Introduce PrecisionPlugin.forward_context()

CHANGELOG.md

@@ -194,6 +194,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `strategy` argument to Trainer ([#8597](https://github.com/PyTorchLightning/pytorch-lightning/pull/8597))
 
 
+- LightningLite:
+    * Added `PrecisionPlugin.forward_context`, making it the default implementation for all `{train,val,test,predict}_step_context()` methods ([#9988](https://github.com/PyTorchLightning/pytorch-lightning/pull/9988))
+
+
 ### Changed
 
 - Setting `Trainer(accelerator="ddp_cpu")` now does not spawn a subprocess if `num_processes` is kept `1` along with `num_nodes > 1` ([#9603](https://github.com/PyTorchLightning/pytorch-lightning/pull/9603)).

pytorch_lightning/plugins/precision/double.py

@@ -92,37 +92,7 @@ def connect(
         return super().connect(model, optimizers, lr_schedulers)
 
     @contextmanager
-    def train_step_context(self) -> Generator[None, None, None]:
-        """A context manager to change the default tensor type.
-
-        See: :meth:`torch.set_default_tensor_type`
-        """
-        torch.set_default_tensor_type(torch.DoubleTensor)
-        yield
-        torch.set_default_tensor_type(torch.FloatTensor)
-
-    @contextmanager
-    def val_step_context(self) -> Generator[None, None, None]:
-        """A context manager to change the default tensor type.
-
-        See: :meth:`torch.set_default_tensor_type`
-        """
-        torch.set_default_tensor_type(torch.DoubleTensor)
-        yield
-        torch.set_default_tensor_type(torch.FloatTensor)
-
-    @contextmanager
-    def test_step_context(self) -> Generator[None, None, None]:
-        """A context manager to change the default tensor type.
-
-        See: :meth:`torch.set_default_tensor_type`
-        """
-        torch.set_default_tensor_type(torch.DoubleTensor)
-        yield
-        torch.set_default_tensor_type(torch.FloatTensor)
-
-    @contextmanager
-    def predict_step_context(self) -> Generator[None, None, None]:
+    def forward_context(self) -> Generator[None, None, None]:
         """A context manager to change the default tensor type.
 
         See: :meth:`torch.set_default_tensor_type`

pytorch_lightning/plugins/precision/native_amp.py

@@ -102,25 +102,7 @@ def autocast_context_manager(self) -> torch.cuda.amp.autocast:
         return torch.cuda.amp.autocast()
 
     @contextmanager
-    def train_step_context(self) -> Generator[None, None, None]:
-        """Enable autocast context."""
-        with self.autocast_context_manager():
-            yield
-
-    @contextmanager
-    def val_step_context(self) -> Generator[None, None, None]:
-        """Enable autocast context."""
-        with self.autocast_context_manager():
-            yield
-
-    @contextmanager
-    def test_step_context(self) -> Generator[None, None, None]:
-        """Enable autocast context."""
-        with self.autocast_context_manager():
-            yield
-
-    @contextmanager
-    def predict_step_context(self) -> Generator[None, None, None]:
+    def forward_context(self) -> Generator[None, None, None]:
         """Enable autocast context."""
         with self.autocast_context_manager():
             yield

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -143,21 +143,30 @@ def post_dispatch(self) -> None:
         """Hook to do something after the training/evaluation/prediction finishes."""
 
     @contextlib.contextmanager
-    def train_step_context(self) -> Generator:
-        """A contextmanager for the training step."""
+    def forward_context(self) -> Generator[None, None, None]:
+        """A contextmanager for managing model forward/training_step/evaluation_step/predict_step."""
         yield
 
     @contextlib.contextmanager
-    def val_step_context(self) -> Generator:
+    def train_step_context(self) -> Generator[None, None, None]:
+        """A contextmanager for the training step."""
+        with self.forward_context():
+            yield
+
+    @contextlib.contextmanager
+    def val_step_context(self) -> Generator[None, None, None]:
         """A contextmanager for the validation step."""
-        yield
+        with self.forward_context():
+            yield
 
     @contextlib.contextmanager
-    def test_step_context(self) -> Generator:
+    def test_step_context(self) -> Generator[None, None, None]:
         """A contextmanager for the test step."""
-        yield
+        with self.forward_context():
+            yield
 
     @contextlib.contextmanager
-    def predict_step_context(self) -> Generator:
+    def predict_step_context(self) -> Generator[None, None, None]:
         """A contextmanager for the predict step."""
-        yield
+        with self.forward_context():
+            yield