optimizing-attention-flashlisted

# 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