These implementations include FlashAttention from HazyResearch, Memory-Efficient Attention from the xFormers project, and a native C++ implementation that is ideal for non-CUDA devices or when high-precision is required.
PyTorch 2.0 will automatically select the optimal implementation for your use case, but you can also toggle them individually for finer-grained control. Additionally, the scaled dot product attention function can be used to build common transformer architecture components.
Learn more with the documentation and this tutorial.
[Beta] functorch -> torch.func