fix: remove ix_moe_bridge — nm -D confirms libixformer.so has NO MoE symbols

真机探测确认:
  nm -D libixformer.so | grep topk_softmax → 空
  ixf_F dir() → 无 vllm_moe_topk_softmax
  ixf_F dir() → 无 vllm_invoke_fused_moe_kernel
  ixf_F dir() → 无 vllm_moe_align_block_size
  _ixformer_torch.so symbols → 仅 cuinfer_gemm 系列, 无 MoE

结论: base 镜像的 MoE 路径:
  fused_moe.py → _custom_ops.topk_softmax → ixf_F.vllm_moe_topk_softmax → AttributeError
  → qwen3_5.py 捕获 → fallback to Python expert loop (这是唯一能工作的路径)

修改:
1. _custom_ops.py topk_softmax: 直接 PyTorch softmax+topk, 不尝试 ixf_F (消除 ERROR 日志)
2. 移除 ix_moe_bridge 加载逻辑 (libixformer.so 没有 MoE 符号, 链接会失败)
3. 移除 patch_ops.sh ix_moe_bridge JIT 编译步骤

comp 168 的 0 分根因不是 MoE fallback (所有参赛者都 fallback),
而是我们的自定义 qwen3_5.py 导致 GDN NaN 99.98% + OOM.
上一个 commit 已修复: 条件部署 qwen3_5.py + max_model_len=80000.

qwen3_6_scripts/_custom_ops.py

@@ -18,72 +18,6 @@ logger = init_logger(__name__)
 
 supports_moe_ops = True
 
-# ============================================================================
-# EX Engine: ix_moe_bridge — JIT-compiled C++ bridge to ixformer::infer MoE ops
-# This is the ONLY way to call topk_softmax, group_gemm, etc. on BI-V100
-# because ixformer.functions Python binding doesn't expose them.
-# ============================================================================
-_ix_moe_bridge = None
-
-def _load_moe_bridge():
-    """Load ix_moe_bridge via torch.utils.cpp_extension JIT compile."""
-    import os, glob
-    bridge = None
-    
-    # Try 1: pre-compiled .so from ex_engine build
-    search_paths = [
-        '/workspace/ex_engine/build',
-        os.path.join(os.path.dirname(__file__), '..', 'model_executor', 'models', 'ex_engine'),
-        '/usr/local/corex/lib/python3/dist-packages/ex_engine',
-    ]
-    for sp in search_paths:
-        so_files = glob.glob(os.path.join(sp, 'ix_moe_bridge*.so'))
-        if so_files:
-            try:
-                import importlib.util
-                spec = importlib.util.spec_from_file_location('ix_moe_bridge', so_files[0])
-                bridge = importlib.util.module_from_spec(spec)
-                spec.loader.exec_module(bridge)
-                logger.info(f"[EX] Loaded ix_moe_bridge from {so_files[0]}")
-                return bridge
-            except Exception as e:
-                logger.warning(f"[EX] Failed to load pre-built bridge {so_files[0]}: {e}")
-    
-    # Try 2: JIT compile ix_moe_bridge.cpp against libixformer.so
-    cpp_search = [
-        '/workspace/ex_engine/csrc/ix_moe_bridge.cpp',
-        os.path.join(os.path.dirname(__file__), 'ix_moe_bridge.cpp'),
-        os.path.join(os.path.dirname(__file__), '..', 'model_executor', 'models', 'ex_engine', 'csrc', 'ix_moe_bridge.cpp'),
-    ]
-    cpp_file = None
-    for p in cpp_search:
-        if os.path.isfile(p):
-            cpp_file = p
-            break
-    
-    if cpp_file:
-        try:
-            from torch.utils.cpp_extension import load
-            bridge = load(
-                name='ix_moe_bridge',
-                sources=[cpp_file],
-                extra_include_paths=['/usr/local/corex/include'],
-                extra_ldflags=[
-                    '-L/usr/local/corex/lib64',
-                    '-L/usr/local/corex/lib64/python3/dist-packages/ixformer',
-                    '-lixformer',
-                    '-Wl,-rpath,/usr/local/corex/lib64/python3/dist-packages/ixformer',
-                ],
-                verbose=False,
-            )
-            logger.info(f"[EX] JIT compiled ix_moe_bridge from {cpp_file}")
-            return bridge
-        except Exception as e:
-            logger.warning(f"[EX] JIT compile failed for {cpp_file}: {e}")
-    
-    logger.warning("[EX] ix_moe_bridge NOT available — topk_softmax will use PyTorch path")
-    return None
-
 if TYPE_CHECKING:
 
     def register_fake(fn):
@@ -896,39 +830,20 @@ def invoke_fused_moe_kernel(
 def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
                  token_expert_indicies: torch.Tensor,
                  gating_output: float) -> None:
-    # EX Engine: algorithm factor replacement for topk_softmax.
-    # ixformer::infer::topk_softmax is in libixformer.so (C++ level)
-    # but NOT exposed via ixformer.functions Python binding.
-    # We call it via ix_moe_bridge (pybind11 JIT-compiled against libixformer.so).
-    # NO FALLBACK — if bridge fails, raise immediately to catch integration bugs.
-    global _ix_moe_bridge
-    if _ix_moe_bridge is None:
-        _ix_moe_bridge = _load_moe_bridge()
-    if _ix_moe_bridge is not None:
-        # Bridge available — call ixformer::infer::topk_softmax via C++
-        if isinstance(gating_output, torch.Tensor):
-            gating_output = gating_output.float().contiguous()
-        topk = topk_weights.shape[1]
-        tw, ti = _ix_moe_bridge.topk_softmax(gating_output, topk, False)
-        topk_weights.copy_(tw.to(topk_weights.dtype))
-        topk_ids.copy_(ti.to(topk_ids.dtype))
-        token_expert_indicies.copy_(
-            torch.arange(topk, device=topk_ids.device, dtype=topk_ids.dtype)
-            .unsqueeze(0).expand_as(topk_ids))
+    # BI-V100 base image: ixf_F.vllm_moe_topk_softmax does NOT exist.
+    # libixformer.so has NO topk_softmax symbol (verified via nm -D).
+    # PyTorch implementation — silent, no ERROR log spam.
+    if isinstance(gating_output, torch.Tensor):
+        probs = torch.softmax(gating_output.float(), dim=-1)
     else:
-        # Bridge not loaded — use PyTorch (for build environments without GPU)
-        # In production this path should NOT be hit
-        if isinstance(gating_output, torch.Tensor):
-            probs = torch.softmax(gating_output.float(), dim=-1)
-        else:
-            probs = torch.softmax(gating_output, dim=-1)
-        topk = topk_weights.shape[1]
-        tw, ti = torch.topk(probs, topk, dim=-1)
-        topk_weights.copy_(tw)
-        topk_ids.copy_(ti.to(topk_ids.dtype))
-        token_expert_indicies.copy_(
-            torch.arange(topk, device=topk_ids.device, dtype=topk_ids.dtype)
-            .unsqueeze(0).expand_as(topk_ids))
+        probs = torch.softmax(gating_output, dim=-1)
+    topk = topk_weights.shape[1]
+    tw, ti = torch.topk(probs, topk, dim=-1)
+    topk_weights.copy_(tw.to(topk_weights.dtype))
+    topk_ids.copy_(ti.to(topk_ids.dtype))
+    token_expert_indicies.copy_(
+        torch.arange(topk, device=topk_ids.device, dtype=topk_ids.dtype)
+        .unsqueeze(0).expand_as(topk_ids))
 
 
 if supports_moe_ops and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):

qwen3_6_scripts/patch_ops.sh

@@ -260,37 +260,7 @@ if [ -d "$EX_ENGINE_SRC/python" ]; then
     echo "[patch_ops] EX Engine Python package deployed to $EX_PY_DST"
 fi
 
-# 7a. JIT compile ix_moe_bridge.cpp → .so (bridge to ixformer::infer C++ API)
-# This is CRITICAL: topk_softmax, group_gemm, etc. are ONLY accessible via C++
-IX_MOE_BRIDGE_CPP="/workspace/ex_engine/csrc/ix_moe_bridge.cpp"
-if [ -f "$IX_MOE_BRIDGE_CPP" ]; then
-    echo "[patch_ops] Pre-compiling ix_moe_bridge.cpp (ixformer C++ bridge)..."
-    python3 -c "
-import torch
-from torch.utils.cpp_extension import load
-try:
-    bridge = load(
-        name='ix_moe_bridge',
-        sources=['$IX_MOE_BRIDGE_CPP'],
-        extra_include_paths=['/usr/local/corex/include'],
-        extra_ldflags=[
-            '-L/usr/local/corex/lib64',
-            '-L/usr/local/corex/lib64/python3/dist-packages/ixformer',
-            '-lixformer',
-            '-Wl,-rpath,/usr/local/corex/lib64/python3/dist-packages/ixformer',
-        ],
-        verbose=True,
-    )
-    print('[patch_ops] ix_moe_bridge compiled successfully')
-    # Test basic function availability
-    print(f'[patch_ops] Bridge functions: {[x for x in dir(bridge) if not x.startswith(\"_\")]}')
-except Exception as e:
-    print(f'[patch_ops] WARNING: ix_moe_bridge compile failed: {e}')
-    print('[patch_ops] topk_softmax will use PyTorch fallback')
-" 2>&1 || echo "[patch_ops] WARNING: ix_moe_bridge pre-compile step failed"
-fi
-
-# 7b. Precompile MoE topk_softmax CUDA kernel (.cu → .so)
+# 7. Precompile MoE topk_softmax CUDA kernel (.cu → .so)
 # This replaces the missing ixf_F.vllm_moe_topk_softmax with our own CUDA kernel
 MOE_TOPK_CU="/workspace/ex_engine/csrc/moe_topk_softmax_v3.cu"
 if [ -f "$MOE_TOPK_CU" ]; then