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Sources (Apache 2.0, cloned 2026-08-09): - Deep-Spark/xllm: Iluvatar's official C++ inference engine - Deep-Spark/vllm: Iluvatar's vllm fork Key files for our EX Engine development: MoE topk_softmax (fixes 2304 calls/token PyTorch fallback): - xllm/kernels/cuda/moe/moe_topk_softmax_kernels.cuh CUB-based fused softmax+topk, power-of-2 expert count optimized For 64 experts: topk_gating_softmax<T,VPT=2,64,WARPS=4,BYTES=4> - xllm/kernels/ilu/ixformer.h Official ixformer C++ API: topk_softmax(), paged_attention(), etc. - xllm/kernels/ilu/fused_moe.cpp How xllm calls ixformer::infer::topk_softmax() - ds_vllm/csrc/moe/topk_softmax_kernels.cu vllm-native topk_softmax (TensorRT-LLM derived, 874 lines) GatedDeltaNet (fixes NaN in 4 GDN layers): - xllm/layers/npu_torch/qwen3_gated_delta_net_base.cpp fp32 state accumulation, proper recurrent update Complete FusedMoE pipeline reference: - xllm/layers/ilu/fused_moe.cpp gate -> topk -> expand -> gemm1 -> act -> gemm2 -> combine
85 lines
4.7 KiB
Markdown
85 lines
4.7 KiB
Markdown
# Upstream Reference: Deep-Spark xllm + vllm
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Source repos (cloned 2026-08-09):
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- `Deep-Spark/xllm` — Iluvatar's C++ inference engine (Apache 2.0)
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- `Deep-Spark/vllm` — Iluvatar's vllm fork (Apache 2.0)
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## Call Chain: MoE topk_softmax on BI-V100
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```
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Our code xllm reference Iluvatar SDK
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───────────────────────────────── ────────────────────────────── ──────────────
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qwen3_5.py
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Qwen3_5MoeSparseBlock.forward()
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_custom_ops.py:topk_softmax()
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ixf_F.vllm_moe_topk_softmax ← MISSING in base image
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│
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├── xllm path (C++ native):
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│ kernels/ilu/fused_moe.cpp
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│ → ixformer::infer::topk_softmax() ← ixformer.h
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│ → CUDA kernel (moe_topk_softmax_kernels.cuh)
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│ topk_gating_softmax<T,VPT,64,4,BYTES_PER_LDG>()
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│
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├── ds_vllm path (Python torch extension):
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│ csrc/moe/topk_softmax_kernels.cu
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│ → torch.ops._moe_C.topk_softmax()
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│ → topk_gating_softmax_kernel_launcher<T>()
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│
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└── Our EX Engine path (dlopen .so):
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ex_engine/csrc/factor_moe_topk_softmax.cu
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→ ex_factor_0.so via ctypes
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→ moe_topk_softmax_kernel()
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```
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## Call Chain: GatedDeltaNet (GDN) on BI-V100
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```
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Our code xllm reference
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───────────────────────────────── ──────────────────────────────
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qwen3_5.py
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GatedDeltaNet.forward()
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prefill path:
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_torch_chunk_gated_delta_rule ← produces NaN (fp16 overflow)
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│
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├── xllm path:
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│ layers/npu_torch/qwen3_gated_delta_net_base.cpp
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│ → process_mixed_qkv() + recurrent state update
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│ → full fp32 accumulation
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│
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└── Our EX Engine path:
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ex_engine/csrc/factor_gdn_chunk_fwd.cu
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→ fp32 state accumulation, tile-based
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```
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## File Index
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### xllm/kernels/cuda/moe/ — CUDA kernels (the actual GPU code)
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- `moe_topk_softmax_kernels.cuh` — **KEY**: fused softmax+topk, CUB-based, power-of-2 expert count optimized
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- `moe_fused_topk.cu` — sigmoid/softmax topk dispatcher
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- `moe_topk.cuh` — topk helper functions
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- `moe_topk_sigmoid_kernels.cuh` — sigmoid variant for DeepSeek-style routing
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### xllm/kernels/ilu/ — Iluvatar ixformer API wrappers
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- `ixformer.h` — **KEY**: official ixformer C++ API declarations (topk_softmax, paged_attention, etc.)
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- `fused_moe.cpp` — how xllm calls ixformer::infer::topk_softmax()
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- `activation.cpp` — silu_and_mul, gelu_and_mul wrappers
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- `attention.cpp` — paged_attention wrappers
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- `norm.cpp` — rms_norm, fused_add_rms_norm wrappers
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- `rope.cpp` — rotary embedding wrappers
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### xllm/layers/ilu/ — Complete FusedMoE layer for Iluvatar
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- `fused_moe.cpp` — **KEY**: full MoE pipeline: gate → topk → expand → gemm1 → act → gemm2 → combine
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- `fused_moe.h` — layer interface
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### xllm/layers/npu_torch/ — GatedDeltaNet implementation
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- `qwen3_gated_delta_net_base.cpp` — base GDN with fp32 state management
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- `qwen3_5_gated_delta_net.cpp` — Qwen3.5 specific GDN
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### ds_vllm/csrc/moe/ — vllm-native MoE CUDA kernels
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- `topk_softmax_kernels.cu` — vllm's topk_softmax (TensorRT-LLM derived)
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- `moeTopKFuncs.cuh` — shared topk reduction primitives
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- `moe_align_sum_kernels.cu` — block alignment for scatter
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### ds_vllm/vllm/ — Python layer
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- `_custom_ops.py` — how vllm calls torch.ops._moe_C.topk_softmax
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