enhance documentation

docs/source-pytorch/extensions/accelerator.rst

@@ -36,45 +36,79 @@ Let's pretend we want to integrate the fictional XPU accelerator and we have acc
 
 .. code-block:: python
 
+    import torch
     import xpulib
 
+    from functools import lru_cache
+    from typing import Any, Dict, Union
+    from lightning.pytorch.accelerators.accelerator import Accelerator
+
+    from typing_extensions import override
+
 
     class XPUAccelerator(Accelerator):
         """Support for a hypothetical XPU, optimized for large-scale machine learning."""
 
+        @override
+        def setup_device(self, device: torch.device) -> None:
+            """
+            Raises:
+                ValueError:
+                    If the selected device is not of type hypothetical XPU.
+            """
+            if device.type != "xpu":
+                raise ValueError(f"Device should be of type 'xpu', got '{device.type}' instead.")
+            if device.index is None:
+                device = torch.device("xpu", 0)
+            xpulib.set_device(device.index)
+
+        @override
+        def teardown(self) -> None:
+            xpulib.empty_cache()
+
         @staticmethod
+        @override
         def parse_devices(devices: Any) -> Any:
             # Put parsing logic here how devices can be passed into the Trainer
             # via the `devices` argument
             return devices
 
         @staticmethod
+        @override
         def get_parallel_devices(devices: Any) -> Any:
             # Here, convert the device indices to actual device objects
             return [torch.device("xpu", idx) for idx in devices]
 
         @staticmethod
+        @override
         def auto_device_count() -> int:
             # Return a value for auto-device selection when `Trainer(devices="auto")`
             return xpulib.available_devices()
 
         @staticmethod
+        @override
         def is_available() -> bool:
             return xpulib.is_available()
 
         def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]:
             # Return optional device statistics for loggers
             return {}
 
+        @staticmethod
+        @override
+        def get_device() -> str:
+            return "xpu"
+
 
 Finally, add the XPUAccelerator to the Trainer:
 
 .. code-block:: python
 
     from lightning.pytorch import Trainer
-
+    from lightning.pytorch.strategies import DDPStrategy
     accelerator = XPUAccelerator()
-    trainer = Trainer(accelerator=accelerator, devices=2)
+    strategy = DDPStrategy(parallel_devices=accelerator.get_parallel_devices(2))
+    trainer = Trainer(accelerator=accelerator, strategy=strategy, devices=2)
 
 
 :doc:`Learn more about Strategies <../extensions/strategy>` and how they interact with the Accelerator.
@@ -93,6 +127,7 @@ If you wish to switch to a custom accelerator from the CLI without code changes,
         ...
 
         @classmethod
+        @override
         def register_accelerators(cls, accelerator_registry):
             accelerator_registry.register(
                 "xpu",