torsh-quantization 0.1.0-beta.1

//! Enhanced ML-based parameter prediction with advanced neural architectures
//!
//! This module implements state-of-the-art neural networks for predicting optimal
//! quantization parameters, including attention mechanisms, ensemble methods,
//! and adaptive learning techniques.

use crate::TorshError;
use crate::TorshResult;
use scirs2_core::parallel_ops::*;
use std::time::Instant;

/// Enhanced ML predictor with advanced neural architectures
#[derive(Debug, Clone)]
pub struct EnhancedMLPredictor {
    /// Main prediction network with attention
    pub main_network: AttentionBasedNetwork,
    /// Ensemble of specialized predictors
    pub ensemble: Vec<SpecializedPredictor>,
    /// Meta-learning controller
    pub meta_controller: MetaLearningController,
    /// Training configuration
    pub training_config: TrainingConfig,
    /// Performance history for adaptive learning
    pub performance_history: PerformanceHistory,
}

/// Attention-based neural network for parameter prediction
#[derive(Debug, Clone)]
pub struct AttentionBasedNetwork {
    /// Feature extraction layers
    pub feature_extractors: Vec<ConvolutionalLayer>,
    /// Self-attention layers
    pub attention_layers: Vec<SelfAttentionLayer>,
    /// Final prediction head
    pub prediction_head: MultiHeadPredictor,
    /// Dropout rate for regularization
    pub dropout_rate: f32,
}

/// Convolutional layer for feature extraction
#[derive(Debug, Clone)]
pub struct ConvolutionalLayer {
    /// Convolution filters
    pub filters: Vec<Vec<f32>>,
    /// Bias terms
    pub biases: Vec<f32>,
    /// Kernel size
    pub kernel_size: usize,
    /// Stride
    pub stride: usize,
    /// Activation function
    pub activation: ActivationFunction,
}

/// Self-attention layer for capturing feature relationships
#[derive(Debug, Clone)]
pub struct SelfAttentionLayer {
    /// Query transformation matrix
    pub query_weights: Vec<Vec<f32>>,
    /// Key transformation matrix
    pub key_weights: Vec<Vec<f32>>,
    /// Value transformation matrix
    pub value_weights: Vec<Vec<f32>>,
    /// Attention head dimension
    pub head_dim: usize,
    /// Number of attention heads
    pub num_heads: usize,
}

/// Multi-head prediction network
#[derive(Debug, Clone)]
pub struct MultiHeadPredictor {
    /// Scale prediction head
    pub scale_head: PredictionHead,
    /// Zero-point prediction head
    pub zero_point_head: PredictionHead,
    /// Bit-width prediction head
    pub bit_width_head: PredictionHead,
    /// Quality prediction head (for uncertainty estimation)
    pub quality_head: PredictionHead,
}

/// Individual prediction head
#[derive(Debug, Clone)]
pub struct PredictionHead {
    /// Layer weights
    pub layers: Vec<DenseLayer>,
    /// Output activation
    pub output_activation: ActivationFunction,
    /// Uncertainty estimation enabled
    pub uncertainty_enabled: bool,
}

/// Dense layer implementation
#[derive(Debug, Clone)]
pub struct DenseLayer {
    /// Weight matrix
    pub weights: Vec<Vec<f32>>,
    /// Bias vector
    pub biases: Vec<f32>,
    /// Activation function
    pub activation: ActivationFunction,
    /// Batch normalization parameters
    pub batch_norm: Option<BatchNormalization>,
}

/// Batch normalization layer
#[derive(Debug, Clone)]
pub struct BatchNormalization {
    /// Running mean
    pub running_mean: Vec<f32>,
    /// Running variance
    pub running_var: Vec<f32>,
    /// Gamma parameter (scale)
    pub gamma: Vec<f32>,
    /// Beta parameter (shift)
    pub beta: Vec<f32>,
    /// Momentum for running statistics
    pub momentum: f32,
}

/// Enhanced activation functions
#[derive(Debug, Clone, PartialEq)]
pub enum ActivationFunction {
    ReLU,
    GELU,
    Swish,
    Mish,
    ELU,
    LeakyReLU(f32),
    Sigmoid,
    Tanh,
    Linear,
}

/// Specialized predictor for specific tensor types
#[derive(Debug, Clone)]
pub struct SpecializedPredictor {
    /// Network for this specialization
    pub network: AttentionBasedNetwork,
    /// Tensor type this predictor specializes in
    pub specialization: TensorSpecialization,
    /// Confidence threshold for using this predictor
    pub confidence_threshold: f32,
    /// Performance metrics
    pub performance_metrics: SpecializationMetrics,
}

/// Tensor specialization types
#[derive(Debug, Clone, PartialEq)]
pub enum TensorSpecialization {
    Weights,
    Activations,
    Gradients,
    Embeddings,
    Convolution,
    FullyConnected,
    BatchNorm,
    LayerNorm,
}

/// Performance metrics for specialized predictors
#[derive(Debug, Clone)]
pub struct SpecializationMetrics {
    /// Average prediction accuracy
    pub accuracy: f32,
    /// Average prediction speed (ms)
    pub speed_ms: f32,
    /// Number of successful predictions
    pub success_count: usize,
    /// Total predictions attempted
    pub total_predictions: usize,
}

/// Meta-learning controller for adaptive learning
#[derive(Debug, Clone)]
pub struct MetaLearningController {
    /// Learning rate scheduler
    pub lr_scheduler: LearningRateScheduler,
    /// Architecture adaptation controller
    pub arch_controller: ArchitectureController,
    /// Data balancing strategy
    pub data_balancer: DataBalancer,
    /// Loss function adaptation
    pub loss_adapter: LossAdapter,
}

/// Learning rate scheduling strategies
#[derive(Debug, Clone)]
pub struct LearningRateScheduler {
    /// Base learning rate
    pub base_lr: f32,
    /// Current learning rate
    pub current_lr: f32,
    /// Scheduling strategy
    pub strategy: LRScheduleStrategy,
    /// Performance-based adaptation
    pub adaptive: bool,
}

#[derive(Debug, Clone)]
pub enum LRScheduleStrategy {
    Constant,
    LinearDecay { decay_rate: f32 },
    ExponentialDecay { decay_rate: f32 },
    CosineAnnealing { t_max: usize },
    ReduceOnPlateau { patience: usize, factor: f32 },
}

/// Architecture adaptation controller
#[derive(Debug, Clone)]
pub struct ArchitectureController {
    /// Available architecture modifications
    pub modifications: Vec<ArchModification>,
    /// Current architecture score
    pub current_score: f32,
    /// Modification history
    pub modification_history: Vec<(ArchModification, f32)>,
}

#[derive(Debug, Clone)]
pub enum ArchModification {
    AddLayer {
        layer_type: LayerType,
        position: usize,
    },
    RemoveLayer {
        position: usize,
    },
    ModifyLayer {
        position: usize,
        modification: LayerModification,
    },
    AdjustDropout {
        new_rate: f32,
    },
    AdjustAttentionHeads {
        new_count: usize,
    },
}

#[derive(Debug, Clone)]
pub enum LayerType {
    Dense { units: usize },
    Attention { heads: usize },
    Convolution { filters: usize },
}

#[derive(Debug, Clone)]
pub enum LayerModification {
    ChangeUnits(usize),
    ChangeActivation(ActivationFunction),
    AddBatchNorm,
    RemoveBatchNorm,
}

/// Data balancing for improved training
#[derive(Debug, Clone)]
pub struct DataBalancer {
    /// Balancing strategy
    pub strategy: BalancingStrategy,
    /// Importance weights for different data types
    pub importance_weights: Vec<f32>,
    /// Data augmentation techniques
    pub augmentation: DataAugmentation,
}

#[derive(Debug, Clone)]
pub enum BalancingStrategy {
    Uniform,
    PerformanceBased,
    FrequencyBased,
    AdversarialBased,
}

/// Data augmentation techniques
#[derive(Debug, Clone)]
pub struct DataAugmentation {
    /// Noise injection probability
    pub noise_prob: f32,
    /// Feature scaling probability
    pub scaling_prob: f32,
    /// Feature permutation probability
    pub permutation_prob: f32,
    /// Synthetic data generation enabled
    pub synthetic_enabled: bool,
}

/// Adaptive loss function
#[derive(Debug, Clone)]
pub struct LossAdapter {
    /// Primary loss function
    pub primary_loss: LossFunction,
    /// Auxiliary loss functions with weights
    pub auxiliary_losses: Vec<(LossFunction, f32)>,
    /// Dynamic loss weighting
    pub dynamic_weighting: bool,
}

#[derive(Debug, Clone)]
pub enum LossFunction {
    MeanSquaredError,
    MeanAbsoluteError,
    Huber { delta: f32 },
    QuantileLoss { quantile: f32 },
    FocalLoss { alpha: f32, gamma: f32 },
}

/// Training configuration
#[derive(Debug, Clone)]
pub struct TrainingConfig {
    /// Batch size
    pub batch_size: usize,
    /// Number of epochs
    pub epochs: usize,
    /// Early stopping patience
    pub early_stopping_patience: usize,
    /// Gradient clipping threshold
    pub grad_clip_threshold: f32,
    /// L1 regularization weight
    pub l1_regularization: f32,
    /// L2 regularization weight
    pub l2_regularization: f32,
    /// Enable mixed precision training
    pub mixed_precision: bool,
}

/// Performance history tracking
#[derive(Debug, Clone)]
pub struct PerformanceHistory {
    /// Training loss history
    pub training_losses: Vec<f32>,
    /// Validation loss history
    pub validation_losses: Vec<f32>,
    /// Prediction accuracy history
    pub accuracy_history: Vec<f32>,
    /// Inference time history
    pub inference_times: Vec<f32>,
    /// Memory usage history
    pub memory_usage: Vec<f32>,
}

/// Enhanced training example with more metadata
#[derive(Debug, Clone)]
pub struct EnhancedTrainingExample {
    /// Input features
    pub features: Vec<f32>,
    /// Target parameters
    pub targets: PredictionTargets,
    /// Quality metrics achieved
    pub quality_metrics: QualityMetrics,
    /// Tensor metadata
    pub tensor_metadata: TensorMetadata,
    /// Timestamp
    pub timestamp: Instant,
}

/// Prediction targets with uncertainty
#[derive(Debug, Clone)]
pub struct PredictionTargets {
    /// Scale target with uncertainty
    pub scale: TargetWithUncertainty,
    /// Zero-point target with uncertainty
    pub zero_point: TargetWithUncertainty,
    /// Bit-width target with uncertainty
    pub bit_width: TargetWithUncertainty,
}

#[derive(Debug, Clone)]
pub struct TargetWithUncertainty {
    /// Target value
    pub value: f32,
    /// Uncertainty estimate
    pub uncertainty: f32,
    /// Confidence level
    pub confidence: f32,
}

/// Quality metrics for training examples
#[derive(Debug, Clone)]
pub struct QualityMetrics {
    /// PSNR achieved
    pub psnr: f32,
    /// SNR achieved
    pub snr: f32,
    /// SSIM achieved
    pub ssim: f32,
    /// Compression ratio achieved
    pub compression_ratio: f32,
    /// Inference speed impact
    pub speed_impact: f32,
}

/// Tensor metadata for specialized prediction
#[derive(Debug, Clone)]
pub struct TensorMetadata {
    /// Tensor shape
    pub shape: Vec<usize>,
    /// Tensor type
    pub tensor_type: TensorSpecialization,
    /// Layer position in model
    pub layer_position: Option<usize>,
    /// Model architecture context
    pub arch_context: String,
}

impl EnhancedMLPredictor {
    /// Create new enhanced ML predictor
    pub fn new() -> Self {
        let feature_dim = 64; // Expanded feature dimension

        Self {
            main_network: AttentionBasedNetwork::new(feature_dim),
            ensemble: Self::create_ensemble(feature_dim),
            meta_controller: MetaLearningController::new(),
            training_config: TrainingConfig::default(),
            performance_history: PerformanceHistory::new(),
        }
    }

    /// Create ensemble of specialized predictors
    fn create_ensemble(feature_dim: usize) -> Vec<SpecializedPredictor> {
        vec![
            SpecializedPredictor::new(feature_dim, TensorSpecialization::Weights),
            SpecializedPredictor::new(feature_dim, TensorSpecialization::Activations),
            SpecializedPredictor::new(feature_dim, TensorSpecialization::Convolution),
            SpecializedPredictor::new(feature_dim, TensorSpecialization::FullyConnected),
        ]
    }

    /// Predict parameters with enhanced capabilities
    pub fn predict_parameters_enhanced(
        &self,
        features: &[f32],
        tensor_metadata: &TensorMetadata,
    ) -> TorshResult<EnhancedPredictionResult> {
        // Select best predictor based on tensor type and confidence
        let selected_predictor = self.select_best_predictor(features, tensor_metadata)?;

        // Main prediction
        let main_prediction = self.main_network.predict(features)?;

        // Ensemble prediction
        let ensemble_predictions: Result<Vec<_>, _> = self
            .ensemble
            .par_iter()
            .map(|predictor| predictor.predict(features))
            .collect();
        let ensemble_predictions = ensemble_predictions?;

        // Combine predictions using meta-learning
        let combined_prediction = self.meta_controller.combine_predictions(
            &main_prediction,
            &ensemble_predictions,
            tensor_metadata,
        )?;

        // Uncertainty estimation
        let uncertainty =
            self.estimate_uncertainty(features, &main_prediction, &ensemble_predictions)?;

        let confidence = self.calculate_confidence(&uncertainty);

        Ok(EnhancedPredictionResult {
            parameters: combined_prediction,
            uncertainty,
            confidence,
            selected_predictor: selected_predictor.specialization.clone(),
        })
    }

    /// Select best predictor for given input
    fn select_best_predictor(
        &self,
        features: &[f32],
        metadata: &TensorMetadata,
    ) -> TorshResult<&SpecializedPredictor> {
        // Find predictor that specializes in this tensor type
        let specialized = self
            .ensemble
            .iter()
            .find(|p| p.specialization == metadata.tensor_type);

        if let Some(predictor) = specialized {
            // Check if confidence is above threshold
            let confidence = self.estimate_predictor_confidence(predictor, features)?;
            if confidence > predictor.confidence_threshold {
                return Ok(predictor);
            }
        }

        // Fall back to best performing general predictor
        self.ensemble
            .iter()
            .max_by(|a, b| {
                a.performance_metrics
                    .accuracy
                    .partial_cmp(&b.performance_metrics.accuracy)
                    .unwrap_or(std::cmp::Ordering::Equal)
            })
            .ok_or_else(|| TorshError::InvalidArgument("No predictor available".to_string()))
    }

    /// Estimate prediction uncertainty
    fn estimate_uncertainty(
        &self,
        features: &[f32],
        _main_pred: &PredictionResult,
        ensemble_preds: &[PredictionResult],
    ) -> TorshResult<UncertaintyEstimate> {
        // Calculate variance across ensemble predictions
        let scale_variance =
            Self::calculate_variance(ensemble_preds.iter().map(|p| p.scale).collect());
        let zp_variance =
            Self::calculate_variance(ensemble_preds.iter().map(|p| p.zero_point as f32).collect());
        let bw_variance =
            Self::calculate_variance(ensemble_preds.iter().map(|p| p.bit_width as f32).collect());

        // Epistemic uncertainty (model uncertainty)
        let epistemic = (scale_variance + zp_variance + bw_variance) / 3.0;

        // Aleatoric uncertainty (data uncertainty) - simplified estimation
        let aleatoric = self.estimate_aleatoric_uncertainty(features)?;

        Ok(UncertaintyEstimate {
            epistemic,
            aleatoric,
            total: epistemic + aleatoric,
        })
    }

    /// Estimate aleatoric uncertainty
    fn estimate_aleatoric_uncertainty(&self, _features: &[f32]) -> TorshResult<f32> {
        // Simplified aleatoric uncertainty estimation
        // In practice, this would use the quality prediction head
        Ok(0.1) // Placeholder
    }

    /// Calculate variance of predictions
    fn calculate_variance(values: Vec<f32>) -> f32 {
        if values.is_empty() {
            return 0.0;
        }

        let mean = values.iter().sum::<f32>() / values.len() as f32;
        let variance = values.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / values.len() as f32;
        variance
    }

    /// Calculate overall confidence from uncertainty
    fn calculate_confidence(&self, uncertainty: &UncertaintyEstimate) -> f32 {
        // Convert uncertainty to confidence (0-1 scale)
        (1.0 / (1.0 + uncertainty.total)).clamp(0.0, 1.0)
    }

    /// Estimate confidence for specific predictor
    fn estimate_predictor_confidence(
        &self,
        predictor: &SpecializedPredictor,
        _features: &[f32],
    ) -> TorshResult<f32> {
        // Use historical performance as confidence proxy
        Ok(predictor.performance_metrics.accuracy)
    }
}

/// Enhanced prediction result
#[derive(Debug, Clone)]
pub struct EnhancedPredictionResult {
    /// Predicted parameters
    pub parameters: PredictionResult,
    /// Uncertainty estimate
    pub uncertainty: UncertaintyEstimate,
    /// Overall confidence
    pub confidence: f32,
    /// Selected predictor type
    pub selected_predictor: TensorSpecialization,
}

/// Basic prediction result
#[derive(Debug, Clone)]
pub struct PredictionResult {
    /// Predicted scale
    pub scale: f32,
    /// Predicted zero point
    pub zero_point: i32,
    /// Predicted bit width
    pub bit_width: u8,
}

/// Uncertainty estimation
#[derive(Debug, Clone)]
pub struct UncertaintyEstimate {
    /// Epistemic uncertainty (model uncertainty)
    pub epistemic: f32,
    /// Aleatoric uncertainty (data uncertainty)
    pub aleatoric: f32,
    /// Total uncertainty
    pub total: f32,
}

// Implementation stubs for complex components
impl AttentionBasedNetwork {
    fn new(_feature_dim: usize) -> Self {
        // Simplified initialization
        Self {
            feature_extractors: vec![],
            attention_layers: vec![],
            prediction_head: MultiHeadPredictor::new(),
            dropout_rate: 0.1,
        }
    }

    fn predict(&self, _features: &[f32]) -> TorshResult<PredictionResult> {
        // Simplified prediction - in practice would run full forward pass
        Ok(PredictionResult {
            scale: 0.1,
            zero_point: 0,
            bit_width: 8,
        })
    }
}

impl MultiHeadPredictor {
    fn new() -> Self {
        Self {
            scale_head: PredictionHead::new(),
            zero_point_head: PredictionHead::new(),
            bit_width_head: PredictionHead::new(),
            quality_head: PredictionHead::new(),
        }
    }
}

impl PredictionHead {
    fn new() -> Self {
        Self {
            layers: vec![],
            output_activation: ActivationFunction::Linear,
            uncertainty_enabled: false,
        }
    }
}

impl SpecializedPredictor {
    fn new(_feature_dim: usize, specialization: TensorSpecialization) -> Self {
        Self {
            network: AttentionBasedNetwork::new(_feature_dim),
            specialization,
            confidence_threshold: 0.8,
            performance_metrics: SpecializationMetrics::new(),
        }
    }

    fn predict(&self, features: &[f32]) -> TorshResult<PredictionResult> {
        self.network.predict(features)
    }
}

impl SpecializationMetrics {
    fn new() -> Self {
        Self {
            accuracy: 0.8,
            speed_ms: 1.0,
            success_count: 0,
            total_predictions: 0,
        }
    }
}

impl MetaLearningController {
    fn new() -> Self {
        Self {
            lr_scheduler: LearningRateScheduler::new(),
            arch_controller: ArchitectureController::new(),
            data_balancer: DataBalancer::new(),
            loss_adapter: LossAdapter::new(),
        }
    }

    fn combine_predictions(
        &self,
        main_pred: &PredictionResult,
        _ensemble_preds: &[PredictionResult],
        _metadata: &TensorMetadata,
    ) -> TorshResult<PredictionResult> {
        // Simplified combination - in practice would use learned weights
        Ok(main_pred.clone())
    }
}

impl LearningRateScheduler {
    fn new() -> Self {
        Self {
            base_lr: 0.001,
            current_lr: 0.001,
            strategy: LRScheduleStrategy::Constant,
            adaptive: true,
        }
    }
}

impl ArchitectureController {
    fn new() -> Self {
        Self {
            modifications: vec![],
            current_score: 0.8,
            modification_history: vec![],
        }
    }
}

impl DataBalancer {
    fn new() -> Self {
        Self {
            strategy: BalancingStrategy::PerformanceBased,
            importance_weights: vec![1.0; 8],
            augmentation: DataAugmentation::new(),
        }
    }
}

impl DataAugmentation {
    fn new() -> Self {
        Self {
            noise_prob: 0.1,
            scaling_prob: 0.1,
            permutation_prob: 0.05,
            synthetic_enabled: true,
        }
    }
}

impl LossAdapter {
    fn new() -> Self {
        Self {
            primary_loss: LossFunction::MeanSquaredError,
            auxiliary_losses: vec![
                (LossFunction::MeanAbsoluteError, 0.3),
                (LossFunction::Huber { delta: 1.0 }, 0.2),
            ],
            dynamic_weighting: true,
        }
    }
}

impl TrainingConfig {
    fn default() -> Self {
        Self {
            batch_size: 32,
            epochs: 100,
            early_stopping_patience: 10,
            grad_clip_threshold: 1.0,
            l1_regularization: 0.0001,
            l2_regularization: 0.0001,
            mixed_precision: true,
        }
    }
}

impl PerformanceHistory {
    fn new() -> Self {
        Self {
            training_losses: vec![],
            validation_losses: vec![],
            accuracy_history: vec![],
            inference_times: vec![],
            memory_usage: vec![],
        }
    }
}

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