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/csrc/moe/moe_combine.cu

@@ -1,105 +0,0 @@
-/* Copyright 2025-2026 The xLLM Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    https://github.com/jd-opensource/xllm/blob/main/LICENSE
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// Fused MoE combine kernel — reorder + weighted sum in one pass.
-// Replaces: torch::zeros + index_copy_ + view + multiply + sum
-//
-// Algorithm per token (each block handles one token):
-//   1. For each of its topk experts, read gemm2 at flat_idx directly
-//      (gemm2 is flat-index-ordered after scatter via index_copy_ with dst_src)
-//   2. Multiply by router weight
-//   3. Accumulate into output[token]
-//
-// Grid:  num_tokens (N) blocks
-// Block: HIDDEN_DIM / HIDDEN_TILE threads
-
-#include <c10/cuda/CUDAGuard.h>
-
-#include "device_utils.cuh"
-#include "kernels/cuda/cuda_ops_api.h"
-
-namespace xllm::kernel::cuda {
-
-constexpr int32_t kCombineBlockSize = 256;
-
-template <typename scalar_t>
-__global__ void XLLM_KERNEL_ATTR(kCombineBlockSize) moe_combine_kernel(
-    const scalar_t* __restrict__ gemm2,       // [N*topk, H] flat-index-ordered
-    const float* __restrict__ reduce_weight,  // [N, topk]
-    scalar_t* __restrict__ output,            // [N, H]
-    int64_t N,
-    int32_t topk,
-    int64_t H) {
-  int64_t token_id = blockIdx.x;  // 0 .. N-1
-  if (token_id >= N) return;
-
-  int32_t tid = threadIdx.x;
-  int32_t stride = kCombineBlockSize;
-
-  // Accumulate over topk experts for this token
-  for (int64_t h = tid; h < H; h += stride) {
-    float acc = 0.0f;
-    for (int32_t k = 0; k < topk; ++k) {
-      int64_t flat_idx = token_id * topk + k;
-      float w = reduce_weight[flat_idx];
-      acc += w * static_cast<float>(gemm2[flat_idx * H + h]);
-    }
-    output[token_id * H + h] = static_cast<scalar_t>(acc);
-  }
-}
-
-// ---- Host-side orchestrator ----
-torch::Tensor moe_combine_result(
-    const torch::Tensor& gemm2,          // [N*topk, H] flat-index-ordered
-    const torch::Tensor& reduce_weight,  // [N, topk] float or same as gemm2
-    int64_t N,
-    int32_t topk) {
-  auto stream = at::cuda::getCurrentCUDAStream();
-  int64_t H = gemm2.size(1);
-  auto dtype = gemm2.scalar_type();
-
-  auto output = torch::empty({N, H}, gemm2.options());
-  auto rw = reduce_weight.to(gemm2.device(), torch::kFloat32).contiguous();
-
-  if (dtype == torch::kFloat16) {
-    moe_combine_kernel<c10::Half>
-        <<<N, kCombineBlockSize, 0, stream>>>(gemm2.data_ptr<c10::Half>(),
-                                              rw.data_ptr<float>(),
-                                              output.data_ptr<c10::Half>(),
-                                              N,
-                                              topk,
-                                              H);
-  } else if (dtype == torch::kBFloat16) {
-    moe_combine_kernel<c10::BFloat16>
-        <<<N, kCombineBlockSize, 0, stream>>>(gemm2.data_ptr<c10::BFloat16>(),
-                                              rw.data_ptr<float>(),
-                                              output.data_ptr<c10::BFloat16>(),
-                                              N,
-                                              topk,
-                                              H);
-  } else {
-    moe_combine_kernel<float>
-        <<<N, kCombineBlockSize, 0, stream>>>(gemm2.data_ptr<float>(),
-                                              rw.data_ptr<float>(),
-                                              output.data_ptr<float>(),
-                                              N,
-                                              topk,
-                                              H);
-  }
-
-  return output;
-}
-
-}  // namespace xllm::kernel::cuda