FlexAttention Adds FlashAttention-4 Backend
π‘2x faster custom attention on Blackwell GPUs via PyTorch FlexAttention update.
β‘ 30-Second TL;DR
What Changed
FlexAttention gains FlashAttention-4 backend on Hopper/Blackwell GPUs
Why It Matters
This boosts transformer model training/inference efficiency on NVIDIA's latest GPUs, reducing memory usage and compute time for LLMs. AI practitioners can now scale larger models with custom attention patterns more easily.
What To Do Next
Install PyTorch nightly and benchmark FlexAttention with FlashAttention-4 on Hopper GPUs.
Key Points
- β’FlexAttention gains FlashAttention-4 backend on Hopper/Blackwell GPUs
- β’PyTorch auto-generates CuTeDSL score/mask modification functions
- β’JIT-instantiation for custom FlashAttention-4 kernels
- β’Enhanced speed and flexibility for attention mechanisms
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Original source: PyTorch Blog β
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