[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/lora/request.py

@@ -0,0 +1,95 @@
+import warnings
+from typing import Optional
+
+import msgspec
+
+from vllm.adapter_commons.request import AdapterRequest
+
+
+class LoRARequest(
+        msgspec.Struct,
+        omit_defaults=True,  # type: ignore[call-arg]
+        array_like=True):  # type: ignore[call-arg]
+    """
+    Request for a LoRA adapter.
+
+    Note that this class should be used internally. For online
+    serving, it is recommended to not allow users to use this class but
+    instead provide another layer of abstraction to prevent users from
+    accessing unauthorized LoRA adapters.
+
+    lora_int_id must be globally unique for a given adapter.
+    This is currently not enforced in vLLM.
+    """
+    __metaclass__ = AdapterRequest
+
+    lora_name: str
+    lora_int_id: int
+    lora_path: str = ""
+    lora_local_path: Optional[str] = msgspec.field(default=None)
+    long_lora_max_len: Optional[int] = None
+    base_model_name: Optional[str] = msgspec.field(default=None)
+
+    def __post_init__(self):
+        if 'lora_local_path' in self.__struct_fields__:
+            warnings.warn(
+                "The 'lora_local_path' attribute is deprecated "
+                "and will be removed in a future version. "
+                "Please use 'lora_path' instead.",
+                DeprecationWarning,
+                stacklevel=2)
+            if not self.lora_path:
+                self.lora_path = self.lora_local_path or ""
+
+        # Ensure lora_path is not empty
+        assert self.lora_path, "lora_path cannot be empty"
+
+    @property
+    def adapter_id(self):
+        return self.lora_int_id
+
+    @property
+    def name(self):
+        return self.lora_name
+
+    @property
+    def path(self):
+        return self.lora_path
+
+    @property
+    def local_path(self):
+        warnings.warn(
+            "The 'local_path' attribute is deprecated "
+            "and will be removed in a future version. "
+            "Please use 'path' instead.",
+            DeprecationWarning,
+            stacklevel=2)
+        return self.lora_path
+
+    @local_path.setter
+    def local_path(self, value):
+        warnings.warn(
+            "The 'local_path' attribute is deprecated "
+            "and will be removed in a future version. "
+            "Please use 'path' instead.",
+            DeprecationWarning,
+            stacklevel=2)
+        self.lora_path = value
+
+    def __eq__(self, value: object) -> bool:
+        """
+        Overrides the equality method to compare LoRARequest
+        instances based on lora_name. This allows for identification
+        and comparison lora adapter across engines.
+        """
+        return isinstance(value,
+                          self.__class__) and self.lora_name == value.lora_name
+
+    def __hash__(self) -> int:
+        """
+        Overrides the hash method to hash LoRARequest instances
+        based on lora_name. This ensures that LoRARequest instances
+        can be used in hash-based collections such as sets and dictionaries,
+        identified by their names across engines.
+        """
+        return hash(self.lora_name)