优化 block_tables 的生成逻辑，增加对混合情况的检测，确保在存在空和非空块时正确计算最大块长度

vllm/attention/backends/utils.py

@@ -245,6 +245,26 @@ class CommonMetadataBuilder(AttentionMetadataBuilder[TAttentionMetadata]):
             block_tables = torch.from_numpy(input_block_tables).pin_memory().to(
                 device, non_blocking=True)
 
+        else:
+            has_empty: bool = any(len(bt) == 0 for bt in self.block_tables)
+            has_non_empty = any(len(bt) > 0 for bt in self.block_tables)
+            max_block_length = 0
+            if has_empty and has_non_empty:
+                for inter_data in self.input_builder.inter_data_list:
+                    block_tables = inter_data.block_tables
+                    if block_tables:
+                        for seq_id in inter_data.seq_ids:
+                            if seq_id in block_tables:
+                                block_table = block_tables[seq_id]
+                                max_block_length = max(max_block_length, len(block_table))
+            if max_block_length >0:
+                block_tables = make_tensor_with_pad(
+                    self.block_tables,
+                    pad=0,
+                    dtype=torch.int,
+                    device=device,
+                    max_len=max_block_length,
+                )
             else:
                  block_tables = make_tensor_with_pad(
                     self.block_tables,