Top Sparse Attention Code Repos | Generated by AI

Home 2026.07 English 中文

---

Question: What open-source code can help us understand sparse attention?

Answer:

If your goal is to understand how sparse attention actually works, I’d recommend reading implementations in roughly this order—from simplest to production-grade.

Project	Best for	Difficulty
lucidrains sparse-attention	Learn the algorithm	⭐
OpenAI Sparse Transformers	Original implementation	⭐⭐
Hugging Face Longformer	Sliding window attention	⭐⭐
Google BigBird	Random + global attention	⭐⭐⭐
Google Reformer	LSH attention	⭐⭐⭐
Dao-AILab FlashAttention	Efficient kernels (mostly dense)	⭐⭐⭐⭐
vLLM	Production serving	⭐⭐⭐⭐

---

1. lucidrains/sparse-attention ⭐ (Best starting point)

Official repo:

lucidrains/sparse-attention

This is probably the easiest codebase to understand.

Typical implementation:

class SparseAttention(nn.Module):
    def forward(self, x):

        q = self.to_q(x)
        k = self.to_k(x)
        v = self.to_v(x)

        # local attention
        local = local_attention(q, k, v)

        # strided attention
        global = strided_attention(q, k, v)

        return local + global

Very little CUDA. Mostly PyTorch.

Excellent for learning the algorithm.

---

2. OpenAI Sparse Transformers ⭐⭐

Paper:

Generating Long Sequences with Sparse Transformers

Repo:

OpenAI sparse_attention

This is the original implementation from the paper.

Contains:

blocksparse/
attention.py
ops.py

You’ll see ideas like

block sparse matrix
block layout
block masks

instead of token-level sparsity.

This project inspired many later sparse implementations.

---

3. Longformer ⭐⭐

Repo:

Longformer

Sliding-window attention.

Instead of

N × N

compute only

N × window

Visualization:

█████
 █████
  █████
   █████

Only nearby tokens attend.

Core code:

longformer/sliding_chunks.py

One of the cleanest sparse implementations ever written.

---

4. BigBird ⭐⭐⭐

Repo:

BigBird

BigBird mixes three patterns:

local

□□□□■■■■□□□□

global

■□□□□□□□□□□■

random

□■□□□■□□□□□

Total attention:

local
+ global
+ random

Reading this code helps understand why sparse attention can remain expressive while reducing complexity.

---

5. Reformer ⭐⭐⭐

Repo:

Reformer

Instead of choosing neighbors,

it hashes queries:

Q

↓

LSH

↓

bucket

↓

attention inside bucket

Complexity:

O(N log N)

instead of

O(N²)

Interesting if you’re studying alternative sparse mechanisms rather than fixed patterns.

---

6. FlashAttention ⭐⭐⭐⭐

Repo:

FlashAttention

Strictly speaking,

FlashAttention is not sparse attention.

It computes the full attention matrix while avoiding materializing it in HBM, using tiled computation.

However, its kernel organization is essential if you’re interested in implementing efficient sparse kernels later.

Look at

flash_attn/
hopper/
csrc/

---

7. xFormers

Repo:

xFormers

Contains multiple attention operators:

Dense

Block Sparse

Memory Efficient

Local

Causal

Flash

Useful for comparing implementations behind a common interface.

---

8. vLLM

Repo:

vLLM

Modern inference engine.

Interesting files:

attention/
paged_attention/
flash_attn/

Although most current models use dense attention, vLLM shows how production systems structure attention kernels, KV cache management, and scheduling.

---

If you want to implement one yourself

A minimal sparse attention implementation is surprisingly short.

scores = q @ k.transpose(-2, -1)

scores.masked_fill_(~mask, float("-inf"))

attn = scores.softmax(-1)

out = attn @ v

The only difference from dense attention is how mask is constructed.

For example, a sliding-window mask:

mask = torch.zeros(n, n, dtype=torch.bool)

for i in range(n):
    left = max(0, i - w)
    right = min(n, i + w + 1)
    mask[i, left:right] = True

Everything else—the softmax and value aggregation—remains unchanged. Optimized implementations avoid creating the full N × N mask by computing only the nonzero blocks or neighborhoods.

Reading order I’d recommend

1. lucidrains/sparse-attention — understand the algorithm in pure PyTorch.
2. Longformer — learn sliding-window attention.
3. OpenAI Sparse Transformers — study block-sparse layouts.
4. BigBird — understand mixed sparse patterns (local + global + random).
5. FlashAttention — learn high-performance attention kernels.
6. vLLM — see how efficient attention is integrated into a production inference engine.

This progression goes from conceptual simplicity to increasingly realistic systems while keeping the implementation complexity manageable.

---

Generated by AI. Curating and sharing still takes effort. If you find it useful, feel free to donate. WeChat: @lzwjava · X: @lzwjava · Say hi 👋

Back Donate