fix auto scale batch size not working with precision=16

CHANGELOG.md

@@ -148,6 +148,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed gathering of results with tensors of varying shape ([#3020](https://github.com/PyTorchLightning/pytorch-lightning/pull/3020))
 
+- Fixed automatic batch scaling not working with half precision ([#3045](https://github.com/PyTorchLightning/pytorch-lightning/pull/3045))
+
 ## [0.8.5] - 2020-07-09
 
 ### Added

pytorch_lightning/trainer/training_io.py

@@ -354,7 +354,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
             checkpoint['lr_schedulers'] = lr_schedulers
 
             # save native amp scaling
-            if self.amp_backend == AMPType.NATIVE and not self.use_tpu:
+            if self.amp_backend == AMPType.NATIVE and not self.use_tpu and self.scaler is not None:
                 checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()
             elif self.amp_backend == AMPType.APEX:
                 checkpoint['amp_scaling_state'] = amp.state_dict()

tests/trainer/test_trainer_tricks.py

@@ -5,6 +5,7 @@
 
 import tests.base.develop_utils as tutils
 from pytorch_lightning import Trainer
+from pytorch_lightning.utilities import AMPType, NATIVE_AMP_AVALAIBLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
 
@@ -257,3 +258,22 @@ def test_error_on_dataloader_passed_to_fit(tmpdir):
 
     with pytest.raises(MisconfigurationException):
         trainer.fit(model, **fit_options)
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+@pytest.mark.skipif(not NATIVE_AMP_AVALAIBLE, reason="test requires native AMP.")
+def test_auto_scale_batch_size_with_amp(tmpdir):
+    model = EvalModelTemplate()
+    batch_size_before = model.batch_size
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_steps=1,
+        auto_scale_batch_size=True,
+        gpus=1,
+        precision=16
+    )
+    trainer.fit(model)
+    batch_size_after = model.batch_size
+    assert trainer.amp_backend == AMPType.NATIVE
+    assert trainer.scaler is not None
+    assert batch_size_after != batch_size_before