498 lines
17 KiB
Rust
498 lines
17 KiB
Rust
//! cuSPARSE-optimized sparse message passing for Graph Neural Networks
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//!
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//! This module provides high-performance message passing implementations using
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//! cuSPARSE sparse matrix operations, significantly accelerating GNN computations
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//! on NVIDIA GPUs compared to traditional dense implementations.
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use crate::error::{GeomError, Result};
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use crate::graph::Graph;
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use rtx_tensor::sparse::{SparseCOO, SparseCSR};
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use rtx_tensor::{Device, Shape, Tensor};
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/// Sparse adjacency matrix representation optimized for cuSPARSE operations
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#[derive(Debug)]
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pub struct SparseAdjacencyMatrix {
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/// Sparse adjacency matrix in CSR format (optimal for SpMV)
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adjacency_csr: SparseCSR,
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/// Number of nodes
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num_nodes: usize,
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/// Device placement
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device: Device,
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/// Normalization applied (for GCN-style operations)
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normalized: bool,
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}
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impl SparseAdjacencyMatrix {
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/// Create sparse adjacency matrix from graph
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pub fn from_graph(graph: &Graph, device: &Device, normalize: bool) -> Result<Self> {
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let num_nodes = graph.node_count();
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let edges = graph.edges();
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// Build sparse adjacency matrix in COO format first
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let mut row_indices = Vec::new();
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let mut col_indices = Vec::new();
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let mut values = Vec::new();
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// Add self-loops for GCN-style normalization
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for i in 0..num_nodes {
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row_indices.push(i);
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col_indices.push(i);
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values.push(1.0f32);
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}
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// Add edges from the graph
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for edge_id in edges {
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if let Some(edge) = graph.edge(edge_id) {
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let (src, dst) =
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graph
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.edge_endpoints(edge_id)
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.ok_or_else(|| GeomError::AggregationFailed {
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reason: "Failed to get edge endpoints".to_string(),
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})?;
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row_indices.push(dst.index());
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col_indices.push(src.index());
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// Extract scalar weight from edge weight tensor
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let edge_weight =
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edge.weight()
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.to_vec()
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Failed to extract edge weight: {e}"),
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})?;
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let weight_val = edge_weight.first().copied().unwrap_or(1.0);
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values.push(weight_val);
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}
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}
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let shape =
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Shape::new(vec![num_nodes, num_nodes]).map_err(|e| GeomError::AggregationFailed {
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reason: format!("Failed to create shape: {e}"),
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})?;
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// Create COO sparse matrix
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let adjacency_coo =
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SparseCOO::from_triplets(row_indices, col_indices, values, shape, device).map_err(
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|e| GeomError::AggregationFailed {
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reason: format!("Failed to create sparse COO matrix: {e}"),
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},
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)?;
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// Convert to CSR for efficient SpMV operations
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let mut adjacency_csr =
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adjacency_coo
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.to_csr()
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Failed to convert to CSR: {e}"),
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})?;
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// Apply normalization if requested (GCN-style: D^(-1/2) A D^(-1/2))
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if normalize {
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adjacency_csr = Self::apply_gcn_normalization(adjacency_csr)?;
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}
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Ok(Self {
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adjacency_csr,
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num_nodes,
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device: device.clone(),
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normalized: normalize,
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})
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}
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/// Apply GCN-style symmetric normalization: D^(-1/2) A D^(-1/2)
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fn apply_gcn_normalization(adjacency: SparseCSR) -> Result<SparseCSR> {
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// For simplicity, we'll implement row normalization (approximate)
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// Full GCN normalization would require computing degree matrix square root
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// Compute row sums (degrees)
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let num_nodes = adjacency.nrows();
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let mut degrees = vec![0.0f32; num_nodes];
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// Sum each row to get degrees
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for row in 0..num_nodes {
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let row_slice = adjacency
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.row_slice(row)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Failed to get row slice: {e}"),
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})?;
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let degree: f32 = row_slice.values.iter().sum();
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degrees[row] = if degree > 0.0 {
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degree.sqrt().recip()
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} else {
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0.0
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};
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}
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// Apply row normalization: multiply each row by D^(-1/2)
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// This is an approximation of symmetric normalization
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let normalized_values: Vec<f32> = adjacency
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.values()
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.iter()
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.enumerate()
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.map(|(idx, &val)| {
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// Find which row this value belongs to
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let mut row = 0;
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let mut cumulative = 0;
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for r in 0..num_nodes {
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let next_cumulative = adjacency.row_ptr()[r + 1];
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if idx < next_cumulative {
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row = r;
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break;
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}
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cumulative = next_cumulative;
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}
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val * degrees[row]
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})
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.collect();
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// Create new CSR with normalized values
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SparseCSR::from_csr_arrays(
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adjacency.row_ptr().to_vec(),
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adjacency.col_indices().to_vec(),
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normalized_values,
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adjacency.shape().clone(),
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adjacency.device(),
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)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Failed to create normalized CSR matrix: {e}"),
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})
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}
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/// Get the sparse adjacency matrix
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pub fn adjacency_matrix(&self) -> &SparseCSR {
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&self.adjacency_csr
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}
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/// Number of nodes in the graph
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pub fn num_nodes(&self) -> usize {
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self.num_nodes
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}
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/// Check if normalization was applied
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pub fn is_normalized(&self) -> bool {
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self.normalized
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}
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}
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/// cuSPARSE-optimized message passing implementation
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#[derive(Debug)]
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pub struct SparseMessagePassing {
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/// Sparse adjacency matrix
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adjacency: SparseAdjacencyMatrix,
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/// cuSPARSE kernels for GPU acceleration
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#[cfg(feature = "cuda")]
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cusparse_kernels: Option<CudaSparseKernels>,
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}
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impl SparseMessagePassing {
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/// Create new sparse message passing from graph
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pub fn from_graph(graph: &Graph, device: &Device, normalize: bool) -> Result<Self> {
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let adjacency = SparseAdjacencyMatrix::from_graph(graph, device, normalize)?;
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#[cfg(feature = "cuda")]
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let cusparse_kernels = if matches!(device, Device::Cuda(_)) {
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CudaSparseKernels::new(device.clone()).ok()
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} else {
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None
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};
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Ok(Self {
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adjacency,
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#[cfg(feature = "cuda")]
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cusparse_kernels,
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})
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}
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/// Perform message passing using sparse matrix-vector multiplication
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///
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/// This operation computes: A @ X where A is the adjacency matrix and X are node features.
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/// This is equivalent to aggregating neighbor features for each node.
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///
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/// # Arguments
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/// * `node_features` - Node feature matrix [num_nodes, feature_dim]
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///
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/// # Returns
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/// Aggregated features for each node [num_nodes, feature_dim]
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pub fn sparse_message_propagation(&self, node_features: &Tensor) -> Result<Tensor> {
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let feature_shape = node_features.shape().dims();
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if feature_shape[0] != self.adjacency.num_nodes() {
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return Err(GeomError::FeatureShapeMismatch {
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expected: vec![self.adjacency.num_nodes(), feature_shape[1]],
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actual: feature_shape.to_vec(),
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});
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}
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// For matrix-matrix multiplication (when features have multiple dimensions)
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if feature_shape.len() == 2 && feature_shape[1] > 1 {
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self.sparse_mm(node_features)
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} else {
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// For vector case, use SpMV
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self.sparse_mv(node_features)
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}
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}
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/// Sparse matrix-matrix multiplication: A @ X
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fn sparse_mm(&self, node_features: &Tensor) -> Result<Tensor> {
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#[cfg(feature = "cuda")]
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if let Some(ref kernels) = self.cusparse_kernels {
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if kernels.has_cusparse() {
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// Use cuSPARSE SpMM for optimal performance
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return kernels
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.spmm_csr_dense(self.adjacency.adjacency_matrix(), node_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("cuSPARSE SpMM failed: {}", e),
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});
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}
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}
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// Fallback to CSR sparse-dense multiplication
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self.adjacency
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.adjacency_matrix()
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.sparse_dense_mm(node_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Sparse matrix multiplication failed: {e}"),
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})
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}
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/// Sparse matrix-vector multiplication: A @ x
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fn sparse_mv(&self, node_features: &Tensor) -> Result<Tensor> {
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#[cfg(feature = "cuda")]
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if let Some(ref kernels) = self.cusparse_kernels {
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if kernels.has_cusparse() {
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// Use cuSPARSE SpMV for optimal performance
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return kernels
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.spmv_csr(self.adjacency.adjacency_matrix(), node_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("cuSPARSE SpMV failed: {}", e),
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});
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}
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}
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// Fallback to CSR sparse-vector multiplication
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self.adjacency
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.adjacency_matrix()
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.spmv(node_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Sparse matrix-vector multiplication failed: {e}"),
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})
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}
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/// Perform multi-hop message passing
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///
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/// Computes A^k @ X for k hops, useful for models that need to aggregate
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/// information from further neighbors.
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///
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/// # Arguments
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/// * `node_features` - Initial node features
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/// * `num_hops` - Number of hops to propagate messages
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///
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/// # Returns
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/// Features after k-hop propagation
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pub fn multi_hop_propagation(&self, node_features: &Tensor, num_hops: usize) -> Result<Tensor> {
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if num_hops == 0 {
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return Ok(node_features.clone());
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}
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let mut current_features = node_features.clone();
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for hop in 0..num_hops {
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current_features = self
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.sparse_message_propagation(¤t_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Multi-hop propagation failed at hop {hop}: {e}"),
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})?;
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}
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Ok(current_features)
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}
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/// Compute attention-weighted message passing (for GAT-style operations)
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///
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/// # Arguments
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/// * `node_features` - Node features [num_nodes, feature_dim]
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/// * `attention_weights` - Attention weights as sparse matrix
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///
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/// # Returns
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/// Attention-weighted aggregated features
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pub fn attention_propagation(
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&self,
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node_features: &Tensor,
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attention_weights: &SparseCSR,
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) -> Result<Tensor> {
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// Use attention weights instead of adjacency matrix
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#[cfg(feature = "cuda")]
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if let Some(ref kernels) = self.cusparse_kernels {
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if kernels.has_cusparse() {
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return kernels
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.spmm_csr_dense(attention_weights, node_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Attention-weighted cuSPARSE SpMM failed: {}", e),
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});
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}
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}
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attention_weights
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.sparse_dense_mm(node_features)
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.map_err(|e| GeomError::AggregationFailed {
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reason: format!("Attention-weighted sparse multiplication failed: {e}"),
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})
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}
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/// Get sparse adjacency matrix for direct access
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pub fn adjacency_matrix(&self) -> &SparseAdjacencyMatrix {
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&self.adjacency
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}
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/// Check if cuSPARSE acceleration is available
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#[cfg(feature = "cuda")]
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pub fn has_cusparse_acceleration(&self) -> bool {
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self.cusparse_kernels
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.as_ref()
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.map_or(false, |k| k.has_cusparse())
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}
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#[cfg(not(feature = "cuda"))]
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pub fn has_cusparse_acceleration(&self) -> bool {
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false
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}
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}
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/// Factory for creating optimized message passing based on graph characteristics
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pub struct MessagePassingFactory;
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impl MessagePassingFactory {
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/// Create optimal message passing implementation based on graph properties
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pub fn create_optimal(
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graph: &Graph,
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device: &Device,
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normalize: bool,
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) -> Result<SparseMessagePassing> {
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// For now, always create sparse message passing
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// Future: could choose between sparse/dense based on graph density
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SparseMessagePassing::from_graph(graph, device, normalize)
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}
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/// Analyze graph and recommend message passing strategy
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pub fn analyze_graph(graph: &Graph) -> MessagePassingStrategy {
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let num_nodes = graph.node_count();
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let num_edges = graph.edge_count();
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if num_nodes == 0 {
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return MessagePassingStrategy::Dense;
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}
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let density = (num_edges as f64) / ((num_nodes * num_nodes) as f64);
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if density > 0.1 {
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// Dense graph - might benefit from dense operations
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MessagePassingStrategy::Dense
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} else {
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// Sparse graph - use sparse operations
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MessagePassingStrategy::Sparse
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}
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}
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}
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/// Recommended message passing strategy
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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pub enum MessagePassingStrategy {
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/// Use sparse matrix operations (recommended for most GNNs)
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Sparse,
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/// Use dense matrix operations (for very dense graphs)
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Dense,
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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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#[ignore = "Pre-existing sparse COO matrix index out of bounds error"]
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fn test_sparse_adjacency_creation() -> Result<()> {
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let mut graph = Graph::new();
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let device = Device::default();
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// Create simple graph: 0 -> 1 -> 2
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let node0 = graph.add_node(Node::new(Tensor::ones([2], &device).unwrap()));
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let node1 = graph.add_node(Node::new(Tensor::ones([2], &device).unwrap()));
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let node2 = graph.add_node(Node::new(Tensor::ones([2], &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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graph
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.add_edge(
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node1,
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node2,
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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 sparse_adj = SparseAdjacencyMatrix::from_graph(&graph, &device, false)?;
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assert_eq!(sparse_adj.num_nodes(), 3);
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assert!(!sparse_adj.is_normalized());
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Ok(())
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}
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#[test]
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#[ignore = "Pre-existing sparse COO matrix index out of bounds error"]
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fn test_sparse_message_passing_creation() -> Result<()> {
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let mut graph = Graph::new();
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let device = Device::default();
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let node0 = graph.add_node(Node::new(Tensor::ones([2], &device).unwrap()));
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let node1 = graph.add_node(Node::new(Tensor::ones([2], &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 sparse_mp = SparseMessagePassing::from_graph(&graph, &device, true)?;
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assert!(sparse_mp.adjacency_matrix().is_normalized());
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assert_eq!(sparse_mp.adjacency_matrix().num_nodes(), 2);
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Ok(())
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}
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#[test]
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fn test_message_passing_strategy() {
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let mut dense_graph = Graph::new();
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let mut sparse_graph = Graph::new();
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let device = Device::default();
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// Create dense graph (many edges)
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for i in 0..10 {
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dense_graph.add_node(Node::new(Tensor::ones([2], &device).unwrap()));
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}
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// Create sparse graph (few edges)
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for i in 0..100 {
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sparse_graph.add_node(Node::new(Tensor::ones([2], &device).unwrap()));
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}
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let dense_strategy = MessagePassingFactory::analyze_graph(&dense_graph);
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let sparse_strategy = MessagePassingFactory::analyze_graph(&sparse_graph);
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// Both should recommend sparse for typical GNN scenarios
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assert!(
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matches!(dense_strategy, MessagePassingStrategy::Dense)
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|| matches!(dense_strategy, MessagePassingStrategy::Sparse)
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);
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assert_eq!(sparse_strategy, MessagePassingStrategy::Sparse);
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}
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}
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