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。

qwen3_6_scripts/patch_worker_profile_override.py

@@ -1,81 +0,0 @@
-from patch_utils import package_root, replace_one_of
-
-WORKER = package_root("vllm") / "worker" / "worker.py"
-
-CLEAN_BLOCK = """\
-        # Profile the memory usage of the model and get the maximum number of
-        # cache blocks that can be allocated with the remaining free memory.
-        torch.cuda.empty_cache()
-
-        # Execute a forward pass with dummy inputs to profile the memory usage
-        # of the model.
-        self.model_runner.profile_run()
-"""
-
-GUARDED_BLOCK = """\
-        # Profile the memory usage of the model and get the maximum number of
-        # cache blocks that can be allocated with the remaining free memory.
-        torch.cuda.empty_cache()
-
-        # Execute a forward pass with dummy inputs to profile the memory usage
-        # of the model. Mark this synthetic pass so BI100_PROFILE can skip
-        # timing it by default; profiling real requests is the useful signal.
-        _bi100_prev_startup_profile = os.environ.get("BI100_IN_STARTUP_PROFILE")
-        os.environ["BI100_IN_STARTUP_PROFILE"] = "1"
-        try:
-            self.model_runner.profile_run()
-        finally:
-            if _bi100_prev_startup_profile is None:
-                os.environ.pop("BI100_IN_STARTUP_PROFILE", None)
-            else:
-                os.environ["BI100_IN_STARTUP_PROFILE"] = _bi100_prev_startup_profile
-"""
-
-NEW_BLOCK = """\
-        # Profile the memory usage of the model and get the maximum number of
-        # cache blocks that can be allocated with the remaining free memory.
-        torch.cuda.empty_cache()
-
-        # BI100: Qwen3.6 batched dummy profile_run can trip GDN non-finite
-        # checks before the server starts. If the operator explicitly provides
-        # --num-gpu-blocks-override, trust that conservative capacity value and
-        # skip only the synthetic profile pass. Real inference still uses the
-        # normal GDN fail-fast path.
-        if self.cache_config.num_gpu_blocks_override is not None:
-            cache_block_size = self.get_cache_block_size_bytes()
-            if cache_block_size == 0:
-                num_cpu_blocks = 0
-            else:
-                num_cpu_blocks = int(self.cache_config.swap_space_bytes //
-                                     cache_block_size)
-            logger.warning(
-                "[BI100] skipping worker.profile_run because "
-                "num_gpu_blocks_override=%d was explicitly set",
-                self.cache_config.num_gpu_blocks_override)
-            gc.collect()
-            torch.cuda.empty_cache()
-            return self.cache_config.num_gpu_blocks_override, max(num_cpu_blocks, 0)
-
-        # Execute a forward pass with dummy inputs to profile the memory usage
-        # of the model. Mark this synthetic pass so BI100_PROFILE can skip
-        # timing it by default; profiling real requests is the useful signal.
-        _bi100_prev_startup_profile = os.environ.get("BI100_IN_STARTUP_PROFILE")
-        os.environ["BI100_IN_STARTUP_PROFILE"] = "1"
-        try:
-            self.model_runner.profile_run()
-        finally:
-            if _bi100_prev_startup_profile is None:
-                os.environ.pop("BI100_IN_STARTUP_PROFILE", None)
-            else:
-                os.environ["BI100_IN_STARTUP_PROFILE"] = _bi100_prev_startup_profile
-"""
-
-replace_one_of(
-    WORKER,
-    [
-        (GUARDED_BLOCK, NEW_BLOCK),
-        (CLEAN_BLOCK, NEW_BLOCK),
-    ],
-    required=True,
-    already_contains="[BI100] skipping worker.profile_run",
-)