lightning/pytorch_lightning/plugins/plugins_registry.py

138 lines
4.9 KiB
Python

# 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)