project6-dev
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2aedf7377b
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ref(upstream): add Deep-Spark/vllm latest + xllm ILU kernel sources
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
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2026-08-10 09:44:11 +00:00 |
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EX Engine
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002f9879b2
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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
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2026-08-10 02:54:03 +00:00 |
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EX Engine
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ea82b00e54
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ref(upstream): add Deep-Spark xllm + vllm MoE/GDN reference code
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
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2026-08-10 02:48:23 +00:00 |
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