wifi-densepose/vendor/ruvector/docs/research/gnn-v2/08-native-sparse-attention.md

Native Sparse Attention - Implementation Plan

Overview

Problem Statement

Current attention mechanisms in GNNs face severe computational bottlenecks:

1. Quadratic Complexity: Standard attention is O(N²) in sequence length, limiting graph size to <100K nodes
2. GPU Underutilization: FlashAttention achieves only 35-50% of theoretical GPU throughput on sparse graphs
3. Memory Bandwidth: Attention matrix materialization requires 4N² bytes, exceeding GPU memory for large graphs
4. Static Sparsity: Hand-crafted sparsity patterns (e.g., k-nearest neighbors) ignore query distribution
5. Poor Tensor Core Utilization: Irregular sparsity patterns prevent use of tensor cores (8x FP16 throughput)

Real-World Impact:

• Large graphs (1M+ nodes) require 16GB+ GPU memory for attention alone
• Attention accounts for 60-80% of GNN training time
• FlashAttention provides only 2-3x speedup vs naive attention (vs theoretical 8-15x)

Proposed Solution

Implement Native Sparse Attention with learned block-sparse patterns optimized for GPU tensor cores:

Core Innovations:

1. Learned Sparsity Patterns:

   • Use query distribution to learn which blocks of the attention matrix are important
   • Prune 85-95% of attention computations with minimal accuracy loss (<1%)
   • Patterns adapt over time via lightweight auxiliary loss

2. Block-Sparse Tensor Core Kernels:

   • Custom CUDA kernels that exploit tensor cores (8x throughput vs CUDA cores)
   • Block sizes tuned for tensor core alignment (16x16, 32x32, 64x64)
   • Fused operations (softmax + dropout + attention) in shared memory

3. Multi-Head Sparse Routing:

   • Different sparsity patterns per attention head
   • Heads specialize on local vs global connectivity
   • Dynamic routing based on query features

4. Hybrid CPU/GPU Execution:

   • Sparse pattern learning on CPU (graph algorithms)
   • Dense block attention on GPU (tensor cores)
   • Zero-copy memory for pattern buffers

Expected Benefits (Quantified)

Metric	Current (FlashAttention)	Native Sparse Attention	Improvement
GPU throughput (tensor core utilization)	35-50%	75-85%	2.1-2.4x
Memory usage (1M nodes, 8 heads)	16GB	2.4GB	6.7x reduction
Training time (100 epochs, 1M graph)	120 min	15 min	8x faster
Inference latency (single query)	8ms	0.6ms	13.3x faster
Maximum graph size (on 16GB GPU)	1M nodes	8M nodes	8x larger
Energy consumption	1.0x	0.2x	5x reduction

Accuracy Preservation:

• 90% sparsity: <0.5% accuracy loss
• 95% sparsity: 1-2% accuracy loss
• Adaptive sparsity: no accuracy loss (learned patterns)

ROI Calculation:

• Training cost: $120/model (8 GPU-hours) → $15/model (1 GPU-hour) = 87% cost reduction
• Inference cost: 8ms/query → 0.6ms/query = 13x more throughput per GPU
• Carbon footprint: 5x reduction in energy consumption

Technical Design

Architecture Diagram (ASCII)

┌─────────────────────────────────────────────────────────────────────┐
│                  Native Sparse Attention Pipeline                    │
└─────────────────────────────────────────────────────────────────────┘
                                  │
                    ┌─────────────┴──────────────┐
                    ▼                            ▼
        ┌──────────────────────┐    ┌──────────────────────┐
        │  Sparsity Pattern    │    │  Sparse Attention    │
        │  Learning (CPU)      │    │  Kernels (GPU)       │
        └──────────────────────┘    └──────────────────────┘
                    │                            │
        ┌───────────┼────────────┐              │
        ▼           ▼            ▼              ▼
    ┌───────┐ ┌─────────┐ ┌──────────┐  ┌─────────────┐
    │Graph  │ │Query    │ │Pattern   │  │Tensor Core  │
    │Analyis│ │Distrib  │ │Pruning   │  │Block Matmul │
    │       │ │Tracking │ │          │  │             │
    └───────┘ └─────────┘ └──────────┘  └─────────────┘
        │           │            │              │
        └───────────┼────────────┘              │
                    ▼                           ▼
        ┌───────────────────────┐     ┌─────────────────┐
        │  Sparse Block Pattern │────▶│ Fused Attention │
        │  (CSR/BSR format)     │     │ (Softmax+Drop)  │
        └───────────────────────┘     └─────────────────┘
                                              │
                                              ▼
                                      ┌──────────────┐
                                      │  Output      │
                                      │  (Dense)     │
                                      └──────────────┘

┌─────────────────────────────────────────────────────────────────────┐
│                     Sparsity Pattern Lifecycle                       │
└─────────────────────────────────────────────────────────────────────┘

Initialization ──▶ Learning ──▶ Pruning ──▶ Execution ──▶ Refinement
      │                │           │            │              │
      │                │           │            │              │
      ▼                ▼           ▼            ▼              ▼
  [K-NN Graph]   [Attn Scores] [Top-K]   [Tensor Core]  [Query Stats]
  [Random]       [Gradient]    [Threshold] [Fused Ops]  [Re-prune]
  [Predefined]   [Importance]  [Blocks]    [Shared Mem] [Adapt]

Data Flow:

1. Pattern Initialization (Pre-training):

   • Analyze graph structure (community detection, centrality)
   • Initialize block-sparse pattern from graph topology
   • Convert to BSR (Block Sparse Row) format for tensor cores

2. Learned Sparsity (Training):

   • Track query distribution over epochs
   • Compute attention importance scores
   • Prune low-importance blocks (threshold or top-k)
   • Update pattern every N epochs

3. Sparse Execution (Inference):

   • Load sparse pattern to GPU constant memory
   • Execute fused block-sparse attention kernel
   • Output dense attention results

Core Data Structures (Rust)

/// Sparse attention configuration with learned patterns
#[derive(Clone, Debug)]
pub struct SparseAttentionConfig {
    /// Block size for tensor cores (16, 32, 64)
    pub block_size: usize,

    /// Sparsity ratio (0.0 = dense, 0.95 = 95% sparse)
    pub sparsity: f32,

    /// Pattern learning strategy
    pub learning_strategy: SparsityLearningStrategy,

    /// Number of attention heads
    pub num_heads: usize,

    /// Head-specific patterns (true) or shared (false)
    pub per_head_patterns: bool,

    /// Pattern update frequency (epochs)
    pub update_frequency: usize,

    /// Pruning method
    pub pruning_method: PruningMethod,
}

#[derive(Clone, Debug)]
pub enum SparsityLearningStrategy {
    /// Static pattern from graph structure
    Static {
        /// Graph-based initialization (KNN, community, random)
        init: StaticPatternInit,
    },

    /// Learn from attention scores during training
    Learned {
        /// Track attention importance over N batches
        importance_window: usize,

        /// Importance aggregation (mean, max, exponential moving average)
        aggregation: ImportanceAggregation,

        /// Re-prune frequency
        reprune_epochs: usize,
    },

    /// Query-distribution-aware routing
    QueryAdaptive {
        /// Cluster queries by similarity
        num_clusters: usize,

        /// Pattern per query cluster
        patterns_per_cluster: HashMap<usize, BlockSparsePattern>,
    },

    /// Hybrid: static initialization + learned refinement
    Hybrid {
        static_init: StaticPatternInit,
        learning_epochs: usize,
    },
}

#[derive(Clone, Debug)]
pub enum StaticPatternInit {
    /// K-nearest neighbors in graph
    KNN { k: usize },

    /// Community structure (Louvain, label propagation)
    Community { algorithm: CommunityAlgorithm },

    /// Random sparsity (baseline)
    Random { seed: u64 },

    /// Predefined pattern (e.g., local + strided)
    Predefined { pattern: PredefinedPattern },
}

#[derive(Clone, Debug)]
pub enum PruningMethod {
    /// Keep top-k% important blocks
    TopK { k: f32 },

    /// Threshold-based pruning
    Threshold { threshold: f32 },

    /// Magnitude-based (L1/L2 norm)
    Magnitude { norm: NormType },

    /// Learned via auxiliary loss
    LearnedMask {
        /// Temperature for Gumbel-Softmax
        temperature: f32,
    },
}

/// Block-sparse attention pattern (BSR format for tensor cores)
#[derive(Clone, Debug)]
pub struct BlockSparsePattern {
    /// Block size (must be 16, 32, or 64 for tensor cores)
    pub block_size: usize,

    /// Number of block rows
    pub num_block_rows: usize,

    /// Number of block columns
    pub num_block_cols: usize,

    /// BSR row pointers (length = num_block_rows + 1)
    pub row_ptr: Vec<i32>,

    /// BSR column indices (length = num_nonzero_blocks)
    pub col_indices: Vec<i32>,

    /// Block importance scores (for pruning)
    pub importance: Option<Vec<f32>>,

    /// GPU buffer handles
    pub gpu_buffers: Option<GpuBuffers>,
}

/// GPU memory buffers for sparse attention
struct GpuBuffers {
    row_ptr_gpu: DeviceBuffer<i32>,
    col_indices_gpu: DeviceBuffer<i32>,
    block_values_gpu: DeviceBuffer<f16>,  // Half precision for tensor cores
}

/// Sparse attention layer with learned patterns
pub struct SparseAttentionLayer {
    /// Configuration
    config: SparseAttentionConfig,

    /// Learned sparse patterns (one per head, or shared)
    patterns: Vec<BlockSparsePattern>,

    /// Query/key/value projection weights
    qkv_weights: [Tensor; 3],

    /// Output projection weights
    output_weight: Tensor,

    /// Attention importance tracker (for learning)
    importance_tracker: Option<ImportanceTracker>,

    /// GPU kernel launcher
    kernel: SparseAttentionKernel,
}

/// Tracks attention importance for pattern learning
struct ImportanceTracker {
    /// Rolling window of attention scores
    score_history: VecDeque<Tensor>,

    /// Aggregated importance per block
    block_importance: Tensor,

    /// Number of batches tracked
    num_batches: usize,
}

/// GPU kernel for sparse attention
struct SparseAttentionKernel {
    /// CUDA module (compiled kernels)
    module: CudaModule,

    /// Kernel function handles
    block_matmul_kernel: CudaFunction,
    fused_softmax_kernel: CudaFunction,
    block_output_kernel: CudaFunction,

    /// Shared memory size (bytes)
    shared_mem_bytes: usize,
}

Key Algorithms (Pseudocode)

Algorithm 1: Sparse Pattern Learning from Query Distribution

function learn_sparse_pattern(attention_layer, training_data, config):
    """
    Learn block-sparse attention pattern from query distribution
    """
    # Step 1: Initialize pattern from graph structure
    if config.learning_strategy is Static:
        pattern = initialize_static_pattern(
            attention_layer.graph,
            config.block_size,
            config.sparsity
        )
    else:
        # Start with KNN baseline
        pattern = initialize_knn_pattern(
            attention_layer.graph,
            k = 32,
            block_size = config.block_size
        )

    # Step 2: Track attention importance during training
    importance_tracker = ImportanceTracker(
        window_size = config.importance_window,
        num_blocks = pattern.num_block_rows * pattern.num_block_cols
    )

    for epoch in 1..config.num_epochs:
        for batch in training_data:
            # Forward pass: compute attention with current pattern
            queries, keys, values = attention_layer.qkv_projection(batch)

            # Compute full attention scores (for learning only)
            if config.learning_strategy is Learned:
                full_attention_scores = queries @ keys.T / sqrt(d_k)
                importance_tracker.update(full_attention_scores)

            # Execute sparse attention (actual computation)
            attention_output = sparse_attention_forward(
                queries, keys, values, pattern, config
            )

            # Backward pass
            loss.backward()
            optimizer.step()

        # Step 3: Update sparse pattern periodically
        if epoch % config.update_frequency == 0:
            if config.learning_strategy is Learned:
                # Compute block importance from tracked scores
                block_importance = importance_tracker.aggregate(
                    method = config.aggregation
                )

                # Prune low-importance blocks
                pattern = prune_blocks(
                    pattern,
                    block_importance,
                    target_sparsity = config.sparsity,
                    method = config.pruning_method
                )

                # Reset tracker
                importance_tracker.reset()

            # Update GPU buffers
            pattern.upload_to_gpu()

    return pattern

function initialize_static_pattern(graph, block_size, sparsity):
    """
    Initialize sparse pattern from graph structure
    """
    num_nodes = graph.num_nodes()
    num_blocks = (num_nodes + block_size - 1) / block_size

    # Build block adjacency matrix
    block_adj = zeros(num_blocks, num_blocks)

    for edge in graph.edges():
        src_block = edge.src / block_size
        dst_block = edge.dst / block_size
        block_adj[src_block][dst_block] += 1  # Count edges per block

    # Prune to target sparsity
    threshold = percentile(block_adj.flatten(), sparsity * 100)
    block_mask = block_adj > threshold

    # Convert to BSR format
    pattern = BlockSparsePattern::from_mask(block_mask, block_size)

    return pattern

function prune_blocks(pattern, importance, target_sparsity, method):
    """
    Prune sparse pattern to target sparsity using importance scores
    """
    current_sparsity = 1.0 - pattern.num_nonzero_blocks / (pattern.num_block_rows * pattern.num_block_cols)

    if current_sparsity >= target_sparsity:
        return pattern  # Already sparse enough

    # Flatten importance scores
    block_importance = []
    for row_idx in 0..pattern.num_block_rows:
        start = pattern.row_ptr[row_idx]
        end = pattern.row_ptr[row_idx + 1]
        for col_offset in start..end:
            col_idx = pattern.col_indices[col_offset]
            block_idx = row_idx * pattern.num_block_cols + col_idx
            block_importance.append((block_idx, importance[block_idx]))

    # Sort by importance (ascending)
    block_importance.sort_by(|a, b| a.1.cmp(&b.1))

    # Compute number of blocks to prune
    target_num_blocks = (1.0 - target_sparsity) * pattern.num_block_rows * pattern.num_block_cols
    num_to_prune = pattern.num_nonzero_blocks - target_num_blocks

    # Prune lowest-importance blocks
    pruned_blocks = set(block_importance[0..num_to_prune].map(|x| x.0))

    # Rebuild BSR structure
    new_row_ptr = [0]
    new_col_indices = []

    for row_idx in 0..pattern.num_block_rows:
        start = pattern.row_ptr[row_idx]
        end = pattern.row_ptr[row_idx + 1]

        for col_offset in start..end:
            col_idx = pattern.col_indices[col_offset]
            block_idx = row_idx * pattern.num_block_cols + col_idx

            if block_idx not in pruned_blocks:
                new_col_indices.append(col_idx)

        new_row_ptr.append(len(new_col_indices))

    return BlockSparsePattern {
        block_size: pattern.block_size,
        num_block_rows: pattern.num_block_rows,
        num_block_cols: pattern.num_block_cols,
        row_ptr: new_row_ptr,
        col_indices: new_col_indices,
        importance: Some(importance),
        gpu_buffers: None  # Will be re-uploaded
    }

Algorithm 2: Fused Block-Sparse Attention Kernel (CUDA)

// CUDA kernel for block-sparse attention using tensor cores
// Input:
//   Q: queries [num_heads, seq_len, head_dim]
//   K: keys [num_heads, seq_len, head_dim]
//   V: values [num_heads, seq_len, head_dim]
//   pattern: BSR sparse pattern
// Output:
//   O: attention output [num_heads, seq_len, head_dim]

__global__ void fused_block_sparse_attention(
    const half* Q,          // Queries (FP16 for tensor cores)
    const half* K,          // Keys (FP16)
    const half* V,          // Values (FP16)
    half* O,                // Output (FP16)
    const int* row_ptr,     // BSR row pointers
    const int* col_indices, // BSR column indices
    int num_heads,
    int seq_len,
    int head_dim,
    int block_size,
    float scale             // 1 / sqrt(head_dim)
) {
    // Thread block processes one output block (block_size x head_dim)
    int block_row = blockIdx.x;  // Which block row
    int head_idx = blockIdx.y;   // Which attention head

    // Shared memory for tile caching
    __shared__ half Q_tile[BLOCK_SIZE][HEAD_DIM];
    __shared__ half K_tile[BLOCK_SIZE][HEAD_DIM];
    __shared__ half V_tile[BLOCK_SIZE][HEAD_DIM];
    __shared__ half S_tile[BLOCK_SIZE][BLOCK_SIZE];  // Attention scores

    // Thread indices within block
    int tx = threadIdx.x;
    int ty = threadIdx.y;

    // Load query block into shared memory (coalesced)
    int q_row_start = block_row * block_size;
    for (int i = ty; i < block_size; i += blockDim.y) {
        for (int j = tx; j < head_dim; j += blockDim.x) {
            int q_idx = head_idx * seq_len * head_dim + (q_row_start + i) * head_dim + j;
            Q_tile[i][j] = Q[q_idx];
        }
    }
    __syncthreads();

    // Initialize output accumulator
    float O_acc[BLOCK_SIZE][HEAD_DIM] = {0};
    float row_max[BLOCK_SIZE] = {-INFINITY};
    float row_sum[BLOCK_SIZE] = {0};

    // Iterate over non-zero blocks in this row
    int block_start = row_ptr[block_row];
    int block_end = row_ptr[block_row + 1];

    for (int block_offset = block_start; block_offset < block_end; block_offset++) {
        int block_col = col_indices[block_offset];
        int k_col_start = block_col * block_size;

        // Load key block into shared memory
        for (int i = ty; i < block_size; i += blockDim.y) {
            for (int j = tx; j < head_dim; j += blockDim.x) {
                int k_idx = head_idx * seq_len * head_dim + (k_col_start + i) * head_dim + j;
                K_tile[i][j] = K[k_idx];
            }
        }
        __syncthreads();

        // Compute attention scores: S = Q @ K^T (using tensor cores)
        // Use wmma (Warp Matrix Multiply-Accumulate) for tensor cores
        nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, BLOCK_SIZE, BLOCK_SIZE, HEAD_DIM, half, nvcuda::wmma::row_major> Q_frag;
        nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, BLOCK_SIZE, BLOCK_SIZE, HEAD_DIM, half, nvcuda::wmma::col_major> K_frag;
        nvcuda::wmma::fragment<nvcuda::wmma::accumulator, BLOCK_SIZE, BLOCK_SIZE, HEAD_DIM, float> S_frag;

        nvcuda::wmma::load_matrix_sync(Q_frag, &Q_tile[0][0], head_dim);
        nvcuda::wmma::load_matrix_sync(K_frag, &K_tile[0][0], head_dim);
        nvcuda::wmma::fill_fragment(S_frag, 0.0f);
        nvcuda::wmma::mma_sync(S_frag, Q_frag, K_frag, S_frag);

        // Scale scores
        for (int i = 0; i < S_frag.num_elements; i++) {
            S_frag.x[i] *= scale;
        }

        // Store scores to shared memory
        nvcuda::wmma::store_matrix_sync(&S_tile[0][0], S_frag, BLOCK_SIZE, nvcuda::wmma::mem_row_major);
        __syncthreads();

        // Online softmax: update running max and sum
        for (int i = ty; i < block_size; i += blockDim.y) {
            float local_max = row_max[i];
            float local_sum = row_sum[i];

            // Find new max
            for (int j = 0; j < block_size; j++) {
                local_max = fmaxf(local_max, S_tile[i][j]);
            }

            // Update sum with new max
            float correction = expf(row_max[i] - local_max);
            local_sum *= correction;

            for (int j = 0; j < block_size; j++) {
                float exp_score = expf(S_tile[i][j] - local_max);
                S_tile[i][j] = exp_score;  // Store normalized score
                local_sum += exp_score;
            }

            row_max[i] = local_max;
            row_sum[i] = local_sum;

            // Rescale previous output
            for (int j = 0; j < head_dim; j++) {
                O_acc[i][j] *= correction;
            }
        }
        __syncthreads();

        // Load value block
        for (int i = ty; i < block_size; i += blockDim.y) {
            for (int j = tx; j < head_dim; j += blockDim.x) {
                int v_idx = head_idx * seq_len * head_dim + (k_col_start + i) * head_dim + j;
                V_tile[i][j] = V[v_idx];
            }
        }
        __syncthreads();

        // Accumulate output: O += S @ V (using tensor cores)
        nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, BLOCK_SIZE, HEAD_DIM, BLOCK_SIZE, half, nvcuda::wmma::row_major> S_half_frag;
        nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, BLOCK_SIZE, HEAD_DIM, BLOCK_SIZE, half, nvcuda::wmma::row_major> V_frag;
        nvcuda::wmma::fragment<nvcuda::wmma::accumulator, BLOCK_SIZE, HEAD_DIM, BLOCK_SIZE, float> O_frag;

        // Convert S_tile to half precision
        for (int i = 0; i < BLOCK_SIZE; i++) {
            for (int j = 0; j < BLOCK_SIZE; j++) {
                S_tile[i][j] = __float2half(S_tile[i][j]);
            }
        }

        nvcuda::wmma::load_matrix_sync(S_half_frag, &S_tile[0][0], BLOCK_SIZE);
        nvcuda::wmma::load_matrix_sync(V_frag, &V_tile[0][0], head_dim);
        nvcuda::wmma::load_matrix_sync(O_frag, &O_acc[0][0], head_dim, nvcuda::wmma::mem_row_major);
        nvcuda::wmma::mma_sync(O_frag, S_half_frag, V_frag, O_frag);
        nvcuda::wmma::store_matrix_sync(&O_acc[0][0], O_frag, head_dim, nvcuda::wmma::mem_row_major);
        __syncthreads();
    }

    // Final softmax normalization
    for (int i = ty; i < block_size; i += blockDim.y) {
        float inv_sum = 1.0f / row_sum[i];
        for (int j = tx; j < head_dim; j += blockDim.x) {
            O_acc[i][j] *= inv_sum;
        }
    }
    __syncthreads();

    // Write output to global memory (coalesced)
    for (int i = ty; i < block_size; i += blockDim.y) {
        for (int j = tx; j < head_dim; j += blockDim.x) {
            int o_idx = head_idx * seq_len * head_dim + (q_row_start + i) * head_dim + j;
            O[o_idx] = __float2half(O_acc[i][j]);
        }
    }
}

API Design (Function Signatures)

// ============================================================
// Public API for Sparse Attention
// ============================================================

pub trait SparseAttention {
    /// Create sparse attention layer with learned patterns
    fn new(
        config: SparseAttentionConfig,
        embedding_dim: usize,
    ) -> Result<Self, AttentionError> where Self: Sized;

    /// Forward pass: compute sparse attention
    fn forward(
        &self,
        queries: &Tensor,
        keys: &Tensor,
        values: &Tensor,
    ) -> Result<Tensor, AttentionError>;

    /// Learn sparse pattern from training data
    fn learn_pattern(
        &mut self,
        training_data: &DataLoader,
        num_epochs: usize,
    ) -> Result<(), AttentionError>;

    /// Get current sparse pattern (for inspection)
    fn get_pattern(&self, head_idx: usize) -> &BlockSparsePattern;

    /// Export learned pattern to file
    fn save_pattern(&self, path: &Path) -> Result<(), io::Error>;

    /// Load pre-trained pattern from file
    fn load_pattern(&mut self, path: &Path) -> Result<(), io::Error>;

    /// Compute sparsity statistics
    fn sparsity_stats(&self) -> SparsityStatistics;
}

// ============================================================
// Configuration Builders
// ============================================================

impl SparseAttentionConfig {
    /// Default configuration for 90% sparsity
    pub fn default_sparse() -> Self {
        Self {
            block_size: 32,
            sparsity: 0.90,
            learning_strategy: SparsityLearningStrategy::Hybrid {
                static_init: StaticPatternInit::KNN { k: 32 },
                learning_epochs: 10,
            },
            num_heads: 8,
            per_head_patterns: true,
            update_frequency: 5,
            pruning_method: PruningMethod::TopK { k: 0.10 },
        }
    }

    /// Aggressive sparsity for large graphs (95%)
    pub fn large_graph() -> Self {
        Self {
            block_size: 64,
            sparsity: 0.95,
            learning_strategy: SparsityLearningStrategy::Learned {
                importance_window: 100,
                aggregation: ImportanceAggregation::ExponentialMovingAverage { alpha: 0.9 },
                reprune_epochs: 10,
            },
            num_heads: 8,
            per_head_patterns: true,
            update_frequency: 5,
            pruning_method: PruningMethod::TopK { k: 0.05 },
        }
    }

    /// Conservative sparsity for high accuracy (80%)
    pub fn high_accuracy() -> Self {
        Self {
            block_size: 16,
            sparsity: 0.80,
            learning_strategy: SparsityLearningStrategy::Static {
                init: StaticPatternInit::Community {
                    algorithm: CommunityAlgorithm::Louvain,
                },
            },
            num_heads: 8,
            per_head_patterns: false,
            update_frequency: 10,
            pruning_method: PruningMethod::Threshold { threshold: 0.01 },
        }
    }
}

// ============================================================
// Pattern Manipulation
// ============================================================

impl BlockSparsePattern {
    /// Create pattern from dense boolean mask
    pub fn from_mask(mask: &Tensor, block_size: usize) -> Self;

    /// Create pattern from graph adjacency matrix
    pub fn from_graph(graph: &Graph, block_size: usize, sparsity: f32) -> Self;

    /// Convert to dense mask (for visualization)
    pub fn to_dense_mask(&self) -> Tensor;

    /// Upload pattern to GPU
    pub fn upload_to_gpu(&mut self) -> Result<(), CudaError>;

    /// Compute block statistics
    pub fn block_stats(&self) -> BlockStatistics;

    /// Merge multiple patterns (for multi-head)
    pub fn merge(patterns: &[BlockSparsePattern]) -> Self;
}

// ============================================================
// Kernel Execution
// ============================================================

pub struct SparseAttentionKernel {
    /// Load CUDA kernels from PTX file
    pub fn load(ptx_path: &Path) -> Result<Self, CudaError>;

    /// Execute sparse attention kernel
    pub fn execute(
        &self,
        queries: &DeviceTensor,
        keys: &DeviceTensor,
        values: &DeviceTensor,
        pattern: &BlockSparsePattern,
        output: &mut DeviceTensor,
    ) -> Result<(), CudaError>;

    /// Benchmark kernel performance
    pub fn benchmark(
        &self,
        config: &SparseAttentionConfig,
        seq_len: usize,
        num_iterations: usize,
    ) -> KernelBenchmark;
}

// ============================================================
// Monitoring and Metrics
// ============================================================

#[derive(Clone, Debug)]
pub struct SparsityStatistics {
    /// Actual sparsity achieved (0-1)
    pub actual_sparsity: f32,

    /// Blocks per row (mean, std)
    pub blocks_per_row: (f32, f32),

    /// Block importance distribution
    pub importance_histogram: Vec<f32>,

    /// Tensor core utilization estimate
    pub tensor_core_utilization: f32,
}

#[derive(Clone, Debug)]
pub struct BlockStatistics {
    pub num_nonzero_blocks: usize,
    pub avg_blocks_per_row: f32,
    pub max_blocks_per_row: usize,
    pub memory_bytes: usize,
}

#[derive(Clone, Debug)]
pub struct KernelBenchmark {
    pub avg_time_ms: f32,
    pub throughput_tflops: f32,
    pub memory_bandwidth_gbps: f32,
    pub tensor_core_efficiency: f32,
}

Integration Points

Affected Crates/Modules

1. ruvector-gnn (Core GNN crate):

   • Add attention/sparse/ module for sparse attention
   • Extend AttentionLayer with sparse variant
   • Add pattern learning algorithms

2. ruvector-cuda (GPU kernels):

   • Implement fused block-sparse attention kernels
   • Add tensor core WMMA wrappers
   • Optimize shared memory usage

3. ruvector-core:

   • Add BSR (Block Sparse Row) sparse matrix format
   • Extend tensor operations with sparse support
   • Add pattern serialization

4. ruvector-gnn-node (Node.js bindings):

   • Expose SparseAttentionLayer to JavaScript
   • Add configuration builders
   • Provide GPU memory profiling

5. ruvector-cli:

   • Add ruvector sparse-attention learn command
   • Add pattern visualization tools
   • Add sparsity profiling

New Modules to Create

crates/ruvector-gnn/src/attention/sparse/
├── mod.rs                    # Public API
├── config.rs                 # SparseAttentionConfig
├── pattern.rs                # BlockSparsePattern + BSR format
├── learning.rs               # Pattern learning algorithms
├── pruning.rs                # Pruning strategies
├── importance.rs             # Importance tracking
└── kernels.rs                # Rust wrapper for CUDA kernels

crates/ruvector-cuda/src/attention/
├── sparse_kernel.cu          # CUDA kernel implementations
├── tensor_core.cuh           # WMMA helpers
├── fused_ops.cu              # Fused softmax/dropout
└── benchmarks.cu             # Kernel benchmarks

crates/ruvector-core/src/sparse/
├── mod.rs                    # Sparse tensor operations
├── bsr.rs                    # Block Sparse Row format
├── csr.rs                    # Compressed Sparse Row format
└── conversions.rs            # Dense <-> sparse conversion

crates/ruvector-gnn-node/attention/
├── sparse_bindings.rs        # NAPI bindings
└── typescript/
    └── sparse_attention.d.ts # TypeScript definitions

Dependencies on Other Features

1. Prerequisite: Attention Mechanisms (Tier 1, Feature #3):

   • Sparse attention extends base attention layer
   • Shares QKV projection logic
   • Action: Refactor base attention into trait for sparse variant

2. Synergy: Graph Condensation (Tier 3, Feature #7):

   • Condensed graph provides natural sparsity pattern (cluster connectivity)
   • Integration: Use condensed graph edges as initial sparse pattern

3. Synergy: Quantum-Inspired Entanglement (Tier 3, Feature #9):

   • Quantum fidelity can guide sparsity (high fidelity = important connection)
   • Integration: Use entanglement scores as importance metric

4. Complementary: Adaptive HNSW (Tier 2, Feature #5):

   • HNSW layers define natural sparse patterns (layer-wise connectivity)
   • Integration: Initialize sparse attention from HNSW graph

Regression Prevention

Existing Functionality at Risk

1. Attention Accuracy:

   • Risk: Sparse patterns lose important long-range dependencies
   • Mitigation:
     • Validate attention output matches dense attention within 1% error
     • Add "importance oracle" test (compare pruned vs full attention scores)
     • Default to conservative 80% sparsity

2. GPU Memory Safety:

   • Risk: Tensor core kernels cause out-of-bounds access or corruption
   • Mitigation:
     • Use cuda-memcheck for validation
     • Add boundary checks in kernel (debug builds)
     • Fuzz testing with random sparse patterns

3. Training Stability:

   • Risk: Pattern updates during training cause loss spikes
   • Mitigation:
     • Freeze pattern for first N epochs
     • Gradual pruning (increase sparsity slowly)
     • Monitor loss and revert pattern if spike detected

4. Backward Compatibility:

   • Risk: Breaking existing attention API
   • Mitigation:
     • Keep dense attention as default
     • Sparse attention is opt-in via separate class
     • Shared trait for both dense and sparse

Test Cases to Prevent Regressions

// Test 1: Attention output correctness
#[test]
fn test_sparse_attention_correctness() {
    let dense_layer = DenseAttentionLayer::new(config);
    let sparse_layer = SparseAttentionLayer::new(sparse_config);

    let (q, k, v) = generate_test_tensors(seq_len=100, dim=64);

    let dense_output = dense_layer.forward(&q, &k, &v).unwrap();
    let sparse_output = sparse_layer.forward(&q, &k, &v).unwrap();

    let relative_error = ((dense_output - sparse_output).norm() / dense_output.norm()).item();
    assert!(relative_error < 0.01, "Sparse attention error: {}", relative_error);
}

// Test 2: GPU kernel correctness
#[test]
fn test_kernel_vs_cpu() {
    let pattern = BlockSparsePattern::from_graph(&test_graph(), 32, 0.9);
    let kernel = SparseAttentionKernel::load("kernels.ptx").unwrap();

    let (q, k, v) = generate_test_tensors(seq_len=512, dim=64);

    // CPU reference implementation
    let cpu_output = sparse_attention_cpu(&q, &k, &v, &pattern);

    // GPU kernel
    let gpu_q = q.to_device();
    let gpu_k = k.to_device();
    let gpu_v = v.to_device();
    let mut gpu_output = Tensor::zeros_like(&cpu_output).to_device();
    kernel.execute(&gpu_q, &gpu_k, &gpu_v, &pattern, &mut gpu_output).unwrap();
    let gpu_output_cpu = gpu_output.to_cpu();

    assert_tensors_close(&cpu_output, &gpu_output_cpu, atol=1e-3);
}

// Test 3: Pattern learning convergence
#[test]
fn test_pattern_learning() {
    let mut layer = SparseAttentionLayer::new(sparse_config);
    let training_data = load_test_data();

    let initial_pattern = layer.get_pattern(0).clone();
    layer.learn_pattern(&training_data, num_epochs=20).unwrap();
    let learned_pattern = layer.get_pattern(0);

    // Pattern should change
    assert_ne!(initial_pattern.num_nonzero_blocks, learned_pattern.num_nonzero_blocks);

    // Learned pattern should improve attention quality
    let test_queries = generate_test_queries(100);
    let initial_quality = evaluate_attention_quality(&layer, &test_queries, &initial_pattern);
    let learned_quality = evaluate_attention_quality(&layer, &test_queries, learned_pattern);

    assert!(learned_quality > initial_quality);
}

// Test 4: Memory usage
#[test]
fn test_memory_reduction() {
    let dense_layer = DenseAttentionLayer::new(config);
    let sparse_layer = SparseAttentionLayer::new(sparse_config);

    let dense_mem = dense_layer.gpu_memory_usage();
    let sparse_mem = sparse_layer.gpu_memory_usage();

    let reduction = dense_mem as f32 / sparse_mem as f32;
    assert!(reduction >= 5.0, "Memory reduction below 5x: {}", reduction);
}

// Test 5: Tensor core utilization
#[test]
fn test_tensor_core_usage() {
    let kernel = SparseAttentionKernel::load("kernels.ptx").unwrap();
    let config = SparseAttentionConfig::default_sparse();

    let benchmark = kernel.benchmark(&config, seq_len=1024, num_iterations=100);

    // Tensor core efficiency should be >70%
    assert!(benchmark.tensor_core_efficiency > 0.70,
            "Tensor core efficiency: {}", benchmark.tensor_core_efficiency);
}

// Test 6: Training stability with pattern updates
#[test]
fn test_training_stability() {
    let mut model = build_test_model_with_sparse_attention();
    let training_data = load_training_data();

    let mut loss_history = vec![];

    for epoch in 0..50 {
        let loss = train_one_epoch(&mut model, &training_data);
        loss_history.push(loss);

        // Check for loss spikes after pattern updates
        if epoch > 0 && epoch % model.sparse_attention.update_frequency == 0 {
            let spike = (loss - loss_history[epoch - 1]).abs() / loss_history[epoch - 1];
            assert!(spike < 0.5, "Loss spike after pattern update: {}", spike);
        }
    }
}

Backward Compatibility Strategy

1. API Level:

   • Keep DenseAttentionLayer as default
   • Add new SparseAttentionLayer (opt-in)
   • Both implement common AttentionLayer trait
   • Configuration flag to switch between dense/sparse

2. Model Serialization:

   • Dense and sparse use different file extensions (.dense_attn, .sparse_attn)
   • Metadata includes attention type + sparsity config
   • Auto-detect type on load

3. Node.js Bindings:

   • new AttentionLayer() defaults to dense
   • new SparseAttentionLayer(config) for sparse
   • Same search API for both

4. CLI:

   • ruvector train defaults to dense attention
   • ruvector train --sparse-attention enables sparse
   • Separate ruvector sparse-attention learn command

Implementation Phases

Phase 1: Core Implementation (Weeks 1-4)

Goals:

• Implement BSR sparse matrix format
• Build basic CUDA kernels (no tensor cores yet)
• Static sparsity patterns (KNN, random)
• CPU reference implementation

Deliverables:

// Week 1-2: Sparse matrix format
crates/ruvector-core/src/sparse/
  ✓ bsr.rs (Block Sparse Row format)
  ✓ conversions.rs (dense <-> sparse)

// Week 3: CPU implementation
crates/ruvector-gnn/src/attention/sparse/
  ✓ sparse_attention_cpu.rs
  ✓ pattern.rs (static patterns)

// Week 4: Basic CUDA kernel
crates/ruvector-cuda/src/attention/
  ✓ sparse_kernel_v1.cu (no tensor cores)
  ✓ Rust FFI bindings

Success Criteria:

• BSR format tests pass
• CPU sparse attention matches dense within 1e-5
• Basic CUDA kernel compiles and runs

Phase 2: Tensor Core Optimization (Weeks 5-8)

Goals:

• Implement tensor core kernels (WMMA)
• Fused operations (softmax + dropout)
• Shared memory optimization
• Pattern learning algorithms

Deliverables:

// Week 5-6: Tensor core kernels
crates/ruvector-cuda/src/attention/
  ✓ sparse_kernel_tc.cu (tensor cores)
  ✓ tensor_core.cuh (WMMA helpers)

// Week 7: Fused operations
crates/ruvector-cuda/src/attention/
  ✓ fused_ops.cu (softmax + dropout + attention)

// Week 8: Pattern learning
crates/ruvector-gnn/src/attention/sparse/
  ✓ learning.rs (importance tracking)
  ✓ pruning.rs (top-k, threshold)

Success Criteria:

• Tensor core kernel achieves >70% utilization
• Speedup vs FlashAttention: 3x+ on 90% sparsity
• Pattern learning converges in <20 epochs

Phase 3: Integration & APIs (Weeks 9-11)

Goals:

• Integrate with existing GNN layers
• Node.js bindings
• CLI tools for pattern visualization
• Multi-head sparse attention

Deliverables:

// Week 9: GNN integration
crates/ruvector-gnn/src/layers/
  ✓ sparse_gnn_layer.rs
  ✓ AttentionLayer trait (shared by dense/sparse)

// Week 10: Node.js bindings
crates/ruvector-gnn-node/attention/
  ✓ sparse_bindings.rs
  ✓ TypeScript definitions

// Week 11: CLI tools
crates/ruvector-cli/src/commands/
  ✓ sparse_attention.rs
  ✓ Pattern visualization (export to PNG)

Success Criteria:

• Multi-head sparse attention works correctly
• Node.js API passes all tests
• CLI can learn and visualize patterns

Phase 4: Production Hardening (Weeks 12-14)

Goals:

• Comprehensive testing (unit, integration, fuzz)
• Documentation + tutorials
• Performance benchmarks vs baselines
• Multi-GPU support

Deliverables:

// Week 12: Testing
tests/sparse_attention/
  ✓ Property-based tests
  ✓ Fuzz testing (cuda-memcheck)
  ✓ Regression suite

// Week 13: Documentation
docs/
  ✓ Sparse Attention Guide
  ✓ Kernel optimization guide
  ✓ Pattern learning tutorial

// Week 14: Benchmarks + multi-GPU
benches/sparse_attention.rs
  ✓ Speedup vs FlashAttention
  ✓ Memory reduction benchmarks
  ✓ Multi-GPU data parallelism

Success Criteria:

• 100% code coverage for core logic
• Documentation complete with 3+ examples
• Benchmarks show 8x+ speedup vs FlashAttention
• Multi-GPU scaling efficiency >85%

Success Metrics

Performance Benchmarks

Benchmark	Metric	Target	Measurement Method
Tensor Core Utilization	GPU efficiency	>75%	nvprof --metrics tensor_precision_fu_utilization
Speedup vs FlashAttention	Training time	8x faster	criterion on 1M graph, 100 epochs
Memory Reduction	GPU memory	6x smaller	nvidia-smi memory usage
Inference Latency	Single query	<0.6ms	criterion on single forward pass
Pattern Learning Time	Offline learning	<5s	Time to learn pattern from 10K samples
Kernel Throughput	TFLOPS	>15 TFLOPS	Theoretical FP16 compute / runtime

Accuracy Metrics

Sparsity Level	Metric	Target	Baseline (Dense)
80% sparse	Attention error (L2)	<0.5%	0%
90% sparse	Attention error (L2)	<1.0%	0%
95% sparse	Attention error (L2)	<2.0%	0%
Learned (adaptive)	Attention error (L2)	<0.3%	0%

Memory/Latency Targets

Configuration	GPU Memory	Inference Latency	Use Case
Dense attention (1M graph)	16GB	8ms	Baseline
80% sparse (static KNN)	4GB	2ms	Conservative
90% sparse (learned)	2.4GB	0.8ms	Recommended
95% sparse (aggressive)	1.6GB	0.6ms	Large graphs

Measurement Tools:

• GPU profiling: nvprof, nsight-compute
• Memory: nvidia-smi, cuda-memcheck
• Latency: criterion (Rust), custom CUDA timers
• Accuracy: Custom attention error calculator

Quality Gates

1. Functional:

   • ✓ All unit tests pass
   • ✓ Kernel output matches CPU reference (< 1e-3 error)
   • ✓ Pattern learning converges

2. Performance:

   • ✓ Tensor core utilization > 70%
   • ✓ Speedup vs FlashAttention >= 6x (90% sparsity)
   • ✓ Memory reduction >= 5x

3. Accuracy:

   • ✓ Attention error < 1% (90% sparsity)
   • ✓ No catastrophic failures (error > 10%)
   • ✓ Learned patterns improve over static

4. Compatibility:

   • ✓ Works on CUDA compute capability >= 7.0 (tensor cores)
   • ✓ Fallback to non-tensor-core kernel on older GPUs
   • ✓ Node.js bindings pass all tests

Risks and Mitigations

Technical Risks

Risk 1: Tensor Core Alignment Constraints

Description: Tensor cores require strict alignment (block sizes must be 16, 32, 64). Arbitrary graph sizes may not fit evenly.

Probability: High (80%)

Impact: Medium (affects all graphs)

Mitigation:

1. Padding: Pad queries/keys to nearest block size (waste < 10% memory)
2. Hybrid Execution: Use tensor cores for aligned blocks, CUDA cores for remainder
3. Dynamic Block Sizing: Choose block size based on graph size (e.g., seq_len % 32 == 0 → block_size=32)
4. Masked Attention: Mask padded elements in softmax

Contingency Plan: If padding overhead exceeds 15%, implement hybrid kernel that splits attention into tensor-core-aligned and unaligned portions.

Risk 2: Sparse Pattern Overhead

Description: Loading sparse pattern (row_ptr, col_indices) from global memory may bottleneck kernel.

Probability: Medium (50%)

Impact: High (negates speedup)

Mitigation:

1. Constant Memory: Store pattern in constant memory (64KB limit)
2. Shared Memory Caching: Cache pattern tiles in shared memory
3. Pattern Compression: Use bitmap for regular patterns (e.g., block-diagonal)
4. Prefetching: Overlap pattern loading with computation

Contingency Plan: If pattern loading exceeds 20% of runtime, move to static patterns (compile-time constants) for critical paths.

Risk 3: Softmax Numerics with Sparse Attention

Description: Online softmax (for numerical stability) is complex with sparse patterns. Risk of NaN/Inf.

Probability: Medium (40%)

Impact: High (blocks training)

Mitigation:

1. Safe Softmax: Use log-sum-exp trick with careful max reduction
2. FP32 Accumulators: Use FP32 for intermediate sums (even with FP16 inputs)
3. NaN Detection: Add debug checks for NaN/Inf in kernels
4. Regularization: Add small epsilon to denominator

Contingency Plan: If softmax instability occurs, fall back to two-pass softmax (separate max reduction + normalization) instead of online version.

Risk 4: Pattern Learning Overfitting

Description: Learned sparse patterns may overfit to training queries, degrading test-time performance.

Probability: Medium (50%)

Impact: Medium (poor generalization)

Mitigation:

1. Regularization: Add L1 penalty on pattern sparsity during learning
2. Validation Set: Monitor pattern quality on held-out queries
3. Ensemble Patterns: Learn multiple patterns and ensemble
4. Conservative Pruning: Keep top 15% blocks instead of exact 10% (margin)

Contingency Plan: If learned patterns degrade test accuracy by >2%, use static patterns (KNN) with conservative sparsity (80%).

Risk 5: Multi-Head Pattern Diversity

Description: Per-head patterns may not be diverse enough (all heads learn similar patterns).

Probability: High (60%)

Impact: Medium (redundant heads)

Mitigation:

1. Diversity Loss: Add auxiliary loss that encourages different patterns per head
2. Head Specialization: Initialize each head with different static patterns
3. Attention Dropout: Apply different dropout masks per head
4. Pattern Visualization: Monitor pattern diversity metrics

Contingency Plan: If heads have >90% pattern overlap, switch to shared pattern across heads (reduce memory).

Operational Risks

Risk 6: CUDA Version Compatibility

Description: Tensor core APIs (WMMA) are only available in CUDA 10+. Users on older CUDA may fail.

Probability: Medium (30%)

Impact: High (blocks usage)

Mitigation:

1. Compile-Time Detection: Check CUDA version and disable tensor cores if < 10.0
2. Fallback Kernels: Provide non-tensor-core sparse kernel for older GPUs
3. Clear Error Messages: Warn users if tensor cores unavailable
4. Documentation: List CUDA version requirements prominently

Risk 7: Debugging Difficulty

Description: Sparse attention bugs are hard to reproduce (pattern-dependent). GPU kernels have limited debugging.

Probability: High (70%)

Impact: Medium (developer experience)

Mitigation:

1. Verbose Logging: Add detailed logging for pattern loading
2. Visualization Tools: Provide pattern heatmap visualization
3. CPU Reference: Always compare against CPU implementation
4. cuda-memcheck: Run all tests with cuda-memcheck
5. Unit Test Coverage: Test each kernel function independently

---

Appendix: Related Research

This design is based on:

1. Sparse Transformers (Child et al., 2019): Block-sparse attention patterns
2. BigBird (Zaheer et al., 2020): Random + window + global sparsity
3. FlashAttention (Dao et al., 2022): Fused attention kernels
4. Reformer (Kitaev et al., 2020): LSH-based sparse attention
5. Tensor Cores (NVIDIA, 2017): Warp matrix multiply-accumulate (WMMA)

Key differences from prior work:

• Novel: Learned sparsity from query distribution (vs static patterns)
• Novel: Tensor core optimization for graph attention (vs NLP transformers)
• Engineering: Production-ready Rust + CUDA implementation
• Integration: Seamless integration with existing GNN layers