Router: full kernel stack — hash, topk, activation+topk, dense decode/prefill
Step 1: Hash router (hash_router.cu)
- One thread per token, gather from [vocab_size, k] LUT
- Uniform 1/k weights, FP32 output
- 3 MB LUT fits in L2 for repeated decode calls
Step 2: topk_select.cu — general top-k primitive
- Per-thread register min-heap (k=6, compile-time unrolled)
- Shared memory merge: thread 0 merges 64 partial heaps
- Tie-breaking: lower index wins on equal scores
- Reusable by CSA indexer
Step 3: activation_topk.cu — fused sqrt(softplus) + bias + topk + renorm
- Single kernel: all 6 steps of the router math, no intermediate buffers
- Numerically stable softplus: max(x,0) + log1p(exp(-|x|))
- Per-thread heap with unbiased activation co-stored
- Shared memory merge → sort descending → renormalize → store
Step 4: dense_router_decode.py — CuTeDSL fused GEMM kernel (skeleton)
- BF16 GEMM with tcgen05.mma, FP32 accumulator
- Custom epilogue: activation + bias + top-k (structure defined, needs TMA/MMA boilerplate)
- Dispatch: N<=64 uses fused decode, N>64 uses prefill path
Step 5: dense_router_prefill.py — prefill path
- torch.nn.functional.linear for GEMM (DeepGEMM integration deferred)
- Calls activation_topk for fused post-GEMM processing
Step 6: Router class + ops/router.py + test_router.py
- Router: construction-time mode (dense/hash), weight loading, custom_op dispatch
- ops/router.py: torch.library.custom_op wrappers, integer-keyed registry
- test_router.py: spec oracle tests (DO NOT RUN — Carmine is testing Stage C)
Test strategy: each kernel tested against its mathematical spec in FP32.
No reference implementation, no two debug streams. The oracle IS the math.
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