feat: ix_moe_bridge + ix_attn_bridge — dlopen bridges for full ixformer::infer API
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
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@@ -149,7 +149,7 @@ torch::Tensor ix_group_gemm(
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output, inputs, weights, token_count,
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/*dst_to_src=*/kNoneTensor,
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/*bias=*/kNoneTensor,
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/*format=*/"NT",
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/*format=*/"TN",
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/*persistent=*/0,
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/*output_n=*/output_n);
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return output;
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