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

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Author: pre-commit-ci[bot]

docs/source-pytorch/accelerators/musa.rst

@@ -10,8 +10,8 @@ MUSA training (Advanced)
 
 MUSAAccelerator Overview
 --------------------
-torch_musa is an extended Python package based on PyTorch that enables full utilization of MooreThreads graphics cards' 
-super computing power. Combined with PyTorch, users can take advantage of the strong power of MooreThreads graphics cards 
+torch_musa is an extended Python package based on PyTorch that enables full utilization of MooreThreads graphics cards'
+super computing power. Combined with PyTorch, users can take advantage of the strong power of MooreThreads graphics cards
 through torch_musa.
 
 PyTorch Lightning automatically finds these weights and ties them after the modules are moved to the

src/lightning/fabric/accelerators/__init__.py

@@ -16,9 +16,9 @@
 from lightning.fabric.accelerators.cpu import CPUAccelerator  # noqa: F401
 from lightning.fabric.accelerators.cuda import CUDAAccelerator, find_usable_cuda_devices  # noqa: F401
 from lightning.fabric.accelerators.mps import MPSAccelerator  # noqa: F401
+from lightning.fabric.accelerators.musa import MUSAAccelerator  # noqa: F401
 from lightning.fabric.accelerators.registry import _AcceleratorRegistry
 from lightning.fabric.accelerators.xla import XLAAccelerator  # noqa: F401
-from lightning.fabric.accelerators.musa import MUSAAccelerator  # noqa: F401
 from lightning.fabric.utilities.registry import _register_classes
 
 ACCELERATOR_REGISTRY = _AcceleratorRegistry()

src/lightning/fabric/connector.py

@@ -23,8 +23,8 @@
 from lightning.fabric.accelerators.accelerator import Accelerator
 from lightning.fabric.accelerators.cuda import CUDAAccelerator
 from lightning.fabric.accelerators.mps import MPSAccelerator
-from lightning.fabric.accelerators.xla import XLAAccelerator
 from lightning.fabric.accelerators.musa import MUSAAccelerator
+from lightning.fabric.accelerators.xla import XLAAccelerator
 from lightning.fabric.plugins import (
     BitsandbytesPrecision,
     CheckpointIO,

src/lightning/fabric/utilities/device_parser.py

@@ -86,15 +86,18 @@ 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_musa=include_musa)
+    gpus = _normalize_parse_gpu_input_to_list(
+        gpus, include_cuda=include_cuda, include_mps=include_mps, include_musa=include_musa
+    )
     if not gpus:
         raise MisconfigurationException("GPUs requested but none are available.")
 
     if (
         torch.distributed.is_available()
         and torch.distributed.is_torchelastic_launched()
         and len(gpus) != 1
-        and len(_get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_musa=include_musa)) == 1
+        and len(_get_all_available_gpus(include_cuda=include_cuda, include_mps=include_mps, include_musa=include_musa))
+        == 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_musa: bool = False) -> list[int]:
+def _sanitize_gpu_ids(
+    gpus: list[int], include_cuda: bool = False, include_mps: bool = False, include_musa: 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.
 
@@ -132,7 +137,9 @@ def _sanitize_gpu_ids(gpus: list[int], include_cuda: bool = False, include_mps:
     """
     if sum((include_cuda, include_mps, include_musa)) == 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_musa=include_musa)
+    all_available_gpus = _get_all_available_gpus(
+        include_cuda=include_cuda, include_mps=include_mps, include_musa=include_musa
+    )
     for gpu in gpus:
         if gpu not in all_available_gpus:
             raise MisconfigurationException(
@@ -157,7 +164,9 @@ 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_musa: bool = False) -> list[int]:
+def _get_all_available_gpus(
+    include_cuda: bool = False, include_mps: bool = False, include_musa: bool = False
+) -> list[int]:
     """
     Returns:
         A list of all available GPUs
@@ -214,8 +223,8 @@ def _select_auto_accelerator() -> str:
     """Choose the accelerator type (str) based on availability."""
     from lightning.fabric.accelerators.cuda import CUDAAccelerator
     from lightning.fabric.accelerators.mps import MPSAccelerator
-    from lightning.fabric.accelerators.xla import XLAAccelerator
     from lightning.fabric.accelerators.musa import MUSAAccelerator
+    from lightning.fabric.accelerators.xla import XLAAccelerator
 
     if XLAAccelerator.is_available():
         return "tpu"

src/lightning/fabric/utilities/testing/_runif.py

@@ -110,7 +110,7 @@ def _runif_reasons(
             reasons.append("MPS")
         elif not mps and MPSAccelerator.is_available():
             reasons.append("not MPS")
-    
+
     if musa is not None:
         if musa and not MUSAAccelerator.is_available():
             reasons.append("MUSA")

src/lightning/pytorch/accelerators/__init__.py

@@ -30,8 +30,8 @@
 from lightning.pytorch.accelerators.cpu import CPUAccelerator
 from lightning.pytorch.accelerators.cuda import CUDAAccelerator
 from lightning.pytorch.accelerators.mps import MPSAccelerator
-from lightning.pytorch.accelerators.xla import XLAAccelerator
 from lightning.pytorch.accelerators.musa import MUSAAccelerator
+from lightning.pytorch.accelerators.xla import XLAAccelerator
 
 AcceleratorRegistry = _AcceleratorRegistry()
 _register_classes(AcceleratorRegistry, "register_accelerators", sys.modules[__name__], Accelerator)

src/lightning/pytorch/accelerators/musa.py

@@ -13,8 +13,6 @@
 # limitations under the License.
 import logging
 import os
-import shutil
-import subprocess
 from typing import Any, Optional, Union
 
 import torch

src/lightning/pytorch/core/saving.py

@@ -34,7 +34,7 @@
 from lightning.fabric.utilities.cloud_io import _load as pl_load
 from lightning.fabric.utilities.data import AttributeDict
 from lightning.fabric.utilities.types import _MAP_LOCATION_TYPE, _PATH
-from lightning.pytorch.accelerators import CUDAAccelerator, MPSAccelerator, XLAAccelerator, MUSAAccelerator
+from lightning.pytorch.accelerators import CUDAAccelerator, MPSAccelerator, MUSAAccelerator, XLAAccelerator
 from lightning.pytorch.utilities.imports import _OMEGACONF_AVAILABLE
 from lightning.pytorch.utilities.migration import pl_legacy_patch
 from lightning.pytorch.utilities.migration.utils import _pl_migrate_checkpoint

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

@@ -35,8 +35,8 @@
 from lightning.pytorch.accelerators.accelerator import Accelerator
 from lightning.pytorch.accelerators.cuda import CUDAAccelerator
 from lightning.pytorch.accelerators.mps import MPSAccelerator
-from lightning.pytorch.accelerators.xla import XLAAccelerator
 from lightning.pytorch.accelerators.musa import MUSAAccelerator
+from lightning.pytorch.accelerators.xla import XLAAccelerator
 from lightning.pytorch.plugins import (
     _PLUGIN_INPUT,
     BitsandbytesPrecision,

src/lightning/pytorch/trainer/setup.py

@@ -18,7 +18,7 @@
 
 import lightning.pytorch as pl
 from lightning.fabric.utilities.warnings import PossibleUserWarning
-from lightning.pytorch.accelerators import CUDAAccelerator, MPSAccelerator, XLAAccelerator, MUSAAccelerator
+from lightning.pytorch.accelerators import CUDAAccelerator, MPSAccelerator, MUSAAccelerator, XLAAccelerator
 from lightning.pytorch.loggers.logger import DummyLogger
 from lightning.pytorch.profilers import (
     AdvancedProfiler,