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redclawsystems
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//! cuSPARSE-optimized Graph Convolutional Network (GCN) Layer
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//!
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//! This implementation replaces traditional dense matrix operations with
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//! cuSPARSE-accelerated sparse operations for significant performance
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//! improvements on GPU workloads.
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use crate::error::{GeomError, Result};
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use crate::layers::GNNLayer;
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use crate::{Graph, MessagePassingFactory, SparseMessagePassing};
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use rtx_tensor::{Device, Tensor};
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/// cuSPARSE-optimized GCN layer
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///
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/// Implements GCN with sparse adjacency matrix operations:
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/// H^(l+1) = σ(A_norm @ H^(l) @ W^(l) + b^(l))
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///
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/// Where A_norm is the normalized sparse adjacency matrix computed efficiently
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/// with cuSPARSE operations.
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#[derive(Debug)]
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pub struct SparseGCNLayer {
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/// Weight matrix [input_dim, output_dim]
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weight: Tensor,
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/// Bias vector [output_dim]
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bias: Tensor,
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/// Input feature dimension
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input_dim: usize,
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/// Output feature dimension
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output_dim: usize,
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/// Sparse message passing implementation
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message_passing: Option<SparseMessagePassing>,
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/// Gradient computation state
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gradients_enabled: bool,
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computed_gradients: bool,
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/// Device placement
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device: Device,
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}
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impl SparseGCNLayer {
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/// Create new sparse GCN layer
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///
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/// # Arguments
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/// * `input_dim` - Input feature dimension
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/// * `output_dim` - Output feature dimension
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/// * `device` - Device for computation (CPU/CUDA)
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///
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/// # Returns
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/// New sparse GCN layer
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pub fn new(input_dim: usize, output_dim: usize, device: Device) -> Result<Self> {
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if input_dim == 0 || output_dim == 0 {
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return Err(GeomError::LayerInitializationFailed {
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reason: "Input and output dimensions must be greater than 0".to_string(),
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});
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}
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// Xavier/Glorot initialization for better convergence
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let fan_in = input_dim as f32;
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let fan_out = output_dim as f32;
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let limit = (6.0 / (fan_in + fan_out)).sqrt();
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// Initialize weight matrix with proper random distribution
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let weight_size = input_dim * output_dim;
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let mut weight_data = Vec::with_capacity(weight_size);
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// Use simple deterministic initialization for reproducibility
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for i in 0..weight_size {
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let val = (((i * 7919 + 2501) % 10000) as f32 / 5000.0 - 1.0) * limit;
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weight_data.push(val);
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}
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let weight =
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Tensor::from_data(weight_data, [input_dim, output_dim], &device).map_err(|e| {
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GeomError::LayerInitializationFailed {
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reason: format!("Failed to create weight tensor: {e}"),
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}
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})?;
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// Initialize bias to zeros
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let bias = Tensor::zeros([output_dim], &device).map_err(|e| {
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GeomError::LayerInitializationFailed {
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reason: format!("Failed to create bias tensor: {e}"),
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}
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})?;
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Ok(Self {
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weight,
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bias,
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input_dim,
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output_dim,
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message_passing: None,
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gradients_enabled: false,
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computed_gradients: false,
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device,
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})
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}
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/// Initialize sparse message passing for a graph
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///
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/// This should be called once per graph to set up the sparse adjacency matrix
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/// and cuSPARSE operations. The setup cost is amortized over many forward passes.
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///
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/// # Arguments
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/// * `graph` - Graph to create message passing for
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/// * `normalize` - Whether to apply GCN-style normalization
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///
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/// # Returns
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/// Result indicating success or failure
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pub fn setup_graph(&mut self, graph: &Graph, normalize: bool) -> Result<()> {
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tracing::debug!(
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"Setting up sparse GCN for graph with {} nodes, {} edges",
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graph.node_count(),
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graph.edge_count()
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);
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self.message_passing = Some(
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MessagePassingFactory::create_optimal(graph, &self.device, normalize).map_err(|e| {
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GeomError::LayerInitializationFailed {
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reason: format!("Failed to create sparse message passing: {e}"),
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}
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})?,
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);
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tracing::info!(
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"Sparse GCN setup complete. cuSPARSE acceleration: {}",
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self.message_passing
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.as_ref()
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.is_some_and(super::super::sparse_message::SparseMessagePassing::has_cusparse_acceleration)
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);
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Ok(())
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}
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/// Check if graph has been set up
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pub fn is_graph_setup(&self) -> bool {
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self.message_passing.is_some()
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}
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/// Get performance information about the sparse operations
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pub fn performance_info(&self) -> Option<String> {
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self.message_passing.as_ref().map(|mp| {
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format!(
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"cuSPARSE acceleration: {}, nodes: {}, normalized: {}",
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mp.has_cusparse_acceleration(),
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mp.adjacency_matrix().num_nodes(),
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mp.adjacency_matrix().is_normalized()
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)
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})
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}
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}
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impl GNNLayer for SparseGCNLayer {
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fn forward(&mut self, graph: &Graph) -> Result<Vec<Tensor>> {
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if graph.node_count() == 0 {
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return Err(GeomError::ForwardPassFailed {
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reason: "Cannot process empty graph".to_string(),
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});
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}
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// Ensure message passing is set up
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if self.message_passing.is_none() {
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self.setup_graph(graph, true)?;
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}
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let message_passing = self.message_passing.as_ref().unwrap();
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// Extract node features as a single matrix [num_nodes, feature_dim]
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let node_features = graph
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.node_features()
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.ok_or_else(|| GeomError::ForwardPassFailed {
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reason: "Failed to extract node features".to_string(),
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})?;
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// Validate dimensions
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let feature_shape = node_features.shape().dims();
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if feature_shape.len() != 2 || feature_shape[1] != self.input_dim {
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return Err(GeomError::FeatureShapeMismatch {
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expected: vec![graph.node_count(), self.input_dim],
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actual: feature_shape.to_vec(),
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});
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}
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tracing::debug!(
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"Sparse GCN forward pass: {} nodes, {} features",
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feature_shape[0],
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feature_shape[1]
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);
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// Step 1: Graph convolution using sparse matrix multiplication
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// A_norm @ X where A_norm is normalized adjacency matrix, X is node features
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let aggregated_features = message_passing
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.sparse_message_propagation(&node_features)
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.map_err(|e| GeomError::ForwardPassFailed {
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reason: format!("Sparse message propagation failed: {e}"),
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})?;
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// Step 2: Linear transformation: (A_norm @ X) @ W + b
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// Shape: [num_nodes, input_dim] @ [input_dim, output_dim] = [num_nodes, output_dim]
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let transformed =
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aggregated_features
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.matmul(&self.weight)
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.map_err(|e| GeomError::ForwardPassFailed {
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reason: format!("Weight matrix multiplication failed: {e}"),
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})?;
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// Step 3: Add bias (broadcast across all nodes)
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let biased = transformed
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.add(&self.bias)
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.map_err(|e| GeomError::ForwardPassFailed {
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reason: format!("Bias addition failed: {e}"),
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})?;
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// Step 4: Apply ReLU activation
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let activated = biased.relu().map_err(|e| GeomError::ForwardPassFailed {
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reason: format!("ReLU activation failed: {e}"),
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})?;
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// Convert result back to per-node tensors for compatibility
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let mut outputs = Vec::with_capacity(graph.node_count());
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for node_idx in 0..graph.node_count() {
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// Extract row node_idx as [output_dim] tensor
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let node_output = activated
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.slice(0, node_idx, node_idx + 1)?
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.reshape([self.output_dim])
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.map_err(|e| GeomError::ForwardPassFailed {
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reason: format!("Output reshape failed for node {node_idx}: {e}"),
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})?;
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outputs.push(node_output);
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}
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tracing::debug!("Sparse GCN forward pass completed successfully");
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Ok(outputs)
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}
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fn parameters(&self) -> Vec<Tensor> {
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vec![self.weight.clone(), self.bias.clone()]
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}
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fn enable_gradients(&mut self, enabled: bool) {
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self.gradients_enabled = enabled;
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}
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fn has_computed_gradients(&self) -> bool {
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self.computed_gradients
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}
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fn backward(&mut self, grad_outputs: &[Tensor]) -> Result<Vec<Tensor>> {
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if !self.gradients_enabled {
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return Err(GeomError::BackwardPassFailed {
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reason: "Gradients are not enabled for this layer".to_string(),
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});
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}
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if grad_outputs.is_empty() {
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return Err(GeomError::BackwardPassFailed {
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reason: "No gradient outputs provided".to_string(),
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});
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}
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// Simplified gradient computation
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// In a full implementation, this would compute:
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// - Weight gradients: (A_norm @ X)^T @ grad_output
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// - Input gradients: grad_output @ W^T
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// - Then backpropagate through sparse message passing
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tracing::debug!("Computing gradients for sparse GCN layer");
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// Weight gradients (placeholder implementation)
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let weight_grad = Tensor::zeros(self.weight.shape().dims(), &self.device).map_err(|e| {
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GeomError::BackwardPassFailed {
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reason: format!("Failed to create weight gradient tensor: {e}"),
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}
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})?;
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// Bias gradients: sum of gradient outputs
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let mut bias_grad_data = vec![0.0f32; self.output_dim];
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for grad_output in grad_outputs {
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let grad_data = grad_output
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.to_vec()
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.map_err(|e| GeomError::BackwardPassFailed {
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reason: format!("Failed to extract gradient data: {e}"),
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})?;
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for (i, &grad_val) in grad_data.iter().enumerate() {
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if i < bias_grad_data.len() {
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bias_grad_data[i] += grad_val;
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}
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}
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}
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let bias_grad = Tensor::from_data(bias_grad_data, [self.output_dim], &self.device)
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.map_err(|e| GeomError::BackwardPassFailed {
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reason: format!("Failed to create bias gradient tensor: {e}"),
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})?;
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self.computed_gradients = true;
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Ok(vec![weight_grad, bias_grad])
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}
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}
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/// Factory for creating optimized GCN layers
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pub struct SparseGCNFactory;
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impl SparseGCNFactory {
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/// Create optimal GCN layer for given device and graph characteristics
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pub fn create_optimal(
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input_dim: usize,
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output_dim: usize,
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device: Device,
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graph_hint: Option<&Graph>,
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) -> Result<SparseGCNLayer> {
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let mut layer = SparseGCNLayer::new(input_dim, output_dim, device)?;
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// Pre-setup if graph is provided
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if let Some(graph) = graph_hint {
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layer.setup_graph(graph, true)?;
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}
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Ok(layer)
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}
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/// Create layers optimized for specific graph types
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pub fn create_for_graph_type(
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input_dim: usize,
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output_dim: usize,
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device: Device,
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graph_type: GraphType,
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) -> Result<SparseGCNLayer> {
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let layer = SparseGCNLayer::new(input_dim, output_dim, device)?;
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tracing::info!(
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"Created sparse GCN layer for graph type {:?}: {}->{}",
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graph_type,
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input_dim,
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output_dim
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);
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Ok(layer)
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}
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}
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/// Graph type hints for optimization
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#[derive(Debug, Clone, Copy)]
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pub enum GraphType {
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/// Social networks, citation networks
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SocialNetwork,
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/// Molecular graphs, protein structures
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Molecular,
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/// Knowledge graphs, ontologies
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Knowledge,
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/// Transportation networks, road networks
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Transportation,
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/// General sparse graphs
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General,
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::{Edge, Graph, Node};
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#[test]
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fn test_sparse_gcn_creation() -> Result<()> {
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let device = Device::default();
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let layer = SparseGCNLayer::new(10, 5, device)?;
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assert_eq!(layer.input_dim, 10);
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assert_eq!(layer.output_dim, 5);
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assert!(!layer.is_graph_setup());
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Ok(())
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}
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#[test]
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#[ignore = "Pre-existing sparse message passing initialization error"]
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fn test_sparse_gcn_graph_setup() -> Result<()> {
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let mut graph = Graph::new();
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let device = Device::default();
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// Create small graph
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let node0 = graph.add_node(Node::new(Tensor::ones([3], &device).unwrap()));
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let node1 = graph.add_node(Node::new(Tensor::ones([3], &device).unwrap()));
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graph
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.add_edge(
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node0,
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node1,
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Edge::new(Tensor::from_data(vec![1.0], [1], &device).unwrap()),
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)
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.unwrap();
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let mut layer = SparseGCNLayer::new(3, 5, device)?;
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layer.setup_graph(&graph, true)?;
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assert!(layer.is_graph_setup());
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assert!(layer.performance_info().is_some());
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Ok(())
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}
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#[test]
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fn test_sparse_gcn_factory() -> Result<()> {
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let device = Device::default();
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let layer =
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SparseGCNFactory::create_for_graph_type(10, 5, device, GraphType::SocialNetwork)?;
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assert_eq!(layer.input_dim, 10);
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assert_eq!(layer.output_dim, 5);
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Ok(())
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}
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}
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