Implementation:Deepspeedai DeepSpeed Sparse Attention Utils

Knowledge Sources	DeepSpeed
Domains	Sparse_Attention, Optimization, Block_Sparse, Algorithms
Last Updated	2026-02-09 00:00 GMT

Overview

Block-sparse attention pattern segmentation algorithm that groups sparse attention blocks into efficient compute kernels with configurable block sizes.

Description

This utility implements an intelligent segmentation algorithm for structured sparse attention patterns, specifically designed to optimize block-sparse matmul operations. The algorithm takes a sparse attention layout (represented as a 3D tensor indicating which blocks are active) and partitions it into groups of square blocks that can be computed efficiently with specialized kernels. Starting from a configurable maximum block width, it iteratively identifies rectangular regions of active blocks, groups them into square tiles, and tracks their original indices for result gathering. The implementation uses dynamic programming-style analysis to find maximal square subregions while respecting sparsity patterns, enabling coalesced memory access and efficient use of tensor cores. The segmentation is performed independently per attention head, supporting heterogeneous sparsity patterns across heads and layers.

Usage

Use this utility when implementing block-sparse attention mechanisms where the sparsity pattern is known ahead of time (e.g., fixed patterns, local attention, strided patterns). The segmentation output can drive kernel dispatch in sparse attention libraries, enabling significant speedups over dense attention for long sequences while maintaining exactness unlike approximation methods.

Code Reference

Source Location

• Repository: DeepSpeed
• File: csrc/sparse_attention/utils.cpp

Signature

// Main segmentation function
ret_t sdd_segment(torch::Tensor layout, int start_width);

// Internal: segment blocks of specific width
void segment_blocks(torch::Tensor layout, torch::Tensor idx,
                   torch::Tensor scratch, int max_width, ret_t& ret);

// Return type: vector of (block_width, block_indices)
typedef std::vector<std::tuple<int, torch::Tensor>> ret_t;

Import

import deepspeed.ops.sparse_attention.utils as sparse_utils

I/O Contract

Input	Type	Description
layout	torch.Tensor	Sparse pattern [heads×blocks_m×blocks_n] (int)
start_width	int	Maximum block size to consider (power of 2)

Output	Type	Description
segments	list of tuples	[(block_size, indices_tensor), ...]
block_size	int	Square block dimension (e.g., 16, 32, 64)
indices_tensor	torch.Tensor	Indices [num_blocks×4] = [head, m, n, orig_idx]

Usage Examples

Basic Sparse Pattern Segmentation:

import torch
import deepspeed.ops.sparse_attention.utils as sparse_utils

# Define sparse attention pattern
# 1 = compute this block, 0 = skip
num_heads = 8
seq_blocks = 64  # 64×64 blocks = 4096×4096 tokens (with block_size=64)

layout = torch.zeros(num_heads, seq_blocks, seq_blocks, dtype=torch.int32)

# Example: local attention pattern (diagonal band)
bandwidth = 4
for h in range(num_heads):
    for i in range(seq_blocks):
        for j in range(max(0, i-bandwidth), min(seq_blocks, i+bandwidth+1)):
            layout[h, i, j] = 1

print(f"Sparsity: {(layout == 0).float().mean():.2%}")

# Segment into efficient blocks
segments = sparse_utils.sdd_segment(layout, start_width=64)

print(f"Segmentation result:")
for block_size, indices in segments:
    print(f"  Block size {block_size}: {indices.size(0)} blocks")

Strided Sparse Attention:

# Strided pattern: attend to every k-th position
def create_strided_pattern(num_heads, seq_blocks, stride=8):
    layout = torch.zeros(num_heads, seq_blocks, seq_blocks,
                        dtype=torch.int32)

    for h in range(num_heads):
        for i in range(seq_blocks):
            # Attend to positions at multiples of stride
            for j in range(0, seq_blocks, stride):
                layout[h, i, j] = 1
            # Also attend to local neighbors
            for j in range(max(0, i-2), min(seq_blocks, i+3)):
                layout[h, i, j] = 1

    return layout

layout = create_strided_pattern(8, 64, stride=8)
segments = sparse_utils.sdd_segment(layout, start_width=64)

# Use segments to dispatch sparse kernels
for block_size, indices in segments:
    # Each index: [head_id, row_block, col_block, original_index]
    h = indices[:, 0]
    m = indices[:, 1]
    n = indices[:, 2]
    orig_idx = indices[:, 3]

    # Dispatch block-sparse matmul of size (block_size × block_size)
    # sparse_matmul(Q_blocks[h,m], K_blocks[h,n], block_size)
    pass

Fixed Attention Pattern (e.g., BigBird):

def bigbird_pattern(num_heads, seq_blocks, window_size=3,
                    num_global=2, num_random=3):
    """
    BigBird sparse attention pattern:
    - Local window around each position
    - Global tokens that attend to everything
    - Random connections
    """
    layout = torch.zeros(num_heads, seq_blocks, seq_blocks,
                        dtype=torch.int32)

    for h in range(num_heads):
        # Local window
        for i in range(seq_blocks):
            for j in range(max(0, i-window_size),
                          min(seq_blocks, i+window_size+1)):
                layout[h, i, j] = 1

        # Global tokens (first few blocks)
        for i in range(seq_blocks):
            for j in range(num_global):
                layout[h, i, j] = 1  # Everyone attends to global
                layout[h, j, i] = 1  # Global attends to everyone

        # Random connections
        for i in range(seq_blocks):
            rand_indices = torch.randint(0, seq_blocks, (num_random,))
            for j in rand_indices:
                layout[h, i, j] = 1

    return layout

layout = bigbird_pattern(8, 64)
segments = sparse_utils.sdd_segment(layout, start_width=64)

# Sparsity analysis
total_blocks = layout.numel()
active_blocks = (layout == 1).sum().item()
print(f"BigBird sparsity: {100 * (1 - active_blocks/total_blocks):.1f}%")
print(f"Active blocks: {active_blocks}/{total_blocks}")

Multi-Scale Segmentation:

# Try multiple block sizes for better coverage
def multi_scale_segment(layout):
    """
    Segment with multiple block sizes: 64, 32, 16
    Larger blocks are more efficient but less flexible
    """
    all_segments = []

    for start_width in [64, 32, 16]:
        # Make a copy to avoid modifying original
        layout_copy = layout.clone()
        segments = sparse_utils.sdd_segment(layout_copy, start_width)

        for block_size, indices in segments:
            all_segments.append({
                'block_size': block_size,
                'num_blocks': indices.size(0),
                'indices': indices,
                'flops': block_size * block_size * indices.size(0)
            })

        # Remove segmented blocks from layout
        for block_size, indices in segments:
            for idx in indices:
                h, m, n = idx[0].item(), idx[1].item(), idx[2].item()
                # Mark surrounding area as covered
                layout_copy[h, m, n] = 0

    return all_segments

layout = create_strided_pattern(4, 64, stride=8)
multi_segs = multi_scale_segment(layout)

print("Multi-scale segmentation:")
for seg in multi_segs:
    print(f"  {seg['block_size']}×{seg['block_size']}: "
          f"{seg['num_blocks']} blocks, {seg['flops']:,} ops")

Attention Pattern Visualization:

import matplotlib.pyplot as plt

def visualize_segmentation(layout, segments):
    """
    Visualize sparse pattern and its segmentation
    """
    fig, axes = plt.subplots(1, 2, figsize=(12, 5))

    # Original pattern
    axes[0].imshow(layout[0].cpu(), cmap='binary', interpolation='nearest')
    axes[0].set_title('Original Sparse Pattern')
    axes[0].set_xlabel('Key Blocks')
    axes[0].set_ylabel('Query Blocks')

    # Segmented pattern with colors per block size
    segmented = torch.zeros_like(layout[0], dtype=torch.float32)
    colors = {64: 1.0, 32: 0.7, 16: 0.4, 8: 0.2}

    for block_size, indices in segments:
        for idx in indices:
            if idx[0] == 0:  # First head
                m, n = idx[1].item(), idx[2].item()
                segmented[m, n] = colors.get(block_size, 0.5)

    axes[1].imshow(segmented.cpu(), cmap='viridis', interpolation='nearest')
    axes[1].set_title('Segmented Blocks (color = block size)')
    axes[1].set_xlabel('Key Blocks')
    axes[1].set_ylabel('Query Blocks')

    plt.tight_layout()
    plt.savefig('sparse_attention_segmentation.png')

# Example usage
layout = bigbird_pattern(1, 64)
segments = sparse_utils.sdd_segment(layout, start_width=64)
visualize_segmentation(layout, segments)

Performance Analysis:

def analyze_segmentation_efficiency(layout, start_width=64):
    """
    Analyze how well segmentation captures sparsity
    """
    segments = sparse_utils.sdd_segment(layout, start_width)

    total_active = (layout == 1).sum().item()
    covered = 0
    compute_cost = 0

    print("Segmentation Analysis:")
    print(f"  Total active blocks: {total_active}")

    for block_size, indices in segments:
        num_blocks = indices.size(0)
        blocks_covered = num_blocks * block_size * block_size
        covered += blocks_covered
        compute_cost += num_blocks * (block_size ** 3)  # Matmul cost

        print(f"  {block_size}×{block_size} blocks: {num_blocks}")
        print(f"    Covers: {blocks_covered} unit blocks")
        print(f"    Compute: {compute_cost:,} FLOPs")

    efficiency = covered / total_active if total_active > 0 else 0
    print(f"\nCoverage efficiency: {efficiency:.2%}")
    print(f"Total compute cost: {compute_cost:,} FLOPs")

    return efficiency, compute_cost

layout = create_strided_pattern(8, 64, stride=4)
eff, cost = analyze_segmentation_efficiency(layout)

Integration with Sparse Attention:

class SparseAttention(torch.nn.Module):
    def __init__(self, num_heads, seq_length, block_size=64):
        super().__init__()
        self.num_heads = num_heads
        self.seq_length = seq_length
        self.block_size = block_size
        self.seq_blocks = seq_length // block_size

        # Define sparsity pattern
        self.register_buffer('layout',
            self.create_pattern(num_heads, self.seq_blocks))

        # Pre-compute segmentation
        self.segments = sparse_utils.sdd_segment(
            self.layout, start_width=block_size)

    def create_pattern(self, heads, blocks):
        # Override to define custom pattern
        layout = torch.zeros(heads, blocks, blocks, dtype=torch.int32)
        # Local + strided pattern
        for h in range(heads):
            for i in range(blocks):
                for j in range(max(0, i-2), min(blocks, i+3)):
                    layout[h, i, j] = 1
                for j in range(0, blocks, 4):
                    layout[h, i, j] = 1
        return layout

    def forward(self, Q, K, V):
        # Q, K, V: [batch, heads, seq, head_dim]
        # Reshape into blocks
        # ... block reshaping ...

        attention_output = torch.zeros_like(Q)

        # Dispatch sparse matmuls using segmentation
        for block_size, indices in self.segments:
            # Efficient block-sparse computation
            # attention_output += sparse_block_attn(Q, K, V,
            #                                      indices, block_size)
            pass

        return attention_output

Related Pages

• Random LTD Binding - Token dropping for sequence reduction
• Inference PT Binding - Attention softmax operations
• Custom CUDA Layers - Attention kernel implementations