Use step parameter when logging metrics with NeptuneLogger (#19126)

src/lightning/pytorch/CHANGELOG.md

@@ -35,6 +35,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - The `LightningModule.load_from_checkpoint()` function now calls `.configure_model()` on the model if it is overridden, to ensure all layers can be loaded from the checkpoint ([#19036](https://github.com/Lightning-AI/lightning/pull/19036))
 
 
+- Restored usage of `step` parameter when logging metrics with `NeptuneLogger` ([#19126](https://github.com/Lightning-AI/pytorch-lightning/pull/19126))
+
+
 - Changed the `TransformerEnginePrecision(dtype=)` argument to `weights_dtype` and made it required ([#19082](https://github.com/Lightning-AI/lightning/pull/19082))
 
 

src/lightning/pytorch/loggers/neptune.py

@@ -447,7 +447,7 @@ def log_metrics(  # type: ignore[override]
 
         Args:
             metrics: Dictionary with metric names as keys and measured quantities as values.
-            step: Step number at which the metrics should be recorded, currently ignored.
+            step: Step number at which the metrics should be recorded
 
         """
         if rank_zero_only.rank != 0:
@@ -456,9 +456,7 @@ def log_metrics(  # type: ignore[override]
         metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
 
         for key, val in metrics.items():
-            # `step` is ignored because Neptune expects strictly increasing step values which
-            # Lightning does not always guarantee.
-            self.run[key].append(val)
+            self.run[key].append(val, step=step)
 
     @override
     @rank_zero_only

tests/tests_pytorch/loggers/test_all.py

@@ -307,7 +307,7 @@ def test_logger_with_prefix_all(mlflow_mock, wandb_mock, comet_mock, neptune_moc
     logger.log_metrics({"test": 1.0}, step=0)
     assert logger.experiment.__getitem__.call_count == 1
     logger.experiment.__getitem__.assert_called_with("tmp/test")
-    logger.experiment.__getitem__().append.assert_called_once_with(1.0)
+    logger.experiment.__getitem__().append.assert_called_once_with(1.0, step=0)
 
     # TensorBoard
     if _TENSORBOARD_AVAILABLE:

tests/tests_pytorch/loggers/test_neptune.py

@@ -166,7 +166,7 @@ def on_validation_epoch_end(self):
     logger, run_instance_mock, _ = _get_logger_with_mocks(api_key="test", project="project")
     _fit_and_test(logger=logger, model=LoggingModel(), tmp_path=tmp_path)
     run_instance_mock.__getitem__.assert_any_call("training/some/key")
-    run_instance_mock.__getitem__.return_value.append.assert_has_calls([call(42)])
+    run_instance_mock.__getitem__.return_value.append.assert_has_calls([call(42, step=2)])
 
 
 def test_log_hyperparams(neptune_mock):
@@ -204,7 +204,7 @@ def test_log_metrics(neptune_mock):
         assert run_instance_mock.__getitem__.call_count == 2
         run_instance_mock.__getitem__.assert_any_call(metrics_foo_key)
         run_instance_mock.__getitem__.assert_any_call(metrics_bar_key)
-        run_attr_mock.append.assert_has_calls([call(42), call(555)])
+        run_attr_mock.append.assert_has_calls([call(42, step=None), call(555, step=None)])
 
 
 def test_log_model_summary(neptune_mock):