optirs-tpu 0.3.1

use std::fmt::Debug;
// Kernel fusion optimization for XLA computations
//
// This module implements various kernel fusion strategies including
// elementwise fusion, producer-consumer fusion, loop fusion, and
// multi-output fusion to reduce memory traffic and improve performance.

use scirs2_core::numeric::Float;
use std::collections::{BTreeMap, HashMap, HashSet, VecDeque};

use super::super::frontend::{
    DataType, OperandId, OperationAttributes, OperationId, OperationType, TensorShape,
    XLAComputation, XLAOperation,
};
use super::{OptimizationPass, OptimizationPipelineConfig};
use crate::error::{OptimError, Result};

/// Kernel fusion engine for XLA computations
pub struct KernelFusionEngine<T: Float + Debug + Send + Sync + 'static> {
    /// Fusion configuration
    config: FusionConfig,

    /// Elementwise fusion pass
    elementwise_fusion: ElementwiseFusionPass<T>,

    /// Producer-consumer fusion pass
    producer_consumer_fusion: ProducerConsumerFusionPass<T>,

    /// Loop fusion pass
    loop_fusion: LoopFusionPass<T>,

    /// Multi-output fusion pass
    multi_output_fusion: MultiOutputFusionPass<T>,

    /// Convolution fusion pass
    convolution_fusion: ConvolutionFusionPass<T>,

    /// Custom fusion pass
    custom_fusion: CustomFusionPass<T>,

    /// Fusion statistics
    fusion_stats: FusionStatistics,
}

/// Fusion configuration
#[derive(Debug, Clone)]
pub struct FusionConfig {
    /// Enable elementwise fusion
    pub enable_elementwise_fusion: bool,

    /// Enable producer-consumer fusion
    pub enable_producer_consumer_fusion: bool,

    /// Enable loop fusion
    pub enable_loop_fusion: bool,

    /// Enable multi-output fusion
    pub enable_multi_output_fusion: bool,

    /// Enable convolution fusion
    pub enable_convolution_fusion: bool,

    /// Maximum fusion cluster size
    pub max_cluster_size: usize,

    /// Memory threshold for fusion (bytes)
    pub memory_threshold: usize,

    /// Enable aggressive fusion
    pub aggressive_fusion: bool,

    /// Minimum operations for fusion
    pub min_ops_for_fusion: usize,
}

/// Fusion statistics tracking
#[derive(Debug, Default)]
pub struct FusionStatistics {
    /// Total fusions performed
    pub total_fusions: usize,

    /// Fusions by type
    pub fusions_by_type: HashMap<String, usize>,

    /// Memory savings (bytes)
    pub memory_savings: usize,

    /// Estimated speedup
    pub estimated_speedup: f64,

    /// Operations before fusion
    pub ops_before_fusion: usize,

    /// Operations after fusion
    pub ops_after_fusion: usize,
}

/// Fusion cluster representing a group of operations to be fused
#[derive(Debug, Clone)]
pub struct FusionCluster<T: Float + Debug + Send + Sync + 'static> {
    /// Cluster identifier
    pub id: String,

    /// Operations in the cluster
    pub operations: Vec<OperationId>,

    /// Cluster inputs (from outside the cluster)
    pub inputs: Vec<OperandId>,

    /// Cluster outputs (used outside the cluster)
    pub outputs: Vec<OperandId>,

    /// Fusion type
    pub fusion_type: FusionType,

    /// Estimated benefit
    pub estimated_benefit: f64,

    /// Memory requirements
    pub memory_requirements: usize,

    /// Execution characteristics
    pub execution_info: ClusterExecutionInfo,

    /// Phantom data for type parameter
    _phantom: std::marker::PhantomData<T>,
}

/// Types of fusion strategies
#[derive(Debug, Clone, PartialEq)]
pub enum FusionType {
    /// Elementwise operations fusion
    Elementwise,

    /// Producer-consumer chain fusion
    ProducerConsumer,

    /// Loop-level fusion
    Loop,

    /// Multi-output fusion
    MultiOutput,

    /// Convolution-related fusion
    Convolution,

    /// Custom fusion pattern
    Custom(String),
}

/// Execution characteristics for fusion cluster
#[derive(Debug, Clone, Default)]
pub struct ClusterExecutionInfo {
    /// Estimated execution time (microseconds)
    pub execution_time_us: u64,

    /// Memory bandwidth utilization
    pub memory_bandwidth_util: f64,

    /// Compute utilization
    pub compute_utilization: f64,

    /// Parallelization factor
    pub parallelization_factor: f64,
}

/// Elementwise fusion pass
pub struct ElementwiseFusionPass<T: Float + Debug + Send + Sync + 'static> {
    /// Fusion clusters found
    clusters: Vec<FusionCluster<T>>,

    /// Supported elementwise operations
    supported_ops: HashSet<OperationType>,
}

/// Producer-consumer fusion pass
pub struct ProducerConsumerFusionPass<T: Float + Debug + Send + Sync + 'static> {
    /// Producer-consumer chains
    chains: Vec<ProducerConsumerChain>,

    /// Maximum chain length
    max_chain_length: usize,

    _phantom: std::marker::PhantomData<T>,
}

/// Producer-consumer chain
#[derive(Debug)]
pub struct ProducerConsumerChain {
    /// Operations in the chain
    pub operations: Vec<OperationId>,

    /// Chain score (fusion benefit)
    pub score: f64,

    /// Memory footprint reduction
    pub memory_reduction: usize,
}

/// Loop fusion pass
pub struct LoopFusionPass<T: Float + Debug + Send + Sync + 'static> {
    /// Detected loops
    loops: Vec<LoopStructure>,

    /// Loop fusion candidates
    fusion_candidates: Vec<LoopFusionCandidate>,

    _phantom: std::marker::PhantomData<T>,
}

/// Loop structure representation
#[derive(Debug)]
pub struct LoopStructure {
    /// Loop identifier
    pub id: String,

    /// Loop body operations
    pub body_operations: Vec<OperationId>,

    /// Loop bounds
    pub bounds: LoopBounds,

    /// Loop iteration count
    pub iteration_count: Option<usize>,
}

/// Loop bounds information
#[derive(Debug)]
pub struct LoopBounds {
    /// Lower bound
    pub lower: i64,

    /// Upper bound
    pub upper: i64,

    /// Step size
    pub step: i64,
}

/// Loop fusion candidate
#[derive(Debug)]
pub struct LoopFusionCandidate {
    /// Loops to be fused
    pub loops: Vec<String>,

    /// Fusion type
    pub fusion_type: LoopFusionType,

    /// Expected benefit
    pub benefit: f64,
}

/// Types of loop fusion
#[derive(Debug)]
pub enum LoopFusionType {
    /// Horizontal fusion (same iteration space)
    Horizontal,

    /// Vertical fusion (nested loops)
    Vertical,

    /// Diagonal fusion (partial overlap)
    Diagonal,
}

/// Multi-output fusion pass
pub struct MultiOutputFusionPass<T: Float + Debug + Send + Sync + 'static> {
    /// Multi-output opportunities
    opportunities: Vec<MultiOutputOpportunity>,

    _phantom: std::marker::PhantomData<T>,
}

/// Multi-output fusion opportunity
#[derive(Debug)]
pub struct MultiOutputOpportunity {
    /// Common computation
    pub common_computation: Vec<OperationId>,

    /// Different outputs
    pub outputs: Vec<OperandId>,

    /// Estimated savings
    pub savings: f64,
}

/// Convolution fusion pass
pub struct ConvolutionFusionPass<T: Float + Debug + Send + Sync + 'static> {
    /// Convolution patterns
    patterns: Vec<ConvolutionPattern>,

    _phantom: std::marker::PhantomData<T>,
}

/// Convolution fusion pattern
#[derive(Debug)]
pub struct ConvolutionPattern {
    /// Pattern name
    pub name: String,

    /// Operations in pattern
    pub operations: Vec<OperationType>,

    /// Fusion benefit
    pub benefit: f64,
}

/// Custom fusion pass
pub struct CustomFusionPass<T: Float + Debug + Send + Sync + 'static> {
    /// Custom patterns
    patterns: Vec<CustomFusionPattern>,

    _phantom: std::marker::PhantomData<T>,
}

/// Custom fusion pattern
#[derive(Debug)]
pub struct CustomFusionPattern {
    /// Pattern name
    pub name: String,

    /// Pattern matching function
    pub matcher: String,

    /// Fusion generator function
    pub generator: String,
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> KernelFusionEngine<T> {
    /// Create new kernel fusion engine
    pub fn new(config: &OptimizationPipelineConfig) -> Self {
        let fusion_config = FusionConfig {
            enable_elementwise_fusion: true,
            enable_producer_consumer_fusion: true,
            enable_loop_fusion: config.aggressive_mode,
            enable_multi_output_fusion: true,
            enable_convolution_fusion: true,
            max_cluster_size: if config.aggressive_mode { 16 } else { 8 },
            memory_threshold: 1024 * 1024, // 1 MB
            aggressive_fusion: config.aggressive_mode,
            min_ops_for_fusion: 2,
        };

        Self {
            config: fusion_config.clone(),
            elementwise_fusion: ElementwiseFusionPass::new(&fusion_config),
            producer_consumer_fusion: ProducerConsumerFusionPass::new(&fusion_config),
            loop_fusion: LoopFusionPass::new(&fusion_config),
            multi_output_fusion: MultiOutputFusionPass::new(&fusion_config),
            convolution_fusion: ConvolutionFusionPass::new(&fusion_config),
            custom_fusion: CustomFusionPass::new(&fusion_config),
            fusion_stats: FusionStatistics::default(),
        }
    }

    /// Fuse kernels in computation
    pub fn fuse_kernels(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        let mut current_computation = computation;
        self.fusion_stats.ops_before_fusion = current_computation.operations.len();

        // Apply elementwise fusion
        if self.config.enable_elementwise_fusion {
            current_computation = self.elementwise_fusion.apply_fusion(current_computation)?;
            self.fusion_stats.fusions_by_type.insert(
                "elementwise".to_string(),
                self.elementwise_fusion.clusters.len(),
            );
        }

        // Apply producer-consumer fusion
        if self.config.enable_producer_consumer_fusion {
            current_computation = self
                .producer_consumer_fusion
                .apply_fusion(current_computation)?;
            self.fusion_stats.fusions_by_type.insert(
                "producer_consumer".to_string(),
                self.producer_consumer_fusion.chains.len(),
            );
        }

        // Apply loop fusion
        if self.config.enable_loop_fusion {
            current_computation = self.loop_fusion.apply_fusion(current_computation)?;
            self.fusion_stats
                .fusions_by_type
                .insert("loop".to_string(), self.loop_fusion.fusion_candidates.len());
        }

        // Apply multi-output fusion
        if self.config.enable_multi_output_fusion {
            current_computation = self.multi_output_fusion.apply_fusion(current_computation)?;
            self.fusion_stats.fusions_by_type.insert(
                "multi_output".to_string(),
                self.multi_output_fusion.opportunities.len(),
            );
        }

        // Apply convolution fusion
        if self.config.enable_convolution_fusion {
            current_computation = self.convolution_fusion.apply_fusion(current_computation)?;
            self.fusion_stats.fusions_by_type.insert(
                "convolution".to_string(),
                self.convolution_fusion.patterns.len(),
            );
        }

        // Apply custom fusion
        current_computation = self.custom_fusion.apply_fusion(current_computation)?;

        self.fusion_stats.ops_after_fusion = current_computation.operations.len();
        self.fusion_stats.total_fusions = self.fusion_stats.fusions_by_type.values().sum();

        Ok(current_computation)
    }

    /// Get fusion statistics
    pub fn get_statistics(&self) -> &FusionStatistics {
        &self.fusion_stats
    }

    /// Reset fusion engine state
    pub fn reset(&mut self) {
        self.elementwise_fusion.clusters.clear();
        self.producer_consumer_fusion.chains.clear();
        self.loop_fusion.loops.clear();
        self.loop_fusion.fusion_candidates.clear();
        self.multi_output_fusion.opportunities.clear();
        self.convolution_fusion.patterns.clear();
        self.custom_fusion.patterns.clear();
        self.fusion_stats = FusionStatistics::default();
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> ElementwiseFusionPass<T> {
    /// Create new elementwise fusion pass
    pub fn new(_config: &FusionConfig) -> Self {
        let mut supported_ops = HashSet::new();
        supported_ops.insert(OperationType::Add);
        supported_ops.insert(OperationType::Multiply);
        supported_ops.insert(OperationType::Subtract);
        supported_ops.insert(OperationType::Divide);
        supported_ops.insert(OperationType::Maximum);
        supported_ops.insert(OperationType::Minimum);
        supported_ops.insert(OperationType::Abs);
        supported_ops.insert(OperationType::Exp);
        supported_ops.insert(OperationType::Log);
        supported_ops.insert(OperationType::Sqrt);

        Self {
            clusters: Vec::new(),
            supported_ops,
        }
    }

    /// Apply elementwise fusion
    pub fn apply_fusion(
        &mut self,
        mut computation: XLAComputation<T>,
    ) -> Result<XLAComputation<T>> {
        self.find_elementwise_clusters(&computation)?;
        self.create_fused_operations(&mut computation)?;
        Ok(computation)
    }

    /// Find elementwise fusion clusters
    fn find_elementwise_clusters(&mut self, computation: &XLAComputation<T>) -> Result<()> {
        self.clusters.clear();
        let mut visited = HashSet::new();

        for operation in &computation.operations {
            if visited.contains(&operation.id) || !self.is_elementwise_operation(&operation.op_type)
            {
                continue;
            }

            let cluster = self.build_elementwise_cluster(operation, computation, &mut visited)?;
            if cluster.operations.len() >= 2 {
                self.clusters.push(cluster);
            }
        }

        Ok(())
    }

    /// Build elementwise cluster starting from an operation
    fn build_elementwise_cluster(
        &self,
        start_op: &XLAOperation<T>,
        computation: &XLAComputation<T>,
        visited: &mut HashSet<OperationId>,
    ) -> Result<FusionCluster<T>> {
        let mut cluster_ops = vec![start_op.id];
        let mut queue = VecDeque::new();
        let mut inputs = HashSet::new();
        let mut outputs = HashSet::new();

        queue.push_back(start_op.id);
        visited.insert(start_op.id);

        while let Some(op_id) = queue.pop_front() {
            if let Some(operation) = computation.operations.iter().find(|op| op.id == op_id) {
                // Add inputs from outside cluster
                for &input_id in &operation.inputs {
                    if let Some(producer) = self.find_producer_operation(input_id, computation) {
                        if self.is_elementwise_operation(&producer.op_type)
                            && !visited.contains(&producer.id)
                            && self.can_fuse_operations(operation, producer)
                        {
                            cluster_ops.push(producer.id);
                            queue.push_back(producer.id);
                            visited.insert(producer.id);
                        } else {
                            inputs.insert(input_id);
                        }
                    } else {
                        inputs.insert(input_id);
                    }
                }

                // Check if output is used outside cluster
                outputs.insert(operation.output);
            }
        }

        let estimated_benefit = self.estimate_elementwise_benefit(&cluster_ops);
        let memory_requirements = self.estimate_memory_requirements(&cluster_ops);

        let cluster = FusionCluster {
            id: format!("elementwise_cluster_{}", start_op.id.0),
            operations: cluster_ops,
            inputs: inputs.into_iter().collect(),
            outputs: outputs.into_iter().collect(),
            fusion_type: FusionType::Elementwise,
            estimated_benefit,
            memory_requirements,
            execution_info: ClusterExecutionInfo::default(),
            _phantom: std::marker::PhantomData,
        };

        Ok(cluster)
    }

    /// Check if operation is elementwise
    fn is_elementwise_operation(&self, op_type: &OperationType) -> bool {
        self.supported_ops.contains(op_type)
    }

    /// Check if two operations can be fused
    fn can_fuse_operations(&self, _op1: &XLAOperation<T>, _op2: &XLAOperation<T>) -> bool {
        // Simplified fusion compatibility check
        true
    }

    /// Find producer operation for operand
    fn find_producer_operation<'a>(
        &self,
        operand_id: OperandId,
        computation: &'a XLAComputation<T>,
    ) -> Option<&'a XLAOperation<T>> {
        computation
            .operations
            .iter()
            .find(|op| op.output == operand_id)
    }

    /// Estimate benefit of elementwise fusion
    fn estimate_elementwise_benefit(&self, operations: &[OperationId]) -> f64 {
        // Benefit increases with cluster size (memory access reduction)
        (operations.len() - 1) as f64 * 0.2
    }

    /// Estimate memory requirements for cluster
    fn estimate_memory_requirements(&self, operations: &[OperationId]) -> usize {
        // Simplified estimation
        operations.len() * 1024 // 1KB per operation
    }

    /// Create fused operations in computation
    fn create_fused_operations(&self, computation: &mut XLAComputation<T>) -> Result<()> {
        for cluster in &self.clusters {
            // Remove original operations
            computation
                .operations
                .retain(|op| !cluster.operations.contains(&op.id));

            // Create fused operation
            let fused_op = XLAOperation {
                id: super::super::frontend::graph_capture::OperationId(
                    computation.operations.len(),
                ),
                op_type: OperationType::Custom(
                    super::super::frontend::graph_capture::CustomOperation {
                        name: format!("fused_{}", cluster.id),
                        custom_attributes: HashMap::new(),
                        backend_config: Some("elementwise_fusion".to_string()),
                    },
                ),
                inputs: cluster.inputs.clone(),
                output: cluster.outputs[0], // Use first output as primary
                attributes: OperationAttributes::default(),
                performance: Default::default(),
                memory_requirements: Default::default(),
                source_location: None,
                _phantom: std::marker::PhantomData,
            };

            computation.operations.push(fused_op);
        }

        Ok(())
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync>
    ProducerConsumerFusionPass<T>
{
    /// Create new producer-consumer fusion pass
    pub fn new(config: &FusionConfig) -> Self {
        Self {
            chains: Vec::new(),
            max_chain_length: config.max_cluster_size,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Apply producer-consumer fusion
    pub fn apply_fusion(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        self.find_producer_consumer_chains(&computation)?;
        // Implementation would create fused operations
        Ok(computation)
    }

    /// Find producer-consumer chains
    fn find_producer_consumer_chains(&mut self, _computation: &XLAComputation<T>) -> Result<()> {
        self.chains.clear();
        // Chain detection logic would go here
        Ok(())
    }
}

// Similar implementations for other fusion passes...
impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> LoopFusionPass<T> {
    pub fn new(_config: &FusionConfig) -> Self {
        Self {
            loops: Vec::new(),
            fusion_candidates: Vec::new(),
            _phantom: std::marker::PhantomData,
        }
    }

    pub fn apply_fusion(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        // Loop fusion implementation
        Ok(computation)
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> MultiOutputFusionPass<T> {
    pub fn new(_config: &FusionConfig) -> Self {
        Self {
            opportunities: Vec::new(),
            _phantom: std::marker::PhantomData,
        }
    }

    pub fn apply_fusion(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        // Multi-output fusion implementation
        Ok(computation)
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> ConvolutionFusionPass<T> {
    pub fn new(_config: &FusionConfig) -> Self {
        let patterns = vec![ConvolutionPattern {
            name: "conv_bias_relu".to_string(),
            operations: vec![
                OperationType::Convolution(
                    super::super::frontend::graph_capture::ConvolutionConfig {
                        strides: vec![1, 1],
                        padding: super::super::frontend::graph_capture::PaddingConfig::Same,
                        dilation: vec![1, 1],
                        feature_group_count: 1,
                        batch_group_count: 1,
                    },
                ),
                OperationType::Add,     // Bias
                OperationType::Maximum, // ReLU
            ],
            benefit: 0.3,
        }];

        Self {
            patterns,
            _phantom: std::marker::PhantomData,
        }
    }

    pub fn apply_fusion(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        // Convolution fusion implementation
        Ok(computation)
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> CustomFusionPass<T> {
    pub fn new(_config: &FusionConfig) -> Self {
        Self {
            patterns: Vec::new(),
            _phantom: std::marker::PhantomData,
        }
    }

    pub fn apply_fusion(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        // Custom fusion implementation
        Ok(computation)
    }
}

#[cfg(test)]
mod tests {
    use super::super::{ComputeCapability, HardwareTarget};
    use super::*;

    #[test]
    fn test_kernel_fusion_engine_creation() {
        let config = OptimizationPipelineConfig {
            optimization_level: crate::main_types::XLAOptimizationLevel::Standard,
            enable_graph_optimization: true,
            enable_kernel_fusion: true,
            enable_memory_optimization: true,
            enable_scheduling_optimization: true,
            max_optimization_time: 300,
            target_hardware: HardwareTarget {
                tpu_version: "v4".to_string(),
                num_cores: 4,
                memory_capacity: 1024 * 1024 * 1024,
                memory_bandwidth: 1600.0,
                compute_capability: ComputeCapability {
                    matrix_unit_dims: (128, 128),
                    vector_unit_width: 256,
                    supported_dtypes: vec!["F32".to_string()],
                    special_instructions: vec![],
                },
            },
            custom_passes: vec![],
            aggressive_mode: false,
            debug_mode: false,
        };

        let engine: KernelFusionEngine<f32> = KernelFusionEngine::new(&config);
        assert_eq!(engine.fusion_stats.total_fusions, 0);
        assert!(engine.config.enable_elementwise_fusion);
    }

    #[test]
    fn test_elementwise_fusion_pass() {
        let config = FusionConfig {
            enable_elementwise_fusion: true,
            enable_producer_consumer_fusion: true,
            enable_loop_fusion: false,
            enable_multi_output_fusion: true,
            enable_convolution_fusion: true,
            max_cluster_size: 8,
            memory_threshold: 1024 * 1024,
            aggressive_fusion: false,
            min_ops_for_fusion: 2,
        };

        let pass: ElementwiseFusionPass<f32> = ElementwiseFusionPass::new(&config);
        assert!(!pass.supported_ops.is_empty());
        assert!(pass.supported_ops.contains(&OperationType::Add));
        assert!(pass.supported_ops.contains(&OperationType::Multiply));
    }
}