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Antigravity Agent
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id: wiki-2026-0508-sparse-attention
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title: Sparse Attention
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Sparse Self-Attention, Local Attention, Efficient Attention]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [transformer, attention, long-context, efficient-llm, sparse]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: PyTorch/FlashAttention
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---
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# Sparse Attention
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## 매 한 줄
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> **"매 O(n²) attention 의 O(n·k) 의 reduce — token pair 의 subset 만 compute"**. Sparse attention 매 long-context Transformer 의 enabler, sliding-window + global tokens (Longformer) 매 base, BigBird/LongNet 매 random + dilated. 매 2026 매 native sparse (DeepSeek NSA, MoBA) + SSM hybrid (Mamba2) + FlashAttention-3 sparse mask 매 production.
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## 매 핵심
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### 매 sparsity pattern
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- **Sliding window**: 매 ±w tokens. Local context. (Longformer, Mistral SWA).
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- **Global tokens**: 매 [CLS] + 특정 token 매 모든 token 의 attend.
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- **Dilated**: 매 stride k. Long-range w/o full O(n²).
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- **Random**: 매 random k tokens. BigBird 의 component.
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- **Block sparse**: 매 block-diagonal + selected blocks. FlashAttention 친화.
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- **Learned/adaptive**: 매 routing network 의 어디 sparse 의 decide (NSA, MoBA 2025).
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### 매 historical landmarks
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- **Sparse Transformer** (OpenAI 2019): factorized attention.
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- **Longformer** (AllenAI 2020): SWA + global. 4k→16k+ tokens.
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- **BigBird** (Google 2020): random + window + global. Theoretically 의 full-attn approximate.
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- **LongNet** (Microsoft 2023): dilated → 1B token claim.
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- **NSA** (DeepSeek 2025): native sparse 매 pretraining.
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- **MoBA** (Moonshot 2025): mixture-of-block-attention, hierarchical sparsity.
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### 매 응용
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1. Long-document QA / summarization.
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2. Code-base wide LLM analysis (Claude 1M context).
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3. Genomics / DNA Transformer.
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4. Video Transformer (frames as tokens).
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## 💻 패턴
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### Sliding window mask
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```python
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import torch
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def sliding_window_mask(n: int, w: int, device='cuda') -> torch.Tensor:
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"""Boolean mask: True = allow attention."""
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idx = torch.arange(n, device=device)
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diff = idx.unsqueeze(0) - idx.unsqueeze(1)
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return diff.abs() <= w
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```
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### Longformer-style (SWA + global)
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```python
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def longformer_mask(n: int, w: int, global_idx: list[int], device='cuda'):
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mask = sliding_window_mask(n, w, device)
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g = torch.zeros(n, dtype=torch.bool, device=device)
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g[global_idx] = True
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# global attends to all + all attend to global
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mask = mask | g.unsqueeze(0) | g.unsqueeze(1)
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return mask
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```
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### BigBird random component
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```python
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def random_attention_mask(n: int, k: int, device='cuda') -> torch.Tensor:
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"""Each token attends to k random others."""
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mask = torch.zeros(n, n, dtype=torch.bool, device=device)
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for i in range(n):
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idx = torch.randperm(n, device=device)[:k]
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mask[i, idx] = True
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return mask
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```
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### FlashAttention-2/3 with custom mask
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```python
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from flash_attn import flash_attn_func
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# (b, s, h, d) — fp16/bf16
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# FA-3 supports block-sparse mask via mask_mod (PyTorch 2.5+ FlexAttention)
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out = flash_attn_func(q, k, v, causal=True, window_size=(512, 0))
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# window_size=(left, right) — Mistral-style SWA
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```
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### FlexAttention (PyTorch 2.5+)
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```python
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from torch.nn.attention.flex_attention import flex_attention, create_block_mask
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def sliding_window(b, h, q_idx, kv_idx):
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return (q_idx - kv_idx).abs() <= 512
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block_mask = create_block_mask(sliding_window, B=None, H=None, Q_LEN=8192, KV_LEN=8192)
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out = flex_attention(q, k, v, block_mask=block_mask)
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```
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### Block-sparse (DeepSeek NSA pseudo)
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```python
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def block_sparse_attn(q, k, v, block_size=64, top_k_blocks=8):
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# 1. Compute block-level importance via mean-pooled K
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n_blocks = k.shape[1] // block_size
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k_blocks = k.view(*k.shape[:1], n_blocks, block_size, *k.shape[2:]).mean(dim=2)
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scores = torch.einsum('bnhd,bmhd->bnmh', q, k_blocks)
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# 2. Select top-k blocks per query
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_, top_idx = scores.topk(top_k_blocks, dim=2)
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# 3. Gather + dense attn within
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return _gather_and_attend(q, k, v, top_idx, block_size)
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```
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### Mistral SWA (HuggingFace)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"mistralai/Mistral-7B-v0.3",
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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sliding_window=4096,
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)
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```
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### Adaptive top-k token (Native Sparse)
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```python
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def topk_attention(q, k, v, top_k=128):
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# (b, h, s, d)
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scores = q @ k.transpose(-2, -1) / q.shape[-1]**0.5
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# Keep top_k per query
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top_v, top_i = scores.topk(top_k, dim=-1)
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sparse = torch.full_like(scores, float('-inf'))
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sparse.scatter_(-1, top_i, top_v)
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attn = sparse.softmax(dim=-1)
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return attn @ v
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```
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## 매 결정 기준
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| 상황 | Approach |
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| 4-32k context, local-mostly | Sliding window (Mistral SWA) |
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| Long-doc QA w/ key-token | Longformer (SWA + global) |
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| 100k+ context, hardware-friendly | Block-sparse + FlashAttention |
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| Native long-context pretraining | NSA / MoBA (2025+) |
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| Inference-only swap | Top-k token sparsification |
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**기본값**: 매 inference 매 SWA + FlashAttention; 매 pretraining 매 native sparse (NSA-like).
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## 🔗 Graph
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- 부모: [[Attention Mechanism]] · [[Transformer]]
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- 변형: [[Flash Attention]] · [[Mamba]]
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- Adjacent: [[KV-Cache]]
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## 🤖 LLM 활용
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**언제**: 매 attention pattern selection rationale, 매 mask code draft, 매 paper distillation (NSA/MoBA).
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**언제 X**: 매 production kernel write (use FA-3 / FlexAttention), 매 perf measurement (real benchmark).
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## ❌ 안티패턴
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- **Naïve mask + softmax**: 매 O(n²) memory still. 매 -inf masking 매 helps compute 의 X.
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- **Random sparsity only**: 매 quality drop 매 catastrophic. 매 hybrid (window + global) needed.
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- **Fixed window for all heads**: 매 head 마다 different need. 매 per-head adaptive 의 better.
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- **No global token**: 매 long-doc QA 매 [CLS]/question token 의 全文 access 의 lose.
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- **Window too small**: 매 perplexity 매 baseline 의 break.
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## 🧪 검증 / 중복
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- Verified (Longformer arXiv:2004.05150; BigBird arXiv:2007.14062; FlashAttention-3 2024; DeepSeek NSA 2025; PyTorch FlexAttention).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — full content (SWA/BigBird/NSA + FlashAttention/FlexAttention patterns) |
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