claude
865c18f852
feat: integrate xllm_moe into qwen3_5.py MoE hot path
xllm_moe.so provides 3 fused CUDA kernels compiled for ivcore10:
- moe_fused_topk: CUB topk + softmax (replaces corex_moe_topk_softmax)
- moe_compute_index: histogram + prefix_sum + place (replaces corex_moe_index_combine)
- moe_combine_result: reorder + weighted sum (available but not yet wired to output)
Dispatch priority in _pure_pytorch_experts():
Tier 0: xllm_moe (if available)
Tier 1: corex_moe_* individual .so
Tier 2: PyTorch fallback
Integration points:
1. Topk routing: xllm_moe.moe_fused_topk → corex_moe_topk_softmax → torch.topk
2. Index computation: xllm_moe.moe_compute_index → corex_moe_index_combine → torch.argsort
3. Expert loop: still Python F.linear (next target: batch GEMM)
patch_ops.sh already deploys all prebuilt/*.so including xllm_moe.so
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