lightning.pytorch.callbacks.model_checkpoint — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

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""" Model Checkpointing

Automatically save model checkpoints during training. """

import logging import os import re import shutil import time import warnings from copy import deepcopy from datetime import timedelta from pathlib import Path from typing import Any, Literal, Optional, Union from weakref import proxy

import torch import yaml from torch import Tensor from typing_extensions import override

import lightning.pytorch as pl from lightning.fabric.utilities.cloud_io import _is_dir, _is_local_file_protocol, get_filesystem from lightning.fabric.utilities.types import _PATH from lightning.pytorch.callbacks import Checkpoint from lightning.pytorch.utilities.exceptions import MisconfigurationException from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_info, rank_zero_warn from lightning.pytorch.utilities.types import STEP_OUTPUT

log = logging.getLogger(name) warning_cache = WarningCache()

[docs]class ModelCheckpoint(Checkpoint): r"""Save the model periodically by monitoring a quantity. Every metric logged with :meth:~lightning.pytorch.core.LightningModule.log or :meth:~lightning.pytorch.core.LightningModule.log_dict is a candidate for the monitor key. For more information, see :ref:checkpointing.

After training finishes, use :attr:`best_model_path` to retrieve the path to the
best checkpoint file and :attr:`best_model_score` to retrieve its score.

Args:
    dirpath: directory to save the model file.

        Example::

            # custom path
            # saves a file like: my/path/epoch=0-step=10.ckpt
            >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')

        By default, dirpath is ``None`` and will be set at runtime to the location
        specified by :class:`~lightning.pytorch.trainer.trainer.Trainer`'s
        :paramref:`~lightning.pytorch.trainer.trainer.Trainer.default_root_dir` argument,
        and if the Trainer uses a logger, the path will also contain logger name and version.

    filename: checkpoint filename. Can contain named formatting options to be auto-filled.

        Example::

            # save any arbitrary metrics like `val_loss`, etc. in name
            # saves a file like: my/path/epoch=2-val_loss=0.02-other_metric=0.03.ckpt
            >>> checkpoint_callback = ModelCheckpoint(
            ...     dirpath='my/path',
            ...     filename='{epoch}-{val_loss:.2f}-{other_metric:.2f}'
            ... )

        By default, filename is ``None`` and will be set to ``'{epoch}-{step}'``, where "epoch" and "step" match
        the number of finished epoch and optimizer steps respectively.
    monitor: quantity to monitor. By default it is ``None`` which saves a checkpoint only for the last epoch.
    verbose: verbosity mode. Default: ``False``.
    save_last: When ``True``, saves a `last.ckpt` copy whenever a checkpoint file gets saved. Can be set to
        ``'link'`` on a local filesystem to create a symbolic link. This allows accessing the latest checkpoint
        in a deterministic manner. Default: ``None``.
    save_top_k: if ``save_top_k == k``,
        the best k models according to the quantity monitored will be saved.
        If ``save_top_k == 0``, no models are saved.
        If ``save_top_k == -1``, all models are saved.
        Please note that the monitors are checked every ``every_n_epochs`` epochs.
        If ``save_top_k >= 2`` and the callback is called multiple times inside an epoch, and the filename remains
        unchanged, the name of the saved file will be appended with a version count starting with ``v1`` to avoid
        collisions unless ``enable_version_counter`` is set to False. The version counter is unrelated to the top-k
        ranking of the checkpoint, and we recommend formatting the filename to include the monitored metric to avoid
        collisions.
    mode: one of {min, max}.
        If ``save_top_k != 0``, the decision to overwrite the current save file is made
        based on either the maximization or the minimization of the monitored quantity.
        For ``'val_acc'``, this should be ``'max'``, for ``'val_loss'`` this should be ``'min'``, etc.
    auto_insert_metric_name: When ``True``, the checkpoints filenames will contain the metric name.
        For example, ``filename='checkpoint_{epoch:02d}-{acc:02.0f}`` with epoch ``1`` and acc ``1.12`` will resolve
        to ``checkpoint_epoch=01-acc=01.ckpt``. Is useful to set it to ``False`` when metric names contain ``/``
        as this will result in extra folders.
        For example, ``filename='epoch={epoch}-step={step}-val_acc={val/acc:.2f}', auto_insert_metric_name=False``
    save_weights_only: if ``True``, then only the model's weights will be
        saved. Otherwise, the optimizer states, lr-scheduler states, etc are added in the checkpoint too.
    every_n_train_steps: Number of training steps between checkpoints.
        If ``every_n_train_steps == None or every_n_train_steps == 0``, we skip saving during training.
        To disable, set ``every_n_train_steps = 0``. This value must be ``None`` or non-negative.
        This must be mutually exclusive with ``train_time_interval`` and ``every_n_epochs``.
    train_time_interval: Checkpoints are monitored at the specified time interval.
        For all practical purposes, this cannot be smaller than the amount
        of time it takes to process a single training batch. This is not
        guaranteed to execute at the exact time specified, but should be close.
        This must be mutually exclusive with ``every_n_train_steps`` and ``every_n_epochs``.
    every_n_epochs: Number of epochs between checkpoints.
        This value must be ``None`` or non-negative.
        To disable saving top-k checkpoints, set ``every_n_epochs = 0``.
        This argument does not impact the saving of ``save_last=True`` checkpoints.
        If all of ``every_n_epochs``, ``every_n_train_steps`` and
        ``train_time_interval`` are ``None``, we save a checkpoint at the end of every epoch
        (equivalent to ``every_n_epochs = 1``).
        If ``every_n_epochs == None`` and either ``every_n_train_steps != None`` or ``train_time_interval != None``,
        saving at the end of each epoch is disabled
        (equivalent to ``every_n_epochs = 0``).
        This must be mutually exclusive with ``every_n_train_steps`` and ``train_time_interval``.
        Setting both ``ModelCheckpoint(..., every_n_epochs=V, save_on_train_epoch_end=False)`` and
        ``Trainer(max_epochs=N, check_val_every_n_epoch=M)``
        will only save checkpoints at epochs 0 < E <= N
        where both values for ``every_n_epochs`` and ``check_val_every_n_epoch`` evenly divide E.
    save_on_train_epoch_end: Whether to run checkpointing at the end of the training epoch.
        If this is ``False``, then the check runs at the end of the validation.
    enable_version_counter: Whether to append a version to the existing file name.
        If this is ``False``, then the checkpoint files will be overwritten.

Note:
    For extra customization, ModelCheckpoint includes the following attributes:

    - ``CHECKPOINT_JOIN_CHAR = "-"``
    - ``CHECKPOINT_EQUALS_CHAR = "="``
    - ``CHECKPOINT_NAME_LAST = "last"``
    - ``FILE_EXTENSION = ".ckpt"``
    - ``STARTING_VERSION = 1``

    For example, you can change the default last checkpoint name by doing
    ``checkpoint_callback.CHECKPOINT_NAME_LAST = "{epoch}-last"``

    If you want to checkpoint every N hours, every M train batches, and/or every K val epochs,
    then you should create multiple ``ModelCheckpoint`` callbacks.

    If the checkpoint's ``dirpath`` changed from what it was before while resuming the training,
    only ``best_model_path`` will be reloaded and a warning will be issued.

Raises:
    MisconfigurationException:
        If ``save_top_k`` is smaller than ``-1``,
        if ``monitor`` is ``None`` and ``save_top_k`` is none of ``None``, ``-1``, and ``0``, or
        if ``mode`` is none of ``"min"`` or ``"max"``.
    ValueError:
        If ``trainer.save_checkpoint`` is ``None``.

Example::

    >>> from lightning.pytorch import Trainer
    >>> from lightning.pytorch.callbacks import ModelCheckpoint

    # saves checkpoints to 'my/path/' at every epoch
    >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
    >>> trainer = Trainer(callbacks=[checkpoint_callback])

    # save epoch and val_loss in name
    # saves a file like: my/path/sample-mnist-epoch=02-val_loss=0.32.ckpt
    >>> checkpoint_callback = ModelCheckpoint(
    ...     monitor='val_loss',
    ...     dirpath='my/path/',
    ...     filename='sample-mnist-{epoch:02d}-{val_loss:.2f}'
    ... )

    # save epoch and val_loss in name, but specify the formatting yourself (e.g. to avoid problems with Tensorboard
    # or Neptune, due to the presence of characters like '=' or '/')
    # saves a file like: my/path/sample-mnist-epoch02-val_loss0.32.ckpt
    >>> checkpoint_callback = ModelCheckpoint(
    ...     monitor='val/loss',
    ...     dirpath='my/path/',
    ...     filename='sample-mnist-epoch{epoch:02d}-val_loss{val/loss:.2f}',
    ...     auto_insert_metric_name=False
    ... )

    # retrieve the best checkpoint after training
    checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
    trainer = Trainer(callbacks=[checkpoint_callback])
    model = ...
    trainer.fit(model)
    checkpoint_callback.best_model_path

.. tip:: Saving and restoring multiple checkpoint callbacks at the same time is supported under variation in the
    following arguments:

    *monitor, mode, every_n_train_steps, every_n_epochs, train_time_interval*

    Read more: :ref:`Persisting Callback State <extensions/callbacks_state:save callback state>`

"""

CHECKPOINT_JOIN_CHAR = "-"
CHECKPOINT_EQUALS_CHAR = "="
CHECKPOINT_NAME_LAST = "last"
FILE_EXTENSION = ".ckpt"
STARTING_VERSION = 1

def __init__(
    self,
    dirpath: Optional[_PATH] = None,
    filename: Optional[str] = None,
    monitor: Optional[str] = None,
    verbose: bool = False,
    save_last: Optional[Union[bool, Literal["link"]]] = None,
    save_top_k: int = 1,
    save_weights_only: bool = False,
    mode: str = "min",
    auto_insert_metric_name: bool = True,
    every_n_train_steps: Optional[int] = None,
    train_time_interval: Optional[timedelta] = None,
    every_n_epochs: Optional[int] = None,
    save_on_train_epoch_end: Optional[bool] = None,
    enable_version_counter: bool = True,
):
    super().__init__()
    self.monitor = monitor
    self.verbose = verbose
    self.save_last = save_last
    self.save_top_k = save_top_k
    self.save_weights_only = save_weights_only
    self.auto_insert_metric_name = auto_insert_metric_name
    self._save_on_train_epoch_end = save_on_train_epoch_end
    self._enable_version_counter = enable_version_counter
    self._last_global_step_saved = 0  # no need to save when no steps were taken
    self._last_time_checked: Optional[float] = None
    self.current_score: Optional[Tensor] = None
    self.best_k_models: dict[str, Tensor] = {}
    self.kth_best_model_path = ""
    self.best_model_score: Optional[Tensor] = None
    self.best_model_path = ""
    self.last_model_path = ""
    self._last_checkpoint_saved = ""

    self.kth_value: Tensor
    self.dirpath: Optional[_PATH]
    self.__init_monitor_mode(mode)
    self.__init_ckpt_dir(dirpath, filename)
    self.__init_triggers(every_n_train_steps, every_n_epochs, train_time_interval)
    self.__validate_init_configuration()

@property
@override
def state_key(self) -> str:
    return self._generate_state_key(
        monitor=self.monitor,
        mode=self.mode,
        every_n_train_steps=self._every_n_train_steps,
        every_n_epochs=self._every_n_epochs,
        train_time_interval=self._train_time_interval,
    )

[docs] @override def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None: dirpath = self.__resolve_ckpt_dir(trainer) dirpath = trainer.strategy.broadcast(dirpath) self.dirpath = dirpath self._fs = get_filesystem(self.dirpath or "") if trainer.is_global_zero and stage == "fit": self.__warn_if_dir_not_empty(self.dirpath) if self.save_last == "link" and not _is_local_file_protocol(self.dirpath): raise ValueError( f"ModelCheckpoint(save_last='link') is only supported for local file paths, got dirpath={dirpath}." )

[docs] @override def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: self._last_time_checked = time.monotonic()

[docs] @override def on_train_batch_end( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: STEP_OUTPUT, batch: Any, batch_idx: int, ) -> None: """Save checkpoint on train batch end if we meet the criteria for every_n_train_steps""" if self._should_skip_saving_checkpoint(trainer): return skip_batch = self._every_n_train_steps < 1 or (trainer.global_step % self._every_n_train_steps != 0)

    train_time_interval = self._train_time_interval
    skip_time = True
    now = time.monotonic()
    if train_time_interval:
        prev_time_check = self._last_time_checked
        skip_time = prev_time_check is None or (now - prev_time_check) < train_time_interval.total_seconds()
        # in case we have time differences across ranks
        # broadcast the decision on whether to checkpoint from rank 0 to avoid possible hangs
        skip_time = trainer.strategy.broadcast(skip_time)

    if skip_batch and skip_time:
        return
    if not skip_time:
        self._last_time_checked = now

    monitor_candidates = self._monitor_candidates(trainer)
    self._save_topk_checkpoint(trainer, monitor_candidates)
    self._save_last_checkpoint(trainer, monitor_candidates)

[docs] @override def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: """Save a checkpoint at the end of the training epoch.""" if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer): monitor_candidates = self._monitor_candidates(trainer) if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0: self._save_topk_checkpoint(trainer, monitor_candidates) self._save_last_checkpoint(trainer, monitor_candidates)

[docs] @override def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: """Save a checkpoint at the end of the validation stage.""" if not self._should_skip_saving_checkpoint(trainer) and not self._should_save_on_train_epoch_end(trainer): monitor_candidates = self._monitor_candidates(trainer) if self._every_n_epochs >= 1 and (trainer.current_epoch + 1) % self._every_n_epochs == 0: self._save_topk_checkpoint(trainer, monitor_candidates) self._save_last_checkpoint(trainer, monitor_candidates)

[docs] @override def state_dict(self) -> dict[str, Any]: return { "monitor": self.monitor, "best_model_score": self.best_model_score, "best_model_path": self.best_model_path, "current_score": self.current_score, "dirpath": self.dirpath, "best_k_models": self.best_k_models, "kth_best_model_path": self.kth_best_model_path, "kth_value": self.kth_value, "last_model_path": self.last_model_path, }

[docs] @override def load_state_dict(self, state_dict: dict[str, Any]) -> None: dirpath_from_ckpt = state_dict.get("dirpath", self.dirpath)

    if self.dirpath == dirpath_from_ckpt:
        self.best_model_score = state_dict["best_model_score"]
        self.kth_best_model_path = state_dict.get("kth_best_model_path", self.kth_best_model_path)
        self.kth_value = state_dict.get("kth_value", self.kth_value)
        self.best_k_models = state_dict.get("best_k_models", self.best_k_models)
        self.last_model_path = state_dict.get("last_model_path", self.last_model_path)
    else:
        warnings.warn(
            f"The dirpath has changed from {dirpath_from_ckpt!r} to {self.dirpath!r},"
            " therefore `best_model_score`, `kth_best_model_path`, `kth_value`, `last_model_path` and"
            " `best_k_models` won't be reloaded. Only `best_model_path` will be reloaded."
        )

    self.best_model_path = state_dict["best_model_path"]


def _save_topk_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: dict[str, Tensor]) -> None:
    if self.save_top_k == 0:
        return

    # validate metric
    if self.monitor is not None:
        if self.monitor not in monitor_candidates:
            m = (
                f"`ModelCheckpoint(monitor={self.monitor!r})` could not find the monitored key in the returned"
                f" metrics: {list(monitor_candidates)}."
                f" HINT: Did you call `log({self.monitor!r}, value)` in the `LightningModule`?"
            )
            if trainer.fit_loop.epoch_loop.val_loop._has_run:
                raise MisconfigurationException(m)
            warning_cache.warn(m)
        self._save_monitor_checkpoint(trainer, monitor_candidates)
    else:
        self._save_none_monitor_checkpoint(trainer, monitor_candidates)

def _save_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
    trainer.save_checkpoint(filepath, self.save_weights_only)

    self._last_global_step_saved = trainer.global_step
    self._last_checkpoint_saved = filepath

    # notify loggers
    if trainer.is_global_zero:
        for logger in trainer.loggers:
            logger.after_save_checkpoint(proxy(self))

@staticmethod
def _link_checkpoint(trainer: "pl.Trainer", filepath: str, linkpath: str) -> None:
    if trainer.is_global_zero:
        if os.path.islink(linkpath) or os.path.isfile(linkpath):
            os.remove(linkpath)
        elif os.path.isdir(linkpath):
            shutil.rmtree(linkpath)
        try:
            os.symlink(os.path.relpath(filepath, os.path.dirname(linkpath)), linkpath)
        except OSError:
            # on Windows, special permissions are required to create symbolic links as a regular user
            # fall back to copying the file
            shutil.copy(filepath, linkpath)
    trainer.strategy.barrier()

def _should_skip_saving_checkpoint(self, trainer: "pl.Trainer") -> bool:
    from lightning.pytorch.trainer.states import TrainerFn

    return (
        bool(trainer.fast_dev_run)  # disable checkpointing with fast_dev_run
        or trainer.state.fn != TrainerFn.FITTING  # don't save anything during non-fit
        or trainer.sanity_checking  # don't save anything during sanity check
        or self._last_global_step_saved == trainer.global_step  # already saved at the last step
    )

def _should_save_on_train_epoch_end(self, trainer: "pl.Trainer") -> bool:
    if self._save_on_train_epoch_end is not None:
        return self._save_on_train_epoch_end

    # if `check_val_every_n_epoch != 1`, we can't say when the validation dataloader will be loaded
    # so let's not enforce saving at every training epoch end
    if trainer.check_val_every_n_epoch != 1:
        return False

    # no validation means save on train epoch end
    num_val_batches = (
        sum(trainer.num_val_batches) if isinstance(trainer.num_val_batches, list) else trainer.num_val_batches
    )
    if num_val_batches == 0:
        return True

    # if the user runs validation multiple times per training epoch, then we run after validation
    # instead of on train epoch end
    return trainer.val_check_interval == 1.0

def __validate_init_configuration(self) -> None:
    if self.save_top_k < -1:
        raise MisconfigurationException(f"Invalid value for save_top_k={self.save_top_k}. Must be >= -1")
    if self._every_n_train_steps < 0:
        raise MisconfigurationException(
            f"Invalid value for every_n_train_steps={self._every_n_train_steps}. Must be >= 0"
        )
    if self._every_n_epochs < 0:
        raise MisconfigurationException(f"Invalid value for every_n_epochs={self._every_n_epochs}. Must be >= 0")

    every_n_train_steps_triggered = self._every_n_train_steps >= 1
    every_n_epochs_triggered = self._every_n_epochs >= 1
    train_time_interval_triggered = self._train_time_interval is not None
    if every_n_train_steps_triggered + every_n_epochs_triggered + train_time_interval_triggered > 1:
        raise MisconfigurationException(
            f"Combination of parameters every_n_train_steps={self._every_n_train_steps}, "
            f"every_n_epochs={self._every_n_epochs} and train_time_interval={self._train_time_interval} "
            "should be mutually exclusive."
        )

    if self.monitor is None and self.save_top_k not in (-1, 0, 1):
        # -1: save all epochs, 0: nothing is saved, 1: save last epoch
        raise MisconfigurationException(
            f"ModelCheckpoint(save_top_k={self.save_top_k}, monitor=None) is not a valid"
            " configuration. No quantity for top_k to track."
        )

def __init_ckpt_dir(self, dirpath: Optional[_PATH], filename: Optional[str]) -> None:
    self._fs = get_filesystem(dirpath if dirpath else "")

    if dirpath and _is_local_file_protocol(dirpath if dirpath else ""):
        dirpath = os.path.realpath(os.path.expanduser(dirpath))

    self.dirpath = dirpath
    self.filename = filename

def __init_monitor_mode(self, mode: str) -> None:
    torch_inf = torch.tensor(torch.inf)
    mode_dict = {"min": (torch_inf, "min"), "max": (-torch_inf, "max")}

    if mode not in mode_dict:
        raise MisconfigurationException(f"`mode` can be {', '.join(mode_dict.keys())} but got {mode}")

    self.kth_value, self.mode = mode_dict[mode]

def __init_triggers(
    self,
    every_n_train_steps: Optional[int],
    every_n_epochs: Optional[int],
    train_time_interval: Optional[timedelta],
) -> None:
    # Default to running once after each validation epoch if neither
    # every_n_train_steps nor every_n_epochs is set
    if every_n_train_steps is None and every_n_epochs is None and train_time_interval is None:
        every_n_epochs = 1
        every_n_train_steps = 0
        log.debug("Both every_n_train_steps and every_n_epochs are not set. Setting every_n_epochs=1")
    else:
        every_n_epochs = every_n_epochs or 0
        every_n_train_steps = every_n_train_steps or 0

    self._train_time_interval: Optional[timedelta] = train_time_interval
    self._every_n_epochs: int = every_n_epochs
    self._every_n_train_steps: int = every_n_train_steps

@property
def every_n_epochs(self) -> Optional[int]:
    return self._every_n_epochs

def check_monitor_top_k(self, trainer: "pl.Trainer", current: Optional[Tensor] = None) -> bool:
    if current is None:
        return False

    if self.save_top_k == -1:
        return True

    less_than_k_models = len(self.best_k_models) < self.save_top_k
    if less_than_k_models:
        return True

    monitor_op = {"min": torch.lt, "max": torch.gt}[self.mode]
    should_update_best_and_save = monitor_op(current, self.best_k_models[self.kth_best_model_path])

    # If using multiple devices, make sure all processes are unanimous on the decision.
    should_update_best_and_save = trainer.strategy.reduce_boolean_decision(bool(should_update_best_and_save))

    return should_update_best_and_save

def _format_checkpoint_name(
    self,
    filename: Optional[str],
    metrics: dict[str, Tensor],
    prefix: str = "",
    auto_insert_metric_name: bool = True,
) -> str:
    if not filename:
        # filename is not set, use default name
        filename = "{epoch}" + self.CHECKPOINT_JOIN_CHAR + "{step}"

    # check and parse user passed keys in the string
    groups = re.findall(r"(\{.*?)[:\}]", filename)

    # sort keys from longest to shortest to avoid replacing substring
    # eg: if keys are "epoch" and "epoch_test", the latter must be replaced first
    groups = sorted(groups, key=lambda x: len(x), reverse=True)

    for group in groups:
        name = group[1:]

        if auto_insert_metric_name:
            filename = filename.replace(group, name + self.CHECKPOINT_EQUALS_CHAR + "{" + name)

        # support for dots: https://stackoverflow.com/a/7934969
        filename = filename.replace(group, f"{{0[{name}]")

        if name not in metrics:
            metrics[name] = torch.tensor(0)
    filename = filename.format(metrics)

    if prefix:
        filename = self.CHECKPOINT_JOIN_CHAR.join([prefix, filename])

    return filename

[docs] def format_checkpoint_name( self, metrics: dict[str, Tensor], filename: Optional[str] = None, ver: Optional[int] = None ) -> str: """Generate a filename according to the defined template.

    Example::

        >>> tmpdir = os.path.dirname(__file__)
        >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}')
        >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=0)))
        'epoch=0.ckpt'
        >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch:03d}')
        >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=5)))
        'epoch=005.ckpt'
        >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{epoch}-{val_loss:.2f}')
        >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.123456)))
        'epoch=2-val_loss=0.12.ckpt'
        >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.12), filename='{epoch:d}'))
        'epoch=2.ckpt'
        >>> ckpt = ModelCheckpoint(dirpath=tmpdir,
        ... filename='epoch={epoch}-validation_loss={val_loss:.2f}',
        ... auto_insert_metric_name=False)
        >>> os.path.basename(ckpt.format_checkpoint_name(dict(epoch=2, val_loss=0.123456)))
        'epoch=2-validation_loss=0.12.ckpt'
        >>> ckpt = ModelCheckpoint(dirpath=tmpdir, filename='{missing:d}')
        >>> os.path.basename(ckpt.format_checkpoint_name({}))
        'missing=0.ckpt'
        >>> ckpt = ModelCheckpoint(filename='{step}')
        >>> os.path.basename(ckpt.format_checkpoint_name(dict(step=0)))
        'step=0.ckpt'

    """
    filename = filename or self.filename
    filename = self._format_checkpoint_name(filename, metrics, auto_insert_metric_name=self.auto_insert_metric_name)

    if ver is not None:
        filename = self.CHECKPOINT_JOIN_CHAR.join((filename, f"v{ver}"))

    ckpt_name = f"{filename}{self.FILE_EXTENSION}"
    return os.path.join(self.dirpath, ckpt_name) if self.dirpath else ckpt_name


def __resolve_ckpt_dir(self, trainer: "pl.Trainer") -> _PATH:
    """Determines model checkpoint save directory at runtime. Reference attributes from the trainer's logger to
    determine where to save checkpoints. The path for saving weights is set in this priority:

    1.  The ``ModelCheckpoint``'s ``dirpath`` if passed in
    2.  The ``Logger``'s ``log_dir`` if the trainer has loggers
    3.  The ``Trainer``'s ``default_root_dir`` if the trainer has no loggers

    The path gets extended with subdirectory "checkpoints".

    """
    if self.dirpath is not None:
        # short circuit if dirpath was passed to ModelCheckpoint
        return self.dirpath

    if len(trainer.loggers) > 0:
        if trainer.loggers[0].save_dir is not None:
            save_dir = trainer.loggers[0].save_dir
        else:
            save_dir = trainer.default_root_dir
        name = trainer.loggers[0].name
        version = trainer.loggers[0].version
        version = version if isinstance(version, str) else f"version_{version}"
        ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
    else:
        # if no loggers, use default_root_dir
        ckpt_path = os.path.join(trainer.default_root_dir, "checkpoints")

    return ckpt_path

def _find_last_checkpoints(self, trainer: "pl.Trainer") -> set[str]:
    # find all checkpoints in the folder
    ckpt_path = self.__resolve_ckpt_dir(trainer)
    last_pattern = rf"^{self.CHECKPOINT_NAME_LAST}(-(\d+))?"

    def _is_last(path: Path) -> bool:
        return path.suffix == self.FILE_EXTENSION and bool(re.match(last_pattern, path.stem))

    if self._fs.exists(ckpt_path):
        return {os.path.normpath(p) for p in self._fs.ls(ckpt_path, detail=False) if _is_last(Path(p))}
    return set()

def __warn_if_dir_not_empty(self, dirpath: _PATH) -> None:
    if self.save_top_k != 0 and _is_dir(self._fs, dirpath, strict=True) and len(self._fs.ls(dirpath)) > 0:
        rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")

def _get_metric_interpolated_filepath_name(
    self, monitor_candidates: dict[str, Tensor], trainer: "pl.Trainer", del_filepath: Optional[str] = None
) -> str:
    filepath = self.format_checkpoint_name(monitor_candidates)

    if self._enable_version_counter:
        version_cnt = self.STARTING_VERSION
        while self.file_exists(filepath, trainer) and filepath != del_filepath:
            filepath = self.format_checkpoint_name(monitor_candidates, ver=version_cnt)
            version_cnt += 1

    return filepath

def _monitor_candidates(self, trainer: "pl.Trainer") -> dict[str, Tensor]:
    monitor_candidates = deepcopy(trainer.callback_metrics)
    # cast to int if necessary because `self.log("epoch", 123)` will convert it to float. if it's not a tensor
    # or does not exist we overwrite it as it's likely an error
    epoch = monitor_candidates.get("epoch")
    monitor_candidates["epoch"] = epoch.int() if isinstance(epoch, Tensor) else torch.tensor(trainer.current_epoch)
    step = monitor_candidates.get("step")
    monitor_candidates["step"] = step.int() if isinstance(step, Tensor) else torch.tensor(trainer.global_step)
    return monitor_candidates

def _save_last_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: dict[str, Tensor]) -> None:
    if not self.save_last:
        return

    filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST)

    if self._enable_version_counter:
        version_cnt = self.STARTING_VERSION
        while self.file_exists(filepath, trainer) and filepath != self.last_model_path:
            filepath = self.format_checkpoint_name(monitor_candidates, self.CHECKPOINT_NAME_LAST, ver=version_cnt)
            version_cnt += 1

    # set the last model path before saving because it will be part of the state.
    previous, self.last_model_path = self.last_model_path, filepath
    if self.save_last == "link" and self._last_checkpoint_saved and self.save_top_k != 0:
        self._link_checkpoint(trainer, self._last_checkpoint_saved, filepath)
    else:
        self._save_checkpoint(trainer, filepath)
    if previous and self._should_remove_checkpoint(trainer, previous, filepath):
        self._remove_checkpoint(trainer, previous)

def _save_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: dict[str, Tensor]) -> None:
    assert self.monitor
    current = monitor_candidates.get(self.monitor)
    if self.check_monitor_top_k(trainer, current):
        assert current is not None
        self._update_best_and_save(current, trainer, monitor_candidates)
    elif self.verbose:
        epoch = monitor_candidates["epoch"]
        step = monitor_candidates["step"]
        rank_zero_info(f"Epoch {epoch:d}, global step {step:d}: {self.monitor!r} was not in top {self.save_top_k}")

def _save_none_monitor_checkpoint(self, trainer: "pl.Trainer", monitor_candidates: dict[str, Tensor]) -> None:
    filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, self.best_model_path)
    # set the best model path before saving because it will be part of the state.
    previous, self.best_model_path = self.best_model_path, filepath
    self._save_checkpoint(trainer, filepath)

    if self.save_top_k == 1 and previous and self._should_remove_checkpoint(trainer, previous, filepath):
        self._remove_checkpoint(trainer, previous)

def _update_best_and_save(
    self, current: Tensor, trainer: "pl.Trainer", monitor_candidates: dict[str, Tensor]
) -> None:
    k = len(self.best_k_models) + 1 if self.save_top_k == -1 else self.save_top_k

    del_filepath = None
    if len(self.best_k_models) == k and k > 0:
        del_filepath = self.kth_best_model_path
        self.best_k_models.pop(del_filepath)

    # do not save nan, replace with +/- inf
    if isinstance(current, Tensor) and torch.isnan(current):
        current = torch.tensor(float("inf" if self.mode == "min" else "-inf"), device=current.device)

    filepath = self._get_metric_interpolated_filepath_name(monitor_candidates, trainer, del_filepath)

    # save the current score
    self.current_score = current
    self.best_k_models[filepath] = current

    if len(self.best_k_models) == k:
        # monitor dict has reached k elements
        _op = max if self.mode == "min" else min
        self.kth_best_model_path = _op(self.best_k_models, key=self.best_k_models.get)  # type: ignore[arg-type]
        self.kth_value = self.best_k_models[self.kth_best_model_path]

    _op = min if self.mode == "min" else max
    self.best_model_path = _op(self.best_k_models, key=self.best_k_models.get)  # type: ignore[arg-type]
    self.best_model_score = self.best_k_models[self.best_model_path]

    if self.verbose:
        epoch = monitor_candidates["epoch"]
        step = monitor_candidates["step"]
        rank_zero_info(
            f"Epoch {epoch:d}, global step {step:d}: {self.monitor!r} reached {current:0.5f}"
            f" (best {self.best_model_score:0.5f}), saving model to {filepath!r} as top {k}"
        )
    self._save_checkpoint(trainer, filepath)

    if del_filepath and self._should_remove_checkpoint(trainer, del_filepath, filepath):
        self._remove_checkpoint(trainer, del_filepath)

[docs] def to_yaml(self, filepath: Optional[_PATH] = None) -> None: """Saves the best_k_models dict containing the checkpoint paths with the corresponding scores to a YAML file.""" best_k = {k: v.item() for k, v in self.best_k_models.items()} if filepath is None: assert self.dirpath filepath = os.path.join(self.dirpath, "best_k_models.yaml") with self._fs.open(filepath, "w") as fp: yaml.dump(best_k, fp)

[docs] def file_exists(self, filepath: _PATH, trainer: "pl.Trainer") -> bool: """Checks if a file exists on rank 0 and broadcasts the result to all other ranks, preventing the internal state to diverge between ranks.""" exists = self._fs.exists(filepath) return trainer.strategy.broadcast(exists)

def _should_remove_checkpoint(self, trainer: "pl.Trainer", previous: str, current: str) -> bool:
    """Checks if the previous checkpoint should be deleted.

    A checkpoint won't be deleted if any of the cases apply:
    - The previous checkpoint is the same as the current checkpoint (means the old was already overwritten by new)
    - The previous checkpoint is not in the current checkpoint directory and the filesystem is local
    - The previous checkpoint is the checkpoint the Trainer resumed from and the filesystem is local

    """
    if previous == current:
        return False
    if not _is_local_file_protocol(previous):
        return True
    previous = Path(previous).absolute()
    resume_path = Path(trainer.ckpt_path).absolute() if trainer.ckpt_path is not None else None
    if resume_path is not None and previous == resume_path:
        return False
    assert self.dirpath is not None
    dirpath = Path(self.dirpath).absolute()
    return dirpath in previous.parents

def _remove_checkpoint(self, trainer: "pl.Trainer", filepath: str) -> None:
    """Calls the strategy to remove the checkpoint file."""
    trainer.strategy.remove_checkpoint(filepath)