[Feature] Bagel: Support tp+cfg parallel using mooncake transfer engine connector (#2705)

Signed-off-by: natureofnature <wzliu@connect.hku.hk>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

vllm_omni/diffusion/distributed/group_coordinator.py

@@ -104,6 +104,7 @@ class GroupCoordinator:
         self.local_rank = local_rank
         self.device_group = None
         self.cpu_group = None
+        self.shm_broadcaster = None
 
         for ranks in group_ranks:
             device_group = torch.distributed.new_group(ranks, backend=torch_distributed_backend)
@@ -316,7 +317,7 @@ class GroupCoordinator:
 
         assert dst < self.world_size, f"Invalid dst rank ({dst})"
 
-        assert dst != self.rank, "Invalid destination rank. Destination rank is the same as the current rank."
+        assert dst != self.rank_in_group, "Invalid destination rank. Destination rank is the same as the current rank."
 
         # Serialize object to tensor and get the size as well
         object_tensor = torch.frombuffer(pickle.dumps(obj), dtype=torch.uint8)
@@ -338,7 +339,7 @@ class GroupCoordinator:
 
         assert src < self.world_size, f"Invalid src rank ({src})"
 
-        assert src != self.rank, "Invalid source rank. Source rank is the same as the current rank."
+        assert src != self.rank_in_group, "Invalid source rank. Source rank is the same as the current rank."
 
         size_tensor = torch.empty(1, dtype=torch.long, device="cpu")