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
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bf4d3fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # Hopper Tensor Core WGMMA Lane
This directory is reserved for the Hopper-native gap-closing kernel lane.
Current state:
- `tensor_core_async` is the owned WMMA guardrail lane.
- `cuBLASLt` is the control ceiling lane.
- `tensor_core_wgmma` is 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: `512x512x512` with reference enabled
- performance lane: `4096x4096x4096` with 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_wgmma` variant if it beats the current owned SM90 baseline without correctness regressions
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