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Source code for lightning_lite.plugins.environments.xla

# 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.
import logging
import os
from typing import Any

from lightning_lite.accelerators.tpu import _XLA_AVAILABLE, TPUAccelerator
from lightning_lite.plugins.environments.cluster_environment import ClusterEnvironment

log = logging.getLogger(__name__)


[docs]class XLAEnvironment(ClusterEnvironment): """Cluster environment for training on a TPU Pod with the `PyTorch/XLA <https://pytorch.org/xla>`_ library. A list of environment variables set by XLA can be found `here <https://github.com/pytorch/xla/blob/master/torch_xla/core/xla_env_vars.py>`_. """ def __init__(self, *args: Any, **kwargs: Any) -> None: if not _XLA_AVAILABLE: raise ModuleNotFoundError(str(_XLA_AVAILABLE)) super().__init__(*args, **kwargs) @property def creates_processes_externally(self) -> bool: return False @property def main_address(self) -> str: import torch_xla.core.xla_env_vars as xenv return os.environ[xenv.TPU_MESH_CTLER_ADDR] @property def main_port(self) -> int: import torch_xla.core.xla_env_vars as xenv return int(os.environ[xenv.TPU_MESH_CTLER_PORT])
[docs] @staticmethod def detect() -> bool: return TPUAccelerator.is_available()
[docs] def world_size(self) -> int: import torch_xla.core.xla_model as xm return xm.xrt_world_size()
def set_world_size(self, size: int) -> None: log.debug("XLAEnvironment.set_world_size was called, but setting world size is not allowed. Ignored.")
[docs] def global_rank(self) -> int: import torch_xla.core.xla_model as xm return xm.get_ordinal()
def set_global_rank(self, rank: int) -> None: log.debug("XLAEnvironment.set_global_rank was called, but setting global rank is not allowed. Ignored.")
[docs] def local_rank(self) -> int: import torch_xla.core.xla_model as xm return xm.get_local_ordinal()
[docs] def node_rank(self) -> int: import torch_xla.core.xla_env_vars as xenv return int(os.environ.get(xenv.HOST_ORDINAL, 0))

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