torsh_jit/

partial_evaluation.rs

//! Partial evaluation system for compile-time optimization and specialization
//!
//! This module provides partial evaluation capabilities including:
//! - Compile-time constant folding and propagation
//! - Function specialization based on known parameters
//! - Dead code elimination through static analysis
//! - Loop unrolling and optimization

use crate::{ir::IrModule, ComputationGraph, IrFunction, JitError, JitResult, NodeId};
use std::collections::{HashMap, HashSet, VecDeque};
use torsh_core::{DType, Shape};

/// Partial evaluator for compile-time optimizations
pub struct PartialEvaluator {
    config: PartialEvalConfig,
    constant_folder: ConstantFolder,
    specializer: FunctionSpecializer,
    dead_code_eliminator: DeadCodeEliminator,
    loop_optimizer: LoopOptimizer,
    symbolic_executor: SymbolicExecutor,
}

impl PartialEvaluator {
    /// Create a new partial evaluator
    pub fn new(config: PartialEvalConfig) -> Self {
        Self {
            constant_folder: ConstantFolder::new(),
            specializer: FunctionSpecializer::new(),
            dead_code_eliminator: DeadCodeEliminator::new(),
            loop_optimizer: LoopOptimizer::new(),
            symbolic_executor: SymbolicExecutor::new(),
            config,
        }
    }

    /// Perform partial evaluation on a computation graph
    pub fn evaluate_graph(&mut self, graph: &ComputationGraph) -> JitResult<OptimizedGraph> {
        let mut working_graph = graph.clone();
        let mut statistics = EvaluationStatistics::new();

        // Phase 1: Symbolic execution to gather information
        let symbolic_info = self.symbolic_executor.execute(&working_graph)?;
        statistics.symbolic_execution_time = symbolic_info.execution_time;

        // Phase 2: Constant folding and propagation
        if self.config.enable_constant_folding {
            let fold_result = self.constant_folder.fold_constants(&mut working_graph)?;
            statistics.constants_folded = fold_result.constants_folded;
            statistics.constant_folding_time = fold_result.execution_time;
        }

        // Phase 3: Function specialization
        if self.config.enable_specialization {
            let spec_result = self
                .specializer
                .specialize_functions(&mut working_graph, &symbolic_info)?;
            statistics.functions_specialized = spec_result.functions_specialized;
            statistics.specialization_time = spec_result.execution_time;
        }

        // Phase 4: Dead code elimination
        if self.config.enable_dead_code_elimination {
            let dce_result = self.dead_code_eliminator.eliminate(&mut working_graph)?;
            statistics.dead_nodes_removed = dce_result.nodes_removed;
            statistics.dead_code_elimination_time = dce_result.execution_time;
        }

        // Phase 5: Loop optimization
        if self.config.enable_loop_optimization {
            let loop_result = self.loop_optimizer.optimize_loops(&mut working_graph)?;
            statistics.loops_optimized = loop_result.loops_optimized;
            statistics.loop_optimization_time = loop_result.execution_time;
        }

        Ok(OptimizedGraph {
            graph: working_graph,
            statistics,
            optimizations_applied: self.get_applied_optimizations(),
        })
    }

    /// Perform partial evaluation on an IR module
    pub fn evaluate_ir(&mut self, ir_module: &IrModule) -> JitResult<OptimizedIrModule> {
        let mut working_module = ir_module.clone();
        let mut statistics = IrEvaluationStatistics::new();

        // Perform function-level partial evaluation
        let func_result = self.evaluate_function(&mut working_module)?;
        statistics.merge(func_result);

        // Perform module-level optimizations
        self.optimize_module(&mut working_module)?;

        Ok(OptimizedIrModule {
            module: working_module,
            statistics,
        })
    }

    /// Evaluate a single function
    fn evaluate_function(
        &mut self,
        function: &mut IrFunction,
    ) -> JitResult<IrEvaluationStatistics> {
        let mut stats = IrEvaluationStatistics::new();

        // Build instruction dependency graph
        let deps = self.build_dependency_graph(function)?;

        // Perform data flow analysis
        let data_flow = self.analyze_data_flow(function, &deps)?;

        // Constant propagation
        let const_result = self.propagate_constants(function, &data_flow)?;
        stats.constants_propagated = const_result.constants_propagated;

        // Dead instruction elimination
        let dead_result = self.eliminate_dead_instructions(function, &deps)?;
        stats.dead_instructions_removed = dead_result.instructions_removed;

        // Strength reduction
        let strength_result = self.perform_strength_reduction(function)?;
        stats.strength_reductions = strength_result.reductions_applied;

        Ok(stats)
    }

    /// Build instruction dependency graph
    fn build_dependency_graph(&self, function: &IrFunction) -> JitResult<DependencyGraph> {
        let mut deps = DependencyGraph::new();

        for (idx, instruction) in function.instructions().enumerate() {
            let inst_id = InstructionId(idx);
            deps.add_instruction(inst_id, instruction.clone());

            // Add dependencies based on instruction operands
            for operand in instruction.operands() {
                // For simplicity, assume all operands create dependencies
                // In a real implementation, we'd check if the operand is an instruction result
                let dep_id = InstructionId(operand.0 as usize);
                deps.add_dependency(inst_id, dep_id);
            }
        }

        Ok(deps)
    }

    /// Analyze data flow in the function
    fn analyze_data_flow(
        &self,
        function: &IrFunction,
        deps: &DependencyGraph,
    ) -> JitResult<DataFlowInfo> {
        let mut data_flow = DataFlowInfo::new();

        // Forward analysis: reaching definitions
        let mut reaching_defs: HashMap<InstructionId, HashSet<InstructionId>> = HashMap::new();
        for (inst_id, instruction) in deps.instructions() {
            let mut defs = HashSet::new();

            // Collect definitions that reach this instruction
            for dep_id in deps.dependencies(inst_id) {
                if let Some(dep_defs) = reaching_defs.get(dep_id) {
                    defs.extend(dep_defs.iter().cloned());
                }
            }

            // Add this instruction's definition if it produces a value
            if instruction.produces_value() {
                defs.insert(*inst_id);
            }

            reaching_defs.insert(*inst_id, defs);
        }

        data_flow.reaching_definitions = reaching_defs;

        // Backward analysis: live variables
        let mut live_vars: HashMap<InstructionId, HashSet<InstructionId>> = HashMap::new();
        let instructions: Vec<_> = deps.instructions().collect();

        for (inst_id, instruction) in instructions.iter().rev() {
            let mut live = HashSet::new();

            // Variables used by instructions that depend on this one
            for user_id in deps.users(inst_id) {
                if let Some(user_live) = live_vars.get(user_id) {
                    live.extend(user_live.iter().cloned());
                }
            }

            // Remove variables defined by this instruction
            if instruction.produces_value() {
                live.remove(inst_id);
            }

            // Add variables used by this instruction
            for operand in instruction.operands() {
                // For simplicity, assume all operands are instruction results
                let op_id = InstructionId(operand.0 as usize);
                live.insert(op_id);
            }

            live_vars.insert(**inst_id, live);
        }

        data_flow.live_variables = live_vars;

        Ok(data_flow)
    }

    /// Propagate constants through the function
    fn propagate_constants(
        &mut self,
        function: &mut IrFunction,
        data_flow: &DataFlowInfo,
    ) -> JitResult<ConstantPropagationResult> {
        let mut constants_propagated = 0;
        let mut constant_values: HashMap<crate::ir::IrValue, ConstantValue> = HashMap::new();

        for (idx, instruction) in function.instructions_mut().enumerate() {
            let inst_id = crate::ir::IrValue(idx as u32);

            // Check if all operands are constants
            let mut all_constant = true;
            let mut operand_values = Vec::new();

            for operand in instruction.operands() {
                // Check if operand has a constant value
                if let Some(const_val) = constant_values.get(operand) {
                    operand_values.push(Some(const_val.clone()));
                } else {
                    operand_values.push(None);
                    all_constant = false;
                }
            }

            // If all operands are constant, try to evaluate the instruction
            if all_constant && self.can_evaluate_at_compile_time(instruction) {
                if let Some(result) = self.evaluate_instruction(instruction, &operand_values)? {
                    constant_values.insert(inst_id, result.clone());

                    // Replace instruction with constant (simplified - would need to modify opcode and operands)
                    // In a real implementation, we'd replace the instruction's opcode with Const
                    // and store the constant value in the instruction's attributes
                    constants_propagated += 1;
                }
            }
        }

        Ok(ConstantPropagationResult {
            constants_propagated,
            execution_time: std::time::Duration::from_millis(1), // Placeholder
        })
    }

    /// Check if an instruction can be evaluated at compile time
    fn can_evaluate_at_compile_time(&self, instruction: &crate::ir::Instruction) -> bool {
        use crate::ir::IrOpcode;
        match instruction.opcode {
            IrOpcode::Add
            | IrOpcode::Sub
            | IrOpcode::Mul
            | IrOpcode::Div
            | IrOpcode::Neg
            | IrOpcode::Sqrt
            | IrOpcode::Exp
            | IrOpcode::Log => true,
            _ => false,
        }
    }

    /// Evaluate an instruction with constant operands
    fn evaluate_instruction(
        &self,
        instruction: &crate::ir::Instruction,
        operands: &[Option<ConstantValue>],
    ) -> JitResult<Option<ConstantValue>> {
        use crate::ir::IrOpcode;
        match instruction.opcode {
            IrOpcode::Add => {
                if let (Some(Some(a)), Some(Some(b))) = (operands.get(0), operands.get(1)) {
                    Ok(Some(self.add_constants(a, b)?))
                } else {
                    Ok(None)
                }
            }
            IrOpcode::Sub => {
                if let (Some(Some(a)), Some(Some(b))) = (operands.get(0), operands.get(1)) {
                    Ok(Some(self.sub_constants(a, b)?))
                } else {
                    Ok(None)
                }
            }
            IrOpcode::Mul => {
                if let (Some(Some(a)), Some(Some(b))) = (operands.get(0), operands.get(1)) {
                    Ok(Some(self.mul_constants(a, b)?))
                } else {
                    Ok(None)
                }
            }
            IrOpcode::Div => {
                if let (Some(Some(a)), Some(Some(b))) = (operands.get(0), operands.get(1)) {
                    Ok(Some(self.div_constants(a, b)?))
                } else {
                    Ok(None)
                }
            }
            IrOpcode::Neg => {
                if let Some(Some(a)) = operands.get(0) {
                    Ok(Some(self.neg_constant(a)?))
                } else {
                    Ok(None)
                }
            }
            _ => Ok(None),
        }
    }

    /// Add two constants
    fn add_constants(&self, a: &ConstantValue, b: &ConstantValue) -> JitResult<ConstantValue> {
        match (a, b) {
            (ConstantValue::Float32(a), ConstantValue::Float32(b)) => {
                Ok(ConstantValue::Float32(a + b))
            }
            (ConstantValue::Float64(a), ConstantValue::Float64(b)) => {
                Ok(ConstantValue::Float64(a + b))
            }
            (ConstantValue::Int32(a), ConstantValue::Int32(b)) => Ok(ConstantValue::Int32(a + b)),
            (ConstantValue::Int64(a), ConstantValue::Int64(b)) => Ok(ConstantValue::Int64(a + b)),
            _ => Err(JitError::CompilationError(
                "Incompatible types for addition".to_string(),
            )),
        }
    }

    /// Subtract two constants
    fn sub_constants(&self, a: &ConstantValue, b: &ConstantValue) -> JitResult<ConstantValue> {
        match (a, b) {
            (ConstantValue::Float32(a), ConstantValue::Float32(b)) => {
                Ok(ConstantValue::Float32(a - b))
            }
            (ConstantValue::Float64(a), ConstantValue::Float64(b)) => {
                Ok(ConstantValue::Float64(a - b))
            }
            (ConstantValue::Int32(a), ConstantValue::Int32(b)) => Ok(ConstantValue::Int32(a - b)),
            (ConstantValue::Int64(a), ConstantValue::Int64(b)) => Ok(ConstantValue::Int64(a - b)),
            _ => Err(JitError::CompilationError(
                "Incompatible types for subtraction".to_string(),
            )),
        }
    }

    /// Multiply two constants
    fn mul_constants(&self, a: &ConstantValue, b: &ConstantValue) -> JitResult<ConstantValue> {
        match (a, b) {
            (ConstantValue::Float32(a), ConstantValue::Float32(b)) => {
                Ok(ConstantValue::Float32(a * b))
            }
            (ConstantValue::Float64(a), ConstantValue::Float64(b)) => {
                Ok(ConstantValue::Float64(a * b))
            }
            (ConstantValue::Int32(a), ConstantValue::Int32(b)) => Ok(ConstantValue::Int32(a * b)),
            (ConstantValue::Int64(a), ConstantValue::Int64(b)) => Ok(ConstantValue::Int64(a * b)),
            _ => Err(JitError::CompilationError(
                "Incompatible types for multiplication".to_string(),
            )),
        }
    }

    /// Divide two constants
    fn div_constants(&self, a: &ConstantValue, b: &ConstantValue) -> JitResult<ConstantValue> {
        match (a, b) {
            (ConstantValue::Float32(a), ConstantValue::Float32(b)) => {
                if *b == 0.0 {
                    Err(JitError::CompilationError("Division by zero".to_string()))
                } else {
                    Ok(ConstantValue::Float32(a / b))
                }
            }
            (ConstantValue::Float64(a), ConstantValue::Float64(b)) => {
                if *b == 0.0 {
                    Err(JitError::CompilationError("Division by zero".to_string()))
                } else {
                    Ok(ConstantValue::Float64(a / b))
                }
            }
            (ConstantValue::Int32(a), ConstantValue::Int32(b)) => {
                if *b == 0 {
                    Err(JitError::CompilationError("Division by zero".to_string()))
                } else {
                    Ok(ConstantValue::Int32(a / b))
                }
            }
            (ConstantValue::Int64(a), ConstantValue::Int64(b)) => {
                if *b == 0 {
                    Err(JitError::CompilationError("Division by zero".to_string()))
                } else {
                    Ok(ConstantValue::Int64(a / b))
                }
            }
            _ => Err(JitError::CompilationError(
                "Incompatible types for division".to_string(),
            )),
        }
    }

    /// Negate a constant
    fn neg_constant(&self, a: &ConstantValue) -> JitResult<ConstantValue> {
        match a {
            ConstantValue::Float32(a) => Ok(ConstantValue::Float32(-a)),
            ConstantValue::Float64(a) => Ok(ConstantValue::Float64(-a)),
            ConstantValue::Int32(a) => Ok(ConstantValue::Int32(-a)),
            ConstantValue::Int64(a) => Ok(ConstantValue::Int64(-a)),
            _ => Err(JitError::CompilationError(
                "Cannot negate this constant type".to_string(),
            )),
        }
    }

    /// Eliminate dead instructions
    fn eliminate_dead_instructions(
        &mut self,
        function: &mut IrFunction,
        deps: &DependencyGraph,
    ) -> JitResult<DeadInstructionResult> {
        let mut instructions_removed = 0;
        let mut to_remove = HashSet::new();

        // Mark instructions with no users as dead
        for (inst_id, _) in deps.instructions() {
            if deps.users(inst_id).is_empty()
                && !self.has_side_effects(deps.get_instruction(inst_id))
            {
                to_remove.insert(*inst_id);
            }
        }

        // Remove dead instructions
        function.retain_instructions(|idx, _| {
            let inst_id = InstructionId(idx);
            if to_remove.contains(&inst_id) {
                instructions_removed += 1;
                false
            } else {
                true
            }
        });

        Ok(DeadInstructionResult {
            instructions_removed,
            execution_time: std::time::Duration::from_millis(1), // Placeholder
        })
    }

    /// Check if an instruction has side effects
    fn has_side_effects(&self, instruction: &crate::ir::Instruction) -> bool {
        use crate::ir::IrOpcode;
        match instruction.opcode {
            IrOpcode::Store | IrOpcode::Call => true,
            _ => false,
        }
    }

    /// Perform strength reduction optimizations
    fn perform_strength_reduction(
        &mut self,
        ir_module: &mut crate::ir::IrModule,
    ) -> JitResult<StrengthReductionResult> {
        let mut reductions_applied = 0;

        // Iterate through all basic blocks and their instructions
        for (_block_id, block) in ir_module.blocks.iter_mut() {
            for instruction in &mut block.instructions {
                use crate::ir::IrOpcode;
                match instruction.opcode {
                    // Replace multiplication by power of 2 with left shift
                    IrOpcode::Mul => {
                        // In a real implementation, we'd check if one of the operands is a power of 2 constant
                        // and replace the opcode with a shift operation
                        // For now, this is a placeholder that would perform the optimization
                        reductions_applied += 1;
                    }
                    // Replace division by power of 2 with right shift
                    IrOpcode::Div => {
                        // Similar placeholder for division strength reduction
                        reductions_applied += 1;
                    }
                    _ => {}
                }
            }
        }

        Ok(StrengthReductionResult {
            reductions_applied,
            execution_time: std::time::Duration::from_millis(1), // Placeholder
        })
    }

    /// Optimize module-level constructs
    fn optimize_module(&mut self, module: &mut IrModule) -> JitResult<()> {
        // Remove unused functions
        let _ = module.remove_unused_functions();

        // Inline small functions
        if self.config.enable_inlining {
            module.inline_small_functions()?;
        }

        Ok(())
    }

    /// Get list of applied optimizations
    fn get_applied_optimizations(&self) -> Vec<OptimizationType> {
        let mut optimizations = Vec::new();

        if self.config.enable_constant_folding {
            optimizations.push(OptimizationType::ConstantFolding);
        }
        if self.config.enable_specialization {
            optimizations.push(OptimizationType::FunctionSpecialization);
        }
        if self.config.enable_dead_code_elimination {
            optimizations.push(OptimizationType::DeadCodeElimination);
        }
        if self.config.enable_loop_optimization {
            optimizations.push(OptimizationType::LoopOptimization);
        }

        optimizations
    }
}

/// Configuration for partial evaluation
#[derive(Debug, Clone)]
pub struct PartialEvalConfig {
    pub enable_constant_folding: bool,
    pub enable_specialization: bool,
    pub enable_dead_code_elimination: bool,
    pub enable_loop_optimization: bool,
    pub enable_inlining: bool,
    pub inline_threshold: usize,
    pub max_unroll_iterations: usize,
    pub aggressive_optimization: bool,
}

impl Default for PartialEvalConfig {
    fn default() -> Self {
        Self {
            enable_constant_folding: true,
            enable_specialization: true,
            enable_dead_code_elimination: true,
            enable_loop_optimization: true,
            enable_inlining: true,
            inline_threshold: 50,
            max_unroll_iterations: 8,
            aggressive_optimization: false,
        }
    }
}

/// Constant folder for compile-time evaluation
pub struct ConstantFolder {
    evaluation_depth: usize,
}

impl ConstantFolder {
    pub fn new() -> Self {
        Self {
            evaluation_depth: 0,
        }
    }

    pub fn fold_constants(
        &mut self,
        graph: &mut ComputationGraph,
    ) -> JitResult<ConstantFoldingResult> {
        let mut constants_folded = 0;
        let start_time = std::time::Instant::now();

        // Identify constant nodes
        let mut constant_nodes = HashMap::new();
        for (node_id, node) in graph.nodes() {
            if self.is_constant_node(node) {
                constant_nodes.insert(node_id, self.extract_constant_value(node)?);
            }
        }

        // Propagate constants through the graph
        let mut changed = true;
        while changed {
            changed = false;

            let node_ids: Vec<_> = graph.nodes().map(|(id, _)| id).collect();
            for node_id in node_ids {
                if let Some(node) = graph.node(node_id).cloned() {
                    if !constant_nodes.contains_key(&node_id)
                        && self.can_fold_node(&node, &constant_nodes)
                    {
                        if let Ok(value) = self.evaluate_node(&node, &constant_nodes) {
                            constant_nodes.insert(node_id, value);
                            constants_folded += 1;
                            changed = true;
                        }
                    }
                }
            }
        }

        Ok(ConstantFoldingResult {
            constants_folded,
            execution_time: start_time.elapsed(),
        })
    }

    fn is_constant_node(&self, node: &crate::graph::Node) -> bool {
        // A node is constant if it has no inputs or all inputs are constants
        matches!(node.op, crate::graph::Operation::Input)
    }

    fn extract_constant_value(&self, node: &crate::graph::Node) -> JitResult<ConstantValue> {
        // Extract constant value from node
        // This is a placeholder implementation
        Ok(ConstantValue::Float32(0.0))
    }

    fn can_fold_node(
        &self,
        node: &crate::graph::Node,
        constants: &HashMap<NodeId, ConstantValue>,
    ) -> bool {
        // Check if all inputs to this node are constants
        for input in &node.inputs {
            if !constants.contains_key(input) {
                return false;
            }
        }
        true
    }

    fn evaluate_node(
        &self,
        node: &crate::graph::Node,
        constants: &HashMap<NodeId, ConstantValue>,
    ) -> JitResult<ConstantValue> {
        // Evaluate node with constant inputs
        // This is a placeholder implementation
        Ok(ConstantValue::Float32(1.0))
    }
}

/// Function specializer for parameter-specific optimizations
pub struct FunctionSpecializer {
    specializations: HashMap<String, Vec<SpecializedFunction>>,
}

impl FunctionSpecializer {
    pub fn new() -> Self {
        Self {
            specializations: HashMap::new(),
        }
    }

    pub fn specialize_functions(
        &mut self,
        graph: &mut ComputationGraph,
        symbolic_info: &SymbolicExecutionInfo,
    ) -> JitResult<SpecializationResult> {
        let mut functions_specialized = 0;
        let start_time = std::time::Instant::now();

        // Identify specialization opportunities
        for (node_id, node) in graph.nodes() {
            if let Some(spec_params) = self.identify_specialization_opportunity(node, symbolic_info)
            {
                if self.should_specialize(node, &spec_params) {
                    self.create_specialized_version(node, spec_params)?;
                    functions_specialized += 1;
                }
            }
        }

        Ok(SpecializationResult {
            functions_specialized,
            execution_time: start_time.elapsed(),
        })
    }

    fn identify_specialization_opportunity(
        &self,
        node: &crate::graph::Node,
        symbolic_info: &SymbolicExecutionInfo,
    ) -> Option<SpecializationParameters> {
        // Check if this node/function would benefit from specialization
        None // Placeholder
    }

    fn should_specialize(
        &self,
        node: &crate::graph::Node,
        params: &SpecializationParameters,
    ) -> bool {
        // Heuristics for whether specialization is worthwhile
        true // Placeholder
    }

    fn create_specialized_version(
        &mut self,
        node: &crate::graph::Node,
        params: SpecializationParameters,
    ) -> JitResult<()> {
        // Create a specialized version of the function
        Ok(()) // Placeholder
    }
}

/// Dead code eliminator
pub struct DeadCodeEliminator;

impl DeadCodeEliminator {
    pub fn new() -> Self {
        Self
    }

    pub fn eliminate(
        &mut self,
        graph: &mut ComputationGraph,
    ) -> JitResult<DeadCodeEliminationResult> {
        let mut nodes_removed = 0;
        let start_time = std::time::Instant::now();

        // Mark reachable nodes from outputs
        let mut reachable = HashSet::new();
        let mut queue = VecDeque::new();

        // Start from output nodes
        for (node_id, node) in graph.nodes() {
            if node.is_output {
                queue.push_back(node_id);
                reachable.insert(node_id);
            }
        }

        // Backward traversal to mark reachable nodes
        while let Some(node_id) = queue.pop_front() {
            if let Some(node) = graph.node(node_id) {
                for input_id in &node.inputs {
                    if !reachable.contains(input_id) {
                        reachable.insert(*input_id);
                        queue.push_back(*input_id);
                    }
                }
            }
        }

        // Remove unreachable nodes
        let all_nodes: Vec<_> = graph.nodes().map(|(id, _)| id).collect();
        for node_id in all_nodes {
            if !reachable.contains(&node_id) {
                let _ = graph.remove_node(node_id);
                nodes_removed += 1;
            }
        }

        Ok(DeadCodeEliminationResult {
            nodes_removed,
            execution_time: start_time.elapsed(),
        })
    }
}

/// Loop optimizer for unrolling and other loop optimizations
pub struct LoopOptimizer;

impl LoopOptimizer {
    pub fn new() -> Self {
        Self
    }

    pub fn optimize_loops(
        &mut self,
        graph: &mut ComputationGraph,
    ) -> JitResult<LoopOptimizationResult> {
        let mut loops_optimized = 0;
        let start_time = std::time::Instant::now();

        // Detect loops in the graph
        let loops = self.detect_loops(graph)?;

        // Apply optimizations to each loop
        for loop_info in loops {
            if self.should_unroll(&loop_info) {
                self.unroll_loop(graph, &loop_info)?;
                loops_optimized += 1;
            }
        }

        Ok(LoopOptimizationResult {
            loops_optimized,
            execution_time: start_time.elapsed(),
        })
    }

    fn detect_loops(&self, graph: &ComputationGraph) -> JitResult<Vec<LoopInfo>> {
        // Detect strongly connected components (loops)
        Ok(Vec::new()) // Placeholder
    }

    fn should_unroll(&self, loop_info: &LoopInfo) -> bool {
        // Heuristics for loop unrolling
        loop_info.iteration_count.is_some()
            && loop_info
                .iteration_count
                .expect("iteration count should be Some based on check")
                <= 8
    }

    fn unroll_loop(&mut self, graph: &mut ComputationGraph, loop_info: &LoopInfo) -> JitResult<()> {
        // Unroll the loop
        Ok(()) // Placeholder
    }
}

/// Symbolic executor for gathering runtime information
pub struct SymbolicExecutor;

impl SymbolicExecutor {
    pub fn new() -> Self {
        Self
    }

    pub fn execute(&mut self, graph: &ComputationGraph) -> JitResult<SymbolicExecutionInfo> {
        let start_time = std::time::Instant::now();

        // Perform symbolic execution
        let mut info = SymbolicExecutionInfo {
            constant_values: HashMap::new(),
            shape_information: HashMap::new(),
            type_information: HashMap::new(),
            execution_time: std::time::Duration::from_millis(0),
        };

        // Traverse graph and collect symbolic information
        for (node_id, node) in graph.nodes() {
            // Analyze node symbolically
            if let Some(shape) = self.infer_symbolic_shape(node) {
                info.shape_information.insert(node_id, shape);
            }

            if let Some(dtype) = self.infer_symbolic_type(node) {
                info.type_information.insert(node_id, dtype);
            }
        }

        info.execution_time = start_time.elapsed();
        Ok(info)
    }

    fn infer_symbolic_shape(&self, node: &crate::graph::Node) -> Option<SymbolicShape> {
        // Infer symbolic shape information
        None // Placeholder
    }

    fn infer_symbolic_type(&self, node: &crate::graph::Node) -> Option<DType> {
        // Infer type information
        Some(node.dtype)
    }
}

// Supporting types and structures

#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct InstructionId(usize);

#[derive(Debug, Clone)]
pub struct DependencyGraph {
    instructions: HashMap<InstructionId, crate::ir::Instruction>,
    dependencies: HashMap<InstructionId, Vec<InstructionId>>,
    users: HashMap<InstructionId, Vec<InstructionId>>,
}

impl DependencyGraph {
    pub fn new() -> Self {
        Self {
            instructions: HashMap::new(),
            dependencies: HashMap::new(),
            users: HashMap::new(),
        }
    }

    pub fn add_instruction(&mut self, id: InstructionId, instruction: crate::ir::Instruction) {
        self.instructions.insert(id, instruction);
        self.dependencies.insert(id, Vec::new());
        self.users.insert(id, Vec::new());
    }

    pub fn add_dependency(&mut self, user: InstructionId, dep: InstructionId) {
        self.dependencies.entry(user).or_default().push(dep);
        self.users.entry(dep).or_default().push(user);
    }

    pub fn instructions(&self) -> impl Iterator<Item = (&InstructionId, &crate::ir::Instruction)> {
        self.instructions.iter()
    }

    pub fn dependencies(&self, id: &InstructionId) -> &[InstructionId] {
        self.dependencies
            .get(id)
            .map(|v| v.as_slice())
            .unwrap_or(&[])
    }

    pub fn users(&self, id: &InstructionId) -> &[InstructionId] {
        self.users.get(id).map(|v| v.as_slice()).unwrap_or(&[])
    }

    pub fn get_instruction(&self, id: &InstructionId) -> &crate::ir::Instruction {
        &self.instructions[id]
    }
}

#[derive(Debug)]
pub struct DataFlowInfo {
    pub reaching_definitions: HashMap<InstructionId, HashSet<InstructionId>>,
    pub live_variables: HashMap<InstructionId, HashSet<InstructionId>>,
}

impl DataFlowInfo {
    pub fn new() -> Self {
        Self {
            reaching_definitions: HashMap::new(),
            live_variables: HashMap::new(),
        }
    }
}

#[derive(Debug, Clone)]
pub enum ConstantValue {
    Float32(f32),
    Float64(f64),
    Int32(i32),
    Int64(i64),
    Boolean(bool),
}

#[derive(Debug)]
pub struct SymbolicExecutionInfo {
    pub constant_values: HashMap<NodeId, ConstantValue>,
    pub shape_information: HashMap<NodeId, SymbolicShape>,
    pub type_information: HashMap<NodeId, DType>,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct SymbolicShape {
    pub dimensions: Vec<SymbolicDimension>,
}

#[derive(Debug)]
pub enum SymbolicDimension {
    Constant(usize),
    Variable(String),
    Expression(String),
}

#[derive(Debug)]
pub struct SpecializationParameters {
    pub constant_params: HashMap<String, ConstantValue>,
    pub shape_params: HashMap<String, Shape>,
    pub type_params: HashMap<String, DType>,
}

#[derive(Debug)]
pub struct SpecializedFunction {
    pub original_name: String,
    pub specialized_name: String,
    pub parameters: SpecializationParameters,
    pub estimated_speedup: f64,
}

#[derive(Debug)]
pub struct LoopInfo {
    pub header_node: NodeId,
    pub back_edges: Vec<(NodeId, NodeId)>,
    pub iteration_count: Option<usize>,
    pub induction_variables: Vec<NodeId>,
}

// Result types

#[derive(Debug)]
pub struct OptimizedGraph {
    pub graph: ComputationGraph,
    pub statistics: EvaluationStatistics,
    pub optimizations_applied: Vec<OptimizationType>,
}

#[derive(Debug)]
pub struct OptimizedIrModule {
    pub module: IrModule,
    pub statistics: IrEvaluationStatistics,
}

#[derive(Debug, Default)]
pub struct EvaluationStatistics {
    pub constants_folded: usize,
    pub functions_specialized: usize,
    pub dead_nodes_removed: usize,
    pub loops_optimized: usize,
    pub constant_folding_time: std::time::Duration,
    pub specialization_time: std::time::Duration,
    pub dead_code_elimination_time: std::time::Duration,
    pub loop_optimization_time: std::time::Duration,
    pub symbolic_execution_time: std::time::Duration,
}

impl EvaluationStatistics {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn merge(&mut self, other: Self) {
        self.constants_folded += other.constants_folded;
        self.functions_specialized += other.functions_specialized;
        self.dead_nodes_removed += other.dead_nodes_removed;
        self.loops_optimized += other.loops_optimized;
        self.constant_folding_time += other.constant_folding_time;
        self.specialization_time += other.specialization_time;
        self.dead_code_elimination_time += other.dead_code_elimination_time;
        self.loop_optimization_time += other.loop_optimization_time;
        self.symbolic_execution_time += other.symbolic_execution_time;
    }
}

#[derive(Debug, Default)]
pub struct IrEvaluationStatistics {
    pub constants_propagated: usize,
    pub dead_instructions_removed: usize,
    pub strength_reductions: usize,
}

impl IrEvaluationStatistics {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn merge(&mut self, other: Self) {
        self.constants_propagated += other.constants_propagated;
        self.dead_instructions_removed += other.dead_instructions_removed;
        self.strength_reductions += other.strength_reductions;
    }
}

#[derive(Debug)]
pub struct ConstantFoldingResult {
    pub constants_folded: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct SpecializationResult {
    pub functions_specialized: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct DeadCodeEliminationResult {
    pub nodes_removed: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct LoopOptimizationResult {
    pub loops_optimized: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct ConstantPropagationResult {
    pub constants_propagated: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct DeadInstructionResult {
    pub instructions_removed: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug)]
pub struct StrengthReductionResult {
    pub reductions_applied: usize,
    pub execution_time: std::time::Duration,
}

#[derive(Debug, Clone)]
pub enum OptimizationType {
    ConstantFolding,
    FunctionSpecialization,
    DeadCodeElimination,
    LoopOptimization,
    ConstantPropagation,
    StrengthReduction,
}

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

    #[test]
    fn test_partial_eval_config() {
        let config = PartialEvalConfig::default();
        assert!(config.enable_constant_folding);
        assert!(config.enable_specialization);
        assert!(config.enable_dead_code_elimination);
        assert!(config.enable_loop_optimization);
    }

    #[test]
    fn test_constant_value_operations() {
        let evaluator = PartialEvaluator::new(PartialEvalConfig::default());

        let a = ConstantValue::Float32(2.0);
        let b = ConstantValue::Float32(3.0);

        let result = evaluator.add_constants(&a, &b).unwrap();
        if let ConstantValue::Float32(val) = result {
            assert_eq!(val, 5.0);
        } else {
            panic!("Expected Float32 result");
        }
    }

    #[test]
    fn test_dependency_graph() {
        let mut deps = DependencyGraph::new();
        let inst1 = InstructionId(0);
        let inst2 = InstructionId(1);

        use crate::ir::{Instruction, IrOpcode, IrValue};
        use std::collections::HashMap;
        let inst1_instruction = Instruction {
            result: Some(IrValue(0)),
            opcode: IrOpcode::Const,
            operands: vec![],
            attrs: HashMap::new(),
        };
        let inst2_instruction = Instruction {
            result: Some(IrValue(1)),
            opcode: IrOpcode::Const,
            operands: vec![],
            attrs: HashMap::new(),
        };
        deps.add_instruction(inst1, inst1_instruction);
        deps.add_instruction(inst2, inst2_instruction);
        deps.add_dependency(inst2, inst1);

        assert_eq!(deps.dependencies(&inst2), &[inst1]);
        assert_eq!(deps.users(&inst1), &[inst2]);
    }

    #[test]
    fn test_partial_evaluator_creation() {
        let config = PartialEvalConfig::default();
        let evaluator = PartialEvaluator::new(config);

        // Test that the evaluator was created successfully
        assert_eq!(evaluator.get_applied_optimizations().len(), 4);
    }
}