2aedf7377b
Cloned from GitHub:
- Deep-Spark/vllm (latest): qwen3_5.py with multimodal support,
transformers configs, multimodal registry, model registry
- jd-opensource/xllm (latest): ILU kernel implementations
(attention, fused_moe, group_gemm, activation, norm, rope, matmul)
+ GatedDeltaNet layer for Qwen3.5
These are the REAL upstream implementations that the base Docker image
is compiled from. Our dlopen modules should match these interfaces:
- ilu_ops_api.h: 14 functions in xllm::kernel::ilu namespace
- ixformer.h: 15 functions in ixformer::infer namespace
Key interface signatures for dlopen targets:
batch_prefill() → ixinfer_flash_attn_unpad_with_block_tables
batch_decode() → xllm_paged_attention
moe_active_topk()→ topk_softmax
moe_gen_idx() → moe_compute_token_index_api
group_gemm() → moe_w16a16_group_gemm
silu_and_mul() → silu_and_mul
rms_norm() → rms_norm + residual_rms_norm
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