count)
}
/// Get comprehensive performance statistics
/// 包括的パフォーマンス統計を取得
pub fn get_performance_stats(&self) -> CacheStatistics {
self.cache.get_cache_stats()
}
/// Record model execution metrics
/// モデル実行メトリクスを記録
pub fn record_execution(&self, key: &str, execution_time: Duration, success: bool) {
self.cache.update_metrics(key, execution_time, success);
}
/// Force cache cleanup
/// キャッシュクリーンアップを強制実行
pub fn force_cleanup(&self) {
self.cache.clear();
}
/// Get optimization recommendations
/// 最適化推奨事項を取得
pub fn get_optimization_recommendations(&self) -> Vec<OptimizationRecommendation> {
let stats = self.get_performance_stats();
let mut recommendations = Vec::new();
// Memory usage recommendations
if stats.memory_usage_mb as f64 / stats.max_memory_mb as f64 > 0.8 {
recommendations.push(OptimizationRecommendation {
category: "Memory".to_string(),
description:
"Memory usage is high. Consider reducing cache size or cleanup threshold."
.to_string(),
priority: RecommendationPriority::High,
});
}
// Success rate recommendations
if stats.average_success_rate < 0.9 {
recommendations.push(OptimizationRecommendation {
category: "Reliability".to_string(),
description:
"Model success rate is low. Check model compatibility and input validation."
.to_string(),
priority: RecommendationPriority::Medium,
});
}
// Cache hit rate recommendations
if stats.cache_hit_rate < 0.5 {
recommendations.push(OptimizationRecommendation {
category: "Performance".to_string(),
description: "Cache hit rate is low. Consider increasing cache size or optimizing model keys.".to_string(),
priority: RecommendationPriority::Low,
});
}
recommendations
}
}
/// Optimization recommendation
/// 最適化推奨事項
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
pub category: String,
pub description: String,
pub priority: RecommendationPriority,
}
#[derive(Debug, Clone)]
pub enum RecommendationPriority {
Low,
Medium,
High,
Critical,
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_model_metrics() {
let mut metrics = ModelMetrics::new();
assert_eq!(metrics.execution_count, 0);
assert_eq!(metrics.success_rate, 1.0);
metrics.update_execution(Duration::from_millis(100), true);
assert_eq!(metrics.execution_count, 1);
assert_eq!(metrics.success_rate, 1.0);
metrics.update_execution(Duration::from_millis(200), false);
assert_eq!(metrics.execution_count, 2);
assert_eq!(metrics.success_rate, 0.5);
}
#[test]
fn test_cache_creation() {
let cache = AdvancedModelCache::new(10, 100);
let stats = cache.get_cache_stats();
assert_eq!(stats.cached_models, 0);
assert_eq!(stats.memory_usage_mb, 0);
assert_eq!(stats.max_memory_mb, 100);
}
#[test]
fn test_model_manager_creation() {
let manager = CoreStringManager::new(OptimizationStrategy::Balanced);
let stats = manager.get_performance_stats();
assert_eq!(stats.cached_models, 0);
}
#[test]
fn test_optimization_recommendations() {
let manager = CoreStringManager::new(OptimizationStrategy::Speed);
let recommendations = manager.get_optimization_recommendations();
// Should provide recommendations based on initial state
assert!(!recommendations.is_empty());
}
}