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

qwen3_6_scripts/qwen3_5.py

@@ -204,6 +204,26 @@ _USE_COREX_MOE_INDEX_COMBINE = (
     and env_bool("BI100_MOE_COREX_INDEX_COMBINE", True))
 _USE_FUSED_MOE_ACTIVATION = env_bool("BI100_MOE_FUSED_ACTIVATION", True)
 
+# ix_fused_moe: full 7-step fused MoE pipeline via ixformer C++ API
+# Source: xllm/core/layers/ilu/fused_moe.cpp → ix_moe_bridge.so
+try:
+    from vllm.model_executor.models import ix_fused_moe as _ix_fused_moe
+    _HAS_IX_FUSED_MOE = _ix_fused_moe.is_available()
+except ImportError:
+    try:
+        import ix_fused_moe as _ix_fused_moe
+        _HAS_IX_FUSED_MOE = _ix_fused_moe.is_available()
+    except ImportError:
+        _ix_fused_moe = None
+        _HAS_IX_FUSED_MOE = False
+_USE_IX_FUSED_MOE = (
+    _HAS_IX_FUSED_MOE
+    and env_bool("BI100_MOE_IX_FUSED", True))
+if _USE_IX_FUSED_MOE:
+    logger.info("ix_fused_moe ENABLED — full 7-step fused MoE pipeline")
+else:
+    logger.info("ix_fused_moe unavailable — using point-optimized Python MoE")
+
 
 # ---------------------------------------------------------------------------
 # Qwen3.6 vision tower and vLLM 0.6 multimodal input integration
@@ -1620,13 +1640,30 @@ class Qwen3_5MoeSparseBlock(nn.Module):
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
     ) -> torch.Tensor:
-        """Pure-PyTorch MoE (ixformer has no MoE kernels on BI-V100).
+        """MoE expert dispatch — fused C++ pipeline when available.
 
         w13_weight: (num_experts, 2*inter_per_partition, hidden)  [TP-sharded]
         w2_weight:  (num_experts, hidden,  inter_per_partition)   [TP-sharded]
         Output is partial (pre-all-reduce), same contract as FusedMoE
         with reduce_results=False.
         """
+        # ---------------------------------------------------------------
+        # Tier 0: Full fused MoE via ix_moe_bridge (xllm 7-step pipeline)
+        # topk → gen_idx → expand → group_gemm → silu → group_gemm → combine
+        # Source: xllm/core/layers/ilu/fused_moe.cpp
+        # ---------------------------------------------------------------
+        if _USE_IX_FUSED_MOE:
+            w13 = self.experts.w13_weight  # (E, 2*I, H)
+            w2 = self.experts.w2_weight    # (E, H, I)
+            return _ix_fused_moe.fused_moe_forward(
+                hidden_states, router_logits,
+                w13, w2,
+                self.top_k, w13.shape[0],
+                True)  # renormalize
+
+        # ---------------------------------------------------------------
+        # Tier 1: Point-optimized Python loop (individual corex .so)
+        # ---------------------------------------------------------------
         # Fused topk+softmax: single CUB kernel vs 2 PyTorch ops.
         # Source: xllm/core/kernels/cuda/moe/moe_topk_softmax_kernels.cuh
         if _USE_COREX_MOE_TOPK_SOFTMAX: