refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-flash-attention
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title: Flash 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: [FlashAttention, FA2, FA3, IO-aware attention, Tri Dao, online softmax]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.98
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verification_status: applied
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tags: [transformer, attention, gpu, optimization, flash-attention, memory-efficient]
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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: CUDA / PyTorch
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framework: flash-attn / xformers / vLLM
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---
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# Flash Attention
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## 매 한 줄
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> **"매 attention 의 IO-aware tile-based exact algorithm"**. Tri Dao 2022 (FA1), 2023 (FA2), 2024 (FA3). 매 quadratic memory 의 fix — 매 O(N²) → 매 O(N) memory. 매 modern transformer 의 standard. 매 vLLM, xformers, native PyTorch.
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## 매 핵심
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### 매 problem (vanilla)
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- **Standard attention**: 매 O(N²) memory (매 N×N attention matrix).
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- **HBM bandwidth**: 매 bottleneck (>FLOPS).
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- **Long context**: 매 OOM.
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### 매 solution (Flash)
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- **Tile** Q, K, V into blocks.
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- **Online softmax**: 매 incremental, no full matrix.
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- **SRAM compute**: 매 fast on-chip.
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- **Recomputation**: 매 backward 의 의 의 trade compute for memory.
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### 매 versions
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- **FA1** (2022): 매 baseline.
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- **FA2** (2023): 매 better parallelism, 2x faster.
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- **FA3** (2024): 매 H100-optimized, async.
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### 매 응용
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1. **All transformer training**.
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2. **Long-context** (100K+).
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3. **Inference** (vLLM, TGI).
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4. **Multi-query / GQA**.
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5. **Sparse / sliding window**.
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## 💻 패턴
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### PyTorch native (FA built-in)
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```python
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import torch
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import torch.nn.functional as F
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# 매 PyTorch 2.0+ scaled_dot_product_attention auto-uses Flash if eligible
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out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
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```
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### flash-attn (Tri Dao package)
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```python
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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# 매 standard
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out = flash_attn_func(q, k, v, dropout_p=0.0, causal=True)
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# 매 variable length (no padding waste)
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out = flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, causal=True)
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```
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### xformers
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```python
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from xformers.ops import memory_efficient_attention
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out = memory_efficient_attention(q, k, v, attn_bias=causal_mask)
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```
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### vLLM (paged attention serving)
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model='meta-llama/Llama-3-8B', dtype='bfloat16')
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# 매 internally uses Flash + paged attention
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outputs = llm.generate(['Hello'], SamplingParams(max_tokens=100))
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```
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### Manual Flash-style (educational, simplified)
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```python
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def flash_attention_simple(Q, K, V, block_size=64):
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"""매 simplified — actual implementation 의 CUDA."""
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N = Q.shape[1]
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O = torch.zeros_like(Q)
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L = torch.zeros(Q.shape[:2]) # 매 max
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M = torch.full(Q.shape[:2], float('-inf')) # 매 normalize
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for j in range(0, N, block_size):
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Kj = K[:, j:j+block_size]
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Vj = V[:, j:j+block_size]
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for i in range(0, N, block_size):
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Qi = Q[:, i:i+block_size]
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Sij = Qi @ Kj.transpose(-1, -2)
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Mij = Sij.max(dim=-1, keepdim=True).values
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Mi_new = torch.maximum(M[:, i:i+block_size, None], Mij)
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Pij = torch.exp(Sij - Mi_new)
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# 매 online normalization
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scale = torch.exp(M[:, i:i+block_size, None] - Mi_new)
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O[:, i:i+block_size] = O[:, i:i+block_size] * scale + Pij @ Vj
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M[:, i:i+block_size] = Mi_new.squeeze(-1)
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return O / L # 매 simplified
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```
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### Sliding window (Mistral-style)
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```python
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from flash_attn import flash_attn_func
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out = flash_attn_func(q, k, v, window_size=(window_left, 0), causal=True)
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```
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### Grouped Query Attention (GQA)
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```python
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class GQA(nn.Module):
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def __init__(self, dim, n_heads, n_kv_heads):
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super().__init__()
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self.q = nn.Linear(dim, n_heads * head_dim)
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self.k = nn.Linear(dim, n_kv_heads * head_dim)
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self.v = nn.Linear(dim, n_kv_heads * head_dim)
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def forward(self, x):
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q = self.q(x).view(...)
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k = self.k(x).view(...).repeat_interleave(n_heads // n_kv_heads, dim=2)
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v = self.v(x).view(...).repeat_interleave(n_heads // n_kv_heads, dim=2)
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return flash_attn_func(q, k, v, causal=True)
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```
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### KV cache (inference)
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```python
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# 매 paged attention
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class PagedKVCache:
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def __init__(self, n_layers, max_seqs, block_size=16):
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self.blocks = {} # 매 logical block → physical
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self.block_size = block_size
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def append(self, seq_id, k_block, v_block):
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physical = self.allocate_block()
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self.blocks[(seq_id, len(self.blocks))] = physical
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# 매 → flash_attn_with_kvcache
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```
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### Backward (recomputation)
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```python
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# 매 forward 의 small statistics + recompute on backward
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# 매 native to flash_attn — automatic
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out = flash_attn_func(q, k, v).backward()
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```
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### Compile + Flash
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```python
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# 매 PyTorch 2.x compile 의 fuse
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model = torch.compile(model)
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# 매 internally uses sdpa (Flash if available)
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```
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### Detect Flash availability
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```python
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def has_flash():
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try:
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from flash_attn import flash_attn_func
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return torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8
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except ImportError:
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return False
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```
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### H100 / FA3
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```python
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# 매 fa3 (2024) — H100 hopper async
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from flash_attn_interface import flash_attn_func
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# 매 same API, 1.5-2x faster on H100
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```
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### Mask custom (block-sparse)
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```python
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# 매 매 custom mask 의 efficient 의 X
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# 매 fully sparse (e.g., longformer global+local) → flash-attn variants
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from flash_attn.flash_attn_triton import flash_attn_func
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out = flash_attn_func(q, k, v, custom_block_mask)
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```
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### vLLM serving
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model meta-llama/Llama-3-8B \
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--dtype bfloat16 \
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--max-model-len 32768
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```
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## 매 결정 기준
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| 상황 | Approach |
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| Default training | PyTorch sdpa (auto) |
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| Long context | flash_attn_varlen |
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| Production serving | vLLM (paged) |
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| Custom mask | xformers / flash-attn variants |
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| H100 | FA3 |
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| Mobile / non-CUDA | Use math fallback |
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**기본값**: 매 PyTorch sdpa + 매 vLLM serving + 매 GQA + 매 paged KV cache + 매 H100 FA3.
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## 🔗 Graph
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- 부모: [[Transformer]] · [[Attention Mechanism]]
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- 변형: [[PagedAttention]] · [[Sliding-Window]] · [[GQA]]
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- 응용: [[LLM_Optimization_and_Deployment_Strategies|vLLM]] · [[Long-Context]]
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- Adjacent: [[LLM_Optimization_and_Deployment_Strategies|Quantization]] · [[Foundation-Models]]
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## 🤖 LLM 활용
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**언제**: 매 모든 transformer training/inference.
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**언제 X**: 매 non-CUDA (mobile).
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## ❌ 안티패턴
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- **Manual attention loop**: 매 slow.
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- **Pad to max in batch**: 매 use varlen.
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- **No KV cache**: 매 inference quadratic.
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- **Old non-Flash 의 prod**: 매 cost ↑.
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## 🧪 검증 / 중복
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- Verified (Dao 2022/2023/2024 FA papers, vLLM Kwon 2023).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-20 | Auto-reinforced |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — algorithm + 매 PyTorch / flash-attn / vLLM / GQA / FA3 code |
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