scirs2_sparse/neural_adaptive_sparse/

transformer.rs

//! Transformer models for advanced pattern recognition in sparse matrices
//!
//! This module contains transformer-based architectures for learning complex
//! patterns in sparse matrix operations and optimizing them adaptively.

use super::neural_network::{ActivationFunction, AttentionHead, LayerNorm};
use crate::error::SparseResult;
use scirs2_core::random::Rng;

/// Transformer model for advanced pattern recognition
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub(crate) struct TransformerModel {
    pub encoder_layers: Vec<TransformerEncoderLayer>,
    pub positional_encoding: Vec<Vec<f64>>,
    pub embedding_dim: usize,
}

/// Transformer encoder layer
#[derive(Debug, Clone)]
pub(crate) struct TransformerEncoderLayer {
    pub self_attention: MultiHeadAttention,
    pub feed_forward: FeedForwardNetwork,
    pub layer_norm1: LayerNorm,
    pub layer_norm2: LayerNorm,
    pub dropout_rate: f64,
}

/// Multi-head attention for transformer
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub(crate) struct MultiHeadAttention {
    pub heads: Vec<AttentionHead>,
    pub output_projection: Vec<Vec<f64>>,
    pub num_heads: usize,
    pub head_dim: usize,
}

/// Feed-forward network
#[derive(Debug, Clone)]
pub(crate) struct FeedForwardNetwork {
    pub layer1: Vec<Vec<f64>>,
    pub layer1_bias: Vec<f64>,
    pub layer2: Vec<Vec<f64>>,
    pub layer2_bias: Vec<f64>,
    pub activation: ActivationFunction,
}

impl TransformerModel {
    /// Create a new transformer model
    pub fn new(
        embedding_dim: usize,
        num_layers: usize,
        num_heads: usize,
        ff_dim: usize,
        max_sequence_length: usize,
    ) -> Self {
        let mut encoder_layers = Vec::new();

        for _ in 0..num_layers {
            encoder_layers.push(TransformerEncoderLayer::new(
                embedding_dim,
                num_heads,
                ff_dim,
            ));
        }

        let positional_encoding =
            Self::create_positional_encoding(max_sequence_length, embedding_dim);

        Self {
            encoder_layers,
            positional_encoding,
            embedding_dim,
        }
    }

    /// Create positional encoding for transformer
    fn create_positional_encoding(max_length: usize, embedding_dim: usize) -> Vec<Vec<f64>> {
        let mut pos_encoding = vec![vec![0.0; embedding_dim]; max_length];

        for pos in 0..max_length {
            for i in (0..embedding_dim).step_by(2) {
                let angle = pos as f64 / 10000.0_f64.powf(i as f64 / embedding_dim as f64);
                pos_encoding[pos][i] = angle.sin();
                if i + 1 < embedding_dim {
                    pos_encoding[pos][i + 1] = angle.cos();
                }
            }
        }

        pos_encoding
    }

    /// Forward pass through the transformer
    pub fn forward(&self, input: &[Vec<f64>]) -> Vec<Vec<f64>> {
        // Add positional encoding
        let mut x = input.to_vec();
        for (i, sequence) in x.iter_mut().enumerate() {
            if i < self.positional_encoding.len() {
                for (j, val) in sequence.iter_mut().enumerate() {
                    if j < self.positional_encoding[i].len() {
                        *val += self.positional_encoding[i][j];
                    }
                }
            }
        }

        // Pass through encoder layers
        for layer in &self.encoder_layers {
            x = layer.forward(&x);
        }

        x
    }

    /// Encode matrix patterns using transformer
    pub fn encode_matrix_pattern(&self, matrix_features: &[f64]) -> Vec<f64> {
        // Convert 1D features to sequence format
        let sequence_length = (matrix_features.len() / self.embedding_dim).max(1);
        let mut sequence = vec![vec![0.0; self.embedding_dim]; sequence_length];

        let mut idx = 0;
        for i in 0..sequence_length {
            for j in 0..self.embedding_dim {
                if idx < matrix_features.len() {
                    sequence[i][j] = matrix_features[idx];
                    idx += 1;
                }
            }
        }

        // Process through transformer
        let encoded = self.forward(&sequence);

        // Pool the encoded sequence (simple mean pooling)
        let mut pooled = vec![0.0; self.embedding_dim];
        for sequence_step in &encoded {
            for (i, &val) in sequence_step.iter().enumerate() {
                if i < pooled.len() {
                    pooled[i] += val / encoded.len() as f64;
                }
            }
        }

        pooled
    }

    /// Update transformer parameters (simplified training step)
    pub fn update_parameters(&mut self, gradients: &TransformerGradients, learning_rate: f64) {
        for (layer_idx, layer) in self.encoder_layers.iter_mut().enumerate() {
            if layer_idx < gradients.layer_gradients.len() {
                layer.update_parameters(&gradients.layer_gradients[layer_idx], learning_rate);
            }
        }
    }
}

impl TransformerEncoderLayer {
    /// Create a new transformer encoder layer
    pub fn new(embedding_dim: usize, num_heads: usize, ff_dim: usize) -> Self {
        Self {
            self_attention: MultiHeadAttention::new(embedding_dim, num_heads),
            feed_forward: FeedForwardNetwork::new(embedding_dim, ff_dim),
            layer_norm1: LayerNorm::new(embedding_dim),
            layer_norm2: LayerNorm::new(embedding_dim),
            dropout_rate: 0.1,
        }
    }

    /// Forward pass through encoder layer
    pub fn forward(&self, input: &[Vec<f64>]) -> Vec<Vec<f64>> {
        // Self-attention with residual connection
        let attention_output = self.self_attention.forward(input);
        let mut norm1_input = Vec::new();

        for (i, attention_seq) in attention_output.iter().enumerate() {
            let mut residual = attention_seq.clone();
            if i < input.len() {
                for (j, &input_val) in input[i].iter().enumerate() {
                    if j < residual.len() {
                        residual[j] += input_val;
                    }
                }
            }
            norm1_input.push(residual);
        }

        // First layer normalization
        let norm1_output: Vec<Vec<f64>> = norm1_input
            .iter()
            .map(|seq| self.layer_norm1.normalize(seq))
            .collect();

        // Feed-forward with residual connection
        let ff_output = self.feed_forward.forward(&norm1_output);
        let mut norm2_input = Vec::new();

        for (i, ff_seq) in ff_output.iter().enumerate() {
            let mut residual = ff_seq.clone();
            if i < norm1_output.len() {
                for (j, &norm_val) in norm1_output[i].iter().enumerate() {
                    if j < residual.len() {
                        residual[j] += norm_val;
                    }
                }
            }
            norm2_input.push(residual);
        }

        // Second layer normalization
        norm2_input
            .iter()
            .map(|seq| self.layer_norm2.normalize(seq))
            .collect()
    }

    /// Update layer parameters
    pub fn update_parameters(&mut self, gradients: &LayerGradients, learning_rate: f64) {
        self.self_attention
            .update_parameters(&gradients.attention_gradients, learning_rate);
        self.feed_forward
            .update_parameters(&gradients.ff_gradients, learning_rate);
    }
}

impl MultiHeadAttention {
    /// Create a new multi-head attention mechanism
    pub fn new(embedding_dim: usize, num_heads: usize) -> Self {
        let head_dim = embedding_dim / num_heads;
        let mut heads = Vec::new();

        for _ in 0..num_heads {
            heads.push(AttentionHead::new(embedding_dim));
        }

        let output_projection = Self::initialize_weights(embedding_dim, embedding_dim);

        Self {
            heads,
            output_projection,
            num_heads,
            head_dim,
        }
    }

    /// Initialize weights
    fn initialize_weights(input_dim: usize, output_dim: usize) -> Vec<Vec<f64>> {
        let mut rng = scirs2_core::random::thread_rng();
        let bound = (6.0 / (input_dim + output_dim) as f64).sqrt();

        (0..output_dim)
            .map(|_| {
                (0..input_dim)
                    .map(|_| rng.gen_range(-bound..bound))
                    .collect()
            })
            .collect()
    }

    /// Forward pass through multi-head attention
    pub fn forward(&self, input: &[Vec<f64>]) -> Vec<Vec<f64>> {
        let mut all_head_outputs = Vec::new();

        // Process each head
        for head in &self.heads {
            let mut head_output = Vec::new();
            for sequence in input {
                head_output.push(head.forward(sequence));
            }
            all_head_outputs.push(head_output);
        }

        // Concatenate head outputs and apply output projection
        let mut result = Vec::new();
        for seq_idx in 0..input.len() {
            let mut concatenated = Vec::new();
            for head_output in &all_head_outputs {
                if seq_idx < head_output.len() {
                    concatenated.extend(&head_output[seq_idx]);
                }
            }

            // Apply output projection
            let projected = self.linear_transform(&concatenated, &self.output_projection);
            result.push(projected);
        }

        result
    }

    /// Linear transformation
    fn linear_transform(&self, input: &[f64], weights: &[Vec<f64>]) -> Vec<f64> {
        let mut output = vec![0.0; weights.len()];

        for (i, neuron_weights) in weights.iter().enumerate() {
            let mut sum = 0.0;
            for (j, &input_val) in input.iter().enumerate() {
                if j < neuron_weights.len() {
                    sum += neuron_weights[j] * input_val;
                }
            }
            output[i] = sum;
        }

        output
    }

    /// Update attention parameters
    pub fn update_parameters(&mut self, gradients: &AttentionGradients, learning_rate: f64) {
        // Simplified parameter update
        // In practice, this would involve proper gradient computation and application
        for (head_idx, head) in self.heads.iter_mut().enumerate() {
            // Update head parameters (simplified)
            // Real implementation would have proper gradient handling
        }
    }
}

impl FeedForwardNetwork {
    /// Create a new feed-forward network
    pub fn new(embedding_dim: usize, ff_dim: usize) -> Self {
        Self {
            layer1: Self::initialize_weights(embedding_dim, ff_dim),
            layer1_bias: vec![0.0; ff_dim],
            layer2: Self::initialize_weights(ff_dim, embedding_dim),
            layer2_bias: vec![0.0; embedding_dim],
            activation: ActivationFunction::ReLU,
        }
    }

    /// Initialize weights
    fn initialize_weights(input_dim: usize, output_dim: usize) -> Vec<Vec<f64>> {
        let mut rng = scirs2_core::random::thread_rng();
        let bound = (6.0 / (input_dim + output_dim) as f64).sqrt();

        (0..output_dim)
            .map(|_| {
                (0..input_dim)
                    .map(|_| rng.gen_range(-bound..bound))
                    .collect()
            })
            .collect()
    }

    /// Forward pass through feed-forward network
    pub fn forward(&self, input: &[Vec<f64>]) -> Vec<Vec<f64>> {
        let mut result = Vec::new();

        for sequence in input {
            // First layer
            let mut layer1_output = vec![0.0; self.layer1.len()];
            for (i, neuron_weights) in self.layer1.iter().enumerate() {
                let mut sum = self.layer1_bias[i];
                for (j, &input_val) in sequence.iter().enumerate() {
                    if j < neuron_weights.len() {
                        sum += neuron_weights[j] * input_val;
                    }
                }
                layer1_output[i] = self.apply_activation(sum);
            }

            // Second layer
            let mut layer2_output = vec![0.0; self.layer2.len()];
            for (i, neuron_weights) in self.layer2.iter().enumerate() {
                let mut sum = self.layer2_bias[i];
                for (j, &layer1_val) in layer1_output.iter().enumerate() {
                    if j < neuron_weights.len() {
                        sum += neuron_weights[j] * layer1_val;
                    }
                }
                layer2_output[i] = sum; // No activation on output layer
            }

            result.push(layer2_output);
        }

        result
    }

    /// Apply activation function
    fn apply_activation(&self, x: f64) -> f64 {
        match self.activation {
            ActivationFunction::ReLU => x.max(0.0),
            ActivationFunction::Sigmoid => 1.0 / (1.0 + (-x).exp()),
            ActivationFunction::Tanh => x.tanh(),
            ActivationFunction::Swish => x * (1.0 / (1.0 + (-x).exp())),
            ActivationFunction::Gelu => 0.5 * x * (1.0 + (x * 0.7978845608028654).tanh()),
        }
    }

    /// Update feed-forward parameters
    pub fn update_parameters(&mut self, gradients: &FFGradients, learning_rate: f64) {
        // Simplified parameter update
        // Real implementation would apply computed gradients
    }
}

/// Gradient structures for transformer training
#[derive(Debug, Clone)]
pub struct TransformerGradients {
    pub layer_gradients: Vec<LayerGradients>,
}

#[derive(Debug, Clone)]
pub struct LayerGradients {
    pub attention_gradients: AttentionGradients,
    pub ff_gradients: FFGradients,
}

#[derive(Debug, Clone)]
pub struct AttentionGradients {
    pub head_gradients: Vec<HeadGradients>,
    pub output_projection_gradients: Vec<Vec<f64>>,
}

#[derive(Debug, Clone)]
pub struct HeadGradients {
    pub query_gradients: Vec<Vec<f64>>,
    pub key_gradients: Vec<Vec<f64>>,
    pub value_gradients: Vec<Vec<f64>>,
}

#[derive(Debug, Clone)]
pub struct FFGradients {
    pub layer1_weight_gradients: Vec<Vec<f64>>,
    pub layer1_bias_gradients: Vec<f64>,
    pub layer2_weight_gradients: Vec<Vec<f64>>,
    pub layer2_bias_gradients: Vec<f64>,
}