optirs-tpu 0.3.1

use std::fmt::Debug;
// XLA optimization passes
//
// This module contains optimization passes for XLA computations,
// including graph optimization, kernel fusion, memory planning, and scheduling.

pub mod graph_optimization;
pub mod kernel_fusion;
pub mod memory_planning;
pub mod scheduling;

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

use super::frontend::{OperandId, OperationId, XLAComputation};
use super::{XLACompilerConfig, XLAOptimizationLevel};
use crate::error::{OptimError, Result};

// Re-export main types selectively to avoid ambiguous glob re-exports
// (MemoryAccessType and MemoryLevel exist in both memory_planning and scheduling)
pub use graph_optimization::*;
pub use kernel_fusion::*;
pub use memory_planning::{MemoryPlan, MemoryPlanner};
pub use scheduling::ExecutionScheduler;

/// Performance analyzer for XLA operations
pub struct PerformanceAnalyzer<T> {
    /// Performance metrics
    metrics: HashMap<String, f64>,
    /// Phantom data for type parameter
    _phantom: std::marker::PhantomData<T>,
}

impl<T> PerformanceAnalyzer<T> {
    /// Create a new performance analyzer
    pub fn new() -> Self {
        Self {
            metrics: HashMap::new(),
            _phantom: std::marker::PhantomData,
        }
    }
}

impl<T> Default for PerformanceAnalyzer<T> {
    fn default() -> Self {
        Self::new()
    }
}

/// Comprehensive optimization pipeline for XLA computations
pub struct OptimizationPipeline<T: Float + Debug + Send + Sync + 'static> {
    /// Pipeline configuration
    config: OptimizationPipelineConfig,

    /// Graph optimization passes
    graph_optimizer: GraphOptimizer<T>,

    /// Kernel fusion engine
    fusion_engine: KernelFusionEngine<T>,

    /// Memory planner
    memory_planner: MemoryPlanner<T>,

    /// Execution scheduler
    scheduler: ExecutionScheduler<T>,

    /// Applied optimization passes
    applied_passes: Vec<String>,

    /// Performance statistics
    performance_stats: OptimizationStats,
}

/// Optimization pipeline configuration
#[derive(Debug, Clone)]
pub struct OptimizationPipelineConfig {
    /// Optimization level
    pub optimization_level: XLAOptimizationLevel,

    /// Enable graph optimizations
    pub enable_graph_optimization: bool,

    /// Enable kernel fusion
    pub enable_kernel_fusion: bool,

    /// Enable memory optimization
    pub enable_memory_optimization: bool,

    /// Enable scheduling optimization
    pub enable_scheduling_optimization: bool,

    /// Maximum optimization time (seconds)
    pub max_optimization_time: u64,

    /// Target hardware configuration
    pub target_hardware: HardwareTarget,

    /// Custom optimization passes
    pub custom_passes: Vec<String>,

    /// Aggressive optimizations
    pub aggressive_mode: bool,

    /// Debug mode
    pub debug_mode: bool,
}

/// Hardware target configuration
#[derive(Debug, Clone)]
pub struct HardwareTarget {
    /// TPU version
    pub tpu_version: String,

    /// Number of cores
    pub num_cores: usize,

    /// Memory capacity (bytes)
    pub memory_capacity: usize,

    /// Memory bandwidth (GB/s)
    pub memory_bandwidth: f64,

    /// Compute capability
    pub compute_capability: ComputeCapability,
}

/// Compute capability information
#[derive(Debug, Clone)]
pub struct ComputeCapability {
    /// Matrix unit dimensions
    pub matrix_unit_dims: (usize, usize),

    /// Vector unit width
    pub vector_unit_width: usize,

    /// Supported data types
    pub supported_dtypes: Vec<String>,

    /// Special instructions
    pub special_instructions: Vec<String>,
}

/// Performance statistics for optimization
#[derive(Debug, Default)]
pub struct OptimizationStats {
    /// Total optimization time
    pub total_time: Duration,

    /// Time per optimization pass
    pub pass_times: HashMap<String, Duration>,

    /// Number of operations optimized
    pub operations_optimized: usize,

    /// Memory savings achieved
    pub memory_savings: usize,

    /// Estimated speedup
    pub estimated_speedup: f64,

    /// Optimization success rate
    pub success_rate: f64,
}

/// Optimization pass trait
pub trait OptimizationPass<T: Float + Debug + Send + Sync + 'static> {
    /// Pass name
    fn name(&self) -> &str;

    /// Apply optimization pass to computation
    fn apply(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>>;

    /// Check if pass is applicable
    fn is_applicable(&self, computation: &XLAComputation<T>) -> bool;

    /// Get pass dependencies
    fn dependencies(&self) -> Vec<String>;

    /// Estimate optimization benefit
    fn estimate_benefit(&self, computation: &XLAComputation<T>) -> f64;
}

impl<T: Float + Debug + Default + std::fmt::Debug + Clone + Send + Sync> OptimizationPipeline<T> {
    /// Create new optimization pipeline
    pub fn new(config: &XLACompilerConfig) -> Self {
        let pipeline_config = OptimizationPipelineConfig {
            optimization_level: config.optimization_level,
            enable_graph_optimization: config.enable_fusion,
            enable_kernel_fusion: config.enable_fusion,
            enable_memory_optimization: config.enable_memory_optimization,
            enable_scheduling_optimization: config.enable_pipeline_optimization,
            max_optimization_time: config.compilation_timeout,
            target_hardware: HardwareTarget::from_tpu_config(&config.target_tpu),
            custom_passes: config.custom_passes.clone(),
            aggressive_mode: matches!(
                config.optimization_level,
                XLAOptimizationLevel::Aggressive | XLAOptimizationLevel::Experimental
            ),
            debug_mode: config.debug_mode,
        };

        let graph_optimizer = GraphOptimizer::new(&pipeline_config);
        let fusion_engine = KernelFusionEngine::new(&pipeline_config);
        let memory_planner = MemoryPlanner::new(config.target_tpu.clone());
        let scheduler = ExecutionScheduler::new(&pipeline_config);

        Self {
            config: pipeline_config,
            graph_optimizer,
            fusion_engine,
            memory_planner,
            scheduler,
            applied_passes: Vec::new(),
            performance_stats: OptimizationStats::default(),
        }
    }

    /// Optimize XLA computation
    pub fn optimize(&mut self, computation: XLAComputation<T>) -> Result<XLAComputation<T>> {
        let start_time = Instant::now();
        let mut current_computation = computation;

        // Graph optimization phase
        if self.config.enable_graph_optimization {
            let pass_start = Instant::now();
            current_computation = self.graph_optimizer.optimize(current_computation)?;
            self.record_pass_time("graph_optimization", pass_start.elapsed());
            self.applied_passes.push("graph_optimization".to_string());
        }

        // Kernel fusion phase
        if self.config.enable_kernel_fusion {
            let pass_start = Instant::now();
            current_computation = self.fusion_engine.fuse_kernels(current_computation)?;
            self.record_pass_time("kernel_fusion", pass_start.elapsed());
            self.applied_passes.push("kernel_fusion".to_string());
        }

        // Memory optimization phase
        if self.config.enable_memory_optimization {
            let pass_start = Instant::now();
            current_computation = self
                .memory_planner
                .optimize_memory_layout(current_computation)?;
            self.record_pass_time("memory_optimization", pass_start.elapsed());
            self.applied_passes.push("memory_optimization".to_string());
        }

        // Scheduling optimization phase
        if self.config.enable_scheduling_optimization {
            let pass_start = Instant::now();
            current_computation = self.scheduler.optimize_schedule(current_computation)?;
            self.record_pass_time("scheduling_optimization", pass_start.elapsed());
            self.applied_passes
                .push("scheduling_optimization".to_string());
        }

        // Apply custom passes
        let custom_passes = self.config.custom_passes.clone();
        for pass_name in &custom_passes {
            let pass_start = Instant::now();
            current_computation = self.apply_custom_pass(pass_name, current_computation)?;
            self.record_pass_time(pass_name, pass_start.elapsed());
            self.applied_passes.push(pass_name.clone());
        }

        self.performance_stats.total_time = start_time.elapsed();
        Ok(current_computation)
    }

    /// Apply custom optimization pass
    fn apply_custom_pass(
        &mut self,
        _pass_name: &str,
        computation: XLAComputation<T>,
    ) -> Result<XLAComputation<T>> {
        // Custom pass application logic would go here
        Ok(computation)
    }

    /// Record optimization pass timing
    fn record_pass_time(&mut self, pass_name: &str, duration: Duration) {
        self.performance_stats
            .pass_times
            .insert(pass_name.to_string(), duration);
    }

    /// Get applied optimization passes
    pub fn get_applied_passes(&self) -> Vec<String> {
        self.applied_passes.clone()
    }

    /// Get optimization statistics
    pub fn get_statistics(&self) -> &OptimizationStats {
        &self.performance_stats
    }

    /// Reset pipeline state
    pub fn reset(&mut self) {
        self.applied_passes.clear();
        self.performance_stats = OptimizationStats::default();
    }
}

impl HardwareTarget {
    /// Create hardware target from TPU configuration
    pub fn from_tpu_config(tpu_config: &super::TPUConfig) -> Self {
        Self {
            tpu_version: format!("{:?}", tpu_config.tpu_version),
            num_cores: tpu_config.num_cores,
            memory_capacity: 16 * 1024 * 1024 * 1024, // Default 16GB
            memory_bandwidth: 900.0,                  // Default 900 GB/s
            compute_capability: ComputeCapability {
                matrix_unit_dims: (128, 128), // Default for TPU
                vector_unit_width: 256,
                supported_dtypes: vec!["BF16".to_string(), "F32".to_string(), "S32".to_string()],
                special_instructions: vec!["MATMUL".to_string(), "CONV".to_string()],
            },
        }
    }
}

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

    #[test]
    fn test_optimization_pipeline_creation() {
        let config = XLACompilerConfig::default();
        let pipeline: OptimizationPipeline<f32> = OptimizationPipeline::new(&config);

        assert_eq!(
            pipeline.config.optimization_level,
            config.optimization_level
        );
        assert_eq!(pipeline.applied_passes.len(), 0);
    }

    #[test]
    fn test_hardware_target_creation() {
        use crate::main_types::{PodTopology, TPUConfig, TPUVersion};

        let tpu_config = TPUConfig {
            tpu_version: TPUVersion::V4,
            num_cores: 8,
            enable_xla: true,
            xla_optimization_level: crate::main_types::XLAOptimizationLevel::Standard,
            mixed_precision: true,
            batch_size_per_core: 32,
            enable_pod_coordination: false,
            pod_topology: PodTopology::Pod2x2,
            memory_optimization: crate::main_types::TPUMemoryOptimization::Balanced,
            gradient_compression: true,
            prefetch_depth: 2,
            experimental_features: false,
        };

        let target = HardwareTarget::from_tpu_config(&tpu_config);
        assert_eq!(target.num_cores, 8);
        // Default memory capacity is 16GB
        assert_eq!(target.memory_capacity, 16 * 1024 * 1024 * 1024);
    }
}