Source code for pytorch_lightning.cli
# Copyright The Lightning AI 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 os
import sys
from functools import partial, update_wrapper
from types import MethodType
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
import torch
from lightning_utilities.core.imports import RequirementCache
from lightning_utilities.core.rank_zero import _warn
from torch.optim import Optimizer
import pytorch_lightning as pl
from lightning_fabric.utilities.cloud_io import get_filesystem
from lightning_fabric.utilities.types import _TORCH_LRSCHEDULER
from pytorch_lightning import Callback, LightningDataModule, LightningModule, seed_everything, Trainer
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.rank_zero import rank_zero_deprecation, rank_zero_warn
_JSONARGPARSE_SIGNATURES_AVAILABLE = RequirementCache("jsonargparse[signatures]>=4.17.0")
if _JSONARGPARSE_SIGNATURES_AVAILABLE:
import docstring_parser
from jsonargparse import (
ActionConfigFile,
ArgumentParser,
class_from_function,
Namespace,
register_unresolvable_import_paths,
set_config_read_mode,
)
register_unresolvable_import_paths(torch) # Required until fix https://github.com/pytorch/pytorch/issues/74483
set_config_read_mode(fsspec_enabled=True)
else:
locals()["ArgumentParser"] = object
locals()["Namespace"] = object
class ReduceLROnPlateau(torch.optim.lr_scheduler.ReduceLROnPlateau):
def __init__(self, optimizer: Optimizer, monitor: str, *args: Any, **kwargs: Any) -> None:
super().__init__(optimizer, *args, **kwargs)
self.monitor = monitor
# LightningCLI requires the ReduceLROnPlateau defined here, thus it shouldn't accept the one from pytorch:
LRSchedulerTypeTuple = (_TORCH_LRSCHEDULER, ReduceLROnPlateau)
LRSchedulerTypeUnion = Union[_TORCH_LRSCHEDULER, ReduceLROnPlateau]
LRSchedulerType = Union[Type[_TORCH_LRSCHEDULER], Type[ReduceLROnPlateau]]
# Type aliases intended for convenience of CLI developers
ArgsType = Optional[Union[List[str], Dict[str, Any], Namespace]]
OptimizerCallable = Callable[[Iterable], Optimizer]
LRSchedulerCallable = Callable[[Optimizer], Union[_TORCH_LRSCHEDULER, ReduceLROnPlateau]]
[docs]class LightningArgumentParser(ArgumentParser):
"""Extension of jsonargparse's ArgumentParser for pytorch-lightning."""
def __init__(
self,
*args: Any,
description: str = "pytorch-lightning trainer command line tool",
env_prefix: str = "PL",
default_env: bool = False,
**kwargs: Any,
) -> None:
"""Initialize argument parser that supports configuration file input.
For full details of accepted arguments see `ArgumentParser.__init__
<https://jsonargparse.readthedocs.io/en/stable/#jsonargparse.ArgumentParser.__init__>`_.
Args:
description: Description of the tool shown when running ``--help``.
env_prefix: Prefix for environment variables. Set ``default_env=True`` to enable env parsing.
default_env: Whether to parse environment variables.
"""
if not _JSONARGPARSE_SIGNATURES_AVAILABLE:
raise ModuleNotFoundError(
f"{_JSONARGPARSE_SIGNATURES_AVAILABLE}. Try `pip install -U 'jsonargparse[signatures]'`."
)
super().__init__(*args, description=description, env_prefix=env_prefix, default_env=default_env, **kwargs)
self.callback_keys: List[str] = []
# separate optimizers and lr schedulers to know which were added
self._optimizers: Dict[str, Tuple[Union[Type, Tuple[Type, ...]], str]] = {}
self._lr_schedulers: Dict[str, Tuple[Union[Type, Tuple[Type, ...]], str]] = {}
[docs] def add_lightning_class_args(
self,
lightning_class: Union[
Callable[..., Union[Trainer, LightningModule, LightningDataModule, Callback]],
Type[Trainer],
Type[LightningModule],
Type[LightningDataModule],
Type[Callback],
],
nested_key: str,
subclass_mode: bool = False,
required: bool = True,
) -> List[str]:
"""Adds arguments from a lightning class to a nested key of the parser.
Args:
lightning_class: A callable or any subclass of {Trainer, LightningModule, LightningDataModule, Callback}.
nested_key: Name of the nested namespace to store arguments.
subclass_mode: Whether allow any subclass of the given class.
required: Whether the argument group is required.
Returns:
A list with the names of the class arguments added.
"""
if callable(lightning_class) and not isinstance(lightning_class, type):
lightning_class = class_from_function(lightning_class)
if isinstance(lightning_class, type) and issubclass(
lightning_class, (Trainer, LightningModule, LightningDataModule, Callback)
):
if issubclass(lightning_class, Callback):
self.callback_keys.append(nested_key)
if subclass_mode:
return self.add_subclass_arguments(lightning_class, nested_key, fail_untyped=False, required=required)
return self.add_class_arguments(
lightning_class,
nested_key,
fail_untyped=False,
instantiate=not issubclass(lightning_class, Trainer),
sub_configs=True,
)
raise MisconfigurationException(
f"Cannot add arguments from: {lightning_class}. You should provide either a callable or a subclass of: "
"Trainer, LightningModule, LightningDataModule, or Callback."
)
[docs] def add_optimizer_args(
self,
optimizer_class: Union[Type[Optimizer], Tuple[Type[Optimizer], ...]] = (Optimizer,),
nested_key: str = "optimizer",
link_to: str = "AUTOMATIC",
) -> None:
"""Adds arguments from an optimizer class to a nested key of the parser.
Args:
optimizer_class: Any subclass of :class:`torch.optim.Optimizer`. Use tuple to allow subclasses.
nested_key: Name of the nested namespace to store arguments.
link_to: Dot notation of a parser key to set arguments or AUTOMATIC.
"""
if isinstance(optimizer_class, tuple):
assert all(issubclass(o, Optimizer) for o in optimizer_class)
else:
assert issubclass(optimizer_class, Optimizer)
kwargs: Dict[str, Any] = {"instantiate": False, "fail_untyped": False, "skip": {"params"}}
if isinstance(optimizer_class, tuple):
self.add_subclass_arguments(optimizer_class, nested_key, **kwargs)
else:
self.add_class_arguments(optimizer_class, nested_key, sub_configs=True, **kwargs)
self._optimizers[nested_key] = (optimizer_class, link_to)
[docs] def add_lr_scheduler_args(
self,
lr_scheduler_class: Union[LRSchedulerType, Tuple[LRSchedulerType, ...]] = LRSchedulerTypeTuple,
nested_key: str = "lr_scheduler",
link_to: str = "AUTOMATIC",
) -> None:
"""Adds arguments from a learning rate scheduler class to a nested key of the parser.
Args:
lr_scheduler_class: Any subclass of ``torch.optim.lr_scheduler.{_LRScheduler, ReduceLROnPlateau}``. Use
tuple to allow subclasses.
nested_key: Name of the nested namespace to store arguments.
link_to: Dot notation of a parser key to set arguments or AUTOMATIC.
"""
if isinstance(lr_scheduler_class, tuple):
assert all(issubclass(o, LRSchedulerTypeTuple) for o in lr_scheduler_class)
else:
assert issubclass(lr_scheduler_class, LRSchedulerTypeTuple)
kwargs: Dict[str, Any] = {"instantiate": False, "fail_untyped": False, "skip": {"optimizer"}}
if isinstance(lr_scheduler_class, tuple):
self.add_subclass_arguments(lr_scheduler_class, nested_key, **kwargs)
else:
self.add_class_arguments(lr_scheduler_class, nested_key, sub_configs=True, **kwargs)
self._lr_schedulers[nested_key] = (lr_scheduler_class, link_to)
[docs]class SaveConfigCallback(Callback):
"""Saves a LightningCLI config to the log_dir when training starts.
Args:
parser: The parser object used to parse the configuration.
config: The parsed configuration that will be saved.
config_filename: Filename for the config file.
overwrite: Whether to overwrite an existing config file.
multifile: When input is multiple config files, saved config preserves this structure.
Raises:
RuntimeError: If the config file already exists in the directory to avoid overwriting a previous run
"""
def __init__(
self,
parser: LightningArgumentParser,
config: Namespace,
config_filename: str = "config.yaml",
overwrite: bool = False,
multifile: bool = False,
) -> None:
self.parser = parser
self.config = config
self.config_filename = config_filename
self.overwrite = overwrite
self.multifile = multifile
self.already_saved = False
[docs] def setup(self, trainer: Trainer, pl_module: LightningModule, stage: str) -> None:
if self.already_saved:
return
log_dir = trainer.log_dir # this broadcasts the directory
assert log_dir is not None
config_path = os.path.join(log_dir, self.config_filename)
fs = get_filesystem(log_dir)
if not self.overwrite:
# check if the file exists on rank 0
file_exists = fs.isfile(config_path) if trainer.is_global_zero else False
# broadcast whether to fail to all ranks
file_exists = trainer.strategy.broadcast(file_exists)
if file_exists:
raise RuntimeError(
f"{self.__class__.__name__} expected {config_path} to NOT exist. Aborting to avoid overwriting"
" results of a previous run. You can delete the previous config file,"
" set `LightningCLI(save_config_callback=None)` to disable config saving,"
' or set `LightningCLI(save_config_kwargs={"overwrite": True})` to overwrite the config file.'
)
# save the file on rank 0
if trainer.is_global_zero:
# save only on rank zero to avoid race conditions.
# the `log_dir` needs to be created as we rely on the logger to do it usually
# but it hasn't logged anything at this point
fs.makedirs(log_dir, exist_ok=True)
self.parser.save(
self.config, config_path, skip_none=False, overwrite=self.overwrite, multifile=self.multifile
)
self.already_saved = True
# broadcast so that all ranks are in sync on future calls to .setup()
self.already_saved = trainer.strategy.broadcast(self.already_saved)
[docs]class LightningCLI:
"""Implementation of a configurable command line tool for pytorch-lightning."""
def __init__(
self,
model_class: Optional[Union[Type[LightningModule], Callable[..., LightningModule]]] = None,
datamodule_class: Optional[Union[Type[LightningDataModule], Callable[..., LightningDataModule]]] = None,
save_config_callback: Optional[Type[SaveConfigCallback]] = SaveConfigCallback,
save_config_kwargs: Optional[Dict[str, Any]] = None,
trainer_class: Union[Type[Trainer], Callable[..., Trainer]] = Trainer,
trainer_defaults: Optional[Dict[str, Any]] = None,
seed_everything_default: Union[bool, int] = True,
parser_kwargs: Optional[Union[Dict[str, Any], Dict[str, Dict[str, Any]]]] = None,
subclass_mode_model: bool = False,
subclass_mode_data: bool = False,
args: ArgsType = None,
run: bool = True,
auto_configure_optimizers: bool = True,
**kwargs: Any, # Remove with deprecations of v2.0.0
) -> None:
"""Receives as input pytorch-lightning classes (or callables which return pytorch-lightning classes), which
are called / instantiated using a parsed configuration file and / or command line args.
Parsing of configuration from environment variables can be enabled by setting ``parser_kwargs={"default_env":
True}``. A full configuration yaml would be parsed from ``PL_CONFIG`` if set. Individual settings are so parsed
from variables named for example ``PL_TRAINER__MAX_EPOCHS``.
For more info, read :ref:`the CLI docs <lightning-cli>`.
.. warning:: ``LightningCLI`` is in beta and subject to change.
Args:
model_class: An optional :class:`~pytorch_lightning.core.module.LightningModule` class to train on or a
callable which returns a :class:`~pytorch_lightning.core.module.LightningModule` instance when
called. If ``None``, you can pass a registered model with ``--model=MyModel``.
datamodule_class: An optional :class:`~pytorch_lightning.core.datamodule.LightningDataModule` class or a
callable which returns a :class:`~pytorch_lightning.core.datamodule.LightningDataModule` instance when
called. If ``None``, you can pass a registered datamodule with ``--data=MyDataModule``.
save_config_callback: A callback class to save the config.
save_config_kwargs: Parameters that will be used to instantiate the save_config_callback.
trainer_class: An optional subclass of the :class:`~pytorch_lightning.trainer.trainer.Trainer` class or a
callable which returns a :class:`~pytorch_lightning.trainer.trainer.Trainer` instance when called.
trainer_defaults: Set to override Trainer defaults or add persistent callbacks. The callbacks added through
this argument will not be configurable from a configuration file and will always be present for
this particular CLI. Alternatively, configurable callbacks can be added as explained in
:ref:`the CLI docs <lightning-cli>`.
seed_everything_default: Number for the :func:`~lightning_fabric.utilities.seed.seed_everything`
seed value. Set to True to automatically choose a seed value.
Setting it to False will avoid calling ``seed_everything``.
parser_kwargs: Additional arguments to instantiate each ``LightningArgumentParser``.
subclass_mode_model: Whether model can be any `subclass
<https://jsonargparse.readthedocs.io/en/stable/#class-type-and-sub-classes>`_
of the given class.
subclass_mode_data: Whether datamodule can be any `subclass
<https://jsonargparse.readthedocs.io/en/stable/#class-type-and-sub-classes>`_
of the given class.
args: Arguments to parse. If ``None`` the arguments are taken from ``sys.argv``. Command line style
arguments can be given in a ``list``. Alternatively, structured config options can be given in a
``dict`` or ``jsonargparse.Namespace``.
run: Whether subcommands should be added to run a :class:`~pytorch_lightning.trainer.trainer.Trainer`
method. If set to ``False``, the trainer and model classes will be instantiated only.
"""
self.save_config_callback = save_config_callback
self.save_config_kwargs = save_config_kwargs or {}
self.trainer_class = trainer_class
self.trainer_defaults = trainer_defaults or {}
self.seed_everything_default = seed_everything_default
self.parser_kwargs = parser_kwargs or {} # type: ignore[var-annotated] # github.com/python/mypy/issues/6463
self.auto_configure_optimizers = auto_configure_optimizers
self._handle_deprecated_params(kwargs)
self.model_class = model_class
# used to differentiate between the original value and the processed value
self._model_class = model_class or LightningModule
self.subclass_mode_model = (model_class is None) or subclass_mode_model
self.datamodule_class = datamodule_class
# used to differentiate between the original value and the processed value
self._datamodule_class = datamodule_class or LightningDataModule
self.subclass_mode_data = (datamodule_class is None) or subclass_mode_data
main_kwargs, subparser_kwargs = self._setup_parser_kwargs(self.parser_kwargs)
self.setup_parser(run, main_kwargs, subparser_kwargs)
self.parse_arguments(self.parser, args)
self.subcommand = self.config["subcommand"] if run else None
self._set_seed()
self.before_instantiate_classes()
self.instantiate_classes()
if self.subcommand is not None:
self._run_subcommand(self.subcommand)
def _handle_deprecated_params(self, kwargs: dict) -> None:
for name in kwargs.keys() & ["save_config_filename", "save_config_overwrite", "save_config_multifile"]:
value = kwargs.pop(name)
key = name.replace("save_config_", "").replace("filename", "config_filename")
self.save_config_kwargs[key] = value
rank_zero_deprecation(
f"LightningCLI's {name!r} init parameter is deprecated from v1.8 and will "
f"be removed in v2.0.0. Use `save_config_kwargs={{'{key}': ...}}` instead."
)
for name in kwargs.keys() & ["description", "env_prefix", "env_parse"]:
value = kwargs.pop(name)
key = name.replace("env_parse", "default_env")
self.parser_kwargs[key] = value
rank_zero_deprecation(
f"LightningCLI's {name!r} init parameter is deprecated from v1.9 and will "
f"be removed in v2.0. Use `parser_kwargs={{'{key}': ...}}` instead."
)
if kwargs:
raise ValueError(f"Unexpected keyword parameters: {kwargs}")
def _setup_parser_kwargs(self, parser_kwargs: Dict[str, Any]) -> Tuple[Dict[str, Any], Dict[str, Any]]:
subcommand_names = self.subcommands().keys()
main_kwargs = {k: v for k, v in parser_kwargs.items() if k not in subcommand_names}
subparser_kwargs = {k: v for k, v in parser_kwargs.items() if k in subcommand_names}
return main_kwargs, subparser_kwargs
[docs] def init_parser(self, **kwargs: Any) -> LightningArgumentParser:
"""Method that instantiates the argument parser."""
kwargs.setdefault("dump_header", [f"pytorch_lightning=={pl.__version__}"])
parser = LightningArgumentParser(**kwargs)
parser.add_argument(
"-c", "--config", action=ActionConfigFile, help="Path to a configuration file in json or yaml format."
)
return parser
[docs] def setup_parser(
self, add_subcommands: bool, main_kwargs: Dict[str, Any], subparser_kwargs: Dict[str, Any]
) -> None:
"""Initialize and setup the parser, subcommands, and arguments."""
self.parser = self.init_parser(**main_kwargs)
if add_subcommands:
self._subcommand_method_arguments: Dict[str, List[str]] = {}
self._add_subcommands(self.parser, **subparser_kwargs)
else:
self._add_arguments(self.parser)
[docs] def add_default_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
"""Adds default arguments to the parser."""
parser.add_argument(
"--seed_everything",
type=Union[bool, int],
default=self.seed_everything_default,
help=(
"Set to an int to run seed_everything with this value before classes instantiation."
"Set to True to use a random seed."
),
)
[docs] def add_core_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
"""Adds arguments from the core classes to the parser."""
parser.add_lightning_class_args(self.trainer_class, "trainer")
trainer_defaults = {"trainer." + k: v for k, v in self.trainer_defaults.items() if k != "callbacks"}
parser.set_defaults(trainer_defaults)
parser.add_lightning_class_args(self._model_class, "model", subclass_mode=self.subclass_mode_model)
if self.datamodule_class is not None:
parser.add_lightning_class_args(self._datamodule_class, "data", subclass_mode=self.subclass_mode_data)
else:
# this should not be required because the user might want to use the `LightningModule` dataloaders
parser.add_lightning_class_args(
self._datamodule_class, "data", subclass_mode=self.subclass_mode_data, required=False
)
def _add_arguments(self, parser: LightningArgumentParser) -> None:
# default + core + custom arguments
self.add_default_arguments_to_parser(parser)
self.add_core_arguments_to_parser(parser)
self.add_arguments_to_parser(parser)
# add default optimizer args if necessary
if self.auto_configure_optimizers:
if not parser._optimizers: # already added by the user in `add_arguments_to_parser`
parser.add_optimizer_args((Optimizer,))
if not parser._lr_schedulers: # already added by the user in `add_arguments_to_parser`
parser.add_lr_scheduler_args(LRSchedulerTypeTuple)
self.link_optimizers_and_lr_schedulers(parser)
[docs] def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
"""Implement to add extra arguments to the parser or link arguments.
Args:
parser: The parser object to which arguments can be added
"""
[docs] @staticmethod
def subcommands() -> Dict[str, Set[str]]:
"""Defines the list of available subcommands and the arguments to skip."""
return {
"fit": {"model", "train_dataloaders", "val_dataloaders", "datamodule"},
"validate": {"model", "dataloaders", "datamodule"},
"test": {"model", "dataloaders", "datamodule"},
"predict": {"model", "dataloaders", "datamodule"},
"tune": {"model", "train_dataloaders", "val_dataloaders", "datamodule"},
}
def _add_subcommands(self, parser: LightningArgumentParser, **kwargs: Any) -> None:
"""Adds subcommands to the input parser."""
self._subcommand_parsers: Dict[str, LightningArgumentParser] = {}
parser_subcommands = parser.add_subcommands()
# the user might have passed a builder function
trainer_class = (
self.trainer_class if isinstance(self.trainer_class, type) else class_from_function(self.trainer_class)
)
# register all subcommands in separate subcommand parsers under the main parser
for subcommand in self.subcommands():
fn = getattr(trainer_class, subcommand)
# extract the first line description in the docstring for the subcommand help message
description = _get_short_description(fn)
subparser_kwargs = kwargs.get(subcommand, {})
subparser_kwargs.setdefault("description", description)
subcommand_parser = self._prepare_subcommand_parser(trainer_class, subcommand, **subparser_kwargs)
self._subcommand_parsers[subcommand] = subcommand_parser
parser_subcommands.add_subcommand(subcommand, subcommand_parser, help=description)
def _prepare_subcommand_parser(self, klass: Type, subcommand: str, **kwargs: Any) -> LightningArgumentParser:
parser = self.init_parser(**kwargs)
self._add_arguments(parser)
# subcommand arguments
skip: Set[Union[str, int]] = set(self.subcommands()[subcommand])
added = parser.add_method_arguments(klass, subcommand, skip=skip)
# need to save which arguments were added to pass them to the method later
self._subcommand_method_arguments[subcommand] = added
return parser
[docs] @staticmethod
def link_optimizers_and_lr_schedulers(parser: LightningArgumentParser) -> None:
"""Creates argument links for optimizers and learning rate schedulers that specified a ``link_to``."""
optimizers_and_lr_schedulers = {**parser._optimizers, **parser._lr_schedulers}
for key, (class_type, link_to) in optimizers_and_lr_schedulers.items():
if link_to == "AUTOMATIC":
continue
if isinstance(class_type, tuple):
parser.link_arguments(key, link_to)
else:
add_class_path = _add_class_path_generator(class_type)
parser.link_arguments(key, link_to, compute_fn=add_class_path)
[docs] def parse_arguments(self, parser: LightningArgumentParser, args: ArgsType) -> None:
"""Parses command line arguments and stores it in ``self.config``."""
if args is not None and len(sys.argv) > 1:
rank_zero_warn(
"LightningCLI's args parameter is intended to run from within Python like if it were from the command "
"line. To prevent mistakes it is not recommended to provide both args and command line arguments, got: "
f"sys.argv[1:]={sys.argv[1:]}, args={args}."
)
if isinstance(args, (dict, Namespace)):
self.config = parser.parse_object(args)
else:
self.config = parser.parse_args(args)
[docs] def before_instantiate_classes(self) -> None:
"""Implement to run some code before instantiating the classes."""
[docs] def instantiate_classes(self) -> None:
"""Instantiates the classes and sets their attributes."""
self.config_init = self.parser.instantiate_classes(self.config)
self.datamodule = self._get(self.config_init, "data")
self.model = self._get(self.config_init, "model")
self._add_configure_optimizers_method_to_model(self.subcommand)
self.trainer = self.instantiate_trainer()
[docs] def instantiate_trainer(self, **kwargs: Any) -> Trainer:
"""Instantiates the trainer.
Args:
kwargs: Any custom trainer arguments.
"""
extra_callbacks = [self._get(self.config_init, c) for c in self._parser(self.subcommand).callback_keys]
trainer_config = {**self._get(self.config_init, "trainer", default={}), **kwargs}
return self._instantiate_trainer(trainer_config, extra_callbacks)
def _instantiate_trainer(self, config: Dict[str, Any], callbacks: List[Callback]) -> Trainer:
key = "callbacks"
if key in config:
if config[key] is None:
config[key] = []
elif not isinstance(config[key], list):
config[key] = [config[key]]
config[key].extend(callbacks)
if key in self.trainer_defaults:
value = self.trainer_defaults[key]
config[key] += value if isinstance(value, list) else [value]
if self.save_config_callback and not config.get("fast_dev_run", False):
config_callback = self.save_config_callback(
self._parser(self.subcommand),
self.config.get(str(self.subcommand), self.config),
**self.save_config_kwargs,
)
config[key].append(config_callback)
else:
rank_zero_warn(
f"The `{self.trainer_class.__qualname__}` class does not expose the `{key}` argument so they will"
" not be included."
)
return self.trainer_class(**config)
def _parser(self, subcommand: Optional[str]) -> LightningArgumentParser:
if subcommand is None:
return self.parser
# return the subcommand parser for the subcommand passed
return self._subcommand_parsers[subcommand]
[docs] @staticmethod
def configure_optimizers(
lightning_module: LightningModule, optimizer: Optimizer, lr_scheduler: Optional[LRSchedulerTypeUnion] = None
) -> Any:
"""Override to customize the :meth:`~pytorch_lightning.core.module.LightningModule.configure_optimizers`
method.
Args:
lightning_module: A reference to the model.
optimizer: The optimizer.
lr_scheduler: The learning rate scheduler (if used).
"""
if lr_scheduler is None:
return optimizer
if isinstance(lr_scheduler, ReduceLROnPlateau):
return {
"optimizer": optimizer,
"lr_scheduler": {"scheduler": lr_scheduler, "monitor": lr_scheduler.monitor},
}
return [optimizer], [lr_scheduler]
def _add_configure_optimizers_method_to_model(self, subcommand: Optional[str]) -> None:
"""Overrides the model's :meth:`~pytorch_lightning.core.module.LightningModule.configure_optimizers` method
if a single optimizer and optionally a scheduler argument groups are added to the parser as 'AUTOMATIC'."""
if not self.auto_configure_optimizers:
return
parser = self._parser(subcommand)
def get_automatic(
class_type: Union[Type, Tuple[Type, ...]], register: Dict[str, Tuple[Union[Type, Tuple[Type, ...]], str]]
) -> List[str]:
automatic = []
for key, (base_class, link_to) in register.items():
if not isinstance(base_class, tuple):
base_class = (base_class,)
if link_to == "AUTOMATIC" and any(issubclass(c, class_type) for c in base_class):
automatic.append(key)
return automatic
optimizers = get_automatic(Optimizer, parser._optimizers)
lr_schedulers = get_automatic(LRSchedulerTypeTuple, parser._lr_schedulers)
if len(optimizers) == 0:
return
if len(optimizers) > 1 or len(lr_schedulers) > 1:
raise MisconfigurationException(
f"`{self.__class__.__name__}.add_configure_optimizers_method_to_model` expects at most one optimizer "
f"and one lr_scheduler to be 'AUTOMATIC', but found {optimizers+lr_schedulers}. In this case the user "
"is expected to link the argument groups and implement `configure_optimizers`, see "
"https://lightning.ai/docs/pytorch/LTS/common/lightning_cli.html"
"#optimizers-and-learning-rate-schedulers"
)
optimizer_class = parser._optimizers[optimizers[0]][0]
optimizer_init = self._get(self.config_init, optimizers[0])
if not isinstance(optimizer_class, tuple):
optimizer_init = _global_add_class_path(optimizer_class, optimizer_init)
if not optimizer_init:
# optimizers were registered automatically but not passed by the user
return
lr_scheduler_init = None
if lr_schedulers:
lr_scheduler_class = parser._lr_schedulers[lr_schedulers[0]][0]
lr_scheduler_init = self._get(self.config_init, lr_schedulers[0])
if not isinstance(lr_scheduler_class, tuple):
lr_scheduler_init = _global_add_class_path(lr_scheduler_class, lr_scheduler_init)
if is_overridden("configure_optimizers", self.model):
_warn(
f"`{self.model.__class__.__name__}.configure_optimizers` will be overridden by "
f"`{self.__class__.__name__}.configure_optimizers`."
)
optimizer = instantiate_class(self.model.parameters(), optimizer_init)
lr_scheduler = instantiate_class(optimizer, lr_scheduler_init) if lr_scheduler_init else None
fn = partial(self.configure_optimizers, optimizer=optimizer, lr_scheduler=lr_scheduler)
update_wrapper(fn, self.configure_optimizers) # necessary for `is_overridden`
# override the existing method
self.model.configure_optimizers = MethodType(fn, self.model)
def _get(self, config: Namespace, key: str, default: Optional[Any] = None) -> Any:
"""Utility to get a config value which might be inside a subcommand."""
return config.get(str(self.subcommand), config).get(key, default)
def _run_subcommand(self, subcommand: str) -> None:
"""Run the chosen subcommand."""
before_fn = getattr(self, f"before_{subcommand}", None)
if callable(before_fn):
before_fn()
default = getattr(self.trainer, subcommand)
fn = getattr(self, subcommand, default)
fn_kwargs = self._prepare_subcommand_kwargs(subcommand)
fn(**fn_kwargs)
after_fn = getattr(self, f"after_{subcommand}", None)
if callable(after_fn):
after_fn()
def _prepare_subcommand_kwargs(self, subcommand: str) -> Dict[str, Any]:
"""Prepares the keyword arguments to pass to the subcommand to run."""
fn_kwargs = {
k: v for k, v in self.config_init[subcommand].items() if k in self._subcommand_method_arguments[subcommand]
}
fn_kwargs["model"] = self.model
if self.datamodule is not None:
fn_kwargs["datamodule"] = self.datamodule
return fn_kwargs
def _set_seed(self) -> None:
"""Sets the seed."""
config_seed = self._get(self.config, "seed_everything")
if config_seed is False:
return
if config_seed is True:
# user requested seeding, choose randomly
config_seed = seed_everything(workers=True)
else:
config_seed = seed_everything(config_seed, workers=True)
self.config["seed_everything"] = config_seed
def _class_path_from_class(class_type: Type) -> str:
return class_type.__module__ + "." + class_type.__name__
def _global_add_class_path(
class_type: Type, init_args: Optional[Union[Namespace, Dict[str, Any]]] = None
) -> Dict[str, Any]:
if isinstance(init_args, Namespace):
init_args = init_args.as_dict()
return {"class_path": _class_path_from_class(class_type), "init_args": init_args or {}}
def _add_class_path_generator(class_type: Type) -> Callable[[Namespace], Dict[str, Any]]:
def add_class_path(init_args: Namespace) -> Dict[str, Any]:
return _global_add_class_path(class_type, init_args)
return add_class_path
def instantiate_class(args: Union[Any, Tuple[Any, ...]], init: Dict[str, Any]) -> Any:
"""Instantiates a class with the given args and init.
Args:
args: Positional arguments required for instantiation.
init: Dict of the form {"class_path":...,"init_args":...}.
Returns:
The instantiated class object.
"""
kwargs = init.get("init_args", {})
if not isinstance(args, tuple):
args = (args,)
class_module, class_name = init["class_path"].rsplit(".", 1)
module = __import__(class_module, fromlist=[class_name])
args_class = getattr(module, class_name)
return args_class(*args, **kwargs)
def _get_short_description(component: object) -> Optional[str]:
if component.__doc__ is None:
return None
try:
docstring = docstring_parser.parse(component.__doc__)
return docstring.short_description
except (ValueError, docstring_parser.ParseError) as ex:
rank_zero_warn(f"Failed parsing docstring for {component}: {ex}")
