Initialize ModelCheckpoint state as early as possible

pytorch_lightning/callbacks/model_checkpoint.py

@@ -247,13 +247,16 @@ def state_key(self) -> str:
             save_on_train_epoch_end=self._save_on_train_epoch_end,
         )
 
-    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
-        """When pretrain routine starts we resolve the ckpt dir on the fly."""
+    def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
+        # NOTE: setting these attributes needs to happen as early as possible BEFORE reloading callback states,
+        # because the attributes are part of the state_key which needs to be fully defined before reloading.
         if self._save_on_train_epoch_end is None:
             # if the user runs validation multiple times per training epoch or multiple training epochs without
             # validation, then we run after validation instead of on train epoch end
             self._save_on_train_epoch_end = trainer.val_check_interval == 1.0 and trainer.check_val_every_n_epoch == 1
 
+    def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+        """When pretrain routine starts we build the ckpt dir on the fly."""
         self.__resolve_ckpt_dir(trainer)
         if trainer.is_global_zero:
             self.__warn_if_dir_not_empty(self.dirpath)

tests/models/test_restore.py

@@ -246,10 +246,11 @@ def get_trainer_args():
         checkpoint = ModelCheckpoint(dirpath=tmpdir, monitor="val_loss", save_last=True)
         trainer_args = dict(
             default_root_dir=tmpdir,
-            max_steps=1,
+            limit_train_batches=1,
+            limit_val_batches=2,
+            max_epochs=1,
             logger=False,
             callbacks=[checkpoint, callback_capture],
-            limit_val_batches=2,
         )
         assert checkpoint.best_model_path == ""
         assert checkpoint.best_model_score is None