[bugfix] Add set_default_tensor_type to torch.DoubleTensor with precision=64 (#7108)

* update

* Update pytorch_lightning/plugins/precision/double.py

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* Update pytorch_lightning/plugins/precision/double.py

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* Update pytorch_lightning/plugins/precision/double.py

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

* resolve tests

Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com>

pytorch_lightning/accelerators/accelerator.py

@@ -242,7 +242,7 @@ class Accelerator:
 
         args[0] = batch
 
-        with self.precision_plugin.predict_context(), self.training_type_plugin.predict_context():
+        with self.precision_plugin.predict_step_context(), self.training_type_plugin.predict_step_context():
             return self.training_type_plugin.predict_step(*args)
 
     def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:

pytorch_lightning/plugins/base_plugin.py

@@ -41,6 +41,6 @@ class Plugin(ABC):
         yield
 
     @contextlib.contextmanager
-    def predict_context(self) -> Generator:
+    def predict_step_context(self) -> Generator:
         """A contextmanager for the predict step"""
         yield

pytorch_lightning/plugins/precision/double.py

@@ -11,8 +11,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import contextlib
 from functools import wraps
-from typing import Any, List, Tuple
+from typing import Any, Generator, List, Tuple
 
 import torch
 import torch.nn as nn
@@ -90,3 +91,18 @@ class DoublePrecisionPlugin(PrecisionPlugin):
     def post_dispatch(self) -> None:
         while len(self.patches) > 0:
             self.patches.pop().teardown()
+
+    @contextlib.contextmanager
+    def tensor_type_context(self) -> Generator:
+        """
+        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)
+
+    train_step_context = tensor_type_context
+    val_step_context = tensor_type_context
+    test_step_context = tensor_type_context
+    predict_step_context = tensor_type_context

pytorch_lightning/plugins/precision/native_amp.py

@@ -115,7 +115,7 @@ class NativeMixedPrecisionPlugin(MixedPrecisionPlugin):
             yield
 
     @contextmanager
-    def predict_context(self) -> Generator[None, None, None]:
+    def predict_step_context(self) -> Generator[None, None, None]:
         """Enable autocast context"""
         with torch.cuda.amp.autocast():
             yield

tests/plugins/test_double_plugin.py

@@ -37,25 +37,37 @@ class DoublePrecisionBoringModel(BoringModel):
 
     def training_step(self, batch, batch_idx):
         float_data, int_data = batch
+        assert torch.tensor([0.]).dtype == torch.float64
+        assert torch.tensor([0.], dtype=torch.float16).dtype == torch.float16
         assert float_data.dtype == torch.float64
         output = self(float_data)
         loss = self.loss(batch, output)
         return {"loss": loss}
 
+    def training_epoch_end(self, outputs) -> None:
+        assert torch.tensor([0.]).dtype == torch.float32
+        return super().training_epoch_end(outputs)
+
     def validation_step(self, batch, batch_idx):
         assert batch.dtype == torch.float64
+        assert torch.tensor([0.]).dtype == torch.float64
+        assert torch.tensor([0.], dtype=torch.float16).dtype == torch.float16
         output = self(batch)
         loss = self.loss(batch, output)
         return {"x": loss}
 
     def test_step(self, batch, batch_idx):
         assert batch.dtype == torch.float64
+        assert torch.tensor([0.]).dtype == torch.float64
+        assert torch.tensor([0.], dtype=torch.float16).dtype == torch.float16
         output = self(batch)
         loss = self.loss(batch, output)
         return {"y": loss}
 
     def predict_step(self, batch, batch_idx, dataloader_idx=None):
         assert batch.dtype == torch.float64
+        assert torch.tensor([0.]).dtype == torch.float64
+        assert torch.tensor([0.], dtype=torch.float16).dtype == torch.float16
         return self(batch)
 
     def on_fit_start(self):