138 lines
4.9 KiB
Python
138 lines
4.9 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib
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import inspect
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from inspect import getmembers, isclass
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional
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from pytorch_lightning.plugins.training_type.training_type_plugin import TrainingTypePlugin
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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class _TrainingTypePluginsRegistry(dict):
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"""This class is a Registry that stores information about the Training Type Plugins.
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The Plugins are mapped to strings. These strings are names that idenitify
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a plugin, e.g., "deepspeed". It also returns Optional description and
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parameters to initialize the Plugin, which were defined durng the
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registration.
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The motivation for having a TrainingTypePluginRegistry is to make it convenient
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for the Users to try different Plugins by passing just strings
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to the plugins flag to the Trainer.
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Example::
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@TrainingTypePluginsRegistry.register("lightning", description="Super fast", a=1, b=True)
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class LightningPlugin:
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def __init__(self, a, b):
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...
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or
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TrainingTypePluginsRegistry.register("lightning", LightningPlugin, description="Super fast", a=1, b=True)
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"""
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def register(
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self,
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name: str,
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plugin: Optional[Callable] = None,
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description: Optional[str] = None,
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override: bool = False,
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**init_params: Any,
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) -> Callable:
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"""Registers a plugin mapped to a name and with required metadata.
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Args:
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name : the name that identifies a plugin, e.g. "deepspeed_stage_3"
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plugin : plugin class
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description : plugin description
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override : overrides the registered plugin, if True
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init_params: parameters to initialize the plugin
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"""
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if not (name is None or isinstance(name, str)):
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raise TypeError(f"`name` must be a str, found {name}")
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if name in self and not override:
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raise MisconfigurationException(f"'{name}' is already present in the registry. HINT: Use `override=True`.")
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data: Dict[str, Any] = {}
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data["description"] = description if description is not None else ""
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data["init_params"] = init_params
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def do_register(plugin: Callable) -> Callable:
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data["plugin"] = plugin
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data["distributed_backend"] = plugin.distributed_backend
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self[name] = data
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return plugin
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if plugin is not None:
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return do_register(plugin)
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return do_register
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def get(self, name: str, default: Optional[Any] = None) -> Any:
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"""Calls the registered plugin with the required parameters and returns the plugin object.
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Args:
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name (str): the name that identifies a plugin, e.g. "deepspeed_stage_3"
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"""
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if name in self:
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data = self[name]
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return data["plugin"](**data["init_params"])
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if default is not None:
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return default
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err_msg = "'{}' not found in registry. Available names: {}"
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available_names = ", ".join(sorted(self.keys())) or "none"
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raise KeyError(err_msg.format(name, available_names))
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def remove(self, name: str) -> None:
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"""Removes the registered plugin by name."""
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self.pop(name)
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def available_plugins(self) -> List:
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"""Returns a list of registered plugins."""
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return list(self.keys())
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def __str__(self) -> str:
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return "Registered Plugins: {}".format(", ".join(self.keys()))
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TrainingTypePluginsRegistry = _TrainingTypePluginsRegistry()
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def is_register_plugins_overridden(plugin: type) -> bool:
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method_name = "register_plugins"
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plugin_attr = getattr(plugin, method_name)
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previous_super_cls = inspect.getmro(plugin)[1]
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if issubclass(previous_super_cls, TrainingTypePlugin):
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super_attr = getattr(previous_super_cls, method_name)
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else:
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return False
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return plugin_attr.__code__ is not super_attr.__code__
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def call_training_type_register_plugins(root: Path, base_module: str) -> None:
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module = importlib.import_module(base_module)
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for _, mod in getmembers(module, isclass):
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if issubclass(mod, TrainingTypePlugin) and is_register_plugins_overridden(mod):
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mod.register_plugins(TrainingTypePluginsRegistry)
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