test: 回退Docker context到26e6cb40完全一致——验证竞赛平台build

Dockerfile/qwen3_6_scripts/ex_engine/computility-run.yaml 全部
还原到26e6cb40的精确内容。删除所有26e6cb40不存在的新增文件
(prebuilt/*.so, wheels/*.whl, vendor_overrides/, 新增.cu/.sh等)。

目的：确认26e6cb40的文件内容在当前git状态下仍能通过竞赛平台build。
如果通过，说明问题在新增文件中；如果不通过，说明问题在git仓库层面。

ex_engine/python/ex_topk_bridge.py

@@ -1,100 +0,0 @@
-"""ex_topk_bridge.py — ctypes bridge for ex_factor_0.so topk_softmax
-
-CCCL pattern: ex_registry → ex_dispatch → kernel
-Python bridge: ctypes.CDLL → ex_dispatch_moe_topk_softmax()
-
-Usage:
-    from ex_engine.python.ex_topk_bridge import ex_topk_softmax
-    ex_topk_softmax(topk_weights, topk_ids, token_expert_indices, gating_output)
-"""
-import ctypes
-import os
-import glob
-import logging
-import torch
-
-logger = logging.getLogger("ex_topk_bridge")
-
-_lib = None
-_dispatch_fn = None
-
-
-def _load():
-    global _lib, _dispatch_fn
-    if _dispatch_fn is not None:
-        return True
-
-    # Search for ex_factor_0.so
-    search = [
-        os.path.join(os.path.dirname(__file__), "..", "build"),
-        "/workspace/ex_engine/build",
-        os.path.join(os.path.dirname(__file__), ".."),
-    ]
-    # Also check vllm model path (where build.sh factor compile puts it)
-    for p in ["/usr/local/corex/lib64/python3/dist-packages/vllm/model_executor/models/ex_engine",
-              "/usr/local/corex/lib/python3/dist-packages/vllm/model_executor/models/ex_engine"]:
-        search.append(p)
-
-    for d in search:
-        so = os.path.join(d, "ex_factor_0.so")
-        if os.path.isfile(so):
-            try:
-                _lib_local = ctypes.CDLL(so)
-                fn = _lib_local.ex_dispatch_moe_topk_softmax
-                fn.restype = ctypes.c_int
-                fn.argtypes = [
-                    ctypes.c_void_p,  # float* topk_weights
-                    ctypes.c_void_p,  # int32_t* topk_ids
-                    ctypes.c_void_p,  # const float* logits
-                    ctypes.c_int,     # T
-                    ctypes.c_int,     # E
-                    ctypes.c_int,     # top_k
-                    ctypes.c_void_p,  # stream
-                ]
-                _lib = _lib_local
-                _dispatch_fn = fn
-                logger.info("ex_factor_0.so loaded from %s", so)
-                return True
-            except Exception as e:
-                logger.warning("Failed to load %s: %s", so, e)
-
-    return False
-
-
-def ex_topk_softmax(topk_weights: torch.Tensor,
-                    topk_ids: torch.Tensor,
-                    token_expert_indices: torch.Tensor,
-                    gating_output: torch.Tensor) -> None:
-    """Drop-in replacement for _custom_ops.topk_softmax using ex_factor_0.so.
-
-    Same interface as vllm._custom_ops.topk_softmax:
-      topk_weights: (T, K) float32, output
-      topk_ids: (T, K) int32, output
-      token_expert_indices: (T, K) int32, output (ignored by ex kernel)
-      gating_output: (T, E) float32, input
-    """
-    if not _load():
-        raise RuntimeError("ex_factor_0.so not available")
-
-    T, E = gating_output.shape
-    K = topk_weights.shape[1]
-
-    # Get CUDA stream
-    stream = torch.cuda.current_stream().cuda_stream
-
-    ret = _dispatch_fn(
-        topk_weights.data_ptr(),
-        topk_ids.data_ptr(),
-        gating_output.data_ptr(),
-        T, E, K,
-        stream,
-    )
-    if ret != 0:
-        raise RuntimeError(f"ex_dispatch_moe_topk_softmax returned {ret}")
-
-    # token_expert_indices: vllm expects (T, K) with values k_idx * T + t_idx
-    # ex kernel doesn't write this, fill it here
-    if token_expert_indices is not None:
-        T_t = torch.arange(T, device=topk_ids.device, dtype=torch.int32)
-        for k in range(K):
-            token_expert_indices[:, k] = k * T + T_t