Source code for lightning.pytorch.accelerators.tpu

# Copyright The Lightning AI 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 lightning.fabric.accelerators.tpu import _parse_tpu_devices, _XLA_AVAILABLE
from lightning.fabric.accelerators.tpu import TPUAccelerator as FabricTPUAccelerator
from lightning.fabric.utilities.types import _DEVICE
from lightning.pytorch.accelerators.accelerator import Accelerator

[docs]class TPUAccelerator(Accelerator): """Accelerator for TPU devices. .. warning:: Use of this accelerator beyond import and instantiation is experimental. """ def __init__(self, *args: Any, **kwargs: Any) -> None: if not _XLA_AVAILABLE: raise ModuleNotFoundError(str(_XLA_AVAILABLE)) super().__init__(*args, **kwargs)
[docs] def setup_device(self, device: torch.device) -> None: pass
[docs] def get_device_stats(self, device: _DEVICE) -> Dict[str, Any]: """Gets stats for the given TPU device. Args: device: TPU device for which to get stats Returns: A dictionary mapping the metrics (free memory and peak memory) to their values. """ import torch_xla.core.xla_model as xm memory_info = xm.get_memory_info(device) free_memory = memory_info["kb_free"] peak_memory = memory_info["kb_total"] - free_memory return { "avg. free memory (MB)": free_memory, "avg. peak memory (MB)": peak_memory, }
[docs] def teardown(self) -> None: pass
[docs] @staticmethod def parse_devices(devices: Union[int, str, List[int]]) -> Optional[Union[int, List[int]]]: """Accelerator device parsing logic.""" return _parse_tpu_devices(devices)
[docs] @staticmethod def get_parallel_devices(devices: Union[int, List[int]]) -> List[int]: """Gets parallel devices for the Accelerator.""" if isinstance(devices, int): return list(range(devices)) return devices
[docs] @staticmethod def auto_device_count() -> int: """Get the devices when set to auto.""" return 8
[docs] @staticmethod def is_available() -> bool: return FabricTPUAccelerator.is_available()
@classmethod def register_accelerators(cls, accelerator_registry: Dict) -> None: accelerator_registry.register( "tpu", cls, description=cls.__class__.__name__, )

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