amberjs 1.17.0

// 模型缓存系统
// 实现智能的模型加载和缓存机制，包括分层缓存、压缩存储和智能预取

use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

/// 模型缓存配置
#[derive(Debug, Clone)]
pub struct ModelCacheConfig {
    pub max_memory_mb: usize,
    pub max_disk_gb: usize,
    pub enable_compression: bool,
    pub enable_prefetch: bool,
}
/// 缓存层级
#[derive(Debug, Clone, PartialEq)]
pub enum CacheTier {
    /// L1: 内存缓存（最快）
    L1Memory,
    /// L2: SSD 缓存（中等速度）
    L2Disk,
    /// L3: 远程缓存（最慢）
    L3Remote,
}
/// 模型缓存条目
#[derive(Debug, Clone)]
struct CacheEntry {
    model_name: String,
    model_data: Arc<ModelData>,
    tier: CacheTier,
    last_access: Instant,
    access_count: u64,
    size_bytes: usize,
}
/// 模型数据
#[derive(Debug, Clone)]
struct ModelData {
    weights: Vec<u8>,
    metadata: ModelMetadata,
}
/// 模型元数据
#[derive(Debug, Clone)]
struct ModelMetadata {
    model_name: String,
    version: String,
    size_mb: usize,
    compression_ratio: f32,
    load_time: Duration,
}
/// 访问模式记录
#[derive(Debug, Clone)]
struct AccessPattern {
    model_name: String,
    access_count: u64,
    last_access: Instant,
    access_intervals: Vec<Duration>,
}
/// 模型缓存管理器
pub struct ModelCache {
    config: ModelCacheConfig,
    l1_cache: Arc<RwLock<HashMap<String, CacheEntry>>>,
    l2_cache: Arc<Mutex<HashMap<String, CacheEntry>>>,
    l3_cache: Arc<Mutex<HashMap<String, CacheEntry>>>,
    access_patterns: Arc<RwLock<HashMap<String, AccessPattern>>>,
    stats: Arc<Mutex<CacheStats>>,
}
/// 缓存统计信息
#[derive(Debug, Clone, Default)]
pub struct CacheStats {
    pub l1_hits: u64,
    pub l2_hits: u64,
    pub l3_hits: u64,
    pub misses: u64,
    pub total_requests: u64,
    pub memory_usage_mb: usize,
    pub disk_usage_mb: usize,
}
/// 缓存结果
#[derive(Debug, Clone)]
pub struct CacheResult {
    pub model_data: Arc<ModelData>,
    pub tier: CacheTier,
    pub load_time: Duration,
    pub from_cache: bool,
}
impl ModelCache {
    /// 创建新的模型缓存实例
    pub fn new(config: ModelCacheConfig) -> Result<Self, String> {
        // AI 模型缓存不需要 V8 Runtime，移除运行时依赖
        Ok(ModelCache {
            config: config.clone(),
            l1_cache: Arc::new(Mutex::new(HashMap::new()))
            l2_cache: Arc::new(Mutex::new(HashMap::new()))
            l3_cache: Arc::new(Mutex::new(HashMap::new()))
            access_patterns: Arc::new(Mutex::new(HashMap::new()))
            stats: Arc::new(Mutex::new(CacheStats::default()))
        })
    }
    /// 加载模型
    pub fn load_model(&mut self, model_name: String) -> Result<CacheResult, String> {
        let start_time: _ = Instant::now();
        // 尝试从 L1 缓存获取
        if let Some(entry) = self.get_from_l1(&model_name) {
            self.update_stats(entry.tier.clone(), true);
            return Ok(CacheResult {
                model_data: entry.model_data.clone(),
                tier: entry.tier.clone(),
                load_time: Duration::from_millis(1),
                from_cache: true,
            });
        }
        // 尝试从 L2 缓存获取
        if let Some(entry) = self.get_from_l2(&model_name) {
            // 提升到 L1
            self.promote_to_l1(entry.clone())?;
            self.update_stats(CacheTier::L2Disk, true);
            return Ok(CacheResult {
                model_data: entry.model_data.clone(),
                tier: CacheTier::L2Disk,
                load_time: Duration::from_millis(5),
                from_cache: true,
            });
        }
        // 尝试从 L3 缓存获取
        if let Some(entry) = self.get_from_l3(&model_name) {
            // 提升到 L2
            self.promote_to_l2(entry.clone())?;
            self.update_stats(CacheTier::L3Remote, true);
            return Ok(CacheResult {
                model_data: entry.model_data.clone(),
                tier: CacheTier::L3Remote,
                load_time: Duration::from_millis(20),
                from_cache: true,
            });
        }
        // 缓存未命中，从磁盘加载
        let model_data: _ = self.load_from_disk(&model_name)?;
        let load_time: _ = start_time.elapsed();
        // 创建缓存条目
        let entry: _ = CacheEntry {
            model_name: model_name.clone(),
            model_data: Arc::new(Mutex::new(model_data)))
            tier: CacheTier::L2Disk,
            last_access: Instant::now(),
            access_count: 1,
            size_bytes: 1024 * 1024, // 假设 1MB
        };
        // 存储到 L2 缓存
        self.store_to_l2(entry.clone())?;
        // 更新统计
        self.update_stats(CacheTier::L2Disk, false);
        Ok(CacheResult {
            model_data: entry.model_data.clone(),
            tier: CacheTier::L2Disk,
            load_time,
            from_cache: false,
        })
    }
    /// 从 L1 缓存获取
    fn get_from_l1(&self, model_name: &str) -> Option<CacheEntry> {
        let cache: _ = self.l1_cache.read().unwrap();
        if let Some(entry) = cache.get(model_name).cloned() {
            // 更新访问信息
            drop(cache);
            self.record_access(model_name);
            Some(entry)
        } else {
            None
        }
    }
    /// 从 L2 缓存获取
    fn get_from_l2(&self, model_name: &str) -> Option<CacheEntry> {
        let cache: _ = self.l2_cache.lock().unwrap();
        if let Some(entry) = cache.get(model_name).cloned() {
            drop(cache);
            self.record_access(model_name);
            Some(entry)
        } else {
            None
        }
    }
    /// 从 L3 缓存获取
    fn get_from_l3(&self, model_name: &str) -> Option<CacheEntry> {
        let cache: _ = self.l3_cache.lock().unwrap();
        if let Some(entry) = cache.get(model_name).cloned() {
            drop(cache);
            self.record_access(model_name);
            Some(entry)
        } else {
            None
        }
    }
    /// 提升到 L1 缓存
    fn promote_to_l1(&self, entry: CacheEntry) -> Result<(), String> {
        let mut l1_cache = self.l1_cache.write().unwrap();
        // 检查内存限制
        if self.get_memory_usage() > self.config.max_memory_mb {
            self.evict_from_l1()?;
        }
        l1_cache.insert(entry.model_name.clone(), entry);
        Ok(())
    }
    /// 提升到 L2 缓存
    fn promote_to_l2(&self, entry: CacheEntry) -> Result<(), String> {
        let mut l2_cache = self.l2_cache.lock().unwrap();
        // 检查磁盘限制
        if self.get_disk_usage() > self.config.max_disk_gb * 1024 {
            self.evict_from_l2()?;
        }
        l2_cache.insert(entry.model_name.clone(), entry);
        Ok(())
    }
    /// 存储到 L2 缓存
    fn store_to_l2(&self, entry: CacheEntry) -> Result<(), String> {
        let mut l2_cache = self.l2_cache.lock().unwrap();
        l2_cache.insert(entry.model_name.clone(), entry);
        Ok(())
    }
    /// 从磁盘加载模型
    fn load_from_disk(&self, model_name: &str) -> Result<ModelData, String> {
        // 模拟从磁盘加载模型数据
        let weights: _ = vec![0u8; 1024 * 1024]; // 1MB 模拟数据
        let metadata: _ = ModelMetadata {
            model_name: model_name.to_string(),
            version: "1.0".to_string(),
            size_mb: 1,
            compression_ratio: 0.8,
            load_time: Duration::from_millis(100),
        };
        Ok(ModelData { weights, metadata })
    }
    /// 记录访问
    fn record_access(&self, model_name: &str) {
        let mut patterns = self.access_patterns.write().unwrap();
        let now: _ = Instant::now();
        if let Some(pattern) = patterns.get_mut(model_name) {
            pattern.access_count += 1;
            let interval: _ = now.duration_since(pattern.last_access);
            pattern.access_intervals.push(interval);
            pattern.last_access = now;
            // 限制历史记录大小
            if pattern.access_intervals.len() > 100 {
                pattern.access_intervals.remove(0);
            }
        } else {
            patterns.insert(
                model_name.to_string(),
                AccessPattern {
                    model_name: model_name.to_string(),
                    access_count: 1,
                    last_access: now,
                    access_intervals: Vec::new(),
                },
            );
        }
    }
    /// 预加载模型
    pub fn preload_model(&mut self, model_name: String) -> Result<(), String> {
        // 基于访问模式预测并预加载
        let patterns: _ = self.access_patterns.read().unwrap();
        if let Some(pattern) = patterns.get(&model_name) {
            if pattern.access_count > 10 {
                drop(patterns);
                let _: _ = self.load_model(model_name);
            }
        }
        Ok(())
    }
    /// 获取预取推荐
    pub fn get_prefetch_recommendations(&self) -> Vec<String> {
        let patterns: _ = self.access_patterns.read().unwrap();
        let mut recommendations = Vec::new();
        for (model_name, pattern) in patterns.iter() {
            if pattern.access_count > 20 {
                let avg_interval: _ = pattern
                    .access_intervals
                    .iter()
                    .sum::<Duration>()
                    .div_f32(pattern.access_intervals.len() as f32);
                // 如果访问间隔稳定，预测下次访问
                let now: _ = Instant::now();
                let time_since_last: _ = now.duration_since(pattern.last_access);
                if time_since_last < avg_interval {
                    recommendations.push(model_name.clone());
                }
            }
        }
        recommendations.sort_by(|a, b| {
            let count_a: _ = patterns.get(a).map(|p| p.access_count).unwrap_or(0);
            let count_b: _ = patterns.get(b).map(|p| p.access_count).unwrap_or(0);
            count_b.cmp(&count_a)
        });
        recommendations.into_iter().take(10).collect()
    }
    /// 从 L1 淘汰
    fn evict_from_l1(&self) -> Result<(), String> {
        let mut cache = self.l1_cache.write().unwrap();
        if cache.is_empty() {
            return Ok(());
        }
        // LRU 淘汰策略
        let oldest_key: _ = cache
            .iter()
            .min_by_key(|(_, entry)| entry.last_access)
            .map(|(key, _)| key.clone())
            .unwrap();
        cache.remove(&oldest_key);
        Ok(())
    }
    /// 从 L2 淘汰
    fn evict_from_l2(&self) -> Result<(), String> {
        let mut cache = self.l2_cache.lock().unwrap();
        if cache.is_empty() {
            return Ok(());
        }
        // LFU 淘汰策略
        let least_frequent_key: _ = cache
            .iter()
            .min_by_key(|(_, entry)| entry.access_count)
            .map(|(key, _)| key.clone())
            .unwrap();
        cache.remove(&least_frequent_key);
        Ok(())
    }
    /// 更新统计信息
    fn update_stats(&self, tier: CacheTier, hit: bool) {
        let mut stats = self.stats.lock().unwrap();
        stats.total_requests += 1;
        match (tier, hit) {
            (CacheTier::L1Memory, true) => stats.l1_hits += 1,
            (CacheTier::L2Disk, true) => stats.l2_hits += 1,
            (CacheTier::L3Remote, true) => stats.l3_hits += 1,
            (_, false) => stats.misses += 1,
            _ => {}
        }
    }
    /// 获取缓存命中率
    pub fn get_hit_rate(&self) -> f64 {
        let stats: _ = self.stats.lock().unwrap();
        if stats.total_requests == 0 {
            return 0.0;
        }
        let hits: _ = stats.l1_hits + stats.l2_hits + stats.l3_hits;
        hits as f64 / stats.total_requests as f64
    }
    /// 获取预取准确率
    pub fn get_prefetch_accuracy(&self) -> f64 {
        // 简化实现：基于访问模式的预测准确率
        let patterns: _ = self.access_patterns.read().unwrap();
        let mut accurate_predictions = 0;
        let mut total_predictions = 0;
        for (_model_name, pattern) in patterns.iter() {
            total_predictions += 1;
            if pattern.access_count > 10 {
                accurate_predictions += 1;
            }
        }
        if total_predictions == 0 {
            0.0
        } else {
            accurate_predictions as f64 / total_predictions as f64
        }
    }
    /// 获取内存使用情况
    pub fn get_memory_usage(&self) -> usize {
        let cache: _ = self.l1_cache.read().unwrap();
        let mut usage = 0;
        for entry in cache.values() {
            usage += entry.size_bytes;
        }
        usage / (1024 * 1024) // 转换为 MB
    }
    /// 获取磁盘使用情况
    pub fn get_disk_usage(&self) -> usize {
        let cache: _ = self.l2_cache.lock().unwrap();
        let mut usage = 0;
        for entry in cache.values() {
            usage += entry.size_bytes;
        }
        usage / (1024 * 1024) // 转换为 MB
    }
    /// 优化缓存
    pub fn optimize(&self) {
        // 清理过期条目
        let now: _ = Instant::now();
        let mut l1_cache = self.l1_cache.write().unwrap();
        l1_cache.retain(|_, entry| {
            now.duration_since(entry.last_access) < Duration::from_secs(3600)
        });
        let mut l2_cache = self.l2_cache.lock().unwrap();
        l2_cache.retain(|_, entry| {
            now.duration_since(entry.last_access) < Duration::from_secs(7200)
        });
    }
    /// 强制垃圾回收
    pub fn force_gc(&self) {
        let mut l1_cache = self.l1_cache.write().unwrap();
        l1_cache.clear();
        let mut l2_cache = self.l2_cache.lock().unwrap();
        l2_cache.clear();
    }
}
#[cfg(test)]
mod tests {
    #[test]
    fn test_cache_creation() {
        let config: _ = ModelCacheConfig {
            max_memory_mb: 1024,
            max_disk_gb: 10,
            enable_compression: true,
            enable_prefetch: true,
        };
        let cache: _ = ModelCache::new(config);
        assert!(cache.is_ok());
    }
    #[test]
    fn test_model_loading() {
        let mut cache = ModelCache::new(ModelCacheConfig {
            max_memory_mb: 1024,
            max_disk_gb: 10,
            enable_compression: true,
            enable_prefetch: false,
        }).unwrap();
        let result: _ = cache.load_model("test-model".to_string());
        assert!(result.is_ok());
    }
    #[test]
    fn test_cache_hit_rate() {
        let mut cache = ModelCache::new(ModelCacheConfig {
            max_memory_mb: 1024,
            max_disk_gb: 10,
            enable_compression: true,
            enable_prefetch: true,
        }).unwrap();
        // 首次加载
        let _: _ = cache.load_model("model-1".to_string());
        // 重复访问
        for _ in 0..10 {
            let _: _ = cache.load_model("model-1".to_string());
        }
        let hit_rate: _ = cache.get_hit_rate();
        println!("Hit rate: {}%", hit_rate * 100.0);
        assert!(hit_rate > 0.8);
    }
    #[test]
    fn test_prefetch_recommendations() {
        let mut cache = ModelCache::new(ModelCacheConfig {
            max_memory_mb: 1024,
            max_disk_gb: 10,
            enable_compression: true,
            enable_prefetch: true,
        }).unwrap();
        // 模拟访问模式
        for _ in 0..30 {
            let _: _ = cache.load_model("popular-model".to_string());
        }
        let recommendations: _ = cache.get_prefetch_recommendations();
        assert!(!recommendations.is_empty());
    }
}