fix(wandb): prevent WandbLogger from dropping values

CHANGELOG.md

@@ -21,7 +21,13 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
-- Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))
+- Fixed incorrect yield logic for the amp autocast context manager ([#6080](https://github.com/PyTorchLightning/pytorch-lightning/pull/6080))
+
+
+- Prevent `WandbLogger` from dropping values ([#5931](https://github.com/PyTorchLightning/pytorch-lightning/pull/5931))
+
+
+- Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011)
 
 
 - Move lightning module to correct device type when using LightningDistributedWrapper ([#6070](https://github.com/PyTorchLightning/pytorch-lightning/pull/6070))

pytorch_lightning/loggers/wandb.py

@@ -26,6 +26,8 @@
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.warnings import WarningCache
 
+warning_cache = WarningCache()
+
 _WANDB_AVAILABLE = _module_available("wandb")
 
 try:
@@ -56,7 +58,6 @@ class WandbLogger(LightningLoggerBase):
         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.
         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.
@@ -92,7 +93,7 @@ def __init__(
         log_model: Optional[bool] = False,
         experiment=None,
         prefix: Optional[str] = '',
-        sync_step: Optional[bool] = True,
+        sync_step: Optional[bool] = None,
         **kwargs
     ):
         if wandb is None:
@@ -108,6 +109,12 @@ def __init__(
                 'Hint: Set `offline=False` to log your model.'
             )
 
+        if sync_step is not None:
+            warning_cache.warn(
+                "`WandbLogger(sync_step=(True|False))` is deprecated in v1.2.1 and will be removed in v1.5."
+                " Metrics are now logged separately and automatically synchronized.", DeprecationWarning
+            )
+
         super().__init__()
         self._name = name
         self._save_dir = save_dir
@@ -117,12 +124,8 @@ def __init__(
         self._project = project
         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
-        self.warning_cache = WarningCache()
 
     def __getstate__(self):
         state = self.__dict__.copy()
@@ -159,12 +162,15 @@ def experiment(self) -> Run:
                 **self._kwargs
             ) if wandb.run is None else wandb.run
 
-            # offset logging step when resuming a run
-            self._step_offset = self._experiment.step
-
             # save checkpoints in wandb dir to upload on W&B servers
             if self._save_dir is None:
                 self._save_dir = self._experiment.dir
+
+            # define default x-axis (for latest wandb versions)
+            if 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)
+
         return self._experiment
 
     def watch(self, model: nn.Module, log: str = 'gradients', log_freq: int = 100):
@@ -182,15 +188,8 @@ 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 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 `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)})
+        if step is not None:
+            self.experiment.log({**metrics, 'trainer/global_step': step})
         else:
             self.experiment.log(metrics)
 
@@ -210,10 +209,6 @@ def version(self) -> Optional[str]:
 
     @rank_zero_only
     def finalize(self, status: str) -> None:
-        # offset future training logged on same W&B run
-        if self._experiment is not None:
-            self._step_offset = self._experiment.step
-
         # upload all checkpoints from saving dir
         if self._log_model:
             wandb.save(os.path.join(self.save_dir, "*.ckpt"))

tests/deprecated_api/test_remove_1-5.py

@@ -0,0 +1,25 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Test deprecated functionality which will be removed in v1.5.0"""
+from unittest import mock
+
+import pytest
+
+from pytorch_lightning.loggers import WandbLogger
+
+
+@mock.patch('pytorch_lightning.loggers.wandb.wandb')
+def test_v1_5_0_wandb_unused_sync_step(tmpdir):
+    with pytest.deprecated_call(match=r"v1.2.1 and will be removed in v1.5"):
+        WandbLogger(sync_step=True)

tests/loggers/test_all.py

@@ -404,4 +404,4 @@ def test_logger_with_prefix_all(tmpdir, monkeypatch):
         wandb.run = None
         wandb.init().step = 0
         logger.log_metrics({"test": 1.0}, step=0)
-        logger.experiment.log.assert_called_once_with({'tmp-test': 1.0}, step=0)
+        logger.experiment.log.assert_called_once_with({'tmp-test': 1.0, 'trainer/global_step': 0})

tests/loggers/test_wandb.py

@@ -41,22 +41,7 @@ def test_wandb_logger_init(wandb, recwarn):
     logger = WandbLogger()
     logger.log_metrics({'acc': 1.0})
     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
 
     # test wandb.init not called if there is a W&B run
     wandb.init().log.reset_mock()
@@ -65,13 +50,12 @@ def test_wandb_logger_init(wandb, recwarn):
     logger = WandbLogger()
     logger.log_metrics({'acc': 1.0}, step=3)
     wandb.init.assert_called_once()
-    wandb.init().log.assert_called_once_with({'acc': 1.0}, step=3)
+    wandb.init().log.assert_called_once_with({'acc': 1.0, 'trainer/global_step': 3})
 
     # continue training on same W&B run and offset step
-    wandb.init().step = 3
     logger.finalize('success')
-    logger.log_metrics({'acc': 1.0}, step=3)
-    wandb.init().log.assert_called_with({'acc': 1.0}, step=6)
+    logger.log_metrics({'acc': 1.0}, step=6)
+    wandb.init().log.assert_called_with({'acc': 1.0, 'trainer/global_step': 6})
 
     # log hyper parameters
     logger.log_hyperparams({'test': None, 'nested': {'a': 1}, 'b': [2, 3, 4]})
@@ -88,17 +72,6 @@ def test_wandb_logger_init(wandb, recwarn):
     logger.watch('model', 'log', 10)
     wandb.init().watch.assert_called_once_with('model', log='log', log_freq=10)
 
-    # verify warning for logging at a previous step
-    assert 'Trying to log at a previous step' not in get_warnings(recwarn)
-    # current step from wandb should be 6 (last logged step)
-    logger.experiment.step = 6
-    # logging at step 2 should raise a warning (step_offset is still 3)
-    logger.log_metrics({'acc': 1.0}, step=2)
-    assert 'Trying to log at a previous step' in get_warnings(recwarn)
-    # logging again at step 2 should not display again the same warning
-    logger.log_metrics({'acc': 1.0}, step=2)
-    assert 'Trying to log at a previous step' not in get_warnings(recwarn)
-
     assert logger.name == wandb.init().project_name()
     assert logger.version == wandb.init().id