FlashAttention: Faster, Memory-Efficient Exact Attention
FlashAttention is an IO-aware algorithm that computes exact attention faster and with less memory. It avoids slow GPU memory transfers, making it a key optimization for training and serving large models on modern GPUs.
FlashAttention is a highly optimized algorithm that computes exact attention much faster and with less memory. Its mental model is being "IO-aware": it minimizes slow data transfers between the GPU's main memory and its fast on-chip cache. This makes it a critical drop-in optimization for training and serving large models with long sequences. The main footgun is assuming it works everywhere; it has strict dependencies on modern GPU architectures (like NVIDIA Ampere/Hopper) and specific CUDA versions for full functionality.
Read the original → pypi.org
- #attention
- #transformers
- #gpu
- #optimization
- #llm
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