voirs_cloning/

model_loading.rs

//! Advanced model loading and caching optimization system
//!
//! This module provides comprehensive model loading optimizations including:
//! - Lazy loading with predictive prefetching
//! - Memory-mapped model loading for large models
//! - Background model warming and preloading
//! - Model versioning and migration support
//! - Intelligent caching with usage pattern analysis

use crate::{
    config::CloningConfig,
    thread_safety::{CacheStats, ModelCache},
    Error, Result,
};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::Arc;
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::{Mutex, RwLock, Semaphore};
use tracing::{debug, error, info, trace, warn};

/// Advanced model loading manager with optimization features
pub struct ModelLoadingManager {
    /// Core model cache
    cache: Arc<ModelCache<Box<dyn ModelInterface>>>,
    /// Model preloader for background loading
    preloader: Arc<ModelPreloader>,
    /// Usage pattern analyzer for predictive loading
    usage_analyzer: Arc<UsagePatternAnalyzer>,
    /// Model memory manager
    memory_manager: Arc<ModelMemoryManager>,
    /// Loading strategy configuration
    config: Arc<RwLock<ModelLoadingConfig>>,
    /// Performance metrics
    metrics: Arc<RwLock<LoadingMetrics>>,
}

/// Configuration for model loading optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelLoadingConfig {
    /// Maximum number of models to keep in cache
    pub max_cached_models: usize,
    /// Maximum memory usage for model cache (in MB)
    pub max_cache_memory_mb: usize,
    /// Number of models to preload based on usage patterns
    pub preload_count: usize,
    /// Background preloading interval
    pub preload_interval: Duration,
    /// Memory mapping threshold (models larger than this use memory mapping)
    pub memory_map_threshold_mb: usize,
    /// Enable predictive loading based on usage patterns
    pub enable_predictive_loading: bool,
    /// Model warming timeout
    pub warming_timeout: Duration,
    /// Enable model compression in cache
    pub enable_compression: bool,
}

impl Default for ModelLoadingConfig {
    fn default() -> Self {
        Self {
            max_cached_models: 50,
            max_cache_memory_mb: 2048, // 2GB
            preload_count: 5,
            preload_interval: Duration::from_secs(300), // 5 minutes
            memory_map_threshold_mb: 100,
            enable_predictive_loading: true,
            warming_timeout: Duration::from_secs(30),
            enable_compression: false,
        }
    }
}

/// Generic model interface for different model types
pub trait ModelInterface: Send + Sync {
    /// Get model size in bytes
    fn size_bytes(&self) -> usize;
    /// Get model type identifier
    fn model_type(&self) -> &str;
    /// Warm up the model (perform any initialization)
    fn warm_up(&mut self) -> Result<()>;
    /// Check if model is ready for use
    fn is_ready(&self) -> bool;
    /// Get model version
    fn version(&self) -> String;
}

/// Model metadata for tracking and optimization
#[derive(Debug, Clone)]
pub struct ModelMetadata {
    pub model_id: String,
    pub model_type: String,
    pub size_bytes: usize,
    pub version: String,
    pub last_accessed: Instant,
    pub access_count: u64,
    pub load_time: Duration,
    pub memory_mapped: bool,
    pub compressed: bool,
}

/// Background model preloader
pub struct ModelPreloader {
    /// Preloading queue
    preload_queue: Arc<RwLock<VecDeque<PreloadRequest>>>,
    /// Preloading semaphore to limit concurrent loads
    preload_semaphore: Arc<Semaphore>,
    /// Active preloading tasks
    active_tasks: Arc<RwLock<HashMap<String, tokio::task::JoinHandle<()>>>>,
}

/// Request for background preloading
#[derive(Debug, Clone)]
pub struct PreloadRequest {
    pub model_id: String,
    pub priority: PreloadPriority,
    pub requested_at: Instant,
    pub estimated_size: usize,
}

/// Priority levels for preloading
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum PreloadPriority {
    Low = 1,
    Medium = 2,
    High = 3,
    Critical = 4,
}

/// Usage pattern analyzer for predictive loading
pub struct UsagePatternAnalyzer {
    /// Historical access patterns
    access_history: Arc<RwLock<VecDeque<AccessRecord>>>,
    /// Model co-occurrence patterns
    co_occurrence: Arc<RwLock<HashMap<String, HashMap<String, f32>>>>,
    /// Time-based usage patterns
    temporal_patterns: Arc<RwLock<HashMap<String, Vec<TimeWindow>>>>,
    /// Analysis configuration
    config: AnalysisConfig,
}

/// Access record for pattern analysis
#[derive(Debug, Clone)]
pub struct AccessRecord {
    pub model_id: String,
    pub access_time: SystemTime,
    pub session_id: String,
    pub access_duration: Duration,
}

/// Time window for temporal pattern analysis
#[derive(Debug, Clone)]
pub struct TimeWindow {
    pub hour: u8,
    pub day_of_week: u8,
    pub access_probability: f32,
}

/// Configuration for usage pattern analysis
#[derive(Debug, Clone)]
pub struct AnalysisConfig {
    pub max_history_size: usize,
    pub analysis_window: Duration,
    pub co_occurrence_threshold: f32,
    pub prediction_confidence_threshold: f32,
}

impl Default for AnalysisConfig {
    fn default() -> Self {
        Self {
            max_history_size: 10000,
            analysis_window: Duration::from_secs(7 * 24 * 60 * 60), // 7 days
            co_occurrence_threshold: 0.3,
            prediction_confidence_threshold: 0.7,
        }
    }
}

/// Model memory manager for efficient memory utilization
pub struct ModelMemoryManager {
    /// Memory usage tracking
    memory_usage: Arc<RwLock<HashMap<String, usize>>>,
    /// Memory-mapped models
    memory_mapped_models: Arc<RwLock<HashMap<String, Arc<MmapModel>>>>,
    /// Compressed model cache
    compressed_cache: Arc<RwLock<HashMap<String, CompressedModel>>>,
    /// Memory pressure monitor
    pressure_monitor: Arc<Mutex<MemoryPressureMonitor>>,
}

/// Memory-mapped model wrapper (placeholder for future implementation)
pub struct MmapModel {
    pub data: Vec<u8>, // Placeholder - would use memmap2::Mmap in production
    pub metadata: ModelMetadata,
}

/// Compressed model for space-efficient storage
#[derive(Debug, Clone)]
pub struct CompressedModel {
    pub compressed_data: Vec<u8>,
    pub compression_ratio: f32,
    pub original_size: usize,
    pub metadata: ModelMetadata,
}

/// Memory pressure monitoring
#[derive(Debug)]
pub struct MemoryPressureMonitor {
    pub current_usage_mb: usize,
    pub peak_usage_mb: usize,
    pub pressure_level: MemoryPressureLevel,
    pub last_cleanup: Instant,
}

impl Default for MemoryPressureMonitor {
    fn default() -> Self {
        Self {
            current_usage_mb: 0,
            peak_usage_mb: 0,
            pressure_level: MemoryPressureLevel::Low,
            last_cleanup: Instant::now(),
        }
    }
}

/// Memory pressure levels
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MemoryPressureLevel {
    Low,
    Medium,
    High,
    Critical,
}

impl Default for MemoryPressureLevel {
    fn default() -> Self {
        MemoryPressureLevel::Low
    }
}

/// Comprehensive loading metrics
#[derive(Debug, Default, Clone)]
pub struct LoadingMetrics {
    pub cache_hits: u64,
    pub cache_misses: u64,
    pub preload_successes: u64,
    pub preload_failures: u64,
    pub prediction_accuracy: f32,
    pub average_load_time: Duration,
    pub memory_efficiency: f32,
    pub total_models_loaded: u64,
    pub models_evicted: u64,
}

impl ModelLoadingManager {
    /// Create new optimized model loading manager
    pub fn new(config: ModelLoadingConfig) -> Self {
        let cache = Arc::new(ModelCache::new(
            config.max_cached_models,
            10, // Max concurrent loads
        ));

        Self {
            cache,
            preloader: Arc::new(ModelPreloader::new(5)), // 5 concurrent preloads
            usage_analyzer: Arc::new(UsagePatternAnalyzer::new(AnalysisConfig::default())),
            memory_manager: Arc::new(ModelMemoryManager::new()),
            config: Arc::new(RwLock::new(config)),
            metrics: Arc::new(RwLock::new(LoadingMetrics::default())),
        }
    }

    /// Load model with full optimization pipeline
    pub async fn load_model<T>(&self, model_id: &str) -> Result<Arc<T>>
    where
        T: ModelInterface + 'static,
    {
        let start_time = Instant::now();

        // Record access for pattern analysis
        self.usage_analyzer.record_access(model_id).await;

        // Try to load from cache first
        if let Some(model) = self.try_load_from_cache(model_id).await? {
            let mut metrics = self.metrics.write().await;
            metrics.cache_hits += 1;
            return Ok(model);
        }

        // Cache miss - need to load the model
        let mut metrics = self.metrics.write().await;
        metrics.cache_misses += 1;
        drop(metrics);

        // Determine loading strategy based on model characteristics
        let loading_strategy = self.determine_loading_strategy(model_id).await?;

        // Load model using optimal strategy
        let model = match loading_strategy {
            LoadingStrategy::Direct => self.load_direct(model_id).await?,
            LoadingStrategy::MemoryMapped => self.load_memory_mapped(model_id).await?,
            LoadingStrategy::Compressed => self.load_compressed(model_id).await?,
            LoadingStrategy::Streaming => self.load_streaming(model_id).await?,
        };

        // Warm up the model
        self.warm_up_model(&model).await?;

        // Update metrics
        let load_time = start_time.elapsed();
        let mut metrics = self.metrics.write().await;
        metrics.total_models_loaded += 1;
        metrics.average_load_time = Duration::from_nanos(
            (metrics.average_load_time.as_nanos() as u64 + load_time.as_nanos() as u64) / 2,
        );

        // Trigger predictive preloading
        if self.config.read().await.enable_predictive_loading {
            self.trigger_predictive_preloading(model_id).await?;
        }

        Ok(model)
    }

    /// Try to load model from cache
    async fn try_load_from_cache<T>(&self, model_id: &str) -> Result<Option<Arc<T>>>
    where
        T: ModelInterface + 'static,
    {
        // Implementation would use the existing ModelCache
        // This is a placeholder for the cache lookup logic
        Ok(None)
    }

    /// Determine optimal loading strategy for a model
    async fn determine_loading_strategy(&self, model_id: &str) -> Result<LoadingStrategy> {
        let config = self.config.read().await;
        let memory_pressure = self.memory_manager.get_memory_pressure().await;

        // Estimate model size (in production, this would query model metadata)
        let estimated_size_mb = self.estimate_model_size(model_id).await?;

        match (estimated_size_mb, memory_pressure) {
            (size, MemoryPressureLevel::Critical) if size > 50 => Ok(LoadingStrategy::Streaming),
            (size, _) if size > config.memory_map_threshold_mb => Ok(LoadingStrategy::MemoryMapped),
            (_, MemoryPressureLevel::High) if config.enable_compression => {
                Ok(LoadingStrategy::Compressed)
            }
            _ => Ok(LoadingStrategy::Direct),
        }
    }

    /// Estimate model size for loading strategy decision
    async fn estimate_model_size(&self, model_id: &str) -> Result<usize> {
        // Placeholder implementation - in production this would query actual model metadata
        match model_id {
            id if id.contains("large") => Ok(500), // 500MB
            id if id.contains("base") => Ok(100),  // 100MB
            id if id.contains("small") => Ok(25),  // 25MB
            _ => Ok(100),                          // Default to 100MB
        }
    }

    /// Load model directly into memory
    async fn load_direct<T>(&self, model_id: &str) -> Result<Arc<T>>
    where
        T: ModelInterface + 'static,
    {
        // Placeholder implementation for direct loading
        Err(Error::Validation(
            "Direct loading not implemented".to_string(),
        ))
    }

    /// Load model using memory mapping
    async fn load_memory_mapped<T>(&self, model_id: &str) -> Result<Arc<T>>
    where
        T: ModelInterface + 'static,
    {
        // Placeholder implementation for memory-mapped loading
        Err(Error::Validation(
            "Memory-mapped loading not implemented".to_string(),
        ))
    }

    /// Load model with compression
    async fn load_compressed<T>(&self, model_id: &str) -> Result<Arc<T>>
    where
        T: ModelInterface + 'static,
    {
        // Placeholder implementation for compressed loading
        Err(Error::Validation(
            "Compressed loading not implemented".to_string(),
        ))
    }

    /// Load model with streaming (for very large models)
    async fn load_streaming<T>(&self, model_id: &str) -> Result<Arc<T>>
    where
        T: ModelInterface + 'static,
    {
        // Placeholder implementation for streaming loading
        Err(Error::Validation(
            "Streaming loading not implemented".to_string(),
        ))
    }

    /// Warm up a freshly loaded model
    async fn warm_up_model<T>(&self, model: &Arc<T>) -> Result<()>
    where
        T: ModelInterface + 'static,
    {
        let config = self.config.read().await;
        let timeout = config.warming_timeout;

        // Use timeout for model warming
        match tokio::time::timeout(timeout, async {
            // Model warming would happen here
            Ok(())
        })
        .await
        {
            Ok(result) => result,
            Err(_) => {
                warn!("Model warm-up timed out after {:?}", timeout);
                Err(Error::Validation("Model warm-up timeout".to_string()))
            }
        }
    }

    /// Trigger predictive preloading based on usage patterns
    async fn trigger_predictive_preloading(&self, current_model: &str) -> Result<()> {
        let predictions = self
            .usage_analyzer
            .predict_next_models(current_model, 3)
            .await?;

        for (model_id, confidence) in predictions {
            if confidence > 0.7 {
                self.preloader
                    .schedule_preload(model_id, PreloadPriority::High)
                    .await?;
            }
        }

        Ok(())
    }

    /// Get comprehensive loading metrics
    pub async fn get_metrics(&self) -> LoadingMetrics {
        self.metrics.read().await.clone()
    }

    /// Optimize cache based on current usage patterns
    pub async fn optimize_cache(&self) -> Result<()> {
        // Analyze usage patterns and adjust cache strategy
        let patterns = self.usage_analyzer.analyze_patterns().await?;

        // Update configuration based on patterns
        let mut config = self.config.write().await;

        // Adjust preload count based on hit rate
        let metrics = self.metrics.read().await;
        let hit_rate =
            metrics.cache_hits as f32 / (metrics.cache_hits + metrics.cache_misses) as f32;

        if hit_rate < 0.8 {
            config.preload_count = (config.preload_count as f32 * 1.2).min(20.0) as usize;
        } else if hit_rate > 0.95 {
            config.preload_count = (config.preload_count as f32 * 0.9).max(3.0) as usize;
        }

        info!(
            "Cache optimization completed. Hit rate: {:.2}%, Preload count: {}",
            hit_rate * 100.0,
            config.preload_count
        );

        Ok(())
    }
}

/// Loading strategy enumeration
#[derive(Debug, Clone, Copy)]
pub enum LoadingStrategy {
    /// Load directly into memory
    Direct,
    /// Use memory mapping for large models
    MemoryMapped,
    /// Load with compression to save memory
    Compressed,
    /// Stream model data for very large models
    Streaming,
}

impl ModelPreloader {
    /// Create new model preloader
    pub fn new(max_concurrent: usize) -> Self {
        Self {
            preload_queue: Arc::new(RwLock::new(VecDeque::new())),
            preload_semaphore: Arc::new(Semaphore::new(max_concurrent)),
            active_tasks: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Schedule model for background preloading
    pub async fn schedule_preload(
        &self,
        model_id: String,
        priority: PreloadPriority,
    ) -> Result<()> {
        let request = PreloadRequest {
            model_id: model_id.clone(),
            priority,
            requested_at: Instant::now(),
            estimated_size: 100 * 1024 * 1024, // 100MB default
        };

        let mut queue = self.preload_queue.write().await;

        // Insert based on priority (higher priority first)
        let insert_pos = queue
            .iter()
            .position(|req| req.priority < priority)
            .unwrap_or(queue.len());

        queue.insert(insert_pos, request);

        debug!(
            "Scheduled preload for model {} with priority {:?}",
            model_id, priority
        );
        Ok(())
    }

    /// Process preload queue (called by background task)
    pub async fn process_queue(&self) -> Result<()> {
        let request = {
            let mut queue = self.preload_queue.write().await;
            queue.pop_front()
        };

        if let Some(req) = request {
            let _permit =
                self.preload_semaphore.acquire().await.map_err(|_| {
                    Error::Validation("Failed to acquire preload permit".to_string())
                })?;

            // Spawn background preload task
            let model_id = req.model_id.clone();
            let handle = tokio::spawn(async move {
                // Actual model preloading would happen here
                tokio::time::sleep(Duration::from_millis(100)).await; // Simulate loading
                trace!("Completed preload for model {}", model_id);
            });

            // Track active task
            let mut active_tasks = self.active_tasks.write().await;
            active_tasks.insert(req.model_id, handle);
        }

        Ok(())
    }
}

impl UsagePatternAnalyzer {
    /// Create new usage pattern analyzer
    pub fn new(config: AnalysisConfig) -> Self {
        Self {
            access_history: Arc::new(RwLock::new(VecDeque::new())),
            co_occurrence: Arc::new(RwLock::new(HashMap::new())),
            temporal_patterns: Arc::new(RwLock::new(HashMap::new())),
            config,
        }
    }

    /// Record model access for pattern analysis
    pub async fn record_access(&self, model_id: &str) {
        let record = AccessRecord {
            model_id: model_id.to_string(),
            access_time: SystemTime::now(),
            session_id: "session_placeholder".to_string(), // Would be actual session ID
            access_duration: Duration::from_millis(100),   // Placeholder
        };

        let mut history = self.access_history.write().await;
        history.push_back(record);

        // Maintain history size limit
        while history.len() > self.config.max_history_size {
            history.pop_front();
        }
    }

    /// Predict next likely models based on current access
    pub async fn predict_next_models(
        &self,
        current_model: &str,
        count: usize,
    ) -> Result<Vec<(String, f32)>> {
        let co_occurrence = self.co_occurrence.read().await;

        if let Some(related_models) = co_occurrence.get(current_model) {
            let mut predictions: Vec<_> = related_models
                .iter()
                .map(|(model, score)| (model.clone(), *score))
                .collect();

            predictions.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
            predictions.truncate(count);

            Ok(predictions)
        } else {
            Ok(Vec::new())
        }
    }

    /// Analyze usage patterns and update internal models
    pub async fn analyze_patterns(&self) -> Result<PatternAnalysisResult> {
        let history = self.access_history.read().await;

        // Analyze co-occurrence patterns
        let mut co_occurrence_map = HashMap::new();
        let window_size = 5; // Look at 5 consecutive accesses

        for window in history.iter().collect::<Vec<_>>().windows(window_size) {
            for (i, record) in window.iter().enumerate() {
                for other in window.iter().skip(i + 1) {
                    let entry = co_occurrence_map
                        .entry(record.model_id.clone())
                        .or_insert_with(HashMap::new);

                    *entry.entry(other.model_id.clone()).or_insert(0.0) += 1.0;
                }
            }
        }

        // Normalize co-occurrence scores
        for (_, related) in co_occurrence_map.iter_mut() {
            let max_score = related.values().fold(0.0f32, |a, b| a.max(*b));
            if max_score > 0.0 {
                for score in related.values_mut() {
                    *score /= max_score;
                }
            }
        }

        // Update internal co-occurrence map
        let mut co_occurrence = self.co_occurrence.write().await;
        *co_occurrence = co_occurrence_map;

        Ok(PatternAnalysisResult {
            total_accesses: history.len(),
            unique_models: co_occurrence.len(),
            average_co_occurrence_strength: 0.5, // Placeholder calculation
        })
    }
}

/// Result of pattern analysis
#[derive(Debug, Clone)]
pub struct PatternAnalysisResult {
    pub total_accesses: usize,
    pub unique_models: usize,
    pub average_co_occurrence_strength: f32,
}

impl Default for ModelMemoryManager {
    fn default() -> Self {
        Self::new()
    }
}

impl ModelMemoryManager {
    /// Create new model memory manager
    pub fn new() -> Self {
        Self {
            memory_usage: Arc::new(RwLock::new(HashMap::new())),
            memory_mapped_models: Arc::new(RwLock::new(HashMap::new())),
            compressed_cache: Arc::new(RwLock::new(HashMap::new())),
            pressure_monitor: Arc::new(Mutex::new(MemoryPressureMonitor::default())),
        }
    }

    /// Get current memory pressure level
    pub async fn get_memory_pressure(&self) -> MemoryPressureLevel {
        let monitor = self.pressure_monitor.lock().await;
        monitor.pressure_level
    }

    /// Update memory pressure based on current usage
    pub async fn update_memory_pressure(&self, current_usage_mb: usize) {
        let mut monitor = self.pressure_monitor.lock().await;
        monitor.current_usage_mb = current_usage_mb;
        monitor.peak_usage_mb = monitor.peak_usage_mb.max(current_usage_mb);

        monitor.pressure_level = match current_usage_mb {
            usage if usage < 512 => MemoryPressureLevel::Low,
            usage if usage < 1024 => MemoryPressureLevel::Medium,
            usage if usage < 1536 => MemoryPressureLevel::High,
            _ => MemoryPressureLevel::Critical,
        };
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use tokio;

    #[tokio::test]
    async fn test_model_loading_manager_creation() {
        let config = ModelLoadingConfig::default();
        let manager = ModelLoadingManager::new(config);

        let metrics = manager.get_metrics().await;
        assert_eq!(metrics.cache_hits, 0);
        assert_eq!(metrics.cache_misses, 0);
    }

    #[tokio::test]
    async fn test_preloader_scheduling() {
        let preloader = ModelPreloader::new(5);

        let result = preloader
            .schedule_preload("test_model".to_string(), PreloadPriority::High)
            .await;

        assert!(result.is_ok());
    }

    #[tokio::test]
    async fn test_usage_pattern_analyzer() {
        let analyzer = UsagePatternAnalyzer::new(AnalysisConfig::default());

        // Record some accesses
        analyzer.record_access("model_a").await;
        analyzer.record_access("model_b").await;
        analyzer.record_access("model_a").await;

        let result = analyzer.analyze_patterns().await;
        assert!(result.is_ok());

        let patterns = result.unwrap();
        assert_eq!(patterns.total_accesses, 3);
    }

    #[tokio::test]
    async fn test_memory_pressure_monitoring() {
        let memory_manager = ModelMemoryManager::new();

        // Initially should be low pressure
        assert_eq!(
            memory_manager.get_memory_pressure().await,
            MemoryPressureLevel::Low
        );

        // Update to high usage
        memory_manager.update_memory_pressure(1200).await;
        assert_eq!(
            memory_manager.get_memory_pressure().await,
            MemoryPressureLevel::High
        );
    }

    #[tokio::test]
    async fn test_loading_config_defaults() {
        let config = ModelLoadingConfig::default();

        assert_eq!(config.max_cached_models, 50);
        assert_eq!(config.max_cache_memory_mb, 2048);
        assert_eq!(config.preload_count, 5);
        assert!(config.enable_predictive_loading);
    }
}