//! TPU (Tensor Processing Unit) compatibility infrastructure
//!
//! This module provides infrastructure for running neural networks on TPUs including:
//! - TPU device detection and enumeration
//! - XLA (Accelerated Linear Algebra) compilation integration
//! - TPU-specific memory management
//! - Distributed TPU pod coordination
//! - TPU kernel optimization and scheduling
//! - Performance profiling and optimization

use crate::error::{NeuralError, Result};
use scirs2_core::ndarray::{ArrayD, Dimension};
use scirs2_core::numeric::Float;
use regex;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};
/// TPU device information and capabilities
#[derive(Debug, Clone)]
pub struct TPUDevice {
    /// Device ID
    pub device_id: u32,
    /// Device name (e.g., "TPU v4")
    pub device_name: String,
    /// TPU generation (v2, v3, v4, v5, etc.)
    pub generation: TPUGeneration,
    /// Number of cores available
    pub num_cores: u32,
    /// Memory capacity per core (bytes)
    pub memory_per_core: usize,
    /// Peak performance (TOPS)
    pub peak_tops: f64,
    /// Available for computation
    pub available: bool,
    /// Current utilization percentage
    pub utilization: f64,
    /// Temperature (Celsius)
    pub temperature: f32,
}
/// TPU generations with different capabilities
#[derive(Debug, Clone, PartialEq)]
pub enum TPUGeneration {
    V2,
    V3,
    V4,
    V5,
    /// Future/custom TPU versions
    Custom(String),
/// TPU Pod configuration for distributed training
pub struct TPUPodConfig {
    /// Pod slice topology (e.g., 2x2x1 for v3-8)
    pub topology: Vec<u32>,
    /// Total number of cores in the pod
    pub total_cores: u32,
    /// Inter-chip communication bandwidth (GB/s)
    pub interconnect_bandwidth: f64,
    /// Pod coordinator host address
    pub coordinator_address: String,
    /// Worker nodes in the pod
    pub worker_nodes: Vec<TPUWorkerNode>,
/// Individual worker node in a TPU pod
pub struct TPUWorkerNode {
    /// Node ID within the pod
    pub node_id: u32,
    /// Host address for communication
    pub host_address: String,
    /// TPU devices on this node
    pub devices: Vec<TPUDevice>,
    /// Node status
    pub status: TPUNodeStatus,
/// Status of a TPU worker node
pub enum TPUNodeStatus {
    Online,
    Offline,
    Busy,
    Error(String),
/// XLA compilation context for TPU operations
pub struct XLACompiler {
    /// Target TPU generation
    target_generation: TPUGeneration,
    /// Compilation cache
    compilation_cache: Arc<RwLock<HashMap<XLAProgram, CompiledTPUKernel>>>,
    /// Optimization settings
    optimization_config: XLAOptimizationConfig,
    /// Compilation statistics
    stats: Arc<RwLock<XLACompilationStats>>,
/// XLA program representation
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct XLAProgram {
    /// Program operations in HLO (High Level Operations) format
    pub hlotext: String,
    /// Input shapes and types
    pub input_specs: Vec<TensorSpec>,
    /// Output shapes and types
    pub output_specs: Vec<TensorSpec>,
    /// Program unique identifier
    pub program_id: String,
/// Tensor specification for XLA
pub struct TensorSpec {
    /// Tensor shape
    pub shape: Vec<usize>,
    /// Data type
    pub dtype: TPUDataType,
    /// Layout specification
    pub layout: Option<TPULayout>,
/// TPU-supported data types
pub enum TPUDataType {
    F32,
    F16,
    BF16,
    F64,
    I8,
    I16,
    I32,
    I64,
    U8,
    U16,
    U32,
    U64,
    BOOL,
    C64,
    C128,
/// Memory layout for TPU tensors
pub struct TPULayout {
    /// Dimension order for memory layout
    pub minor_to_major: Vec<usize>,
    /// Padding and alignment information
    pub paddedshape: Vec<usize>,
    /// Memory space (HBM, VMEM, etc.)
    pub memory_space: TPUMemorySpace,
/// TPU memory spaces
pub enum TPUMemorySpace {
    /// High Bandwidth Memory (main memory)
    HBM,
    /// Vector Memory (on-chip cache)
    VMEM,
    /// Scalar Memory
    SMEM,
/// Compiled TPU kernel
pub struct CompiledTPUKernel {
    /// Compiled binary code
    pub binary_code: Vec<u8>,
    /// Kernel metadata
    pub metadata: TPUKernelMetadata,
    /// Memory allocation requirements
    pub memory_requirements: TPUMemoryRequirements,
    /// Performance estimates
    pub performance_estimates: TPUPerformanceEstimates,
    /// Compilation timestamp
    pub compiled_at: Instant,
/// TPU kernel metadata
pub struct TPUKernelMetadata {
    /// Kernel name
    pub name: String,
    pub target_generation: TPUGeneration,
    /// Required core count
    pub required_cores: u32,
    /// Estimated execution time (microseconds)
    pub estimated_execution_time_us: u64,
    /// Memory footprint (bytes)
    pub memory_footprint: usize,
/// TPU memory allocation requirements
pub struct TPUMemoryRequirements {
    /// HBM allocation size
    pub hbm_bytes: usize,
    /// VMEM allocation size
    pub vmem_bytes: usize,
    /// SMEM allocation size
    pub smem_bytes: usize,
    /// Memory alignment requirements
    pub alignment: usize,
    /// Persistent memory allocation
    pub persistent: bool,
/// TPU performance estimates
pub struct TPUPerformanceEstimates {
    /// Estimated FLOPS
    pub estimated_flops: u64,
    /// Memory bandwidth utilization
    pub memory_bandwidth_utilization: f64,
    /// Compute utilization
    pub compute_utilization: f64,
    /// Expected latency (microseconds)
    pub expected_latency_us: u64,
    /// Expected throughput (operations/second)
    pub expected_throughput: f64,
/// XLA optimization configuration
pub struct XLAOptimizationConfig {
    /// Enable auto-sharding for distributed execution
    pub auto_sharding: bool,
    /// Enable operator fusion
    pub operator_fusion: bool,
    /// Enable layout optimization
    pub layout_optimization: bool,
    /// Enable constant folding
    pub constant_folding: bool,
    /// Enable algebraic simplification
    pub algebraic_simplification: bool,
    /// Maximum fusion depth
    pub max_fusion_depth: u32,
    /// Memory optimization level
    pub memory_optimization_level: u32,
impl Default for XLAOptimizationConfig {
    fn default() -> Self {
        Self {
            auto_sharding: true,
            operator_fusion: true,
            layout_optimization: true,
            constant_folding: true,
            algebraic_simplification: true,
            max_fusion_depth: 8,
            memory_optimization_level: 2,
        }
    }
/// XLA compilation statistics
#[derive(Debug, Clone, Default)]
pub struct XLACompilationStats {
    /// Number of programs compiled
    pub programs_compiled: u64,
    /// Total compilation time (milliseconds)
    pub total_compile_time_ms: f64,
    /// Average compilation time (milliseconds)
    pub avg_compile_time_ms: f64,
    /// Cache hit rate
    pub cache_hit_rate: f64,
    /// Memory usage for compilation (bytes)
    pub compilation_memory_usage: usize,
/// TPU memory manager for efficient allocation
pub struct TPUMemoryManager {
    /// Available TPU devices
    devices: Vec<TPUDevice>,
    /// Memory pools per device
    memory_pools: HashMap<u32, TPUMemoryPool>,
    /// Allocation tracker
    allocations: Arc<RwLock<HashMap<AllocationId, TPUAllocation>>>,
    /// Memory statistics
    stats: Arc<RwLock<TPUMemoryStats>>,
/// TPU memory pool for a specific device
#[derive(Debug)]
pub struct TPUMemoryPool {
    device_id: u32,
    /// Available memory blocks
    free_blocks: VecDeque<MemoryBlock>,
    /// Allocated memory blocks
    allocated_blocks: HashMap<AllocationId, MemoryBlock>,
    /// Total pool size
    total_size: usize,
    /// Currently allocated size
    allocated_size: usize,
/// Memory block in TPU memory
pub struct MemoryBlock {
    /// Start offset in device memory
    pub offset: usize,
    /// Block size in bytes
    pub size: usize,
    /// Memory space type
    /// Alignment requirements
/// Unique allocation identifier
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct AllocationId(pub u64);
/// TPU memory allocation
pub struct TPUAllocation {
    /// Allocation ID
    pub id: AllocationId,
    /// Device ID where allocated
    /// Memory block details
    pub block: MemoryBlock,
    /// Allocation timestamp
    pub allocated_at: Instant,
    /// Reference count for shared allocations
    pub ref_count: Arc<Mutex<u32>>,
/// TPU memory usage statistics
pub struct TPUMemoryStats {
    /// Total allocations made
    pub total_allocations: u64,
    /// Currently active allocations
    pub active_allocations: u64,
    /// Peak memory usage across all devices
    pub peak_memory_usage: usize,
    /// Current memory usage
    pub current_memory_usage: usize,
    /// Memory fragmentation percentage
    pub fragmentation_percentage: f64,
/// TPU scheduler for optimal workload distribution
pub struct TPUScheduler {
    /// Task queue
    task_queue: Arc<Mutex<VecDeque<TPUTask>>>,
    /// Running tasks
    running_tasks: Arc<RwLock<HashMap<TaskId, RunningTask>>>,
    /// Scheduling statistics
    stats: Arc<RwLock<TPUSchedulingStats>>,
    /// Scheduling policy
    policy: SchedulingPolicy,
/// TPU task for execution
pub struct TPUTask {
    /// Task unique identifier
    pub task_id: TaskId,
    /// Compiled kernel to execute
    pub kernel: CompiledTPUKernel,
    /// Input tensors
    pub inputs: Vec<TPUTensor>,
    /// Expected output shapes
    pub outputshapes: Vec<Vec<usize>>,
    /// Task priority
    pub priority: TaskPriority,
    /// Maximum execution time allowed
    pub timeout: Duration,
    /// Callback for completion
    pub completion_callback: Option<Box<dyn Fn(TaskResult) + Send + Sync>>,
/// Task unique identifier
pub struct TaskId(pub u64);
/// Task priority levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum TaskPriority {
    Low,
    Normal,
    High,
    Critical,
/// Running task information
pub struct RunningTask {
    /// Task details
    pub task: TPUTask,
    /// Assigned device
    /// Start time
    pub started_at: Instant,
    /// Expected completion time
    pub expected_completion: Instant,
/// Task execution result
pub enum TaskResult {
    Success {
        /// Output tensors
        outputs: Vec<TPUTensor>,
        /// Execution time
        execution_time: Duration,
        /// Device utilization during execution
        device_utilization: f64,
    },
    Error {
        /// Error description
        error: String,
        /// Partial results if any
        partial_outputs: Option<Vec<TPUTensor>>,
    Timeout {
        /// Time elapsed before timeout
        elapsed: Duration,
/// TPU scheduling statistics
pub struct TPUSchedulingStats {
    /// Total tasks scheduled
    pub total_tasks: u64,
    /// Successfully completed tasks
    pub completed_tasks: u64,
    /// Failed tasks
    pub failed_tasks: u64,
    /// Timed out tasks
    pub timeout_tasks: u64,
    /// Average execution time
    pub avg_execution_time_ms: f64,
    /// Average queue wait time
    pub avg_queue_wait_time_ms: f64,
    /// Device utilization across all devices
    pub avg_device_utilization: f64,
/// Scheduling policies
pub enum SchedulingPolicy {
    /// First-Come-First-Served
    FCFS,
    /// Shortest Job First
    SJF,
    /// Priority-based scheduling
    Priority,
    /// Round-robin across devices
    RoundRobin,
    /// Load balancing based on device utilization
    LoadBalance,
    /// Custom scheduling algorithm
    Custom(Box<dyn Fn(&[TPUDevice], &TPUTask) -> Option<u32> + Send + Sync>),
/// TPU operation types for neural network computations
pub enum TPUOperation<F: Float> {
    /// Matrix multiplication operation
    MatMul {
        /// Transpose first input
        transpose_a: bool,
        /// Transpose second input
        transpose_b: bool,
        /// Phantom data for type parameter
        _phantom: std::marker::PhantomData<F>,
    /// Element-wise operations
    ElementWise {
        /// Operation type
        op: ElementWiseTPUOp,
    /// Convolution operation
    Convolution {
        /// Stride values
        stride: (usize, usize),
        /// Padding values
        padding: (usize, usize),
        /// Dilation values
        dilation: (usize, usize),
    /// Activation functions
    Activation {
        /// Activation type
        activation: TPUActivationType,
    /// Reduction operations
    Reduction {
        /// Reduction type
        reduction: TPUReductionType,
        /// Reduction axes
        axes: Vec<usize>,
        /// Keep dimensions
        keep_dims: bool,
/// Element-wise operations for TPU
pub enum ElementWiseTPUOp {
    Add,
    Subtract,
    Multiply,
    Divide,
    Maximum,
    Minimum,
    Power,
/// TPU activation function types
pub enum TPUActivationType {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
    Swish,
    Softmax,
/// TPU reduction operation types
pub enum TPUReductionType {
    Sum,
    Mean,
    Max,
    Min,
    Product,
/// TPU tensor representation
pub struct TPUTensor {
    /// Tensor data
    pub data: ArrayD<f32>, // Simplified to f32 for now
    /// Tensor specification
    pub spec: TensorSpec,
    /// Device where tensor is allocated
    pub device_id: Option<u32>,
    /// Memory allocation ID
    pub allocation_id: Option<AllocationId>,
/// Main TPU runtime for coordinating all TPU operations
pub struct TPURuntime {
    /// XLA compiler
    xla_compiler: XLACompiler,
    /// Memory manager
    memory_manager: TPUMemoryManager,
    /// Task scheduler
    scheduler: TPUScheduler,
    /// Available devices
    /// Pod configuration (if using distributed setup)
    pod_config: Option<TPUPodConfig>,
    /// Runtime statistics
    stats: Arc<RwLock<TPURuntimeStats>>,
/// TPU runtime statistics
pub struct TPURuntimeStats {
    /// Runtime uptime
    pub uptime: Duration,
    /// Total operations executed
    pub total_operations: u64,
    /// Total data processed (bytes)
    pub total_data_processed: u64,
    /// Average operations per second
    pub avg_ops_per_second: f64,
    /// Power consumption (watts)
    pub power_consumption: f64,
    /// Thermal status across devices
    pub thermal_status: HashMap<u32, f32>,
impl TPURuntime {
    /// Initialize TPU runtime with device discovery
    pub fn initialize() -> Result<Self> {
        // Discover available TPU devices
        let devices = Self::discover_tpu_devices()?;
        if devices.is_empty() {
            return Err(NeuralError::DeviceError("No TPU devices found".to_string()));
        // Initialize XLA compiler for the first available device
        let target_generation = devices[0].generation.clone();
        let xla_compiler = XLACompiler::new(target_generation)?;
        // Initialize memory manager
        let memory_manager = TPUMemoryManager::new(&devices)?;
        // Initialize scheduler
        let scheduler = TPUScheduler::new(devices.clone(), SchedulingPolicy::LoadBalance)?;
        Ok(Self {
            xla_compiler,
            memory_manager,
            scheduler,
            devices,
            pod_config: None,
            stats: Arc::new(RwLock::new(TPURuntimeStats::default())),
        })
    /// Discover available TPU devices on the system
    fn discover_tpu_devices() -> Result<Vec<TPUDevice>> {
        let mut devices = Vec::new();
        // Method 1: Check TPU_VISIBLE_DEVICES environment variable
        if let Ok(visible_devices) = std::env::var("TPU_VISIBLE_DEVICES") {
            if visible_devices != "" {
                let device_ids: Vec<u32> = visible_devices
                    .split(',')
                    .filter_map(|s| s.trim().parse().ok())
                    .collect();
                for device_id in device_ids {
                    devices.push(Self::create_simulated_device(device_id)?);
                }
            }
        // Method 2: Check for TPU_NAME environment variable (Cloud TPU)
        if let Ok(tpu_name) = std::env::var("TPU_NAME") {
            if !tpu_name.is_empty() && devices.is_empty() {
                // Parse TPU configuration from name
                let (generation, num_cores) = Self::parse_tpu_config(&tpu_name)?;
                devices.push(TPUDevice {
                    device_id: 0,
                    device_name: tpu_name.clone(),
                    generation: generation.clone(),
                    num_cores,
                    memory_per_core: Self::get_memory_per_core(&generation),
                    peak_tops: Self::get_peak_tops(&generation),
                    available: true,
                    utilization: 0.0,
                    temperature: 40.0,
                });
        // Method 3: Check for local TPU detection via /dev/accel* devices
            devices.extend(Self::discover_local_tpus()?);
        // Method 4: Check TPU_WORKER_ID for multi-worker setups
        if let Ok(worker_id) = std::env::var("TPU_WORKER_ID") {
            if let Ok(id) = worker_id.parse::<u32>() {
                // In multi-worker setup, each worker gets a subset of devices
                devices = devices
                    .into_iter()
                    .filter(|d| d.device_id % 8 == id)
        Ok(devices)
    /// Create a simulated TPU device for testing
    fn create_simulated_device(_deviceid: u32) -> Result<TPUDevice> {
        // Determine generation based on environment or default to v4
        let generation = if let Ok(gen) = std::env::var("TPU_GENERATION") {
            match gen.as_str() {
                "v2" => TPUGeneration::V2,
                "v3" => TPUGeneration::V3,
                "v4" => TPUGeneration::V4,
                "v5" => TPUGeneration::V5,
                custom => TPUGeneration::Custom(custom.to_string()),
        } else {
            TPUGeneration::V4
        };
        Ok(TPUDevice {
            device_id,
            device_name: format!("TPU {:?}-{}", generation, device_id),
            generation: generation.clone(),
            num_cores: Self::get_default_cores(&generation),
            memory_per_core: Self::get_memory_per_core(&generation),
            peak_tops: Self::get_peak_tops(&generation),
            available: true,
            utilization: 0.0,
            temperature: 40.0 + (device_id as f32 * 2.0), // Vary temperature slightly
    /// Parse TPU configuration from TPU name
    fn parse_tpu_config(_tpuname: &str) -> Result<(TPUGeneration, u32)> {
        // Parse names like "v4-8", "v3-32", etc.
        if let Some(caps) = regex::Regex::new(r"v(\d+)-(\d+)")
            .expect("Operation failed")
            .captures(_tpu_name)
        {
            let version = caps.get(1).expect("Operation failed").as_str();
            let cores =
                caps.get(2).expect("Operation failed").as_str().parse::<u32>().map_err(|_| {
                    NeuralError::InvalidArgument("Invalid TPU core count".to_string())
                })?;
            let generation = match version {
                "2" => TPUGeneration::V2,
                "3" => TPUGeneration::V3,
                "4" => TPUGeneration::V4,
                "5" => TPUGeneration::V5_ => TPUGeneration::Custom(format!("v{}", version)),
            };
            Ok((generation, cores))
            // Default fallback
            Ok((TPUGeneration::V4, 4))
    /// Discover local TPU devices via system interfaces
    fn discover_local_tpus() -> Result<Vec<TPUDevice>> {
        // Check for TPU accelerator devices in /dev/
        if let Ok(entries) = std::fs::read_dir("/dev") {
            for entry in entries.flatten() {
                if let Some(name) = entry.file_name().to_str() {
                    if name.starts_with("accel") {
                        // Parse device ID from name like "accel0", "accel1"
                        if let Some(id_str) = name.strip_prefix("accel") {
                            if let Ok(device_id) = id_str.parse::<u32>() {
                                devices.push(Self::create_simulated_device(device_id)?);
                            }
                        }
                    }
    /// Get default number of cores for TPU generation
    fn get_default_cores(generation: &TPUGeneration) -> u32 {
        match _generation {
            TPUGeneration::V2 => 4,
            TPUGeneration::V3 => 8,
            TPUGeneration::V4 => 4,
            TPUGeneration::V5 => 4,
            TPUGeneration::Custom(_) => 4,
    /// Get memory per core for TPU _generation
    fn get_memory_per_core(generation: &TPUGeneration) -> usize {
            TPUGeneration::V2 => 8 * 1024 * 1024 * 1024,  // 8GB
            TPUGeneration::V3 => 16 * 1024 * 1024 * 1024, // 16GB
            TPUGeneration::V4 => 32 * 1024 * 1024 * 1024, // 32GB
            TPUGeneration::V5 => 64 * 1024 * 1024 * 1024, // 64GB
            TPUGeneration::Custom(_) => 16 * 1024 * 1024 * 1024, // Default 16GB
    /// Get peak TOPS for TPU _generation
    fn get_peak_tops(generation: &TPUGeneration) -> f64 {
            TPUGeneration::V2 => 45.0,         // 45 TOPS
            TPUGeneration::V3 => 123.0,        // 123 TOPS
            TPUGeneration::V4 => 275.0,        // 275 TOPS
            TPUGeneration::V5 => 459.0,        // 459 TOPS (estimated)
            TPUGeneration::Custom(_) => 100.0, // Conservative estimate
    /// Configure the runtime for distributed TPU pod execution
    pub fn configure_pod(&mut self, podconfig: TPUPodConfig) -> Result<()> {
        // Validate pod configuration
        if pod_config.total_cores == 0 {
            return Err(NeuralError::InvalidArgument(
                "Pod must have at least one core".to_string(),
            ));
        // Initialize distributed communication
        self.initialize_pod_communication(&pod_config)?;
        self.pod_config = Some(pod_config);
        Ok(())
    /// Initialize pod communication infrastructure
    fn initialize_pod_communication(&self, _podconfig: &TPUPodConfig) -> Result<()> {
        // In a real implementation, this would set up gRPC/MPI communication
        // between pod workers
    /// Compile and execute a neural network operation on TPU
    pub fn compile_and_execute<F: Float + Debug>(
        &mut self,
        operation: &TPUOperation<F>,
        inputs: &[&ArrayD<F>],
    ) -> Result<Vec<ArrayD<F>>> {
        // Convert to XLA program
        let xla_program = self.convert_to_xla_program(operation, inputs)?;
        // Compile the program
        let compiled_kernel = self.xla_compiler.compile_program(&xla_program)?;
        // Convert inputs to TPU tensors
        let tpu_inputs = self.convert_inputs_to_tpu_tensors(inputs)?;
        // Create TPU task
        let task = TPUTask {
            task_id: TaskId(self.generate_task_id()),
            kernel: compiled_kernel,
            inputs: tpu_inputs,
            outputshapes: self.infer_outputshapes(operation, inputs)?,
            priority: TaskPriority::Normal,
            timeout: Duration::from_secs(60),
            completion_callback: None,
        // Schedule and execute the task
        let result = self.scheduler.execute_task(task)?;
        // Convert results back to standard tensors
        match result {
            TaskResult::Success { outputs, .. } => {
                Ok(outputs.into_iter().map(|t| t.data).collect())
            TaskResult::Error { error, .. } => Err(NeuralError::ComputationError(format!(
                "TPU execution failed: {}",
                error
            ))),
            TaskResult::Timeout { elapsed } => Err(NeuralError::ComputationError(format!(
                "TPU execution timed out after {:?}",
                elapsed
    /// Allocate memory on TPU device
    pub fn allocate_memory(
        device_id: u32,
        size: usize,
        memory_space: TPUMemorySpace,
    ) -> Result<AllocationId> {
        self.memory_manager.allocate(device_id, size, memory_space)
    /// Free TPU memory allocation
    pub fn free_memory(&mut self, allocationid: AllocationId) -> Result<()> {
        self.memory_manager.free(allocation_id)
    /// Get runtime statistics
    pub fn get_statistics(&self) -> TPURuntimeStats {
        if let Ok(stats) = self.stats.read() {
            stats.clone()
            TPURuntimeStats::default()
    /// Convert operation to XLA program
    fn convert_to_xla_program<F: Float + Debug>(
        &self,
    ) -> Result<XLAProgram> {
        // Convert operation to HLO (High Level Operations) format
        let hlotext = self.generate_hlo_for_operation(operation, inputs)?;
        let input_specs = inputs
            .iter()
            .map(|input| {
                TensorSpec {
                    shape: input.shape().to_vec(),
                    dtype: TPUDataType::F32, // Simplified
                    layout: None,
            })
            .collect();
        let output_specs = self.infer_output_specs(operation, inputs)?;
        Ok(XLAProgram {
            hlotext,
            input_specs,
            output_specs,
            program_id: self.generate_program_id(),
    /// Generate HLO code for operation
    fn generate_hlo_for_operation<F: Float + Debug>(
    ) -> Result<String> {
        match operation {
            TPUOperation::MatMul {
                transpose_a,
                transpose_b,
                ..
            } => {
                let mut hlo = String::new();
                hlo.push_str("HloModule matmul\n");
                hlo.push_str("ENTRY computation {\n");
                hlo.push_str("  arg0 = f32[?, ?] parameter(0)\n");
                hlo.push_str("  arg1 = f32[?, ?] parameter(1)\n");
                if *transpose_a || *transpose_b {
                    if *transpose_a {
                        hlo.push_str("  arg0_t = f32[?, ?] transpose(arg0), dimensions={1,0}\n");
                    if *transpose_b {
                        hlo.push_str("  arg1_t = f32[?, ?] transpose(arg1), dimensions={1,0}\n");
                let lhs = if *transpose_a { "arg0_t" } else { "arg0" };
                let rhs = if *transpose_b { "arg1_t" } else { "arg1" };
                hlo.push_str(&format!("  ROOT result = f32[?, ?] dot({}, {}), lhs_contracting_dims={{1}}, rhs_contracting_dims={{0}}\n", lhs, rhs));
                hlo.push_str("}\n");
                // Replace ? with actual dimensions
                for (i, input) in inputs.iter().enumerate() {
                    let shape_str = input
                        .shape()
                        .iter()
                        .map(|&d| d.to_string())
                        .collect::<Vec<_>>()
                        .join(",");
                    hlo = hlo.replace(
                        &format!("f32[?, ?] parameter({})", i),
                        &format!("f32[{}] parameter({})", shape_str, i),
                    );
                Ok(hlo)
            TPUOperation::ElementWise { op } => {
                hlo.push_str("HloModule elementwise\n");
                hlo.push_str("  arg0 = f32[?] parameter(0)\n");
                hlo.push_str("  arg1 = f32[?] parameter(1)\n");
                let op_name = match op {
                    ElementWiseTPUOp::Add => "add",
                    ElementWiseTPUOp::Multiply => "multiply",
                    ElementWiseTPUOp::Subtract => "subtract",
                    ElementWiseTPUOp::Divide => "divide",
                    ElementWiseTPUOp::Maximum => "maximum",
                    ElementWiseTPUOp::Minimum => "minimum",
                    ElementWiseTPUOp::Power => "power",
                };
                hlo.push_str(&format!("  ROOT result = f32[?] {}(arg0, arg1)\n", op_name));
            TPUOperation::Convolution { .. } => {
                // Simplified convolution HLO
                Ok("HloModule conv\nENTRY computation {\n  input = f32[?,?,?,?] parameter(0)\n  weight = f32[?,?,?,?] parameter(1)\n  ROOT result = f32[?,?,?,?] convolution(input, weight), window={size=3x3}, dim_labels=b01f_01io->b01f\n}\n".to_string())
            TPUOperation::Activation { activation } => {
                hlo.push_str("HloModule activation\n");
                let op_name = match activation {
                    TPUActivationType::ReLU => "maximum(arg0, constant(0))",
                    TPUActivationType::Sigmoid => "logistic(arg0)",
                    TPUActivationType::Tanh => "tanh(arg0)",
                    TPUActivationType::GELU => "multiply(arg0, multiply(constant(0.5), add(constant(1), tanh(multiply(constant(0.7978845608), add(arg0, multiply(constant(0.044715), power(arg0, constant(3))))))))",
                    TPUActivationType::Swish => "multiply(arg0, logistic(arg0))",
                    TPUActivationType::Softmax => "divide(exponential(arg0), reduce(exponential(arg0), constant(0), add))",
                hlo.push_str(&format!("  ROOT result = f32[?] {}\n", op_name));
            TPUOperation::Reduction {
                reduction,
                axes,
                keep_dims: _,
                hlo.push_str("HloModule reduction\n");
                let (op_name, init_value) = match reduction {
                    TPUReductionType::Sum => ("add", "0"),
                    TPUReductionType::Mean => ("add", "0"), // Will divide by count later
                    TPUReductionType::Max => ("maximum", "-inf"),
                    TPUReductionType::Min => ("minimum", "inf"),
                    TPUReductionType::Product => ("multiply", "1"),
                let axes_str = axes
                    .iter()
                    .map(|&axis| axis.to_string())
                    .collect::<Vec<_>>()
                    .join(",");
                hlo.push_str(&format!(
                    "  ROOT result = f32[?] reduce(arg0, constant({}), {}), dimensions={{{}}}\n",
                    init_value, op_name, axes_str
                ));
    /// Generate unique program ID
    fn generate_program_id(&self) -> String {
        format!(
            "prog_{}",
            std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .expect("Operation failed")
                .as_nanos()
        )
    /// Generate unique task ID
    fn generate_task_id(&self) -> u64 {
        std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .as_nanos() as u64
    /// Convert inputs to TPU tensors
    fn convert_inputs_to_tpu_tensors<F: Float + Debug>(
    ) -> Result<Vec<TPUTensor>> {
        inputs
                // Convert to f32 (simplified)
                let f32_data = input.mapv(|x| x.to_f32().unwrap_or(0.0));
                Ok(TPUTensor {
                    data: f32, data,
                    spec: TensorSpec {
                        shape: input.shape().to_vec(),
                        dtype: TPUDataType::F32,
                        layout: None,
                    },
                    device_id: None,
                    allocation_id: None,
                })
            .collect()
    /// Infer output shapes for operation
    fn infer_outputshapes<F: Float + Debug>(
    ) -> Result<Vec<Vec<usize>>> {
                if inputs.len() < 2 {
                    return Err(NeuralError::InvalidArgument(
                        "MatMul requires 2 inputs".to_string(),
                    ));
                let ashape = inputs[0].shape();
                let bshape = inputs[1].shape();
                let m = if *transpose_a { ashape[1] } else { ashape[0] };
                let n = if *transpose_b { bshape[0] } else { bshape[1] };
                Ok(vec![vec![m, n]])
            TPUOperation::ElementWise { .. } => {
                if inputs.is_empty() {
                        "ElementWise requires at least 1 input".to_string(),
                Ok(vec![inputs[0].shape().to_vec()])
                        "Convolution requires at least 1 input".to_string(),
                // Simplified - return input shape (should calculate actual output shape)
            TPUOperation::Activation { .. } => {
                        "Activation requires at least 1 input".to_string(),
                // Activation functions preserve input shape
                axes, keep_dims, ..
                        "Reduction requires at least 1 input".to_string(),
                let inputshape = inputs[0].shape();
                let mut outputshape = inputshape.to_vec();
                // Remove or keep dimensions based on axes and keep_dims
                if *keep_dims {
                    // Keep dimensions but set size to 1 for reduced axes
                    for &axis in axes {
                        if axis < outputshape.len() {
                            outputshape[axis] = 1;
                } else {
                    // Remove dimensions in reverse order to avoid index issues
                    let mut sorted_axes = axes.clone();
                    sorted_axes.sort_unstable();
                    sorted_axes.reverse();
                    for &axis in &sorted_axes {
                            outputshape.remove(axis);
                Ok(vec![outputshape])
    /// Infer output specifications for operation
    fn infer_output_specs<F: Float + Debug>(
    ) -> Result<Vec<TensorSpec>> {
        let outputshapes = self.infer_outputshapes(operation, inputs)?;
        Ok(outputshapes
            .into_iter()
            .map(|shape| {
                    shape,
            .collect())
impl XLACompiler {
    /// Create a new XLA compiler
    pub fn new(_targetgeneration: TPUGeneration) -> Result<Self> {
        let optimization_config = XLAOptimizationConfig {
            max_fusion_depth: 4,
            target_generation,
            compilation_cache: Arc::new(RwLock::new(HashMap::new())),
            optimization_config,
            stats: Arc::new(RwLock::new(XLACompilationStats::default())),
    /// Compile XLA program to TPU kernel
    pub fn compile_program(&self, program: &XLAProgram) -> Result<CompiledTPUKernel> {
        let start_time = Instant::now();
        // Check cache first
        if let Some(cached_kernel) = self.get_cached_kernel(program) {
            self.update_cache_stats(true);
            return Ok(cached_kernel);
        let compiled_kernel = self.perform_compilation(program)?;
        // Cache the result
        self.cache_kernel(program.clone(), compiled_kernel.clone());
        // Update statistics
        let compile_time = start_time.elapsed().as_millis() as f64;
        self.update_compile_stats(compile_time);
        self.update_cache_stats(false);
        Ok(compiled_kernel)
    /// Perform actual compilation
    fn perform_compilation(&self, program: &XLAProgram) -> Result<CompiledTPUKernel> {
        // In a real implementation, this would call into XLA/JAX compilation
        // For now, we simulate the compilation process
        let binary_code = self.generate_tpu_binary(program)?;
        let metadata = self.generate_kernel_metadata(program)?;
        let memory_requirements = self.analyze_memory_requirements(program)?;
        let performance_estimates = self.estimate_performance(program)?;
        Ok(CompiledTPUKernel {
            binary_code,
            metadata,
            memory_requirements,
            performance_estimates,
            compiled_at: Instant::now(),
    /// Generate TPU binary code (simulated)
    fn generate_tpu_binary(&self, program: &XLAProgram) -> Result<Vec<u8>> {
        // Simulate binary generation based on HLO
        let binary_size = program.hlotext.len() * 4; // Rough estimate
        Ok(vec![0u8; binary_size])
    /// Generate kernel metadata
    fn generate_kernel_metadata(&self, program: &XLAProgram) -> Result<TPUKernelMetadata> {
        let estimated_flops = self.estimate_flops(&program.hlotext)?;
        let estimated_execution_time = (estimated_flops / 1_000_000) as u64; // Rough estimate
        Ok(TPUKernelMetadata {
            name: program.program_id.clone(),
            target_generation: self._target_generation.clone(),
            required_cores: 1, // Simplified
            estimated_execution_time_us: estimated_execution_time,
            memory_footprint: program
                .input_specs
                .iter()
                .map(|spec| {
                    spec.shape.iter().product::<usize>() * 4 // 4 bytes per f32
                .sum(),
    /// Estimate FLOPS from HLO text (simplified)
    fn estimate_flops(&self, hlotext: &str) -> Result<u64> {
        let mut flops = 0u64;
        if hlotext.contains("dot") {
            flops += 1_000_000; // Rough estimate for matrix multiplication
        if hlotext.contains("add") || hlotext.contains("multiply") {
            flops += 100_000; // Rough estimate for element-wise ops
        if hlotext.contains("convolution") {
            flops += 10_000_000; // Rough estimate for convolution
        Ok(flops.max(1000))
    /// Analyze memory requirements for program
    fn analyze_memory_requirements(&self, program: &XLAProgram) -> Result<TPUMemoryRequirements> {
        let total_input_size: usize = program
            .input_specs
            .map(|spec| spec.shape.iter().product::<usize>() * self.dtype_size(&spec.dtype))
            .sum();
        let total_output_size: usize = program
            .output_specs
        let working_memory = (total_input_size + total_output_size) / 2; // Rough estimate
        Ok(TPUMemoryRequirements {
            hbm_bytes: total_input_size + total_output_size,
            vmem_bytes: working_memory,
            smem_bytes: 1024, // Small amount for scalars
            alignment: 128,   // 128-byte alignment for TPU
            persistent: false,
    /// Get size in bytes for TPU data type
    fn dtype_size(&self, dtype: &TPUDataType) -> usize {
        match dtype {
            TPUDataType::F32 | TPUDataType::I32 | TPUDataType::U32 => 4,
            TPUDataType::F64 | TPUDataType::I64 | TPUDataType::U64 | TPUDataType::C64 => 8,
            TPUDataType::F16 | TPUDataType::BF16 | TPUDataType::I16 | TPUDataType::U16 => 2,
            TPUDataType::I8 | TPUDataType::U8 | TPUDataType::BOOL => 1,
            TPUDataType::C128 => 16,
    /// Estimate performance characteristics
    fn estimate_performance(&self, program: &XLAProgram) -> Result<TPUPerformanceEstimates> {
        let memory_bandwidth = match self._target_generation {
            TPUGeneration::V4 => 1200.0, // GB/s for TPU v4
            TPUGeneration::V3 => 900.0,  // GB/s for TPU v3
            _ => 600.0,                  // Conservative estimate
        let compute_throughput = match self._target_generation {
            TPUGeneration::V4 => 275e12, // 275 TOPS
            TPUGeneration::V3 => 123e12, // 123 TOPS
            _ => 50e12,                  // Conservative estimate
        let memory_time_us =
            (memory_requirements.hbm_bytes as f64 / (memory_bandwidth * 1e9)) * 1e6;
        let compute_time_us = (estimated_flops as f64 / compute_throughput) * 1e6;
        let expected_latency_us = memory_time_us.max(compute_time_us) as u64;
        let memory_bandwidth_util = memory_time_us / (memory_time_us + compute_time_us);
        let compute_utilization = compute_time_us / (memory_time_us + compute_time_us);
        Ok(TPUPerformanceEstimates {
            estimated_flops,
            memory_bandwidth_utilization: memory_bandwidth_util,
            compute_utilization,
            expected_latency_us,
            expected_throughput: if expected_latency_us > 0 {
                1_000_000.0 / expected_latency_us as f64
            } else {
                0.0
            },
    /// Get cached kernel if available
    fn get_cached_kernel(&self, program: &XLAProgram) -> Option<CompiledTPUKernel> {
        if let Ok(cache) = self.compilation_cache.read() {
            cache.get(program).cloned()
            None
    /// Cache compiled kernel
    fn cache_kernel(&self, program: XLAProgram, kernel: CompiledTPUKernel) {
        if let Ok(mut cache) = self.compilation_cache.write() {
            cache.insert(program, kernel);
    /// Update cache statistics
    fn update_cache_stats(&self, cachehit: bool) {
        if let Ok(mut stats) = self.stats.write() {
            if cache_hit {
                let total = stats.programs_compiled + 1;
                stats.cache_hit_rate =
                    ((stats.cache_hit_rate * stats.programs_compiled as f64) + 1.0) / total as f64;
    /// Update compilation statistics
    fn update_compile_stats(&self, compile_timems: f64) {
            stats.programs_compiled += 1;
            stats.total_compile_time_ms += compile_time_ms;
            stats.avg_compile_time_ms =
                stats.total_compile_time_ms / stats.programs_compiled as f64;
impl TPUMemoryManager {
    /// Create a new TPU memory manager
    pub fn new(devices: &[TPUDevice]) -> Result<Self> {
        let mut memory_pools = HashMap::new();
        for device in _devices {
            let pool = TPUMemoryPool::new(
                device.device_id,
                device.memory_per_core * device.num_cores as usize,
            )?;
            memory_pools.insert(device.device_id, pool);
            devices: devices.to_vec(),
            memory_pools,
            allocations: Arc::new(RwLock::new(HashMap::new())),
            stats: Arc::new(RwLock::new(TPUMemoryStats::default())),
    /// Allocate memory on specific device
    pub fn allocate(
        // Generate allocation ID first to avoid borrowing conflicts
        let allocation_id = AllocationId(self.generate_allocation_id());
        
        let pool = self
            .memory_pools
            .get_mut(&device_id)
            .ok_or_else(|| NeuralError::DeviceError(format!("Device {} not found", device_id)))?;
        let block = pool.allocate(size, memory_space)?;
        let allocation = TPUAllocation {
            id: allocation_id,
            block,
            allocated_at: Instant::now(),
            ref_count: Arc::new(Mutex::new(1)),
        if let Ok(mut allocations) = self.allocations.write() {
            allocations.insert(allocation_id, allocation);
        self.update_allocation_stats(size, true);
        Ok(allocation_id)
    /// Free memory allocation
    pub fn free(&mut self, allocationid: AllocationId) -> Result<()> {
        let allocation = {
            if let Ok(mut allocations) = self.allocations.write() {
                allocations.remove(&allocation_id)
                return Err(NeuralError::MemoryError(
                    "Failed to access allocations".to_string(),
        if let Some(allocation) = allocation {
            let pool = self
                .memory_pools
                .get_mut(&allocation.device_id)
                .ok_or_else(|| {
                    NeuralError::DeviceError(format!("Device {} not found", allocation.device_id))
            let block_size = allocation.block.size;
            pool.free(allocation.block)?;
            self.update_allocation_stats(block_size, false);
    /// Generate unique allocation ID
    fn generate_allocation_id(&self) -> u64 {
    /// Update allocation statistics
    fn update_allocation_stats(&self, size: usize, allocated: bool) {
            if allocated {
                stats.total_allocations += 1;
                stats.active_allocations += 1;
                stats.current_memory_usage += size;
                stats.peak_memory_usage = stats.peak_memory_usage.max(stats.current_memory_usage);
                stats.active_allocations = stats.active_allocations.saturating_sub(1);
                stats.current_memory_usage = stats.current_memory_usage.saturating_sub(size);
impl TPUMemoryPool {
    /// Create a new memory pool for device
    pub fn new(_device_id: u32, totalsize: usize) -> Result<Self> {
        let mut free_blocks = VecDeque::new();
        // Initialize with one large free block
        free_blocks.push_back(MemoryBlock {
            offset: 0,
            size: total_size,
            memory_space: TPUMemorySpace::HBM,
            alignment: 128,
        });
            free_blocks,
            allocated_blocks: HashMap::new(),
            total_size,
            allocated_size: 0,
    /// Allocate memory block
    pub fn allocate(&mut self, size: usize, memoryspace: TPUMemorySpace) -> Result<MemoryBlock> {
        // Find suitable free block
        let mut block_index = None;
        for (i, block) in self.free_blocks.iter().enumerate() {
            if block.size >= size && block.memory_space == memory_space {
                block_index = Some(i);
                break;
        let block_index = block_index.ok_or_else(|| {
            NeuralError::MemoryError(format!(
                "Insufficient memory: requested {}, available {}",
                size,
                self.total_size - self.allocated_size
            ))
        })?;
        let mut free_block = self.free_blocks.remove(block_index).expect("Operation failed");
        // Create allocated block
        let allocated_block = MemoryBlock {
            offset: free_block.offset,
            size,
            memory_space,
            alignment: free_block.alignment,
        // Return remaining space to free blocks if any
        if free_block.size > size {
            free_block.offset += size;
            free_block.size -= size;
            self.free_blocks.push_back(free_block);
        self.allocated_size += size;
        Ok(allocated_block)
    /// Free memory block
    pub fn free(&mut self, block: MemoryBlock) -> Result<()> {
        self.allocated_size = self.allocated_size.saturating_sub(block.size);
        // Add block back to free list and coalesce adjacent blocks
        self.free_blocks.push_back(block);
        self.coalesce_free_blocks();
    /// Coalesce adjacent free blocks to reduce fragmentation
    fn coalesce_free_blocks(&mut self) {
        // Sort free blocks by offset
        let mut blocks: Vec<MemoryBlock> = self.free_blocks.drain(..).collect();
        blocks.sort_by_key(|b| b.offset);
        let mut coalesced_blocks = VecDeque::new();
        if blocks.is_empty() {
            return;
        let mut current_block = blocks[0].clone();
        for block in blocks.into_iter().skip(1) {
            // Check if blocks are adjacent and in the same memory space
            if current_block.offset + current_block.size == block.offset 
                && current_block.memory_space == block.memory_space 
                && current_block.alignment == block.alignment {
                // Merge blocks
                current_block.size += block.size;
                // Add current block to coalesced list and start new block
                coalesced_blocks.push_back(current_block);
                current_block = block;
        // Add the final block
        coalesced_blocks.push_back(current_block);
        self.free_blocks = coalesced_blocks;
