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2026-03-04 00:08:42 +00:00
commit 4d88dc0584
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+//! Graph Transformer components for graph-structured data
+//!
+//! This module provides attention-based learning capabilities on graph-structured data,
+//! supporting both homogeneous and heterogeneous graphs with batch processing.
+
+use crate::Result;
+use rtx_tensor::{Tensor, Device};
+
+pub mod graph_transformer_simple;
+pub mod graph_attention_simple;
+pub mod graph_pooling_simple;
+pub mod positional_encoding_simple;
+
+#[cfg(all(test, feature = "disabled_tests"))]
+pub mod graph_transformer_tests;
+
+pub use graph_transformer_simple::{GraphTransformer, GraphTransformerConfig, GraphOutput};
+pub use graph_attention_simple::{GraphAttention, GraphAttentionConfig, AttentionOutput};
+pub use graph_pooling_simple::{GraphPooling, GlobalPooling, HierarchicalPooling, Set2SetPooling, AttentionPooling};
+pub use positional_encoding_simple::{GraphPositionalEncoding, GraphPEConfig, GraphPEType};
+
+/// Graph data structure for batch processing
+#[derive(Debug, Clone)]
+pub struct GraphBatch {
+    /// Node features: [total_nodes, node_dim]
+    pub node_features: Tensor,
+    /// Edge features: [total_edges, edge_dim]
+    pub edge_features: Tensor,
+    /// Edge indices: [2, total_edges] (source, target)
+    pub edge_indices: Tensor,
+    /// Graph boundaries for batch processing: [batch_size + 1]
+    pub graph_boundaries: Vec<usize>,
+    /// Number of graphs in batch
+    pub batch_size: usize,
+    /// Device
+    pub device: Device,
+}
+
+impl GraphBatch {
+    /// Create a new graph batch
+    pub fn new(
+        node_features: Tensor,
+        edge_features: Tensor,
+        edge_indices: Tensor,
+        graph_boundaries: Vec<usize>,
+        device: Device,
+    ) -> Result<Self> {
+        let batch_size = graph_boundaries.len().saturating_sub(1);
+        
+        Ok(Self {
+            node_features,
+            edge_features,
+            edge_indices,
+            graph_boundaries,
+            batch_size,
+            device,
+        })
+    }
+    
+    /// Get the number of nodes in the batch
+    pub fn num_nodes(&self) -> usize {
+        self.node_features.shape().dims()[0]
+    }
+    
+    /// Get the number of edges in the batch
+    pub fn num_edges(&self) -> usize {
+        self.edge_features.shape().dims()[0]
+    }
+    
+    /// Check if batch is homogeneous (all graphs have same structure)
+    pub fn is_homogeneous(&self) -> bool {
+        // For now, assume heterogeneous - can be optimized later
+        false
+    }
+}
+
+/// Graph attention mechanism types
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub enum GraphAttentionType {
+    /// Standard graph attention (GAT)
+    Standard,
+    /// Graph attention with edge features
+    EdgeAware,
+    /// Multi-head graph attention
+    MultiHead,
+    /// Graph attention with gating mechanism
+    Gated,
+}
+
+/// Attention mechanism types
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub enum AttentionMechanism {
+    /// Dot-product attention
+    DotProduct,
+    /// Additive attention
+    Additive,
+    /// Scaled dot-product attention
+    ScaledDotProduct,
+}
+
+/// Graph transformer layer trait
+pub trait GraphLayer: Send + Sync {
+    /// Forward pass through the graph layer
+    fn forward(&self, graph: &GraphBatch) -> Result<GraphOutput>;
+    
+    /// Get the layer type name
+    fn layer_type(&self) -> &'static str;
+    
+    /// Get the device this layer is on
+    fn device(&self) -> &Device;
+    
+    /// Get layer parameters for optimization
+    fn parameters(&self) -> Vec<&Tensor>;
+    
+    /// Get mutable layer parameters for optimization
+    fn parameters_mut(&mut self) -> Vec<&mut Tensor>;
+}