Ignore parameters causing ValueError when dumping to YAML (#19804)

src/lightning/pytorch/CHANGELOG.md

@@ -60,6 +60,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed an issue with the LightningCLI not being able to set the `ModelCheckpoint(save_last=...)` argument ([#19808](https://github.com/Lightning-AI/pytorch-lightning/pull/19808))
 
+- Fixed an issue causing ValueError for certain object such as TorchMetrics when dumping hyperparameters to YAML ([#19804](https://github.com/Lightning-AI/pytorch-lightning/pull/19804))
+
 
 ## [2.2.2] - 2024-04-11
 

src/lightning/pytorch/core/saving.py

@@ -359,7 +359,7 @@ def save_hparams_to_yaml(config_yaml: _PATH, hparams: Union[dict, Namespace], us
         try:
             v = v.name if isinstance(v, Enum) else v
             yaml.dump(v)
-        except TypeError:
+        except (TypeError, ValueError):
             warn(f"Skipping '{k}' parameter because it is not possible to safely dump to YAML.")
             hparams[k] = type(v).__name__
         else:

tests/tests_pytorch/models/test_hparams.py

@@ -552,7 +552,7 @@ def test_hparams_pickle_warning(tmp_path):
         trainer.fit(model)
 
 
-def test_hparams_save_yaml(tmp_path):
+def test_save_hparams_to_yaml(tmp_path):
     class Options(str, Enum):
         option1name = "option1val"
         option2name = "option2val"
@@ -590,6 +590,14 @@ def _compare_params(loaded_params, default_params: dict):
         _compare_params(load_hparams_from_yaml(path_yaml), hparams)
 
 
+def test_save_hparams_to_yaml_warning(tmp_path):
+    """Test that we warn about unserializable parameters that need to be dropped."""
+    path_yaml = tmp_path / "hparams.yaml"
+    hparams = {"torch_type": torch.float32}
+    with pytest.warns(UserWarning, match="Skipping 'torch_type' parameter"):
+        save_hparams_to_yaml(path_yaml, hparams)
+
+
 class NoArgsSubClassBoringModel(CustomBoringModel):
     def __init__(self):
         super().__init__()