Remove the deprecated resume_from_checkpoint Trainer argument (#16167)

Author: carmocca

docs/source-pytorch/common/trainer.rst

@@ -1142,31 +1142,6 @@ By setting to False, you have to add your own distributed sampler:
 
 .. note:: For iterable datasets, we don't do this automatically.
 
-resume_from_checkpoint
-^^^^^^^^^^^^^^^^^^^^^^
-
-.. warning:: ``resume_from_checkpoint`` is deprecated in v1.5 and will be removed in v2.0.
-    Please pass ``trainer.fit(ckpt_path="some/path/to/my_checkpoint.ckpt")`` instead.
-
-
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/resume_from_checkpoint.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/resume_from_checkpoint.mp4"></video>
-
-|
-
-To resume training from a specific checkpoint pass in the path here. If resuming from a mid-epoch
-checkpoint, training will start from the beginning of the next epoch.
-
-.. testcode::
-
-    # default used by the Trainer
-    trainer = Trainer(resume_from_checkpoint=None)
-
-    # resume from a specific checkpoint
-    trainer = Trainer(resume_from_checkpoint="some/path/to/my_checkpoint.ckpt")
 
 strategy
 ^^^^^^^^

src/pytorch_lightning/CHANGELOG.md

@@ -48,6 +48,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
   * Removed the `Trainer(ipus=...)` argument
   * Removed the `Trainer(num_processes=...)` argument
 
+- Removed the deprecated `resume_from_checkpoint` Trainer argument ([#16167](https://github.com/Lightning-AI/lightning/pull/16167))
+
 
 ### Fixed
 

src/pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -36,7 +36,7 @@
 from pytorch_lightning.utilities.imports import _fault_tolerant_training
 from pytorch_lightning.utilities.migration import pl_legacy_patch
 from pytorch_lightning.utilities.migration.utils import _pl_migrate_checkpoint
-from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
 
 if _OMEGACONF_AVAILABLE:
     from omegaconf import Container
@@ -46,16 +46,9 @@
 
 
 class CheckpointConnector:
-    def __init__(self, trainer: "pl.Trainer", resume_from_checkpoint: Optional[_PATH] = None) -> None:
+    def __init__(self, trainer: "pl.Trainer") -> None:
         self.trainer = trainer
         self.resume_checkpoint_path: Optional[_PATH] = None
-        # TODO: remove resume_from_checkpoint_fit_path in v2.0
-        self.resume_from_checkpoint_fit_path: Optional[_PATH] = resume_from_checkpoint
-        if resume_from_checkpoint is not None:
-            rank_zero_deprecation(
-                "Setting `Trainer(resume_from_checkpoint=)` is deprecated in v1.5 and"
-                " will be removed in v2.0. Please pass `Trainer.fit(ckpt_path=)` directly instead."
-            )
         self._loaded_checkpoint: Dict[str, Any] = {}
 
     @property
@@ -193,12 +186,6 @@ def resume_end(self) -> None:
                 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}")
-        # TODO: remove resume_from_checkpoint_fit_path in v2.0
-        if (
-            self.trainer.state.fn == TrainerFn.FITTING
-            and self.resume_checkpoint_path == self.resume_from_checkpoint_fit_path
-        ):
-            self.resume_from_checkpoint_fit_path = None
         self.resume_checkpoint_path = None
         self._loaded_checkpoint = {}
 

src/pytorch_lightning/trainer/trainer.py

@@ -29,7 +29,6 @@
 from contextlib import contextmanager
 from copy import deepcopy
 from datetime import timedelta
-from pathlib import Path
 from typing import Any, Dict, Generator, Iterable, List, Optional, Type, Union
 from weakref import proxy
 
@@ -150,7 +149,6 @@ def __init__(
         precision: _PRECISION_INPUT = 32,
         enable_model_summary: bool = True,
         num_sanity_val_steps: int = 2,
-        resume_from_checkpoint: Optional[Union[Path, str]] = None,
         profiler: Optional[Union[Profiler, str]] = None,
         benchmark: Optional[bool] = None,
         deterministic: Optional[Union[bool, _LITERAL_WARN]] = None,
@@ -316,14 +314,6 @@ def __init__(
                 train sampler and ``shuffle=False`` for val/test sampler. If you want to customize it,
                 you can set ``replace_sampler_ddp=False`` and add your own distributed sampler.
 
-            resume_from_checkpoint: Path/URL of the checkpoint from which training is resumed. If there is
-                no checkpoint file at the path, an exception is raised. If resuming from mid-epoch checkpoint,
-                training will start from the beginning of the next epoch.
-
-                .. deprecated:: v1.5
-                    ``resume_from_checkpoint`` is deprecated in v1.5 and will be removed in v2.0.
-                    Please pass the path to ``Trainer.fit(..., ckpt_path=...)`` instead.
-
             strategy: Supports different training strategies with aliases
                 as well custom strategies.
                 Default: ``None``.
@@ -384,7 +374,7 @@ def __init__(
         )
         self._logger_connector = LoggerConnector(self)
         self._callback_connector = CallbackConnector(self)
-        self._checkpoint_connector = CheckpointConnector(self, resume_from_checkpoint)
+        self._checkpoint_connector = CheckpointConnector(self)
         self._signal_connector = SignalConnector(self)
         self.tuner = Tuner(self)
 
@@ -586,11 +576,10 @@ def _fit_impl(
             model, train_dataloaders=train_dataloaders, val_dataloaders=val_dataloaders, datamodule=datamodule
         )
 
-        # TODO: ckpt_path only in v2.0
-        ckpt_path = ckpt_path or self.resume_from_checkpoint
+        ckpt_path = ckpt_path
         self._ckpt_path = self._checkpoint_connector._set_ckpt_path(
             self.state.fn,
-            ckpt_path,  # type: ignore[arg-type]
+            ckpt_path,
             model_provided=True,
             model_connected=self.lightning_module is not None,
         )
@@ -1823,18 +1812,6 @@ def progress_bar_callback(self) -> Optional[ProgressBarBase]:
                 return c
         return None
 
-    @property
-    def resume_from_checkpoint(self) -> Optional[Union[str, Path]]:
-        resume_from_checkpoint = self._checkpoint_connector.resume_from_checkpoint_fit_path
-        if resume_from_checkpoint is not None:
-            rank_zero_deprecation(
-                "`trainer.resume_from_checkpoint` is deprecated in v1.5 and will be removed in v2.0."
-                " Specify the fit checkpoint path with `trainer.fit(ckpt_path=)` instead.",
-                stacklevel=5,
-            )
-
-        return resume_from_checkpoint
-
     @property
     def ckpt_path(self) -> Optional[str]:
         """Set to the path/URL of a checkpoint loaded via :meth:`~pytorch_lightning.trainer.trainer.Trainer.fit`,

tests/tests_pytorch/deprecated_api/test_remove_2-0.py

@@ -68,50 +68,9 @@
 from pytorch_lightning.utilities.optimizer import optimizer_to_device, optimizers_to_device
 from pytorch_lightning.utilities.seed import pl_worker_init_function, reset_seed, seed_everything
 from pytorch_lightning.utilities.xla_device import inner_f, pl_multi_process, XLADeviceUtils
-from tests_pytorch.callbacks.test_callbacks import OldStatefulCallback
 from tests_pytorch.helpers.runif import RunIf
 
 
-def test_v2_0_0_resume_from_checkpoint_trainer_constructor(tmpdir):
-    # test resume_from_checkpoint still works until v2.0 deprecation
-    model = BoringModel()
-    callback = OldStatefulCallback(state=111)
-    trainer = Trainer(default_root_dir=tmpdir, max_steps=1, callbacks=[callback])
-    trainer.fit(model)
-    ckpt_path = trainer.checkpoint_callback.best_model_path
-
-    callback = OldStatefulCallback(state=222)
-    with pytest.deprecated_call(match=r"Setting `Trainer\(resume_from_checkpoint=\)` is deprecated in v1.5"):
-        trainer = Trainer(default_root_dir=tmpdir, max_steps=2, callbacks=[callback], resume_from_checkpoint=ckpt_path)
-    with pytest.deprecated_call(match=r"trainer.resume_from_checkpoint` is deprecated in v1.5"):
-        _ = trainer.resume_from_checkpoint
-    assert trainer._checkpoint_connector.resume_checkpoint_path is None
-    assert trainer._checkpoint_connector.resume_from_checkpoint_fit_path == ckpt_path
-    trainer.validate(model=model, ckpt_path=ckpt_path)
-    assert callback.state == 222
-    assert trainer._checkpoint_connector.resume_checkpoint_path is None
-    assert trainer._checkpoint_connector.resume_from_checkpoint_fit_path == ckpt_path
-    with pytest.deprecated_call(match=r"trainer.resume_from_checkpoint` is deprecated in v1.5"):
-        trainer.fit(model)
-    ckpt_path = trainer.checkpoint_callback.best_model_path  # last `fit` replaced the `best_model_path`
-    assert callback.state == 111
-    assert trainer._checkpoint_connector.resume_checkpoint_path is None
-    assert trainer._checkpoint_connector.resume_from_checkpoint_fit_path is None
-    trainer.predict(model=model, ckpt_path=ckpt_path)
-    assert trainer._checkpoint_connector.resume_checkpoint_path is None
-    assert trainer._checkpoint_connector.resume_from_checkpoint_fit_path is None
-    trainer.fit(model)
-    assert trainer._checkpoint_connector.resume_checkpoint_path is None
-    assert trainer._checkpoint_connector.resume_from_checkpoint_fit_path is None
-
-    # test fit(ckpt_path=) precedence over Trainer(resume_from_checkpoint=) path
-    model = BoringModel()
-    with pytest.deprecated_call(match=r"Setting `Trainer\(resume_from_checkpoint=\)` is deprecated in v1.5"):
-        trainer = Trainer(resume_from_checkpoint="trainer_arg_path")
-    with pytest.raises(FileNotFoundError, match="Checkpoint at fit_arg_ckpt_path not found. Aborting training."):
-        trainer.fit(model, ckpt_path="fit_arg_ckpt_path")
-
-
 def test_v2_0_0_callback_on_load_checkpoint_hook(tmpdir):
     class TestCallbackLoadHook(Callback):
         def on_load_checkpoint(self, trainer, pl_module, callback_state):