# coding=utf-8
__author__ = "Dimitris Karkalousos"
# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/optimizers.py
import copy
from functools import partial
from typing import Any, Dict, Optional, Union
import hydra
import torch
from omegaconf import DictConfig, OmegaConf
from torch import optim
from torch.optim import adadelta, adagrad, adamax, rmsprop, rprop
from torch.optim.optimizer import Optimizer
from atommic.core.conf.optimizers import OptimizerParams, get_optimizer_config, register_optimizer_params
from atommic.core.optim.adafactor import Adafactor
from atommic.core.optim.lion import Lion
from atommic.core.optim.novograd import Novograd
from atommic.core.optim.radam import RAdam
from atommic.utils.model_utils import maybe_update_config_version
AVAILABLE_OPTIMIZERS = {
"sgd": optim.SGD,
"adam": optim.Adam,
"adamw": optim.AdamW,
"adadelta": adadelta.Adadelta,
"adamax": adamax.Adamax,
"adagrad": adagrad.Adagrad,
"rmsprop": rmsprop.RMSprop,
"rprop": rprop.Rprop,
"novograd": Novograd,
"adafactor": Adafactor,
"radam": RAdam,
"lion": Lion,
}
__all__ = ["AVAILABLE_OPTIMIZERS", "get_optimizer", "register_optimizer", "parse_optimizer_args"]
def parse_optimizer_args(
optimizer_name: str, optimizer_kwargs: Union[DictConfig, Dict[str, Any]]
) -> Union[Dict[str, Any], DictConfig]:
"""Parses a list of strings, of the format "key=value" or "key2=val1,val2,..." into a dictionary of type
{key=value, key2=[val1, val2], ...}. This dictionary is then used to instantiate the chosen Optimizer.
Parameters
----------
optimizer_name : str
String name of the optimizer, used for auto resolution of params.
optimizer_kwargs : Union[DictConfig, Dict[str, Any]]
Either a list of strings in a specified format, or a dictionary. If a dictionary is provided, it is assumed the
dictionary is the final parsed value, and simply returned. If a list of strings is provided, each item in the
list is parsed into a new dictionary.
Returns
-------
dict
A dictionary of the parsed arguments.
"""
kwargs: Dict[Any, Any] = {}
if optimizer_kwargs is None:
return kwargs
optimizer_kwargs = copy.deepcopy(optimizer_kwargs)
optimizer_kwargs = maybe_update_config_version(optimizer_kwargs)
if isinstance(optimizer_kwargs, DictConfig):
optimizer_kwargs = OmegaConf.to_container(optimizer_kwargs, resolve=True)
# If it is a dictionary, perform stepwise resolution
if hasattr(optimizer_kwargs, "keys"):
# Attempt class path resolution
if "_target_" in optimizer_kwargs: # captures (target, _target_)
optimizer_kwargs_config = OmegaConf.create(optimizer_kwargs)
optimizer_instance = hydra.utils.instantiate(optimizer_kwargs_config) # type: DictConfig
optimizer_instance = vars(optimizer_instance)
return optimizer_instance
# If class path was not provided, perhaps `name` is provided for resolution
if "name" in optimizer_kwargs:
# If `auto` is passed as name for resolution of optimizer name,
# then lookup optimizer name and resolve its parameter config
if optimizer_kwargs["name"] == "auto":
optimizer_params_name = f"{optimizer_name}_params"
optimizer_kwargs.pop("name")
else:
optimizer_params_name = optimizer_kwargs.pop("name")
# Override arguments provided in the config yaml file
if "params" in optimizer_kwargs:
# If optimizer kwarg overrides are wrapped in yaml `params`
optimizer_params_override = optimizer_kwargs.get("params")
else:
# If the kwargs themselves are a DictConfig
optimizer_params_override = optimizer_kwargs
if isinstance(optimizer_params_override, DictConfig):
optimizer_params_override = OmegaConf.to_container(optimizer_params_override, resolve=True)
optimizer_params_cls = get_optimizer_config(optimizer_params_name, **optimizer_params_override)
# If we are provided just a Config object, simply return the dictionary of that object
if optimizer_params_name is None:
optimizer_params = vars(optimizer_params_cls)
return optimizer_params
# If we are provided a partial class instantiation of a Config, instantiate it and retrieve its vars
# as a dictionary.
# instantiate the parameters object
optimizer_params = vars(optimizer_params_cls)
return optimizer_params
# simply return the dictionary that was provided
return optimizer_kwargs
return kwargs
[docs]def register_optimizer(name: str, optimizer: Optimizer, optimizer_params: OptimizerParams):
"""Checks if the optimizer name exists in the registry, and if it doesn't, adds it. This allows custom optimizers
to be added and called by name during instantiation.
Parameters
----------
name : str
Name of the optimizer. Will be used as key to retrieve the optimizer.
optimizer : Optimizer
Optimizer class.
optimizer_params : OptimizerParams
The parameters as a dataclass of the optimizer.
"""
if name in AVAILABLE_OPTIMIZERS:
raise ValueError(f"Cannot override pre-existing optimizers. Conflicting optimizer name = {name}")
AVAILABLE_OPTIMIZERS[name] = optimizer
optim_name = f"{optimizer.__name__}_params"
register_optimizer_params(name=optim_name, optimizer_params=optimizer_params)
def get_optimizer(name: str, **kwargs: Optional[Dict[str, Any]]) -> partial:
"""Convenience method to obtain an Optimizer class and partially instantiate it with optimizer kwargs.
Parameters
----------
name : str
Name of the optimizer. Will be used as key to retrieve the optimizer.
kwargs : Optional[Dict[str, Any]]
Optional kwargs of the optimizer used during instantiation.
Returns
-------
partial
A partially instantiated Optimizer.
"""
if name not in AVAILABLE_OPTIMIZERS:
raise ValueError(
f"Cannot resolve optimizer '{name}'. Available optimizers are : " f"{AVAILABLE_OPTIMIZERS.keys()}"
)
if name == "fused_adam" and not torch.cuda.is_available():
raise ValueError("CUDA must be available to use fused_adam.")
optimizer = AVAILABLE_OPTIMIZERS[name]
optimizer = partial(optimizer, **kwargs)
return optimizer
