ronn-providers 0.1.0

//! Example TPU (Tensor Processing Unit) provider implementation.
//!
//! This module provides a reference implementation for integrating Google TPU
//! or TPU-like tensor processing units. It demonstrates high-throughput matrix
//! operations and specialized tensor computation patterns.

use std::any::Any;
use std::collections::HashMap;
use std::sync::{Arc, Mutex, RwLock};
use std::time::Instant;

use anyhow::{Result, anyhow};
use ronn_core::{CompiledKernel, DataType, KernelStats, MemoryUsage, SubGraph, Tensor};

use super::traits::{
    CustomHardwareProvider, CustomKernel, DeviceBuffer, DeviceMemory, DeviceMemoryInfo,
    HardwareCapability, HardwareProfiler, KernelInfo, PowerProfile, ProfilingResults,
    ProfilingSession, ProfilingSummary, ProviderStats,
};

/// Configuration for TPU provider.
#[derive(Debug, Clone)]
pub struct TpuConfig {
    /// TPU core count.
    pub core_count: u32,
    /// TPU generation (v2, v3, v4, v5).
    pub generation: TpuGeneration,
    /// Memory per core in bytes.
    pub memory_per_core_bytes: u64,
    /// Enable bfloat16 precision.
    pub enable_bfloat16: bool,
    /// Matrix unit dimensions.
    pub matrix_unit_size: u32,
    /// Interconnect topology.
    pub interconnect: TpuInterconnect,
    /// XLA compilation settings.
    pub xla_config: XlaConfig,
    /// Pod scaling configuration.
    pub pod_config: Option<TpuPodConfig>,
}

impl Default for TpuConfig {
    fn default() -> Self {
        Self {
            core_count: 8,
            generation: TpuGeneration::V4,
            memory_per_core_bytes: 32 * 1024 * 1024 * 1024, // 32GB per core
            enable_bfloat16: true,
            matrix_unit_size: 128,
            interconnect: TpuInterconnect::ICI,
            xla_config: XlaConfig::default(),
            pod_config: None,
        }
    }
}

/// TPU generation variants.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TpuGeneration {
    V2,
    V3,
    V4,
    V5,
}

impl TpuGeneration {
    fn peak_tops(&self) -> f64 {
        match self {
            TpuGeneration::V2 => 45.0,
            TpuGeneration::V3 => 123.0,
            TpuGeneration::V4 => 275.0,
            TpuGeneration::V5 => 459.0,
        }
    }

    fn memory_bandwidth_gbps(&self) -> f64 {
        match self {
            TpuGeneration::V2 => 600.0,
            TpuGeneration::V3 => 900.0,
            TpuGeneration::V4 => 1200.0,
            TpuGeneration::V5 => 1600.0,
        }
    }
}

/// TPU interconnect types.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TpuInterconnect {
    /// Inter-chip interconnect for single-pod communication.
    ICI,
    /// Data center networking for multi-pod communication.
    DCN,
}

/// XLA (Accelerated Linear Algebra) compiler configuration.
#[derive(Debug, Clone)]
pub struct XlaConfig {
    /// Enable auto-sharding.
    pub auto_sharding: bool,
    /// Optimization passes to enable.
    pub optimization_passes: Vec<String>,
    /// Memory optimization level.
    pub memory_optimization: u8,
    /// Enable experimental features.
    pub experimental_features: bool,
    /// Custom HLO (High Level Operations) passes.
    pub custom_hlo_passes: Vec<String>,
}

impl Default for XlaConfig {
    fn default() -> Self {
        Self {
            auto_sharding: true,
            optimization_passes: vec![
                "constant-folding".to_string(),
                "dead-code-elimination".to_string(),
                "algebraic-simplifier".to_string(),
                "hlo-cse".to_string(),
            ],
            memory_optimization: 3,
            experimental_features: false,
            custom_hlo_passes: Vec::new(),
        }
    }
}

/// TPU Pod configuration for multi-device setups.
#[derive(Debug, Clone)]
pub struct TpuPodConfig {
    /// Pod slice topology (e.g., "2x2", "4x4", "8x8").
    pub topology: String,
    /// Number of chips per host.
    pub chips_per_host: u32,
    /// Number of hosts in the pod.
    pub host_count: u32,
    /// Enable mesh parallelism.
    pub enable_mesh_parallelism: bool,
    /// Data parallel replica count.
    pub data_parallel_replicas: u32,
    /// Model parallel partitions.
    pub model_parallel_partitions: u32,
}

/// Example TPU provider implementation.
#[derive(Debug)]
pub struct TpuProvider {
    config: TpuConfig,
    device_memory: Arc<TpuDeviceMemory>,
    profiler: Arc<Mutex<TpuProfiler>>,
    stats: Arc<RwLock<ProviderStats>>,
    xla_service: Arc<Mutex<XlaService>>,
    initialized: bool,
    hardware_capability: HardwareCapability,
}

impl TpuProvider {
    /// Create a new TPU provider with the given configuration.
    pub fn new(config: TpuConfig) -> Self {
        let total_memory = config.core_count as u64 * config.memory_per_core_bytes;
        let device_memory = Arc::new(TpuDeviceMemory::new(total_memory, config.core_count));
        let profiler = Arc::new(Mutex::new(TpuProfiler::new()));
        let xla_service = Arc::new(Mutex::new(XlaService::new(config.xla_config.clone())));
        let stats = Arc::new(RwLock::new(ProviderStats {
            total_operations: 0,
            average_execution_time_us: 0.0,
            memory_usage_bytes: 0,
            peak_memory_bytes: 0,
            hardware_utilization: 0.0,
            current_power_watts: 200.0,
            total_energy_joules: 0.0,
        }));

        // Configure hardware capabilities based on TPU generation
        let peak_tops = config.generation.peak_tops() * config.core_count as f64;
        let memory_bandwidth = config.generation.memory_bandwidth_gbps();

        let hardware_capability = HardwareCapability {
            vendor: "Google".to_string(),
            model: format!("TPU {:?}", config.generation),
            architecture_version: match config.generation {
                TpuGeneration::V2 => "2.0".to_string(),
                TpuGeneration::V3 => "3.0".to_string(),
                TpuGeneration::V4 => "4.0".to_string(),
                TpuGeneration::V5 => "5.0".to_string(),
            },
            supported_data_types: vec![
                DataType::F32,
                DataType::F16,
                DataType::I32,
                DataType::I8,
                DataType::U8,
            ],
            max_memory_bytes: total_memory,
            peak_tops,
            memory_bandwidth_gbps: memory_bandwidth,
            supported_operations: vec![
                "MatMul".to_string(),
                "Conv2D".to_string(),
                "Conv3D".to_string(),
                "BatchMatMul".to_string(),
                "Einsum".to_string(),
                "Dot".to_string(),
                "Reduce".to_string(),
                "Transpose".to_string(),
                "Reshape".to_string(),
                "Slice".to_string(),
                "Concatenate".to_string(),
                "AllReduce".to_string(),
                "AllGather".to_string(),
                "ReduceScatter".to_string(),
            ],
            features: {
                let mut features = HashMap::new();
                features.insert("bfloat16".to_string(), config.enable_bfloat16.to_string());
                features.insert(
                    "matrix_unit_size".to_string(),
                    config.matrix_unit_size.to_string(),
                );
                features.insert("xla_compilation".to_string(), "true".to_string());
                features.insert("core_count".to_string(), config.core_count.to_string());
                if config.pod_config.is_some() {
                    features.insert("pod_scaling".to_string(), "true".to_string());
                }
                features
            },
            power_profile: PowerProfile {
                idle_power_watts: 50.0,
                peak_power_watts: 250.0 * config.core_count as f64 / 8.0, // Scale with cores
                tdp_watts: 200.0 * config.core_count as f64 / 8.0,
                efficiency_tops_per_watt: peak_tops / (200.0 * config.core_count as f64 / 8.0),
            },
        };

        Self {
            config,
            device_memory,
            profiler,
            stats,
            xla_service,
            initialized: false,
            hardware_capability,
        }
    }

    /// Get TPU mesh dimensions for the current configuration.
    pub fn get_mesh_dimensions(&self) -> Result<(u32, u32)> {
        if let Some(ref pod_config) = self.config.pod_config {
            let dims: Vec<&str> = pod_config.topology.split('x').collect();
            if dims.len() == 2 {
                let x = dims[0].parse::<u32>()?;
                let y = dims[1].parse::<u32>()?;
                Ok((x, y))
            } else {
                Err(anyhow!("Invalid pod topology: {}", pod_config.topology))
            }
        } else {
            // Single-device mesh
            Ok((1, 1))
        }
    }

    /// Configure mesh parallelism for model sharding.
    pub fn configure_mesh_parallelism(
        &mut self,
        data_parallel: u32,
        model_parallel: u32,
    ) -> Result<()> {
        if let Some(ref mut pod_config) = self.config.pod_config {
            pod_config.data_parallel_replicas = data_parallel;
            pod_config.model_parallel_partitions = model_parallel;
            pod_config.enable_mesh_parallelism = true;
            tracing::info!(
                "Configured mesh parallelism: {}x{} (data x model)",
                data_parallel,
                model_parallel
            );
            Ok(())
        } else {
            Err(anyhow!("Pod configuration required for mesh parallelism"))
        }
    }

    /// Get XLA compilation statistics.
    pub fn get_xla_stats(&self) -> Result<XlaStats> {
        let xla_service = self.xla_service.lock().unwrap();
        Ok(xla_service.get_stats())
    }
}

impl CustomHardwareProvider for TpuProvider {
    fn provider_name(&self) -> &str {
        "example_tpu"
    }

    fn get_hardware_capability(&self) -> HardwareCapability {
        self.hardware_capability.clone()
    }

    fn is_hardware_available(&self) -> bool {
        // In a real implementation, this would check TPU availability via Cloud API
        true
    }

    fn initialize(&mut self) -> Result<()> {
        if self.initialized {
            return Ok(());
        }

        tracing::info!(
            "Initializing TPU provider: {} cores, {:?}",
            self.config.core_count,
            self.config.generation
        );

        // Initialize XLA service
        {
            let mut xla_service = self.xla_service.lock().unwrap();
            xla_service.initialize()?;
        }

        // Configure mesh parallelism if pod config is available
        if let Some(ref pod_config) = self.config.pod_config {
            tracing::info!("Setting up TPU pod with topology: {}", pod_config.topology);
            // Simulate pod initialization
            std::thread::sleep(std::time::Duration::from_millis(500));
        }

        self.initialized = true;
        tracing::info!("TPU provider initialized successfully");
        Ok(())
    }

    fn compile_subgraph(&self, subgraph: &SubGraph) -> Result<Box<dyn CustomKernel>> {
        if !self.initialized {
            return Err(anyhow!("TPU provider not initialized"));
        }

        let kernel = TpuKernel::compile(
            subgraph,
            &self.config,
            Arc::clone(&self.device_memory),
            Arc::clone(&self.profiler),
            Arc::clone(&self.xla_service),
        )?;

        Ok(Box::new(kernel))
    }

    fn get_device_memory(&self) -> &dyn DeviceMemory {
        self.device_memory.as_ref()
    }

    fn get_performance_stats(&self) -> ProviderStats {
        self.stats.read().unwrap().clone()
    }

    fn shutdown(&mut self) -> Result<()> {
        if !self.initialized {
            return Ok(());
        }

        tracing::info!("Shutting down TPU provider");

        // Shutdown XLA service
        {
            let mut xla_service = self.xla_service.lock().unwrap();
            xla_service.shutdown()?;
        }

        // Clear memory allocations
        self.device_memory.clear_all_allocations();
        self.initialized = false;

        tracing::info!("TPU provider shutdown complete");
        Ok(())
    }

    fn as_any(&self) -> &dyn Any {
        self
    }

    fn as_any_mut(&mut self) -> &mut dyn Any {
        self
    }
}

/// TPU-specific compiled kernel.
#[derive(Debug)]
pub struct TpuKernel {
    name: String,
    operations: Vec<String>,
    device_memory: Arc<TpuDeviceMemory>,
    profiler: Arc<Mutex<TpuProfiler>>,
    xla_service: Arc<Mutex<XlaService>>,
    kernel_info: KernelInfo,
    hlo_module: Vec<u8>, // Compiled HLO (High Level Operations)
    mesh_config: Option<MeshConfig>,
}

/// Mesh configuration for distributed execution.
#[derive(Debug, Clone)]
struct MeshConfig {
    data_parallel_replicas: u32,
    model_parallel_partitions: u32,
    mesh_shape: (u32, u32),
}

impl TpuKernel {
    fn compile(
        subgraph: &SubGraph,
        config: &TpuConfig,
        device_memory: Arc<TpuDeviceMemory>,
        profiler: Arc<Mutex<TpuProfiler>>,
        xla_service: Arc<Mutex<XlaService>>,
    ) -> Result<Self> {
        let compilation_start = Instant::now();

        let operations: Vec<String> = subgraph
            .nodes
            .iter()
            .map(|node| node.op_type.clone())
            .collect();

        let name = format!(
            "tpu_kernel_{}",
            subgraph.name.as_deref().unwrap_or("unnamed")
        );

        // Compile with XLA
        let mut xla = xla_service.lock().unwrap();
        let hlo_module = xla.compile_subgraph(subgraph, config)?;

        // Estimate memory and execution time based on operations and TPU generation
        let base_memory_per_op = match config.generation {
            TpuGeneration::V2 => 512 * 1024,      // 512KB
            TpuGeneration::V3 => 1024 * 1024,     // 1MB
            TpuGeneration::V4 => 2 * 1024 * 1024, // 2MB
            TpuGeneration::V5 => 4 * 1024 * 1024, // 4MB
        };
        let estimated_memory_bytes = operations.len() as u64 * base_memory_per_op;

        let base_execution_time_us = match config.generation {
            TpuGeneration::V2 => 200.0,
            TpuGeneration::V3 => 150.0,
            TpuGeneration::V4 => 100.0,
            TpuGeneration::V5 => 75.0,
        };
        let estimated_execution_time_us = operations.len() as f64 * base_execution_time_us;

        let compilation_time_ms = compilation_start.elapsed().as_millis() as f64;

        let kernel_info = KernelInfo {
            name: name.clone(),
            operations: operations.clone(),
            estimated_memory_bytes,
            estimated_execution_time_us,
            hardware_utilization: 0.9, // TPUs typically achieve high utilization
            compilation_time_ms,
        };

        // Configure mesh if pod config is available
        let mesh_config = config.pod_config.as_ref().map(|pod| {
            let mesh_shape = if pod.topology.contains('x') {
                let dims: Vec<&str> = pod.topology.split('x').collect();
                if dims.len() == 2 {
                    (dims[0].parse().unwrap_or(1), dims[1].parse().unwrap_or(1))
                } else {
                    (1, 1)
                }
            } else {
                (1, 1)
            };

            MeshConfig {
                data_parallel_replicas: pod.data_parallel_replicas,
                model_parallel_partitions: pod.model_parallel_partitions,
                mesh_shape,
            }
        });

        Ok(Self {
            name,
            operations,
            device_memory,
            profiler,
            xla_service,
            kernel_info,
            hlo_module,
            mesh_config,
        })
    }
}

impl CustomKernel for TpuKernel {
    fn execute(&self, inputs: &[Tensor]) -> Result<Vec<Tensor>> {
        let start_time = Instant::now();

        // Start profiling
        let mut profiler = self.profiler.lock().unwrap();
        let session = profiler.start_profiling(&self.name)?;
        drop(profiler);

        // Execute with XLA runtime
        let mut xla_service = self.xla_service.lock().unwrap();
        let outputs = xla_service.execute_hlo(&self.hlo_module, inputs, &self.mesh_config)?;
        drop(xla_service);

        // Stop profiling
        let mut profiler = self.profiler.lock().unwrap();
        let _results = profiler.stop_profiling(session)?;

        tracing::debug!(
            "TPU kernel '{}' executed in {:?}",
            self.name,
            start_time.elapsed()
        );

        Ok(outputs)
    }

    fn get_memory_usage(&self) -> MemoryUsage {
        MemoryUsage {
            device_bytes: self.kernel_info.estimated_memory_bytes,
            host_bytes: 0,
            shared_bytes: 0,
        }
    }

    fn get_performance_stats(&self) -> KernelStats {
        KernelStats {
            total_executions: 1,
            average_execution_time_us: self.kernel_info.estimated_execution_time_us,
            min_execution_time_us: self.kernel_info.estimated_execution_time_us * 0.8,
            max_execution_time_us: self.kernel_info.estimated_execution_time_us * 1.2,
            total_memory_allocated: self.kernel_info.estimated_memory_bytes,
            peak_memory_usage: self.kernel_info.estimated_memory_bytes,
        }
    }

    fn get_kernel_info(&self) -> KernelInfo {
        self.kernel_info.clone()
    }

    fn warmup(&self) -> Result<()> {
        tracing::debug!("Warming up TPU kernel '{}'", self.name);
        // TPU warmup involves preloading the HLO module
        std::thread::sleep(std::time::Duration::from_millis(50));
        Ok(())
    }
}

/// XLA (Accelerated Linear Algebra) compilation service.
#[derive(Debug)]
struct XlaService {
    config: XlaConfig,
    compiled_modules: HashMap<String, Vec<u8>>,
    compilation_stats: XlaStats,
}

impl XlaService {
    fn new(config: XlaConfig) -> Self {
        Self {
            config,
            compiled_modules: HashMap::new(),
            compilation_stats: XlaStats::default(),
        }
    }

    fn initialize(&mut self) -> Result<()> {
        tracing::info!("Initializing XLA service");
        // Simulate XLA initialization
        std::thread::sleep(std::time::Duration::from_millis(100));
        Ok(())
    }

    fn shutdown(&mut self) -> Result<()> {
        tracing::info!("Shutting down XLA service");
        self.compiled_modules.clear();
        Ok(())
    }

    fn compile_subgraph(&mut self, subgraph: &SubGraph, _config: &TpuConfig) -> Result<Vec<u8>> {
        let module_name = subgraph.name.as_deref().unwrap_or("unnamed");

        if let Some(cached) = self.compiled_modules.get(module_name) {
            tracing::debug!("Using cached HLO module for {}", module_name);
            return Ok(cached.clone());
        }

        tracing::debug!("Compiling subgraph to HLO: {}", module_name);

        // Simulate HLO compilation
        let compilation_time =
            std::time::Duration::from_millis(100 + subgraph.nodes.len() as u64 * 20);
        std::thread::sleep(compilation_time);

        // Generate dummy HLO module
        let hlo_module = format!(
            "HloModule {}\n\nENTRY main {{\n  // Operations: {:?}\n}}",
            module_name,
            subgraph
                .nodes
                .iter()
                .map(|n| &n.op_type)
                .collect::<Vec<_>>()
        )
        .into_bytes();

        // Update compilation stats
        self.compilation_stats.total_compilations += 1;
        self.compilation_stats.total_compilation_time_ms += compilation_time.as_millis() as u64;

        self.compiled_modules
            .insert(module_name.to_string(), hlo_module.clone());
        Ok(hlo_module)
    }

    fn execute_hlo(
        &self,
        hlo_module: &[u8],
        inputs: &[Tensor],
        mesh_config: &Option<MeshConfig>,
    ) -> Result<Vec<Tensor>> {
        // Simulate HLO execution
        let execution_delay = if mesh_config.is_some() {
            // Distributed execution is faster due to parallelism
            std::time::Duration::from_micros(inputs.len() as u64 * 50)
        } else {
            std::time::Duration::from_micros(inputs.len() as u64 * 100)
        };
        std::thread::sleep(execution_delay);

        // For this example, pass through inputs as outputs
        let outputs = inputs.to_vec();

        tracing::debug!(
            "Executed HLO module ({} bytes) with {} inputs",
            hlo_module.len(),
            inputs.len()
        );
        Ok(outputs)
    }

    fn get_stats(&self) -> XlaStats {
        self.compilation_stats.clone()
    }
}

/// XLA compilation and execution statistics.
#[derive(Debug, Clone, Default)]
pub struct XlaStats {
    /// Total number of HLO compilations.
    pub total_compilations: u64,
    /// Total compilation time in milliseconds.
    pub total_compilation_time_ms: u64,
    /// Average compilation time in milliseconds.
    pub average_compilation_time_ms: f64,
    /// Cache hit ratio for compiled modules.
    pub cache_hit_ratio: f64,
}

/// TPU device memory implementation with HBM support.
#[derive(Debug)]
pub struct TpuDeviceMemory {
    max_memory_bytes: u64,
    core_count: u32,
    memory_per_core: u64,
    allocated_buffers: Arc<Mutex<HashMap<u64, DeviceBuffer>>>,
    next_handle: Arc<Mutex<u64>>,
    total_allocated: Arc<Mutex<u64>>,
}

impl TpuDeviceMemory {
    fn new(max_memory_bytes: u64, core_count: u32) -> Self {
        Self {
            max_memory_bytes,
            core_count,
            memory_per_core: max_memory_bytes / core_count as u64,
            allocated_buffers: Arc::new(Mutex::new(HashMap::new())),
            next_handle: Arc::new(Mutex::new(1)),
            total_allocated: Arc::new(Mutex::new(0)),
        }
    }

    fn clear_all_allocations(&self) {
        let mut buffers = self.allocated_buffers.lock().unwrap();
        buffers.clear();
        let mut total = self.total_allocated.lock().unwrap();
        *total = 0;
    }
}

impl DeviceMemory for TpuDeviceMemory {
    fn allocate(&self, size: usize, alignment: usize) -> Result<DeviceBuffer> {
        let mut total = self.total_allocated.lock().unwrap();
        if *total + size as u64 > self.max_memory_bytes {
            return Err(anyhow!("TPU memory exhausted"));
        }

        let mut next_handle = self.next_handle.lock().unwrap();
        let handle = *next_handle;
        *next_handle += 1;

        // Assign to core based on handle (round-robin)
        let core_id = (handle % self.core_count as u64) as u32;

        let buffer = DeviceBuffer {
            handle,
            size,
            alignment,
            device_id: core_id,
            memory_type: "HBM2".to_string(),
        };

        let mut buffers = self.allocated_buffers.lock().unwrap();
        buffers.insert(handle, buffer.clone());
        *total += size as u64;

        tracing::debug!(
            "TPU allocated {} bytes on core {}, handle: {}",
            size,
            core_id,
            handle
        );
        Ok(buffer)
    }

    fn deallocate(&self, buffer: DeviceBuffer) -> Result<()> {
        let mut buffers = self.allocated_buffers.lock().unwrap();
        if buffers.remove(&buffer.handle).is_some() {
            let mut total = self.total_allocated.lock().unwrap();
            *total = total.saturating_sub(buffer.size as u64);
            tracing::debug!(
                "TPU deallocated {} bytes from core {}, handle: {}",
                buffer.size,
                buffer.device_id,
                buffer.handle
            );
            Ok(())
        } else {
            Err(anyhow!("Invalid TPU buffer handle: {}", buffer.handle))
        }
    }

    fn copy_to_device(&self, host_data: &[u8], device_buffer: &DeviceBuffer) -> Result<()> {
        if host_data.len() > device_buffer.size {
            return Err(anyhow!("Host data larger than device buffer"));
        }

        // Simulate high-bandwidth memory copy
        let bandwidth_gbps = 1200.0; // TPU v4 bandwidth
        let copy_time_us = (host_data.len() as f64) / (bandwidth_gbps * 1e9 / 8.0) * 1e6;
        std::thread::sleep(std::time::Duration::from_micros(copy_time_us as u64));

        tracing::debug!(
            "TPU copied {} bytes to core {} in {:.2}us",
            host_data.len(),
            device_buffer.device_id,
            copy_time_us
        );
        Ok(())
    }

    fn copy_from_device(&self, device_buffer: &DeviceBuffer, host_data: &mut [u8]) -> Result<()> {
        if host_data.len() > device_buffer.size {
            return Err(anyhow!("Host buffer smaller than device data"));
        }

        // Simulate high-bandwidth memory copy
        let bandwidth_gbps = 1200.0;
        let copy_time_us = (host_data.len() as f64) / (bandwidth_gbps * 1e9 / 8.0) * 1e6;
        std::thread::sleep(std::time::Duration::from_micros(copy_time_us as u64));

        tracing::debug!(
            "TPU copied {} bytes from core {} in {:.2}us",
            host_data.len(),
            device_buffer.device_id,
            copy_time_us
        );
        Ok(())
    }

    fn get_memory_info(&self) -> DeviceMemoryInfo {
        let total_allocated = *self.total_allocated.lock().unwrap();
        DeviceMemoryInfo {
            total_bytes: self.max_memory_bytes,
            available_bytes: self.max_memory_bytes.saturating_sub(total_allocated),
            allocated_bytes: total_allocated,
            bandwidth_gbps: 1200.0, // TPU v4 HBM2 bandwidth
            memory_type: "HBM2".to_string(),
        }
    }

    fn synchronize(&self) -> Result<()> {
        // TPU synchronization across all cores
        std::thread::sleep(std::time::Duration::from_micros(20));
        Ok(())
    }

    fn can_access(&self, buffer1: &DeviceBuffer, buffer2: &DeviceBuffer) -> bool {
        // All TPU cores can access each other's memory via ICI
        true
    }
}

/// TPU profiler with XLA integration.
#[derive(Debug)]
pub struct TpuProfiler {
    active_sessions: HashMap<u64, ProfilingSession>,
    completed_sessions: Vec<ProfilingResults>,
    next_session_id: u64,
}

impl TpuProfiler {
    fn new() -> Self {
        Self {
            active_sessions: HashMap::new(),
            completed_sessions: Vec::new(),
            next_session_id: 1,
        }
    }
}

impl HardwareProfiler for TpuProfiler {
    fn start_profiling(&mut self, operation_name: &str) -> Result<ProfilingSession> {
        let session_id = self.next_session_id;
        self.next_session_id += 1;

        let session = ProfilingSession {
            session_id,
            operation_name: operation_name.to_string(),
            start_time: Instant::now(),
        };

        self.active_sessions.insert(session_id, session.clone());
        Ok(session)
    }

    fn stop_profiling(&mut self, session: ProfilingSession) -> Result<ProfilingResults> {
        self.active_sessions.remove(&session.session_id);

        let execution_time_us = session.start_time.elapsed().as_micros() as f64;

        let results = ProfilingResults {
            operation_name: session.operation_name,
            execution_time_us,
            memory_usage_bytes: 32 * 1024 * 1024, // 32MB typical
            hardware_utilization: 92.0,           // TPUs typically achieve high utilization
            power_consumption_watts: 200.0,
            energy_consumed_mj: execution_time_us * 200.0 / 1_000_000.0,
            custom_metrics: {
                let mut metrics = HashMap::new();
                metrics.insert(
                    "tpu_ops_per_second".to_string(),
                    10_000_000.0 / execution_time_us,
                );
                metrics.insert("matrix_unit_utilization".to_string(), 0.95);
                metrics.insert("hbm_bandwidth_utilization".to_string(), 0.85);
                metrics.insert("xla_compilation_overhead_us".to_string(), 1000.0);
                metrics
            },
        };

        self.completed_sessions.push(results.clone());
        Ok(results)
    }

    fn get_profiling_summary(&self) -> ProfilingSummary {
        if self.completed_sessions.is_empty() {
            return ProfilingSummary {
                total_operations: 0,
                total_execution_time_us: 0.0,
                average_execution_time_us: 0.0,
                peak_memory_bytes: 0,
                average_utilization: 0.0,
                total_energy_joules: 0.0,
                top_operations_by_time: Vec::new(),
            };
        }

        let total_operations = self.completed_sessions.len() as u64;
        let total_execution_time_us: f64 = self
            .completed_sessions
            .iter()
            .map(|r| r.execution_time_us)
            .sum();
        let average_execution_time_us = total_execution_time_us / total_operations as f64;
        let peak_memory_bytes = self
            .completed_sessions
            .iter()
            .map(|r| r.memory_usage_bytes)
            .max()
            .unwrap_or(0);
        let average_utilization: f64 = self
            .completed_sessions
            .iter()
            .map(|r| r.hardware_utilization)
            .sum::<f64>()
            / total_operations as f64;
        let total_energy_joules: f64 = self
            .completed_sessions
            .iter()
            .map(|r| r.energy_consumed_mj / 1000.0)
            .sum();

        // Get top 5 operations by execution time
        let mut operations_by_time: Vec<(String, f64)> = self
            .completed_sessions
            .iter()
            .map(|r| (r.operation_name.clone(), r.execution_time_us))
            .collect();
        operations_by_time
            .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
        operations_by_time.truncate(5);

        ProfilingSummary {
            total_operations,
            total_execution_time_us,
            average_execution_time_us,
            peak_memory_bytes,
            average_utilization,
            total_energy_joules,
            top_operations_by_time: operations_by_time,
        }
    }

    fn reset_profiling(&mut self) {
        self.active_sessions.clear();
        self.completed_sessions.clear();
        self.next_session_id = 1;
    }
}

/// Create a new TPU provider with default configuration.
pub fn create_tpu_provider() -> Result<TpuProvider> {
    Ok(TpuProvider::new(TpuConfig::default()))
}

/// Create a new TPU provider with custom configuration.
pub fn create_tpu_provider_with_config(config: TpuConfig) -> Result<TpuProvider> {
    Ok(TpuProvider::new(config))
}

/// Create a TPU provider configured for pod deployment.
pub fn create_tpu_pod_provider(topology: &str, generation: TpuGeneration) -> Result<TpuProvider> {
    let dims: Vec<&str> = topology.split('x').collect();
    if dims.len() != 2 {
        return Err(anyhow!("Invalid pod topology: {}", topology));
    }

    let x: u32 = dims[0].parse()?;
    let y: u32 = dims[1].parse()?;
    let total_chips = x * y;

    let pod_config = TpuPodConfig {
        topology: topology.to_string(),
        chips_per_host: 8,                 // Typical TPU host configuration
        host_count: (total_chips + 7) / 8, // Round up
        enable_mesh_parallelism: true,
        data_parallel_replicas: x,
        model_parallel_partitions: y,
    };

    let config = TpuConfig {
        core_count: total_chips * 2, // 2 cores per chip
        generation,
        pod_config: Some(pod_config),
        ..TpuConfig::default()
    };

    Ok(TpuProvider::new(config))
}

#[cfg(test)]
mod tests {
    use super::*;
    use ronn_core::{Node, OpType};

    #[test]
    fn test_tpu_provider_creation() {
        let provider = create_tpu_provider().unwrap();
        assert_eq!(provider.provider_name(), "example_tpu");
        assert!(provider.is_hardware_available());
    }

    #[test]
    fn test_tpu_provider_initialization() {
        let mut provider = create_tpu_provider().unwrap();
        assert!(provider.initialize().is_ok());
    }

    #[test]
    fn test_tpu_memory_allocation() {
        let provider = create_tpu_provider().unwrap();
        let memory = provider.get_device_memory();

        let buffer = memory.allocate(1024 * 1024, 4096).unwrap(); // 1MB
        assert_eq!(buffer.size, 1024 * 1024);
        assert_eq!(buffer.memory_type, "HBM2");

        assert!(memory.deallocate(buffer).is_ok());
    }

    #[test]
    fn test_tpu_pod_creation() {
        let provider = create_tpu_pod_provider("4x4", TpuGeneration::V4).unwrap();
        assert_eq!(provider.config.core_count, 32); // 4x4x2
        assert!(provider.config.pod_config.is_some());

        let mesh_dims = provider.get_mesh_dimensions().unwrap();
        assert_eq!(mesh_dims, (4, 4));
    }

    #[test]
    fn test_tpu_mesh_parallelism() {
        let mut provider = create_tpu_pod_provider("2x2", TpuGeneration::V3).unwrap();
        assert!(provider.configure_mesh_parallelism(2, 2).is_ok());

        if let Some(ref pod_config) = provider.config.pod_config {
            assert_eq!(pod_config.data_parallel_replicas, 2);
            assert_eq!(pod_config.model_parallel_partitions, 2);
        }
    }

    #[test]
    fn test_tpu_kernel_compilation() {
        let mut provider = create_tpu_provider().unwrap();
        provider.initialize().unwrap();

        let subgraph = SubGraph {
            name: Some("test_matmul".to_string()),
            nodes: vec![Node {
                name: "matmul1".to_string(),
                op_type: "MatMul".to_string(),
                inputs: vec!["a".to_string(), "b".to_string()],
                outputs: vec!["c".to_string()],
                attributes: std::collections::HashMap::new(),
            }],
            inputs: vec!["a".to_string(), "b".to_string()],
            outputs: vec!["c".to_string()],
        };

        let kernel = provider.compile_subgraph(&subgraph).unwrap();
        let info = kernel.get_kernel_info();
        assert_eq!(info.name, "tpu_kernel_test_matmul");
        assert_eq!(info.operations, vec!["MatMul"]);
    }

    #[test]
    fn test_xla_service() {
        let mut xla = XlaService::new(XlaConfig::default());
        assert!(xla.initialize().is_ok());

        let subgraph = SubGraph {
            name: Some("test".to_string()),
            nodes: vec![],
            inputs: vec![],
            outputs: vec![],
        };

        let config = TpuConfig::default();
        let hlo_module = xla.compile_subgraph(&subgraph, &config).unwrap();
        assert!(!hlo_module.is_empty());

        let stats = xla.get_stats();
        assert_eq!(stats.total_compilations, 1);
    }

    #[test]
    fn test_tpu_profiler() {
        let mut profiler = TpuProfiler::new();

        let session = profiler.start_profiling("test_matmul").unwrap();
        std::thread::sleep(std::time::Duration::from_millis(10));
        let results = profiler.stop_profiling(session).unwrap();

        assert_eq!(results.operation_name, "test_matmul");
        assert!(results.execution_time_us > 0.0);
        assert!(results.hardware_utilization > 90.0); // TPUs achieve high utilization

        let summary = profiler.get_profiling_summary();
        assert_eq!(summary.total_operations, 1);
    }

    #[test]
    fn test_tpu_generation_specs() {
        assert_eq!(TpuGeneration::V2.peak_tops(), 45.0);
        assert_eq!(TpuGeneration::V4.peak_tops(), 275.0);
        assert_eq!(TpuGeneration::V5.peak_tops(), 459.0);
    }
}