lightning: add tests for log_model

src/dvclive/lightning.py

@@ -130,21 +130,21 @@ def log_metrics(self, metrics: Dict[str, Any], step: Optional[int] = None):
             self.experiment.next_step()
 
     def after_save_checkpoint(self, checkpoint_callback: ModelCheckpoint) -> None:
-        self._checkpoint_callback = checkpoint_callback
+        if self._log_model in [True, "all"]:
+            self._checkpoint_callback = checkpoint_callback
+            self._scan_checkpoints(checkpoint_callback)
         if self._log_model == "all" or (
             self._log_model is True and checkpoint_callback.save_top_k == -1
         ):
             self._save_checkpoints(checkpoint_callback)
 
     @rank_zero_only
     def finalize(self, status: str) -> None:
-        checkpoint_callback = self._checkpoint_callback
-        # Save model checkpoints.
-        if self._log_model is True:
-            self._save_checkpoints(checkpoint_callback)
         # Log best model.
-        if self._log_model in (True, "all"):
-            best_model_path = checkpoint_callback.best_model_path
+        if self._checkpoint_callback:
+            self._scan_checkpoints(self._checkpoint_callback)
+            self._save_checkpoints(self._checkpoint_callback)
+            best_model_path = self._checkpoint_callback.best_model_path
             self.experiment.log_artifact(
                 best_model_path, name="best", type="model", cache=False
             )

tests/test_frameworks/test_lightning.py

@@ -8,8 +8,9 @@
 
 try:
     import torch
-    from pytorch_lightning import LightningModule
-    from pytorch_lightning.trainer import Trainer
+    from lightning import LightningModule
+    from lightning.pytorch import Trainer
+    from lightning.pytorch.callbacks import ModelCheckpoint
     from torch import nn
     from torch.nn import functional as F  # noqa: N812
     from torch.optim import SGD, Adam
@@ -18,7 +19,7 @@
     from dvclive import Live
     from dvclive.lightning import DVCLiveLogger
 except ImportError:
-    pytest.skip("skipping pytorch_lightning tests", allow_module_level=True)
+    pytest.skip("skipping lightning tests", allow_module_level=True)
 
 
 class XORDataset(Dataset):
@@ -161,6 +162,38 @@ def test_lightning_kwargs(tmp_dir):
     assert dvclive_logger.experiment._cache_images is True
 
 
+@pytest.mark.parametrize("log_model", [False, True, "all"])
+@pytest.mark.parametrize("save_top_k", [1, -1])
+def test_lightning_log_model(tmp_dir, mocker, log_model, save_top_k):
+    model = LitXOR()
+    dvclive_logger = DVCLiveLogger(dir="dir", log_model=log_model)
+    checkpoint = ModelCheckpoint(dirpath="model", save_top_k=save_top_k)
+    trainer = Trainer(
+        logger=dvclive_logger,
+        max_epochs=2,
+        log_every_n_steps=1,
+        callbacks=[checkpoint],
+    )
+    log_artifact = mocker.patch.object(dvclive_logger.experiment, "log_artifact")
+    trainer.fit(model)
+
+    # Check that log_artifact is called.
+    if log_model is False:
+        log_artifact.assert_not_called()
+    elif (log_model is True) and (save_top_k != -1):
+        # called once to cache, then again to log best artifact
+        assert log_artifact.call_count == 2
+    else:
+        # once per epoch plus two calls at the end (see above)
+        assert log_artifact.call_count == 4
+
+    # Check that checkpoint files does not grow with each run.
+    num_checkpoints = len(os.listdir(tmp_dir / "model"))
+    if log_model in [True, "all"]:
+        trainer.fit(model)
+        assert len(os.listdir(tmp_dir / "model")) == num_checkpoints
+
+
 def test_lightning_steps(tmp_dir, mocker):
     model = LitXOR()
     # Handle kwargs passed to Live.