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/ilu/ilu_ops_api.h

@@ -1,141 +0,0 @@
-/* ilu_ops_api.h — Standalone header for project_6 ex_engine.
- *
- * Adapted from xllm/core/kernels/ilu/ilu_ops_api.h.
- * Removes xllm-internal deps (glog, kernels/kernels.h, framework/*).
- * Only requires: torch, ixformer.h (ixformer::infer namespace).
- */
-#pragma once
-
-#include <torch/all.h>
-// #include <optional>  // use c10::optional instead
-#include <iostream>
-#include <stdexcept>
-
-#include "ixformer.h"
-
-using namespace ixformer;
-
-/* ---- Minimal LOG(FATAL) replacement ------------------------------------ */
-#ifndef LOG
-struct FatalLogStream {
-  std::ostringstream ss;
-  [[noreturn]] ~FatalLogStream() noexcept(false) {
-    std::cerr << ss.str() << std::endl;
-    throw std::runtime_error(ss.str());
-  }
-  template <typename T> FatalLogStream& operator<<(const T& v) {
-    ss << v; return *this;
-  }
-};
-#define LOG(level) FatalLogStream()
-#endif
-
-namespace xllm::kernel::ilu {
-
-void apply_rope_pos_ids_cos_sin_cache(torch::Tensor& query,
-                                      torch::Tensor& key,
-                                      torch::Tensor& cos_sin_cache,
-                                      torch::Tensor& positions,
-                                      bool interleave);
-
-void act_and_mul(torch::Tensor out,
-                 torch::Tensor input,
-                 const std::string& act_mode);
-
-void reshape_paged_cache(
-    torch::Tensor& key,
-    c10::optional<torch::Tensor>& value,
-    torch::Tensor& key_cache,
-    c10::optional<torch::Tensor>& value_cache,
-    torch::Tensor& slot_mapping);
-
-void batch_prefill(torch::Tensor& query,
-                   const torch::Tensor& key,
-                   const c10::optional<torch::Tensor>& value,
-                   torch::Tensor& output,
-                   c10::optional<torch::Tensor>& output_lse,
-                   const c10::optional<torch::Tensor>& q_cu_seq_lens,
-                   const c10::optional<torch::Tensor>& kv_cu_seq_lens,
-                   const c10::optional<torch::Tensor>& alibi_slope,
-                   const c10::optional<torch::Tensor>& attn_bias,
-                   const c10::optional<torch::Tensor>& q_quant_scale,
-                   const c10::optional<torch::Tensor>& k_quant_scale,
-                   const c10::optional<torch::Tensor>& v_quant_scale,
-                   const torch::Tensor& block_tables,
-                   int64_t max_query_len,
-                   int64_t max_seq_len,
-                   float scale,
-                   bool is_causal,
-                   int64_t window_size_left,
-                   int64_t window_size_right,
-                   const std::string& compute_dtype,
-                   bool return_lse);
-
-void batch_decode(torch::Tensor& query,
-                  const torch::Tensor& k_cache,
-                  torch::Tensor& output,
-                  const torch::Tensor& block_table,
-                  const torch::Tensor& seq_lens,
-                  const c10::optional<torch::Tensor>& v_cache,
-                  c10::optional<torch::Tensor>& output_lse,
-                  const c10::optional<torch::Tensor>& q_quant_scale,
-                  const c10::optional<torch::Tensor>& k_cache_quant_scale,
-                  const c10::optional<torch::Tensor>& v_cache_quant_scale,
-                  const c10::optional<torch::Tensor>& out_quant_scale,
-                  const c10::optional<torch::Tensor>& alibi_slope,
-                  const c10::optional<torch::Tensor>& mask,
-                  const std::string& compute_dtype,
-                  int64_t max_seq_len,
-                  int64_t window_size_left,
-                  int64_t window_size_right,
-                  float scale,
-                  bool return_lse,
-                  bool is_causal,
-                  int64_t kv_cache_quant_bit_size);
-
-void residual_layer_norm(torch::Tensor& input,
-                         torch::Tensor& output,
-                         c10::optional<torch::Tensor>& residual,
-                         torch::Tensor& weight,
-                         c10::optional<torch::Tensor>& bias,
-                         c10::optional<torch::Tensor>& residual_out,
-                         double eps);
-
-void rms_norm(torch::Tensor& output,
-              torch::Tensor& input,
-              torch::Tensor& weight,
-              double eps);
-
-torch::Tensor matmul(torch::Tensor a,
-                     torch::Tensor b,
-                     c10::optional<torch::Tensor> bias);
-
-std::tuple<torch::Tensor, torch::Tensor> moe_active_topk(
-    const torch::Tensor& input,
-    int64_t topk,
-    int64_t num_expert_group,
-    int64_t topk_group,
-    bool normalize,
-    const c10::optional<torch::Tensor>& mask,
-    const std::string& normed_by,
-    const std::string& scoring_func,
-    double route_scale,
-    const c10::optional<torch::Tensor>& e_score_correction_bias);
-
-std::vector<torch::Tensor> moe_gen_idx(torch::Tensor& expert_id,
-                                       int64_t expert_num);
-
-torch::Tensor moe_expand_input(const torch::Tensor& input,
-                               const torch::Tensor& gather_index,
-                               const torch::Tensor& combine_idx,
-                               int64_t topk);
-
-torch::Tensor group_gemm(torch::Tensor& input,
-                         torch::Tensor& weight,
-                         torch::Tensor& tokens_per_experts,
-                         const c10::optional<torch::Tensor>& dst_to_src,
-                         torch::Tensor& output);
-
-torch::Tensor moe_combine_result(torch::Tensor& input, torch::Tensor& weight);
-
-}  // namespace xllm::kernel::ilu