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Skill

optimizing-attention-flash

From NousResearch

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.

Facts

Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/mlops/flash-attention/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# 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 A
View full source on GitHub →

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