[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/block.py

@@ -0,0 +1,88 @@
+"""Token blocks."""
+from typing import TYPE_CHECKING, Iterator, List, Optional
+
+from vllm.utils import Device
+
+DEFAULT_LAST_ACCESSED_TIME: float = -1
+
+
+class PhysicalTokenBlock:
+    """Represents the state of a block in the KV cache."""
+
+    def __init__(
+        self,
+        device: Device,
+        block_number: int,
+        block_size: int,
+        block_hash: int,
+        num_hashed_tokens: int,
+    ) -> None:
+        self.device = device
+        self.block_number = block_number
+        self.block_size = block_size
+        self.block_hash = block_hash
+        self.num_hashed_tokens = num_hashed_tokens
+
+        self.ref_count = 0
+        self.last_accessed = DEFAULT_LAST_ACCESSED_TIME
+
+        self.computed = False
+
+    def __repr__(self) -> str:
+        return (f'PhysicalTokenBlock(device={self.device}, '
+                f'block_number={self.block_number}, '
+                f'num_hashed_tokens={self.num_hashed_tokens}, '
+                f'ref_count={self.ref_count}, '
+                f'last_accessed={self.last_accessed}, '
+                f'computed={self.computed})')
+
+
+class BlockTable:
+    """Holds a list of blocks with caching of their associated block_ids 
+    """
+
+    def __init__(self, blocks: Optional[List[PhysicalTokenBlock]] = None):
+        self._blocks: List[PhysicalTokenBlock] = []
+        self._block_ids: List[int] = []
+
+        if blocks is not None:
+            for block in blocks:
+                self.append(block)
+
+    def append(self, block: PhysicalTokenBlock):
+        self._blocks.append(block)
+        self._block_ids.append(block.block_number)
+
+    def __len__(self) -> int:
+        return len(self._blocks)
+
+    def __getitem__(self, key):
+        return self._blocks[key]
+
+    if TYPE_CHECKING:
+
+        def __iter__(self) -> Iterator[PhysicalTokenBlock]:
+            raise RuntimeError("Method should be automatically generated")
+
+    def __setitem__(self, key, value):
+        if isinstance(key, slice):
+            blocks = value
+            self._blocks[key] = blocks
+            self._block_ids[key] = [b.block_number for b in blocks]
+        else:
+            block = value
+            self._blocks[key] = block
+            self._block_ids[key] = block.block_number
+
+    def reset(self):
+        self._blocks = []
+        self._block_ids = []
+
+    def copy(self) -> "BlockTable":
+        return BlockTable(self._blocks)
+
+    def list(self) -> List[PhysicalTokenBlock]:
+        return self._blocks
+
+    def ids(self) -> List[int]:
+        return self._block_ids