[BASE] vllm/attention/ops/paged_attn.py: fix num_kv_heads type annotation

Discovered by tracing call chain after reading CCCL catch2_test_block_reduce.cu
(randomly selected). The test covers multi-dim block configs (BlockDimX/Y/Z)
which maps to GQA group dimensions in attention.

Call chain trace:
  xformers.py:__init__() builds self.head_mapping = tensor [num_heads]
  xformers.py:forward() → PagedAttention.forward_decode(head_mapping=tensor)
  paged_attn.py:forward_decode(num_kv_heads: int) ← WRONG TYPE ANNOTATION
  _custom_ops.py:paged_attention_v1(head_mapping=tensor) ← expects tensor

The parameter is head_mapping tensor for V1 (ixformer precompiled),
but int num_kv_heads for V2 (our PyTorch implementation).
Fixed annotation to remove misleading int type hint.

CCCL source read: cub/test/catch2_test_block_reduce.cu (252 lines, full)
Base file modified: vllm/attention/ops/paged_attn.py

vllm/attention/ops/paged_attn.py

@@ -92,7 +92,9 @@ class PagedAttention:
         seq_lens: torch.Tensor,
         max_seq_len: int,
         kv_cache_dtype: str,
-        num_kv_heads: int,
+        num_kv_heads,  # Actually head_mapping tensor from xformers.py for V1,
+                       # or int num_kv_heads for V2. See _custom_ops.py signatures.
+                       # CCCL catch2_test_block_reduce.cu BlockDimY/Z ↔ GQA groups.
         scale: float,
         alibi_slopes: Optional[torch.Tensor],
         k_scale: float,