Save ModelCheckpoint's last.ckpt as symlink if possible

src/lightning/pytorch/CHANGELOG.md

@@ -90,6 +90,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - The `ModelCheckpoint` no longer deletes files under the save-top-k mechanism when resuming from a folder that is not the same as the current checkpoint folder ([#18750](https://github.com/Lightning-AI/lightning/pull/18750))
 - The `ModelCheckpoint` no longer deletes the file that was passed to `Trainer.fit(ckpt_path=...)` ([#18750](https://github.com/Lightning-AI/lightning/pull/18750))
 - Calling `trainer.fit()` twice now raises an error with strategies that spawn subprocesses through `multiprocessing` (ddp_spawn, xla) ([#18776](https://github.com/Lightning-AI/lightning/pull/18776))
+- The `ModelCheckpoint` now saves a symbolic link if `save_last=True` and `save_top_k != 0` ([#18748](https://github.com/Lightning-AI/lightning/pull/18748))
 
 ### Deprecated
 

src/lightning/pytorch/callbacks/model_checkpoint.py

@@ -81,8 +81,9 @@ class ModelCheckpoint(Checkpoint):
             the number of finished epoch and optimizer steps respectively.
         monitor: quantity to monitor. By default it is ``None`` which saves a checkpoint only for the last epoch.
         verbose: verbosity mode. Default: ``False``.
-        save_last: When ``True``, saves an exact copy of the checkpoint to a file `last.ckpt` whenever a checkpoint
-            file gets saved. This allows accessing the latest checkpoint in a deterministic manner. Default: ``None``.
+        save_last: When ``True``, saves a `last.ckpt` whenever a checkpoint file gets saved. On a local filesystem,
+            this will be a symbolic link, and otherwise a copy of the checkpoint file. This allows accessing the latest
+            checkpoint in a deterministic manner. Default: ``None``.
         save_top_k: if ``save_top_k == k``,
             the best k models according to the quantity monitored will be saved.
             if ``save_top_k == 0``, no models are saved.
@@ -241,6 +242,7 @@ def __init__(
         self.best_model_score: Optional[Tensor] = None
         self.best_model_path = ""
         self.last_model_path = ""
+        self._last_checkpoint_saved = ""
 
         self.kth_value: Tensor
         self.dirpath: Optional[_PATH]
@@ -371,12 +373,21 @@ def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
         trainer.save_checkpoint(filepath, self.save_weights_only)
 
         self._last_global_step_saved = trainer.global_step
+        self._last_checkpoint_saved = filepath
 
         # notify loggers
         if trainer.is_global_zero:
             for logger in trainer.loggers:
                 logger.after_save_checkpoint(proxy(self))
 
+    @staticmethod
+    def _link_checkpoint(trainer: "pl.Trainer", filepath: str, linkpath: str) -> None:
+        if trainer.is_global_zero:
+            if os.path.lexists(linkpath):
+                os.remove(linkpath)
+            os.symlink(filepath, linkpath)
+        trainer.strategy.barrier()
+
     def _should_skip_saving_checkpoint(self, trainer: "pl.Trainer") -> bool:
         from lightning.pytorch.trainer.states import TrainerFn
 
@@ -427,19 +438,12 @@ def __validate_init_configuration(self) -> None:
                 "should be mutually exclusive."
             )
 
-        if self.monitor is None:
+        if self.monitor is None and self.save_top_k not in (-1, 0, 1):
             # -1: save all epochs, 0: nothing is saved, 1: save last epoch
-            if self.save_top_k not in (-1, 0, 1):
-                raise MisconfigurationException(
-                    f"ModelCheckpoint(save_top_k={self.save_top_k}, monitor=None) is not a valid"
-                    " configuration. No quantity for top_k to track."
-                )
-
-            if self.save_top_k == -1 and self.save_last:
-                rank_zero_info(
-                    "ModelCheckpoint(save_last=True, save_top_k=-1, monitor=None)"
-                    " will duplicate the last checkpoint saved."
-                )
+            raise MisconfigurationException(
+                f"ModelCheckpoint(save_top_k={self.save_top_k}, monitor=None) is not a valid"
+                " configuration. No quantity for top_k to track."
+            )
 
     def __init_ckpt_dir(self, dirpath: Optional[_PATH], filename: Optional[str]) -> None:
         self._fs = get_filesystem(dirpath if dirpath else "")
@@ -662,7 +666,10 @@ def _save_last_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: Dict[
 
         # set the last model path before saving because it will be part of the state.
         previous, self.last_model_path = self.last_model_path, filepath
-        self._save_checkpoint(trainer, filepath)
+        if self._fs.protocol == "file" and self._last_checkpoint_saved and self.save_top_k != 0:
+            self._link_checkpoint(trainer, self._last_checkpoint_saved, filepath)
+        else:
+            self._save_checkpoint(trainer, filepath)
         if previous and self._should_remove_checkpoint(trainer, previous, filepath):
             self._remove_checkpoint(trainer, previous)
 

tests/tests_pytorch/checkpointing/test_checkpoint_callback_frequency.py

@@ -62,7 +62,7 @@ def __init__(self):
             self.last_coeff = 10.0
 
         def training_step(self, batch, batch_idx):
-            loss = self.step(torch.ones(32))
+            loss = self.step(torch.ones(32, device=self.device))
             loss = loss / (loss + 0.0000001)
             loss += self.last_coeff
             self.log("my_loss", loss)
@@ -80,8 +80,7 @@ def training_step(self, batch, batch_idx):
     trainer.fit(model)
 
     if save_last:
-        # last epochs are saved every step (so double the save calls)
-        expected = expected * 2
+        expected = expected
     assert save_mock.call_count == expected
 
 

tests/tests_pytorch/checkpointing/test_model_checkpoint.py

@@ -18,7 +18,6 @@
 import time
 from argparse import Namespace
 from datetime import timedelta
-from logging import INFO
 from pathlib import Path
 from typing import Union
 from unittest import mock
@@ -510,7 +509,8 @@ def test_model_checkpoint_save_last(tmpdir):
     assert set(os.listdir(tmpdir)) == set(
         [f"epoch={i}-step={j}.ckpt" for i, j in zip(range(epochs), [10, 20, 30])] + [last_filename]
     )
-
+    assert os.path.islink(tmpdir / last_filename)
+    assert os.path.realpath(tmpdir / last_filename) == model_checkpoint._last_checkpoint_saved
     ModelCheckpoint.CHECKPOINT_NAME_LAST = "last"
 
 
@@ -589,10 +589,7 @@ def test_model_checkpoint_save_last_none_monitor(tmpdir, caplog):
         max_epochs=epochs,
         logger=False,
     )
-
-    with caplog.at_level(INFO):
-        trainer.fit(model)
-        assert "will duplicate the last checkpoint saved" in caplog.text
+    trainer.fit(model)
 
     # these should not be set if monitor is None
     assert checkpoint_callback.monitor is None
@@ -606,6 +603,7 @@ def test_model_checkpoint_save_last_none_monitor(tmpdir, caplog):
     expected = [f"epoch={i}-step={j}.ckpt" for i, j in zip(range(epochs), [10, 20])]
     expected.append("last.ckpt")
     assert set(os.listdir(tmpdir)) == set(expected)
+    assert os.path.islink(tmpdir / "last.ckpt")
 
 
 @pytest.mark.parametrize("every_n_epochs", list(range(4)))
@@ -709,6 +707,8 @@ def test_model_checkpoint_topk_zero(tmpdir):
     # check that only the last ckpt was created
     assert os.listdir(tmpdir) == ["last.ckpt"]
     assert checkpoint_callback.last_model_path == tmpdir / "last.ckpt"
+    # 'last.ckpt' is not a symlink because there are no top-k checkpoints to link
+    assert not os.path.islink(checkpoint_callback.last_model_path)
 
 
 def test_model_checkpoint_topk_all(tmpdir):
@@ -814,6 +814,7 @@ def test_model_checkpoint_save_last_checkpoint_contents(tmpdir):
     path_last = str(tmpdir / "last.ckpt")
     assert path_last == model_checkpoint.last_model_path
     assert os.path.isfile(path_last_epoch)
+    assert os.path.islink(path_last)
 
     ckpt_last_epoch = torch.load(path_last_epoch)
     ckpt_last = torch.load(path_last)
@@ -1343,7 +1344,7 @@ def test_save_last_saves_correct_last_model_path(tmpdir):
     trainer = Trainer(callbacks=mc)
     trainer.strategy.connect(BoringModel())
 
-    mc._save_last_checkpoint(trainer, {"foo": 1})
+    mc._save_last_checkpoint(trainer, {"foo": torch.tensor(1)})
     expected = "foo=1-last.ckpt"
     assert os.listdir(tmpdir) == [expected]
     full_path = str(tmpdir / expected)
@@ -1366,6 +1367,8 @@ def test_save_last_versioning(tmpdir):
         )
         trainer.fit(model)
     assert {"last.ckpt", "last-v1.ckpt"} == set(os.listdir(tmpdir))
+    # 'last.ckpt' is not a symlink since `save_top_k=0` didn't save any other checkpoints to link to
+    assert all(not os.path.islink(tmpdir / path) for path in set(os.listdir(tmpdir)))
 
 
 def test_none_monitor_saves_correct_best_model_path(tmpdir):
@@ -1385,7 +1388,7 @@ def test_last_global_step_saved():
     # this should not save anything
     model_checkpoint = ModelCheckpoint(save_top_k=0, save_last=False, monitor="foo")
     trainer = Mock()
-    monitor_candidates = {"foo": 123}
+    monitor_candidates = {"foo": torch.tensor(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/tests_pytorch/models/test_restore.py

@@ -311,9 +311,11 @@ def get_trainer_args():
                 "best_k_models",
                 "kth_best_model_path",
                 "kth_value",
-                "last_model_path",
             ):
-                assert getattr(before, attribute) == getattr(after, attribute)
+                assert getattr(before, attribute) == getattr(after, attribute), f"{attribute}"
+            # `before.last_model_path` is a symlink pointing to a checkpoint saved before that symlink was created,
+            # hence reloading that checkpoint will restore `after.last_model_path = ""`
+            assert after.last_model_path == ""
 
 
 @RunIf(sklearn=True)

tests/tests_pytorch/plugins/test_checkpoint_io_plugin.py

@@ -47,6 +47,7 @@ def test_checkpoint_plugin_called(tmpdir):
     model = BoringModel()
     trainer = Trainer(
         default_root_dir=tmpdir,
+        accelerator="cpu",
         strategy=SingleDeviceStrategy("cpu", checkpoint_io=checkpoint_plugin),
         callbacks=ck,
         max_epochs=2,
@@ -60,7 +61,7 @@ def test_checkpoint_plugin_called(tmpdir):
     assert ckpt_files == {"epoch=1-step=2.ckpt", "last.ckpt"}
     assert trainer.checkpoint_callback.best_model_path == tmpdir / "epoch=1-step=2.ckpt"
     assert trainer.checkpoint_callback.last_model_path == tmpdir / "last.ckpt"
-    assert checkpoint_plugin.save_checkpoint.call_count == 4
+    assert checkpoint_plugin.save_checkpoint.call_count == 2
     assert checkpoint_plugin.remove_checkpoint.call_count == 1
 
     trainer.test(model, ckpt_path=ck.last_model_path)
@@ -72,6 +73,7 @@ def test_checkpoint_plugin_called(tmpdir):
     model = BoringModel()
     trainer = Trainer(
         default_root_dir=tmpdir,
+        accelerator="cpu",
         strategy=SingleDeviceStrategy("cpu"),
         plugins=[checkpoint_plugin],
         callbacks=ck,
@@ -86,7 +88,7 @@ def test_checkpoint_plugin_called(tmpdir):
     assert ckpt_files == {"epoch=1-step=2.ckpt", "last.ckpt", "epoch=1-step=2-v1.ckpt", "last-v1.ckpt"}
     assert trainer.checkpoint_callback.best_model_path == tmpdir / "epoch=1-step=2-v1.ckpt"
     assert trainer.checkpoint_callback.last_model_path == tmpdir / "last-v1.ckpt"
-    assert checkpoint_plugin.save_checkpoint.call_count == 4
+    assert checkpoint_plugin.save_checkpoint.call_count == 2
     assert checkpoint_plugin.remove_checkpoint.call_count == 1
 
     trainer.test(model, ckpt_path=ck.last_model_path)