051b02d3cd
Bridge architecture (from xllm/core/kernels/ilu/ixformer.h): ix_moe_bridge.so (MoE 7-step fused pipeline): - topk_softmax → moe_compute_token_index_api → moe_expand_input - moe_w16a16_group_gemm (x2) → silu_and_mul → moe_output_reduce_sum - fused_moe_forward(): replaces entire Python expert loop - Fix: group_gemm format NT→TN (match xllm trans_b=true) ix_attn_bridge.so (attention + linear): - ixinfer_flash_attn_unpad_with_block_tables (fused prefill) - xllm_paged_attention (fused paged decode) - ixformer_linear (matmul + activation) - residual_rms_norm (fused residual + norm) Integration: - ix_fused_moe.py: Python loader (prebuilt .so → JIT → unavailable) - qwen3_5.py: Tier 0 dispatch in _pure_pytorch_experts() - patch_ops.sh: deploys ix_fused_moe.py + all prebuilt/*.so Source: jd-opensource/xllm (fresh clone, all ILU kernels verified SAME) Sync: upstream_ref/xllm_latest/models/llm/qwen3_next_hybrid_base.h (+32 lines) Build on real machine: bash qwen3_6_scripts/build_ix_moe_bridge.sh bash qwen3_6_scripts/build_ix_attn_bridge.sh
Upstream Reference: Deep-Spark xllm + vllm (FULL TREE)
Source repos (cloned 2026-08-09, Apache 2.0):
Deep-Spark/xllm— Iluvatar official C++ LLM inference engine (1470 files)Deep-Spark/vllm— Iluvatar official vllm fork (703 files, csrc + model layer)
What's here
xllm/ (complete source minus git/binaries/submodules)
天数智芯官方下一代推理引擎，C++ 原生，多平台(CUDA/ILU/MLU/NPU)。 包含 kernels → layers → models → runtime → scheduler → api_service 完整栈。
Key subtrees:
xllm/core/kernels/ilu/— ixformer API wrappers (ixformer.h是金矿)xllm/core/kernels/cuda/moe/— MoE CUDA kernels (topk_softmax, fused_topk)xllm/core/kernels/cuda/— activation, norm, rope, attention CUDA kernelsxllm/core/layers/ilu/— Iluvatar FusedMoE完整pipelinexllm/core/layers/npu_torch/— GatedDeltaNet C++ implementationxllm/models/llm/qwen3_5.h— Qwen3.5 model definitionxllm/compiler/tilelang/— GDN kernel code generation
ds_vllm/ (csrc + model layers + fused_moe)
天数智芯官方vllm fork，Python + CUDA torch extension。
csrc/— ALL CUDA source (attention, moe, quantization, cache)csrc/libtorch_stable/moe/topk_softmax_kernels.cu— vllm topk_softmaxvllm/_custom_ops.py— Python → torch.ops._moe_C bridgevllm/model_executor/models/qwen3_5.py— ds_vllm的qwen3_5实现vllm/model_executor/layers/fused_moe/— vllm FusedMoE Python layer