trustformers-models 0.1.1

Model implementations for TrustformeRS
Documentation 
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use trustformers_core::{
    errors::Result,
    layers::{Embedding, LayerNorm, Linear},
    quantum::QuantumEmbeddingLayer,
    tensor::Tensor,
    traits::Layer,
};

use super::{config::QuantumClassicalConfig, model::QuantumClassicalModelOutput};

/// Quantum embedding model
#[derive(Debug)]
pub struct QuantumEmbeddingModel {
    /// Configuration
    pub config: QuantumClassicalConfig,
    /// Classical embeddings
    pub classical_embeddings: Embedding,
    /// Quantum embedding layer
    pub quantum_embedding: QuantumEmbeddingLayer,
    /// Output projection
    pub output_projection: Linear,
    /// Layer normalization
    pub layer_norm: LayerNorm,
}

impl QuantumEmbeddingModel {
    /// Create a new quantum embedding model
    pub fn new(config: &QuantumClassicalConfig) -> Result<Self> {
        let classical_embeddings = Embedding::new(config.vocab_size, config.d_model, None)?;
        let quantum_embedding = QuantumEmbeddingLayer::new(
            config.d_model,
            config.get_quantum_dimension(),
            config.quantum_encoding.clone(),
        )?;
        let output_projection = Linear::new(
            config.get_quantum_dimension(),
            config.d_model,
            config.use_bias,
        );
        let layer_norm = LayerNorm::new(vec![config.d_model], 1e-12)?;

        Ok(Self {
            config: config.clone(),
            classical_embeddings,
            quantum_embedding,
            output_projection,
            layer_norm,
        })
    }

    /// Forward pass through quantum embedding
    pub fn forward(&mut self, input: &Tensor) -> Result<QuantumClassicalModelOutput> {
        // For this implementation, we'll assume input is already float tensor
        // In practice, this would handle token IDs
        let _batch_size = input.shape()[0];
        let seq_len = input.shape()[1];

        let mut outputs = Vec::new();
        let mut quantum_measurements = Vec::new();

        // Process each sequence position
        for t in 0..seq_len {
            let input_slice = input.slice(1, t, t + 1)?;
            let input_t = input_slice.squeeze(1)?;

            // Quantum embedding
            let quantum_output = self.quantum_embedding.forward(&input_t)?;
            quantum_measurements.push(quantum_output.clone());

            // Project back to classical dimension
            let projected = self.output_projection.forward(quantum_output)?;
            outputs.push(projected);
        }

        // Stack outputs
        let output = Tensor::concat(&outputs, 1)?;

        let normalized = self.layer_norm.forward(output.clone())?;

        let combined_measurements = if !quantum_measurements.is_empty() {
            let mut combined = quantum_measurements[0].clone();
            for measurement in quantum_measurements.iter().skip(1) {
                combined = combined.add(measurement)?;
            }
            Some(combined)
        } else {
            None
        };

        Ok(QuantumClassicalModelOutput {
            hidden_states: normalized,
            quantum_measurements: combined_measurements,
            classical_activations: Some(output),
            quantum_attention_weights: None,
            quantum_fidelity_scores: None,
            quantum_entanglement_measures: None,
            quantum_error_mitigation: None,
        })
    }

    /// Get parameter count
    pub fn parameter_count(&self) -> usize {
        let classical_params = self.config.vocab_size * self.config.d_model; // Embedding parameters
        let quantum_params = self.config.d_model * self.config.get_quantum_dimension(); // Quantum layer params (approximate)
        let projection_params = self.config.get_quantum_dimension() * self.config.d_model; // Linear projection
        let norm_params =
            if self.config.use_bias { 2 * self.config.d_model } else { self.config.d_model }; // LayerNorm weights + bias

        classical_params + quantum_params + projection_params + norm_params
    }

    /// Get memory usage in MB
    pub fn memory_usage(&self) -> f32 {
        self.parameter_count() as f32 * 4.0 / 1_000_000.0
    }
}