data: port complete MoE + xllm layer call chains from upstream repos
MoE call chain from ds_vllm (vllm-project/vllm latest):
ex_engine/moe/ — 20 files, 8736 lines
- modular_kernel.py (1630 lines) — base classes for modular MoE
- experts/fused_batched_moe.py (972 lines) — NaiveBatchedExperts
- prepare_finalize/batched.py (171 lines) — token grouping by expert
- topk_weight_and_reduce.py (176 lines) — scatter-add finalize
- fused_moe.py (1740 lines) — main fused_moe dispatch
- config.py (1407 lines) — FusedMoEQuantConfig
- activation.py, utils.py, layer.py, etc.
xllm layer code (jd-opensource/xllm):
ex_engine/xllm_layers/ — 39 files, 5859 lines
- ilu/fused_moe.cpp (797 lines) — production ixformer 7-step MoE pipeline
- ilu/attention.cpp (189 lines) — paged_attention + flash_attn bridge
- npu_torch/qwen3_gated_delta_net_base.cpp (576 lines) — GDN reference
- common/rms_norm.cpp, rotary_embedding.cpp, activation.cpp, dense_mlp.cpp
xllm ILU kernels — synced 10 files to upstream (diffs from prior edits)
These are reference implementations, NOT hand-written.
Source repos: vllm-project/vllm, jd-opensource/xllm
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