Move accelerator-specific parsing functions with their accelerators

src/lightning_lite/accelerators/cpu.py

@@ -16,7 +16,6 @@
 import torch
 
 from lightning_lite.accelerators.accelerator import Accelerator
-from lightning_lite.utilities import device_parser
 
 
 class CPUAccelerator(Accelerator):
@@ -37,13 +36,13 @@ def teardown(self) -> None:
     @staticmethod
     def parse_devices(devices: Union[int, str, List[int]]) -> int:
         """Accelerator device parsing logic."""
-        devices = device_parser.parse_cpu_cores(devices)
+        devices = parse_cpu_cores(devices)
         return devices
 
     @staticmethod
     def get_parallel_devices(devices: Union[int, str, List[int]]) -> List[torch.device]:
         """Gets parallel devices for the Accelerator."""
-        devices = device_parser.parse_cpu_cores(devices)
+        devices = parse_cpu_cores(devices)
         return [torch.device("cpu")] * devices
 
     @staticmethod
@@ -63,3 +62,26 @@ def register_accelerators(cls, accelerator_registry: Dict) -> None:
             cls,
             description=cls.__class__.__name__,
         )
+
+
+def parse_cpu_cores(cpu_cores: Union[int, str, List[int]]) -> int:
+    """Parses the cpu_cores given in the format as accepted by the ``devices`` argument in the
+    :class:`~pytorch_lightning.trainer.Trainer`.
+
+    Args:
+        cpu_cores: An int > 0.
+
+    Returns:
+        An int representing the number of processes
+
+    Raises:
+        MisconfigurationException:
+            If cpu_cores is not an int > 0
+    """
+    if isinstance(cpu_cores, str) and cpu_cores.strip().isdigit():
+        cpu_cores = int(cpu_cores)
+
+    if not isinstance(cpu_cores, int) or cpu_cores <= 0:
+        raise TypeError("`devices` selected with `CPUAccelerator` should be an int > 0.")
+
+    return cpu_cores

src/lightning_lite/accelerators/cuda.py

@@ -11,12 +11,13 @@
 # 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 multiprocessing
 from typing import Dict, List, Optional, Union
 
 import torch
 
 from lightning_lite.accelerators.accelerator import Accelerator
-from lightning_lite.utilities import device_parser
+from lightning_lite.strategies.launchers.multiprocessing import _is_forking_disabled
 
 
 class CUDAAccelerator(Accelerator):
@@ -39,7 +40,9 @@ def teardown(self) -> None:
     @staticmethod
     def parse_devices(devices: Union[int, str, List[int]]) -> Optional[List[int]]:
         """Accelerator device parsing logic."""
-        return device_parser.parse_gpu_ids(devices, include_cuda=True)
+        from lightning_lite.utilities.device_parser import parse_gpu_ids
+
+        return parse_gpu_ids(devices, include_cuda=True)
 
     @staticmethod
     def get_parallel_devices(devices: List[int]) -> List[torch.device]:
@@ -49,11 +52,11 @@ def get_parallel_devices(devices: List[int]) -> List[torch.device]:
     @staticmethod
     def auto_device_count() -> int:
         """Get the devices when set to auto."""
-        return device_parser.num_cuda_devices()
+        return num_cuda_devices()
 
     @staticmethod
     def is_available() -> bool:
-        return device_parser.num_cuda_devices() > 0
+        return num_cuda_devices() > 0
 
     @classmethod
     def register_accelerators(cls, accelerator_registry: Dict) -> None:
@@ -62,3 +65,35 @@ def register_accelerators(cls, accelerator_registry: Dict) -> None:
             cls,
             description=cls.__class__.__name__,
         )
+
+
+def _get_all_available_cuda_gpus() -> List[int]:
+    """
+    Returns:
+         A list of all available CUDA GPUs
+    """
+    return list(range(num_cuda_devices()))
+
+
+def num_cuda_devices() -> int:
+    """Returns the number of GPUs available.
+
+    Unlike :func:`torch.cuda.device_count`, this function does its best not to create a CUDA context for fork support,
+    if the platform allows it.
+    """
+    if "fork" not in torch.multiprocessing.get_all_start_methods() or _is_forking_disabled():
+        return torch.cuda.device_count()
+    with multiprocessing.get_context("fork").Pool(1) as pool:
+        return pool.apply(torch.cuda.device_count)
+
+
+def is_cuda_available() -> bool:
+    """Returns a bool indicating if CUDA is currently available.
+
+    Unlike :func:`torch.cuda.is_available`, this function does its best not to create a CUDA context for fork support,
+    if the platform allows it.
+    """
+    if "fork" not in torch.multiprocessing.get_all_start_methods() or _is_forking_disabled():
+        return torch.cuda.is_available()
+    with multiprocessing.get_context("fork").Pool(1) as pool:
+        return pool.apply(torch.cuda.is_available)

src/lightning_lite/accelerators/mps.py

@@ -18,7 +18,6 @@
 import torch
 
 from lightning_lite.accelerators.accelerator import Accelerator
-from lightning_lite.utilities import device_parser
 from lightning_lite.utilities.imports import _TORCH_GREATER_EQUAL_1_12
 
 
@@ -40,15 +39,16 @@ def teardown(self) -> None:
     @staticmethod
     def parse_devices(devices: Union[int, str, List[int]]) -> Optional[List[int]]:
         """Accelerator device parsing logic."""
-        parsed_devices = device_parser.parse_gpu_ids(devices, include_mps=True)
+        from lightning_lite.utilities.device_parser import parse_gpu_ids
+
+        parsed_devices = parse_gpu_ids(devices, include_mps=True)
         return parsed_devices
 
     @staticmethod
     def get_parallel_devices(devices: Union[int, str, List[int]]) -> List[torch.device]:
         """Gets parallel devices for the Accelerator."""
         parsed_devices = MPSAccelerator.parse_devices(devices)
         assert parsed_devices is not None
-
         return [torch.device("mps", i) for i in range(len(parsed_devices))]
 
     @staticmethod
@@ -72,3 +72,11 @@ def register_accelerators(cls, accelerator_registry: Dict) -> None:
             cls,
             description=cls.__class__.__name__,
         )
+
+
+def _get_all_available_mps_gpus() -> List[int]:
+    """
+    Returns:
+        A list of all available MPS GPUs
+    """
+    return [0] if MPSAccelerator.is_available() else []

src/lightning_lite/accelerators/tpu.py

@@ -11,12 +11,12 @@
 # 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.
-from typing import Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 
 from lightning_lite.accelerators.accelerator import Accelerator
-from lightning_lite.utilities import device_parser
+from lightning_lite.utilities.device_parser import _check_data_type
 from lightning_lite.utilities.imports import _TPU_AVAILABLE
 
 
@@ -32,7 +32,7 @@ def teardown(self) -> None:
     @staticmethod
     def parse_devices(devices: Union[int, str, List[int]]) -> Optional[Union[int, List[int]]]:
         """Accelerator device parsing logic."""
-        return device_parser.parse_tpu_cores(devices)
+        return parse_tpu_cores(devices)
 
     @staticmethod
     def get_parallel_devices(devices: Union[int, List[int]]) -> List[int]:
@@ -57,3 +57,54 @@ def register_accelerators(cls, accelerator_registry: Dict) -> None:
             cls,
             description=cls.__class__.__name__,
         )
+
+
+def parse_tpu_cores(tpu_cores: Optional[Union[int, str, List[int]]]) -> Optional[Union[int, List[int]]]:
+    """
+    Parses the tpu_cores given in the format as accepted by the
+    :class:`~pytorch_lightning.trainer.Trainer`.
+
+    Args:
+        tpu_cores: An int of 1 or string '1' indicates that 1 core with multi-processing should be used
+            An int 8 or string '8' indicates that all 8 cores with multi-processing should be used
+            A list of ints or a strings containing a list of comma separated integers
+            indicates the specific TPU core to use.
+
+    Returns:
+        A list of tpu_cores to be used or ``None`` if no TPU cores were requested
+
+    Raises:
+        MisconfigurationException:
+            If TPU cores aren't 1, 8 or [<1-8>]
+    """
+    _check_data_type(tpu_cores)
+
+    if isinstance(tpu_cores, str):
+        tpu_cores = _parse_tpu_cores_str(tpu_cores.strip())
+
+    if not _tpu_cores_valid(tpu_cores):
+        raise TypeError("`tpu_cores` can only be 1, 8 or [<1-8>]")
+
+    return tpu_cores
+
+
+def _tpu_cores_valid(tpu_cores: Any) -> bool:
+    # allow 1 or 8 cores
+    if tpu_cores in (1, 8, None):
+        return True
+
+    # allow picking 1 of 8 indexes
+    if isinstance(tpu_cores, (list, tuple, set)):
+        has_1_tpu_idx = len(tpu_cores) == 1
+        is_valid_tpu_idx = 1 <= list(tpu_cores)[0] <= 8
+
+        is_valid_tpu_core_choice = has_1_tpu_idx and is_valid_tpu_idx
+        return is_valid_tpu_core_choice
+
+    return False
+
+
+def _parse_tpu_cores_str(tpu_cores: str) -> Union[int, List[int]]:
+    if tpu_cores in ("1", "8"):
+        return int(tpu_cores)
+    return [int(x.strip()) for x in tpu_cores.split(",") if len(x) > 0]

src/lightning_lite/connector.py

@@ -53,7 +53,8 @@
     XLAStrategy,
 )
 from lightning_lite.strategies.ddp_spawn import _DDP_FORK_ALIASES
-from lightning_lite.utilities import _StrategyType, device_parser, rank_zero_deprecation, rank_zero_info, rank_zero_warn
+from lightning_lite.utilities import _StrategyType, rank_zero_deprecation, rank_zero_info, rank_zero_warn
+from lightning_lite.utilities.device_parser import determine_root_gpu_device
 from lightning_lite.utilities.imports import _HPU_AVAILABLE, _IPU_AVAILABLE, _IS_INTERACTIVE, _TPU_AVAILABLE
 
 _PLUGIN = Union[Strategy, Precision, ClusterEnvironment, CheckpointIO]
@@ -438,7 +439,7 @@ def _choose_strategy(self) -> Union[Strategy, str]:
             if isinstance(self._accelerator_flag, (CUDAAccelerator, MPSAccelerator)) or (
                 isinstance(self._accelerator_flag, str) and self._accelerator_flag in ("cuda", "gpu", "mps")
             ):
-                device = device_parser.determine_root_gpu_device(self._parallel_devices)
+                device = determine_root_gpu_device(self._parallel_devices)
             else:
                 device = "cpu"
             # TODO: lazy initialized device, then here could be self._strategy_flag = "single_device"