ronn-providers 0.1.0

//! Example NPU (Neural Processing Unit) provider implementation.
//!
//! This module provides a reference implementation for integrating NPU hardware
//! accelerators. It demonstrates the patterns and interfaces needed to create
//! custom hardware providers for specialized AI processors.

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 NPU provider.
#[derive(Debug, Clone)]
pub struct NpuConfig {
    /// NPU device identifier.
    pub device_id: String,
    /// Maximum memory pool size in bytes.
    pub max_memory_pool_bytes: u64,
    /// Enable quantization optimizations.
    pub enable_quantization: bool,
    /// Target precision for operations.
    pub target_precision: String,
    /// Power management mode.
    pub power_mode: NpuPowerMode,
    /// Custom compiler flags.
    pub compiler_flags: Vec<String>,
    /// Profiling configuration.
    pub enable_profiling: bool,
}

impl Default for NpuConfig {
    fn default() -> Self {
        Self {
            device_id: "npu0".to_string(),
            max_memory_pool_bytes: 2 * 1024 * 1024 * 1024, // 2GB
            enable_quantization: true,
            target_precision: "int8".to_string(),
            power_mode: NpuPowerMode::Balanced,
            compiler_flags: vec![
                "-O3".to_string(),
                "-ffast-math".to_string(),
                "-march=native".to_string(),
            ],
            enable_profiling: false,
        }
    }
}

/// NPU power management modes.
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NpuPowerMode {
    /// Maximum performance, higher power consumption.
    Performance,
    /// Balanced performance and power.
    Balanced,
    /// Power-efficient mode, lower performance.
    PowerSaver,
}

/// Example NPU provider implementation.
#[derive(Debug)]
pub struct NpuProvider {
    config: NpuConfig,
    device_memory: Arc<NpuDeviceMemory>,
    profiler: Arc<Mutex<NpuProfiler>>,
    stats: Arc<RwLock<ProviderStats>>,
    initialized: bool,
    hardware_capability: HardwareCapability,
}

impl NpuProvider {
    /// Create a new NPU provider with the given configuration.
    pub fn new(config: NpuConfig) -> Self {
        let device_memory = Arc::new(NpuDeviceMemory::new(config.max_memory_pool_bytes));
        let profiler = Arc::new(Mutex::new(NpuProfiler::new()));
        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: 15.0,
            total_energy_joules: 0.0,
        }));

        // Simulate NPU hardware capabilities
        let hardware_capability = HardwareCapability {
            vendor: "Example NPU Corp".to_string(),
            model: "NPU-X1000".to_string(),
            architecture_version: "2.1".to_string(),
            supported_data_types: vec![
                DataType::I8,
                DataType::U8,
                DataType::I16,
                DataType::F16,
                DataType::F32,
            ],
            max_memory_bytes: config.max_memory_pool_bytes,
            peak_tops: 50.0, // 50 TOPS
            memory_bandwidth_gbps: 400.0,
            supported_operations: vec![
                "Conv2D".to_string(),
                "DepthwiseConv2D".to_string(),
                "MatMul".to_string(),
                "BatchNorm".to_string(),
                "ReLU".to_string(),
                "MaxPool".to_string(),
                "AvgPool".to_string(),
                "Softmax".to_string(),
                "Add".to_string(),
                "Mul".to_string(),
            ],
            features: {
                let mut features = HashMap::new();
                features.insert("quantization".to_string(), "int8,int4".to_string());
                features.insert(
                    "sparsity".to_string(),
                    "structured,unstructured".to_string(),
                );
                features.insert("batch_processing".to_string(), "1-32".to_string());
                features
            },
            power_profile: PowerProfile {
                idle_power_watts: 5.0,
                peak_power_watts: 25.0,
                tdp_watts: 20.0,
                efficiency_tops_per_watt: 2.5,
            },
        };

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

    /// Set the power management mode.
    pub fn set_power_mode(&mut self, mode: NpuPowerMode) -> Result<()> {
        self.config.power_mode = mode;
        // In a real implementation, this would configure the hardware
        tracing::info!("NPU power mode set to {:?}", mode);
        Ok(())
    }

    /// Get the current thermal state of the NPU.
    pub fn get_thermal_state(&self) -> NpuThermalState {
        // Simulate thermal monitoring
        NpuThermalState {
            temperature_celsius: 45.0,
            thermal_throttling_active: false,
            max_safe_temperature: 85.0,
        }
    }
}

impl CustomHardwareProvider for NpuProvider {
    fn provider_name(&self) -> &str {
        "example_npu"
    }

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

    fn is_hardware_available(&self) -> bool {
        // In a real implementation, this would check hardware presence
        // For the example, we'll simulate availability based on device_id
        !self.config.device_id.is_empty()
    }

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

        tracing::info!("Initializing NPU provider: {}", self.config.device_id);

        // Simulate NPU initialization
        std::thread::sleep(std::time::Duration::from_millis(100));

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

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

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

        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 NPU provider");

        // Cleanup resources
        self.device_memory.clear_all_allocations();
        self.initialized = false;

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

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

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

/// NPU thermal state information.
#[derive(Debug, Clone)]
pub struct NpuThermalState {
    /// Current temperature in Celsius.
    pub temperature_celsius: f64,
    /// Whether thermal throttling is active.
    pub thermal_throttling_active: bool,
    /// Maximum safe operating temperature.
    pub max_safe_temperature: f64,
}

/// NPU-specific compiled kernel.
#[derive(Debug)]
pub struct NpuKernel {
    name: String,
    operations: Vec<String>,
    device_memory: Arc<NpuDeviceMemory>,
    profiler: Arc<Mutex<NpuProfiler>>,
    kernel_info: KernelInfo,
    compiled_code: Vec<u8>, // Simulated compiled kernel
}

impl NpuKernel {
    fn compile(
        subgraph: &SubGraph,
        config: &NpuConfig,
        device_memory: Arc<NpuDeviceMemory>,
        profiler: Arc<Mutex<NpuProfiler>>,
    ) -> Result<Self> {
        let compilation_start = Instant::now();

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

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

        // Estimate memory usage and execution time based on operations
        let estimated_memory_bytes = operations.len() as u64 * 1024 * 1024; // 1MB per op
        let estimated_execution_time_us = operations.len() as f64 * 100.0; // 100us per op

        // Simulate compilation with delay
        let compilation_delay = std::time::Duration::from_millis(50 + operations.len() as u64 * 10);
        std::thread::sleep(compilation_delay);

        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: 0.8, // Assume 80% utilization
            compilation_time_ms,
        };

        // Generate simulated compiled code
        let compiled_code = vec![0u8; 1024]; // Dummy compiled kernel

        Ok(Self {
            name,
            operations,
            device_memory,
            profiler,
            kernel_info,
            compiled_code,
        })
    }
}

impl CustomKernel for NpuKernel {
    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); // Release lock

        // Simulate kernel execution
        let execution_delay =
            std::time::Duration::from_micros(self.kernel_info.estimated_execution_time_us as u64);
        std::thread::sleep(execution_delay);

        // Create dummy output tensors
        let outputs: Vec<Tensor> = inputs
            .iter()
            .map(|input| {
                // For this example, just pass through the input as output
                input.clone()
            })
            .collect();

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

        tracing::debug!(
            "NPU 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 * 0.9,
            max_execution_time_us: self.kernel_info.estimated_execution_time_us * 1.1,
            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 NPU kernel '{}'", self.name);
        // Simulate warmup with a quick execution
        std::thread::sleep(std::time::Duration::from_millis(10));
        Ok(())
    }
}

/// NPU-specific device memory implementation.
#[derive(Debug)]
pub struct NpuDeviceMemory {
    max_pool_size: u64,
    allocated_buffers: Arc<Mutex<HashMap<u64, DeviceBuffer>>>,
    next_handle: Arc<Mutex<u64>>,
    total_allocated: Arc<Mutex<u64>>,
}

impl NpuDeviceMemory {
    fn new(max_pool_size: u64) -> Self {
        Self {
            max_pool_size,
            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 NpuDeviceMemory {
    fn allocate(&self, size: usize, alignment: usize) -> Result<DeviceBuffer> {
        let mut total = self.total_allocated.lock().unwrap();
        if *total + size as u64 > self.max_pool_size {
            return Err(anyhow!("NPU memory pool exhausted"));
        }

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

        let buffer = DeviceBuffer {
            handle,
            size,
            alignment,
            device_id: 0,
            memory_type: "NPU_HBM".to_string(),
        };

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

        tracing::debug!("NPU allocated {} bytes, handle: {}", size, 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!(
                "NPU deallocated {} bytes, handle: {}",
                buffer.size,
                buffer.handle
            );
            Ok(())
        } else {
            Err(anyhow!("Invalid NPU 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 memory copy
        let copy_delay = std::time::Duration::from_nanos(host_data.len() as u64);
        std::thread::sleep(copy_delay);

        tracing::debug!("NPU copied {} bytes to device", host_data.len());
        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 memory copy
        let copy_delay = std::time::Duration::from_nanos(host_data.len() as u64);
        std::thread::sleep(copy_delay);

        tracing::debug!("NPU copied {} bytes from device", host_data.len());
        Ok(())
    }

    fn get_memory_info(&self) -> DeviceMemoryInfo {
        let total_allocated = *self.total_allocated.lock().unwrap();
        DeviceMemoryInfo {
            total_bytes: self.max_pool_size,
            available_bytes: self.max_pool_size.saturating_sub(total_allocated),
            allocated_bytes: total_allocated,
            bandwidth_gbps: 400.0,
            memory_type: "HBM2E".to_string(),
        }
    }

    fn synchronize(&self) -> Result<()> {
        // Simulate synchronization
        std::thread::sleep(std::time::Duration::from_micros(10));
        Ok(())
    }

    fn can_access(&self, _buffer1: &DeviceBuffer, _buffer2: &DeviceBuffer) -> bool {
        // For NPU, all buffers are in the same address space
        true
    }
}

/// NPU profiler implementation.
#[derive(Debug)]
pub struct NpuProfiler {
    active_sessions: HashMap<u64, ProfilingSession>,
    completed_sessions: Vec<ProfilingResults>,
    next_session_id: u64,
}

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

impl HardwareProfiler for NpuProfiler {
    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: 1024 * 1024, // 1MB
            hardware_utilization: 85.0,
            power_consumption_watts: 20.0,
            energy_consumed_mj: execution_time_us * 20.0 / 1_000_000.0,
            custom_metrics: {
                let mut metrics = HashMap::new();
                metrics.insert(
                    "npu_ops_per_second".to_string(),
                    1_000_000.0 / execution_time_us,
                );
                metrics.insert("npu_cache_hit_rate".to_string(), 0.95);
                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 NPU provider with default configuration.
pub fn create_npu_provider() -> Result<NpuProvider> {
    Ok(NpuProvider::new(NpuConfig::default()))
}

/// Create a new NPU provider with custom configuration.
pub fn create_npu_provider_with_config(config: NpuConfig) -> Result<NpuProvider> {
    Ok(NpuProvider::new(config))
}

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

    #[test]
    fn test_npu_provider_creation() {
        let provider = create_npu_provider().unwrap();
        assert_eq!(provider.provider_name(), "example_npu");
        assert!(provider.is_hardware_available());
    }

    #[test]
    fn test_npu_provider_initialization() {
        let mut provider = create_npu_provider().unwrap();
        assert!(provider.initialize().is_ok());
        assert!(provider.initialize().is_ok()); // Should be idempotent
    }

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

        let buffer = memory.allocate(1024, 256).unwrap();
        assert_eq!(buffer.size, 1024);
        assert_eq!(buffer.alignment, 256);
        assert_eq!(buffer.memory_type, "NPU_HBM");

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

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

        let subgraph = SubGraph {
            name: Some("test_subgraph".to_string()),
            nodes: vec![Node {
                name: "conv1".to_string(),
                op_type: "Conv2D".to_string(),
                inputs: vec!["input".to_string()],
                outputs: vec!["conv1_out".to_string()],
                attributes: std::collections::HashMap::new(),
            }],
            inputs: vec!["input".to_string()],
            outputs: vec!["conv1_out".to_string()],
        };

        let kernel = provider.compile_subgraph(&subgraph).unwrap();
        let info = kernel.get_kernel_info();
        assert_eq!(info.name, "npu_kernel_test_subgraph");
        assert_eq!(info.operations, vec!["Conv2D"]);
    }

    #[test]
    fn test_npu_power_mode() {
        let mut provider = create_npu_provider().unwrap();

        assert!(provider.set_power_mode(NpuPowerMode::Performance).is_ok());
        assert_eq!(provider.config.power_mode, NpuPowerMode::Performance);

        assert!(provider.set_power_mode(NpuPowerMode::PowerSaver).is_ok());
        assert_eq!(provider.config.power_mode, NpuPowerMode::PowerSaver);
    }

    #[test]
    fn test_npu_thermal_monitoring() {
        let provider = create_npu_provider().unwrap();
        let thermal_state = provider.get_thermal_state();

        assert!(thermal_state.temperature_celsius > 0.0);
        assert!(thermal_state.max_safe_temperature > thermal_state.temperature_celsius);
    }

    #[test]
    fn test_npu_profiler() {
        let mut profiler = NpuProfiler::new();

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

        assert_eq!(results.operation_name, "test_op");
        assert!(results.execution_time_us > 0.0);

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