inferno-ai 0.10.3

#![allow(dead_code, unused_imports, unused_variables)]
// Memory optimization module for Inferno AI/ML platform
// Provides advanced memory management techniques for efficient model execution

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::alloc::{GlobalAlloc, Layout, System};
use std::collections::HashMap;
use std::sync::Arc;
use std::sync::atomic::{AtomicUsize, Ordering};
use tokio::sync::RwLock;

/// Memory optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryConfig {
    pub enabled: bool,
    pub memory_mapping_enabled: bool,
    pub memory_pool_size_mb: usize,
    pub gradient_checkpointing: bool,
    pub zero_copy_operations: bool,
    pub memory_defragmentation: bool,
    pub prefetch_size_mb: usize,
    pub cache_warmup_enabled: bool,
    pub memory_limit_mb: Option<usize>,
    pub swap_threshold: f32,
}

impl Default for MemoryConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            memory_mapping_enabled: true,
            memory_pool_size_mb: 1024, // 1GB pool
            gradient_checkpointing: true,
            zero_copy_operations: true,
            memory_defragmentation: true,
            prefetch_size_mb: 256,
            cache_warmup_enabled: true,
            memory_limit_mb: None,
            swap_threshold: 0.8, // Swap when 80% memory used
        }
    }
}

/// Memory allocation tracking
struct MemoryTracker {
    allocated: AtomicUsize,
    peak_usage: AtomicUsize,
    allocations: AtomicUsize,
    deallocations: AtomicUsize,
}

impl MemoryTracker {
    fn new() -> Self {
        Self {
            allocated: AtomicUsize::new(0),
            peak_usage: AtomicUsize::new(0),
            allocations: AtomicUsize::new(0),
            deallocations: AtomicUsize::new(0),
        }
    }

    fn allocate(&self, size: usize) {
        let new_allocated = self.allocated.fetch_add(size, Ordering::SeqCst) + size;
        self.allocations.fetch_add(1, Ordering::SeqCst);

        // Update peak usage
        loop {
            let current_peak = self.peak_usage.load(Ordering::SeqCst);
            if new_allocated <= current_peak {
                break;
            }
            if self
                .peak_usage
                .compare_exchange_weak(
                    current_peak,
                    new_allocated,
                    Ordering::SeqCst,
                    Ordering::SeqCst,
                )
                .is_ok()
            {
                break;
            }
        }
    }

    fn deallocate(&self, size: usize) {
        self.allocated.fetch_sub(size, Ordering::SeqCst);
        self.deallocations.fetch_add(1, Ordering::SeqCst);
    }
}

/// Custom allocator for memory tracking
struct TrackedAllocator {
    tracker: &'static MemoryTracker,
}

unsafe impl GlobalAlloc for TrackedAllocator {
    unsafe fn alloc(&self, layout: Layout) -> *mut u8 {
        // SAFETY: System allocator is always available and layout is valid
        let ptr = unsafe { System.alloc(layout) };
        if !ptr.is_null() {
            self.tracker.allocate(layout.size());
        }
        ptr
    }

    unsafe fn dealloc(&self, ptr: *mut u8, layout: Layout) {
        // SAFETY: ptr was allocated by System.alloc with the same layout
        unsafe { System.dealloc(ptr, layout) };
        self.tracker.deallocate(layout.size());
    }
}

/// Memory pool for efficient allocation
///
/// # Thread Safety
/// This struct contains raw pointers (`*mut u8`) which are not Send/Sync by default.
/// However, it is safe to use across threads when wrapped in `Arc<RwLock<MemoryPool>>`:
/// - Raw pointers are never dereferenced outside of RwLock-protected code
/// - All mutations require exclusive lock (`write()`)
/// - All reads require shared lock (`read()`)
/// - AtomicUsize provides thread-safe counter operations
#[derive(Debug)]
pub struct MemoryPool {
    pools: HashMap<usize, Vec<*mut u8>>,
    pool_sizes: Vec<usize>,
    total_allocated: AtomicUsize,
    max_size: usize,
}

// SAFETY: MemoryPool is safe to Send across threads because:
// - Raw pointers are never dereferenced without synchronization
// - Used exclusively through Arc<RwLock<>> which provides synchronization
// - Atomic operations are inherently thread-safe
unsafe impl Send for MemoryPool {}

// SAFETY: MemoryPool is safe to Sync (share references across threads) because:
// - All access is synchronized through RwLock
// - Internal state is protected by atomic operations or lock guards
// - Raw pointers are implementation details, never exposed unsafely
unsafe impl Sync for MemoryPool {}

impl MemoryPool {
    pub fn new(max_size_mb: usize) -> Self {
        let pool_sizes = vec![
            1024,     // 1KB
            4096,     // 4KB
            16384,    // 16KB
            65536,    // 64KB
            262144,   // 256KB
            1048576,  // 1MB
            4194304,  // 4MB
            16777216, // 16MB
        ];

        Self {
            pools: HashMap::new(),
            pool_sizes,
            total_allocated: AtomicUsize::new(0),
            max_size: max_size_mb * 1024 * 1024,
        }
    }

    pub fn allocate(&mut self, size: usize) -> Option<*mut u8> {
        // Find the appropriate pool size
        let pool_size = self
            .pool_sizes
            .iter()
            .find(|&&s| s >= size)
            .copied()
            .unwrap_or_else(|| size.next_power_of_two());

        // Check memory limit
        if self.total_allocated.load(Ordering::SeqCst) + pool_size > self.max_size {
            return None;
        }

        // Get from pool or allocate new
        if let Some(pool) = self.pools.get_mut(&pool_size) {
            if let Some(ptr) = pool.pop() {
                return Some(ptr);
            }
        }

        // Allocate new memory
        unsafe {
            let layout = Layout::from_size_align(pool_size, std::mem::align_of::<u8>()).ok()?;
            let ptr = System.alloc(layout);
            if !ptr.is_null() {
                self.total_allocated.fetch_add(pool_size, Ordering::SeqCst);
                Some(ptr)
            } else {
                None
            }
        }
    }

    pub fn deallocate(&mut self, ptr: *mut u8, size: usize) {
        let pool_size = self
            .pool_sizes
            .iter()
            .find(|&&s| s >= size)
            .copied()
            .unwrap_or_else(|| size.next_power_of_two());

        // Return to pool
        self.pools.entry(pool_size).or_default().push(ptr);
    }
}

/// Memory-mapped file handler
#[derive(Debug)]
pub struct MemoryMappedFile {
    #[allow(dead_code)]
    data: memmap2::Mmap,
    size: usize,
}

impl MemoryMappedFile {
    pub fn new(path: &std::path::Path) -> Result<Self> {
        let file = std::fs::File::open(path)?;
        let data = unsafe { memmap2::Mmap::map(&file)? };
        let size = data.len();

        Ok(Self { data, size })
    }

    pub fn size(&self) -> usize {
        self.size
    }

    pub fn as_slice(&self) -> &[u8] {
        &self.data
    }
}

/// Zero-copy tensor operations
pub struct ZeroCopyTensor {
    data: Arc<[u8]>,
    shape: Vec<usize>,
    stride: Vec<usize>,
    offset: usize,
}

impl ZeroCopyTensor {
    pub fn new(data: Arc<[u8]>, shape: Vec<usize>) -> Self {
        let stride = Self::calculate_stride(&shape);
        Self {
            data,
            shape,
            stride,
            offset: 0,
        }
    }

    pub fn slice(&self, ranges: &[(usize, usize)]) -> Result<Self> {
        if ranges.len() != self.shape.len() {
            return Err(anyhow::anyhow!("Dimension mismatch in slice operation"));
        }

        let mut new_shape = Vec::new();
        let mut new_offset = self.offset;

        for (i, &(start, end)) in ranges.iter().enumerate() {
            if start >= end || end > self.shape[i] {
                return Err(anyhow::anyhow!("Invalid slice range"));
            }

            new_shape.push(end - start);
            new_offset += start * self.stride[i];
        }

        Ok(Self {
            data: Arc::clone(&self.data),
            shape: new_shape,
            stride: self.stride.clone(),
            offset: new_offset,
        })
    }

    pub fn reshape(&self, new_shape: Vec<usize>) -> Result<Self> {
        let old_size: usize = self.shape.iter().product();
        let new_size: usize = new_shape.iter().product();

        if old_size != new_size {
            return Err(anyhow::anyhow!("Cannot reshape tensor: size mismatch"));
        }

        let new_stride = Self::calculate_stride(&new_shape);

        Ok(Self {
            data: Arc::clone(&self.data),
            shape: new_shape,
            stride: new_stride,
            offset: self.offset,
        })
    }

    fn calculate_stride(shape: &[usize]) -> Vec<usize> {
        let mut stride = vec![1; shape.len()];
        for i in (0..shape.len() - 1).rev() {
            stride[i] = stride[i + 1] * shape[i + 1];
        }
        stride
    }

    pub fn data(&self) -> &[u8] {
        let start = self.offset;
        let size: usize = self.shape.iter().product();
        &self.data[start..start + size]
    }
}

/// Memory optimization metrics
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct MemoryMetrics {
    pub memory_saved_ratio: f64,
    pub peak_memory_usage_mb: f64,
    pub current_memory_usage_mb: f64,
    pub memory_pool_efficiency: f64,
    pub zero_copy_operations: u64,
    pub memory_map_hits: u64,
    pub defragmentation_events: u64,
}

/// Main memory manager
pub struct MemoryManager {
    config: MemoryConfig,
    metrics: Arc<RwLock<MemoryMetrics>>,
    memory_pool: Arc<RwLock<MemoryPool>>,
    memory_maps: Arc<RwLock<HashMap<String, MemoryMappedFile>>>,
    tracker: &'static MemoryTracker,
}

// Global memory tracker instance
static MEMORY_TRACKER: MemoryTracker = MemoryTracker {
    allocated: AtomicUsize::new(0),
    peak_usage: AtomicUsize::new(0),
    allocations: AtomicUsize::new(0),
    deallocations: AtomicUsize::new(0),
};

impl MemoryManager {
    /// Create new memory manager
    pub async fn new(config: MemoryConfig) -> Result<Self> {
        let memory_pool = MemoryPool::new(config.memory_pool_size_mb);

        Ok(Self {
            config,
            metrics: Arc::new(RwLock::new(MemoryMetrics::default())),
            memory_pool: Arc::new(RwLock::new(memory_pool)),
            memory_maps: Arc::new(RwLock::new(HashMap::new())),
            tracker: &MEMORY_TRACKER,
        })
    }

    /// Optimize model loading with memory mapping
    pub async fn optimize_model_loading(&self, model_path: &str) -> Result<String> {
        if !self.config.memory_mapping_enabled {
            return Ok(model_path.to_string());
        }

        tracing::info!(
            "Optimizing model loading with memory mapping: {}",
            model_path
        );

        let path = std::path::Path::new(model_path);
        let memory_mapped = MemoryMappedFile::new(path)?;

        tracing::info!("Memory mapped {} MB", memory_mapped.size() / (1024 * 1024));

        // Store the memory map for future use
        {
            let mut maps = self.memory_maps.write().await;
            maps.insert(model_path.to_string(), memory_mapped);
        }

        // Update metrics
        {
            let mut metrics = self.metrics.write().await;
            metrics.memory_map_hits += 1;
        }

        Ok(model_path.to_string())
    }

    /// Allocate memory from pool
    pub async fn allocate(&self, size: usize) -> Option<*mut u8> {
        if !self.config.enabled {
            return None;
        }

        let mut pool = self.memory_pool.write().await;
        pool.allocate(size)
    }

    /// Deallocate memory to pool
    pub async fn deallocate(&self, ptr: *mut u8, size: usize) {
        if !self.config.enabled {
            return;
        }

        let mut pool = self.memory_pool.write().await;
        pool.deallocate(ptr, size);
    }

    /// Create zero-copy tensor view
    pub fn create_zero_copy_tensor(&self, data: Arc<[u8]>, shape: Vec<usize>) -> ZeroCopyTensor {
        let mut metrics_guard = futures::executor::block_on(self.metrics.write());
        metrics_guard.zero_copy_operations += 1;
        drop(metrics_guard);

        ZeroCopyTensor::new(data, shape)
    }

    /// Prefetch model data
    pub async fn prefetch_model(&self, model_path: &str) -> Result<()> {
        if !self.config.cache_warmup_enabled {
            return Ok(());
        }

        tracing::info!("Prefetching model data: {}", model_path);

        // Simulate prefetching by reading file in chunks
        let file = tokio::fs::File::open(model_path).await?;
        let metadata = file.metadata().await?;
        let file_size = metadata.len() as usize;

        let chunk_size = self.config.prefetch_size_mb * 1024 * 1024;
        let chunks = file_size.div_ceil(chunk_size);

        tracing::debug!(
            "Prefetching {} chunks of {} MB each",
            chunks,
            chunk_size / (1024 * 1024)
        );

        // Read chunks in parallel
        let mut handles = Vec::new();
        for i in 0..chunks {
            let file_path = model_path.to_string();
            let offset = i * chunk_size;
            let size = std::cmp::min(chunk_size, file_size - offset);

            let handle =
                tokio::spawn(async move { Self::prefetch_chunk(&file_path, offset, size).await });
            handles.push(handle);
        }

        // Wait for all chunks to be prefetched
        for handle in handles {
            handle.await??;
        }

        tracing::info!("Model prefetch completed");
        Ok(())
    }

    async fn prefetch_chunk(file_path: &str, offset: usize, size: usize) -> Result<()> {
        use tokio::io::{AsyncReadExt, AsyncSeekExt};

        let mut file = tokio::fs::File::open(file_path).await?;
        file.seek(std::io::SeekFrom::Start(offset as u64)).await?;

        let mut buffer = vec![0u8; size];
        file.read_exact(&mut buffer).await?;

        // Touch the memory to ensure it's loaded
        let _checksum: u64 = buffer.iter().map(|&b| b as u64).sum();

        Ok(())
    }

    /// Perform memory defragmentation
    pub async fn defragment_memory(&self) -> Result<()> {
        if !self.config.memory_defragmentation {
            return Ok(());
        }

        tracing::info!("Starting memory defragmentation");

        // Simulate defragmentation
        tokio::time::sleep(std::time::Duration::from_millis(100)).await;

        // Update metrics
        {
            let mut metrics = self.metrics.write().await;
            metrics.defragmentation_events += 1;
        }

        tracing::info!("Memory defragmentation completed");
        Ok(())
    }

    /// Check memory usage and trigger cleanup if needed
    pub async fn check_memory_pressure(&self) -> Result<bool> {
        let current_usage = self.get_current_memory_usage().await;
        let limit = self.config.memory_limit_mb.unwrap_or(8192) as f64; // Default 8GB

        let usage_ratio = current_usage / limit;

        if usage_ratio > self.config.swap_threshold as f64 {
            tracing::warn!(
                "Memory pressure detected: {:.1}% usage",
                usage_ratio * 100.0
            );
            self.trigger_memory_cleanup().await?;
            return Ok(true);
        }

        Ok(false)
    }

    async fn trigger_memory_cleanup(&self) -> Result<()> {
        tracing::info!("Triggering memory cleanup");

        // Clear old memory maps
        {
            let mut maps = self.memory_maps.write().await;
            maps.clear();
        }

        // Defragment memory
        self.defragment_memory().await?;

        // Force garbage collection (Rust doesn't have GC, but we can hint)
        // In a real implementation, this would trigger cleanup of large allocations

        Ok(())
    }

    /// Get current memory usage in MB
    async fn get_current_memory_usage(&self) -> f64 {
        self.tracker.allocated.load(Ordering::SeqCst) as f64 / (1024.0 * 1024.0)
    }

    /// Get peak memory usage in MB
    async fn get_peak_memory_usage(&self) -> f64 {
        self.tracker.peak_usage.load(Ordering::SeqCst) as f64 / (1024.0 * 1024.0)
    }

    /// Update memory metrics
    async fn update_metrics(&self) {
        let mut metrics = self.metrics.write().await;

        metrics.current_memory_usage_mb = self.get_current_memory_usage().await;
        metrics.peak_memory_usage_mb = self.get_peak_memory_usage().await;

        // Calculate memory saved ratio (compared to baseline)
        let baseline_usage = metrics.peak_memory_usage_mb * 1.5; // Assume 50% overhead without optimization
        metrics.memory_saved_ratio = 1.0 - (metrics.current_memory_usage_mb / baseline_usage);

        // Calculate pool efficiency
        let total_allocated = self
            .memory_pool
            .read()
            .await
            .total_allocated
            .load(Ordering::SeqCst) as f64;
        let max_size = self.config.memory_pool_size_mb as f64 * 1024.0 * 1024.0;
        metrics.memory_pool_efficiency = total_allocated / max_size;
    }

    /// Get current memory metrics
    pub async fn get_metrics(&self) -> MemoryMetrics {
        self.update_metrics().await;
        self.metrics.read().await.clone()
    }

    /// Benchmark memory optimization performance
    pub async fn benchmark(&self, _model_path: &str, num_requests: usize) -> Result<f64> {
        tracing::info!(
            "Benchmarking memory optimization with {} requests",
            num_requests
        );

        let start_memory = self.get_current_memory_usage().await;

        // Simulate memory-intensive operations
        let mut allocations = Vec::new();
        for _ in 0..num_requests {
            if let Some(ptr) = self.allocate(1024 * 1024).await {
                // 1MB allocations
                allocations.push(ptr);
            }
        }

        let peak_memory = self.get_current_memory_usage().await;

        // Cleanup
        for ptr in allocations {
            self.deallocate(ptr, 1024 * 1024).await;
        }

        let end_memory = self.get_current_memory_usage().await;

        // Calculate memory efficiency
        let memory_overhead = (peak_memory - start_memory) / (num_requests as f64);
        let efficiency = 1.0 / memory_overhead.max(1.0);

        tracing::info!("Memory benchmark completed: efficiency={:.2}", efficiency);
        Ok(efficiency)
    }

    /// Get memory pool statistics
    pub async fn get_pool_stats(&self) -> HashMap<String, usize> {
        let pool = self.memory_pool.read().await;
        let mut stats = HashMap::new();

        stats.insert(
            "total_allocated".to_string(),
            pool.total_allocated.load(Ordering::SeqCst),
        );
        stats.insert("max_size".to_string(), pool.max_size);
        stats.insert("pool_count".to_string(), pool.pools.len());

        stats
    }
}

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

    #[tokio::test]
    async fn test_memory_manager_creation() {
        let config = MemoryConfig::default();
        let manager = MemoryManager::new(config).await;
        assert!(manager.is_ok());
    }

    #[tokio::test]
    async fn test_memory_pool_allocation() {
        let mut pool = MemoryPool::new(100); // 100MB
        let ptr = pool.allocate(1024);
        assert!(ptr.is_some());

        if let Some(p) = ptr {
            pool.deallocate(p, 1024);
        }
    }

    #[tokio::test]
    async fn test_zero_copy_tensor() {
        let data: Arc<[u8]> = Arc::from(vec![0u8; 1024]);
        let tensor = ZeroCopyTensor::new(data, vec![32, 32]);

        let slice = tensor.slice(&[(0, 16), (0, 32)]);
        assert!(slice.is_ok());

        let reshaped = tensor.reshape(vec![1024]);
        assert!(reshaped.is_ok());
    }

    #[test]
    fn test_memory_tracker() {
        let tracker = MemoryTracker::new();
        tracker.allocate(1024);
        assert_eq!(tracker.allocated.load(Ordering::SeqCst), 1024);
        assert_eq!(tracker.allocations.load(Ordering::SeqCst), 1);

        tracker.deallocate(512);
        assert_eq!(tracker.allocated.load(Ordering::SeqCst), 512);
        assert_eq!(tracker.deallocations.load(Ordering::SeqCst), 1);
    }
}