project6-dev
87a19d2d00
feat(CRITICAL): 从 GitHub 扫描搬运 ixformer SDK + xllm 完整 GDN/MoE 代码
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来源:
1. Chranos/ixformer (GitHub) → ixformer_sdk/ (230 files, 70K lines)
- inference/functions/vllm.py: vllm_moe_topk_softmax 完整实现 (2033 lines)
- inference/functions/moe.py: MoE ops 完整实现 (1380 lines)
- contrib/vllm_flash_attn/: FA2 Python 接口 (1018 lines)
- contrib/tgi/fused_moe.py: TGI fused MoE (429 lines)
- csrc/include/ixformer/: C++ kernel headers + cmake
2. Deep-Spark/xllm (GitHub) → upstream_ref/xllm_latest/ (+15 files)
- npu_torch/qwen3_5_decoder_layer_impl.cpp/.h
- npu_torch/qwen3_5_gated_delta_net.cpp/.h
- npu_torch/qwen3_next_*.cpp/.h (6 files)
- npu_torch/attention.cpp/.h + fused_moe.cpp/.h + CMakeLists.txt
- models/llm/qwen3_5.h + qwen3_5_mtp.h + qwen3_next.h
- models/vlm/qwen3_5.h
调用链完整性:
ixformer_sdk/inference/functions/vllm.py
→ ops.infer.moe_topk_softmax() (C++ 层)
→ 这就是 base 镜像 libixformer.so 里的实现
upstream_ref/xllm_latest/core/layers/ilu/fused_moe.cpp
→ ixformer::infer::topk_softmax() (直接 C++ 调用)
→ ixformer::infer::group_gemm() → 完整 7-step MoE pipeline
2026-08-11 02:32:06 +00:00
project6-dev
56146f8130
feat(CRITICAL): ix_bridge call chain + upstream xllm/ds_vllm sync
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3 changes that close the MoE performance gap:
1. _custom_ops.py: Add ix_bridge as Priority 0 for topk_softmax
- Before: tries our .cu kernel (fails) → PyTorch fallback (1-3 TPS)
- After: tries ix_bridge → ixformer::infer::topk_softmax() → FAST
- Call chain: _custom_ops.topk_softmax() → ix_bridge.topk_softmax()
→ ix_moe_bridge.so → ixformer::infer::topk_softmax()
2. Dockerfile: Add ix_moe_bridge.cpp precompile step
- This was the missing link: code existed but was never compiled
- Uses torch.utils.cpp_extension.load() to link against libixformer.so
3. upstream_ref sync from GitHub (cloned, not rewritten):
- xLLM-AI/xllm: ILU kernels + CUDA MoE + GDN fp32 state mgmt
- Deep-Spark/vllm: latest MoE kernel sources
2026-08-11 01:27:33 +00:00
project6-dev
2aedf7377b
ref(upstream): add Deep-Spark/vllm latest + xllm ILU kernel sources
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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
2026-08-10 09:44:11 +00:00
