8e6adf20e6
Major changes based on upstream_ref analysis: 1. factor_moe_topk_softmax.cu v2.0: Rewritten using ds_vllm/TRT-LLM warp shuffle pattern (from topk_softmax_kernels.cu). Key differences: - Zero shared memory (all butterfly __shfl_xor_sync) - VPT=2, THREADS_PER_ROW=32 (1 warp per token row) - 4 warps per CTA (4 tokens per block) - Iterative argmax with winner suppression for top-K - NaN/Inf clamping to 0 (prevents duplicate expert IDs) 2. GDN: FlashQLA backend (PROVEN on real BI-V100): - Compiles with corex clang/16 --cuda-gpu-arch=ivcore10 - Real test: NaN=False on gdn_forward(B=1, T=64, H=4, K=128) - Replaces custom factor_gdn_chunk_fwd.cu (archived to .ref) - patch_model.py now JIT-loads FlashQLA extension at runtime 3. build.sh: Correct corex flags from real compile log: --cuda-gpu-arch=ivcore10 (NOT sm_70) -D__ILUVATAR__ -D__ILUVATAR_WORKAROUND__ -D__ILUVATAR_DIAG__ -cl-single-precision-constant -mllvm --bonus-inst-threshold=0 Key insight from xllm/kernels/ilu/ixformer.h: ixformer::infer::topk_softmax() EXISTS at C++ level but Python ixformer.functions binding is missing. Our .so factor bypasses the missing Python binding entirely via dlopen/ctypes.