You can take advantage of the new fused SDPA kernels either by calling the new SDPA operator directly (as described in the SDPA tutorial), or transparently via integration into the pre-existing PyTorch Transformer API.  All features of the PyTorch Transformer API will continue to work compatibly, with many features mapped to high-performance SDPA kernels, while other features are impossible to support with higher performance (e.g., need_weights, as per below) while expanded high-performance support for other features may still be under active development.  \
 \