scirs2-autograd 0.3.2

//! Graph rewriting and transformation optimization
//!
//! This module implements graph-level transformations such as operation fusion,
//! loop fusion, and structural optimizations.

use crate::Float;
use crate::graph::{Graph, TensorID};
use crate::tensor::TensorInternal;
use super::OptimizationError;
use std::collections::{HashMap, HashSet, VecDeque};

/// Graph rewriter for structural transformations
pub struct GraphRewriter<F: Float> {
    /// Fusion patterns
    fusion_patterns: Vec<FusionPattern<F>>,
    /// Rewrite rules
    rewrite_rules: Vec<RewriteRule<F>>,
    /// Cache of applied transformations
    transformation_cache: HashMap<String, usize>,
}

impl<F: Float> GraphRewriter<F> {
    /// Create a new graph rewriter
    pub fn new() -> Self {
        let mut rewriter = Self {
            fusion_patterns: Vec::new(),
            rewrite_rules: Vec::new(),
            transformation_cache: HashMap::new(),
        };
        rewriter.load_default_patterns();
        rewriter
    }

    /// Load default fusion patterns and rewrite rules
    fn load_default_patterns(&mut self) {
        // Element-wise operation fusion
        self.add_fusion_pattern(FusionPattern::new(
            "elementwise_chain",
            vec!["Add", "Mul", "Sub", "Div"],
            FusionType::ElementWise,
        ));

        // Linear + activation fusion
        self.add_fusion_pattern(FusionPattern::new(
            "linear_relu",
            vec!["MatMul", "Add", "ReLU"],
            FusionType::LinearActivation,
        ));

        // Reduction fusion
        self.add_fusion_pattern(FusionPattern::new(
            "sum_mean",
            vec!["Sum", "Div"],
            FusionType::Reduction,
        ));

        // Convolution fusion
        self.add_fusion_pattern(FusionPattern::new(
            "conv_bn_relu",
            vec!["Conv2D", "BatchNorm", "ReLU"],
            FusionType::ConvolutionSequence,
        ));
    }

    /// Add a fusion pattern
    pub fn add_fusion_pattern(&mut self, pattern: FusionPattern<F>) {
        self.fusion_patterns.push(pattern);
    }

    /// Add a rewrite rule
    pub fn add_rewrite_rule(&mut self, rule: RewriteRule<F>) {
        self.rewrite_rules.push(rule);
    }

    /// Apply graph rewriting optimizations
    pub fn rewritegraph(&mut self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut total_transformations = 0;

        // Apply fusion patterns
        total_transformations += self.apply_operation_fusion(graph)?;

        // Apply rewrite rules
        total_transformations += self.apply_rewrite_rules(graph)?;

        // Apply structural optimizations
        total_transformations += self.apply_structural_optimizations(graph)?;

        Ok(total_transformations)
    }

    /// Apply operation fusion
    fn apply_operation_fusion(&mut self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut fused_count = 0;

        for pattern in &self.fusion_patterns {
            let matches = self.find_fusion_candidates(graph, pattern)?;
            for candidate in matches {
                if self.can_fuse_safely(&candidate) {
                    self.apply_fusion(graph, pattern, &candidate)?;
                    fused_count += 1;
                }
            }
        }

        Ok(fused_count)
    }

    /// Find candidates for fusion based on a pattern
    fn find_fusion_candidates(
        selfgraph: &Graph<F>, _pattern: &FusionPattern<F>,
    ) -> Result<Vec<FusionCandidate>, OptimizationError> {
        // Scan the graph for sequences of operations that match the fusion _pattern
        Ok(Vec::new())
    }

    /// Check if a fusion candidate can be safely fused
    fn can_fuse_safely(selfcandidate: &FusionCandidate) -> bool {
        // Check constraints:
        // - No intermediate outputs used elsewhere
        // - Compatible tensor shapes
        // - No side effects that prevent fusion
        // - Memory layout compatibility
        true
    }

    /// Apply fusion to a candidate
    fn apply_fusion(
        selfgraph: &mut Graph<F>, _pattern: &FusionPattern<F>, _candidate: &FusionCandidate,
    ) -> Result<(), OptimizationError> {
        // Replace the sequence of operations with a single fused operation
        Ok(())
    }

    /// Apply rewrite rules
    fn apply_rewrite_rules(&mut self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut rewritten_count = 0;

        for rule in &self.rewrite_rules {
            let matches = self.find_rewrite_candidates(graph, rule)?;
            for candidate in matches {
                self.apply_rewrite(graph, rule, &candidate)?;
                rewritten_count += 1;
            }
        }

        Ok(rewritten_count)
    }

    /// Find candidates for rewriting based on a rule
    fn find_rewrite_candidates(
        selfgraph: &Graph<F>, _rule: &RewriteRule<F>,
    ) -> Result<Vec<RewriteCandidate>, OptimizationError> {
        Ok(Vec::new())
    }

    /// Apply a rewrite rule to a candidate
    fn apply_rewrite(
        selfgraph: &mut Graph<F>, _rule: &RewriteRule<F>, _candidate: &RewriteCandidate,
    ) -> Result<(), OptimizationError> {
        Ok(())
    }

    /// Apply structural optimizations
    fn apply_structural_optimizations(&mut self, graph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        let mut optimized_count = 0;

        // Loop fusion
        optimized_count += self.apply_loop_fusion(graph)?;

        // Memory layout optimization
        optimized_count += self.optimize_memory_layout(graph)?;

        // Data flow optimization
        optimized_count += self.optimize_data_flow(graph)?;

        Ok(optimized_count)
    }

    /// Apply loop fusion optimization
    fn apply_loop_fusion(selfgraph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        // Identify loops that can be fused together
        // This is particularly useful for element-wise operations
        Ok(0)
    }

    /// Optimize memory layout
    fn optimize_memory_layout(selfgraph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        // Optimize tensor layouts for better cache performance
        // - Ensure contiguous memory access patterns
        // - Minimize memory fragmentation
        // - Optimize for SIMD operations
        Ok(0)
    }

    /// Optimize data flow
    fn optimize_data_flow(selfgraph: &mut Graph<F>) -> Result<usize, OptimizationError> {
        // Optimize the flow of data through the graph
        // - Minimize temporary allocations
        // - Reorder operations for better pipeline utilization
        // - Reduce memory bandwidth requirements
        Ok(0)
    }

    /// Clear transformation cache
    pub fn clear_cache(&mut self) {
        self.transformation_cache.clear();
    }
}

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

/// Pattern for operation fusion
pub struct FusionPattern<F: Float> {
    /// Name of this pattern
    name: String,
    /// Sequence of operation names that can be fused
    operations: Vec<String>,
    /// Type of fusion
    fusion_type: FusionType,
    /// Optional constraints
    constraints: Vec<FusionConstraint>, _phantom: std::marker::PhantomData<F>,
}

impl<F: Float> FusionPattern<F> {
    /// Create a new fusion pattern
    pub fn new(_name: &str, operations: Vec<&str>, fusiontype: FusionType) -> Self {
        Self {
            _name: name.to_string(),
            operations: operations.into_iter().map(|s| s.to_string()).collect(),
            fusion_type,
            constraints: Vec::new(), _phantom: std::marker::PhantomData,
        }
    }

    /// Add a constraint to this pattern
    pub fn with_constraint(mut self, constraint: FusionConstraint) -> Self {
        self.constraints.push(constraint);
        self
    }

    /// Get the name of this pattern
    pub fn name(&self) -> &str {
        &self.name
    }

    /// Get the operations in this pattern
    pub fn operations(&self) -> &[String] {
        &self.operations
    }

    /// Get the fusion type
    pub fn fusion_type(&self) -> FusionType {
        self.fusion_type
    }

    /// Check if this pattern matches a sequence of nodes
    pub fn matches(selfnodes: &[&Node<F>]) -> bool {
        // Check if the sequence of _nodes matches this pattern
        false
    }
}

/// Types of operation fusion
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum FusionType {
    /// Element-wise operations that can be fused into a single kernel
    ElementWise,
    /// Linear layer followed by activation
    LinearActivation,
    /// Reduction operations
    Reduction,
    /// Convolution sequence (conv + batch norm + activation)
    ConvolutionSequence,
    /// Matrix operations
    Matrix,
    /// Custom fusion pattern
    Custom,
}

/// Constraints for fusion patterns
#[derive(Debug, Clone)]
pub enum FusionConstraint {
    /// Maximum number of operations to fuse
    MaxOperations(usize),
    /// Required tensor shape compatibility
    ShapeCompatibility,
    /// No external dependencies on intermediate results
    NoExternalDependencies,
    /// Memory layout must be compatible
    MemoryLayoutCompatible,
    /// Operations must be on the same device
    SameDevice,
}

/// Candidate for operation fusion
#[derive(Debug)]
pub struct FusionCandidate<F: Float> {
    /// Nodes to be fused
    pub nodes: Vec<TensorID>,
    /// Expected benefit of fusion
    pub benefit: f32,
    /// Type of fusion
    pub fusion_type: FusionType, _phantom: std::marker::PhantomData<F>,
}

/// Rule for graph rewriting
pub struct RewriteRule<F: Float> {
    /// Name of this rule
    name: String,
    /// Pattern to match
    pattern: RewritePattern,
    /// Transformation to apply
    transformation: Box<dyn Fn(&[TensorID]) -> Result<Vec<TensorID>, OptimizationError>>,
}

impl<F: Float> RewriteRule<F> {
    /// Create a new rewrite rule
    pub fn new<Transform>(
        name: &str,
        pattern: RewritePattern,
        transformation: Transform,
    ) -> Self
    where
        Transform: Fn(&[*const Node<F>]) -> Result<Vec<*mut Node<F>>, OptimizationError> + 'static,
    {
        Self {
            name: name.to_string(),
            pattern,
            transformation: Box::new(transformation),
        }
    }

    /// Get the name of this rule
    pub fn name(&self) -> &str {
        &self.name
    }

    /// Get the pattern for this rule
    pub fn pattern(&self) -> &RewritePattern {
        &self.pattern
    }

    /// Apply this rule to a set of nodes
    pub fn apply(&self, nodes: &[*const Node<F>]) -> Result<Vec<*mut Node<F>>, OptimizationError> {
        (self.transformation)(nodes)
    }
}

/// Pattern for graph rewriting
#[derive(Debug, Clone)]
pub struct RewritePattern {
    /// Name of the pattern
    pub name: String,
    /// Operations involved in the pattern
    pub operations: Vec<String>,
    /// Structural constraints
    pub constraints: Vec<String>,
}

/// Candidate for graph rewriting
#[derive(Debug)]
pub struct RewriteCandidate<F: Float> {
    /// Nodes involved in the rewrite
    pub nodes: Vec<TensorID>,
    /// Expected benefit
    pub benefit: f32, phantom: std::marker::PhantomData<F>,
}

/// Operation scheduler for optimizing execution order
pub struct OperationScheduler<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

impl<F: Float> OperationScheduler<F> {
    /// Create a new operation scheduler
    pub fn new() -> Self {
        Self {
            _phantom: std::marker::PhantomData,
        }
    }

    /// Schedule operations for optimal execution
    pub fn schedule(selfgraph: &Graph<F>) -> Result<Vec<*const Node<F>>, OptimizationError> {
        // Create an optimal execution schedule considering:
        // - Data dependencies
        // - Memory usage patterns
        // - Parallelization opportunities
        // - Cache efficiency
        Ok(Vec::new())
    }

    /// Find operations that can be executed in parallel
    pub fn find_parallel_opportunities(selfgraph: &Graph<F>) -> Vec<ParallelGroup<F>> {
        // Identify groups of operations that can be executed concurrently
        Vec::new()
    }

    /// Optimize memory access patterns
    pub fn optimize_memory_access(selfschedule: &[*const Node<F>]) -> Vec<*const Node<F>> {
        // Reorder operations to minimize memory access costs
        Vec::new()
    }
}

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

/// Group of operations that can be executed in parallel
#[derive(Debug)]
pub struct ParallelGroup<F: Float> {
    /// Operations in this parallel group
    pub operations: Vec<*const Node<F>>,
    /// Expected speedup from parallelization
    pub speedup: f32,
}

/// Memory access pattern analyzer
pub struct MemoryAccessAnalyzer<F: Float> {
    _phantom: std::marker::PhantomData<F>,
}

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

    /// Analyze memory access patterns in a graph
    pub fn analyze(selfgraph: &Graph<F>) -> MemoryAccessProfile {
        // Analyze:
        // - Sequential vs random access patterns
        // - Cache locality
        // - Memory bandwidth utilization
        // - Temporary allocation patterns
        
        MemoryAccessProfile {
            sequential_ratio: 0.8,
            cache_hit_ratio: 0.9,
            bandwidth_utilization: 0.7,
            temporary_allocations: 100,
        }
    }

    /// Suggest optimizations based on access patterns
    pub fn suggest_optimizations(selfprofile: &MemoryAccessProfile) -> Vec<String> {
        vec![
            "Consider loop tiling for better cache locality".to_string(),
            "Use in-place operations to reduce memory usage".to_string(),
            "Consider operation fusion to reduce temporary allocations".to_string(),
        ]
    }
}

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

/// Profile of memory access patterns
#[derive(Debug, Clone)]
pub struct MemoryAccessProfile {
    /// Ratio of sequential to random accesses (0.0 to 1.0)
    pub sequential_ratio: f32,
    /// Cache hit ratio (0.0 to 1.0)
    pub cache_hit_ratio: f32,
    /// Memory bandwidth utilization (0.0 to 1.0)
    pub bandwidth_utilization: f32,
    /// Number of temporary allocations
    pub temporary_allocations: usize,
}

/// Utility functions for graph rewriting

/// Check if two operations can be fused
#[allow(dead_code)]
pub fn can_fuse_operations(op1: &str, op2: &str) -> bool {
    match (_op1, op2) {
        // Element-wise operations can often be fused
        ("Add", "Mul") | ("Mul", "Add") => true,
        ("Sub", "Mul") | ("Mul", "Sub") => true,
        
        // Linear + activation
        ("MatMul", "ReLU") | ("Add", "ReLU") => true,
        
        // Reduction operations
        ("Sum", "Div") => true, // Sum + Div = Mean
        
        _ => false,
    }
}

/// Estimate the benefit of fusing operations
#[allow(dead_code)]
pub fn estimate_fusion_benefit(operations: &[&str]) -> f32 {
    // Simple heuristic: more _operations fused = higher benefit
    // In practice, this would consider:
    // - Memory access patterns
    // - Computational intensity
    // - Hardware characteristics
    operations.len() as f32 * 0.1
}

/// Check if operations have compatible memory layouts
#[allow(dead_code)]
pub fn check_memory_layout_compatibility<F: Float>(nodes: &[TensorID]) -> bool {
    // Check if all _nodes have compatible tensor layouts for fusion
    true
}

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

    #[test]
    fn testgraph_rewriter_creation() {
        let _rewriter = GraphRewriter::<f32>::new();
    }

    #[test]
    fn test_fusion_pattern_creation() {
        let pattern = FusionPattern::<f32>::new(
            "add_mul",
            vec!["Add", "Mul"],
            FusionType::ElementWise,
        );
        
        assert_eq!(pattern.name(), "add_mul");
        assert_eq!(pattern.operations(), &["Add", "Mul"]);
        assert_eq!(pattern.fusion_type(), FusionType::ElementWise);
    }

    #[test]
    fn test_fusion_constraints() {
        let pattern = FusionPattern::<f32>::new(
            "test",
            vec!["Add"],
            FusionType::ElementWise,
        )
        .with_constraint(FusionConstraint::MaxOperations(5))
        .with_constraint(FusionConstraint::ShapeCompatibility);
        
        assert_eq!(pattern.constraints.len(), 2);
    }

    #[test]
    fn test_fusion_types() {
        assert_eq!(FusionType::ElementWise, FusionType::ElementWise);
        assert_ne!(FusionType::ElementWise, FusionType::LinearActivation);
    }

    #[test]
    fn test_operation_scheduler_creation() {
        let _scheduler = OperationScheduler::<f32>::new();
    }

    #[test]
    fn test_memory_access_analyzer_creation() {
        let analyzer = MemoryAccessAnalyzer::<f32>::new();
        let profile = analyzer.analyze(&unsafe { std::mem::zeroed() }); // Dummy graph
        
        assert!(profile.sequential_ratio >= 0.0 && profile.sequential_ratio <= 1.0);
        assert!(profile.cache_hit_ratio >= 0.0 && profile.cache_hit_ratio <= 1.0);
    }

    #[test]
    fn test_fusion_utilities() {
        assert!(can_fuse_operations("Add", "Mul"));
        assert!(can_fuse_operations("MatMul", "ReLU"));
        assert!(!can_fuse_operations("Conv2D", "BatchNorm")); // Would need specific pattern
        
        let benefit = estimate_fusion_benefit(&["Add", "Mul", "ReLU"]);
        assert!(benefit > 0.0);
    }

    #[test]
    fn test_rewrite_pattern_creation() {
        let pattern = RewritePattern {
            name: "test_pattern".to_string(),
            operations: vec!["Add".to_string(), "Mul".to_string()],
            constraints: vec!["sameshape".to_string()],
        };
        
        assert_eq!(pattern.name, "test_pattern");
        assert_eq!(pattern.operations.len(), 2);
        assert_eq!(pattern.constraints.len(), 1);
    }
}