Source code for pytorch_lightning.utilities.rank_zero

# 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
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"""Utilities that can be used for calling functions on a particular rank."""
import logging
import os
import warnings
from functools import partial, wraps
from platform import python_version
from typing import Any, Callable, Optional, Union

log = logging.getLogger(__name__)


def rank_zero_only(fn: Callable) -> Callable:
    """Function that can be used as a decorator to enable a function/method being called only on rank 0."""

    @wraps(fn)
    def wrapped_fn(*args: Any, **kwargs: Any) -> Optional[Any]:
        if rank_zero_only.rank == 0:
            return fn(*args, **kwargs)
        return None

    return wrapped_fn


# TODO: this should be part of the cluster environment
def _get_rank() -> int:
    # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing,
    # therefore LOCAL_RANK needs to be checked first
    rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK")
    for key in rank_keys:
        rank = os.environ.get(key)
        if rank is not None:
            return int(rank)
    return 0


# add the attribute to the function but don't overwrite in case Trainer has already set it
rank_zero_only.rank = getattr(rank_zero_only, "rank", _get_rank())


def _info(*args: Any, stacklevel: int = 2, **kwargs: Any) -> None:
    if python_version() >= "3.8.0":
        kwargs["stacklevel"] = stacklevel
    log.info(*args, **kwargs)


def _debug(*args: Any, stacklevel: int = 2, **kwargs: Any) -> None:
    if python_version() >= "3.8.0":
        kwargs["stacklevel"] = stacklevel
    log.debug(*args, **kwargs)


@rank_zero_only
def rank_zero_debug(*args: Any, stacklevel: int = 4, **kwargs: Any) -> None:
    """Function used to log debug-level messages only on rank 0."""
    _debug(*args, stacklevel=stacklevel, **kwargs)


@rank_zero_only
def rank_zero_info(*args: Any, stacklevel: int = 4, **kwargs: Any) -> None:
    """Function used to log info-level messages only on rank 0."""
    _info(*args, stacklevel=stacklevel, **kwargs)


def _warn(message: Union[str, Warning], stacklevel: int = 2, **kwargs: Any) -> None:
    if type(stacklevel) is type and issubclass(stacklevel, Warning):
        rank_zero_deprecation(
            "Support for passing the warning category positionally is deprecated in v1.6 and will be removed in v1.8"
            f" Please, use `category={stacklevel.__name__}`."
        )
        kwargs["category"] = stacklevel
        stacklevel = kwargs.pop("stacklevel", 2)
    warnings.warn(message, stacklevel=stacklevel, **kwargs)


@rank_zero_only
def rank_zero_warn(message: Union[str, Warning], stacklevel: int = 4, **kwargs: Any) -> None:
    """Function used to log warn-level messages only on rank 0."""
    _warn(message, stacklevel=stacklevel, **kwargs)


class LightningDeprecationWarning(DeprecationWarning):
    """Deprecation warnings raised by PyTorch Lightning."""


rank_zero_deprecation = partial(rank_zero_warn, category=LightningDeprecationWarning)