Add a trainer.ckpt_path setter for stateful loading (#16187)

src/pytorch_lightning/CHANGELOG.md

@@ -11,6 +11,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added migration logic to warn about checkpoints with apex AMP state ([#16161](https://github.com/Lightning-AI/lightning/pull/16161))
 
+- Added the `Trainer.ckpt_path = ...` setter to statefully set the checkpoint path to load. This can act as a replacement for the removed `Trainer(resume_from_checkpoint=...)` flag ([#16187](https://github.com/Lightning-AI/lightning/pull/16187))
+
 ### Removed
 
 - Removed the `pytorch_lightning.lite` module in favor of `lightning_fabric` ([#15953](https://github.com/Lightning-AI/lightning/pull/15953))

src/pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -48,7 +48,9 @@
 class CheckpointConnector:
     def __init__(self, trainer: "pl.Trainer") -> None:
         self.trainer = trainer
-        self.resume_checkpoint_path: Optional[_PATH] = None
+        self._ckpt_path: Optional[_PATH] = None
+        # flag to know if the user is changing the checkpoint path statefully. See `trainer.ckpt_path.setter`
+        self._user_managed: bool = False
         self._loaded_checkpoint: Dict[str, Any] = {}
 
     @property
@@ -73,7 +75,7 @@ def resume_start(self, checkpoint_path: Optional[_PATH] = None) -> None:
         3. from `checkpoint_path` file if provided
         4. don't restore
         """
-        self.resume_checkpoint_path = checkpoint_path
+        self._ckpt_path = checkpoint_path
         if not checkpoint_path:
             log.detail("`checkpoint_path` not specified. Skipping checkpoint loading.")
             return
@@ -83,9 +85,41 @@ def resume_start(self, checkpoint_path: Optional[_PATH] = None) -> None:
             loaded_checkpoint = self.trainer.strategy.load_checkpoint(checkpoint_path)
         self._loaded_checkpoint = _pl_migrate_checkpoint(loaded_checkpoint, checkpoint_path)
 
-    def _set_ckpt_path(
-        self, state_fn: TrainerFn, ckpt_path: Optional[str], model_provided: bool, model_connected: bool
-    ) -> Optional[str]:
+    def _select_ckpt_path(
+        self, state_fn: TrainerFn, ckpt_path: Optional[_PATH], model_provided: bool, model_connected: bool
+    ) -> Optional[_PATH]:
+        """Called by the ``Trainer`` to select the checkpoint path source."""
+        if self._user_managed:
+            if ckpt_path:
+                rank_zero_warn(
+                    f"`trainer.ckpt_path = {self._ckpt_path!r}` was called but then you"
+                    f" passed `trainer.fit(ckpt_path={ckpt_path!r})`. The latter will be loaded."
+                )
+                # reset the previous path
+                self._ckpt_path = None
+                self._user_managed = False
+                ckpt_path = self._parse_ckpt_path(
+                    state_fn,
+                    ckpt_path,
+                    model_provided=model_provided,
+                    model_connected=model_connected,
+                )
+            else:
+                ckpt_path = self._ckpt_path
+        else:
+            ckpt_path = self._parse_ckpt_path(
+                state_fn,
+                ckpt_path,
+                model_provided=model_provided,
+                model_connected=model_connected,
+            )
+        return ckpt_path
+
+    def _parse_ckpt_path(
+        self, state_fn: TrainerFn, ckpt_path: Optional[_PATH], model_provided: bool, model_connected: bool
+    ) -> Optional[_PATH]:
+        """Converts the ``ckpt_path`` special values into an actual filepath, depending on the trainer
+        configuration."""
         if ckpt_path is None and SLURMEnvironment.detect() and self._hpc_resume_path is not None:
             ckpt_path = "hpc"
 
@@ -181,15 +215,12 @@ def resume_end(self) -> None:
         """Signal the connector that all states have resumed and memory for the checkpoint object can be
         released."""
         assert self.trainer.state.fn is not None
-        if self.resume_checkpoint_path:
-            if self.trainer.state.fn == TrainerFn.FITTING:
-                rank_zero_info(f"Restored all states from the checkpoint file at {self.resume_checkpoint_path}")
-            elif self.trainer.state.fn in (TrainerFn.VALIDATING, TrainerFn.TESTING, TrainerFn.PREDICTING):
-                rank_zero_info(f"Loaded model weights from checkpoint at {self.resume_checkpoint_path}")
-        self.resume_checkpoint_path = None
-        self._loaded_checkpoint = {}
+        if self._ckpt_path:
+            message = "Restored all states" if self.trainer.state.fn == TrainerFn.FITTING else "Loaded model weights"
+            rank_zero_info(f"{message} from the checkpoint at {self._ckpt_path}")
 
-        # clear cache after restore
+        # free memory
+        self._loaded_checkpoint = {}
         torch.cuda.empty_cache()
 
         # wait for all to catch up
@@ -391,9 +422,15 @@ def restore_lr_schedulers(self) -> None:
         for config, lrs_state in zip(self.trainer.lr_scheduler_configs, lr_schedulers):
             config.scheduler.load_state_dict(lrs_state)
 
-    # ----------------------------------
-    # PRIVATE OPS
-    # ----------------------------------
+    def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None) -> None:
+        # restore modules after setup
+        self.resume_start(checkpoint_path)
+        self._restore_quantization_callbacks()
+        self.restore_model()
+        self.restore_datamodule()
+        if self.trainer.state.fn == TrainerFn.FITTING:
+            # restore callback states
+            self.restore_callbacks()
 
     def dump_checkpoint(self, weights_only: bool = False) -> dict:
         """Creating a model checkpoint dictionary object from various component states.

src/pytorch_lightning/trainer/trainer.py

@@ -392,9 +392,6 @@ def __init__(
         # default .predict() loop
         self.predict_loop = PredictionLoop()
 
-        # set when a checkpoint is loaded via `Trainer.{fit,validate,test,predict}`.
-        self._ckpt_path: Optional[str] = None
-
         # init callbacks
         # Declare attributes to be set in _callback_connector on_trainer_init
         self._callback_connector.on_trainer_init(
@@ -574,14 +571,13 @@ def _fit_impl(
             model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
         )
 
-        ckpt_path = ckpt_path
-        self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
+        ckpt_path = self._checkpoint_connector._select_ckpt_path(
             self.state.fn,
             ckpt_path,
             model_provided=True,
             model_connected=self.lightning_module is not None,
         )
-        self._run(model, ckpt_path=self.ckpt_path)
+        self._run(model, ckpt_path=ckpt_path)
 
         assert self.state.stopped
         self.training = False
@@ -665,14 +661,10 @@ def _validate_impl(
         # links data to the trainer
         self._data_connector.attach_data(model, val_dataloaders=dataloaders, datamodule=datamodule)
 
-        self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
+        ckpt_path = self._checkpoint_connector._select_ckpt_path(
             self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
         )
-
-        self._validated_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
-
-        # run validate
-        results = self._run(model, ckpt_path=self.ckpt_path)
+        results = self._run(model, ckpt_path=ckpt_path)
 
         assert self.state.stopped
         self.validating = False
@@ -758,14 +750,10 @@ def _test_impl(
         # links data to the trainer
         self._data_connector.attach_data(model, test_dataloaders=dataloaders, datamodule=datamodule)
 
-        self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
+        ckpt_path = self._checkpoint_connector._select_ckpt_path(
             self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
         )
-
-        self._tested_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
-
-        # run test
-        results = self._run(model, ckpt_path=self.ckpt_path)
+        results = self._run(model, ckpt_path=ckpt_path)
 
         assert self.state.stopped
         self.testing = False
@@ -851,13 +839,10 @@ def _predict_impl(
         # links data to the trainer
         self._data_connector.attach_data(model, predict_dataloaders=dataloaders, datamodule=datamodule)
 
-        self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
+        ckpt_path = self._checkpoint_connector._select_ckpt_path(
             self.state.fn, ckpt_path, model_provided=model_provided, model_connected=self.lightning_module is not None
         )
-
-        self._predicted_ckpt_path = self.ckpt_path  # TODO: remove in v1.8
-
-        results = self._run(model, ckpt_path=self.ckpt_path)
+        results = self._run(model, ckpt_path=ckpt_path)
 
         assert self.state.stopped
         self.predicting = False
@@ -918,18 +903,8 @@ def tune(
 
         return result
 
-    def _restore_modules_and_callbacks(self, checkpoint_path: Optional[_PATH] = None) -> None:
-        # restore modules after setup
-        self._checkpoint_connector.resume_start(checkpoint_path)
-        self._checkpoint_connector._restore_quantization_callbacks()
-        self._checkpoint_connector.restore_model()
-        self._checkpoint_connector.restore_datamodule()
-        if self.state.fn == TrainerFn.FITTING:
-            # restore callback states
-            self._checkpoint_connector.restore_callbacks()
-
     def _run(
-        self, model: "pl.LightningModule", ckpt_path: Optional[str] = None
+        self, model: "pl.LightningModule", ckpt_path: Optional[_PATH] = None
     ) -> Optional[Union[_EVALUATE_OUTPUT, _PREDICT_OUTPUT]]:
         if model._compiler_ctx is not None:
             supported_strategies = [SingleDeviceStrategy, DDPStrategy, DDPFullyShardedNativeStrategy]
@@ -978,7 +953,7 @@ def _run(
         # check if we should delay restoring checkpoint till later
         if not self.strategy.restore_checkpoint_after_setup:
             log.detail(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}")
-            self._restore_modules_and_callbacks(ckpt_path)
+            self._checkpoint_connector._restore_modules_and_callbacks(ckpt_path)
 
         log.detail(f"{self.__class__.__name__}: configuring sharded model")
         self._call_configure_sharded_model()  # allow user to setup in model sharded environment
@@ -1026,7 +1001,7 @@ def _run(
 
         if self.strategy.restore_checkpoint_after_setup:
             log.detail(f"{self.__class__.__name__}: restoring module and callbacks from checkpoint path: {ckpt_path}")
-            self._restore_modules_and_callbacks(ckpt_path)
+            self._checkpoint_connector._restore_modules_and_callbacks(ckpt_path)
 
         # restore optimizers, etc.
         log.detail(f"{self.__class__.__name__}: restoring training state")
@@ -1811,12 +1786,30 @@ def progress_bar_callback(self) -> Optional[ProgressBarBase]:
         return None
 
     @property
-    def ckpt_path(self) -> Optional[str]:
+    def ckpt_path(self) -> Optional[_PATH]:
         """Set to the path/URL of a checkpoint loaded via :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`,
         :meth:`~pytorch_lightning.trainer.trainer.Trainer.validate`,
         :meth:`~pytorch_lightning.trainer.trainer.Trainer.test`, or
         :meth:`~pytorch_lightning.trainer.trainer.Trainer.predict`. ``None`` otherwise."""
-        return self._ckpt_path
+        return self._checkpoint_connector._ckpt_path
+
+    @ckpt_path.setter
+    def ckpt_path(self, ckpt_path: Optional[_PATH]) -> None:
+        """Allows you to manage which checkpoint is loaded statefully.
+
+        Examples::
+
+            trainer = Trainer()
+            trainer.ckpt_path = "my/checkpoint/file.ckpt"
+            trainer.fit(model)
+            ...
+
+            # you will be in charge of resetting this
+            trainer.ckpt_path = None
+            trainer.test(model)
+        """
+        self._checkpoint_connector._ckpt_path = ckpt_path
+        self._checkpoint_connector._user_managed = bool(ckpt_path)
 
     def save_checkpoint(
         self, filepath: _PATH, weights_only: bool = False, storage_options: Optional[Any] = None

tests/tests_pytorch/core/test_datamodules.py

@@ -230,7 +230,7 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
 
     for trainer_fn in TrainerFn:
         trainer.state.fn = trainer_fn
-        trainer._restore_modules_and_callbacks(checkpoint_path)
+        trainer._checkpoint_connector._restore_modules_and_callbacks(checkpoint_path)
         assert dm.my_state_dict == {"my": "state_dict"}
 
 

tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 import os
 from unittest import mock
+from unittest.mock import Mock
 
 import pytest
 import torch
@@ -21,6 +22,7 @@
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.demos.boring_classes import BoringModel
 from pytorch_lightning.trainer.states import TrainerFn
+from pytorch_lightning.utilities.migration.utils import _set_version
 
 
 def test_preloaded_checkpoint_lifecycle(tmpdir):
@@ -31,26 +33,27 @@ def test_preloaded_checkpoint_lifecycle(tmpdir):
 
     connector = trainer._checkpoint_connector
 
-    assert not connector.resume_checkpoint_path
+    assert not connector._ckpt_path
     assert not connector._loaded_checkpoint
 
     connector.resume_start()
-    assert not connector.resume_checkpoint_path
+    assert not connector._ckpt_path
     assert not connector._loaded_checkpoint
     connector.resume_end()
-    assert not connector.resume_checkpoint_path
+    assert not connector._ckpt_path
     assert not connector._loaded_checkpoint
 
     ckpt_path = trainer.checkpoint_callback.best_model_path
     trainer = Trainer(default_root_dir=tmpdir, max_steps=2)
     connector = trainer._checkpoint_connector
     connector.resume_start(ckpt_path)
-    assert connector.resume_checkpoint_path == ckpt_path
+    assert connector._ckpt_path == ckpt_path
     assert connector._loaded_checkpoint
     assert isinstance(connector._loaded_checkpoint, dict)
     trainer.state.fn = TrainerFn.FITTING
     connector.resume_end()
-    assert not connector.resume_checkpoint_path
+    # not cleared until next restoration, as the user might access it through `trainer.ckpt_path`
+    assert connector._ckpt_path == ckpt_path
     assert not connector._loaded_checkpoint
 
 
@@ -166,3 +169,54 @@ def test_loops_restore(tmpdir):
             if fn2 != fn:
                 trainer_loop2 = getattr(trainer, f"{fn2}_loop")
                 trainer_loop2.load_state_dict.assert_not_called()
+
+
+def test_stateful_trainer_ckpt_path_support(tmp_path):
+    """Tests support for the pattern used by NeMo's experiment manager."""
+    model = BoringModel()
+
+    # dummy ckpt data
+    ckpt_data = {"state_dict": model.state_dict(), "optimizer_states": {}, "lr_schedulers": {}}
+    _set_version(ckpt_data, "2.0.0")
+
+    # save a "checkpoint"
+    ckpt_path = tmp_path / "foo.ckpt"
+    torch.save(ckpt_data, ckpt_path)
+
+    # mock model checkpoint instance that has saved a last checkpoint
+    model_checkpoint = Mock(spec=ModelCheckpoint)
+    last_path = tmp_path / "last.ckpt"
+    torch.save(ckpt_data, last_path)
+    model_checkpoint._find_last_checkpoints.return_value = {last_path}
+
+    trainer = Trainer(default_root_dir=tmp_path, fast_dev_run=True, callbacks=model_checkpoint)
+
+    # set the ckpt path statefully
+    trainer.ckpt_path = ckpt_path
+    trainer.fit(model)
+    assert trainer.ckpt_path == ckpt_path  # not automatically cleaned
+    assert trainer._checkpoint_connector._user_managed
+
+    # now conflict with ckpt_path functionally
+    with pytest.warns(UserWarning, match="trainer.ckpt_path =.*but then you passed"):
+        trainer.fit(model, ckpt_path="last")
+    assert trainer.ckpt_path == last_path
+    assert not trainer._checkpoint_connector._user_managed
+
+    # mock model checkpoint instance that has saved a last checkpoint
+    best_path = tmp_path / "best.ckpt"
+    torch.save(ckpt_data, best_path)
+    model_checkpoint.best_model_path = best_path
+
+    # `trainer.test` will use this over "best" if statefully set
+    trainer.ckpt_path = ckpt_path
+    trainer.test()
+    assert trainer.ckpt_path == ckpt_path
+
+    # ckpt_path = "best" still works if it's reset
+    trainer.ckpt_path = None
+    # the state is cleared
+    assert trainer._checkpoint_connector._ckpt_path is None
+    assert not trainer._checkpoint_connector._user_managed
+    trainer.test()
+    assert trainer.ckpt_path == best_path