Enable logging hparams only if there are any (#11105)

Author: rohitgr7

CHANGELOG.md

@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed a bug to disable logging hyperparameters in logger if there are no hparams ([#11105](https://github.com/PyTorchLightning/pytorch-lightning/issues/11105))
 - Fixed an issue when torch-scripting a `LightningModule` after training with `Trainer(sync_batchnorm=True)` ([#11078](https://github.com/PyTorchLightning/pytorch-lightning/pull/11078))
 - Fixed an `AttributeError` occuring when using a `CombinedLoader` (multiple dataloaders) for prediction ([#11111](https://github.com/PyTorchLightning/pytorch-lightning/pull/11111))
 

pytorch_lightning/core/mixins/hparams_mixin.py

@@ -28,7 +28,7 @@ class HyperparametersMixin:
 
     def __init__(self) -> None:
         super().__init__()
-        self._log_hyperparams = True
+        self._log_hyperparams = False
 
     def save_hyperparameters(
         self,

tests/callbacks/test_gpu_stats_monitor.py

@@ -83,7 +83,7 @@ def test_gpu_stats_monitor_no_queries(tmpdir):
     with mock.patch("pytorch_lightning.loggers.tensorboard.TensorBoardLogger.log_metrics") as log_metrics_mock:
         trainer.fit(model)
 
-    assert log_metrics_mock.mock_calls[2:] == [
+    assert log_metrics_mock.mock_calls[1:] == [
         mock.call({"batch_time/intra_step (ms)": mock.ANY}, step=0),
         mock.call({"batch_time/inter_step (ms)": mock.ANY}, step=1),
         mock.call({"batch_time/intra_step (ms)": mock.ANY}, step=1),

tests/loggers/test_all.py

@@ -144,10 +144,8 @@ def log_metrics(self, metrics, step):
     log_metric_names = [(s, sorted(m.keys())) for s, m in logger.history]
     if logger_class == TensorBoardLogger:
         expected = [
-            (0, ["hp_metric"]),
             (0, ["epoch", "train_some_val"]),
             (0, ["early_stop_on", "epoch", "val_loss"]),
-            (0, ["hp_metric"]),
             (1, ["epoch", "test_loss"]),
         ]
         assert log_metric_names == expected

tests/loggers/test_base.py

@@ -111,7 +111,6 @@ def training_step(self, batch, batch_idx):
     trainer = Trainer(max_steps=2, log_every_n_steps=1, logger=logger, default_root_dir=tmpdir)
     trainer.fit(model)
     assert trainer.state.finished, f"Training failed with {trainer.state}"
-    assert logger.hparams_logged == model.hparams
     assert logger.metrics_logged != {}
     assert logger.after_save_checkpoint_called
     assert logger.finalized_status == "success"
@@ -133,11 +132,11 @@ def training_step(self, batch, batch_idx):
     trainer.fit(model)
     assert trainer.state.finished, f"Training failed with {trainer.state}"
 
-    assert logger1.hparams_logged == model.hparams
+    assert logger1.hparams_logged is None
     assert logger1.metrics_logged != {}
     assert logger1.finalized_status == "success"
 
-    assert logger2.hparams_logged == model.hparams
+    assert logger2.hparams_logged is None
     assert logger2.metrics_logged != {}
     assert logger2.finalized_status == "success"
 

tests/models/test_hparams.py

@@ -776,7 +776,10 @@ def test_adding_datamodule_hparams(tmpdir, model, data):
     # Merged hparams were logged
     merged_hparams = copy.deepcopy(org_model_hparams)
     merged_hparams.update(org_data_hparams)
-    mock_logger.log_hyperparams.assert_called_with(merged_hparams)
+    if merged_hparams:
+        mock_logger.log_hyperparams.assert_called_with(merged_hparams)
+    else:
+        mock_logger.log_hyperparams.assert_not_called()
 
 
 def test_no_datamodule_for_hparams(tmpdir):

tests/trainer/logging_/test_distributed_logging.py

@@ -112,7 +112,6 @@ def on_fit_start(self, trainer, pl_module):
 
         def on_train_start(self, trainer, pl_module):
             assert trainer.logger.method_call
-            trainer.logger.log_hyperparams.assert_called_once()
             trainer.logger.log_graph.assert_called_once()
 
     logger = Mock()

tests/trainer/logging_/test_eval_loop_logging.py

@@ -510,6 +510,10 @@ class ExtendedModel(BoringModel):
 
         val_losses = []
 
+        def __init__(self, some_val=7):
+            super().__init__()
+            self.save_hyperparameters()
+
         def training_step(self, batch, batch_idx):
             output = self.layer(batch)
             loss = self.loss(batch, output)