Environment: Windows 11, RTX 5070 Ti (compute capability 12.0), Python 3.13.11, PyTorch 2.10.0+cu130.
The current sageattention-2.2.0+cu130torch2.10-cp313-cp313-win_amd64.whl fails on Blackwell with cudaErrorNoKernelImageForDevice for longer attention workloads.
packages/sageattention.yml has global arch_list: "8.0 9.0". The cu130/Torch 2.9 and Torch 2.11 combinations override this with "8.0 9.0 10.0 12.0", but the cu130/Torch 2.10 combination does not.
Inspecting the installed _fused.cp313-win_amd64.pyd and _qattn_sm89.cp313-win_amd64.pyd shows only sm_80 and sm_90a markers, with no sm_120/sm_120a.
Minimal reproduction with GQA 32:8, head_dim=128, L=4096 produces CUDA error: no kernel image is available for execution on the device, while L=128 works.
Adding arch_list: "8.0 9.0 10.0 12.0" to the cu130/Torch 2.10 matrix entry and rebuilding the wheel appears to be the expected fix.
Environment: Windows 11, RTX 5070 Ti (compute capability 12.0), Python 3.13.11, PyTorch 2.10.0+cu130.
The current sageattention-2.2.0+cu130torch2.10-cp313-cp313-win_amd64.whl fails on Blackwell with cudaErrorNoKernelImageForDevice for longer attention workloads.
packages/sageattention.yml has global arch_list: "8.0 9.0". The cu130/Torch 2.9 and Torch 2.11 combinations override this with "8.0 9.0 10.0 12.0", but the cu130/Torch 2.10 combination does not.
Inspecting the installed _fused.cp313-win_amd64.pyd and _qattn_sm89.cp313-win_amd64.pyd shows only sm_80 and sm_90a markers, with no sm_120/sm_120a.
Minimal reproduction with GQA 32:8, head_dim=128, L=4096 produces CUDA error: no kernel image is available for execution on the device, while L=128 works.
Adding arch_list: "8.0 9.0 10.0 12.0" to the cu130/Torch 2.10 matrix entry and rebuilding the wheel appears to be the expected fix.