Instructions to use AethronPhantom/pyc-kernels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use AethronPhantom/pyc-kernels with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("AethronPhantom/pyc-kernels") - Notebooks
- Google Colab
- Kaggle
Hopper Tensor Core WGMMA Lane
This directory is reserved for the Hopper-native gap-closing kernel lane.
Current state:
tensor_core_asyncis the owned WMMA guardrail lane.cuBLASLtis the control ceiling lane.tensor_core_wgmmais the next implementation lane intended to close the remaining device-side gap.
Minimum contract for the first implementation:
- target shape:
4096x4096x4096 - dtype: BF16 input, FP32 accumulation
- architecture:
sm90 - correctness lane:
512x512x512with reference enabled - performance lane:
4096x4096x4096with reference disabled
Design goals:
- use warpgroup MMA rather than per-warp WMMA
- move toward TMA-backed staging for the long-term feed path
- preserve a standalone harness so the lane can be benchmarked by
kernel_lab - keep the first fast path simple: no generalized epilogue work unless it is proven necessary
Promotion rule:
- only promote a
tensor_core_wgmmavariant if it beats the current owned SM90 baseline without correctness regressions