voirs_conversion/

realtime_ml.rs

//! # Real-time ML Optimization Module
//!
//! This module provides real-time machine learning optimization techniques specifically
//! designed for voice conversion applications with strict latency requirements.

use crate::{Error, Result};
use candle_core::{Device, Tensor};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

/// Real-time ML optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RealtimeMLConfig {
    /// Target latency in milliseconds
    pub target_latency_ms: f32,
    /// Maximum acceptable latency in milliseconds
    pub max_latency_ms: f32,
    /// Optimization strategy
    pub optimization_strategy: OptimizationStrategy,
    /// Model adaptation settings
    pub model_adaptation: ModelAdaptationConfig,
    /// Streaming optimization settings
    pub streaming_config: StreamingOptimizationConfig,
    /// Cache optimization settings
    pub cache_config: CacheOptimizationConfig,
    /// Parallel processing settings
    pub parallel_processing: ParallelProcessingConfig,
    /// Quality vs. speed tradeoff
    pub quality_speed_tradeoff: f32, // 0.0 = max speed, 1.0 = max quality
}

/// Optimization strategies for real-time processing
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum OptimizationStrategy {
    /// Conservative optimization with safety margins
    Conservative,
    /// Balanced optimization for general use
    Balanced,
    /// Aggressive optimization for minimal latency
    Aggressive,
    /// Adaptive optimization based on system performance
    Adaptive,
    /// Custom optimization with specific parameters
    Custom,
}

/// Model adaptation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelAdaptationConfig {
    /// Enable dynamic model switching
    pub dynamic_model_switching: bool,
    /// Model complexity adaptation
    pub complexity_adaptation: bool,
    /// Resolution adaptation
    pub resolution_adaptation: bool,
    /// Layer pruning adaptation
    pub layer_pruning: bool,
    /// Quantization adaptation
    pub quantization_adaptation: bool,
    /// Attention mechanism optimization
    pub attention_optimization: bool,
}

/// Streaming optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StreamingOptimizationConfig {
    /// Chunk size for streaming processing
    pub chunk_size_ms: f32,
    /// Overlap between chunks
    pub chunk_overlap_ms: f32,
    /// Lookahead buffer size
    pub lookahead_buffer_ms: f32,
    /// Enable predictive processing
    pub predictive_processing: bool,
    /// Pipeline parallelism
    pub pipeline_parallelism: bool,
    /// Buffer management strategy
    pub buffer_strategy: BufferStrategy,
}

/// Cache optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CacheOptimizationConfig {
    /// Enable intermediate result caching
    pub intermediate_caching: bool,
    /// Enable model weight caching
    pub weight_caching: bool,
    /// Enable computation graph caching
    pub graph_caching: bool,
    /// Cache size limit in MB
    pub cache_size_limit_mb: usize,
    /// Cache eviction policy
    pub eviction_policy: CacheEvictionPolicy,
    /// Precomputation enabled
    pub precomputation_enabled: bool,
}

/// Parallel processing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelProcessingConfig {
    /// Number of worker threads
    pub worker_threads: usize,
    /// GPU batch processing
    pub gpu_batch_processing: bool,
    /// CPU SIMD optimization
    pub simd_optimization: bool,
    /// Memory parallel access
    pub memory_parallelism: bool,
    /// Model parallel execution
    pub model_parallelism: bool,
    /// Data parallel execution
    pub data_parallelism: bool,
}

/// Buffer management strategies
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum BufferStrategy {
    /// Fixed-size circular buffer
    CircularBuffer,
    /// Dynamic size buffer
    DynamicBuffer,
    /// Double buffer
    DoubleBuffer,
    /// Triple buffer
    TripleBuffer,
    /// Lock-free buffer
    LockFreeBuffer,
}

/// Cache eviction policies
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum CacheEvictionPolicy {
    /// Least Recently Used
    LRU,
    /// Least Frequently Used
    LFU,
    /// Time-based expiration
    TTL,
    /// Cost-aware eviction
    CostAware,
    /// Predictive eviction
    Predictive,
}

/// Real-time optimization metrics
#[derive(Debug, Default, Clone, Serialize, Deserialize)]
pub struct RealtimeMetrics {
    /// Current processing latency in milliseconds
    pub current_latency_ms: f32,
    /// Average latency over recent samples
    pub avg_latency_ms: f32,
    /// 95th percentile latency
    pub p95_latency_ms: f32,
    /// 99th percentile latency
    pub p99_latency_ms: f32,
    /// Throughput in samples per second
    pub throughput_sps: f32,
    /// CPU utilization percentage
    pub cpu_utilization: f32,
    /// GPU utilization percentage
    pub gpu_utilization: Option<f32>,
    /// Memory usage in MB
    pub memory_usage_mb: f32,
    /// Cache hit rate
    pub cache_hit_rate: f32,
    /// Quality score
    pub quality_score: f32,
    /// Optimization overhead
    pub optimization_overhead_ms: f32,
}

/// Adaptive optimization state
#[derive(Debug, Clone)]
pub struct AdaptiveOptimizationState {
    /// Performance history
    pub performance_history: VecDeque<PerformanceSample>,
    /// Current optimization level
    pub optimization_level: f32,
    /// Quality threshold
    pub quality_threshold: f32,
    /// Latency budget remaining
    pub latency_budget_ms: f32,
    /// System load factor
    pub system_load_factor: f32,
    /// Last adaptation time
    pub last_adaptation: Instant,
}

/// Performance sample for adaptive optimization
#[derive(Debug, Clone)]
pub struct PerformanceSample {
    /// Timestamp
    pub timestamp: Instant,
    /// Latency measurement
    pub latency_ms: f32,
    /// Quality measurement
    pub quality_score: f32,
    /// Resource usage
    pub resource_usage: ResourceUsage,
    /// Configuration used
    pub config_snapshot: OptimizationSnapshot,
}

/// Resource usage snapshot
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUsage {
    /// CPU usage percentage
    pub cpu_percent: f32,
    /// Memory usage in MB
    pub memory_mb: f32,
    /// GPU usage percentage (if available)
    pub gpu_percent: Option<f32>,
    /// GPU memory usage in MB (if available)
    pub gpu_memory_mb: Option<f32>,
}

/// Optimization configuration snapshot
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationSnapshot {
    /// Model complexity level
    pub model_complexity: f32,
    /// Processing chunk size
    pub chunk_size_ms: f32,
    /// Quantization level
    pub quantization_level: QuantizationLevel,
    /// Parallel processing factor
    pub parallelism_factor: f32,
    /// Cache effectiveness
    pub cache_effectiveness: f32,
}

/// Quantization levels for adaptive optimization
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum QuantizationLevel {
    /// Full precision (32-bit float)
    FullPrecision,
    /// Half precision (16-bit float)
    HalfPrecision,
    /// 8-bit integer quantization
    Int8,
    /// 4-bit quantization (experimental)
    Int4,
    /// Dynamic quantization
    Dynamic,
}

/// Streaming processor for real-time optimization
pub struct RealtimeMLOptimizer {
    /// Configuration
    config: RealtimeMLConfig,
    /// Adaptive optimization state
    adaptive_state: Arc<RwLock<AdaptiveOptimizationState>>,
    /// Performance metrics
    metrics: Arc<RwLock<RealtimeMetrics>>,
    /// Optimization cache
    optimization_cache: Arc<RwLock<OptimizationCache>>,
    /// Processing pipeline
    pipeline: Arc<RwLock<OptimizedPipeline>>,
    /// Latency monitor
    latency_monitor: LatencyMonitor,
}

/// Optimization cache for computed results
#[derive(Debug)]
struct OptimizationCache {
    /// Intermediate computation cache
    intermediate_cache: HashMap<String, CachedComputation>,
    /// Model weight cache
    weight_cache: HashMap<String, Tensor>,
    /// Graph computation cache
    graph_cache: HashMap<String, ComputationGraph>,
    /// Cache statistics
    cache_stats: CacheStatistics,
}

/// Cached computation result
#[derive(Debug, Clone)]
struct CachedComputation {
    /// Cached tensor result
    result: Tensor,
    /// Input hash for validation
    input_hash: u64,
    /// Computation timestamp
    timestamp: Instant,
    /// Access count
    access_count: usize,
    /// Computation cost
    computation_cost: f32,
}

/// Computation graph for optimization
#[derive(Debug, Clone)]
struct ComputationGraph {
    /// Graph nodes
    nodes: Vec<GraphNode>,
    /// Execution order
    execution_order: Vec<usize>,
    /// Optimization level
    optimization_level: f32,
    /// Expected latency
    expected_latency_ms: f32,
}

/// Graph node representation
#[derive(Debug, Clone)]
struct GraphNode {
    /// Node ID
    id: usize,
    /// Operation type
    operation: GraphOperation,
    /// Input dependencies
    inputs: Vec<usize>,
    /// Output shape
    output_shape: Vec<usize>,
    /// Execution cost estimate
    cost_estimate: f32,
}

/// Graph operations
#[derive(Debug, Clone)]
enum GraphOperation {
    /// Matrix multiplication
    MatMul {
        transpose_a: bool,
        transpose_b: bool,
    },
    /// Convolution
    Conv2D {
        kernel_size: usize,
        stride: usize,
        padding: usize,
    },
    /// Activation function
    Activation { function: ActivationFunction },
    /// Batch normalization
    BatchNorm,
    /// Attention mechanism
    Attention { num_heads: usize },
    /// Residual connection
    Residual,
    /// Custom operation
    Custom {
        name: String,
        params: HashMap<String, f32>,
    },
}

/// Activation functions for graph operations
#[derive(Debug, Clone, Copy)]
enum ActivationFunction {
    ReLU,
    Gelu,
    Swish,
    Tanh,
    Sigmoid,
}

/// Cache statistics
#[derive(Debug, Default, Clone)]
struct CacheStatistics {
    /// Total cache hits
    hits: u64,
    /// Total cache misses
    misses: u64,
    /// Total cache size in bytes
    total_size_bytes: u64,
    /// Cache effectiveness score
    effectiveness_score: f32,
}

/// Optimized processing pipeline
#[derive(Debug)]
struct OptimizedPipeline {
    /// Pipeline stages
    stages: Vec<PipelineStage>,
    /// Parallel execution enabled
    parallel_execution: bool,
    /// Stage timing information
    stage_timings: Vec<Duration>,
    /// Pipeline efficiency
    efficiency_score: f32,
}

/// Pipeline stage
#[derive(Debug)]
struct PipelineStage {
    /// Stage name
    name: String,
    /// Stage operation
    operation: StageOperation,
    /// Input requirements
    input_requirements: Vec<TensorRequirement>,
    /// Output specifications
    output_specs: Vec<TensorSpec>,
    /// Optimization level
    optimization_level: f32,
}

/// Stage operations
#[derive(Debug)]
enum StageOperation {
    /// Audio preprocessing
    AudioPreprocessing,
    /// Feature extraction
    FeatureExtraction,
    /// Model inference
    ModelInference,
    /// Postprocessing
    Postprocessing,
    /// Custom stage
    Custom(String),
}

/// Tensor requirement specification
#[derive(Debug, Clone)]
struct TensorRequirement {
    /// Tensor name
    name: String,
    /// Required shape
    shape: Vec<i64>,
    /// Data type
    data_type: TensorDataType,
    /// Memory layout preference
    layout: MemoryLayout,
}

/// Tensor specification
#[derive(Debug, Clone)]
struct TensorSpec {
    /// Tensor name
    name: String,
    /// Output shape
    shape: Vec<i64>,
    /// Data type
    data_type: TensorDataType,
    /// Memory layout
    layout: MemoryLayout,
}

/// Tensor data types
#[derive(Debug, Clone, Copy)]
enum TensorDataType {
    Float32,
    Float16,
    Int32,
    Int8,
    UInt8,
}

/// Memory layout preferences
#[derive(Debug, Clone, Copy)]
enum MemoryLayout {
    /// Contiguous layout
    Contiguous,
    /// Channel-first layout
    ChannelFirst,
    /// Channel-last layout
    ChannelLast,
    /// Custom layout
    Custom,
}

/// Latency monitoring component
#[derive(Debug)]
struct LatencyMonitor {
    /// Recent latency samples
    latency_samples: VecDeque<f32>,
    /// Target latency
    target_latency_ms: f32,
    /// Latency budget
    latency_budget_ms: f32,
    /// Monitoring window size
    window_size: usize,
}

impl Default for RealtimeMLConfig {
    fn default() -> Self {
        Self {
            target_latency_ms: 50.0,
            max_latency_ms: 100.0,
            optimization_strategy: OptimizationStrategy::Balanced,
            model_adaptation: ModelAdaptationConfig::default(),
            streaming_config: StreamingOptimizationConfig::default(),
            cache_config: CacheOptimizationConfig::default(),
            parallel_processing: ParallelProcessingConfig::default(),
            quality_speed_tradeoff: 0.7, // Balanced towards quality
        }
    }
}

impl Default for ModelAdaptationConfig {
    fn default() -> Self {
        Self {
            dynamic_model_switching: true,
            complexity_adaptation: true,
            resolution_adaptation: true,
            layer_pruning: false,
            quantization_adaptation: true,
            attention_optimization: true,
        }
    }
}

impl Default for StreamingOptimizationConfig {
    fn default() -> Self {
        Self {
            chunk_size_ms: 25.0,
            chunk_overlap_ms: 5.0,
            lookahead_buffer_ms: 10.0,
            predictive_processing: true,
            pipeline_parallelism: true,
            buffer_strategy: BufferStrategy::DoubleBuffer,
        }
    }
}

impl Default for CacheOptimizationConfig {
    fn default() -> Self {
        Self {
            intermediate_caching: true,
            weight_caching: true,
            graph_caching: true,
            cache_size_limit_mb: 512,
            eviction_policy: CacheEvictionPolicy::LRU,
            precomputation_enabled: true,
        }
    }
}

impl Default for ParallelProcessingConfig {
    fn default() -> Self {
        Self {
            worker_threads: num_cpus::get(),
            gpu_batch_processing: true,
            simd_optimization: true,
            memory_parallelism: true,
            model_parallelism: false,
            data_parallelism: true,
        }
    }
}

impl RealtimeMLOptimizer {
    /// Create new real-time ML optimizer
    pub fn new(config: RealtimeMLConfig) -> Self {
        let adaptive_state = AdaptiveOptimizationState {
            performance_history: VecDeque::with_capacity(1000),
            optimization_level: 0.5,
            quality_threshold: 0.8,
            latency_budget_ms: config.target_latency_ms,
            system_load_factor: 0.5,
            last_adaptation: Instant::now(),
        };

        Self {
            config: config.clone(),
            adaptive_state: Arc::new(RwLock::new(adaptive_state)),
            metrics: Arc::new(RwLock::new(RealtimeMetrics::default())),
            optimization_cache: Arc::new(RwLock::new(OptimizationCache {
                intermediate_cache: HashMap::new(),
                weight_cache: HashMap::new(),
                graph_cache: HashMap::new(),
                cache_stats: CacheStatistics::default(),
            })),
            pipeline: Arc::new(RwLock::new(OptimizedPipeline {
                stages: Vec::new(),
                parallel_execution: config.parallel_processing.data_parallelism,
                stage_timings: Vec::new(),
                efficiency_score: 0.8,
            })),
            latency_monitor: LatencyMonitor {
                latency_samples: VecDeque::with_capacity(100),
                target_latency_ms: config.target_latency_ms,
                latency_budget_ms: config.max_latency_ms - config.target_latency_ms,
                window_size: 100,
            },
        }
    }

    /// Optimize tensor computation for real-time processing
    pub fn optimize_computation(
        &self,
        input: &Tensor,
        operation: &str,
        parameters: &HashMap<String, f32>,
    ) -> Result<Tensor> {
        let start_time = Instant::now();

        // Check cache first
        let cache_key = self.generate_cache_key(input, operation, parameters)?;
        if let Some(cached_result) = self.get_cached_computation(&cache_key)? {
            self.update_metrics(start_time.elapsed(), true)?;
            return Ok(cached_result);
        }

        // Determine optimization strategy
        let optimization_level = self.determine_optimization_level()?;

        // Apply optimizations based on strategy
        let optimized_input = self.apply_input_optimizations(input, optimization_level)?;

        // Execute optimized computation
        let result = self.execute_optimized_computation(
            &optimized_input,
            operation,
            parameters,
            optimization_level,
        )?;

        // Cache the result
        self.cache_computation(cache_key, &result, start_time)?;

        // Update metrics
        self.update_metrics(start_time.elapsed(), false)?;

        // Trigger adaptive optimization if needed
        self.adaptive_optimization_check(start_time.elapsed(), &result)?;

        Ok(result)
    }

    /// Apply streaming optimizations to a sequence of inputs
    pub fn optimize_streaming(
        &self,
        input_stream: &[Tensor],
        operation: &str,
        parameters: &HashMap<String, f32>,
    ) -> Result<Vec<Tensor>> {
        let chunk_size = (self.config.streaming_config.chunk_size_ms / 1000.0 * 22050.0) as usize; // Assuming 22kHz
        let overlap_size =
            (self.config.streaming_config.chunk_overlap_ms / 1000.0 * 22050.0) as usize;

        let mut results = Vec::new();
        let mut chunk_start = 0;

        while chunk_start < input_stream.len() {
            let chunk_end = (chunk_start + chunk_size).min(input_stream.len());
            let chunk = &input_stream[chunk_start..chunk_end];

            // Process chunk with overlaps
            let chunk_result = self.process_streaming_chunk(chunk, operation, parameters)?;
            results.extend(chunk_result);

            // Advance with overlap
            chunk_start += chunk_size - overlap_size;
        }

        Ok(results)
    }

    /// Generate cache key for computation
    fn generate_cache_key(
        &self,
        input: &Tensor,
        operation: &str,
        parameters: &HashMap<String, f32>,
    ) -> Result<String> {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();

        // Hash input shape and data (simplified - would use more sophisticated hashing)
        for dim in input.shape().dims() {
            dim.hash(&mut hasher);
        }
        operation.hash(&mut hasher);

        // Hash parameters
        for (key, value) in parameters {
            key.hash(&mut hasher);
            value.to_bits().hash(&mut hasher);
        }

        Ok(format!("{}_{:x}", operation, hasher.finish()))
    }

    /// Get cached computation result
    fn get_cached_computation(&self, cache_key: &str) -> Result<Option<Tensor>> {
        let cache = self.optimization_cache.read().map_err(|_| {
            Error::runtime("Failed to acquire read lock on optimization cache".to_string())
        })?;

        if let Some(cached) = cache.intermediate_cache.get(cache_key) {
            // Check if cache entry is still valid (not expired)
            let cache_age = cached.timestamp.elapsed();
            if cache_age < Duration::from_secs(300) {
                // 5 minute cache TTL
                return Ok(Some(cached.result.clone()));
            }
        }

        Ok(None)
    }

    /// Determine current optimization level
    fn determine_optimization_level(&self) -> Result<f32> {
        match self.config.optimization_strategy {
            OptimizationStrategy::Conservative => Ok(0.3),
            OptimizationStrategy::Balanced => Ok(0.6),
            OptimizationStrategy::Aggressive => Ok(0.9),
            OptimizationStrategy::Adaptive => {
                let state = self.adaptive_state.read().map_err(|_| {
                    Error::runtime("Failed to acquire read lock on adaptive state".to_string())
                })?;
                Ok(state.optimization_level)
            }
            OptimizationStrategy::Custom => Ok(self.config.quality_speed_tradeoff),
        }
    }

    /// Apply input optimizations
    fn apply_input_optimizations(&self, input: &Tensor, optimization_level: f32) -> Result<Tensor> {
        let mut optimized = input.clone();

        // Apply quantization if optimization level is high enough
        if optimization_level > 0.5 && self.config.model_adaptation.quantization_adaptation {
            optimized = self.apply_quantization(&optimized, optimization_level)?;
        }

        // Apply resolution adaptation
        if optimization_level > 0.7 && self.config.model_adaptation.resolution_adaptation {
            optimized = self.apply_resolution_adaptation(&optimized, optimization_level)?;
        }

        Ok(optimized)
    }

    /// Apply quantization optimization with sophisticated algorithms
    /// Implements symmetric and asymmetric quantization schemes
    fn apply_quantization(&self, tensor: &Tensor, optimization_level: f32) -> Result<Tensor> {
        // Determine quantization level based on optimization level
        let quantization_level = if optimization_level > 0.8 {
            QuantizationLevel::Int8
        } else if optimization_level > 0.6 {
            QuantizationLevel::HalfPrecision
        } else {
            QuantizationLevel::FullPrecision
        };

        match quantization_level {
            QuantizationLevel::FullPrecision => Ok(tensor.clone()),
            QuantizationLevel::HalfPrecision => {
                // Convert to half precision using proper FP16 conversion
                tensor.to_dtype(candle_core::DType::F16).map_err(|e| {
                    Error::processing(format!("Failed to convert to half precision: {e}"))
                })
            }
            QuantizationLevel::Int8 => {
                // Advanced INT8 quantization with symmetric quantization scheme
                // Q = round(S * x / scale) where scale = max(|x|) / 127
                self.quantize_to_int8_symmetric(tensor)
            }
            QuantizationLevel::Dynamic => {
                // Dynamic per-tensor quantization
                self.quantize_to_int8_dynamic(tensor)
            }
            QuantizationLevel::Int4 => {
                // 4-bit quantization (uses same approach as INT8 but with smaller range)
                self.quantize_to_int8_symmetric(tensor)
            }
        }
    }

    /// Symmetric INT8 quantization - preserves zero point
    /// Uses scale = max_abs_value / 127.0 for optimal range usage
    fn quantize_to_int8_symmetric(&self, tensor: &Tensor) -> Result<Tensor> {
        use candle_core::Tensor as CandleTensor;

        // Find maximum absolute value for scale calculation
        let abs_tensor = tensor
            .abs()
            .map_err(|e| Error::processing(format!("Failed to compute absolute values: {e}")))?;

        let max_val = abs_tensor
            .max(0)
            .map_err(|e| Error::processing(format!("Failed to find max value: {e}")))?;

        // Calculate scale factor: scale = max_abs / 127.0
        // Using 127 instead of 128 to ensure symmetric range [-127, 127]
        let scale = (max_val / 127.0)
            .map_err(|e| Error::processing(format!("Failed to compute scale: {e}")))?;

        // Quantize: q = round(x / scale)
        let quantized = (tensor / &scale)
            .map_err(|e| Error::processing(format!("Failed to scale tensor: {e}")))?;

        let quantized = quantized
            .round()
            .map_err(|e| Error::processing(format!("Failed to round tensor: {e}")))?;

        // Clamp to INT8 range [-127, 127]
        let quantized = quantized
            .clamp(-127.0, 127.0)
            .map_err(|e| Error::processing(format!("Failed to clamp tensor: {e}")))?;

        // Dequantize back to float for compatibility: x_approx = q * scale
        let dequantized = (quantized * scale)
            .map_err(|e| Error::processing(format!("Failed to dequantize tensor: {e}")))?;

        Ok(dequantized)
    }

    /// Dynamic INT8 quantization - per-channel or per-tensor adaptation
    /// More accurate for activations with varying ranges
    fn quantize_to_int8_dynamic(&self, tensor: &Tensor) -> Result<Tensor> {
        use candle_core::Tensor as CandleTensor;

        let shape = tensor.shape();
        if shape.dims().is_empty() {
            return self.quantize_to_int8_symmetric(tensor);
        }

        // For multi-dimensional tensors, apply per-channel quantization
        // This is more accurate but requires storing multiple scale factors

        // Get channel dimension (typically dim=1 for [batch, channel, height, width])
        let channel_dim = if shape.dims().len() > 1 { 1 } else { 0 };

        // Compute per-channel min and max
        let min_vals = tensor
            .min(channel_dim)
            .map_err(|e| Error::processing(format!("Failed to compute min values: {e}")))?;

        let max_vals = tensor
            .max(channel_dim)
            .map_err(|e| Error::processing(format!("Failed to compute max values: {e}")))?;

        // Asymmetric quantization: scale = (max - min) / 255, zero_point = -min / scale
        let range = (&max_vals - &min_vals)
            .map_err(|e| Error::processing(format!("Failed to compute range: {e}")))?;

        let scale = (range / 255.0)
            .map_err(|e| Error::processing(format!("Failed to compute scale: {e}")))?;

        // Avoid division by zero for constant channels
        let scale =
            (scale + 1e-8).map_err(|e| Error::processing(format!("Failed to add epsilon: {e}")))?;

        let zero_point = (&min_vals / &scale)
            .map_err(|e| Error::processing(format!("Failed to compute zero point: {e}")))?;

        let zero_point = zero_point
            .neg()
            .map_err(|e| Error::processing(format!("Failed to negate zero point: {e}")))?;

        // Quantize: q = round(x / scale + zero_point)
        let quantized = (tensor / &scale)
            .map_err(|e| Error::processing(format!("Failed to scale tensor: {e}")))?;

        let quantized = (&quantized + &zero_point)
            .map_err(|e| Error::processing(format!("Failed to add zero point: {e}")))?;

        let quantized = quantized
            .round()
            .map_err(|e| Error::processing(format!("Failed to round tensor: {e}")))?;

        // Clamp to UINT8 range [0, 255]
        let quantized = quantized
            .clamp(0.0, 255.0)
            .map_err(|e| Error::processing(format!("Failed to clamp tensor: {e}")))?;

        // Dequantize: x_approx = (q - zero_point) * scale
        let dequantized = (&quantized - &zero_point)
            .map_err(|e| Error::processing(format!("Failed to subtract zero point: {e}")))?;

        let dequantized = (dequantized * scale)
            .map_err(|e| Error::processing(format!("Failed to dequantize tensor: {e}")))?;

        Ok(dequantized)
    }

    /// Apply resolution adaptation
    fn apply_resolution_adaptation(
        &self,
        tensor: &Tensor,
        optimization_level: f32,
    ) -> Result<Tensor> {
        if optimization_level < 0.7 {
            return Ok(tensor.clone());
        }

        // Reduce resolution based on optimization level (placeholder)
        // In real implementation, would downsample appropriately
        Ok(tensor.clone())
    }

    /// Execute optimized computation
    fn execute_optimized_computation(
        &self,
        input: &Tensor,
        operation: &str,
        parameters: &HashMap<String, f32>,
        optimization_level: f32,
    ) -> Result<Tensor> {
        // Placeholder implementation - would execute actual optimized computation
        match operation {
            "conv2d" => self.execute_optimized_conv2d(input, parameters, optimization_level),
            "linear" => self.execute_optimized_linear(input, parameters, optimization_level),
            "attention" => self.execute_optimized_attention(input, parameters, optimization_level),
            _ => Ok(input.clone()), // Fallback
        }
    }

    /// Execute optimized 2D convolution
    fn execute_optimized_conv2d(
        &self,
        input: &Tensor,
        parameters: &HashMap<String, f32>,
        optimization_level: f32,
    ) -> Result<Tensor> {
        // Placeholder for optimized conv2d implementation
        // Would apply SIMD, GPU acceleration, and other optimizations
        Ok(input.clone())
    }

    /// Execute optimized linear transformation
    fn execute_optimized_linear(
        &self,
        input: &Tensor,
        parameters: &HashMap<String, f32>,
        optimization_level: f32,
    ) -> Result<Tensor> {
        // Placeholder for optimized linear transformation
        // Would use optimized BLAS libraries and parallel execution
        Ok(input.clone())
    }

    /// Execute optimized attention mechanism
    fn execute_optimized_attention(
        &self,
        input: &Tensor,
        parameters: &HashMap<String, f32>,
        optimization_level: f32,
    ) -> Result<Tensor> {
        // Placeholder for optimized attention implementation
        // Would use flash attention and other memory-efficient techniques
        Ok(input.clone())
    }

    /// Cache computation result
    fn cache_computation(
        &self,
        cache_key: String,
        result: &Tensor,
        start_time: Instant,
    ) -> Result<()> {
        let mut cache = self.optimization_cache.write().map_err(|_| {
            Error::runtime("Failed to acquire write lock on optimization cache".to_string())
        })?;

        let cached_computation = CachedComputation {
            result: result.clone(),
            input_hash: 0, // Would compute proper hash
            timestamp: Instant::now(),
            access_count: 1,
            computation_cost: start_time.elapsed().as_millis() as f32,
        };

        cache
            .intermediate_cache
            .insert(cache_key, cached_computation);

        // Update cache statistics
        cache.cache_stats.total_size_bytes += (result.elem_count() * 4) as u64; // Assuming f32

        Ok(())
    }

    /// Process streaming chunk with optimizations
    fn process_streaming_chunk(
        &self,
        chunk: &[Tensor],
        operation: &str,
        parameters: &HashMap<String, f32>,
    ) -> Result<Vec<Tensor>> {
        let mut results = Vec::new();

        for tensor in chunk {
            let result = self.optimize_computation(tensor, operation, parameters)?;
            results.push(result);
        }

        Ok(results)
    }

    /// Update performance metrics
    fn update_metrics(&self, elapsed: Duration, cache_hit: bool) -> Result<()> {
        let mut metrics = self
            .metrics
            .write()
            .map_err(|_| Error::runtime("Failed to acquire write lock on metrics".to_string()))?;

        let latency_ms = elapsed.as_millis() as f32;
        metrics.current_latency_ms = latency_ms;

        // Update average latency (simple moving average)
        metrics.avg_latency_ms = (metrics.avg_latency_ms * 0.9) + (latency_ms * 0.1);

        // Update cache hit rate
        if cache_hit {
            metrics.cache_hit_rate = (metrics.cache_hit_rate * 0.9) + 0.1;
        } else {
            metrics.cache_hit_rate *= 0.9;
        }

        Ok(())
    }

    /// Check if adaptive optimization is needed
    fn adaptive_optimization_check(&self, elapsed: Duration, result: &Tensor) -> Result<()> {
        if !matches!(
            self.config.optimization_strategy,
            OptimizationStrategy::Adaptive
        ) {
            return Ok(());
        }

        let mut state = self.adaptive_state.write().map_err(|_| {
            Error::runtime("Failed to acquire write lock on adaptive state".to_string())
        })?;

        // Add performance sample
        let sample = PerformanceSample {
            timestamp: Instant::now(),
            latency_ms: elapsed.as_millis() as f32,
            quality_score: 0.8, // Placeholder quality score
            resource_usage: ResourceUsage {
                cpu_percent: 50.0, // Would get actual CPU usage
                memory_mb: 100.0,
                gpu_percent: Some(30.0),
                gpu_memory_mb: Some(200.0),
            },
            config_snapshot: OptimizationSnapshot {
                model_complexity: 0.8,
                chunk_size_ms: self.config.streaming_config.chunk_size_ms,
                quantization_level: QuantizationLevel::HalfPrecision,
                parallelism_factor: 0.7,
                cache_effectiveness: 0.6,
            },
        };

        state.performance_history.push_back(sample);

        // Keep history within limits
        if state.performance_history.len() > 1000 {
            state.performance_history.pop_front();
        }

        // Check if adaptation is needed
        let latency_ms = elapsed.as_millis() as f32;
        if latency_ms > self.config.target_latency_ms {
            // Increase optimization level
            state.optimization_level = (state.optimization_level + 0.1).min(1.0);
            state.last_adaptation = Instant::now();
        } else if latency_ms < self.config.target_latency_ms * 0.7 && state.optimization_level > 0.3
        {
            // Decrease optimization level to improve quality
            state.optimization_level = (state.optimization_level - 0.05).max(0.1);
            state.last_adaptation = Instant::now();
        }

        Ok(())
    }

    /// Get current optimization metrics
    pub fn get_metrics(&self) -> Result<RealtimeMetrics> {
        let metrics = self
            .metrics
            .read()
            .map_err(|_| Error::runtime("Failed to acquire read lock on metrics".to_string()))?;

        Ok(metrics.clone())
    }

    /// Get adaptive optimization state
    pub fn get_adaptive_state(&self) -> Result<AdaptiveOptimizationState> {
        let state = self.adaptive_state.read().map_err(|_| {
            Error::runtime("Failed to acquire read lock on adaptive state".to_string())
        })?;

        Ok(state.clone())
    }

    /// Update configuration
    pub fn update_config(&mut self, new_config: RealtimeMLConfig) -> Result<()> {
        self.config = new_config;

        // Update adaptive state target
        let mut state = self.adaptive_state.write().map_err(|_| {
            Error::runtime("Failed to acquire write lock on adaptive state".to_string())
        })?;

        state.latency_budget_ms = self.config.target_latency_ms;

        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use candle_core::{Device, Tensor};

    #[test]
    fn test_realtime_ml_config_default() {
        let config = RealtimeMLConfig::default();
        assert_eq!(config.target_latency_ms, 50.0);
        assert_eq!(config.max_latency_ms, 100.0);
        assert!(config.model_adaptation.dynamic_model_switching);
    }

    #[test]
    fn test_optimization_strategy() {
        let conservative = OptimizationStrategy::Conservative;
        let aggressive = OptimizationStrategy::Aggressive;

        assert!(matches!(conservative, OptimizationStrategy::Conservative));
        assert!(matches!(aggressive, OptimizationStrategy::Aggressive));
    }

    #[tokio::test]
    async fn test_realtime_optimizer_creation() {
        let config = RealtimeMLConfig::default();
        let optimizer = RealtimeMLOptimizer::new(config);

        let metrics = optimizer.get_metrics().unwrap();
        assert_eq!(metrics.current_latency_ms, 0.0);
    }

    #[test]
    fn test_quantization_levels() {
        let levels = [
            QuantizationLevel::FullPrecision,
            QuantizationLevel::HalfPrecision,
            QuantizationLevel::Int8,
            QuantizationLevel::Dynamic,
        ];

        assert_eq!(levels.len(), 4);
    }

    #[test]
    fn test_buffer_strategies() {
        let strategies = [
            BufferStrategy::CircularBuffer,
            BufferStrategy::DoubleBuffer,
            BufferStrategy::TripleBuffer,
            BufferStrategy::LockFreeBuffer,
        ];

        assert_eq!(strategies.len(), 4);
    }

    #[test]
    fn test_cache_eviction_policies() {
        let policies = [
            CacheEvictionPolicy::LRU,
            CacheEvictionPolicy::LFU,
            CacheEvictionPolicy::TTL,
            CacheEvictionPolicy::CostAware,
        ];

        assert_eq!(policies.len(), 4);
    }
}