2 Commits

Author	SHA1	Message	Date
project6-dev	56146f8130	feat(CRITICAL): ix_bridge call chain + upstream xllm/ds_vllm sync ... 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 ... 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