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.
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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

    import habana_frameworks.torch.hpu as torch_hpu

[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]: """Returns a map of the following metrics with their values: - Limit: amount of total memory on HPU device. - InUse: amount of allocated memory at any instance. - MaxInUse: amount of total active memory allocated. - NumAllocs: number of allocations. - NumFrees: number of freed chunks. - ActiveAllocs: number of active allocations. - MaxAllocSize: maximum allocated size. - TotalSystemAllocs: total number of system allocations. - TotalSystemFrees: total number of system frees. - TotalActiveAllocs: total number of active allocations. """ try: return torch_hpu.hpu.memory_stats(device) except (AttributeError, NameError): rank_zero_debug("HPU `get_device_stats` failed") 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: """Returns the number of HPU devices when the devices is set to auto.""" try: return torch_hpu.device_count() except (AttributeError, NameError): rank_zero_debug("HPU `auto_device_count` failed, returning default count of 8.") return 8
[docs] @staticmethod def is_available() -> bool: """Returns a bool indicating if HPU is currently available.""" try: return torch_hpu.is_available() except (AttributeError, NameError): return False
[docs] @staticmethod def get_device_name() -> str: """Returns the name of the HPU device.""" try: return torch_hpu.get_device_name() except (AttributeError, NameError): return ""
@classmethod def register_accelerators(cls, accelerator_registry: Dict) -> None: accelerator_registry.register( "hpu", cls, description=f"{cls.__class__.__name__}", )

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