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
#
#     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 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


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)

    def setup_device(self, device: torch.device) -> None:
        pass

    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
        device_stats = {
            "avg. free memory (MB)": free_memory,
            "avg. peak memory (MB)": peak_memory,
        }
        return device_stats

    def teardown(self) -> None:
        pass

    @staticmethod
    def parse_devices(devices: Union[int, str, List[int]]) -> Optional[Union[int, List[int]]]:
        """Accelerator device parsing logic."""
        return _parse_tpu_devices(devices)

    @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

    @staticmethod
    def auto_device_count() -> int:
        """Get the devices when set to auto."""
        return 8

    @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__,
        )