Fix propagation of device and dtype properties in Lite modules

CHANGELOG.md

@@ -163,7 +163,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed sampler replacement logic with `overfit_batches` to only replace the sample when `SequentialSampler` is not used ([#10486](https://github.com/PyTorchLightning/pytorch-lightning/issues/10486))
 
 
--
+- Fixed propagation of device and dtype information to submodules of LightningLite when they inherit from `DeviceDtypeModuleMixin` ([#10559](https://github.com/PyTorchLightning/pytorch-lightning/issues/10559))
 
 
 -

pytorch_lightning/lite/wrappers.py

@@ -24,6 +24,7 @@
 from torch.utils.data import DataLoader
 
 from pytorch_lightning.accelerators import Accelerator
+from pytorch_lightning.core.mixins import DeviceDtypeModuleMixin
 from pytorch_lightning.plugins import PrecisionPlugin
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
 
@@ -64,7 +65,7 @@ def step(self, closure: Optional[Callable] = None) -> None:
         )
 
 
-class _LiteModule(nn.Module):
+class _LiteModule(DeviceDtypeModuleMixin):
     def __init__(self, module: nn.Module, precision_plugin: PrecisionPlugin) -> None:
         """The LiteModule is a thin wrapper around the :class:`torch.nn.Module` and handles precision / autocast
         automatically for the forward pass.

tests/lite/test_wrappers.py

@@ -17,6 +17,7 @@
 import torch
 from torch.utils.data.dataloader import DataLoader
 
+from pytorch_lightning.core.mixins import DeviceDtypeModuleMixin
 from pytorch_lightning.lite import LightningLite
 from pytorch_lightning.lite.wrappers import _LiteDataLoader, _LiteModule, _LiteOptimizer
 from tests.helpers.runif import RunIf
@@ -65,6 +66,27 @@ def check_autocast(forward_input):
     assert out.dtype == input_type or out.dtype == torch.get_default_dtype()
 
 
+@pytest.mark.parametrize(
+    "device", [torch.device("cpu"), pytest.param(torch.device("cuda", 0), marks=RunIf(min_gpus=1))]
+)
+@pytest.mark.parametrize("dtype", [torch.float32, torch.float16])
+def test_lite_module_device_dtype_propagation(device, dtype):
+    """Test that the LiteModule propagates device and dtype properties to its submodules (e.g. torchmetrics)."""
+
+    class DeviceModule(DeviceDtypeModuleMixin):
+        pass
+
+    device_module = DeviceModule()
+    lite_module = _LiteModule(device_module, Mock())
+    lite_module.to(device)
+    assert device_module.device == device
+    assert lite_module.device == device
+
+    lite_module.to(dtype)
+    assert device_module.dtype == dtype
+    assert lite_module.dtype == dtype
+
+
 def test_lite_dataloader_iterator():
     """Test that the iteration over a LiteDataLoader wraps the iterator of the underlying dataloader (no automatic
     device placement)."""