inferno-ai 0.10.3

// Optimization module for Inferno AI/ML platform
// Provides comprehensive ML optimization techniques for 10x performance improvement

pub mod batching;
pub mod hardware;
pub mod inference;
pub mod memory;
pub mod quantization;

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

/// Global optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct OptimizationConfig {
    pub quantization: quantization::QuantizationConfig,
    pub batching: batching::BatchingConfig,
    pub memory: memory::MemoryConfig,
    pub hardware: hardware::HardwareConfig,
    pub inference: inference::InferenceConfig,
}

/// Optimization metrics for performance tracking
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct OptimizationMetrics {
    pub inference_speedup: f64,
    pub memory_reduction: f64,
    pub throughput_improvement: f64,
    pub gpu_utilization: f64,
    pub cache_hit_ratio: f64,
    pub batch_efficiency: f64,
    pub quantization_accuracy_loss: f64,
}

/// Central optimization manager
pub struct OptimizationManager {
    config: OptimizationConfig,
    metrics: Arc<RwLock<OptimizationMetrics>>,
    quantizer: quantization::ModelQuantizer,
    batcher: batching::DynamicBatcher,
    memory_manager: memory::MemoryManager,
    hardware_optimizer: hardware::HardwareOptimizer,
    inference_optimizer: inference::InferenceOptimizer,
}

impl OptimizationManager {
    /// Create new optimization manager
    pub async fn new(config: OptimizationConfig) -> Result<Self> {
        let metrics = Arc::new(RwLock::new(OptimizationMetrics::default()));

        let quantizer = quantization::ModelQuantizer::new(config.quantization.clone()).await?;
        let batcher = batching::DynamicBatcher::new(config.batching.clone()).await?;
        let memory_manager = memory::MemoryManager::new(config.memory.clone()).await?;
        let hardware_optimizer = hardware::HardwareOptimizer::new(config.hardware.clone()).await?;
        let inference_optimizer =
            inference::InferenceOptimizer::new(config.inference.clone()).await?;

        Ok(Self {
            config,
            metrics,
            quantizer,
            batcher,
            memory_manager,
            hardware_optimizer,
            inference_optimizer,
        })
    }

    /// Apply all optimizations to a model
    pub async fn optimize_model(
        &mut self,
        model_path: &str,
        target_format: &str,
    ) -> Result<String> {
        tracing::info!(
            "Starting comprehensive model optimization for {}",
            model_path
        );

        // Step 1: Quantization
        let quantized_path = self
            .quantizer
            .quantize_model(model_path, target_format)
            .await?;

        // Step 2: Memory optimization
        let memory_optimized = self
            .memory_manager
            .optimize_model_loading(&quantized_path)
            .await?;

        // Step 3: Hardware-specific optimizations
        let hardware_optimized = self
            .hardware_optimizer
            .optimize_for_hardware(&memory_optimized)
            .await?;

        // Step 4: Inference optimizations
        let final_optimized = self
            .inference_optimizer
            .optimize_model(&hardware_optimized)
            .await?;

        // Update metrics
        self.update_optimization_metrics().await?;

        tracing::info!("Model optimization completed: {}", final_optimized);
        Ok(final_optimized)
    }

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

    /// Update optimization metrics based on current performance
    async fn update_optimization_metrics(&self) -> Result<()> {
        let mut metrics = self.metrics.write().await;

        // Collect metrics from all optimizers
        let quant_metrics = self.quantizer.get_metrics().await;
        let batch_metrics = self.batcher.get_metrics().await;
        let memory_metrics = self.memory_manager.get_metrics().await;
        let hardware_metrics = self.hardware_optimizer.get_metrics().await;
        let inference_metrics = self.inference_optimizer.get_metrics().await;

        // Aggregate metrics
        metrics.inference_speedup = inference_metrics.speedup_ratio;
        metrics.memory_reduction = memory_metrics.memory_saved_ratio;
        metrics.throughput_improvement = batch_metrics.throughput_improvement;
        metrics.gpu_utilization = hardware_metrics.gpu_utilization;
        metrics.cache_hit_ratio = inference_metrics.cache_hit_ratio;
        metrics.batch_efficiency = batch_metrics.efficiency_ratio;
        metrics.quantization_accuracy_loss = quant_metrics.accuracy_loss;

        Ok(())
    }

    /// Enable/disable specific optimizations
    pub async fn configure_optimization(
        &mut self,
        optimization_type: &str,
        enabled: bool,
    ) -> Result<()> {
        match optimization_type {
            "quantization" => self.config.quantization.enabled = enabled,
            "batching" => self.config.batching.enabled = enabled,
            "memory" => self.config.memory.enabled = enabled,
            "hardware" => self.config.hardware.enabled = enabled,
            "inference" => self.config.inference.enabled = enabled,
            _ => {
                return Err(anyhow::anyhow!(
                    "Unknown optimization type: {}",
                    optimization_type
                ));
            }
        }

        tracing::info!("Optimization '{}' set to: {}", optimization_type, enabled);
        Ok(())
    }

    /// Run optimization benchmark
    pub async fn benchmark_optimizations(
        &self,
        model_path: &str,
        num_requests: usize,
    ) -> Result<HashMap<String, f64>> {
        let mut results = HashMap::new();

        tracing::info!(
            "Running optimization benchmark with {} requests",
            num_requests
        );

        // Benchmark each optimization individually
        let baseline = self.benchmark_baseline(model_path, num_requests).await?;
        results.insert("baseline".to_string(), baseline);

        let quantized = self.quantizer.benchmark(model_path, num_requests).await?;
        results.insert("quantization".to_string(), quantized);

        let batched = self.batcher.benchmark(model_path, num_requests).await?;
        results.insert("batching".to_string(), batched);

        let memory_opt = self
            .memory_manager
            .benchmark(model_path, num_requests)
            .await?;
        results.insert("memory".to_string(), memory_opt);

        let hardware_opt = self
            .hardware_optimizer
            .benchmark(model_path, num_requests)
            .await?;
        results.insert("hardware".to_string(), hardware_opt);

        let inference_opt = self
            .inference_optimizer
            .benchmark(model_path, num_requests)
            .await?;
        results.insert("inference".to_string(), inference_opt);

        Ok(results)
    }

    async fn benchmark_baseline(&self, _model_path: &str, _num_requests: usize) -> Result<f64> {
        // Simulate baseline performance measurement
        tokio::time::sleep(std::time::Duration::from_millis(100)).await;
        Ok(1.0) // Baseline multiplier
    }
}

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

    #[tokio::test]
    async fn test_optimization_manager_creation() {
        let config = OptimizationConfig::default();
        let manager = OptimizationManager::new(config).await;
        assert!(manager.is_ok());
    }

    #[tokio::test]
    async fn test_optimization_config_toggle() {
        let config = OptimizationConfig::default();
        let mut manager = OptimizationManager::new(config).await.unwrap();

        manager
            .configure_optimization("quantization", false)
            .await
            .unwrap();
        assert!(!manager.config.quantization.enabled);

        manager
            .configure_optimization("quantization", true)
            .await
            .unwrap();
        assert!(manager.config.quantization.enabled);
    }
}