[doc][distributed] add both gloo and nccl tests

docs/source/getting_started/debugging.rst

@@ -28,8 +28,8 @@ If it crashes, and the error trace shows somewhere around ``self.graph.replay()`
 
 Here are some common issues that can cause hangs:
 
-- **Incorrect network setup**: The vLLM instance cannot get the correct IP address. You can find the log such as ``DEBUG 06-10 21:32:17 parallel_state.py:88] world_size=8 rank=0 local_rank=0 distributed_init_method=tcp://xxx.xxx.xxx.xxx:54641 backend=nccl``. The IP address should be the correct one. If not, override the IP address by setting the environment variable ``export VLLM_HOST_IP=your_ip_address``.
-- **Incorrect hardware/driver**: GPU communication cannot be established. You can run the following sanity check script to see if the GPU communication is working correctly.
+- **Incorrect network setup**: The vLLM instance cannot get the correct IP address if you have complicated network config. You can find the log such as ``DEBUG 06-10 21:32:17 parallel_state.py:88] world_size=8 rank=0 local_rank=0 distributed_init_method=tcp://xxx.xxx.xxx.xxx:54641 backend=nccl``. The IP address should be the correct one. If not, override the IP address by setting the environment variable ``export VLLM_HOST_IP=your_ip_address``. You might also need to set ``export NCCL_SOCKET_IFNAME=your_network_interface`` and ``export GLOO_SOCKET_IFNAME=your_network_interface`` to specify the network interface for the IP address.
+- **Incorrect hardware/driver**: GPU/CPU communication cannot be established. You can run the following sanity check script to see if the GPU/CPU communication is working correctly.
 
 .. code-block:: python
 
@@ -41,7 +41,14 @@ Here are some common issues that can cause hangs:
     dist.all_reduce(data, op=dist.ReduceOp.SUM)
     torch.cuda.synchronize()
     value = data.mean().item()
-    assert value == dist.get_world_size()
+    world_size = dist.get_world_size()
+    assert value == world_size, f"Expected {world_size}, got {value}"
+
+    gloo_group = dist.new_group(ranks=list(range(world_size)), backend="gloo")
+    cpu_data = torch.FloatTensor([1,] * 128)
+    dist.all_reduce(cpu_data, op=dist.ReduceOp.SUM, group=gloo_group)
+    value = cpu_data.mean().item()
+    assert value == world_size, f"Expected {world_size}, got {value}"
 
 .. tip::