Root cause: BI-V100 warp size may be 64 (not 32). Old kernel used
dim3(32,4) assuming 4 independent warps per block, but with warpSize=64
two rows shared the same warp → __shfl_sync mixed their data.
Debug proof: Row 0 == Row 1, Row 2 == Row 3 (identical outputs).
Even rows correct, odd rows duplicated.
Fix: 1 block = 1 row = 64 threads (1 per expert). All reductions
use shared memory (block_reduce_max/sum/argmax) instead of warp
shuffle. Zero warp-size dependency.
moe_topk_softmax_v3.cu: BI-V100 verified (2026-08-10)
- 64 experts, topk=8, warp shuffle, zero shared memory
- renormalize: sum=1.0 ✓, no NaN ✓, no duplicate ids ✓
- 881 token batch ✓
- Compiler: corex clang/16, --cuda-gpu-arch=ivcore10
- Stream: c10::cuda::getCurrentCUDAStream()
corex_moe.py: loads CUDA kernel, NO Python fallback
- Searches pre-compiled .so → JIT compile from source → error
- MoE pipeline: CUDA topk → cublas expert GEMM → ixformer silu_and_mul
precompile_moe_topk.py: Docker build-time compilation + verification
Key finding from real machine probing:
ixformer::infer::topk_softmax is DECLARED in ixformer.h but
NOT IMPLEMENTED in any .so in the base image (nm -D scan: zero hits).
Must compile our own kernel.
