Merge branch 'release/1.2-dev' into fix/update-mypy

pytorch_lightning/loggers/wandb.py

@@ -49,19 +49,20 @@ class WandbLogger(LightningLoggerBase):
 
     Args:
         name: Display name for the run.
-        save_dir: Path where data is saved.
+        save_dir: Path where data is saved (wandb dir by default).
         offline: Run offline (data can be streamed later to wandb servers).
         id: Sets the version, mainly used to resume a previous run.
+        version: Same as id.
         anonymous: Enables or explicitly disables anonymous logging.
-        version: Sets the version, mainly used to resume a previous run.
         project: The name of the project to which this run will belong.
         log_model: Save checkpoints in wandb dir to upload on W&B servers.
-        experiment: WandB experiment object.
         prefix: A string to put at the beginning of metric keys.
+        sync_step: Sync Trainer step with wandb step.
+        experiment: WandB experiment object. Automatically set when creating a run.
         \**kwargs: Additional arguments like `entity`, `group`, `tags`, etc. used by
             :func:`wandb.init` can be passed as keyword arguments in this logger.
 
-    Example::
+    Example:
 
     .. code-block:: python
 
@@ -74,9 +75,9 @@ class WandbLogger(LightningLoggerBase):
     make sure to use `commit=False` so the logging step does not increase.
 
     See Also:
-        - `Tutorial <https://app.wandb.ai/cayush/pytorchlightning/reports/
-          Use-Pytorch-Lightning-with-Weights-%26-Biases--Vmlldzo2NjQ1Mw>`__
-          on how to use W&B with Pytorch Lightning.
+        - `Tutorial <https://colab.research.google.com/drive/16d1uctGaw2y9KhGBlINNTsWpmlXdJwRW?usp=sharing>`__
+          on how to use W&B with PyTorch Lightning
+        - `W&B Documentation <https://docs.wandb.ai/integrations/lightning>`__
 
     """
 
@@ -86,14 +87,15 @@ def __init__(
         self,
         name: Optional[str] = None,
         save_dir: Optional[str] = None,
-        offline: bool = False,
+        offline: Optional[bool] = False,
         id: Optional[str] = None,
-        anonymous: bool = False,
+        anonymous: Optional[bool] = False,
         version: Optional[str] = None,
         project: Optional[str] = None,
-        log_model: bool = False,
+        log_model: Optional[bool] = False,
         experiment=None,
-        prefix: str = '',
+        prefix: Optional[str] = '',
+        sync_step: Optional[bool] = True,
         **kwargs
     ):
         if wandb is None:
@@ -102,13 +104,14 @@ def __init__(
         super().__init__()
         self._name = name
         self._save_dir = save_dir
-        self._anonymous = 'allow' if anonymous else None
+        self._offline = offline
         self._id = version or id
+        self._anonymous = 'allow' if anonymous else None
         self._project = project
-        self._experiment = experiment
-        self._offline = offline
         self._log_model = log_model
         self._prefix = prefix
+        self._sync_step = sync_step
+        self._experiment = experiment
         self._kwargs = kwargs
         # logging multiple Trainer on a single W&B run (k-fold, resuming, etc)
         self._step_offset = 0
@@ -164,11 +167,16 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) ->
         assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'
 
         metrics = self._add_prefix(metrics)
-        if step is not None and step + self._step_offset < self.experiment.step:
+        if self._sync_step and step is not None and step + self._step_offset < self.experiment.step:
             self.warning_cache.warn(
-                'Trying to log at a previous step. Use `commit=False` when logging metrics manually.'
-            )
-        self.experiment.log(metrics, step=(step + self._step_offset) if step is not None else None)
+                'Trying to log at a previous step. Use `WandbLogger(sync_step=False)`'
+                ' or try logging with `commit=False` when calling manually `wandb.log`.')
+        if self._sync_step:
+            self.experiment.log(metrics, step=(step + self._step_offset) if step is not None else None)
+        elif step is not None:
+            self.experiment.log({**metrics, 'trainer_step': (step + self._step_offset)})
+        else:
+            self.experiment.log(metrics)
 
     @property
     def save_dir(self) -> Optional[str]:

tests/loggers/test_wandb.py

@@ -40,6 +40,18 @@ def test_wandb_logger_init(wandb, recwarn):
     wandb.init.assert_called_once()
     wandb.init().log.assert_called_once_with({'acc': 1.0}, step=None)
 
+    # test sync_step functionality
+    wandb.init().log.reset_mock()
+    wandb.init.reset_mock()
+    wandb.run = None
+    wandb.init().step = 0
+    logger = WandbLogger(sync_step=False)
+    logger.log_metrics({'acc': 1.0})
+    wandb.init().log.assert_called_once_with({'acc': 1.0})
+    wandb.init().log.reset_mock()
+    logger.log_metrics({'acc': 1.0}, step=3)
+    wandb.init().log.assert_called_once_with({'acc': 1.0, 'trainer_step': 3})
+
     # mock wandb step
     wandb.init().step = 0