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Source code for pytorch_lightning.accelerators.hpu

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 Any, Dict, List, Optional, Union

import torch

from pytorch_lightning.accelerators.accelerator import Accelerator
from pytorch_lightning.utilities import _HPU_AVAILABLE, device_parser
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.rank_zero import rank_zero_debug


[docs]class HPUAccelerator(Accelerator): """Accelerator for HPU devices."""
[docs] def setup_environment(self, root_device: torch.device) -> None: """ Raises: MisconfigurationException: If the selected device is not HPU. """ super().setup_environment(root_device) if root_device.type != "hpu": raise MisconfigurationException(f"Device should be HPU, got {root_device} instead.")
[docs] def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]: """HPU device stats aren't supported yet.""" rank_zero_debug("HPU device stats aren't supported yet.") return {}
[docs] @staticmethod def parse_devices(devices: Union[int, str, List[int]]) -> Optional[int]: """Accelerator device parsing logic.""" return device_parser.parse_hpus(devices)
[docs] @staticmethod def get_parallel_devices(devices: int) -> List[torch.device]: """Gets parallel devices for the Accelerator.""" return [torch.device("hpu")] * devices
[docs] @staticmethod def auto_device_count() -> int: """Get the devices when set to auto.""" # TODO(@kaushikb11): Update this when api is exposed by the Habana team return 8
[docs] @staticmethod def is_available() -> bool: return _HPU_AVAILABLE
@classmethod def register_accelerators(cls, accelerator_registry: Dict) -> None: accelerator_registry.register( "hpu", cls, description=f"{cls.__class__.__name__}", )

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