Update Accelerator Connector for Registry

pytorch_lightning/plugins/plugins_registry.py

@@ -82,6 +82,7 @@ def register(
 
         def do_register(plugin: Callable) -> Callable:
             data["plugin"] = plugin
+            data["distributed_backend"] = plugin.distributed_backend
             self[name] = data
             return plugin
 

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -112,6 +112,16 @@ def __init__(
         self._training_type_plugin: Optional[TrainingTypePlugin] = None
         self._cluster_environment: Optional[ClusterEnvironment] = None
 
+        plugins = plugins if plugins is not None else []
+
+        if isinstance(plugins, str):
+            plugins = [plugins]
+
+        if not isinstance(plugins, Sequence):
+            plugins = [plugins]
+
+        self.plugins = plugins
+
         # for gpus allow int, string and gpu list
         if auto_select_gpus and isinstance(gpus, int):
             self.gpus = pick_multiple_gpus(gpus)
@@ -121,7 +131,7 @@ def __init__(
         self.set_distributed_mode()
         self.configure_slurm_ddp()
 
-        self.handle_given_plugins(plugins)
+        self.handle_given_plugins()
 
         self.accelerator = self.select_accelerator()
 
@@ -148,22 +158,13 @@ def __init__(
 
         self.replace_sampler_ddp = replace_sampler_ddp
 
-    def handle_given_plugins(
-        self, plugins: Optional[Union[ClusterEnvironment, TrainingTypePlugin, PrecisionPlugin, Sequence]]
-    ):
-        plugins = plugins if plugins is not None else []
-
-        if isinstance(plugins, str):
-            plugins = [plugins]
-
-        if not isinstance(plugins, Sequence):
-            plugins = [plugins]
+    def handle_given_plugins(self) -> None:
 
         training_type = None
         precision = None
         cluster_environment = None
 
-        for plug in plugins:
+        for plug in self.plugins:
             if isinstance(plug, str) and plug in TrainingTypePluginsRegistry:
                 if training_type is None:
                     training_type = TrainingTypePluginsRegistry.get(plug)
@@ -173,7 +174,7 @@ def handle_given_plugins(
                         ' Found more than 1 training type plugin:'
                         f' {TrainingTypePluginsRegistry[plug]["plugin"]} registered to {plug}'
                     )
-            elif isinstance(plug, str):
+            if isinstance(plug, str):
                 # Reset the distributed type as the user has overridden training type
                 # via the plugins argument
                 self._distrib_type = None
@@ -310,6 +311,10 @@ def parallel_devices(self) -> List[Union[torch.device, int]]:
     def root_gpu(self) -> Optional[int]:
         return self.accelerator.root_device.index if not isinstance(self.accelerator, TPUAccelerator) else None
 
+    @property
+    def is_training_type_in_plugins(self) -> bool:
+        return any(isinstance(plug, str) and plug in TrainingTypePluginsRegistry for plug in self.plugins)
+
     @property
     def is_using_torchelastic(self) -> bool:
         """
@@ -492,7 +497,12 @@ def select_cluster_environment(self) -> ClusterEnvironment:
 
     def set_distributed_mode(self, distributed_backend: Optional[str] = None):
 
-        if distributed_backend is not None:
+        if distributed_backend is None and self.is_training_type_in_plugins:
+            return
+
+        if distributed_backend is not None and distributed_backend in TrainingTypePluginsRegistry:
+            self.distributed_backend = TrainingTypePluginsRegistry[distributed_backend]["distributed_backend"]
+        elif distributed_backend is not None:
             self.distributed_backend = distributed_backend
 
         if isinstance(self.distributed_backend, Accelerator):

tests/plugins/test_plugins_registry.py

@@ -23,6 +23,8 @@ def test_training_type_plugins_registry_with_new_plugin():
 
     class TestPlugin:
 
+        distributed_backend = "test_plugin"
+
         def __init__(self, param1, param2):
             self.param1 = param1
             self.param2 = param2
@@ -37,6 +39,7 @@ def __init__(self, param1, param2):
     assert plugin_name in TrainingTypePluginsRegistry
     assert TrainingTypePluginsRegistry[plugin_name]["description"] == plugin_description
     assert TrainingTypePluginsRegistry[plugin_name]["init_params"] == {"param1": "abc", "param2": 123}
+    assert TrainingTypePluginsRegistry[plugin_name]["distributed_backend"] == "test_plugin"
     assert isinstance(TrainingTypePluginsRegistry.get(plugin_name), TestPlugin)
 
     TrainingTypePluginsRegistry.remove(plugin_name)