biondizzle a813d2824b Router: clean up dense_router_decode.py — realistic architecture, no fake code ...

The first draft had a fake CuTeDSL kernel body with pass statements and
Python lists as register heaps. That is not the right way. This commit
replaces it with honest documentation of what the kernel does and what
needs to happen.

Current working path:
- All N routes through torch.nn.functional.linear + activation_topk.cu
- activation_topk is a single-pass fused CUDA kernel (all 6 steps)
- This is correct and performant for all N

CuTeDSL fused decode kernel (DenseRouterDecodeKernel):
- Class structure and warp specialization defined
- Full documentation of the TMA/MMA/epilogue pipeline
- The novel part is the row-level top-k epilogue (cross-subtile heap)
- EFC framework does not apply — our epilogue is not per-element
- Implementation deferred until profiling shows the GMEM round-trip
  on logits matters for decode latency

No fake code. No pass statements. No Python lists as GPU registers.
The working path is the activation_topk kernel. The CuTeDSL kernel
will be built on top of it when the optimization is needed.

2026-05-21 21:58:31 +00:00

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