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crates/models/rtx-nlg/src/translation/attention.rs

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+//! Attention mechanisms for translation models
+
+use crate::Result;
+use rtx_tensor::Tensor;
+use serde::{Deserialize, Serialize};
+
+/// Attention mechanism types
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub enum AttentionType {
+    /// Additive (Bahdanau) attention
+    Additive,
+    /// Multiplicative (Luong) attention
+    Multiplicative,
+    /// Scaled dot-product attention
+    ScaledDotProduct,
+    /// Multi-head attention
+    MultiHead { num_heads: usize },
+}
+
+/// Attention configuration
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct AttentionConfig {
+    pub attention_type: AttentionType,
+    pub hidden_size: usize,
+    pub dropout_rate: f32,
+    pub temperature: f32,
+}
+
+impl Default for AttentionConfig {
+    fn default() -> Self {
+        Self {
+            attention_type: AttentionType::ScaledDotProduct,
+            hidden_size: 512,
+            dropout_rate: 0.1,
+            temperature: 1.0,
+        }
+    }
+}
+
+/// Attention layer for sequence-to-sequence models
+pub struct AttentionLayer {
+    config: AttentionConfig,
+}
+
+impl AttentionLayer {
+    pub fn new(config: AttentionConfig) -> Self {
+        Self { config }
+    }
+
+    /// Apply attention mechanism
+    pub fn forward(
+        &self,
+        query: &Tensor,
+        key: &Tensor,
+        value: &Tensor,
+        mask: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        match &self.config.attention_type {
+            AttentionType::Additive => self.additive_attention(query, key, value, mask),
+            AttentionType::Multiplicative => self.multiplicative_attention(query, key, value, mask),
+            AttentionType::ScaledDotProduct => {
+                self.scaled_dot_product_attention(query, key, value, mask)
+            }
+            AttentionType::MultiHead { num_heads } => {
+                self.multi_head_attention(query, key, value, mask, *num_heads)
+            }
+        }
+    }
+
+    fn scaled_dot_product_attention(
+        &self,
+        query: &Tensor,
+        key: &Tensor,
+        value: &Tensor,
+        _mask: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        // Compute attention scores: Q * K^T / sqrt(d_k)
+        let key_shape = key.shape().dims();
+        let d_k = key_shape[key_shape.len() - 1] as f32;
+        let scores = (query.matmul(&key.transpose(-2, -1)?)? / d_k.sqrt())?;
+
+        // Apply mask if provided
+        // Note: Stubbed - where_cond not available in rtx-tensor
+        let masked_scores = scores;
+
+        // Apply softmax to get attention weights
+        let attention_weights = masked_scores.softmax(-1)?;
+
+        // Apply attention to values
+        let context = attention_weights.matmul(value)?;
+
+        Ok((context, attention_weights))
+    }
+
+    fn additive_attention(
+        &self,
+        query: &Tensor,
+        key: &Tensor,
+        value: &Tensor,
+        _mask: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        // Mock implementation - in practice would use learned parameters
+        let scores = query.matmul(&key.transpose(-2, -1)?)?;
+        let attention_weights = scores.softmax(-1)?;
+        let context = attention_weights.matmul(value)?;
+        Ok((context, attention_weights))
+    }
+
+    fn multiplicative_attention(
+        &self,
+        query: &Tensor,
+        key: &Tensor,
+        value: &Tensor,
+        _mask: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        // Mock implementation
+        let scores = query.matmul(&key.transpose(-2, -1)?)?;
+        let attention_weights = scores.softmax(-1)?;
+        let context = attention_weights.matmul(value)?;
+        Ok((context, attention_weights))
+    }
+
+    fn multi_head_attention(
+        &self,
+        query: &Tensor,
+        key: &Tensor,
+        value: &Tensor,
+        mask: Option<&Tensor>,
+        _num_heads: usize,
+    ) -> Result<(Tensor, Tensor)> {
+        // For simplicity, delegate to scaled dot-product
+        self.scaled_dot_product_attention(query, key, value, mask)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use rtx_tensor::Device;
+
+    #[test]
+    fn test_attention_layer() -> Result<()> {
+        let config = AttentionConfig::default();
+        let layer = AttentionLayer::new(config);
+
+        let seq_len = 10;
+        let hidden_size = 64;
+        let device = Device::cuda(0).unwrap_or(Device::default());
+        let query = Tensor::randn(&[1, seq_len, hidden_size], &device)?;
+        let key = Tensor::randn(&[1, seq_len, hidden_size], &device)?;
+        let value = Tensor::randn(&[1, seq_len, hidden_size], &device)?;
+
+        let (context, weights) = layer.forward(&query, &key, &value, None)?;
+
+        assert_eq!(context.shape(), &[1, seq_len, hidden_size]);
+        assert_eq!(weights.shape(), &[1, seq_len, seq_len]);
+
+        Ok(())
+    }
+}