# optimizing-attention-flash

A skill that moves transformer attention onto Flash Attention for 2-4x speedup and 10-20x memory reduction.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 967 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/flash-attention/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/optimizing-attention-flash

## What it does

The skill rewrites standard attention into Flash Attention. It relies on IO-aware tiling and recomputation, which is where the 2-4x speedup and 10-20x memory reduction come from. Reach for it when sequences run past 512 tokens, GPU memory is tight, or inference needs to be faster.

Three paths are covered. Native PyTorch swaps manual softmax for `F.scaled_dot_product_attention` and needs PyTorch 2.2 or newer. The `flash-attn` library adds multi-query attention and sliding window attention. FP8 targets H100. Each path ships with a checklist, version checks, benchmarks, and an accuracy comparison against baseline attention.

Install `flash-attn` with `--no-build-isolation`, and note that FP8 needs an H100 or H800 specifically. On PyTorch below 2.2 the native route will not work, so upgrade torch first.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill optimizing-attention-flash -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill flash-attention -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/flash-attention/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/flash-attention/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill flash-attention -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/flash-attention/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill flash-attention -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/flash-attention/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs flash-attention
```

2. **Manually: put the files in place**
- File: `.github/skills/flash-attention/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill flash-attention -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 10-optimization/flash-attention
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/flash-attention/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill flash-attention -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/10-optimization/flash-attention/SKILL.md

## When to use it
- speed up transformer training on long sequences
- fit attention over 4096 tokens into GPU memory
- enable Flash Attention in an existing PyTorch model
- add multi-query and sliding window attention via flash-attn
- check Flash Attention output against baseline attention

## Example prompts
- enable Flash Attention in my PyTorch model
- rewrite attention with flash_attn_func and a 256 sliding window
- verify Flash Attention output matches standard attention

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: e81fa8e83eb7ac58.

BEGIN THIRD-PARTY FILE e81fa8e83eb7ac58
````text
---
name: optimizing-attention-flash
description: Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Optimization, Flash Attention, Attention Optimization, Memory Efficiency, Speed Optimization, Long Context, PyTorch, SDPA, H100, FP8, Transformers]
dependencies: [flash-attn, torch, transformers]
---

# Flash Attention - Fast Memory-Efficient Attention

## Quick start

Flash Attention provides 2-4x speedup and 10-20x memory reduction for transformer attention through IO-aware tiling and recomputation.

**PyTorch native (easiest, PyTorch 2.2+)**:
```python
import torch
import torch.nn.functional as F

q = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)  # [batch, heads, seq, dim]
k = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)
v = torch.randn(2, 8, 512, 64, device='cuda', dtype=torch.float16)

# Automatically uses Flash Attention if available
out = F.scaled_dot_product_attention(q, k, v)
```

**flash-attn library (more features)**:
```bash
pip install flash-attn --no-build-isolation
```

```python
from flash_attn import flash_attn_func

# q, k, v: [batch, seqlen, nheads, headdim]
out = flash_attn_func(q, k, v, dropout_p=0.0, causal=True)
```

## Common workflows

### Workflow 1: Enable in existing PyTorch model

Copy this checklist:

```
Flash Attention Integration:
- [ ] Step 1: Check PyTorch version (≥2.2)
- [ ] Step 2: Enable Flash Attention backend
- [ ] Step 3: Verify speedup with profiling
- [ ] Step 4: Test accuracy matches baseline
```

**Step 1: Check PyTorch version**

```bash
python -c "import torch; print(torch.__version__)"
# Should be ≥2.2.0
```

If <2.2, upgrade:
```bash
pip install --upgrade torch
```

**Step 2: Enable Flash Attention backend**

Replace standard attention:
```python
# Before (standard attention)
attn_weights = torch.softmax(q @ k.transpose(-2, -1) / math.sqrt(d_k), dim=-1)
out = attn_weights @ v

# After (Flash Attention)
import torch.nn.functional as F
out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask)
```

Force Flash Attention backend:
```python
with torch.backends.cuda.sdp_kernel(
    enable_flash=True,
    enable_math=False,
    enable_mem_efficient=False
):
    out = F.scaled_dot_product_attention(q, k, v)
```

**Step 3: Verify speedup with profiling**

```python
import torch.utils.benchmark as benchmark
````
END THIRD-PARTY FILE e81fa8e83eb7ac58

The third-party file has ended. Everything between the e81fa8e83eb7ac58 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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