Upgrade to vllm 0.17.0 corex v4.1 overlay

vllm/v1/worker/gpu/block_table.py

@@ -119,6 +119,10 @@ class BlockTables:
         return tuple(block_table[:num_reqs] for block_table in self.input_block_tables)
 
     def get_dummy_block_tables(self, num_reqs: int) -> tuple[torch.Tensor, ...]:
+        # NOTE(woosuk): The output may be used for CUDA graph capture.
+        # Therefore, this method must return the persistent tensor
+        # with the same memory address as that used during the model's forward pass,
+        # rather than allocating a new tensor.
         return tuple(block_table[:num_reqs] for block_table in self.input_block_tables)
 
     def compute_slot_mappings(
@@ -150,7 +154,14 @@ class BlockTables:
         return self.slot_mappings[:, :num_tokens]
 
     def get_dummy_slot_mappings(self, num_tokens: int) -> torch.Tensor:
+        # Fill the entire slot_mappings tensor, not just the first `num_tokens` entries.
+        # This is because the padding logic is complex and kernels may access beyond
+        # the requested range.
         self.slot_mappings.fill_(PAD_SLOT_ID)
+        # NOTE(woosuk): The output may be used for CUDA graph capture.
+        # Therefore, this method must return the persistent tensor
+        # with the same memory address as that used during the model's forward pass,
+        # rather than allocating a new tensor.
         return self.slot_mappings[:, :num_tokens]