CUTLASS 3.1

• New CUTLASS Python interface that aims to provide an ease-of-use interface for instantiating, emitting, compiling, and running CUTLASS kernels via Python. More details here and new examples.
• New efficient epilogues using TMA for Hopper.
• Support for fused epilogues, such Bias, ReLU and GELU, using the new efficient epilogues.
• New warp-specialized TensorFloat-32 (TF32) GEMM kernels targeting Hopper TMA.
• New warp-specialized persistent cooperative kernel design that allows for larger tile sizes and improves performance on Hopper.
• An example showcasing GEMM-Like Tensor-Tensor Contraction (GETT) capability on Hopper.
• Epilogue builders. Similar to mainloop builders (see example 49), epilogue builders aim to generate the best-possible epilogue while exposing incremental opt-ins for greater customization.
• Profiler support for overriding kernel and epilogue builder auto schedules for 3.x API kernels, allowing specific policies to be run in the CUTLASS profiler.
• Performance optimizations for the warp-specialized persistent ping-pong kernel.
• Changes to the GEMM API 3.x, involving the host-facing arguments and the underlying Params structs.
• FMHA Backward Pass from Meta xFormers.
• Streamk GEMM with Broadcast enables epilogue broadcast with StreamK GEMM.
• Batched B2B GEMM now can run multiple Back-to-Back GEMM with the same problem size in parallel.
• Batched Strided GEMV support both row major and column major input matrix.
• Permute + GEMM fusion can fuse Permute with following GEMM now. Before, we only support fusing GEMM with Permute in the epilogue.
• Row Broadcast can be fused in the epilogue.
• The GitHub branch is renamed from master to main in this release.
• Optimal performance using CUDA 12.1
• Updates and bugfixes from the community (thanks!)