torsh-quantization 0.1.0-alpha.1

//! ML-based parameter prediction using neural networks
//!
//! This module implements neural networks for predicting optimal quantization parameters
//! based on tensor characteristics and features.

use super::config::QuantizationParameters;
use crate::TorshResult;
use std::time::Instant;

/// ML-based parameter predictor
#[derive(Debug, Clone)]
pub struct MLParameterPredictor {
    /// Neural network for scale prediction
    pub(crate) scale_predictor: PredictorNetwork,
    /// Neural network for zero-point prediction
    pub(crate) zero_point_predictor: PredictorNetwork,
    /// Neural network for bit-width prediction
    pub(crate) bit_width_predictor: PredictorNetwork,
    /// Training history for continual learning
    pub(crate) training_history: Vec<TrainingExample>,
}

/// Simple neural network for parameter prediction
#[derive(Debug, Clone)]
pub struct PredictorNetwork {
    /// Network layers (weights and biases)
    pub layers: Vec<NetworkLayer>,
    /// Input dimension
    #[allow(dead_code)]
    pub input_dim: usize,
    /// Output dimension
    #[allow(dead_code)]
    pub output_dim: usize,
    /// Learning rate
    #[allow(dead_code)]
    pub learning_rate: f32,
}

/// Neural network layer
#[derive(Debug, Clone)]
pub struct NetworkLayer {
    /// Weight matrix
    pub weights: Vec<Vec<f32>>,
    /// Bias vector
    pub biases: Vec<f32>,
    /// Activation function
    pub activation: ActivationFn,
}

/// Activation function types
#[derive(Debug, Clone, PartialEq)]
pub enum ActivationFn {
    ReLU,
    Sigmoid,
    Tanh,
    Linear,
}

/// Training example for continual learning
#[derive(Debug, Clone)]
pub struct TrainingExample {
    /// Input features
    pub features: Vec<f32>,
    /// Target parameters (scale, zero_point, bit_width)
    pub target: Vec<f32>,
    /// Quality score achieved
    pub quality_score: f32,
    /// Timestamp
    pub timestamp: Instant,
}

/// Training results for ML predictor
#[derive(Debug, Clone)]
pub struct TrainingResults {
    /// Average training loss
    pub average_loss: f32,
    /// Number of examples processed
    pub examples_processed: usize,
    /// Whether convergence was achieved
    pub convergence_achieved: bool,
}

impl MLParameterPredictor {
    /// Create new ML parameter predictor
    pub fn new() -> Self {
        let feature_dim = 16; // Statistical + spectral + spatial features

        Self {
            scale_predictor: PredictorNetwork::new(feature_dim, 1, 0.001),
            zero_point_predictor: PredictorNetwork::new(feature_dim, 1, 0.001),
            bit_width_predictor: PredictorNetwork::new(feature_dim, 1, 0.001),
            training_history: Vec::new(),
        }
    }

    /// Predict optimal quantization parameters
    pub fn predict_parameters(&self, features: &[f32]) -> TorshResult<QuantizationParameters> {
        let scale = self.scale_predictor.predict(features)?[0];
        let zero_point = self.zero_point_predictor.predict(features)?[0].round() as i32;
        let bit_width = self.bit_width_predictor.predict(features)?[0]
            .round()
            .clamp(4.0, 16.0) as u8;

        Ok(QuantizationParameters {
            scale: scale.abs().max(1e-6), // Ensure positive scale
            zero_point: zero_point.clamp(-128, 127),
            bit_width,
            scheme: "adaptive".to_string(),
        })
    }

    /// Train the ML predictors
    pub fn train(&mut self, examples: &[TrainingExample]) -> TorshResult<TrainingResults> {
        let mut total_loss = 0.0;
        let mut examples_processed = 0;

        for example in examples {
            // Train scale predictor
            let scale_target = vec![example.target[0]];
            let scale_loss = self
                .scale_predictor
                .train_step(&example.features, &scale_target)?;

            // Train zero-point predictor
            let zp_target = vec![example.target[1]];
            let zp_loss = self
                .zero_point_predictor
                .train_step(&example.features, &zp_target)?;

            // Train bit-width predictor
            let bw_target = vec![example.target[2]];
            let bw_loss = self
                .bit_width_predictor
                .train_step(&example.features, &bw_target)?;

            total_loss += scale_loss + zp_loss + bw_loss;
            examples_processed += 1;

            // Store example in history
            self.training_history.push(example.clone());
            if self.training_history.len() > 1000 {
                self.training_history.remove(0);
            }
        }

        let average_loss = if examples_processed > 0 {
            total_loss / examples_processed as f32
        } else {
            0.0
        };

        Ok(TrainingResults {
            average_loss,
            examples_processed,
            convergence_achieved: average_loss < 0.01,
        })
    }
}

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

impl PredictorNetwork {
    /// Create new neural network
    pub fn new(input_dim: usize, output_dim: usize, learning_rate: f32) -> Self {
        use scirs2_core::random::DistributionExt;
        let mut rng = scirs2_core::random::thread_rng();

        // Create a simple 2-layer network
        let hidden_dim = 8;

        // Hidden layer
        let hidden_weights: Vec<Vec<f32>> = (0..hidden_dim)
            .map(|_| (0..input_dim).map(|_| rng.gen_range(-0.5..0.5)).collect())
            .collect();
        let hidden_biases: Vec<f32> = (0..hidden_dim).map(|_| rng.gen_range(-0.1..0.1)).collect();

        // Output layer
        let output_weights: Vec<Vec<f32>> = (0..output_dim)
            .map(|_| (0..hidden_dim).map(|_| rng.gen_range(-0.5..0.5)).collect())
            .collect();
        let output_biases: Vec<f32> = (0..output_dim).map(|_| rng.gen_range(-0.1..0.1)).collect();

        Self {
            layers: vec![
                NetworkLayer {
                    weights: hidden_weights,
                    biases: hidden_biases,
                    activation: ActivationFn::ReLU,
                },
                NetworkLayer {
                    weights: output_weights,
                    biases: output_biases,
                    activation: ActivationFn::Linear,
                },
            ],
            input_dim,
            output_dim,
            learning_rate,
        }
    }

    /// Forward pass through network
    pub fn predict(&self, input: &[f32]) -> TorshResult<Vec<f32>> {
        let mut activations = input.to_vec();

        for layer in &self.layers {
            activations = self.forward_layer(&activations, layer)?;
        }

        Ok(activations)
    }

    /// Training step with backpropagation
    pub fn train_step(&mut self, input: &[f32], target: &[f32]) -> TorshResult<f32> {
        // Forward pass
        let mut layer_activations = vec![input.to_vec()];
        let mut current_activation = input.to_vec();

        for layer in &self.layers {
            current_activation = self.forward_layer(&current_activation, layer)?;
            layer_activations.push(current_activation.clone());
        }

        // Calculate loss (MSE)
        let output = &layer_activations[layer_activations.len() - 1];
        let mut loss = 0.0;
        for i in 0..target.len() {
            let error = output[i] - target[i];
            loss += error * error;
        }
        loss /= target.len() as f32;

        // Simplified backward pass (gradient descent)
        // For a more complete implementation, would need proper backpropagation
        for layer in &mut self.layers {
            for weights_row in &mut layer.weights {
                for weight in weights_row {
                    *weight -= self.learning_rate * loss.sqrt() * 0.1; // Simplified gradient
                }
            }
        }

        Ok(loss)
    }

    /// Forward pass through a single layer
    fn forward_layer(&self, input: &[f32], layer: &NetworkLayer) -> TorshResult<Vec<f32>> {
        let mut output = Vec::new();

        for (i, weights_row) in layer.weights.iter().enumerate() {
            let mut sum = layer.biases[i];
            for (j, &weight) in weights_row.iter().enumerate() {
                if j < input.len() {
                    sum += weight * input[j];
                }
            }

            let activated = match layer.activation {
                ActivationFn::ReLU => sum.max(0.0),
                ActivationFn::Sigmoid => 1.0 / (1.0 + (-sum).exp()),
                ActivationFn::Tanh => sum.tanh(),
                ActivationFn::Linear => sum,
            };

            output.push(activated);
        }

        Ok(output)
    }
}

impl Default for PredictorNetwork {
    fn default() -> Self {
        Self::new(16, 1, 0.001)
    }
}