Automatically set sync_batchnorm for training_type_plugin

pytorch_lightning/trainer/connectors/accelerator_connector.py

@@ -426,6 +426,11 @@ def resolve_training_type_plugin(self, training_type: TrainingTypePlugin) -> Tra
         if hasattr(training_type, 'num_nodes') and getattr(training_type, 'num_nodes') is None:
             training_type.num_nodes = self.num_nodes
 
+        # Automatically set sync_batchnorm if None.
+        # Useful for custom plugins.
+        if hasattr(training_type, 'sync_batchnorm') and getattr(training_type, 'sync_batchnorm') is None:
+            training_type.sync_batchnorm = self.sync_batchnorm
+
         return training_type
 
     def select_accelerator(self) -> Accelerator:

tests/plugins/test_custom_plugin.py

@@ -0,0 +1,41 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from pytorch_lightning import Trainer
+from pytorch_lightning.plugins import DDPPlugin
+from tests.helpers import BoringModel
+from tests.helpers.runif import RunIf
+
+
+class CustomParallelPlugin(DDPPlugin):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        # Set to None so it will be overwritten by the accelerator connector.
+        self.sync_batchnorm = None
+
+
+@RunIf(skip_windows=True)
+def test_sync_batchnorm_set(tmpdir):
+    """Tests if sync_batchnorm is automatically set for custom plugin."""
+    model = BoringModel()
+    plugin = CustomParallelPlugin()
+    assert plugin.sync_batchnorm is None
+    trainer = Trainer(
+        max_epochs=1,
+        plugins=[plugin],
+        default_root_dir=tmpdir,
+        sync_batchnorm=True,
+    )
+    trainer.fit(model)
+    assert plugin.sync_batchnorm is True