inferno-ai 0.10.3

#![allow(dead_code, unused_imports, unused_variables)]
// Hardware acceleration module for Inferno AI/ML platform
// Provides CUDA, ROCm, Metal, and CPU SIMD optimizations

use anyhow::Result;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;

/// Hardware acceleration configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareConfig {
    pub enabled: bool,
    pub gpu_acceleration: bool,
    pub cpu_simd_optimization: bool,
    pub mixed_precision: bool,
    pub multi_gpu_support: bool,
    pub gpu_memory_limit_mb: Option<usize>,
    pub preferred_gpu_vendor: GpuVendor,
    pub cpu_thread_count: Option<usize>,
    pub tensor_core_usage: bool,
    pub dynamic_optimization: bool,
}

impl Default for HardwareConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            gpu_acceleration: true,
            cpu_simd_optimization: true,
            mixed_precision: true,
            multi_gpu_support: false,
            gpu_memory_limit_mb: None,
            preferred_gpu_vendor: GpuVendor::Auto,
            cpu_thread_count: None,
            tensor_core_usage: true,
            dynamic_optimization: true,
        }
    }
}

/// GPU vendor preferences
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GpuVendor {
    Auto,
    Nvidia,
    Amd,
    Intel,
    Apple,
}

/// Hardware capabilities detection
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareCapabilities {
    pub cpu_cores: usize,
    pub cpu_threads: usize,
    pub cpu_simd_support: Vec<SimdInstructionSet>,
    pub gpu_devices: Vec<GpuDevice>,
    pub total_memory_mb: usize,
    pub gpu_memory_mb: usize,
    pub supports_mixed_precision: bool,
    pub supports_tensor_cores: bool,
}

/// SIMD instruction set support
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SimdInstructionSet {
    SSE2,
    SSE3,
    SSE4_1,
    SSE4_2,
    AVX,
    AVX2,
    AVX512,
    NEON, // ARM
}

/// GPU device information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GpuDevice {
    pub id: usize,
    pub name: String,
    pub vendor: GpuVendor,
    pub memory_mb: usize,
    pub compute_capability: Option<String>,
    pub supports_fp16: bool,
    pub supports_int8: bool,
    pub max_threads_per_block: usize,
    pub multiprocessor_count: usize,
}

/// Hardware optimization metrics
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct HardwareMetrics {
    pub gpu_utilization: f64,
    pub cpu_utilization: f64,
    pub memory_bandwidth_utilization: f64,
    pub tensor_throughput_gops: f64,
    pub mixed_precision_speedup: f64,
    pub simd_operations_per_second: f64,
    pub gpu_memory_usage_mb: f64,
}

/// CUDA-specific optimizations
pub struct CudaOptimizer {
    device_count: usize,
    devices: Vec<GpuDevice>,
    current_device: Option<usize>,
}

impl CudaOptimizer {
    pub fn new() -> Result<Self> {
        // Simulate CUDA initialization
        let devices = Self::detect_cuda_devices()?;
        let device_count = devices.len();

        Ok(Self {
            device_count,
            devices,
            current_device: if device_count > 0 { Some(0) } else { None },
        })
    }

    fn detect_cuda_devices() -> Result<Vec<GpuDevice>> {
        // Simulate CUDA device detection
        // In a real implementation, this would use CUDA runtime API
        let mut devices = Vec::new();

        // Mock device for testing
        if cfg!(feature = "cuda") || std::env::var("SIMULATE_CUDA").is_ok() {
            devices.push(GpuDevice {
                id: 0,
                name: "NVIDIA GeForce RTX 4090".to_string(),
                vendor: GpuVendor::Nvidia,
                memory_mb: 24576, // 24GB
                compute_capability: Some("8.9".to_string()),
                supports_fp16: true,
                supports_int8: true,
                max_threads_per_block: 1024,
                multiprocessor_count: 128,
            });
        }

        Ok(devices)
    }

    pub async fn optimize_for_cuda(&self, model_data: &[u8]) -> Result<Vec<u8>> {
        if self.current_device.is_none() {
            return Ok(model_data.to_vec());
        }

        tracing::info!("Applying CUDA optimizations");

        // Simulate CUDA-specific optimizations
        tokio::time::sleep(std::time::Duration::from_millis(50)).await;

        // In a real implementation, this would:
        // - Optimize kernel launches
        // - Configure memory coalescing
        // - Set up tensor core usage
        // - Apply mixed precision optimizations

        Ok(model_data.to_vec())
    }

    pub fn get_device_info(&self, device_id: usize) -> Option<&GpuDevice> {
        self.devices.get(device_id)
    }
}

/// ROCm (AMD GPU) optimizations
pub struct RocmOptimizer {
    devices: Vec<GpuDevice>,
}

impl RocmOptimizer {
    pub fn new() -> Result<Self> {
        let devices = Self::detect_rocm_devices()?;
        Ok(Self { devices })
    }

    fn detect_rocm_devices() -> Result<Vec<GpuDevice>> {
        let mut devices = Vec::new();

        // Mock ROCm device for testing
        if cfg!(feature = "rocm") || std::env::var("SIMULATE_ROCM").is_ok() {
            devices.push(GpuDevice {
                id: 0,
                name: "AMD Radeon RX 7900 XTX".to_string(),
                vendor: GpuVendor::Amd,
                memory_mb: 24576, // 24GB
                compute_capability: Some("gfx1100".to_string()),
                supports_fp16: true,
                supports_int8: true,
                max_threads_per_block: 1024,
                multiprocessor_count: 96,
            });
        }

        Ok(devices)
    }

    pub async fn optimize_for_rocm(&self, model_data: &[u8]) -> Result<Vec<u8>> {
        if self.devices.is_empty() {
            return Ok(model_data.to_vec());
        }

        tracing::info!("Applying ROCm optimizations");
        tokio::time::sleep(std::time::Duration::from_millis(50)).await;

        Ok(model_data.to_vec())
    }
}

/// Metal (Apple GPU) optimizations
pub struct MetalOptimizer {
    devices: Vec<GpuDevice>,
}

impl MetalOptimizer {
    pub fn new() -> Result<Self> {
        let devices = Self::detect_metal_devices()?;
        Ok(Self { devices })
    }

    fn detect_metal_devices() -> Result<Vec<GpuDevice>> {
        let mut devices = Vec::new();

        // Mock Metal device for testing on macOS
        if cfg!(target_os = "macos") || std::env::var("SIMULATE_METAL").is_ok() {
            devices.push(GpuDevice {
                id: 0,
                name: "Apple M2 Ultra".to_string(),
                vendor: GpuVendor::Apple,
                memory_mb: 196608, // 192GB unified memory
                compute_capability: Some("Metal 3.1".to_string()),
                supports_fp16: true,
                supports_int8: true,
                max_threads_per_block: 1024,
                multiprocessor_count: 76, // GPU cores
            });
        }

        Ok(devices)
    }

    pub async fn optimize_for_metal(&self, model_data: &[u8]) -> Result<Vec<u8>> {
        if self.devices.is_empty() {
            return Ok(model_data.to_vec());
        }

        tracing::info!("Applying Metal Performance Shaders optimizations");
        tokio::time::sleep(std::time::Duration::from_millis(30)).await;

        // In a real implementation, this would use Metal Performance Shaders
        // for optimized matrix operations and neural network layers

        Ok(model_data.to_vec())
    }
}

/// CPU SIMD optimizations
pub struct SimdOptimizer {
    supported_instruction_sets: Vec<SimdInstructionSet>,
    optimal_vector_size: usize,
}

impl Default for SimdOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

impl SimdOptimizer {
    pub fn new() -> Self {
        let supported_instruction_sets = Self::detect_simd_support();
        let optimal_vector_size = Self::determine_optimal_vector_size(&supported_instruction_sets);

        Self {
            supported_instruction_sets,
            optimal_vector_size,
        }
    }

    fn detect_simd_support() -> Vec<SimdInstructionSet> {
        let mut supported = Vec::new();

        // Simulate SIMD detection - in practice, use cpuid or similar
        #[cfg(target_arch = "x86_64")]
        {
            supported.push(SimdInstructionSet::SSE2);
            supported.push(SimdInstructionSet::SSE3);
            supported.push(SimdInstructionSet::SSE4_1);
            supported.push(SimdInstructionSet::SSE4_2);
            supported.push(SimdInstructionSet::AVX);

            if std::env::var("ENABLE_AVX2").is_ok() {
                supported.push(SimdInstructionSet::AVX2);
            }

            if std::env::var("ENABLE_AVX512").is_ok() {
                supported.push(SimdInstructionSet::AVX512);
            }
        }

        #[cfg(target_arch = "aarch64")]
        {
            supported.push(SimdInstructionSet::NEON);
        }

        supported
    }

    fn determine_optimal_vector_size(instruction_sets: &[SimdInstructionSet]) -> usize {
        if let Some(instruction_set) = instruction_sets.iter().next_back() {
            match instruction_set {
                SimdInstructionSet::AVX512 => return 64, // 512 bits = 64 bytes
                SimdInstructionSet::AVX2 | SimdInstructionSet::AVX => return 32, // 256 bits = 32 bytes
                SimdInstructionSet::SSE4_2
                | SimdInstructionSet::SSE4_1
                | SimdInstructionSet::SSE3
                | SimdInstructionSet::SSE2 => return 16, // 128 bits = 16 bytes
                SimdInstructionSet::NEON => return 16, // 128 bits = 16 bytes
            }
        }
        8 // Fallback to 64-bit operations
    }

    pub async fn optimize_tensor_operations(&self, data: &[f32]) -> Result<Vec<f32>> {
        tracing::debug!(
            "Applying SIMD optimizations with vector size: {}",
            self.optimal_vector_size
        );

        // Simulate SIMD-optimized operations
        let mut optimized_data = Vec::with_capacity(data.len());

        // Vectorized operations (simplified simulation)
        for chunk in data.chunks(self.optimal_vector_size / 4) {
            // f32 = 4 bytes
            let processed_chunk: Vec<f32> = chunk
                .iter()
                .map(|&x| x * 1.1 + 0.01) // Simulate some computation
                .collect();
            optimized_data.extend(processed_chunk);
        }

        Ok(optimized_data)
    }

    pub fn get_performance_characteristics(&self) -> HashMap<String, f64> {
        let mut characteristics = HashMap::new();

        // Estimate performance based on supported instruction sets
        let mut base_performance = 1.0;

        for instruction_set in &self.supported_instruction_sets {
            match instruction_set {
                SimdInstructionSet::AVX512 => base_performance *= 4.0,
                SimdInstructionSet::AVX2 => base_performance *= 2.5,
                SimdInstructionSet::AVX => base_performance *= 2.0,
                SimdInstructionSet::SSE4_2 => base_performance *= 1.5,
                SimdInstructionSet::NEON => base_performance *= 2.0,
                _ => base_performance *= 1.2,
            }
        }

        characteristics.insert("simd_speedup".to_string(), base_performance);
        characteristics.insert("vector_size".to_string(), self.optimal_vector_size as f64);
        characteristics.insert(
            "instruction_sets".to_string(),
            self.supported_instruction_sets.len() as f64,
        );

        characteristics
    }
}

/// Main hardware optimizer
pub struct HardwareOptimizer {
    config: HardwareConfig,
    capabilities: HardwareCapabilities,
    metrics: Arc<RwLock<HardwareMetrics>>,
    cuda_optimizer: Option<CudaOptimizer>,
    rocm_optimizer: Option<RocmOptimizer>,
    metal_optimizer: Option<MetalOptimizer>,
    simd_optimizer: SimdOptimizer,
}

impl HardwareOptimizer {
    /// Create new hardware optimizer
    pub async fn new(config: HardwareConfig) -> Result<Self> {
        let capabilities = Self::detect_hardware_capabilities().await?;

        // Initialize GPU optimizers based on availability and preference
        let cuda_optimizer = if config.gpu_acceleration {
            CudaOptimizer::new().ok()
        } else {
            None
        };

        let rocm_optimizer = if config.gpu_acceleration {
            RocmOptimizer::new().ok()
        } else {
            None
        };

        let metal_optimizer = if config.gpu_acceleration {
            MetalOptimizer::new().ok()
        } else {
            None
        };

        let simd_optimizer = SimdOptimizer::new();

        Ok(Self {
            config,
            capabilities,
            metrics: Arc::new(RwLock::new(HardwareMetrics::default())),
            cuda_optimizer,
            rocm_optimizer,
            metal_optimizer,
            simd_optimizer,
        })
    }

    /// Detect hardware capabilities
    async fn detect_hardware_capabilities() -> Result<HardwareCapabilities> {
        let cpu_cores = num_cpus::get_physical();
        let cpu_threads = num_cpus::get();

        // Detect SIMD support
        let cpu_simd_support = SimdOptimizer::detect_simd_support();

        // Detect GPU devices
        let mut gpu_devices = Vec::new();
        let mut gpu_memory_mb = 0;

        // Try CUDA
        if let Ok(cuda) = CudaOptimizer::new() {
            for device in &cuda.devices {
                gpu_memory_mb += device.memory_mb;
                gpu_devices.push(device.clone());
            }
        }

        // Try ROCm
        if let Ok(rocm) = RocmOptimizer::new() {
            for device in &rocm.devices {
                gpu_memory_mb += device.memory_mb;
                gpu_devices.push(device.clone());
            }
        }

        // Try Metal
        if let Ok(metal) = MetalOptimizer::new() {
            for device in &metal.devices {
                gpu_memory_mb += device.memory_mb;
                gpu_devices.push(device.clone());
            }
        }

        // Get system memory (simplified)
        let total_memory_mb = 16384; // Mock 16GB - in practice, use sysinfo crate

        let supports_mixed_precision =
            !gpu_devices.is_empty() && gpu_devices.iter().any(|d| d.supports_fp16);

        let supports_tensor_cores = gpu_devices
            .iter()
            .any(|d| matches!(d.vendor, GpuVendor::Nvidia) && d.supports_fp16);

        Ok(HardwareCapabilities {
            cpu_cores,
            cpu_threads,
            cpu_simd_support,
            gpu_devices,
            total_memory_mb,
            gpu_memory_mb,
            supports_mixed_precision,
            supports_tensor_cores,
        })
    }

    /// Optimize model for current hardware
    pub async fn optimize_for_hardware(&self, model_path: &str) -> Result<String> {
        tracing::info!("Optimizing model for hardware: {}", model_path);

        // Read model data
        let model_data = tokio::fs::read(model_path).await?;

        // Apply hardware-specific optimizations
        let optimized_data = self.apply_hardware_optimizations(model_data).await?;

        // Save optimized model
        let optimized_path = format!("{}.hw_optimized", model_path);
        tokio::fs::write(&optimized_path, optimized_data).await?;

        // Update metrics
        self.update_metrics().await;

        tracing::info!("Hardware optimization completed: {}", optimized_path);
        Ok(optimized_path)
    }

    async fn apply_hardware_optimizations(&self, mut model_data: Vec<u8>) -> Result<Vec<u8>> {
        // Apply GPU optimizations based on preference and availability
        match self.config.preferred_gpu_vendor {
            GpuVendor::Nvidia | GpuVendor::Auto => {
                if let Some(ref cuda) = self.cuda_optimizer {
                    model_data = cuda.optimize_for_cuda(&model_data).await?;
                }
            }
            GpuVendor::Amd => {
                if let Some(ref rocm) = self.rocm_optimizer {
                    model_data = rocm.optimize_for_rocm(&model_data).await?;
                }
            }
            GpuVendor::Apple => {
                if let Some(ref metal) = self.metal_optimizer {
                    model_data = metal.optimize_for_metal(&model_data).await?;
                }
            }
            GpuVendor::Intel => {
                // Intel GPU optimizations would go here
                tracing::debug!("Intel GPU optimizations not yet implemented");
            }
        }

        // Apply CPU SIMD optimizations if no GPU or as fallback
        if self.config.cpu_simd_optimization {
            // For simplicity, assume model contains f32 data we can optimize
            // In practice, this would parse the actual model format
        }

        Ok(model_data)
    }

    /// Get optimal device for inference
    pub fn get_optimal_device(&self) -> Option<&GpuDevice> {
        if !self.config.gpu_acceleration || self.capabilities.gpu_devices.is_empty() {
            return None;
        }

        // Select best device based on memory and compute capability
        self.capabilities.gpu_devices.iter().max_by(|a, b| {
            // Primary: memory size
            let memory_cmp = a.memory_mb.cmp(&b.memory_mb);
            if memory_cmp != std::cmp::Ordering::Equal {
                return memory_cmp;
            }

            // Secondary: multiprocessor count
            a.multiprocessor_count.cmp(&b.multiprocessor_count)
        })
    }

    /// Configure mixed precision settings
    pub async fn configure_mixed_precision(&self) -> Result<()> {
        if !self.config.mixed_precision || !self.capabilities.supports_mixed_precision {
            return Ok(());
        }

        tracing::info!("Configuring mixed precision inference");

        // Configure automatic mixed precision
        // In practice, this would set up AMP for PyTorch or TensorRT

        Ok(())
    }

    /// Update hardware metrics
    async fn update_metrics(&self) {
        let mut metrics = self.metrics.write().await;

        // Simulate hardware metrics collection
        metrics.gpu_utilization = 85.0; // Mock 85% GPU utilization
        metrics.cpu_utilization = 45.0; // Mock 45% CPU utilization
        metrics.memory_bandwidth_utilization = 70.0;
        metrics.tensor_throughput_gops = 500.0; // Mock 500 GOPS
        metrics.mixed_precision_speedup = if self.config.mixed_precision {
            1.8
        } else {
            1.0
        };

        // Calculate SIMD performance
        let simd_chars = self.simd_optimizer.get_performance_characteristics();
        metrics.simd_operations_per_second =
            simd_chars.get("simd_speedup").unwrap_or(&1.0) * 1000000.0;

        // GPU memory usage
        if let Some(device) = self.get_optimal_device() {
            metrics.gpu_memory_usage_mb = device.memory_mb as f64 * 0.6; // Mock 60% usage
        }
    }

    /// Get current hardware metrics
    pub async fn get_metrics(&self) -> HardwareMetrics {
        self.metrics.read().await.clone()
    }

    /// Get hardware capabilities
    pub fn get_capabilities(&self) -> &HardwareCapabilities {
        &self.capabilities
    }

    /// Benchmark hardware optimization performance
    pub async fn benchmark(&self, _model_path: &str, num_requests: usize) -> Result<f64> {
        tracing::info!(
            "Benchmarking hardware optimization with {} requests",
            num_requests
        );

        let start_time = std::time::Instant::now();

        // Simulate hardware-accelerated inference
        for _ in 0..num_requests {
            // Mock inference time based on hardware capabilities
            let inference_time = if self.get_optimal_device().is_some() {
                std::time::Duration::from_millis(10) // Fast GPU inference
            } else {
                std::time::Duration::from_millis(50) // Slower CPU inference
            };

            tokio::time::sleep(inference_time).await;
        }

        let total_time = start_time.elapsed();
        let requests_per_second = num_requests as f64 / total_time.as_secs_f64();

        // Calculate performance multiplier
        let baseline_rps = 10.0; // Baseline requests per second
        let performance_multiplier = requests_per_second / baseline_rps;

        tracing::info!(
            "Hardware benchmark completed: {:.2} requests/second ({:.2}x speedup)",
            requests_per_second,
            performance_multiplier
        );

        Ok(performance_multiplier)
    }
}

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

    #[tokio::test]
    async fn test_hardware_optimizer_creation() {
        let config = HardwareConfig::default();
        let optimizer = HardwareOptimizer::new(config).await;
        assert!(optimizer.is_ok());
    }

    #[test]
    fn test_simd_optimizer() {
        let optimizer = SimdOptimizer::new();
        assert!(optimizer.optimal_vector_size >= 8);
        assert!(!optimizer.supported_instruction_sets.is_empty());
    }

    #[tokio::test]
    async fn test_simd_tensor_operations() {
        let optimizer = SimdOptimizer::new();
        let test_data = vec![1.0f32; 1000];
        let result = optimizer.optimize_tensor_operations(&test_data).await;
        assert!(result.is_ok());
        assert_eq!(result.unwrap().len(), test_data.len());
    }

    #[test]
    fn test_cuda_optimizer_creation() {
        // This will only succeed if CUDA is available or simulated
        // SAFETY: No other threads are accessing this environment variable in this test
        unsafe { std::env::set_var("SIMULATE_CUDA", "1") };
        let optimizer = CudaOptimizer::new();
        assert!(optimizer.is_ok());
    }

    #[test]
    fn test_metal_optimizer_creation() {
        // SAFETY: No other threads are accessing this environment variable in this test
        unsafe { std::env::set_var("SIMULATE_METAL", "1") };
        let optimizer = MetalOptimizer::new();
        assert!(optimizer.is_ok());
    }
}