Fix disabled grads after call to predict (#6657)

CHANGELOG.md

@@ -15,6 +15,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `DummyLogger.log_hyperparams` raising a `TypeError` when running with `fast_dev_run=True` ([#6398](https://github.com/PyTorchLightning/pytorch-lightning/pull/6398))
 - Fixed error on TPUs when there was no `ModelCheckpoint` ([#6654](https://github.com/PyTorchLightning/pytorch-lightning/pull/6654))
 - Fixed `trainer.test` freeze on TPUs ([#6654](https://github.com/PyTorchLightning/pytorch-lightning/pull/6654))
+- Fixed a bug where gradients were disabled after calling `Trainer.predict` ([#6657](https://github.com/PyTorchLightning/pytorch-lightning/pull/6657))
 
 
 ## [1.2.5] - 2021-03-23

pytorch_lightning/trainer/trainer.py

@@ -834,6 +834,10 @@ def run_predict(self):
                     self.predict_loop.predict(batch, batch_idx, dataloader_idx)
 
         results = self.predict_loop.on_predict_epoch_end()
+
+        # re-enable grads
+        torch.set_grad_enabled(True)
+
         return results
 
     def run_sanity_check(self, ref_model):

tests/trainer/test_trainer.py

@@ -1410,12 +1410,12 @@ def predict_dataloader(self):
         return self._dataloaders
 
 
-def predict(tmpdir, accelerator, gpus, num_processes, plugins=None, datamodule=True):
+def predict(tmpdir, accelerator, gpus, num_processes, model=None, plugins=None, datamodule=True):
 
     dataloaders = [torch.utils.data.DataLoader(RandomDataset(32, 2)), torch.utils.data.DataLoader(RandomDataset(32, 2))]
 
-    model = BoringModel()
-    datamodule = TestLightningDataModule(dataloaders)
+    model = model or BoringModel()
+    dm = TestLightningDataModule(dataloaders)
 
     trainer = Trainer(
         default_root_dir=tmpdir,
@@ -1428,7 +1428,7 @@ def predict(tmpdir, accelerator, gpus, num_processes, plugins=None, datamodule=T
         plugins=plugins,
     )
     if datamodule:
-        results = trainer.predict(model, datamodule=datamodule)
+        results = trainer.predict(model, datamodule=dm)
     else:
         results = trainer.predict(model, dataloaders=dataloaders)
 
@@ -1439,6 +1439,19 @@ def predict(tmpdir, accelerator, gpus, num_processes, plugins=None, datamodule=T
     assert results[0][0].shape == torch.Size([1, 2])
 
 
+def test_trainer_predict_grad(tmpdir):
+    class CustomBoringModel(BoringModel):
+
+        def predict_step(self, batch, batch_idx, dataloader_idx=None):
+            assert batch.expand_as(batch).grad_fn is None
+            return super().predict_step(batch, batch_idx, dataloader_idx)
+
+    predict(tmpdir, None, None, 1, model=CustomBoringModel())
+
+    x = torch.zeros(1, requires_grad=True)
+    assert x.expand_as(x).grad_fn is not None
+
+
 @pytest.mark.parametrize('datamodule', [False, True])
 def test_trainer_predict_cpu(tmpdir, datamodule):
     predict(tmpdir, None, None, 1, datamodule=datamodule)