scirs2-linalg 0.4.0

//! Advanced GPU kernel fusion and kernel management
//!
//! This module implements cutting-edge GPU acceleration techniques including:
//! - Dynamic kernel fusion for complex operation chains
//! - Kernel optimization and compilation
//! - Performance modeling and prediction

use crate::error::{LinalgError, LinalgResult};
use crate::gpu::operations::kernels::GpuKernelManager;
use crate::gpu::{GpuBackend, GpuContext, GpuDeviceType};
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::numeric::{Float, NumAssign, Zero};
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::sync::{Arc, Mutex, RwLock};

/// Advanced-advanced GPU kernel fusion engine
pub struct AdvancedGpuKernelFusion<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    /// Operation dependency graph
    pub operation_graph: Arc<RwLock<OperationDependencyGraph<T>>>,
    /// Kernel fusion optimizer
    pub fusion_optimizer: Arc<Mutex<KernelFusionEngine>>,
}

/// Operation dependency graph for kernel fusion
#[derive(Debug)]
pub struct OperationDependencyGraph<T> {
    /// Graph nodes representing operations
    pub nodes: Vec<OperationNode<T>>,
    /// Dependency edges between operations
    pub edges: Vec<DependencyEdge>,
    /// Fusion opportunities
    pub fusion_candidates: Vec<FusionCandidate>,
}

/// Individual operation node in the dependency graph
#[derive(Debug)]
pub struct OperationNode<T> {
    /// Unique operation ID
    pub id: usize,
    /// Operation type
    pub op_type: GpuOperationType,
    /// Input tensor shapes
    pub input_shapes: Vec<TensorShape>,
    /// Output tensor shape
    pub output_shape: TensorShape,
    /// Memory requirements
    pub memory_requirements: MemoryRequirements,
    /// Execution cost estimate
    pub cost_estimate: f64,
    /// Kernel specifications
    pub kernel_spec: KernelSpecification<T>,
}

/// GPU operation types supported for fusion
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum GpuOperationType {
    MatrixMultiplication,
    MatrixAddition,
    MatrixSubtraction,
    ElementwiseMultiplication,
    ElementwiseAddition,
    ElementwiseDivision,
    MatrixTranspose,
    VectorNorm,
    MatrixNorm,
    Reduction,
    BroadcastOperation,
    ConvolutionalOperation,
    Convolution,
    ActivationFunction,
    BatchNormalization,
    Transpose,
    Normalization,
    Custom(String),
}

/// Tensor shape representation
#[derive(Debug, Clone, PartialEq)]
pub struct TensorShape {
    pub dimensions: Vec<usize>,
    pub element_type: ElementType,
    pub memory_layout: MemoryLayout,
}

/// Element types supported
#[derive(Debug, Clone, PartialEq)]
pub enum ElementType {
    F32,
    F64,
    F16,
    BF16,
    Int32,
    Int16,
    Int8,
    UInt8,
}

/// Memory layout types
#[derive(Debug, Clone, PartialEq)]
pub enum MemoryLayout {
    RowMajor,
    ColumnMajor,
    Blocked(usize, usize),
    Custom(String),
}

/// Memory requirements for an operation
#[derive(Debug, Clone)]
pub struct MemoryRequirements {
    /// Input memory requirement in bytes
    pub input_memory: usize,
    /// Output memory requirement in bytes
    pub output_memory: usize,
    /// Temporary memory requirement in bytes
    pub temp_memory: usize,
    /// Memory bandwidth requirement in GB/s
    pub bandwidth_requirement: f64,
}

/// Kernel specification for GPU operations
#[derive(Debug)]
pub struct KernelSpecification<T> {
    /// Kernel name
    pub name: String,
    /// Thread block dimensions
    pub block_dims: (u32, u32, u32),
    /// Grid dimensions
    pub grid_dims: (u32, u32, u32),
    /// Shared memory requirement
    pub shared_memory: usize,
    /// Register requirement per thread
    pub registers_per_thread: u32,
    /// Kernel parameters
    pub parameters: Vec<KernelParameter<T>>,
}

/// Kernel parameters
#[derive(Debug)]
pub enum KernelParameter<T> {
    Scalar(T),
    Vector(Vec<T>),
    Matrix(Array2<T>),
    Pointer(*mut T),
}

/// Dependency edge between operations
#[derive(Debug, Clone)]
pub struct DependencyEdge {
    /// Source operation ID
    pub source: usize,
    /// Target operation ID
    pub target: usize,
    /// Data dependency type
    pub dependency_type: DependencyType,
    /// Data size flowing through the edge
    pub data_size: usize,
}

/// Types of dependencies between operations
#[derive(Debug, Clone, PartialEq)]
pub enum DependencyType {
    /// True data dependency (RAW - Read After Write)
    TrueDependency,
    /// Anti-dependency (WAR - Write After Read)
    AntiDependency,
    /// Output dependency (WAW - Write After Write)
    OutputDependency,
    /// Control dependency
    ControlDependency,
}

/// Fusion candidate representing operations that can be fused
#[derive(Debug, Clone)]
pub struct FusionCandidate {
    /// Operations to fuse
    pub operations: Vec<usize>,
    /// Expected performance benefit
    pub benefit_score: f64,
    /// Memory savings estimate
    pub memory_savings: usize,
    /// Fusion complexity
    pub complexity: f64,
}

/// Kernel fusion engine
#[derive(Debug)]
pub struct KernelFusionEngine {
    /// Fusion strategies
    fusion_strategies: Vec<FusionStrategy>,
    /// Fusion rules
    fusion_rules: FusionRuleSet,
    /// Performance models
    performance_models: HashMap<String, PerformanceModel>,
    /// Optimization parameters
    optimization_params: FusionOptimizationParams,
}

/// Kernel fusion strategies
#[derive(Debug, Clone)]
pub enum FusionStrategy {
    /// Fuse elementwise operations
    ElementwiseFusion,
    /// Fuse matrix operations
    MatrixOperationFusion,
    /// Fuse reduction operations
    ReductionFusion,
    /// Fuse memory-bound operations
    MemoryBoundFusion,
    /// Fuse compute-bound operations
    ComputeBoundFusion,
    /// Custom fusion strategy
    Custom(String),
}

/// Fusion rule set
#[derive(Debug)]
pub struct FusionRuleSet {
    /// Compatibility rules between operation types
    compatibility_rules: HashMap<(GpuOperationType, GpuOperationType), bool>,
    /// Memory constraint rules
    memory_rules: Vec<MemoryConstraintRule>,
    /// Performance constraint rules
    performance_rules: Vec<PerformanceConstraintRule>,
}

/// Memory constraint rule for fusion
#[derive(Debug)]
pub struct MemoryConstraintRule {
    /// Maximum memory usage for fused operation
    pub max_memory: usize,
    /// Maximum number of operations to fuse
    pub max_operations: usize,
    /// Memory hierarchy considerations
    pub memory_hierarchy: MemoryHierarchyConstraint,
}

/// Memory hierarchy constraints
#[derive(Debug)]
pub struct MemoryHierarchyConstraint {
    /// L1 cache limit
    pub l1_cache_limit: usize,
    /// L2 cache limit
    pub l2_cache_limit: usize,
    /// Shared memory limit
    pub shared_memory_limit: usize,
    /// Global memory bandwidth
    pub global_memory_bandwidth: f64,
}

/// Performance constraint rule
#[derive(Debug)]
pub struct PerformanceConstraintRule {
    /// Minimum performance improvement required
    pub min_improvement: f64,
    /// Maximum fusion complexity allowed
    pub max_complexity: f64,
    /// Thread divergence threshold
    pub divergence_threshold: f64,
}

/// Performance model for operations
#[derive(Debug)]
pub struct PerformanceModel {
    /// Execution time predictor
    pub execution_time_fn: fn(&TensorShape, &TensorShape) -> f64,
    /// Memory bandwidth utilization
    pub bandwidth_utilization: f64,
    /// Compute utilization
    pub compute_utilization: f64,
    /// Accuracy of the model
    pub model_accuracy: f64,
}

/// Fusion optimization parameters
#[derive(Debug)]
pub struct FusionOptimizationParams {
    /// Weight for performance improvement
    pub performance_weight: f64,
    /// Weight for memory savings
    pub memory_weight: f64,
    /// Weight for complexity penalty
    pub complexity_weight: f64,
    /// Maximum fusion depth
    pub max_fusion_depth: usize,
    /// Enable aggressive optimization
    pub aggressive_optimization: bool,
}

impl<T> AdvancedGpuKernelFusion<T>
where
    T: Float + NumAssign + Zero + Send + Sync + Debug + 'static,
{
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            operation_graph: Arc::new(RwLock::new(OperationDependencyGraph::new())),
            fusion_optimizer: Arc::new(Mutex::new(KernelFusionEngine::new()?)),
        })
    }

    /// Add operation to the fusion graph
    pub fn add_operation(&self, operation: OperationNode<T>) -> LinalgResult<usize> {
        let mut graph = self.operation_graph.write().expect("Operation failed");
        let id = operation.id;
        graph.nodes.push(operation);
        Ok(id)
    }

    /// Add dependency between operations
    pub fn add_dependency(&self, edge: DependencyEdge) -> LinalgResult<()> {
        let mut graph = self.operation_graph.write().expect("Operation failed");
        graph.edges.push(edge);
        Ok(())
    }

    /// Analyze fusion opportunities
    pub fn analyze_fusion_opportunities(&self) -> LinalgResult<Vec<FusionCandidate>> {
        let graph = self.operation_graph.read().expect("Operation failed");
        let optimizer = self.fusion_optimizer.lock().expect("Operation failed");

        let mut candidates = Vec::new();

        // Find connected components that can be fused
        for (i, node1) in graph.nodes.iter().enumerate() {
            for (j, node2) in graph.nodes.iter().enumerate().skip(i + 1) {
                if optimizer.can_fuse_operations(node1, node2) {
                    let benefit = optimizer.estimate_fusion_benefit(node1, node2);
                    let memory_savings = optimizer.estimate_memory_savings(node1, node2);

                    candidates.push(FusionCandidate {
                        operations: vec![node1.id, node2.id],
                        benefit_score: benefit,
                        memory_savings,
                        complexity: 1.0, // Simple binary fusion
                    });
                }
            }
        }

        Ok(candidates)
    }
}

impl<T> OperationDependencyGraph<T> {
    pub fn new() -> Self {
        Self {
            nodes: Vec::new(),
            edges: Vec::new(),
            fusion_candidates: Vec::new(),
        }
    }
}

impl KernelFusionEngine {
    pub fn new() -> LinalgResult<Self> {
        Ok(Self {
            fusion_strategies: vec![
                FusionStrategy::ElementwiseFusion,
                FusionStrategy::MatrixOperationFusion,
                FusionStrategy::ReductionFusion,
            ],
            fusion_rules: FusionRuleSet::default(),
            performance_models: HashMap::new(),
            optimization_params: FusionOptimizationParams::default(),
        })
    }

    fn can_fuse_operations<T>(&self, op1: &OperationNode<T>, op2: &OperationNode<T>) -> bool {
        // Check if operations are compatible for fusion
        match (&op1.op_type, &op2.op_type) {
            (
                GpuOperationType::ElementwiseAddition,
                GpuOperationType::ElementwiseMultiplication,
            ) => true,
            (GpuOperationType::MatrixMultiplication, GpuOperationType::MatrixAddition) => true,
            (GpuOperationType::MatrixTranspose, GpuOperationType::MatrixMultiplication) => true,
            _ => false,
        }
    }

    fn estimate_fusion_benefit<T>(&self, op1: &OperationNode<T>, op2: &OperationNode<T>) -> f64 {
        // Simplified performance benefit estimation
        let memory_transfer_saved =
            op1.output_shape.dimensions.iter().product::<usize>() as f64 * 4.0;
        memory_transfer_saved / 1e9 // Benefit in GB/s saved
    }

    fn estimate_memory_savings<T>(&self, op1: &OperationNode<T>, op2: &OperationNode<T>) -> usize {
        // Memory saved by not storing intermediate result
        op1.output_shape.dimensions.iter().product::<usize>() * 4
    }
}

// Default implementations
impl Default for FusionRuleSet {
    fn default() -> Self {
        Self {
            compatibility_rules: HashMap::new(),
            memory_rules: Vec::new(),
            performance_rules: Vec::new(),
        }
    }
}

impl Default for FusionOptimizationParams {
    fn default() -> Self {
        Self {
            performance_weight: 0.5,
            memory_weight: 0.3,
            complexity_weight: 0.2,
            max_fusion_depth: 5,
            aggressive_optimization: false,
        }
    }
}

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

    #[test]
    fn test_kernel_fusion_engine_creation() {
        let engine = KernelFusionEngine::new().expect("Operation failed");
        assert_eq!(engine.fusion_strategies.len(), 3);
    }

    #[test]
    fn test_operation_dependency_graph() {
        let graph = OperationDependencyGraph::<f32>::new();
        assert!(graph.nodes.is_empty());
        assert!(graph.edges.is_empty());
    }

    #[test]
    fn test_advanced_gpu_kernel_fusion_creation() {
        let fusion = AdvancedGpuKernelFusion::<f32>::new().expect("Operation failed");
        assert!(fusion
            .operation_graph
            .read()
            .expect("Operation failed")
            .nodes
            .is_empty());
    }
}