# 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. import importlib import inspect from inspect import getmembers, isclass from pathlib import Path from typing import Any, Callable, Dict, List, Optional from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin from pytorch_lightning.utilities.exceptions import MisconfigurationException class _TrainingTypePluginsRegistry(dict): """This class is a Registry that stores information about the Training Type Plugins. The Plugins are mapped to strings. These strings are names that idenitify a plugin, e.g., "deepspeed". It also returns Optional description and parameters to initialize the Plugin, which were defined durng the registration. The motivation for having a TrainingTypePluginRegistry is to make it convenient for the Users to try different Plugins by passing just strings to the plugins flag to the Trainer. Example:: @TrainingTypePluginsRegistry.register("lightning", description="Super fast", a=1, b=True) class LightningPlugin: def __init__(self, a, b): ... or TrainingTypePluginsRegistry.register("lightning", LightningPlugin, description="Super fast", a=1, b=True) """ def register( self, name: str, plugin: Optional[Callable] = None, description: Optional[str] = None, override: bool = False, **init_params: Any, ) -> Callable: """Registers a plugin mapped to a name and with required metadata. Args: name : the name that identifies a plugin, e.g. "deepspeed_stage_3" plugin : plugin class description : plugin description override : overrides the registered plugin, if True init_params: parameters to initialize the plugin """ if not (name is None or isinstance(name, str)): raise TypeError(f"`name` must be a str, found {name}") if name in self and not override: raise MisconfigurationException(f"'{name}' is already present in the registry. HINT: Use `override=True`.") data: Dict[str, Any] = {} data["description"] = description if description is not None else "" data["init_params"] = init_params def do_register(plugin: Callable) -> Callable: data["plugin"] = plugin data["distributed_backend"] = plugin.distributed_backend self[name] = data return plugin if plugin is not None: return do_register(plugin) return do_register def get(self, name: str, default: Optional[Any] = None) -> Any: """Calls the registered plugin with the required parameters and returns the plugin object. Args: name (str): the name that identifies a plugin, e.g. "deepspeed_stage_3" """ if name in self: data = self[name] return data["plugin"](**data["init_params"]) if default is not None: return default err_msg = "'{}' not found in registry. Available names: {}" available_names = ", ".join(sorted(self.keys())) or "none" raise KeyError(err_msg.format(name, available_names)) def remove(self, name: str) -> None: """Removes the registered plugin by name.""" self.pop(name) def available_plugins(self) -> List: """Returns a list of registered plugins.""" return list(self.keys()) def __str__(self) -> str: return "Registered Plugins: {}".format(", ".join(self.keys())) TrainingTypePluginsRegistry = _TrainingTypePluginsRegistry() def is_register_plugins_overridden(plugin: type) -> bool: method_name = "register_plugins" plugin_attr = getattr(plugin, method_name) previous_super_cls = inspect.getmro(plugin)[1] if issubclass(previous_super_cls, TrainingTypePlugin): super_attr = getattr(previous_super_cls, method_name) else: return False return plugin_attr.__code__ is not super_attr.__code__ def call_training_type_register_plugins(root: Path, base_module: str) -> None: module = importlib.import_module(base_module) for _, mod in getmembers(module, isclass): if issubclass(mod, TrainingTypePlugin) and is_register_plugins_overridden(mod): mod.register_plugins(TrainingTypePluginsRegistry)