Deprecate `ModelCheckpoint.save_checkpoint` (#12456)

CHANGELOG.md

@@ -582,6 +582,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated `ParallelPlugin.torch_distributed_backend` in favor of `DDPStrategy.process_group_backend` property ([#11745](https://github.com/PyTorchLightning/pytorch-lightning/pull/11745))
 
 
+- Deprecated `ModelCheckpoint.save_checkpoint` in favor of `Trainer.save_checkpoint` ([#12456](https://github.com/PyTorchLightning/pytorch-lightning/pull/12456))
+
+
 - Deprecated `Trainer.devices` in favor of `Trainer.num_devices` and `Trainer.device_ids` ([#12151](https://github.com/PyTorchLightning/pytorch-lightning/pull/12151))
 
 

pytorch_lightning/callbacks/model_checkpoint.py

@@ -37,7 +37,7 @@ from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.logger import _name, _version
-from pytorch_lightning.utilities.rank_zero import rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_info, rank_zero_warn
 from pytorch_lightning.utilities.types import _METRIC, _PATH, STEP_OUTPUT
 from pytorch_lightning.utilities.warnings import WarningCache
 
@@ -353,7 +353,10 @@ class ModelCheckpoint(Callback):
         This method runs on all ranks. It is the responsibility of `trainer.save_checkpoint` to correctly handle the
         behaviour in distributed training, i.e., saving only on rank 0 for data parallel use cases.
         """
-        # TODO: unused method. deprecate it
+        rank_zero_deprecation(
+            f"`{self.__class__.__name__}.save_checkpoint()` was deprecated in v1.6 and will be removed in v1.8."
+            " Instead, you can use `trainer.save_checkpoint()` to manually save a checkpoint."
+        )
         monitor_candidates = self._monitor_candidates(trainer)
         self._save_topk_checkpoint(trainer, monitor_candidates)
         self._save_last_checkpoint(trainer, monitor_candidates)

tests/checkpointing/test_model_checkpoint.py

@@ -22,7 +22,7 @@ from logging import INFO
 from pathlib import Path
 from typing import Union
 from unittest import mock
-from unittest.mock import call, MagicMock, Mock, patch
+from unittest.mock import call, Mock, patch
 
 import cloudpickle
 import pytest
@@ -834,7 +834,7 @@ def test_checkpointing_with_nan_as_first(tmpdir, mode):
         val_check_interval=1.0,
         max_epochs=len(monitor),
     )
-    trainer.save_checkpoint = MagicMock()
+    trainer.save_checkpoint = Mock()
 
     trainer.fit(model)
 
@@ -1309,9 +1309,10 @@ def test_none_monitor_saves_correct_best_model_path(tmpdir):
 def test_last_global_step_saved():
     # this should not save anything
     model_checkpoint = ModelCheckpoint(save_top_k=0, save_last=False, monitor="foo")
-    trainer = MagicMock()
-    trainer.callback_metrics = {"foo": 123}
-    model_checkpoint.save_checkpoint(trainer)
+    trainer = Mock()
+    monitor_candidates = {"foo": 123}
+    model_checkpoint._save_topk_checkpoint(trainer, monitor_candidates)
+    model_checkpoint._save_last_checkpoint(trainer, monitor_candidates)
     assert model_checkpoint._last_global_step_saved == 0
 
 

tests/deprecated_api/test_remove_1-8.py

@@ -24,6 +24,7 @@ from torch import optim
 
 import pytorch_lightning
 from pytorch_lightning import Callback, Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import CSVLogger, LightningLoggerBase, LoggerCollection
 from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
 from pytorch_lightning.plugins.training_type.ddp import DDPPlugin
@@ -1055,6 +1056,15 @@ def test_trainer_data_parallel_device_ids(monkeypatch, trainer_kwargs, expected_
         assert trainer.data_parallel_device_ids == expected_data_parallel_device_ids
 
 
+def test_deprecated_mc_save_checkpoint():
+    mc = ModelCheckpoint()
+    trainer = Trainer()
+    with mock.patch.object(trainer, "save_checkpoint"), pytest.deprecated_call(
+        match=r"ModelCheckpoint.save_checkpoint\(\)` was deprecated in v1.6"
+    ):
+        mc.save_checkpoint(trainer)
+
+
 def test_v1_8_0_callback_on_load_checkpoint_hook(tmpdir):
     class TestCallbackLoadHook(Callback):
         def on_load_checkpoint(self, trainer, pl_module, callback_state):