foxhunt/ml/src/memory_optimization/lazy_loader.rs

//! Lazy checkpoint loading system
//!
//! Loads model weights on-demand rather than eagerly loading entire checkpoints.

use candle_core::{DType, Device, Tensor};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use std::sync::{Arc, Mutex};
use tracing::{debug, info};

use crate::MLError;

/// Loading strategy for checkpoint components
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum LoadStrategy {
    /// Load all weights immediately (default)
    Eager,

    /// Load weights only when accessed
    Lazy,

    /// Load only critical weights, defer rest
    Selective,
}

/// Lazy checkpoint loader
#[derive(Debug)]
pub struct LazyCheckpointLoader {
    /// Path to checkpoint file
    checkpoint_path: PathBuf,

    /// Loading strategy
    strategy: LoadStrategy,

    /// Cached tensors (name -> tensor)
    cache: Arc<Mutex<HashMap<String, Tensor>>>,

    /// Device for tensor allocation
    device: Device,

    /// Metadata about available tensors
    tensor_metadata: HashMap<String, TensorMetadata>,
}

/// Metadata for a tensor in the checkpoint
#[derive(Debug, Clone)]
struct TensorMetadata {
    /// Tensor name/key
    name: String,

    /// Shape of the tensor
    shape: Vec<usize>,

    /// Data type
    dtype: DType,

    /// Size in bytes
    size_bytes: usize,

    /// File offset (for lazy loading)
    offset: usize,

    /// Whether this is a critical tensor (e.g., embedding layers)
    critical: bool,
}

impl LazyCheckpointLoader {
    /// Create a new lazy checkpoint loader
    pub fn new<P: AsRef<Path>>(
        checkpoint_path: P,
        strategy: LoadStrategy,
        device: Device,
    ) -> Result<Self, MLError> {
        let checkpoint_path = checkpoint_path.as_ref().to_path_buf();

        if !checkpoint_path.exists() {
            return Err(MLError::ModelError(format!(
                "Checkpoint not found: {}",
                checkpoint_path.display()
            )));
        }

        info!(
            "Initializing lazy checkpoint loader: {} (strategy: {:?})",
            checkpoint_path.display(),
            strategy
        );

        // Parse checkpoint metadata without loading weights
        let tensor_metadata = Self::parse_checkpoint_metadata(&checkpoint_path)?;

        debug!("Found {} tensors in checkpoint", tensor_metadata.len());

        Ok(Self {
            checkpoint_path,
            strategy,
            cache: Arc::new(Mutex::new(HashMap::new())),
            device,
            tensor_metadata,
        })
    }

    /// Parse checkpoint metadata without loading full weights
    ///
    /// TODO: Implement checkpoint header parsing to extract tensor shapes/dtypes
    /// without loading full weight data. This would parse safetensors/pickle headers.
    /// For now, returns empty metadata - tensors will be loaded lazily on first access.
    fn parse_checkpoint_metadata(
        _checkpoint_path: &Path,
    ) -> Result<HashMap<String, TensorMetadata>, MLError> {
        // Stub implementation - no metadata extraction yet
        // When implemented, would use:
        // - safetensors: parse header JSON to get tensor names/shapes/dtypes
        // - pickle: use limited parsing to read __metadata__ without unpickling arrays
        Ok(HashMap::new())
    }

    /// Load a tensor by name
    pub fn load_tensor(&self, name: &str) -> Result<Tensor, MLError> {
        // Check cache first
        {
            let cache = self.cache.lock().map_err(|e| MLError::ConcurrencyError {
                operation: format!("lock cache: {}", e),
            })?;

            if let Some(tensor) = cache.get(name) {
                debug!("Cache hit for tensor: {}", name);
                return Ok(tensor.clone());
            }
        }

        // Load from checkpoint
        debug!("Loading tensor from checkpoint: {}", name);
        let tensor = self.load_tensor_from_file(name)?;

        // Cache if using lazy/selective strategy
        if self.strategy != LoadStrategy::Eager {
            let mut cache = self.cache.lock().map_err(|e| MLError::ConcurrencyError {
                operation: format!("lock cache for insert: {}", e),
            })?;
            cache.insert(name.to_string(), tensor.clone());
        }

        Ok(tensor)
    }

    /// Load tensor from checkpoint file
    fn load_tensor_from_file(&self, name: &str) -> Result<Tensor, MLError> {
        // In production, this would:
        // 1. Seek to tensor offset in file
        // 2. Read tensor data
        // 3. Deserialize to Tensor

        // For now, return a placeholder
        let metadata = self.tensor_metadata.get(name).ok_or_else(|| {
            MLError::ModelError(format!("Tensor not found in checkpoint: {}", name))
        })?;

        // Create zero tensor as placeholder
        Tensor::zeros(&metadata.shape[..], metadata.dtype, &self.device).map_err(|e| {
            MLError::TensorCreationError {
                operation: format!("create tensor {}", name),
                reason: e.to_string(),
            }
        })
    }

    /// Preload critical tensors (for selective strategy)
    pub fn preload_critical(&self) -> Result<(), MLError> {
        if self.strategy != LoadStrategy::Selective {
            return Ok(());
        }

        info!("Preloading critical tensors...");

        let critical_tensors: Vec<_> = self
            .tensor_metadata
            .iter()
            .filter(|(_, meta)| meta.critical)
            .map(|(name, _)| name.clone())
            .collect();

        for name in critical_tensors {
            self.load_tensor(&name)?;
        }

        info!(
            "Preloaded {} critical tensors",
            self.cache.lock().unwrap().len()
        );
        Ok(())
    }

    /// Get memory usage statistics
    pub fn memory_stats(&self) -> Result<MemoryStatistics, MLError> {
        let cache = self.cache.lock().map_err(|e| MLError::ConcurrencyError {
            operation: format!("lock cache for stats: {}", e),
        })?;

        let cached_tensors = cache.len();
        let total_tensors = self.tensor_metadata.len();

        let cached_memory_mb: f64 = cache
            .values()
            .map(|t| {
                let elem_count = t.dims().iter().product::<usize>();
                let bytes = elem_count * 4; // Assume float32
                bytes as f64 / 1_048_576.0
            })
            .sum();

        Ok(MemoryStatistics {
            cached_tensors,
            total_tensors,
            cached_memory_mb,
            cache_hit_rate: 0.0, // Would track hits/misses in production
        })
    }

    /// Clear cache to free memory
    pub fn clear_cache(&self) -> Result<(), MLError> {
        let mut cache = self.cache.lock().map_err(|e| MLError::ConcurrencyError {
            operation: format!("lock cache for clear: {}", e),
        })?;

        let count = cache.len();
        cache.clear();

        info!("Cleared {} tensors from cache", count);
        Ok(())
    }
}

/// Memory statistics for lazy loader
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryStatistics {
    pub cached_tensors: usize,
    pub total_tensors: usize,
    pub cached_memory_mb: f64,
    pub cache_hit_rate: f64,
}

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

    #[test]
    fn test_load_strategy() {
        assert_eq!(LoadStrategy::Lazy, LoadStrategy::Lazy);
        assert_ne!(LoadStrategy::Eager, LoadStrategy::Lazy);
    }
}