[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/compilation/decorators.py

@@ -0,0 +1,113 @@
+import inspect
+from typing import Dict, List, Union
+
+import torch
+
+import vllm.envs as envs
+from vllm.compilation.levels import CompilationLevel
+from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
+from vllm.sequence import IntermediateTensors
+from vllm.utils import supports_dynamo
+
+
+def support_torch_compile(dynamic_arg_dims: Dict[str, Union[int, List[int]]]):
+    """
+    A decorator to add support for compiling the forward method of a class.
+
+    `dynamic_arg_dims` is a dictionary that maps argument names to the dynamic
+    dimensions of the argument. The dynamic dimensions can be either a single
+    integer or a list of integers.
+
+    Depending on the value of arguments:
+
+    - if it is a single integer, the corresponding dimension of the argument
+        will be marked as dynamic.
+    - if it is `None`, ignored.
+    - if it is `IntermediateTensors`, all the tensors in the intermediate
+        tensors will be marked as dynamic.
+    - otherwise, it will raise an error.
+
+    NOTE: if an argument is `None`, it should always be passed as `None` during
+    the lifetime of the model, otherwise, it cannot be captured as a single
+    computation graph.
+    """
+
+    def cls_decorator_helper(cls: type):
+        # helper to pass `dynamic_arg_dims`` to `_support_torch_compile``
+        # to avoid too much indentation for `_support_torch_compile``
+        sig = inspect.signature(cls.forward)
+        for k in dynamic_arg_dims:
+            if k not in sig.parameters:
+                raise ValueError(
+                    f"Argument {k} not found in the forward method of {cls}")
+        return _support_torch_compile(cls, dynamic_arg_dims)
+
+    return cls_decorator_helper
+
+
+def _support_torch_compile(cls: type,
+                           dynamic_arg_dims: Dict[str, Union[int, List[int]]]):
+    """
+    A decorator to add support for compiling the forward method of a class.
+    """
+
+    # for CompilationLevel.DYNAMO_AS_IS , the upper level model runner
+    # will handle the compilation, so we don't need to do anything here.
+    if envs.VLLM_TORCH_COMPILE_LEVEL in [
+            CompilationLevel.NO_COMPILATION, CompilationLevel.DYNAMO_AS_IS
+    ] or not supports_dynamo():
+        return cls
+
+    # take care of method resolution order
+    # make sure super().__init__ is called on the base class
+    #  other than TorchCompileWrapperWithCustomDispatcher
+    cls.__bases__ = cls.__bases__ + (TorchCompileWrapperWithCustomDispatcher, )
+
+    old_init = cls.__init__
+
+    def __init__(self, *args, **kwargs):
+        old_init(self, *args, **kwargs)
+        TorchCompileWrapperWithCustomDispatcher.__init__(self)
+
+    cls.__init__ = __init__
+
+    def __call__(self, *args, **kwargs):
+        # torch.compiler.is_compiling() means we are inside the compilation
+        # e.g. TPU has the compilation logic in model runner, so we don't
+        # need to compile the model inside.
+        if torch.compiler.is_compiling():
+            return self.forward(*args, **kwargs)
+
+        # the first compilation needs to have dynamic shapes marked
+        if len(self.compiled_codes) < 1:
+            sig = inspect.signature(self.__class__.forward)
+            bound_args = sig.bind(self, *args, **kwargs)
+            bound_args.apply_defaults()
+            for k, dims in dynamic_arg_dims.items():
+                arg = bound_args.arguments.get(k)
+                if arg is not None:
+                    if isinstance(arg, torch.Tensor):
+                        torch._dynamo.mark_dynamic(arg, dims)
+                    elif isinstance(arg, IntermediateTensors):
+                        for tensor in arg.tensors.values():
+                            torch._dynamo.mark_dynamic(tensor, dims)
+                    else:
+                        raise ValueError(
+                            "Unsupported dynamic dimensions"
+                            f" {dims} for argument {k} with type {type(arg)}.")
+
+        # if we don't use custom dispatcher, we can directly call the
+        # compiled function and let torch.compile handle the dispatching,
+        # with the overhead of guard evaluation and recompilation.
+        if len(self.compiled_codes) < 1 or not self.use_custom_dispatcher:
+            return self.compiled_callable(*args, **kwargs)
+
+        # usually, capturing the model once is enough, and then we can
+        # dispatch to the compiled code directly, without going through
+        # the Dynamo guard mechanism.
+        with self.dispatch_to_code(0):
+            model_output = self.forward(*args, **kwargs)
+            return model_output
+
+    cls.__call__ = __call__
+    return cls