fix docs references

Author: awaelchli

docs/source/advanced/multi_gpu.rst

@@ -580,9 +580,9 @@ Below are the possible configurations we support.
 
 Implement Your Own Distributed (DDP) training
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.plugins.legacy.ddp_plugin.DDPPlugin.init_ddp_connection`.
+If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.plugins.training_type.ddp.DDPPlugin.init_ddp_connection`.
 
-If you also need to use your own DDP implementation, override :meth:`pytorch_lightning.plugins.legacy.ddp_plugin.DDPPlugin.configure_ddp`.
+If you also need to use your own DDP implementation, override :meth:`pytorch_lightning.plugins.training_type.ddp.DDPPlugin.configure_ddp`.
 
 
 ----------
@@ -692,7 +692,7 @@ This should be kept within the ``sequential_module`` variable within your ``Ligh
 
 .. code-block:: python
 
-    from pytorch_lightning.plugins.legacy.ddp_sequential_plugin import DDPSequentialPlugin
+    from pytorch_lightning.plugins.training_type.rpc_sequential import RPCSequentialPlugin
     from pytorch_lightning import LightningModule
 
     class MyModel(LightningModule):
@@ -702,7 +702,7 @@ This should be kept within the ``sequential_module`` variable within your ``Ligh
 
     # Split my module across 4 gpus, one layer each
     model = MyModel()
-    plugin = DDPSequentialPlugin(balance=[1, 1, 1, 1])
+    plugin = RPCSequentialPlugin(balance=[1, 1, 1, 1])
     trainer = Trainer(accelerator='ddp', gpus=4, plugins=[plugin])
     trainer.fit(model)