# 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.
"""
Model Checkpointing
===================
Automatically save model checkpoints during training.
"""
import logging
import os
import re
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, Optional, Union
import numpy as np
import torch
import yaml
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.warnings import WarningCache
log = logging.getLogger(__name__)
warning_cache = WarningCache()
[docs]class ModelCheckpoint(Callback):
r"""
Save the model after every epoch by monitoring a quantity.
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:`~pytorch_lightning.trainer.trainer.Trainer`'s
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.default_root_dir` or
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.weights_save_path` arguments,
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}'``.
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``, always saves the model at the end of the epoch to
a file `last.ckpt`. 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 ``period`` epochs.
if ``save_top_k >= 2`` and the callback is called multiple
times inside an epoch, the name of the saved file will be
appended with a version count starting with ``v1``.
mode: one of {auto, 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. In `auto` mode, the direction is
automatically inferred from the name of the monitored quantity.
.. warning::
Setting ``mode='auto'`` has been deprecated in v1.1 and will be removed in v1.3.
save_weights_only: if ``True``, then only the model's weights will be
saved (``model.save_weights(filepath)``), else the full model
is saved (``model.save(filepath)``).
period: Interval (number of epochs) between checkpoints.
prefix: A string to put at the beginning of checkpoint filename.
.. warning::
This argument has been deprecated in v1.1 and will be removed in v1.3
Note:
For extra customization, ModelCheckpoint includes the following attributes:
- ``CHECKPOINT_JOIN_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"``
Raises:
MisconfigurationException:
If ``save_top_k`` is neither ``None`` nor more than or equal to ``-1``,
if ``monitor`` is ``None`` and ``save_top_k`` is none of ``None``, ``-1``, and ``0``, or
if ``mode`` is none of ``"min"``, ``"max"``, and ``"auto"``.
ValueError:
If ``trainer.save_checkpoint`` is ``None``.
Example::
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.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}'
... )
# 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
"""
CHECKPOINT_JOIN_CHAR = "-"
CHECKPOINT_NAME_LAST = "last"
FILE_EXTENSION = ".ckpt"
STARTING_VERSION = 1
def __init__(
self,
dirpath: Optional[Union[str, Path]] = None,
filename: Optional[str] = None,
monitor: Optional[str] = None,
verbose: bool = False,
save_last: Optional[bool] = None,
save_top_k: Optional[int] = None,
save_weights_only: bool = False,
mode: str = "auto",
period: int = 1,
prefix: str = "",
):
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.period = period
self._last_global_step_saved = -1
self.prefix = prefix
self.current_score = None
self.best_k_models = {}
self.kth_best_model_path = ""
self.best_model_score = None
self.best_model_path = ""
self.last_model_path = ""
self.save_function = None
self.warned_result_obj = False
if prefix:
rank_zero_warn(
'Argument `prefix` is deprecated in v1.1 and will be removed in v1.3.'
' Please prepend your prefix in `filename` instead.', DeprecationWarning
)
self.__init_monitor_mode(monitor, mode)
self.__init_ckpt_dir(dirpath, filename, save_top_k)
self.__validate_init_configuration()
[docs] def on_pretrain_routine_start(self, trainer, pl_module):
"""
When pretrain routine starts we build the ckpt dir on the fly
"""
self.__resolve_ckpt_dir(trainer)
self.save_function = trainer.save_checkpoint
[docs] def on_validation_end(self, trainer, pl_module):
"""
checkpoints can be saved at the end of the val loop
"""
self.save_checkpoint(trainer, pl_module)
[docs] def on_save_checkpoint(self, trainer, pl_module, checkpoint: Dict[str, Any]) -> 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
}
[docs] def on_load_checkpoint(self, callback_state: Dict[str, Any]):
self.best_model_score = callback_state["best_model_score"]
self.best_model_path = callback_state["best_model_path"]
[docs] def save_checkpoint(self, trainer, pl_module):
"""
Performs the main logic around saving a checkpoint.
This method runs on all ranks, it is the responsibility of `self.save_function`
to handle correct behaviour in distributed training, i.e., saving only on rank 0.
"""
epoch = trainer.current_epoch
global_step = trainer.global_step
if (
trainer.fast_dev_run # disable checkpointing with fast_dev_run
or self.save_top_k == 0 # no models are saved
or self.period < 1 # no models are saved
or (epoch + 1) % self.period # skip epoch
or trainer.running_sanity_check # don't save anything during sanity check
or self._last_global_step_saved == global_step # already saved at the last step
):
return
self._add_backward_monitor_support(trainer)
self._validate_monitor_key(trainer)
# track epoch when ckpt was last checked
self._last_global_step_saved = global_step
# what can be monitored
monitor_candidates = self._monitor_candidates(trainer)
# callback supports multiple simultaneous modes
# here we call each mode sequentially
# Mode 1: save all checkpoints OR only the top k
if self.save_top_k:
self._save_top_k_checkpoints(trainer, pl_module, monitor_candidates)
# Mode 2: save the last checkpoint
self._save_last_checkpoint(trainer, pl_module, monitor_candidates)
def __validate_init_configuration(self):
if self.save_top_k is not None and self.save_top_k < -1:
raise MisconfigurationException(f'Invalid value for save_top_k={self.save_top_k}. Must be None or >= -1')
if self.monitor is None:
# None: save last epoch, -1: save all epochs, 0: nothing is saved
if self.save_top_k not in [None, -1, 0]:
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.'
)
if self.save_last:
rank_zero_warn(
'ModelCheckpoint(save_last=True, monitor=None) is a redundant configuration.'
' You can save the last checkpoint with ModelCheckpoint(save_top_k=None, monitor=None).'
)
def __init_ckpt_dir(self, dirpath, filename, save_top_k):
self._fs = get_filesystem(str(dirpath) if dirpath else '')
if (
save_top_k is not None and save_top_k > 0 and dirpath is not None and self._fs.isdir(dirpath)
and len(self._fs.ls(dirpath)) > 0
):
rank_zero_warn(f"Checkpoint directory {dirpath} exists and is not empty.")
if dirpath and self._fs.protocol == 'file':
dirpath = os.path.realpath(dirpath)
self.dirpath: Union[str, None] = dirpath or None
self.filename = filename or None
def __init_monitor_mode(self, monitor, mode):
torch_inf = torch.tensor(np.Inf)
mode_dict = {
"min": (torch_inf, "min"),
"max": (-torch_inf, "max"),
}
if mode not in mode_dict and mode != 'auto':
raise MisconfigurationException(f"`mode` can be auto, {', '.join(mode_dict.keys())}, got {mode}")
# TODO: Update with MisconfigurationException when auto mode is removed in v1.3
if mode == 'auto':
rank_zero_warn(
"mode='auto' is deprecated in v1.1 and will be removed in v1.3."
" Default value for mode with be 'min' in v1.3.", DeprecationWarning
)
_condition = monitor is not None and ("acc" in monitor or monitor.startswith("fmeasure"))
mode_dict['auto'] = ((-torch_inf, "max") if _condition else (torch_inf, "min"))
self.kth_value, self.mode = mode_dict[mode]
@rank_zero_only
def _del_model(self, filepath: str):
if self._fs.exists(filepath):
self._fs.rm(filepath)
log.debug(f"Removed checkpoint: {filepath}")
def _save_model(self, filepath: str, trainer, pl_module):
# Todo: required argument `pl_module` is not used
# in debugging, track when we save checkpoints
trainer.dev_debugger.track_checkpointing_history(filepath)
# make paths
if trainer.is_global_zero:
self._fs.makedirs(os.path.dirname(filepath), exist_ok=True)
# delegate the saving to the trainer
if self.save_function is not None:
self.save_function(filepath, self.save_weights_only)
else:
raise ValueError(".save_function() not set")
def check_monitor_top_k(self, trainer, current: Optional[torch.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
if not isinstance(current, torch.Tensor):
rank_zero_warn(
f"{current} is supposed to be a `torch.Tensor`. Saving checkpoint may not work correctly."
f" HINT: check the value of {self.monitor} in your validation loop",
RuntimeWarning,
)
current = torch.tensor(current)
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.training_type_plugin.reduce_boolean_decision(should_update_best_and_save)
return should_update_best_and_save
@classmethod
def _format_checkpoint_name(
cls,
filename: Optional[str],
epoch: int,
step: int,
metrics: Dict[str, Any],
prefix: str = "",
) -> str:
if not filename:
# filename is not set, use default name
filename = "{epoch}" + cls.CHECKPOINT_JOIN_CHAR + "{step}"
# check and parse user passed keys in the string
groups = re.findall(r"(\{.*?)[:\}]", filename)
if len(groups) >= 0:
metrics.update({"epoch": epoch, 'step': step})
for group in groups:
name = group[1:]
filename = filename.replace(group, name + "={" + name)
if name not in metrics:
metrics[name] = 0
filename = filename.format(**metrics)
if prefix:
filename = cls.CHECKPOINT_JOIN_CHAR.join([prefix, filename])
return filename
def __resolve_ckpt_dir(self, trainer):
"""
Determines model checkpoint save directory at runtime. References attributes from the
trainer's logger to determine where to save checkpoints.
The base path for saving weights is set in this priority:
1. Checkpoint callback's path (if passed in)
2. The default_root_dir from trainer if trainer has no logger
3. The weights_save_path from trainer, if user provides it
4. User provided weights_saved_path
The base path gets extended with logger name and version (if these are available)
and subfolder "checkpoints".
"""
# Todo: required argument `pl_module` is not used
if self.dirpath is not None:
return # short circuit
if trainer.logger is not None:
if trainer.weights_save_path != trainer.default_root_dir:
# the user has changed weights_save_path, it overrides anything
save_dir = trainer.weights_save_path
else:
save_dir = trainer.logger.save_dir or trainer.default_root_dir
version = (
trainer.logger.version
if isinstance(trainer.logger.version, str) else f"version_{trainer.logger.version}"
)
version, name = trainer.training_type_plugin.broadcast((version, trainer.logger.name))
ckpt_path = os.path.join(save_dir, str(name), version, "checkpoints")
else:
ckpt_path = os.path.join(trainer.weights_save_path, "checkpoints")
self.dirpath = ckpt_path
if not trainer.fast_dev_run and trainer.is_global_zero:
self._fs.makedirs(self.dirpath, exist_ok=True)
def _add_backward_monitor_support(self, trainer):
metrics = trainer.logger_connector.callback_metrics
deprecation_warning = False
if self.monitor is None and 'val_loss' in metrics:
self.monitor = 'val_loss'
deprecation_warning = True
if self.save_top_k is None and self.monitor is not None:
# TODO: Remove `Optional` from `save_top_k` when this is deleted in v1.4
self.save_top_k = 1
if deprecation_warning:
warning_cache.warn(
"Relying on `self.log('val_loss', ...)` to set the ModelCheckpoint monitor is deprecated in v1.2"
" and will be removed in v1.4. Please, create your own `mc = ModelCheckpoint(monitor='your_monitor')`"
" and use it as `Trainer(callbacks=[mc])`.", DeprecationWarning
)
def _validate_monitor_key(self, trainer):
metrics = trainer.logger_connector.callback_metrics
# validate metric
if self.monitor is not None and not self._is_valid_monitor_key(metrics):
m = (
f"ModelCheckpoint(monitor='{self.monitor}') not found in the returned metrics:"
f" {list(metrics.keys())}. "
f"HINT: Did you call self.log('{self.monitor}', value) in the LightningModule?"
)
raise MisconfigurationException(m)
def _get_metric_interpolated_filepath_name(
self,
ckpt_name_metrics: Dict[str, Any],
epoch: int,
step: int,
trainer,
del_filepath: Optional[str] = None,
) -> str:
filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics)
version_cnt = self.STARTING_VERSION
while self.file_exists(filepath, trainer) and filepath != del_filepath:
filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics, ver=version_cnt)
version_cnt += 1
return filepath
def _monitor_candidates(self, trainer):
monitor_candidates = deepcopy(trainer.logger_connector.callback_metrics)
monitor_candidates.update(step=trainer.global_step, epoch=trainer.current_epoch)
return monitor_candidates
def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
should_save_last = self.monitor is None or self.save_last
if not should_save_last:
return
# when user ALSO asked for the 'last.ckpt' change the name
if self.save_last:
last_filepath = self._format_checkpoint_name(
self.CHECKPOINT_NAME_LAST,
trainer.current_epoch,
trainer.global_step,
ckpt_name_metrics,
prefix=self.prefix
)
last_filepath = os.path.join(self.dirpath, f"{last_filepath}{self.FILE_EXTENSION}")
else:
last_filepath = self._get_metric_interpolated_filepath_name(
ckpt_name_metrics,
trainer.current_epoch,
trainer.global_step,
trainer,
)
if trainer.training_type_plugin.rpc_enabled:
# RPCPlugin manages saving all model states
trainer.training_type_plugin.rpc_save_model(self._save_model, last_filepath, trainer, pl_module)
else:
self._save_model(last_filepath, trainer, pl_module)
if (
self.last_model_path and self.last_model_path != last_filepath
and (self.save_top_k != -1 or self.save_last) and trainer.is_global_zero
):
self._del_model(self.last_model_path)
self.last_model_path = last_filepath
if self.monitor is None:
self.best_model_path = self.last_model_path
def _save_top_k_checkpoints(self, trainer, pl_module, metrics):
current = metrics.get(self.monitor)
epoch = metrics.get("epoch")
step = metrics.get("step")
if self.check_monitor_top_k(trainer, current):
self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)
elif self.verbose:
rank_zero_info(f"Epoch {epoch:d}, step {step:d}: {self.monitor} was not in top {self.save_top_k}")
def _is_valid_monitor_key(self, metrics):
return self.monitor in metrics or len(metrics) == 0
def _update_best_and_save(
self, current: torch.Tensor, epoch: int, step: int, trainer, pl_module, ckpt_name_metrics
):
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, torch.Tensor) and torch.isnan(current):
current = torch.tensor(float('inf' if self.mode == "min" else '-inf'))
filepath = self._get_metric_interpolated_filepath_name(ckpt_name_metrics, epoch, step, 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)
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)
self.best_model_score = self.best_k_models[self.best_model_path]
if self.verbose:
rank_zero_info(
f"Epoch {epoch:d}, global step {step:d}: {self.monitor} reached {current:0.5f}"
f' (best {self.best_model_score:0.5f}), saving model to "{filepath}" as top {k}'
)
self._save_model(filepath, trainer, pl_module)
if del_filepath is not None and filepath != del_filepath:
self._del_model(del_filepath)
[docs] def to_yaml(self, filepath: Optional[Union[str, Path]] = 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:
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: Union[str, Path], 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.training_type_plugin.broadcast(exists)
