[DEPLOY] Complete submission: baseline + all optimizations

Adds ALL files needed for Dockerfile build:
  - qwen3_6_scripts/ (baseline patches + our optimizations)
  - vllm/ (full vllm package)
  - paged_attention_v2_pytorch.py (V2 with single-bmm optimization)
  - Dockerfile + computility-run.yaml

Our optimizations vs baseline:
  1. paged_attn.py: pre-gathered context KV (eliminates 194 gather calls),
     Triton try/fallback, V2 heuristic, threshold 32K→64K
  2. paged_attention_v2_pytorch.py: fills NotImplementedError,
     single-bmm Phase 1 (195 launches → 3)
  3. patch_enable_triton.py: HAS_TRITON=True with safety fallback
  4. patch_triton_tuning.py: BLOCK=64, NUM_WARPS=4 for BI-V100
  5. computility-run.yaml: gpu-memory-utilization 0.9→0.95,
     max-num-batched-tokens 8192→16384

This repo can now be submitted to dev.modelhub.org.cn as-is.

vllm/executor/ray_xpu_executor.py

@@ -0,0 +1,37 @@
+import asyncio
+from typing import List, Optional
+
+import vllm.envs as envs
+from vllm.executor.ray_gpu_executor import RayGPUExecutor, RayGPUExecutorAsync
+from vllm.executor.xpu_executor import XPUExecutor
+from vllm.logger import init_logger
+from vllm.utils import get_vllm_instance_id, make_async
+
+logger = init_logger(__name__)
+
+
+class RayXPUExecutor(RayGPUExecutor, XPUExecutor):
+
+    def _get_env_vars_to_be_updated(self):
+        # Get the set of GPU IDs used on each node.
+        worker_node_and_gpu_ids = self._run_workers("get_node_and_gpu_ids",
+                                                    use_dummy_driver=True)
+
+        VLLM_INSTANCE_ID = get_vllm_instance_id()
+
+        # Set environment variables for the driver and workers.
+        all_args_to_update_environment_variables = [({
+            "VLLM_INSTANCE_ID":
+            VLLM_INSTANCE_ID,
+            "VLLM_TRACE_FUNCTION":
+            str(envs.VLLM_TRACE_FUNCTION),
+        }, ) for (_, _) in worker_node_and_gpu_ids]
+        return all_args_to_update_environment_variables
+
+
+class RayXPUExecutorAsync(RayXPUExecutor, RayGPUExecutorAsync):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.driver_exec_method = make_async(self.driver_worker.execute_method)
+        self.pp_locks: Optional[List[asyncio.Lock]] = None