[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

src/lightning/fabric/accelerators/__init__.py

@@ -17,10 +17,11 @@
 from lightning.fabric.accelerators.registry import _AcceleratorRegistry, call_register_accelerators
 from lightning.fabric.accelerators.xla import XLAAccelerator  # noqa: F401
 from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
+
 _ACCELERATORS_BASE_MODULE = "lightning.fabric.accelerators"
 ACCELERATOR_REGISTRY = _AcceleratorRegistry()
 call_register_accelerators(ACCELERATOR_REGISTRY, _ACCELERATORS_BASE_MODULE)
-if _LIGHTNING_XPU_AVAILABLE:
-    if "xpu" not in ACCELERATOR_REGISTRY:
-        from lightning_xpu.fabric import XPUAccelerator
-        XPUAccelerator.register_accelerators(ACCELERATOR_REGISTRY)
+if _LIGHTNING_XPU_AVAILABLE and "xpu" not in ACCELERATOR_REGISTRY:
+    from lightning_xpu.fabric import XPUAccelerator
+
+    XPUAccelerator.register_accelerators(ACCELERATOR_REGISTRY)

src/lightning/fabric/cli.py

@@ -158,6 +158,7 @@ def _get_num_processes(accelerator: str, devices: str) -> int:
         raise ValueError("Launching processes for TPU through the CLI is not supported.")
     elif accelerator == "xpu":
         from lightning_xpu.fabric import XPUAccelerator
+
         parsed_devices = XPUAccelerator.parse_devices(devices)
     else:
         return CPUAccelerator.parse_devices(devices)

src/lightning/fabric/connector.py

@@ -23,7 +23,6 @@
 from lightning.fabric.accelerators.cuda import CUDAAccelerator
 from lightning.fabric.accelerators.mps import MPSAccelerator
 from lightning.fabric.accelerators.xla import XLAAccelerator
-from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
 from lightning.fabric.plugins import (
     CheckpointIO,
     DeepSpeedPrecision,
@@ -64,7 +63,7 @@
 from lightning.fabric.strategies.fsdp import _FSDP_ALIASES, FSDPStrategy
 from lightning.fabric.utilities import rank_zero_info, rank_zero_warn
 from lightning.fabric.utilities.device_parser import _determine_root_gpu_device
-from lightning.fabric.utilities.imports import _IS_INTERACTIVE
+from lightning.fabric.utilities.imports import _IS_INTERACTIVE, _LIGHTNING_XPU_AVAILABLE
 
 _PLUGIN = Union[Precision, ClusterEnvironment, CheckpointIO]
 _PLUGIN_INPUT = Union[_PLUGIN, str]
@@ -323,6 +322,7 @@ def _choose_auto_accelerator(self) -> str:
             return "cuda"
         if _LIGHTNING_XPU_AVAILABLE:
             from lightning_xpu.fabric import XPUAccelerator
+
             if XPUAccelerator.is_available():
                 return "xpu"
 
@@ -336,6 +336,7 @@ def _choose_gpu_accelerator_backend() -> str:
             return "cuda"
         if _LIGHTNING_XPU_AVAILABLE:
             from lightning_xpu.fabric import XPUAccelerator
+
             if XPUAccelerator.is_available():
                 return "xpu"
         raise RuntimeError("No supported gpu backend found!")
@@ -399,6 +400,7 @@ def _choose_strategy(self) -> Union[Strategy, str]:
             supported_accelerators_str = ["cuda", "gpu", "mps"]
             if _LIGHTNING_XPU_AVAILABLE:
                 from lightning_xpu.fabric import XPUAccelerator
+
                 supported_accelerators.append(XPUAccelerator)
                 supported_accelerators_str.append("xpu")
             if isinstance(self._accelerator_flag, tuple(supported_accelerators)) or (

src/lightning/fabric/utilities/device_parser.py

@@ -16,8 +16,8 @@
 import lightning.fabric.accelerators as accelerators  # avoid circular dependency
 from lightning.fabric.plugins.environments.torchelastic import TorchElasticEnvironment
 from lightning.fabric.utilities.exceptions import MisconfigurationException
-from lightning.fabric.utilities.types import _DEVICE
 from lightning.fabric.utilities.imports import _LIGHTNING_XPU_AVAILABLE
+from lightning.fabric.utilities.types import _DEVICE
 
 
 def _determine_root_gpu_device(gpus: List[_DEVICE]) -> Optional[_DEVICE]:
@@ -87,14 +87,17 @@ def _parse_gpu_ids(
     # We know the user requested GPUs therefore if some of the
     # requested GPUs are not available an exception is thrown.
     gpus = _normalize_parse_gpu_string_input(gpus)
-    gpus = _normalize_parse_gpu_input_to_list(gpus, include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)
+    gpus = _normalize_parse_gpu_input_to_list(
+        gpus, include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu
+    )
     if not gpus:
         raise MisconfigurationException("GPUs requested but none are available.")
 
     if (
         TorchElasticEnvironment.detect()
         and len(gpus) != 1
-        and len(_get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)) == 1
+        and len(_get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu))
+        == 1
     ):
         # Omit sanity check on torchelastic because by default it shows one visible GPU per process
         return gpus
@@ -115,7 +118,9 @@ def _normalize_parse_gpu_string_input(s: Union[int, str, List[int]]) -> Union[in
     return int(s.strip())
 
 
-def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps: bool = False, include_xpu: bool = False) -> List[int]:
+def _sanitize_gpu_ids(
+    gpus: List[int], include_cuda: bool = False, include_mps: bool = False, include_xpu: bool = False
+) -> List[int]:
     """Checks that each of the GPUs in the list is actually available. Raises a MisconfigurationException if any of
     the GPUs is not available.
 
@@ -131,7 +136,9 @@ def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps:
     """
     if sum((include_cuda, include_mps, include_xpu)) == 0:
         raise ValueError("At least one gpu type should be specified!")
-    all_available_gpus = _get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu)
+    all_available_gpus = _get_all_available_gpus(
+        include_cuda=include_cuda, include_mps=include_mps, include_xpu=include_xpu
+    )
     for gpu in gpus:
         if gpu not in all_available_gpus:
             raise MisconfigurationException(
@@ -141,7 +148,10 @@ def _sanitize_gpu_ids(gpus: List[int], include_cuda: bool = False, include_mps:
 
 
 def _normalize_parse_gpu_input_to_list(
-    gpus: Union[int, List[int], Tuple[int, ...]], include_cuda: bool, include_mps: bool, include_xpu: bool,
+    gpus: Union[int, List[int], Tuple[int, ...]],
+    include_cuda: bool,
+    include_mps: bool,
+    include_xpu: bool,
 ) -> Optional[List[int]]:
     assert gpus is not None
     if isinstance(gpus, (MutableSequence, tuple)):
@@ -156,7 +166,9 @@ def _normalize_parse_gpu_input_to_list(
     return list(range(gpus))
 
 
-def _get_all_available_gpus(include_cuda: bool = False, include_mps: bool = False, include_xpu: bool = False) -> List[int]:
+def _get_all_available_gpus(
+    include_cuda: bool = False, include_mps: bool = False, include_xpu: bool = False
+) -> List[int]:
     """
     Returns:
         A list of all available GPUs
@@ -166,6 +178,7 @@ def _get_all_available_gpus(include_cuda: bool = False, include_mps: bool = Fals
     xpu_gpus = []
     if _LIGHTNING_XPU_AVAILABLE:
         import lightning_xpu.fabric as accelerator_xpu
+
         xpu_gpus += accelerator_xpu._get_all_visible_xpu_devices() if include_xpu else []
     return cuda_gpus + mps_gpus + xpu_gpus
 

src/lightning/pytorch/trainer/connectors/accelerator_connector.py

@@ -447,13 +447,12 @@ def _choose_strategy(self) -> Union[Strategy, str]:
             from lightning_habana import SingleHPUStrategy
 
             return SingleHPUStrategy(device=torch.device("hpu"))
-        if self._accelerator_flag == "xpu":
-            if not _LIGHTNING_XPU_AVAILABLE:
-                raise ImportError(
-                    "You have asked for XPU but you miss install related integration."
-                    " Please run `pip install lightning-xpu` or see for further instructions"
-                    " in https://github.com/Lightning-AI/lightning-XPU/."
-                )
+        if self._accelerator_flag == "xpu" and not _LIGHTNING_XPU_AVAILABLE:
+            raise ImportError(
+                "You have asked for XPU but you miss install related integration."
+                " Please run `pip install lightning-xpu` or see for further instructions"
+                " in https://github.com/Lightning-AI/lightning-XPU/."
+            )
         if self._accelerator_flag == "tpu" or isinstance(self._accelerator_flag, XLAAccelerator):
             if self._parallel_devices and len(self._parallel_devices) > 1:
                 return XLAStrategy.strategy_name

src/lightning/pytorch/trainer/setup.py

@@ -28,7 +28,11 @@
     XLAProfiler,
 )
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
-from lightning.pytorch.utilities.imports import _LIGHTNING_GRAPHCORE_AVAILABLE, _LIGHTNING_HABANA_AVAILABLE, _LIGHTNING_XPU_AVAILABLE
+from lightning.pytorch.utilities.imports import (
+    _LIGHTNING_GRAPHCORE_AVAILABLE,
+    _LIGHTNING_HABANA_AVAILABLE,
+    _LIGHTNING_XPU_AVAILABLE,
+)
 from lightning.pytorch.utilities.rank_zero import rank_zero_info, rank_zero_warn