Merge branch 'master' into metrics/fix_states

CHANGELOG.md

@@ -38,7 +38,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
-
+- Tuner algorithms will be skipped if `fast_dev_run=True` ([#3903](https://github.com/PyTorchLightning/pytorch-lightning/pull/3903))
 
 ### Deprecated
 

pytorch_lightning/tuner/batch_size_scaling.py

@@ -69,6 +69,10 @@ def scale_batch_size(trainer,
         **fit_kwargs: remaining arguments to be passed to .fit(), e.g., dataloader
             or datamodule.
     """
+    if trainer.fast_dev_run:
+        rank_zero_warn('Skipping batch size scaler since `fast_dev_run=True`', UserWarning)
+        return
+
     if not lightning_hasattr(model, batch_arg_name):
         raise MisconfigurationException(
             f'Field {batch_arg_name} not found in both `model` and `model.hparams`')

pytorch_lightning/tuner/lr_finder.py

@@ -29,6 +29,7 @@
 from pytorch_lightning.loggers.base import DummyLogger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.parsing import lightning_hasattr, lightning_setattr
+from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 
 # check if ipywidgets is installed before importing tqdm.auto
@@ -42,6 +43,10 @@
 def _run_lr_finder_internally(trainer, model: LightningModule):
     """ Call lr finder internally during Trainer.fit() """
     lr_finder = lr_find(trainer, model)
+
+    if lr_finder is None:
+        return
+
     lr = lr_finder.suggestion()
 
     # TODO: log lr.results to self.logger
@@ -131,6 +136,10 @@ def lr_find(
         trainer.fit(model)
 
     """
+    if trainer.fast_dev_run:
+        rank_zero_warn('Skipping learning rate finder since `fast_dev_run=True`', UserWarning)
+        return
+
     save_path = os.path.join(trainer.default_root_dir, 'lr_find_temp_model.ckpt')
 
     __lr_finder_dump_params(trainer, model)

tests/trainer/flags/test_fast_dev_run.py

@@ -0,0 +1,21 @@
+import pytest
+from pytorch_lightning import Trainer
+from tests.base import EvalModelTemplate
+
+
+@pytest.mark.parametrize('tuner_alg', ['batch size scaler', 'learning rate finder'])
+def test_skip_on_fast_dev_run_batch_scaler(tmpdir, tuner_alg):
+    """ Test that tuner algorithms are skipped if fast dev run is enabled """
+
+    hparams = EvalModelTemplate.get_default_hparams()
+    model = EvalModelTemplate(**hparams)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=2,
+        auto_scale_batch_size=True if tuner_alg == 'batch size scaler' else False,
+        auto_lr_find=True if tuner_alg == 'learning rate finder' else False,
+        fast_dev_run=True
+    )
+    expected_message = f'Skipping {tuner_alg} since `fast_dev_run=True`'
+    with pytest.warns(UserWarning, match=expected_message):
+        trainer.tune(model)