ref(upstream): FULL TREE — Deep-Spark xllm (1470) + ds_vllm csrc/models (703)
Replaces cherry-picked upstream_ref with complete source trees.
xllm/ — Iluvatar official C++ inference engine (15MB, 1470 files)
Complete: kernels → layers → models → runtime → scheduler → api
Excluded: .git, binary images, third_party submodule checkouts
ds_vllm/ — Iluvatar official vllm fork (8MB, 703 files)
Included: csrc/ (ALL CUDA kernels), fused_moe/, qwen3_5 model, _custom_ops
Excluded: tests, benchmarks, docs, examples (not needed for reference)
Critical call chains now fully traceable:
MoE: moe_topk_softmax_kernels.cuh → ixformer.h → fused_moe.cpp → layer
GDN: qwen3_gated_delta_net_base.cpp → qwen3_5_gated_delta_net.cpp
Attention: ixformer.h → xllm_paged_attention → attention.cpp
2026-08-10 02:53:54 +00:00
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# Upstream Reference: Deep-Spark xllm + vllm (FULL TREE)
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Source repos (cloned 2026-08-09, Apache 2.0):
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- `Deep-Spark/xllm` — Iluvatar official C++ LLM inference engine (1470 files)
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- `Deep-Spark/vllm` — Iluvatar official vllm fork (703 files, csrc + model layer)
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## What's here
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### xllm/ (complete source minus git/binaries/submodules)
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天数智芯官方下一代推理引擎，C++ 原生，多平台(CUDA/ILU/MLU/NPU)。
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包含 kernels → layers → models → runtime → scheduler → api_service 完整栈。
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Key subtrees:
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- `xllm/core/kernels/ilu/` — ixformer API wrappers (ixformer.h是金矿)
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- `xllm/core/kernels/cuda/moe/` — MoE CUDA kernels (topk_softmax, fused_topk)
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- `xllm/core/kernels/cuda/` — activation, norm, rope, attention CUDA kernels
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- `xllm/core/layers/ilu/` — Iluvatar FusedMoE完整pipeline
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- `xllm/core/layers/npu_torch/` — GatedDeltaNet C++ implementation
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- `xllm/models/llm/qwen3_5.h` — Qwen3.5 model definition
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- `xllm/compiler/tilelang/` — GDN kernel code generation
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### ds_vllm/ (csrc + model layers + fused_moe)
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天数智芯官方vllm fork，Python + CUDA torch extension。
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- `csrc/` — ALL CUDA source (attention, moe, quantization, cache)
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- `csrc/libtorch_stable/moe/topk_softmax_kernels.cu` — vllm topk_softmax
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- `vllm/_custom_ops.py` — Python → torch.ops._moe_C bridge
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- `vllm/model_executor/models/qwen3_5.py` — ds_vllm的qwen3_5实现
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- `vllm/model_executor/layers/fused_moe/` — vllm FusedMoE Python layer
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