scirs2_autograd/optimization/

memory_optimization.rs

//! Memory optimization for computation graphs
//!
//! This module provides memory optimization techniques including gradient
//! checkpointing, memory pooling, and tensor lifetime analysis.

use super::OptimizationError;
use crate::graph::Graph;
use crate::Float;
use std::collections::HashMap;

/// Memory optimizer for computation graphs
pub struct MemoryOptimizer<F: Float> {
    /// Configuration for memory optimization
    config: MemoryOptimizationConfig,
    /// Analysis results
    analysis: Option<MemoryAnalysis>,
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float> MemoryOptimizer<F> {
    /// Create a new memory optimizer
    pub fn new() -> Self {
        Self {
            config: MemoryOptimizationConfig::default(),
            analysis: None,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Create a memory optimizer with custom configuration
    pub fn with_config(config: MemoryOptimizationConfig) -> Self {
        Self {
            config,
            analysis: None,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Optimize memory usage in a computation graph
    pub fn optimize(
        &mut self,
        graph: &mut Graph<F>,
    ) -> Result<MemoryOptimizationReport, OptimizationError> {
        let mut report = MemoryOptimizationReport::new();

        // Analyze memory usage patterns
        self.analysis = Some(self.analyze_memory_usage(graph)?);

        if self.config.enable_gradient_checkpointing {
            let checkpoints = self.apply_gradient_checkpointing(graph)?;
            report.gradient_checkpoints_added = checkpoints;
        }

        if self.config.enable_memory_pooling {
            let pools = self.setup_memory_pooling(graph)?;
            report.memory_pools_created = pools;
        }

        if self.config.enable_in_place_operations {
            let in_place_ops = self.apply_in_place_operations(graph)?;
            report.in_place_operations_applied = in_place_ops;
        }

        if self.config.enable_tensor_reuse {
            let reused = self.apply_tensor_reuse(graph)?;
            report.tensors_reused = reused;
        }

        if self.config.enable_lifetime_optimization {
            let optimized = self.optimize_tensor_lifetimes(graph)?;
            report.lifetime_optimizations = optimized;
        }

        Ok(report)
    }

    /// Analyze memory usage patterns in the graph
    fn analyze_memory_usage(&self, graph: &Graph<F>) -> Result<MemoryAnalysis, OptimizationError> {
        let mut analysis = MemoryAnalysis::new();

        // Analyze:
        // - Tensor sizes and lifetimes
        // - Memory allocation patterns
        // - Peak memory usage
        // - Opportunities for optimization

        analysis.total_memory_allocated = 1024 * 1024; // Placeholder
        analysis.peak_memory_usage = 512 * 1024; // Placeholder
        analysis.num_allocations = 100; // Placeholder
        analysis.num_deallocations = 90; // Placeholder

        Ok(analysis)
    }

    /// Apply gradient checkpointing
    fn apply_gradient_checkpointing(
        &self,
        graph: &mut Graph<F>,
    ) -> Result<usize, OptimizationError> {
        let mut checkpoints_added = 0;

        // Strategy: Insert checkpoints at points where:
        // 1. Memory usage is high
        // 2. Recomputation cost is relatively low
        // 3. It provides significant memory savings

        let candidates = self.find_checkpoint_candidates(graph)?;

        for candidate in candidates {
            if self.should_checkpoint(&candidate) {
                self.insert_checkpoint(graph, &candidate)?;
                checkpoints_added += 1;
            }
        }

        Ok(checkpoints_added)
    }

    /// Find candidates for gradient checkpointing
    fn find_checkpoint_candidates(
        &self,
        graph: &Graph<F>,
    ) -> Result<Vec<CheckpointCandidate<F>>, OptimizationError> {
        let candidates = Vec::new();

        // Look for:
        // - Nodes with large memory footprint
        // - Nodes in long computation chains
        // - Nodes where recomputation is cheaper than storage

        Ok(candidates)
    }

    /// Check if a node should be checkpointed
    fn should_checkpoint(&self, candidate: &CheckpointCandidate<F>) -> bool {
        // Decision criteria:
        // - Memory savings > threshold
        // - Recomputation cost < threshold
        // - Not already checkpointed

        candidate.memory_savings > self.config.checkpoint_memory_threshold
            && candidate.recomputation_cost < self.config.checkpoint_compute_threshold
    }

    /// Insert a checkpoint at a specific location
    fn insert_checkpoint(
        &self,
        graph: &mut Graph<F>,
        _candidate: &CheckpointCandidate<F>,
    ) -> Result<(), OptimizationError> {
        // Insert a checkpoint operation that:
        // 1. Saves the forward pass result
        // 2. Releases intermediate computations
        // 3. Recomputes on backward pass when needed

        Ok(())
    }

    /// Setup memory pooling
    fn setup_memory_pooling(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut pools_created = 0;

        // Analyze tensor size patterns
        let size_patterns = self.analyze_tensor_sizes(graph)?;

        // Create pools for common sizes
        for (size, frequency) in size_patterns {
            if frequency >= self.config.pool_frequency_threshold {
                MemoryOptimizer::<F>::create_memory_pool(size)?;
                pools_created += 1;
            }
        }

        Ok(pools_created)
    }

    /// Analyze tensor size patterns
    fn analyze_tensor_sizes(
        &self,
        graph: &Graph<F>,
    ) -> Result<HashMap<usize, usize>, OptimizationError> {
        let size_frequency = HashMap::new();

        // Count frequency of different tensor sizes
        // This would traverse the graph and collect size information

        Ok(size_frequency)
    }

    /// Create a memory pool for a specific size
    fn create_memory_pool(size: usize) -> Result<(), OptimizationError> {
        // Create a memory pool that can reuse buffers of the specified _size
        Ok(())
    }

    /// Apply in-place operations where safe
    fn apply_in_place_operations(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut in_place_applied = 0;

        // Find operations that can be done in-place:
        // - Element-wise operations where the input won't be used again
        // - Operations where the output has the same shape as input
        // - No aliasing issues

        let candidates = self.find_in_place_candidates(graph)?;

        for candidate in candidates {
            if MemoryOptimizer::<F>::can_apply_in_place(&candidate) {
                self.convert_to_in_place(graph, &candidate)?;
                in_place_applied += 1;
            }
        }

        Ok(in_place_applied)
    }

    /// Find candidates for in-place operations
    fn find_in_place_candidates(
        &self,
        graph: &Graph<F>,
    ) -> Result<Vec<InPlaceCandidate<F>>, OptimizationError> {
        // Look for operations like:
        // - Element-wise arithmetic
        // - Activation functions
        // - Normalization operations
        // where the input tensor is not used elsewhere

        Ok(Vec::new())
    }

    /// Check if an operation can be safely converted to in-place
    fn can_apply_in_place(candidate: &InPlaceCandidate<F>) -> bool {
        // Safety checks:
        // - Input tensor is not used by other operations
        // - No gradient computation conflicts
        // - Compatible tensor layouts
        // - No aliasing issues

        true
    }

    /// Convert an operation to in-place
    fn convert_to_in_place(
        &self,
        graph: &mut Graph<F>,
        _candidate: &InPlaceCandidate<F>,
    ) -> Result<(), OptimizationError> {
        // Replace the operation with an in-place version
        Ok(())
    }

    /// Apply tensor reuse optimization
    fn apply_tensor_reuse(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut reused_count = 0;

        // Find tensors that can be reused:
        // - Tensors with non-overlapping lifetimes
        // - Compatible shapes and types
        // - No aliasing conflicts

        let reuse_groups = self.find_tensor_reuse_opportunities(graph)?;

        for group in reuse_groups {
            self.apply_tensor_reuse_group(graph, &group)?;
            reused_count += group.tensors.len() - 1; // All but one reuse the same memory
        }

        Ok(reused_count)
    }

    /// Find opportunities for tensor reuse
    fn find_tensor_reuse_opportunities(
        &self,
        graph: &Graph<F>,
    ) -> Result<Vec<TensorReuseGroup<F>>, OptimizationError> {
        // Analyze tensor lifetimes and find non-overlapping tensors
        // that can share the same memory

        Ok(Vec::new())
    }

    /// Apply tensor reuse for a group of tensors
    fn apply_tensor_reuse_group(
        &self,
        graph: &mut Graph<F>,
        _group: &TensorReuseGroup<F>,
    ) -> Result<(), OptimizationError> {
        // Modify the graph to reuse memory for tensors in the _group
        Ok(())
    }

    /// Optimize tensor lifetimes
    fn optimize_tensor_lifetimes(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut optimizations = 0;

        // Strategies:
        // - Release tensors as early as possible
        // - Defer allocations as late as possible
        // - Reorder operations to minimize peak memory

        optimizations += self.apply_early_release(graph)?;
        optimizations += self.apply_late_allocation(graph)?;
        optimizations += self.reorder_for_memory(graph)?;

        Ok(optimizations)
    }

    /// Apply early release of tensors
    fn apply_early_release(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        // Insert explicit release operations as soon as tensors are no longer needed
        Ok(0)
    }

    /// Apply late allocation of tensors
    fn apply_late_allocation(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        // Delay tensor allocation until just before they're needed
        Ok(0)
    }

    /// Reorder operations to minimize peak memory usage
    fn reorder_for_memory(&self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        // Reorder operations (where dependencies allow) to reduce peak memory
        Ok(0)
    }

    /// Get the current memory analysis
    pub fn get_analysis(&self) -> Option<&MemoryAnalysis> {
        self.analysis.as_ref()
    }
}

impl<F: Float> Default for MemoryOptimizer<F> {
    fn default() -> Self {
        Self::new()
    }
}

/// Configuration for memory optimization
#[derive(Debug, Clone)]
pub struct MemoryOptimizationConfig {
    /// Enable gradient checkpointing
    pub enable_gradient_checkpointing: bool,
    /// Enable memory pooling
    pub enable_memory_pooling: bool,
    /// Enable in-place operations
    pub enable_in_place_operations: bool,
    /// Enable tensor reuse
    pub enable_tensor_reuse: bool,
    /// Enable tensor lifetime optimization
    pub enable_lifetime_optimization: bool,
    /// Memory threshold for checkpointing (bytes)
    pub checkpoint_memory_threshold: usize,
    /// Compute threshold for checkpointing (relative cost)
    pub checkpoint_compute_threshold: f32,
    /// Frequency threshold for creating memory pools
    pub pool_frequency_threshold: usize,
    /// Maximum memory usage target (bytes)
    pub max_memory_usage: Option<usize>,
}

impl Default for MemoryOptimizationConfig {
    fn default() -> Self {
        Self {
            enable_gradient_checkpointing: true,
            enable_memory_pooling: true,
            enable_in_place_operations: true,
            enable_tensor_reuse: true,
            enable_lifetime_optimization: true,
            checkpoint_memory_threshold: 1024 * 1024, // 1MB
            checkpoint_compute_threshold: 2.0,        // 2x recomputation cost
            pool_frequency_threshold: 5,              // At least 5 uses
            max_memory_usage: None,
        }
    }
}

/// Results of memory analysis
#[derive(Debug, Clone, Default)]
pub struct MemoryAnalysis {
    /// Total memory allocated (bytes)
    pub total_memory_allocated: usize,
    /// Peak memory usage (bytes)
    pub peak_memory_usage: usize,
    /// Number of allocations
    pub num_allocations: usize,
    /// Number of deallocations
    pub num_deallocations: usize,
    /// Average tensor size
    pub average_tensor_size: usize,
    /// Largest tensor size
    pub largest_tensor_size: usize,
    /// Memory fragmentation ratio
    pub fragmentation_ratio: f32,
    /// Opportunities for optimization
    pub optimization_opportunities: Vec<String>,
}

impl MemoryAnalysis {
    /// Create a new memory analysis
    pub fn new() -> Self {
        Self::default()
    }

    /// Calculate memory efficiency
    pub fn memory_efficiency(&self) -> f32 {
        if self.total_memory_allocated == 0 {
            return 1.0;
        }
        self.peak_memory_usage as f32 / self.total_memory_allocated as f32
    }

    /// Get allocation/deallocation balance
    pub fn allocation_balance(&self) -> i32 {
        self.num_allocations as i32 - self.num_deallocations as i32
    }
}

/// Report of memory optimization results
#[derive(Debug, Clone, Default)]
pub struct MemoryOptimizationReport {
    /// Number of gradient checkpoints added
    pub gradient_checkpoints_added: usize,
    /// Number of memory pools created
    pub memory_pools_created: usize,
    /// Number of in-place operations applied
    pub in_place_operations_applied: usize,
    /// Number of tensors reused
    pub tensors_reused: usize,
    /// Number of lifetime optimizations
    pub lifetime_optimizations: usize,
    /// Estimated memory savings (bytes)
    pub estimated_memory_savings: usize,
}

impl MemoryOptimizationReport {
    /// Create a new optimization report
    pub fn new() -> Self {
        Self::default()
    }

    /// Get total optimizations applied
    pub fn total_optimizations(&self) -> usize {
        self.gradient_checkpoints_added
            + self.memory_pools_created
            + self.in_place_operations_applied
            + self.tensors_reused
            + self.lifetime_optimizations
    }

    /// Print a summary of the memory optimization results
    pub fn print_summary(&self) {
        println!("Memory Optimization Report:");
        println!("==========================");
        println!("Total optimizations: {}", self.total_optimizations());

        if self.gradient_checkpoints_added > 0 {
            println!(
                "  Gradient checkpoints: {}",
                self.gradient_checkpoints_added
            );
        }
        if self.memory_pools_created > 0 {
            println!("  Memory pools created: {}", self.memory_pools_created);
        }
        if self.in_place_operations_applied > 0 {
            println!(
                "  In-place operations: {}",
                self.in_place_operations_applied
            );
        }
        if self.tensors_reused > 0 {
            println!("  Tensors reused: {}", self.tensors_reused);
        }
        if self.lifetime_optimizations > 0 {
            println!("  Lifetime optimizations: {}", self.lifetime_optimizations);
        }
        if self.estimated_memory_savings > 0 {
            println!(
                "  Estimated memory savings: {} bytes",
                self.estimated_memory_savings
            );
        }
    }
}

/// Candidate for gradient checkpointing
#[derive(Debug)]
pub(crate) struct CheckpointCandidate<F: Float> {
    /// Node to potentially checkpoint
    #[allow(dead_code)]
    pub node: *const crate::tensor::TensorInternal<F>,
    /// Estimated memory savings
    pub memory_savings: usize,
    /// Estimated recomputation cost
    pub recomputation_cost: f32,
    /// Priority for checkpointing
    #[allow(dead_code)]
    pub priority: f32,
}

/// Candidate for in-place operation
#[derive(Debug)]
pub(crate) struct InPlaceCandidate<F: Float> {
    /// Node to convert to in-place
    #[allow(dead_code)]
    pub node: *const crate::tensor::TensorInternal<F>,
    /// Estimated memory savings
    #[allow(dead_code)]
    pub memory_savings: usize,
    /// Safety score (higher is safer)
    #[allow(dead_code)]
    pub safety_score: f32,
}

/// Group of tensors that can reuse memory
#[derive(Debug)]
pub(crate) struct TensorReuseGroup<F: Float> {
    /// Tensors that can share memory
    pub tensors: Vec<*const crate::tensor::TensorInternal<F>>,
    /// Total memory that can be saved
    #[allow(dead_code)]
    pub memory_savings: usize,
}

/// Tensor lifetime analyzer
pub struct TensorLifetimeAnalyzer<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float> TensorLifetimeAnalyzer<F> {
    /// Create a new tensor lifetime analyzer
    pub fn new() -> Self {
        Self {
            _phantom: std::marker::PhantomData,
        }
    }

    /// Analyze tensor lifetimes in a graph
    #[allow(dead_code)]
    pub(crate) fn analyze(
        &self,
        graph: &Graph<F>,
    ) -> Result<HashMap<*const crate::tensor::TensorInternal<F>, TensorLifetime>, OptimizationError>
    {
        let lifetimes = HashMap::new();

        // For each tensor, determine:
        // - When it's first created/allocated
        // - When it's last used
        // - Peak memory usage contribution
        // - Overlap with other tensors

        Ok(lifetimes)
    }

    /// Find overlapping tensor lifetimes
    #[allow(dead_code)]
    pub(crate) fn find_overlapping_lifetimes(
        self_lifetimes: &HashMap<*const crate::tensor::TensorInternal<F>, TensorLifetime>,
    ) -> Vec<Vec<*const crate::tensor::TensorInternal<F>>> {
        // Group tensors with overlapping _lifetimes
        // These cannot share memory
        Vec::new()
    }

    /// Find non-overlapping tensor groups
    #[allow(dead_code)]
    pub(crate) fn find_reusable_groups(
        self_lifetimes: &HashMap<*const crate::tensor::TensorInternal<F>, TensorLifetime>,
    ) -> Vec<Vec<*const crate::tensor::TensorInternal<F>>> {
        // Group tensors with non-overlapping _lifetimes
        // These can potentially share memory
        Vec::new()
    }
}

impl<F: Float> Default for TensorLifetimeAnalyzer<F> {
    fn default() -> Self {
        Self::new()
    }
}

/// Information about a tensor's lifetime
#[derive(Debug, Clone)]
pub struct TensorLifetime {
    /// When the tensor is allocated
    pub allocation_time: usize,
    /// When the tensor is last used
    pub deallocation_time: usize,
    /// Size of the tensor
    pub size: usize,
    /// Peak usage during lifetime
    pub peak_usage: usize,
}

impl TensorLifetime {
    /// Check if this lifetime overlaps with another
    pub fn overlaps_with(&self, other: &TensorLifetime) -> bool {
        !(self.deallocation_time <= other.allocation_time
            || other.deallocation_time <= self.allocation_time)
    }

    /// Get the duration of this lifetime
    pub fn duration(&self) -> usize {
        self.deallocation_time.saturating_sub(self.allocation_time)
    }
}

/// Memory pool manager
pub struct MemoryPoolManager<F: Float> {
    /// Pools organized by size
    pools: HashMap<usize, Vec<Vec<F>>>,
    /// Pool usage statistics
    stats: MemoryPoolStats,
}

impl<F: Float> MemoryPoolManager<F> {
    /// Create a new memory pool manager
    pub fn new() -> Self {
        Self {
            pools: HashMap::new(),
            stats: MemoryPoolStats::default(),
        }
    }

    /// Get a buffer from the pool
    pub fn get_buffer(&mut self, size: usize) -> Vec<F> {
        if let Some(pool) = self.pools.get_mut(&size) {
            if let Some(buffer) = pool.pop() {
                self.stats.pool_hits += 1;
                return buffer;
            }
        }

        self.stats.pool_misses += 1;
        vec![F::zero(); size]
    }

    /// Return a buffer to the pool
    pub fn return_buffer(&mut self, mut buffer: Vec<F>) {
        let size = buffer.len();
        buffer.clear();
        buffer.resize(size, F::zero());

        self.pools.entry(size).or_default().push(buffer);
        self.stats.buffers_returned += 1;
    }

    /// Get pool statistics
    pub fn get_stats(&self) -> &MemoryPoolStats {
        &self.stats
    }

    /// Clear all pools
    pub fn clear(&mut self) {
        self.pools.clear();
        self.stats = MemoryPoolStats::default();
    }
}

impl<F: Float> Default for MemoryPoolManager<F> {
    fn default() -> Self {
        Self::new()
    }
}

/// Statistics for memory pools
#[derive(Debug, Clone, Default)]
pub struct MemoryPoolStats {
    /// Number of pool hits
    pub pool_hits: usize,
    /// Number of pool misses
    pub pool_misses: usize,
    /// Number of buffers returned
    pub buffers_returned: usize,
    /// Total memory pooled
    pub total_pooled_memory: usize,
}

impl MemoryPoolStats {
    /// Calculate pool hit ratio
    pub fn hit_ratio(&self) -> f32 {
        let total_requests = self.pool_hits + self.pool_misses;
        if total_requests == 0 {
            return 0.0;
        }
        self.pool_hits as f32 / total_requests as f32
    }
}

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

    #[test]
    fn test_memory_optimizer_creation() {
        let _optimizer = MemoryOptimizer::<f32>::new();
        let _optimizer_with_config =
            MemoryOptimizer::<f32>::with_config(MemoryOptimizationConfig::default());
    }

    #[test]
    fn test_memory_optimization_config() {
        let config = MemoryOptimizationConfig::default();
        assert!(config.enable_gradient_checkpointing);
        assert!(config.enable_memory_pooling);
        assert!(config.enable_in_place_operations);
        assert!(config.enable_tensor_reuse);
        assert!(config.enable_lifetime_optimization);
    }

    #[test]
    fn test_memory_analysis() {
        let mut analysis = MemoryAnalysis::new();
        analysis.total_memory_allocated = 1000;
        analysis.peak_memory_usage = 800;
        analysis.num_allocations = 10;
        analysis.num_deallocations = 8;

        assert_eq!(analysis.memory_efficiency(), 0.8);
        assert_eq!(analysis.allocation_balance(), 2);
    }

    #[test]
    fn test_memory_optimization_report() {
        let mut report = MemoryOptimizationReport::new();
        report.gradient_checkpoints_added = 5;
        report.memory_pools_created = 3;
        report.in_place_operations_applied = 10;

        assert_eq!(report.total_optimizations(), 18);
    }

    #[test]
    fn test_tensor_lifetime() {
        let lifetime1 = TensorLifetime {
            allocation_time: 0,
            deallocation_time: 10,
            size: 100,
            peak_usage: 100,
        };

        let lifetime2 = TensorLifetime {
            allocation_time: 5,
            deallocation_time: 15,
            size: 200,
            peak_usage: 200,
        };

        let lifetime3 = TensorLifetime {
            allocation_time: 20,
            deallocation_time: 30,
            size: 150,
            peak_usage: 150,
        };

        assert!(lifetime1.overlaps_with(&lifetime2));
        assert!(!lifetime1.overlaps_with(&lifetime3));
        assert_eq!(lifetime1.duration(), 10);
    }

    #[test]
    fn test_memory_pool_manager() {
        let mut manager = MemoryPoolManager::<f32>::new();

        // Get a buffer
        let buffer = manager.get_buffer(100);
        assert_eq!(buffer.len(), 100);
        assert_eq!(manager.get_stats().pool_misses, 1);

        // Return the buffer
        manager.return_buffer(buffer);
        assert_eq!(manager.get_stats().buffers_returned, 1);

        // Get another buffer of the same size - should come from pool
        let buffer2 = manager.get_buffer(100);
        assert_eq!(buffer2.len(), 100);
        assert_eq!(manager.get_stats().pool_hits, 1);
    }

    #[test]
    fn test_memory_pool_stats() {
        let stats = MemoryPoolStats {
            pool_hits: 8,
            pool_misses: 2,
            ..Default::default()
        };

        assert_eq!(stats.hit_ratio(), 0.8);
    }

    #[test]
    fn test_tensor_lifetime_analyzer() {
        let _analyzer = TensorLifetimeAnalyzer::<f32>::new();
    }
}