areno-develop-kernel

name	areno-develop-kernel
description	Develop, optimize, debug, and validate an AReno CUDA, Triton, fused, attention, convolution, routing, or MoE operator. Use when changes touch areno/accel or a runtime kernel boundary and require forward, backward, dtype, layout, CUDA graph, or benchmark validation.

Develop an AReno Kernel

Define the mathematical reference, shapes, dtype, layout, supported devices, and backward contract before implementation.

Develop and commit on a local branch. A remote GPU checkout is validation-only: pull the committed branch there, and never edit or copy source files into it.

Generic harnesses

Both callables must accept one tensor and return one tensor:

python .agents/skills/areno-develop-kernel/scripts/check_operator.py \
  --reference package.module:reference --candidate package.module:candidate \
  --shape 8,16 --dtype float32 --device cuda

python .agents/skills/areno-develop-kernel/scripts/benchmark_operator.py \
  --callable package.module:candidate --shape 8,16 --dtype float16 --device cuda

For multi-input or stateful kernels, extend a focused test under tests/ rather than weakening this harness.

Workflow

1. Locate Python wrapper, extension registration, C++/CUDA source, engine layer, and model call site.
2. Add a small PyTorch reference and shape/dtype assertions.
3. Implement forward and backward before using it in training.
4. Test representative shapes, boundary shapes, supported non-contiguous layouts, finite output, and gradients.
5. Validate TP/sequence-parallel local shapes and CUDA graph capture/replay.
6. After pulling a branch that changes areno/accel, rebuild remotely with pip install -e . --no-deps --no-build-isolation. Do not reinstall for Python-only changes.
7. Benchmark only after correctness. Read references/kernel-checklist.md.

Do not add a silent production fallback for a required kernel. Report unsupported cases explicitly.