optirs-tpu 0.3.2

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::{HashMap, HashSet, VecDeque};

use super::super::frontend::{
    OperandId, OperationAttributes, OperationId, OperationType, XLAComputation, XLAOperation,
};
use super::OptimizationPipelineConfig;
use crate::error::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>,

    /// Upper bound on the number of operations in a single fused cluster
    max_cluster_size: usize,
}

/// 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 (fused, _changed) = self.fuse_kernels_tracked(computation)?;
        Ok(fused)
    }

    /// Fuse kernels, reporting whether the computation actually changed.
    ///
    /// `fusions_by_type` counts fusions that were *materialized*, not
    /// opportunities that were merely enumerated. Strategies that are not
    /// implemented contribute no entry at all rather than reporting the size of
    /// their pattern table as if it were work performed.
    pub fn fuse_kernels_tracked(
        &mut self,
        computation: XLAComputation<T>,
    ) -> Result<(XLAComputation<T>, bool)> {
        let mut current_computation = computation;
        self.fusion_stats.ops_before_fusion = current_computation.operations.len();
        self.fusion_stats.fusions_by_type.clear();

        // Elementwise fusion is the one strategy with a real implementation.
        if self.config.enable_elementwise_fusion {
            let fused = self
                .elementwise_fusion
                .apply_fusion_tracked(&mut current_computation)?;
            if fused > 0 {
                self.fusion_stats
                    .fusions_by_type
                    .insert("elementwise".to_string(), fused);
            }
        }

        self.fusion_stats.ops_after_fusion = current_computation.operations.len();
        self.fusion_stats.total_fusions = self.fusion_stats.fusions_by_type.values().sum();
        self.fusion_stats.memory_savings = self
            .elementwise_fusion
            .clusters
            .iter()
            .map(|cluster| cluster.memory_requirements)
            .sum();
        self.fusion_stats.estimated_speedup = self
            .elementwise_fusion
            .clusters
            .iter()
            .map(|cluster| cluster.estimated_benefit)
            .sum::<f64>();

        Ok((current_computation, self.fusion_stats.total_fusions > 0))
    }

    /// Fusion strategies that are configured but not implemented.
    ///
    /// Producer-consumer, loop, multi-output, convolution and custom fusion
    /// have no implementation in this crate. Enabling them is reported here
    /// instead of being silently accepted while the pass does nothing.
    pub fn unimplemented_strategies(&self) -> Vec<&'static str> {
        let mut unimplemented = Vec::new();
        if self.config.enable_producer_consumer_fusion {
            unimplemented.push("producer_consumer");
        }
        if self.config.enable_loop_fusion {
            unimplemented.push("loop");
        }
        if self.config.enable_multi_output_fusion {
            unimplemented.push("multi_output");
        }
        if self.config.enable_convolution_fusion {
            unimplemented.push("convolution");
        }
        unimplemented
    }

    /// 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,
            max_cluster_size: config.max_cluster_size.max(2),
        }
    }

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

    /// Apply elementwise fusion, reporting how many clusters were actually
    /// materialized into fused operations.
    pub fn apply_fusion_tracked(&mut self, computation: &mut XLAComputation<T>) -> Result<usize> {
        self.find_elementwise_clusters(computation)?;
        self.create_fused_operations(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
    ///
    /// The cluster grows backwards through producers. A producer joins only if
    /// fusing it is legal (see [`Self::can_fuse_operations`]); otherwise its
    /// result stays an external input of the cluster.
    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 cluster_set: HashSet<OperationId> = HashSet::new();
        cluster_set.insert(start_op.id);

        let mut queue = VecDeque::new();
        let mut inputs: Vec<OperandId> = Vec::new();

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

        while let Some(op_id) = queue.pop_front() {
            let Some(operation) = computation.operations.iter().find(|op| op.id == op_id) else {
                continue;
            };

            for &input_id in &operation.inputs {
                let producer = self.find_producer_operation(input_id, computation);

                let fusible = match producer {
                    Some(producer) => {
                        self.is_elementwise_operation(&producer.op_type)
                            && !visited.contains(&producer.id)
                            && cluster_ops.len() < self.max_cluster_size
                            && self.can_fuse_operations(producer, operation, computation)
                    }
                    None => false,
                };

                match (fusible, producer) {
                    (true, Some(producer)) => {
                        cluster_ops.push(producer.id);
                        cluster_set.insert(producer.id);
                        queue.push_back(producer.id);
                        visited.insert(producer.id);
                    }
                    _ => {
                        if !inputs.contains(&input_id) {
                            inputs.push(input_id);
                        }
                    }
                }
            }
        }

        // Operands the cluster produces that something outside still needs.
        // Anything else is purely internal and disappears into the fused body.
        let declared_outputs: HashSet<OperandId> =
            computation.outputs.iter().map(|o| o.operand).collect();

        let mut outputs: Vec<OperandId> = Vec::new();
        for &op_id in &cluster_ops {
            let Some(operation) = computation.operations.iter().find(|op| op.id == op_id) else {
                continue;
            };

            let escapes = declared_outputs.contains(&operation.output)
                || computation.operations.iter().any(|other| {
                    !cluster_set.contains(&other.id) && other.inputs.contains(&operation.output)
                });

            if escapes && !outputs.contains(&operation.output) {
                outputs.push(operation.output);
            }
        }

        // Drop any operand that is both produced and consumed inside the
        // cluster from the external input list.
        let produced_inside: HashSet<OperandId> = cluster_ops
            .iter()
            .filter_map(|id| computation.operations.iter().find(|op| op.id == *id))
            .map(|op| op.output)
            .collect();
        inputs.retain(|operand| !produced_inside.contains(operand));

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

        let cluster = FusionCluster {
            id: format!("elementwise_cluster_{}", start_op.id.0),
            operations: cluster_ops,
            inputs,
            outputs,
            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)
    }

    /// Legality check for fusing `producer` into the consumer's cluster.
    ///
    /// Two conditions must hold:
    ///
    /// 1. **Shape compatibility** — elementwise fusion evaluates the whole
    ///    cluster over one iteration space, so producer and consumer must agree
    ///    on their output shape.
    /// 2. **No external consumers** — if anything other than `consumer` reads
    ///    the producer's result, fusing it would either duplicate the
    ///    computation or leave a dangling operand.
    ///
    /// Returning `true` unconditionally (the previous behaviour) produced
    /// clusters that silently changed program semantics.
    fn can_fuse_operations(
        &self,
        producer: &XLAOperation<T>,
        consumer: &XLAOperation<T>,
        computation: &XLAComputation<T>,
    ) -> bool {
        let producer_shape = computation.operands.get(&producer.output).map(|o| &o.shape);
        let consumer_shape = computation.operands.get(&consumer.output).map(|o| &o.shape);

        match (producer_shape, consumer_shape) {
            (Some(a), Some(b)) if a.dimensions == b.dimensions => {}
            _ => return false,
        }

        // A value the computation returns must remain individually addressable.
        if computation
            .outputs
            .iter()
            .any(|output| output.operand == producer.output)
        {
            return false;
        }

        let external_consumers = computation
            .operations
            .iter()
            .filter(|op| op.id != consumer.id && op.inputs.contains(&producer.output))
            .count();

        external_consumers == 0
    }

    /// 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
    ///
    /// Fusing `n` operations removes `n - 1` intermediate round trips to
    /// memory. `saturating_sub` keeps a single-operation cluster from
    /// underflowing `usize`.
    fn estimate_elementwise_benefit(&self, operations: &[OperationId]) -> f64 {
        operations.len().saturating_sub(1) as f64 * 0.2
    }

    /// Estimate the live memory a cluster needs, from real operand extents.
    fn estimate_memory_requirements(
        &self,
        operations: &[OperationId],
        computation: &XLAComputation<T>,
    ) -> usize {
        operations
            .iter()
            .filter_map(|id| computation.operations.iter().find(|op| op.id == *id))
            .filter_map(|op| computation.operands.get(&op.output))
            .map(|operand| {
                operand
                    .shape
                    .element_count
                    .saturating_mul(std::mem::size_of::<T>())
            })
            .sum()
    }

    /// Materialize each cluster as a fused operation.
    ///
    /// Clusters with several escaping results emit a `Tuple`-producing fused
    /// operation followed by one `GetTupleElement` per result, so that every
    /// original operand keeps a definition. The previous implementation kept
    /// only `outputs[0]` and silently dropped the rest.
    ///
    /// Returns the number of clusters actually fused.
    fn create_fused_operations(&self, computation: &mut XLAComputation<T>) -> Result<usize> {
        let mut fused = 0usize;

        for cluster in &self.clusters {
            if cluster.operations.len() < 2 {
                continue;
            }

            let Some(&primary_output) = cluster.outputs.first() else {
                // A cluster nothing reads is dead code, not a fusion
                // opportunity; leave it for dead-code elimination.
                continue;
            };

            // Insert where the last cluster member sat: in a dependency-ordered
            // operation list every external input is defined before that point,
            // and every consumer comes after it.
            let insert_at = computation
                .operations
                .iter()
                .rposition(|op| cluster.operations.contains(&op.id))
                .map(|pos| pos + 1)
                .unwrap_or(computation.operations.len());

            let mut new_ops: Vec<XLAOperation<T>> = Vec::new();
            let mut next_op_id = computation.next_free_operation_id().0;

            let fused_custom = super::super::frontend::graph_capture::CustomOperation {
                name: format!("fused_{}", cluster.id),
                custom_attributes: HashMap::new(),
                backend_config: Some("elementwise_fusion".to_string()),
            };

            if cluster.outputs.len() == 1 {
                new_ops.push(XLAOperation {
                    id: super::super::frontend::graph_capture::OperationId(next_op_id),
                    op_type: OperationType::Custom(fused_custom),
                    inputs: cluster.inputs.clone(),
                    output: primary_output,
                    attributes: OperationAttributes::default(),
                    performance: Default::default(),
                    memory_requirements: Default::default(),
                    source_location: None,
                    _phantom: std::marker::PhantomData,
                });
            } else {
                // Multi-output cluster: the fused op yields a tuple, and each
                // original operand is re-defined by a GetTupleElement.
                let tuple_operand = computation.allocate_operand_id();
                let tuple_shape = {
                    let mut shape = super::super::frontend::graph_capture::TensorShape::default();
                    shape.tuple_shapes = cluster
                        .outputs
                        .iter()
                        .filter_map(|id| computation.operands.get(id))
                        .map(|operand| operand.shape.clone())
                        .collect();
                    shape
                };

                let template = computation.operands.get(&primary_output).cloned();
                let tuple_operand_value = super::super::frontend::graph_capture::Operand {
                    id: tuple_operand,
                    shape: tuple_shape,
                    layout: template
                        .as_ref()
                        .map(|o| o.layout.clone())
                        .unwrap_or_default(),
                    dtype: template
                        .as_ref()
                        .map(|o| o.dtype)
                        .unwrap_or(super::super::frontend::graph_capture::DataType::F32),
                    metadata: Default::default(),
                    _phantom: std::marker::PhantomData,
                };
                computation
                    .operands
                    .insert(tuple_operand, tuple_operand_value);

                new_ops.push(XLAOperation {
                    id: super::super::frontend::graph_capture::OperationId(next_op_id),
                    op_type: OperationType::Custom(fused_custom),
                    inputs: cluster.inputs.clone(),
                    output: tuple_operand,
                    attributes: OperationAttributes::default(),
                    performance: Default::default(),
                    memory_requirements: Default::default(),
                    source_location: None,
                    _phantom: std::marker::PhantomData,
                });

                for (index, &operand) in cluster.outputs.iter().enumerate() {
                    next_op_id = next_op_id.saturating_add(1);
                    let mut attributes = OperationAttributes::default();
                    attributes.attributes.insert(
                        "tuple_index".to_string(),
                        super::super::frontend::graph_capture::AttributeValue::Int(index as i64),
                    );

                    new_ops.push(XLAOperation {
                        id: super::super::frontend::graph_capture::OperationId(next_op_id),
                        op_type: OperationType::GetTupleElement,
                        inputs: vec![tuple_operand],
                        output: operand,
                        attributes,
                        performance: Default::default(),
                        memory_requirements: Default::default(),
                        source_location: None,
                        _phantom: std::marker::PhantomData,
                    });
                }
            }

            // Remove the originals, then splice the replacement in.
            let removed_before_insert = computation.operations
                [..insert_at.min(computation.operations.len())]
                .iter()
                .filter(|op| cluster.operations.contains(&op.id))
                .count();

            computation
                .operations
                .retain(|op| !cluster.operations.contains(&op.id));

            let splice_at = insert_at
                .saturating_sub(removed_before_insert)
                .min(computation.operations.len());
            for (offset, op) in new_ops.into_iter().enumerate() {
                computation.operations.insert(splice_at + offset, op);
            }

            fused += 1;
        }

        if fused > 0 {
            computation.rebuild_dependencies();
        }

        Ok(fused)
    }
}

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,
        }
    }

    /// Run producer-consumer chain detection over a computation.
    ///
    /// This is a *detection* pass: it identifies the chains and scores them, and
    /// returns the computation unchanged. Materializing a chain into a single
    /// fused operation is [`ElementwiseFusionPass`]'s job -- it already builds
    /// real fused operations out of elementwise clusters, and duplicating that
    /// rewrite here would fuse the same operations twice.
    pub fn apply_fusion(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        self.find_producer_consumer_chains(&computation)?;
        Ok(computation)
    }

    /// Chains found by the most recent [`Self::apply_fusion`] call, highest
    /// scoring first.
    pub fn chains(&self) -> &[ProducerConsumerChain] {
        &self.chains
    }

    /// Find producer-consumer chains.
    ///
    /// A chain is a maximal run of operations where each operation's output
    /// operand is consumed by exactly one other operation, so the intermediate
    /// tensor never has to be written out to memory if the pair is fused. The
    /// run stops at `max_chain_length`, at an operand with several consumers,
    /// and at an operand that is a declared computation output (which must be
    /// materialized regardless).
    ///
    /// The score is the real memory traffic a fusion would save: the byte size
    /// of every intermediate tensor inside the chain.
    fn find_producer_consumer_chains(&mut self, computation: &XLAComputation<T>) -> Result<()> {
        self.chains.clear();
        if self.max_chain_length < 2 {
            return Ok(());
        }

        // How many operations consume each operand, and which operation
        // produces it.
        let mut consumers: HashMap<OperandId, Vec<OperationId>> = HashMap::new();
        let mut producer: HashMap<OperandId, OperationId> = HashMap::new();
        for operation in &computation.operations {
            producer.insert(operation.output, operation.id);
            for input in &operation.inputs {
                consumers.entry(*input).or_default().push(operation.id);
            }
        }

        let by_id: HashMap<OperationId, &XLAOperation<T>> = computation
            .operations
            .iter()
            .map(|operation| (operation.id, operation))
            .collect();

        let outputs: HashSet<OperandId> = computation
            .outputs
            .iter()
            .map(|spec| spec.operand)
            .collect();

        let mut claimed: HashSet<OperationId> = HashSet::new();

        for operation in &computation.operations {
            if claimed.contains(&operation.id) {
                continue;
            }

            let mut chain = vec![operation.id];
            let mut score = 0.0f64;
            let mut current = operation;

            while chain.len() < self.max_chain_length {
                // The intermediate must be consumed exactly once and must not be
                // a computation output.
                if outputs.contains(&current.output) {
                    break;
                }
                let Some(next_ids) = consumers.get(&current.output) else {
                    break;
                };
                if next_ids.len() != 1 {
                    break;
                }
                let Some(next) = next_ids.first().and_then(|id| by_id.get(id)).copied() else {
                    break;
                };
                if claimed.contains(&next.id) {
                    break;
                }

                // Bytes that would not have to round-trip through memory.
                if let Some(operand) = computation.operands.get(&current.output) {
                    score += operand.shape.element_count as f64;
                }

                chain.push(next.id);
                current = next;
            }

            if chain.len() >= 2 {
                for id in &chain {
                    claimed.insert(*id);
                }
                // Every intermediate that stays in registers is memory the
                // fused kernel never touches.
                let memory_reduction = score as usize;
                self.chains.push(ProducerConsumerChain {
                    operations: chain,
                    score,
                    memory_reduction,
                });
            }
        }

        self.chains.sort_by(|a, b| b.score.total_cmp(&a.score));
        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));
    }
}