Fix access to trainer object within configure optimizers

pytorch_lightning/trainer/connectors/model_connector.py

@@ -42,6 +42,6 @@ def get_model(self):
         return self._get_reference_model(self.trainer.model)
 
     def _get_reference_model(self, model):
-        if self.trainer.accelerator_backend:
+        if self.trainer.accelerator_backend and self.trainer.accelerator_backend.lightning_module:
             return self.trainer.accelerator_backend.lightning_module
         return model

pytorch_lightning/trainer/trainer.py

@@ -405,12 +405,6 @@ def setup_trainer(self, model: LightningModule):
         Args:
             model: The model to run sanity test on.
         """
-        # --------------------------
-        # Setup??
-        # --------------------------
-
-        # set local properties on the model
-        self.model_connector.copy_trainer_model_properties(model)
 
         # init amp. Must be done here instead of __init__ to allow ddp to work
         if self.amp_backend == AMPType.NATIVE and self.precision == 16 and self._device_type != DeviceType.TPU:
@@ -449,6 +443,9 @@ def fit(
         self._state = TrainerState.RUNNING
         self._set_wide_running_stage(RunningStage.TRAINING)
 
+        # set local properties on the model
+        self.model_connector.copy_trainer_model_properties(model)
+
         # ----------------------------
         # LINK DATA
         # ----------------------------

tests/trainer/test_trainer.py

@@ -1726,3 +1726,17 @@ def training_epoch_end(self, *args, **kwargs):
     assert trainer.current_epoch == current_epoch
     assert model.training_step_invoked == should_train, f"`training_step` {error_string}"
     assert model.training_epoch_end_invoked == should_train, f"`training_epoch_end` {error_string}"
+
+
+def test_trainer_access_in_configure_optimizers(tmpdir):
+
+    class TestModel(BoringModel):
+
+        def configure_optimizers(self):
+            assert self.trainer is not None, "Expect to have access to the trainer within `configure_optimizers`"
+
+    train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
+
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
+    trainer.fit(model, train_data)