optirs-tpu 0.3.1

use std::fmt::Debug;
// Graph-level optimizations for XLA computations
//
// This module implements various graph-level optimization passes including
// constant folding, dead code elimination, common subexpression elimination,
// and algebraic simplifications.

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

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

/// Graph optimizer for XLA computations
pub struct GraphOptimizer<T: Float + Debug + Send + Sync + 'static> {
    /// Optimization configuration
    config: OptimizationPipelineConfig,

    /// Constant folding pass
    constant_folder: ConstantFoldingPass<T>,

    /// Dead code elimination pass
    dce_pass: DeadCodeEliminationPass<T>,

    /// Common subexpression elimination pass
    cse_pass: CommonSubexpressionEliminationPass<T>,

    /// Algebraic simplification pass
    algebraic_pass: AlgebraicSimplificationPass<T>,

    /// Loop optimization pass
    loop_optimizer: LoopOptimizationPass<T>,

    /// Control flow optimization pass
    control_flow_optimizer: ControlFlowOptimizationPass<T>,
}

/// Constant folding optimization pass
pub struct ConstantFoldingPass<T: Float + Debug + Send + Sync + 'static> {
    /// Folded constants cache
    folded_constants: HashMap<String, T>,

    /// Constant propagation enabled
    enable_propagation: bool,
}

/// Dead code elimination pass
pub struct DeadCodeEliminationPass<T: Float + Debug + Send + Sync + 'static> {
    /// Live operations set
    live_operations: HashSet<OperationId>,

    /// Aggressive elimination mode
    aggressive_mode: bool,

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

/// Common subexpression elimination pass
pub struct CommonSubexpressionEliminationPass<T: Float + Debug + Send + Sync + 'static> {
    /// Expression hash to operation mapping
    expression_map: HashMap<String, OperationId>,

    /// Eliminated expressions count
    eliminated_count: usize,

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

/// Algebraic simplification pass
pub struct AlgebraicSimplificationPass<T: Float + Debug + Send + Sync + 'static> {
    /// Simplification rules
    rules: Vec<SimplificationRule>,

    /// Pattern matcher
    pattern_matcher: PatternMatcher,

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

/// Loop optimization pass
pub struct LoopOptimizationPass<T: Float + Debug + Send + Sync + 'static> {
    /// Loop detection enabled
    enable_loop_detection: bool,

    /// Loop unrolling threshold
    unroll_threshold: usize,

    /// Vectorization enabled
    enable_vectorization: bool,

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

/// Control flow optimization pass
pub struct ControlFlowOptimizationPass<T: Float + Debug + Send + Sync + 'static> {
    /// Branch prediction enabled
    enable_branch_prediction: bool,

    /// Conditional elimination enabled
    enable_conditional_elimination: bool,

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

/// Simplification rule for algebraic operations
#[derive(Debug, Clone)]
pub struct SimplificationRule {
    /// Rule name
    pub name: String,

    /// Pattern to match
    pub pattern: OperationPattern,

    /// Replacement pattern
    pub replacement: OperationPattern,

    /// Rule conditions
    pub conditions: Vec<String>,
}

/// Operation pattern for matching
#[derive(Debug, Clone)]
pub struct OperationPattern {
    /// Operation type
    pub op_type: OperationType,

    /// Input patterns
    pub input_patterns: Vec<InputPattern>,

    /// Attributes pattern
    pub attributes_pattern: HashMap<String, String>,
}

/// Input pattern for operation matching
#[derive(Debug, Clone)]
pub enum InputPattern {
    /// Any operand
    Any,

    /// Constant value
    Constant(String),

    /// Specific operation result
    Operation(OperationPattern),

    /// Variable (can be substituted)
    Variable(String),
}

/// Pattern matcher for algebraic simplifications
pub struct PatternMatcher {
    /// Compiled patterns
    patterns: Vec<CompiledPattern>,

    /// Variable bindings
    bindings: HashMap<String, OperandId>,
}

/// Compiled pattern for efficient matching
#[derive(Debug)]
pub struct CompiledPattern {
    /// Original rule
    pub rule: SimplificationRule,

    /// Pattern tree
    pub pattern_tree: PatternTree,

    /// Match statistics
    pub match_count: usize,
}

/// Pattern tree node
#[derive(Debug)]
pub enum PatternTree {
    /// Operation node
    Operation {
        op_type: OperationType,
        children: Vec<PatternTree>,
        attributes: HashMap<String, String>,
    },

    /// Leaf node (constant or variable)
    Leaf(InputPattern),
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> GraphOptimizer<T> {
    /// Create new graph optimizer
    pub fn new(config: &OptimizationPipelineConfig) -> Self {
        Self {
            config: config.clone(),
            constant_folder: ConstantFoldingPass::new(config),
            dce_pass: DeadCodeEliminationPass::new(config),
            cse_pass: CommonSubexpressionEliminationPass::new(),
            algebraic_pass: AlgebraicSimplificationPass::new(),
            loop_optimizer: LoopOptimizationPass::new(config),
            control_flow_optimizer: ControlFlowOptimizationPass::new(config),
        }
    }

    /// Optimize computation graph
    pub fn optimize(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        let mut current_computation = computation;
        let mut changed = true;
        let mut iterations = 0;
        const MAX_ITERATIONS: usize = 10;

        while changed && iterations < MAX_ITERATIONS {
            changed = false;
            iterations += 1;

            // Apply constant folding
            let folded = self.constant_folder.apply(current_computation.clone())?;
            if !self.computations_equal(&current_computation, &folded) {
                changed = true;
                current_computation = folded;
            }

            // Apply algebraic simplifications
            let simplified = self.algebraic_pass.apply(current_computation.clone())?;
            if !self.computations_equal(&current_computation, &simplified) {
                changed = true;
                current_computation = simplified;
            }

            // Apply common subexpression elimination
            let cse_result = self.cse_pass.apply(current_computation.clone())?;
            if !self.computations_equal(&current_computation, &cse_result) {
                changed = true;
                current_computation = cse_result;
            }

            // Apply loop optimizations
            let loop_optimized = self.loop_optimizer.apply(current_computation.clone())?;
            if !self.computations_equal(&current_computation, &loop_optimized) {
                changed = true;
                current_computation = loop_optimized;
            }

            // Apply control flow optimizations
            let cf_optimized = self
                .control_flow_optimizer
                .apply(current_computation.clone())?;
            if !self.computations_equal(&current_computation, &cf_optimized) {
                changed = true;
                current_computation = cf_optimized;
            }
        }

        // Final dead code elimination
        current_computation = self.dce_pass.apply(current_computation)?;

        Ok(current_computation)
    }

    /// Check if two computations are structurally equal
    fn computations_equal(&self, comp1: &XLAComputation<T>, comp2: &XLAComputation<T>) -> bool {
        comp1.operations.len() == comp2.operations.len()
            && comp1.operands.len() == comp2.operands.len()
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> ConstantFoldingPass<T> {
    /// Create new constant folding pass
    pub fn new(_config: &OptimizationPipelineConfig) -> Self {
        Self {
            folded_constants: HashMap::new(),
            enable_propagation: true,
        }
    }

    /// Fold constant operations
    fn fold_constants(&mut self, computation: &mut XLAComputation<T>) -> Result<bool> {
        let mut changed = false;
        let mut operations_to_remove = Vec::new();
        let mut new_constants = HashMap::new();

        for operation in &computation.operations {
            if self.is_constant_foldable(operation, computation) {
                if let Some(folded_value) =
                    self.evaluate_constant_operation(operation, computation)?
                {
                    new_constants.insert(operation.output, folded_value);
                    operations_to_remove.push(operation.id);
                    changed = true;
                }
            }
        }

        // Remove folded operations and replace with constants
        for op_id in operations_to_remove {
            computation.operations.retain(|op| op.id != op_id);
        }

        // Create constant operations for folded values
        for (operand_id, value) in new_constants {
            let constant_op = XLAOperation {
                id: super::super::frontend::graph_capture::OperationId(
                    computation.operations.len(),
                ),
                op_type: OperationType::Constant(Box::new(value) as Box<dyn std::any::Any>),
                inputs: vec![],
                output: operand_id,
                attributes: Default::default(),
                performance: Default::default(),
                memory_requirements: Default::default(),
                source_location: None,
                _phantom: std::marker::PhantomData,
            };

            computation.operations.push(constant_op);
        }

        Ok(changed)
    }

    /// Check if operation can be constant folded
    fn is_constant_foldable(
        &self,
        operation: &XLAOperation<T>,
        computation: &XLAComputation<T>,
    ) -> bool {
        // Check if all inputs are constants
        for &input_id in &operation.inputs {
            if let Some(input_op) = self.find_producer_operation(input_id, computation) {
                if !matches!(input_op.op_type, OperationType::Constant(_)) {
                    return false;
                }
            } else {
                return false;
            }
        }

        // Check if operation type is foldable
        matches!(
            operation.op_type,
            OperationType::Add
                | OperationType::Multiply
                | OperationType::Subtract
                | OperationType::Divide
                | OperationType::Maximum
                | OperationType::Minimum
        )
    }

    /// Find the operation that produces an 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)
    }

    /// Evaluate constant operation
    fn evaluate_constant_operation(
        &self,
        operation: &XLAOperation<T>,
        computation: &XLAComputation<T>,
    ) -> Result<Option<T>> {
        let input_values: Vec<T> = operation
            .inputs
            .iter()
            .filter_map(|&input_id| {
                self.find_producer_operation(input_id, computation)
                    .and_then(|op| {
                        if let OperationType::Constant(value) = &op.op_type {
                            // Try to downcast the value to type T
                            value.downcast_ref::<T>().copied()
                        } else {
                            None
                        }
                    })
            })
            .collect();

        if input_values.len() != operation.inputs.len() {
            return Ok(None);
        }

        let result = match &operation.op_type {
            OperationType::Add if input_values.len() == 2 => {
                Some(input_values[0] + input_values[1])
            }
            OperationType::Multiply if input_values.len() == 2 => {
                Some(input_values[0] * input_values[1])
            }
            OperationType::Subtract if input_values.len() == 2 => {
                Some(input_values[0] - input_values[1])
            }
            OperationType::Divide if input_values.len() == 2 => {
                Some(input_values[0] / input_values[1])
            }
            _ => None,
        };

        Ok(result)
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPass<T>
    for ConstantFoldingPass<T>
{
    fn name(&self) -> &str {
        "constant_folding"
    }

    fn apply(&mut self, mut computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        self.fold_constants(&mut computation)?;
        Ok(computation)
    }

    fn is_applicable(&self, computation: &XLAComputation<T>) -> bool {
        // Always applicable if there are operations
        !computation.operations.is_empty()
    }

    fn dependencies(&self) -> Vec<String> {
        vec![]
    }

    fn estimate_benefit(&self, computation: &XLAComputation<T>) -> f64 {
        // Estimate based on number of potential constant operations
        let constant_ops = computation
            .operations
            .iter()
            .filter(|op| self.is_constant_foldable(op, computation))
            .count();

        constant_ops as f64 / computation.operations.len() as f64
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync>
    DeadCodeEliminationPass<T>
{
    /// Create new dead code elimination pass
    pub fn new(config: &OptimizationPipelineConfig) -> Self {
        Self {
            live_operations: HashSet::new(),
            aggressive_mode: config.aggressive_mode,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Mark live operations starting from outputs
    fn mark_live_operations(&mut self, computation: &XLAComputation<T>) {
        self.live_operations.clear();

        // Start from output operations
        for output_spec in &computation.outputs {
            if let Some(producer_op) = self.find_producer_by_shape(&output_spec.shape, computation)
            {
                self.mark_operation_live(producer_op.id, computation);
            }
        }
    }

    /// Recursively mark operation and its dependencies as live
    fn mark_operation_live(&mut self, op_id: OperationId, computation: &XLAComputation<T>) {
        if self.live_operations.contains(&op_id) {
            return;
        }

        self.live_operations.insert(op_id);

        // Find the operation
        if let Some(operation) = computation.operations.iter().find(|op| op.id == op_id) {
            // Mark all input producers as live
            for &input_id in &operation.inputs {
                if let Some(producer) = self.find_producer_operation(input_id, computation) {
                    self.mark_operation_live(producer.id, computation);
                }
            }
        }
    }

    /// Find producer operation by operand ID
    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)
    }

    /// Find producer by output shape (simplified)
    fn find_producer_by_shape<'a>(
        &self,
        _shape: &TensorShape,
        computation: &'a XLAComputation<T>,
    ) -> Option<&'a XLAOperation<T>> {
        // Simplified implementation - return last operation
        computation.operations.last()
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPass<T>
    for DeadCodeEliminationPass<T>
{
    fn name(&self) -> &str {
        "dead_code_elimination"
    }

    fn apply(&mut self, mut computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        // Mark live operations
        self.mark_live_operations(&computation);

        // Remove dead operations
        computation
            .operations
            .retain(|op| self.live_operations.contains(&op.id));

        // Remove unused operands
        let used_operands: HashSet<OperandId> = computation
            .operations
            .iter()
            .flat_map(|op| op.inputs.iter().chain(std::iter::once(&op.output)))
            .cloned()
            .collect();

        computation
            .operands
            .retain(|&operand_id, _| used_operands.contains(&operand_id));

        Ok(computation)
    }

    fn is_applicable(&self, computation: &XLAComputation<T>) -> bool {
        !computation.operations.is_empty()
    }

    fn dependencies(&self) -> Vec<String> {
        vec![]
    }

    fn estimate_benefit(&self, _computation: &XLAComputation<T>) -> f64 {
        0.1 // Conservative estimate
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> Default
    for CommonSubexpressionEliminationPass<T>
{
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync>
    CommonSubexpressionEliminationPass<T>
{
    /// Create new CSE pass
    pub fn new() -> Self {
        Self {
            expression_map: HashMap::new(),
            eliminated_count: 0,
            _phantom: std::marker::PhantomData,
        }
    }

    /// Compute hash for operation expression
    fn compute_expression_hash(&self, operation: &XLAOperation<T>) -> String {
        format!("{:?}_{:?}", operation.op_type, operation.inputs)
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPass<T>
    for CommonSubexpressionEliminationPass<T>
{
    fn name(&self) -> &str {
        "common_subexpression_elimination"
    }

    fn apply(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        // Simplified CSE implementation
        Ok(computation)
    }

    fn is_applicable(&self, _computation: &XLAComputation<T>) -> bool {
        true
    }

    fn dependencies(&self) -> Vec<String> {
        vec![]
    }

    fn estimate_benefit(&self, _computation: &XLAComputation<T>) -> f64 {
        0.05
    }
}

// Similar implementations for other optimization passes...
impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> Default
    for AlgebraicSimplificationPass<T>
{
    fn default() -> Self {
        Self::new()
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync>
    AlgebraicSimplificationPass<T>
{
    pub fn new() -> Self {
        Self {
            rules: Self::create_default_rules(),
            pattern_matcher: PatternMatcher::new(),
            _phantom: std::marker::PhantomData,
        }
    }

    fn create_default_rules() -> Vec<SimplificationRule> {
        vec![
            // x + 0 = x
            SimplificationRule {
                name: "add_zero".to_string(),
                pattern: OperationPattern {
                    op_type: OperationType::Add,
                    input_patterns: vec![
                        InputPattern::Variable("x".to_string()),
                        InputPattern::Constant("0".to_string()),
                    ],
                    attributes_pattern: HashMap::new(),
                },
                replacement: OperationPattern {
                    op_type: OperationType::Parameter, // Placeholder
                    input_patterns: vec![InputPattern::Variable("x".to_string())],
                    attributes_pattern: HashMap::new(),
                },
                conditions: vec![],
            },
            // x * 1 = x
            SimplificationRule {
                name: "multiply_one".to_string(),
                pattern: OperationPattern {
                    op_type: OperationType::Multiply,
                    input_patterns: vec![
                        InputPattern::Variable("x".to_string()),
                        InputPattern::Constant("1".to_string()),
                    ],
                    attributes_pattern: HashMap::new(),
                },
                replacement: OperationPattern {
                    op_type: OperationType::Parameter, // Placeholder
                    input_patterns: vec![InputPattern::Variable("x".to_string())],
                    attributes_pattern: HashMap::new(),
                },
                conditions: vec![],
            },
        ]
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPass<T>
    for AlgebraicSimplificationPass<T>
{
    fn name(&self) -> &str {
        "algebraic_simplification"
    }
    fn apply(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        Ok(computation)
    }
    fn is_applicable(&self, _: &XLAComputation<T>) -> bool {
        true
    }
    fn dependencies(&self) -> Vec<String> {
        vec![]
    }
    fn estimate_benefit(&self, _: &XLAComputation<T>) -> f64 {
        0.1
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> LoopOptimizationPass<T> {
    pub fn new(_config: &OptimizationPipelineConfig) -> Self {
        Self {
            enable_loop_detection: true,
            unroll_threshold: 8,
            enable_vectorization: true,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPass<T>
    for LoopOptimizationPass<T>
{
    fn name(&self) -> &str {
        "loop_optimization"
    }
    fn apply(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        Ok(computation)
    }
    fn is_applicable(&self, _: &XLAComputation<T>) -> bool {
        true
    }
    fn dependencies(&self) -> Vec<String> {
        vec![]
    }
    fn estimate_benefit(&self, _: &XLAComputation<T>) -> f64 {
        0.15
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync>
    ControlFlowOptimizationPass<T>
{
    pub fn new(_config: &OptimizationPipelineConfig) -> Self {
        Self {
            enable_branch_prediction: true,
            enable_conditional_elimination: true,
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPass<T>
    for ControlFlowOptimizationPass<T>
{
    fn name(&self) -> &str {
        "control_flow_optimization"
    }
    fn apply(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        Ok(computation)
    }
    fn is_applicable(&self, _: &XLAComputation<T>) -> bool {
        true
    }
    fn dependencies(&self) -> Vec<String> {
        vec![]
    }
    fn estimate_benefit(&self, _: &XLAComputation<T>) -> f64 {
        0.08
    }
}

impl Default for PatternMatcher {
    fn default() -> Self {
        Self::new()
    }
}

impl PatternMatcher {
    pub fn new() -> Self {
        Self {
            patterns: Vec::new(),
            bindings: HashMap::new(),
        }
    }
}

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

    #[test]
    fn test_constant_folding_pass() {
        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 pass: ConstantFoldingPass<f32> = ConstantFoldingPass::new(&config);
        assert_eq!(pass.name(), "constant_folding");
        assert_eq!(pass.dependencies().len(), 0);
    }
}