Source code for pytorch_lightning.utilities.cli

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import os
import sys
from argparse import Namespace
from types import MethodType, ModuleType
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from unittest import mock

import torch
import yaml
from torch.optim import Optimizer

import pytorch_lightning as pl
from pytorch_lightning import Callback, LightningDataModule, LightningModule, seed_everything, Trainer
from pytorch_lightning.utilities import _JSONARGPARSE_AVAILABLE, rank_zero_warn, warnings
from pytorch_lightning.utilities.cloud_io import get_filesystem
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.types import LRSchedulerType, LRSchedulerTypeTuple

    from jsonargparse import ActionConfigFile, ArgumentParser, class_from_function, set_config_read_mode
    from jsonargparse.actions import _ActionSubCommands
    from jsonargparse.optionals import import_docstring_parse

    ArgumentParser = object

class _Registry(dict):
    def __call__(self, cls: Type, key: Optional[str] = None, override: bool = False) -> Type:
        """Registers a class mapped to a name.

            cls: the class to be mapped.
            key: the name that identifies the provided class.
            override: Whether to override an existing key.
        if key is None:
            key = cls.__name__
        elif not isinstance(key, str):
            raise TypeError(f"`key` must be a str, found {key}")

        if key in self and not override:
            raise MisconfigurationException(f"'{key}' is already present in the registry. HINT: Use `override=True`.")
        self[key] = cls
        return cls

    def register_classes(self, module: ModuleType, base_cls: Type, override: bool = False) -> None:
        """This function is an utility to register all classes from a module."""
        for _, cls in inspect.getmembers(module, predicate=inspect.isclass):
            if issubclass(cls, base_cls) and cls != base_cls:
                self(cls=cls, override=override)

    def names(self) -> List[str]:
        """Returns the registered names."""
        return list(self.keys())

    def classes(self) -> Tuple[Type, ...]:
        """Returns the registered classes."""
        return tuple(self.values())

    def __str__(self) -> str:
        return f"Registered objects: {self.names}"

OPTIMIZER_REGISTRY.register_classes(torch.optim, Optimizer)

LR_SCHEDULER_REGISTRY.register_classes(torch.optim.lr_scheduler, torch.optim.lr_scheduler._LRScheduler)

CALLBACK_REGISTRY.register_classes(pl.callbacks, pl.callbacks.Callback)

MODEL_REGISTRY = _Registry()


[docs]class LightningArgumentParser(ArgumentParser): """Extension of jsonargparse's ArgumentParser for pytorch-lightning.""" # use class attribute because `parse_args` is only called on the main parser _choices: Dict[str, Tuple[Tuple[Type, ...], bool]] = {} def __init__(self, *args: Any, parse_as_dict: bool = True, **kwargs: Any) -> None: """Initialize argument parser that supports configuration file input. For full details of accepted arguments see `ArgumentParser.__init__ <>`_. """ if not _JSONARGPARSE_AVAILABLE: raise ModuleNotFoundError( "`jsonargparse` is not installed but it is required for the CLI." " Install it with `pip install jsonargparse[signatures]`." ) super().__init__(*args, parse_as_dict=parse_as_dict, **kwargs) self.add_argument( "--config", action=ActionConfigFile, help="Path to a configuration file in json or yaml format." ) 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], ...]], 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 torch.optim.Optimizer. 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 = {"instantiate": False, "fail_untyped": False, "skip": {"params"}} if isinstance(optimizer_class, tuple): self.add_subclass_arguments(optimizer_class, nested_key, **kwargs) self.set_choices(nested_key, optimizer_class) 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, ...]], 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}``. 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 = {"instantiate": False, "fail_untyped": False, "skip": {"optimizer"}} if isinstance(lr_scheduler_class, tuple): self.add_subclass_arguments(lr_scheduler_class, nested_key, **kwargs) self.set_choices(nested_key, lr_scheduler_class) else: self.add_class_arguments(lr_scheduler_class, nested_key, sub_configs=True, **kwargs) self._lr_schedulers[nested_key] = (lr_scheduler_class, link_to)
def parse_args(self, *args: Any, **kwargs: Any) -> Dict[str, Any]: argv = sys.argv for k, v in self._choices.items(): if not any(arg.startswith(f"--{k}") for arg in argv): # the key wasn't passed - maybe defined in a config, maybe it's optional continue classes, is_list = v # knowing whether the argument is a list type automatically would be too complex if is_list: argv = self._convert_argv_issue_85(classes, k, argv) else: argv = self._convert_argv_issue_84(classes, k, argv) self._choices.clear() with mock.patch("sys.argv", argv): return super().parse_args(*args, **kwargs)
[docs] def set_choices(self, nested_key: str, classes: Tuple[Type, ...], is_list: bool = False) -> None: """Adds support for shorthand notation for a particular nested key. Args: nested_key: The key whose choices will be set. classes: A tuple of classes to choose from. is_list: Whether the argument is a ``List[object]`` type. """ self._choices[nested_key] = (classes, is_list)
@staticmethod def _convert_argv_issue_84(classes: Tuple[Type, ...], nested_key: str, argv: List[str]) -> List[str]: """Placeholder for Adds support for shorthand notation for ``object`` arguments. """ passed_args, clean_argv = {}, [] argv_key = f"--{nested_key}" # get the argv args for this nested key i = 0 while i < len(argv): arg = argv[i] if arg.startswith(argv_key): if "=" in arg: key, value = arg.split("=") else: key = arg i += 1 value = argv[i] passed_args[key] = value else: clean_argv.append(arg) i += 1 # the user requested a help message help_key = argv_key + ".help" if help_key in passed_args: argv_class = passed_args[help_key] if "." in argv_class: # user passed the class path directly class_path = argv_class else: # convert shorthand format to the classpath for cls in classes: if cls.__name__ == argv_class: class_path = _class_path_from_class(cls) break else: raise ValueError(f"Could not generate get the class_path for {repr(argv_class)}") return clean_argv + [help_key, class_path] # generate the associated config file argv_class = passed_args.pop(argv_key, "") if not argv_class: # the user passed a config as a str class_path = passed_args[f"{argv_key}.class_path"] init_args_key = f"{argv_key}.init_args" init_args = {k[len(init_args_key) + 1 :]: v for k, v in passed_args.items() if k.startswith(init_args_key)} config = str({"class_path": class_path, "init_args": init_args}) elif argv_class.startswith("{"): # the user passed a config as a dict config = argv_class else: # the user passed the shorthand format init_args = {k[len(argv_key) + 1 :]: v for k, v in passed_args.items()} # +1 to account for the period for cls in classes: if cls.__name__ == argv_class: config = str(_global_add_class_path(cls, init_args)) break else: raise ValueError(f"Could not generate a config for {repr(argv_class)}") return clean_argv + [argv_key, config] @staticmethod def _convert_argv_issue_85(classes: Tuple[Type, ...], nested_key: str, argv: List[str]) -> List[str]: """Placeholder for Adds support for shorthand notation for ``List[object]`` arguments. """ passed_args, clean_argv = [], [] passed_configs = {} argv_key = f"--{nested_key}" # get the argv args for this nested key i = 0 while i < len(argv): arg = argv[i] if arg.startswith(argv_key): if "=" in arg: key, value = arg.split("=") else: key = arg i += 1 value = argv[i] if "class_path" in value: # the user passed a config as a dict passed_configs[key] = yaml.safe_load(value) else: passed_args.append((key, value)) else: clean_argv.append(arg) i += 1 # generate the associated config file config = [] i, n = 0, len(passed_args) while i < n - 1: ki, vi = passed_args[i] # convert class name to class path for cls in classes: if cls.__name__ == vi: cls_type = cls break else: raise ValueError(f"Could not generate a config for {repr(vi)}") config.append(_global_add_class_path(cls_type)) # get any init args j = i + 1 # in case the j-loop doesn't run for j in range(i + 1, n): kj, vj = passed_args[j] if ki == kj: break if kj.startswith(ki): init_arg_name = kj.split(".")[-1] config[-1]["init_args"][init_arg_name] = vj i = j # update at the end to preserve the order for k, v in passed_configs.items(): config.extend(v) if not config: return clean_argv return clean_argv + [argv_key, str(config)]
[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: Union[Namespace, Dict[str, Any]], config_filename: str, overwrite: bool = False, multifile: bool = False, ) -> None: self.parser = parser self.config = config self.config_filename = config_filename self.overwrite = overwrite self.multifile = multifile
[docs] def setup(self, trainer: Trainer, pl_module: LightningModule, stage: Optional[str] = None) -> None: # save the config in `setup` because (1) we want it to save regardless of the trainer function run # and we want to save before processes are spawned log_dir = trainer.log_dir assert log_dir is not None config_path = os.path.join(log_dir, self.config_filename) if not self.overwrite and os.path.isfile(config_path): 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_overwrite=True)` to overwrite the config file." ) if trainer.is_global_zero: # save only on rank zero to avoid race conditions on DDP. # 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 get_filesystem(log_dir).makedirs(log_dir, exist_ok=True) self.config, config_path, skip_none=False, overwrite=self.overwrite, multifile=self.multifile )
def __reduce__(self) -> Tuple[Type["SaveConfigCallback"], Tuple, Dict]: # `ArgumentParser` is un-pickleable. Drop it return self.__class__, (None, self.config, self.config_filename), {}
[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_filename: str = "config.yaml", save_config_overwrite: bool = False, save_config_multifile: bool = False, trainer_class: Union[Type[Trainer], Callable[..., Trainer]] = Trainer, trainer_defaults: Optional[Dict[str, Any]] = None, seed_everything_default: Optional[int] = None, description: str = "pytorch-lightning trainer command line tool", env_prefix: str = "PL", env_parse: bool = False, parser_kwargs: Optional[Union[Dict[str, Any], Dict[str, Dict[str, Any]]]] = None, subclass_mode_model: bool = False, subclass_mode_data: bool = False, run: bool = True, ) -> 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 ``env_parse=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 <common/lightning_cli:LightningCLI>`. .. warning:: ``LightningCLI`` is in beta and subject to change. Args: model_class: An optional :class:`~pytorch_lightning.core.lightning.LightningModule` class to train on or a callable which returns a :class:`~pytorch_lightning.core.lightning.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 training config. save_config_filename: Filename for the config file. save_config_overwrite: Whether to overwrite an existing config file. save_config_multifile: When input is multiple config files, saved config preserves this structure. 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. seed_everything_default: Default value for the :func:`~pytorch_lightning.utilities.seed.seed_everything` seed argument. description: Description of the tool shown when running ``--help``. env_prefix: Prefix for environment variables. env_parse: Whether environment variable parsing is enabled. parser_kwargs: Additional arguments to instantiate each ``LightningArgumentParser``. subclass_mode_model: Whether model can be any `subclass <>`_ of the given class. subclass_mode_data: Whether datamodule can be any `subclass <>`_ of the given class. 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_filename = save_config_filename self.save_config_overwrite = save_config_overwrite self.save_config_multifile = save_config_multifile self.trainer_class = trainer_class self.trainer_defaults = trainer_defaults or {} self.seed_everything_default = seed_everything_default 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( parser_kwargs or {}, # type: ignore # {"description": description, "env_prefix": env_prefix, "default_env": env_parse}, ) self.setup_parser(run, main_kwargs, subparser_kwargs) self.parse_arguments(self.parser) self.subcommand = self.config["subcommand"] if run else None seed = self._get(self.config, "seed_everything") if seed is not None: seed_everything(seed, workers=True) self.before_instantiate_classes() self.instantiate_classes() if self.subcommand is not None: self._run_subcommand(self.subcommand) def _setup_parser_kwargs( self, kwargs: Dict[str, Any], defaults: Dict[str, Any] ) -> Tuple[Dict[str, Any], Dict[str, Any]]: if kwargs.keys() & self.subcommands().keys(): # `kwargs` contains arguments per subcommand return defaults, kwargs main_kwargs = defaults main_kwargs.update(kwargs) return main_kwargs, {}
[docs] def init_parser(self, **kwargs: Any) -> LightningArgumentParser: """Method that instantiates the argument parser.""" return LightningArgumentParser(**kwargs)
[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=Optional[int], default=self.seed_everything_default, help="Set to an int to run seed_everything with this value before classes instantiation", )
[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") parser.set_choices("trainer.callbacks", CALLBACK_REGISTRY.classes, is_list=True) 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.model_class is None and len(MODEL_REGISTRY): # did not pass a model and there are models registered parser.set_choices("model", MODEL_REGISTRY.classes) if self.datamodule_class is not None: parser.add_lightning_class_args(self._datamodule_class, "data", subclass_mode=self.subclass_mode_data) elif len(DATAMODULE_REGISTRY): # 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 ) parser.set_choices("data", DATAMODULE_REGISTRY.classes)
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 not parser._optimizers: # already added by the user in `add_arguments_to_parser` parser.add_optimizer_args(OPTIMIZER_REGISTRY.classes) if not parser._lr_schedulers: # already added by the user in `add_arguments_to_parser` parser.add_lr_scheduler_args(LR_SCHEDULER_REGISTRY.classes) 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", "train_dataloader", "val_dataloaders", "datamodule"}, "validate": {"model", "dataloaders", "val_dataloaders", "datamodule"}, "test": {"model", "dataloaders", "test_dataloaders", "datamodule"}, "predict": {"model", "dataloaders", "datamodule"}, "tune": {"model", "train_dataloaders", "train_dataloader", "val_dataloaders", "datamodule"}, }
def _add_subcommands(self, parser: LightningArgumentParser, **kwargs: Any) -> None: """Adds subcommands to the input parser.""" 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(): subcommand_parser = self._prepare_subcommand_parser(trainer_class, subcommand, **kwargs.get(subcommand, {})) fn = getattr(trainer_class, subcommand) # extract the first line description in the docstring for the subcommand help message description = _get_short_description(fn) 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 = 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] def parse_arguments(self, parser: LightningArgumentParser) -> None: """Parses command line arguments and stores it in ``self.config``.""" self.config = parser.parse_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"), **kwargs} return self._instantiate_trainer(trainer_config, extra_callbacks)
def _instantiate_trainer(self, config: Dict[str, Any], callbacks: List[Callback]) -> Trainer: config["callbacks"] = config["callbacks"] or [] config["callbacks"].extend(callbacks) if "callbacks" in self.trainer_defaults: if isinstance(self.trainer_defaults["callbacks"], list): config["callbacks"].extend(self.trainer_defaults["callbacks"]) else: config["callbacks"].append(self.trainer_defaults["callbacks"]) if self.save_config_callback and not config["fast_dev_run"]: config_callback = self.save_config_callback( self.parser, self.config, self.save_config_filename, overwrite=self.save_config_overwrite, multifile=self.save_config_multifile, ) config["callbacks"].append(config_callback) 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 action_subcommands = [a for a in self.parser._actions if isinstance(a, _ActionSubCommands)] action_subcommand = action_subcommands[0] return action_subcommand._name_parser_map[subcommand] def _add_configure_optimizers_method_to_model(self, subcommand: Optional[str]) -> None: """Adds to the model an automatically generated ``configure_optimizers`` method. If a single optimizer and optionally a scheduler argument groups are added to the parser as 'AUTOMATIC', then a `configure_optimizers` method is automatically implemented in the model class. """ 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 " "" "#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) def configure_optimizers( self: LightningModule, ) -> Union[Optimizer, Tuple[List[Optimizer], List[LRSchedulerType]]]: optimizer = instantiate_class(self.parameters(), optimizer_init) if not lr_scheduler_init: return optimizer lr_scheduler = instantiate_class(optimizer, lr_scheduler_init) return [optimizer], [lr_scheduler] if is_overridden("configure_optimizers", self.model): warnings._warn( f"`{self.model.__class__.__name__}.configure_optimizers` will be overridden by " f"`{self.__class__.__name__}.add_configure_optimizers_method_to_model`." ) self.model.configure_optimizers = MethodType(configure_optimizers, self.model) def _get(self, config: Dict[str, Any], key: str, default: Optional[Any] = None) -> Any: """Utility to get a config value which might be inside a subcommand.""" if self.subcommand is not None: return config[self.subcommand].get(key, default) return 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 _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[[Dict[str, Any]], Dict[str, Any]]: def add_class_path(init_args: Dict[str, Any]) -> Dict[str, Any]: return _global_add_class_path(class_type, init_args) return add_class_path
[docs]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]: parse = import_docstring_parse("LightningCLI(run=True)") try: docstring = parse(component.__doc__) return docstring.short_description except ValueError: rank_zero_warn(f"Failed parsing docstring for {component}")

© Copyright Copyright (c) 2018-2023, William Falcon et al...

Built with Sphinx using a theme provided by Read the Docs.