fix: Set tensorboard's global_step as the default wandb x-axis if sync_tensorboard=True

src/lightning/pytorch/CHANGELOG.md

@@ -8,16 +8,27 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+-
+
+
 ### Changed
 
-- Added a new `checkpoint_path_prefix` parameter to the MLflow logger which can control the path to where the MLflow artifacts for the model checkpoints are stored.
+- Change `wandb` default x-axis to `tensorboard`'s `global_step` when `sync_tensorboard=True` ([#20611](https://github.com/Lightning-AI/pytorch-lightning/pull/20611))
+
+
+- Added a new `checkpoint_path_prefix` parameter to the MLflow logger which can control the path to where the MLflow artifacts for the model checkpoints are stored ([#20538](https://github.com/Lightning-AI/pytorch-lightning/pull/20538))
+
 
 ### Removed
 
+-
+
+
 ### Fixed
 
 - Fix CSVLogger logging hyperparameter at every write which increase latency  ([#20594](https://github.com/Lightning-AI/pytorch-lightning/pull/20594))
 
+
 - Always call `WandbLogger.experiment` first in `_call_setup_hook` to ensure `tensorboard` logs can sync to `wandb` ([#20610](https://github.com/Lightning-AI/pytorch-lightning/pull/20610))
 
 

src/lightning/pytorch/loggers/wandb.py

@@ -410,8 +410,11 @@ def experiment(self) -> Union["Run", "RunDisabled"]:
                 if isinstance(self._experiment, (Run, RunDisabled)) and getattr(
                     self._experiment, "define_metric", None
                 ):
-                    self._experiment.define_metric("trainer/global_step")
-                    self._experiment.define_metric("*", step_metric="trainer/global_step", step_sync=True)
+                    if self._wandb_init.get("sync_tensorboard"):
+                        self._experiment.define_metric("*", step_metric="global_step")
+                    else:
+                        self._experiment.define_metric("trainer/global_step")
+                        self._experiment.define_metric("*", step_metric="trainer/global_step", step_sync=True)
 
         return self._experiment
 
@@ -434,7 +437,7 @@ def log_metrics(self, metrics: Mapping[str, float], step: Optional[int] = None)
         assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0"
 
         metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
-        if step is not None:
+        if step is not None and not self._wandb_init.get("sync_tensorboard"):
             self.experiment.log(dict(metrics, **{"trainer/global_step": step}))
         else:
             self.experiment.log(metrics)

tests/tests_pytorch/loggers/conftest.py

@@ -55,6 +55,7 @@ class RunType:  # to make isinstance checks pass
         watch=Mock(),
         log_artifact=Mock(),
         use_artifact=Mock(),
+        define_metric=Mock(),
         id="run_id",
     )
 

tests/tests_pytorch/loggers/test_wandb.py

@@ -126,6 +126,24 @@ def test_wandb_logger_init(wandb_mock):
     assert logger.version == wandb_mock.init().id
 
 
+def test_wandb_logger_sync_tensorboard(wandb_mock):
+    logger = WandbLogger(sync_tensorboard=True)
+    wandb_mock.run = None
+    logger.experiment
+
+    # test that tensorboard's global_step is set as the default x-axis if sync_tensorboard=True
+    wandb_mock.init.return_value.define_metric.assert_called_once_with("*", step_metric="global_step")
+
+
+def test_wandb_logger_sync_tensorboard_log_metrics(wandb_mock):
+    logger = WandbLogger(sync_tensorboard=True)
+    metrics = {"loss": 1e-3, "accuracy": 0.99}
+    logger.log_metrics(metrics)
+
+    # test that trainer/global_step is not added to the logged metrics if sync_tensorboard=True
+    wandb_mock.run.log.assert_called_once_with(metrics)
+
+
 def test_wandb_logger_init_before_spawn(wandb_mock):
     logger = WandbLogger()
     assert logger._experiment is None