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Source code for pytorch_lightning.accelerators.tpu

# 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 device_parser
from pytorch_lightning.utilities.imports import _TPU_AVAILABLE, _XLA_AVAILABLE

if _XLA_AVAILABLE:
    import torch_xla.core.xla_model as xm


[docs]class TPUAccelerator(Accelerator): """Accelerator for TPU devices."""
[docs] def get_device_stats(self, device: Union[str, torch.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. """ 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
[docs] @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)
[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 _TPU_AVAILABLE
@classmethod def register_accelerators(cls, accelerator_registry: Dict) -> None: accelerator_registry.register( "tpu", cls, description=f"{cls.__class__.__name__}", )

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