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""" Optimizer Factory w/ custom Weight Decay & Layer Decay support
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
from fnmatch import fnmatch
import importlib
import torch
import torch.nn as nn
import torch.optim
from ._param_groups import param_groups_layer_decay, param_groups_weight_decay
from ._types import ParamsT, OptimType, OptimizerCallable
from .adabelief import AdaBelief
from .adafactor import Adafactor
from .adafactor_bv import AdafactorBigVision
from .adahessian import Adahessian
from .adamp import AdamP
from .adamw import AdamWLegacy
from .adan import Adan
from .adopt import Adopt
from .lamb import Lamb
from .laprop import LaProp
from .lars import Lars
from .lion import Lion
from .lookahead import Lookahead
from .madgrad import MADGRAD
from .mars import Mars
from .nadam import NAdamLegacy
from .nadamw import NAdamW
from .nvnovograd import NvNovoGrad
from .radam import RAdamLegacy
from .rmsprop_tf import RMSpropTF
from .sgdp import SGDP
from .sgdw import SGDW
_logger = logging.getLogger(__name__)
def _import_class(class_string: str) -> Type:
"""Dynamically import a class from a string."""
try:
module_name, class_name = class_string.rsplit(".", 1)
module = importlib.import_module(module_name)
return getattr(module, class_name)
except (ImportError, AttributeError) as e:
raise ImportError(f"Could not import {class_string}: {e}")
@dataclass(frozen=True)
class OptimInfo:
"""Immutable configuration for an optimizer.
Attributes:
name: Unique identifier for the optimizer
opt_class: The optimizer class
description: Brief description of the optimizer's characteristics and behavior
has_eps: Whether the optimizer accepts epsilon parameter
has_momentum: Whether the optimizer accepts momentum parameter
has_betas: Whether the optimizer accepts a tuple of beta parameters
num_betas: number of betas in tuple (valid IFF has_betas = True)
defaults: Optional default parameters for the optimizer
"""
name: str
opt_class: Union[str, OptimType]
description: str = ''
has_eps: bool = True
has_momentum: bool = False
has_betas: bool = False
num_betas: int = 2
second_order: bool = False
defaults: Optional[Dict[str, Any]] = None
class OptimizerRegistry:
"""Registry managing optimizer configurations and instantiation.
This class provides a central registry for optimizer configurations and handles
their instantiation with appropriate parameter groups and settings.
"""
def __init__(self) -> None:
self._optimizers: Dict[str, OptimInfo] = {}
self._foreach_defaults: Set[str] = {'lion'}
def register(self, info: OptimInfo) -> None:
"""Register an optimizer configuration.
Args:
info: The OptimInfo configuration containing name, type and description
"""
name = info.name.lower()
if name in self._optimizers:
_logger.warning(f'Optimizer {name} already registered, overwriting')
self._optimizers[name] = info
def register_alias(self, alias: str, target: str) -> None:
"""Register an alias for an existing optimizer.
Args:
alias: The alias name
target: The target optimizer name
Raises:
KeyError: If target optimizer doesn't exist
"""
target = target.lower()
if target not in self._optimizers:
raise KeyError(f'Cannot create alias for non-existent optimizer {target}')
self._optimizers[alias.lower()] = self._optimizers[target]
def register_foreach_default(self, name: str) -> None:
"""Register an optimizer as defaulting to foreach=True."""
self._foreach_defaults.add(name.lower())
def list_optimizers(
self,
filter: Union[str, List[str]] = '',
exclude_filters: Optional[List[str]] = None,
with_description: bool = False
) -> List[Union[str, Tuple[str, str]]]:
"""List available optimizer names, optionally filtered.
Args:
filter: Wildcard style filter string (e.g., 'adam*')
exclude_filters: Optional list of wildcard patterns to exclude
with_description: If True, return tuples of (name, description)
Returns:
List of either optimizer names or (name, description) tuples
"""
names = sorted(self._optimizers.keys())
if filter:
if isinstance(filter, str):
filters = [filter]
else:
filters = filter
filtered_names = set()
for f in filters:
filtered_names.update(n for n in names if fnmatch(n, f))
names = sorted(filtered_names)
if exclude_filters:
for exclude_filter in exclude_filters:
names = [n for n in names if not fnmatch(n, exclude_filter)]
if with_description:
return [(name, self._optimizers[name].description) for name in names]
return names
def get_optimizer_info(self, name: str) -> OptimInfo:
"""Get the OptimInfo for an optimizer.
Args:
name: Name of the optimizer
Returns:
OptimInfo configuration
Raises:
ValueError: If optimizer is not found
"""
name = name.lower()
if name not in self._optimizers:
raise ValueError(f'Optimizer {name} not found in registry')
return self._optimizers[name]
def get_optimizer_class(
self,
name_or_info: Union[str, OptimInfo],
bind_defaults: bool = True,
) -> Union[OptimType, OptimizerCallable]:
"""Get the optimizer class with any default arguments applied.
This allows direct instantiation of optimizers with their default configs
without going through the full factory.
Args:
name_or_info: Name of the optimizer
bind_defaults: Bind default arguments to optimizer class via `partial` before returning
Returns:
Optimizer class or partial with defaults applied
Raises:
ValueError: If optimizer not found
"""
if isinstance(name_or_info, str):
opt_info = self.get_optimizer_info(name_or_info)
else:
assert isinstance(name_or_info, OptimInfo)
opt_info = name_or_info
if isinstance(opt_info.opt_class, str):
# Special handling for APEX and BNB optimizers
if opt_info.opt_class.startswith('apex.'):
assert torch.cuda.is_available(), 'CUDA required for APEX optimizers'
try:
opt_class = _import_class(opt_info.opt_class)
except ImportError as e:
raise ImportError('APEX optimizers require apex to be installed') from e
elif opt_info.opt_class.startswith('bitsandbytes.'):
assert torch.cuda.is_available(), 'CUDA required for bitsandbytes optimizers'
try:
opt_class = _import_class(opt_info.opt_class)
except ImportError as e:
raise ImportError('bitsandbytes optimizers require bitsandbytes to be installed') from e
else:
opt_class = _import_class(opt_info.opt_class)
else:
opt_class = opt_info.opt_class
# Return class or partial with defaults
if bind_defaults and opt_info.defaults:
opt_class = partial(opt_class, **opt_info.defaults)
return opt_class
def create_optimizer(
self,
model_or_params: Union[nn.Module, ParamsT],
opt: str,
lr: Optional[float] = None,
weight_decay: float = 0.,
momentum: float = 0.9,
foreach: Optional[bool] = None,
weight_decay_exclude_1d: bool = True,
layer_decay: Optional[float] = None,
param_group_fn: Optional[Callable[[nn.Module], ParamsT]] = None,
**kwargs: Any,
) -> torch.optim.Optimizer:
"""Create an optimizer instance.
Args:
model_or_params: Model or parameters to optimize
opt: Name of optimizer to create
lr: Learning rate
weight_decay: Weight decay factor
momentum: Momentum factor for applicable optimizers
foreach: Enable/disable foreach operation
weight_decay_exclude_1d: Whether to skip weight decay for 1d params (biases and norm affine)
layer_decay: Layer-wise learning rate decay
param_group_fn: Optional custom parameter grouping function
**kwargs: Additional optimizer-specific arguments
Returns:
Configured optimizer instance
Raises:
ValueError: If optimizer not found or configuration invalid
"""
# Get parameters to optimize
if isinstance(model_or_params, nn.Module):
# Extract parameters from a nn.Module, build param groups w/ weight-decay and/or layer-decay applied
no_weight_decay = getattr(model_or_params, 'no_weight_decay', lambda: set())()
if param_group_fn:
# run custom fn to generate param groups from nn.Module
params = param_group_fn(model_or_params)
elif layer_decay is not None:
params = param_groups_layer_decay(
model_or_params,
weight_decay=weight_decay,
layer_decay=layer_decay,
no_weight_decay_list=no_weight_decay,
weight_decay_exclude_1d=weight_decay_exclude_1d,
)
weight_decay = 0.
elif weight_decay and weight_decay_exclude_1d:
params = param_groups_weight_decay(
model_or_params,
weight_decay=weight_decay,
no_weight_decay_list=no_weight_decay,
)
weight_decay = 0.
else:
params = model_or_params.parameters()
else:
# pass parameters / parameter groups through to optimizer
params = model_or_params
# Parse optimizer name
opt_split = opt.lower().split('_')
opt_name = opt_split[-1]
use_lookahead = opt_split[0] == 'lookahead' if len(opt_split) > 1 else False
opt_info = self.get_optimizer_info(opt_name)
# Build optimizer arguments
opt_args: Dict[str, Any] = {'weight_decay': weight_decay, **kwargs}
# Add LR to args, if None optimizer default is used, some optimizers manage LR internally if None.
if lr is not None:
opt_args['lr'] = lr
# Apply optimizer-specific settings
if opt_info.defaults:
for k, v in opt_info.defaults.items():
opt_args.setdefault(k, v)
# timm has always defaulted momentum to 0.9 if optimizer supports momentum, keep for backward compat.
if opt_info.has_momentum:
opt_args.setdefault('momentum', momentum)
# Remove commonly used kwargs that aren't always supported
if not opt_info.has_eps:
opt_args.pop('eps', None)
if not opt_info.has_betas:
opt_args.pop('betas', None)
if foreach is not None:
# Explicitly activate or deactivate multi-tensor foreach impl.
# Not all optimizers support this, and those that do usually default to using
# multi-tensor impl if foreach is left as default 'None' and can be enabled.
opt_args.setdefault('foreach', foreach)
# Create optimizer
opt_class = self.get_optimizer_class(opt_info, bind_defaults=False)
optimizer = opt_class(params, **opt_args)
# Apply Lookahead if requested
if use_lookahead:
optimizer = Lookahead(optimizer)
return optimizer
def _register_sgd_variants(registry: OptimizerRegistry) -> None:
"""Register SGD-based optimizers"""
sgd_optimizers = [
OptimInfo(
name='sgd',
opt_class=torch.optim.SGD,
description='torch.Optim Stochastic Gradient Descent (SGD) with Nesterov momentum',
has_eps=False,
has_momentum=True,
defaults={'nesterov': True}
),
OptimInfo(
name='momentum',
opt_class=torch.optim.SGD,
description='torch.Optim Stochastic Gradient Descent (SGD) with classical momentum',
has_eps=False,
has_momentum=True,
defaults={'nesterov': False}
),
OptimInfo(
name='sgdp',
opt_class=SGDP,
description='SGD with built-in projection to unit norm sphere',
has_momentum=True,
defaults={'nesterov': True}
),
OptimInfo(
name='sgdw',
opt_class=SGDW,
description='SGD with decoupled weight decay and Nesterov momentum',
has_eps=False,
has_momentum=True,
defaults={'nesterov': True}
),
]
for opt in sgd_optimizers:
registry.register(opt)
def _register_adam_variants(registry: OptimizerRegistry) -> None:
"""Register Adam-based optimizers"""
adam_optimizers = [
OptimInfo(
name='adam',
opt_class=torch.optim.Adam,
description='torch.optim.Adam, Adaptive Moment Estimation',
has_betas=True
),
OptimInfo(
name='adamw',
opt_class=torch.optim.AdamW,
description='torch.optim.AdamW, Adam with decoupled weight decay',
has_betas=True
),
OptimInfo(
name='adamwlegacy',
opt_class=AdamWLegacy,
description='legacy impl of AdamW that pre-dates inclusion to torch.optim',
has_betas=True
),
OptimInfo(
name='adamp',
opt_class=AdamP,
description='Adam with built-in projection to unit norm sphere',
has_betas=True,
defaults={'wd_ratio': 0.01, 'nesterov': True}
),
OptimInfo(
name='nadam',
opt_class=torch.optim.NAdam,
description='torch.optim.NAdam, Adam with Nesterov momentum',
has_betas=True
),
OptimInfo(
name='nadamlegacy',
opt_class=NAdamLegacy,
description='legacy impl of NAdam that pre-dates inclusion in torch.optim',
has_betas=True
),
OptimInfo(
name='nadamw',
opt_class=NAdamW,
description='Adam with Nesterov momentum and decoupled weight decay, mlcommons/algorithmic-efficiency impl',
has_betas=True
),
OptimInfo(
name='radam',
opt_class=torch.optim.RAdam,
description='torch.optim.RAdam, Rectified Adam with variance adaptation',
has_betas=True
),
OptimInfo(
name='radamlegacy',
opt_class=RAdamLegacy,
description='legacy impl of RAdam that predates inclusion in torch.optim',
has_betas=True
),
OptimInfo(
name='radamw',
opt_class=torch.optim.RAdam,
description='torch.optim.RAdamW, Rectified Adam with variance adaptation and decoupled weight decay',
has_betas=True,
defaults={'decoupled_weight_decay': True}
),
OptimInfo(
name='adamax',
opt_class=torch.optim.Adamax,
description='torch.optim.Adamax, Adam with infinity norm for more stable updates',
has_betas=True
),
OptimInfo(
name='adafactor',
opt_class=Adafactor,
description='Memory-efficient implementation of Adam with factored gradients',
),
OptimInfo(
name='adafactorbv',
opt_class=AdafactorBigVision,
description='Big Vision variant of Adafactor with factored gradients, half precision momentum',
),
OptimInfo(
name='adopt',
opt_class=Adopt,
description='Modified Adam that can converge with any β2 with the optimal rate',
),
OptimInfo(
name='adoptw',
opt_class=Adopt,
description='Modified AdamW (decoupled decay) that can converge with any β2 with the optimal rate',
defaults={'decoupled': True}
),
]
for opt in adam_optimizers:
registry.register(opt)
def _register_lamb_lars(registry: OptimizerRegistry) -> None:
"""Register LAMB and LARS variants"""
lamb_lars_optimizers = [
OptimInfo(
name='lamb',
opt_class=Lamb,
description='Layer-wise Adaptive Moments for batch optimization',
has_betas=True
),
OptimInfo(
name='lambc',
opt_class=Lamb,
description='LAMB with trust ratio clipping for stability',
has_betas=True,
defaults={'trust_clip': True}
),
OptimInfo(
name='lambw',
opt_class=Lamb,
description='LAMB with decoupled weight decay',
has_betas=True,
defaults={'decoupled_decay': True}
),
OptimInfo(
name='lambcw',
opt_class=Lamb,
description='LAMB with trust ratio clipping for stability and decoupled decay',
has_betas=True,
defaults={'trust_clip': True, 'decoupled_decay': True}
),
OptimInfo(
name='lars',
opt_class=Lars,
description='Layer-wise Adaptive Rate Scaling',
has_momentum=True
),
OptimInfo(
name='larc',
opt_class=Lars,
description='LARS with trust ratio clipping for stability',
has_momentum=True,
defaults={'trust_clip': True}
),
OptimInfo(
name='nlars',
opt_class=Lars,
description='LARS with Nesterov momentum',
has_momentum=True,
defaults={'nesterov': True}
),
OptimInfo(
name='nlarc',
opt_class=Lars,
description='LARS with Nesterov momentum & trust ratio clipping',
has_momentum=True,
defaults={'nesterov': True, 'trust_clip': True}
),
]
for opt in lamb_lars_optimizers:
registry.register(opt)
def _register_cautious_optimizers(registry: OptimizerRegistry) -> None:
cautious_optimizers = [
OptimInfo(
name='cadafactor',
opt_class=Adafactor,
description='Cautious Adafactor',
defaults={'caution': True}
),
OptimInfo(
name='cadafactorbv',
opt_class=AdafactorBigVision,
description='Cautious Big Vision Adafactor',
defaults={'caution': True}
),
OptimInfo(
name='cadamw',
opt_class=AdamWLegacy,
description='Cautious AdamW',
has_betas=True,
defaults={'caution': True}
),
OptimInfo(
name='cadopt',
opt_class=Adopt,
description='Cautious Adopt',
defaults={'caution': True}
),
OptimInfo(
name='cadan',
opt_class=Adan,
description='Cautious Adaptive Nesterov Momentum Algorithm',
defaults={'caution': True, 'no_prox': False},
has_betas=True,
num_betas=3
),
OptimInfo(
name='cadanw',
opt_class=Adan,
description='Cautious Adaptive Nesterov Momentum with decoupled weight decay',
defaults={'caution': True, 'no_prox': True},
has_betas=True,
num_betas=3
),
OptimInfo(
name='cadoptw',
opt_class=Adopt,
description='Cautious AdoptW (decoupled decay)',
defaults={'decoupled': True, 'caution': True}
),
OptimInfo(
name='clamb',
opt_class=Lamb,
description='Cautious LAMB',
has_betas=True,
defaults={'caution': True}
),
OptimInfo(
name='clambw',
opt_class=Lamb,
description='Cautious LAMB with decoupled weight decay',
has_betas=True,
defaults={'caution': True, 'decoupled_decay': True}
),
OptimInfo(
name='claprop',
opt_class=LaProp,
description='Cautious LaProp',
has_betas=True,
defaults={'caution': True}
),
OptimInfo(
name='clion',
opt_class=Lion,
description='Cautious Lion',
has_eps=False,
has_betas=True,
defaults = {'caution': True}
),
OptimInfo(
name='cmars',
opt_class=Mars,
description='Cautious MARS',
has_betas=True,
defaults={'caution': True}
),
OptimInfo(
name='cnadamw',
opt_class=NAdamW,
description='Cautious NAdamW',
has_betas=True,
defaults={'caution': True}
),
OptimInfo(
name='crmsproptf',
opt_class=RMSpropTF,
description='Cautious TensorFlow-style RMSprop',
has_momentum=True,
defaults={'alpha': 0.9, 'caution': True}
),
OptimInfo(
name='csgdw',
opt_class=SGDW,
description='Cautious SGD with decoupled weight decay and Nesterov momentum',
has_eps=False,
has_momentum=True,
defaults={'nesterov': True, 'caution': True}
),
]
for opt in cautious_optimizers:
registry.register(opt)
def _register_other_optimizers(registry: OptimizerRegistry) -> None:
"""Register miscellaneous optimizers"""
other_optimizers = [
OptimInfo(
name='adabelief',
opt_class=AdaBelief,
description='Adapts learning rate based on gradient prediction error',
has_betas=True,
defaults={'rectify': False}
),
OptimInfo(
name='radabelief',
opt_class=AdaBelief,
description='Rectified AdaBelief with variance adaptation',
has_betas=True,
defaults={'rectify': True}
),
OptimInfo(
name='adadelta',
opt_class=torch.optim.Adadelta,
description='torch.optim.Adadelta, Adapts learning rates based on running windows of gradients'
),
OptimInfo(
name='adagrad',
opt_class=torch.optim.Adagrad,
description='torch.optim.Adagrad, Adapts learning rates using cumulative squared gradients',
defaults={'eps': 1e-8}
),
OptimInfo(
name='adan',
opt_class=Adan,
description='Adaptive Nesterov Momentum Algorithm',
defaults={'no_prox': False},
has_betas=True,
num_betas=3
),
OptimInfo(
name='adanw',
opt_class=Adan,
description='Adaptive Nesterov Momentum with decoupled weight decay',
defaults={'no_prox': True},
has_betas=True,
num_betas=3
),
OptimInfo(
name='adahessian',
opt_class=Adahessian,
description='An Adaptive Second Order Optimizer',
has_betas=True,
second_order=True,
),
OptimInfo(
name='laprop',
opt_class=LaProp,
description='Separating Momentum and Adaptivity in Adam',
has_betas=True,
),
OptimInfo(
name='lion',
opt_class=Lion,
description='Evolved Sign Momentum optimizer for improved convergence',
has_eps=False,
has_betas=True
),
OptimInfo(
name='madgrad',
opt_class=MADGRAD,
description='Momentum-based Adaptive gradient method',
has_momentum=True
),
OptimInfo(
name='madgradw',
opt_class=MADGRAD,
description='MADGRAD with decoupled weight decay',
has_momentum=True,
defaults={'decoupled_decay': True}
),
OptimInfo(
name='mars',
opt_class=Mars,
description='Unleashing the Power of Variance Reduction for Training Large Models',
has_betas=True,
),
OptimInfo(
name='novograd',
opt_class=NvNovoGrad,
description='Normalized Adam with L2 norm gradient normalization',
has_betas=True
),
OptimInfo(
name='rmsprop',
opt_class=torch.optim.RMSprop,
description='torch.optim.RMSprop, Root Mean Square Propagation',
has_momentum=True,
defaults={'alpha': 0.9}
),
OptimInfo(
name='rmsproptf',
opt_class=RMSpropTF,
description='TensorFlow-style RMSprop implementation, Root Mean Square Propagation',
has_momentum=True,
defaults={'alpha': 0.9}
),
]
for opt in other_optimizers:
registry.register(opt)
registry.register_foreach_default('lion')
def _register_apex_optimizers(registry: OptimizerRegistry) -> None:
"""Register APEX optimizers (lazy import)"""
apex_optimizers = [
OptimInfo(
name='fusedsgd',
opt_class='apex.optimizers.FusedSGD',
description='NVIDIA APEX fused SGD implementation for faster training',
has_eps=False,
has_momentum=True,
defaults={'nesterov': True}
),
OptimInfo(
name='fusedadam',
opt_class='apex.optimizers.FusedAdam',
description='NVIDIA APEX fused Adam implementation',
has_betas=True,
defaults={'adam_w_mode': False}
),
OptimInfo(
name='fusedadamw',
opt_class='apex.optimizers.FusedAdam',
description='NVIDIA APEX fused AdamW implementation',
has_betas=True,
defaults={'adam_w_mode': True}
),
OptimInfo(
name='fusedlamb',
opt_class='apex.optimizers.FusedLAMB',
description='NVIDIA APEX fused LAMB implementation',
has_betas=True
),
OptimInfo(
name='fusednovograd',
opt_class='apex.optimizers.FusedNovoGrad',
description='NVIDIA APEX fused NovoGrad implementation',
has_betas=True,
defaults={'betas': (0.95, 0.98)}
),
]
for opt in apex_optimizers:
registry.register(opt)
def _register_bnb_optimizers(registry: OptimizerRegistry) -> None:
"""Register bitsandbytes optimizers (lazy import)"""
bnb_optimizers = [
OptimInfo(
name='bnbsgd',
opt_class='bitsandbytes.optim.SGD',
description='bitsandbytes SGD',
has_eps=False,
has_momentum=True,
defaults={'nesterov': True}
),
OptimInfo(
name='bnbsgd8bit',
opt_class='bitsandbytes.optim.SGD8bit',
description='bitsandbytes 8-bit SGD with dynamic quantization',
has_eps=False,
has_momentum=True,
defaults={'nesterov': True}
),
OptimInfo(
name='bnbadam',
opt_class='bitsandbytes.optim.Adam',
description='bitsandbytes Adam',
has_betas=True
),
OptimInfo(
name='bnbadam8bit',
opt_class='bitsandbytes.optim.Adam',
description='bitsandbytes 8-bit Adam with dynamic quantization',
has_betas=True
),
OptimInfo(
name='bnbadamw',
opt_class='bitsandbytes.optim.AdamW',
description='bitsandbytes AdamW',
has_betas=True
),
OptimInfo(
name='bnbadamw8bit',
opt_class='bitsandbytes.optim.AdamW',
description='bitsandbytes 8-bit AdamW with dynamic quantization',
has_betas=True
),
OptimInfo(
'bnblion',
'bitsandbytes.optim.Lion',
description='bitsandbytes Lion',
has_eps=False,
has_betas=True
),
OptimInfo(
'bnblion8bit',
'bitsandbytes.optim.Lion8bit',
description='bitsandbytes 8-bit Lion with dynamic quantization',
has_eps=False,
has_betas=True
),
OptimInfo(
'bnbademamix',
'bitsandbytes.optim.AdEMAMix',
description='bitsandbytes AdEMAMix',
has_betas=True,
num_betas=3,
),
OptimInfo(
'bnbademamix8bit',
'bitsandbytes.optim.AdEMAMix8bit',
description='bitsandbytes 8-bit AdEMAMix with dynamic quantization',
has_betas=True,
num_betas=3,
),
]
for opt in bnb_optimizers:
registry.register(opt)
default_registry = OptimizerRegistry()
def _register_default_optimizers() -> None:
"""Register all default optimizers to the global registry."""
# Register all optimizer groups
_register_sgd_variants(default_registry)
_register_adam_variants(default_registry)
_register_lamb_lars(default_registry)
_register_other_optimizers(default_registry)
_register_apex_optimizers(default_registry)
_register_bnb_optimizers(default_registry)
_register_cautious_optimizers(default_registry)
# Register aliases
default_registry.register_alias('nesterov', 'sgd')
default_registry.register_alias('nesterovw', 'sgdw')
# Initialize default registry
_register_default_optimizers()
# Public API
def list_optimizers(
filter: Union[str, List[str]] = '',
exclude_filters: Optional[List[str]] = None,
with_description: bool = False,
) -> List[Union[str, Tuple[str, str]]]:
"""List available optimizer names, optionally filtered.
List all registered optimizers, with optional filtering using wildcard patterns.
Optimizers can be filtered using include and exclude patterns, and can optionally
return descriptions with each optimizer name.
Args:
filter: Wildcard style filter string or list of filter strings
(e.g., 'adam*' for all Adam variants, or ['adam*', '*8bit'] for
Adam variants and 8-bit optimizers). Empty string means no filtering.
exclude_filters: Optional list of wildcard patterns to exclude. For example,
['*8bit', 'fused*'] would exclude 8-bit and fused implementations.
with_description: If True, returns tuples of (name, description) instead of
just names. Descriptions provide brief explanations of optimizer characteristics.
Returns:
If with_description is False:
List of optimizer names as strings (e.g., ['adam', 'adamw', ...])
If with_description is True:
List of tuples of (name, description) (e.g., [('adam', 'Adaptive Moment...'), ...])
Examples:
>>> list_optimizers()
['adam', 'adamw', 'sgd', ...]
>>> list_optimizers(['la*', 'nla*']) # List lamb & lars
['lamb', 'lambc', 'larc', 'lars', 'nlarc', 'nlars']
>>> list_optimizers('*adam*', exclude_filters=['bnb*', 'fused*']) # Exclude bnb & apex adam optimizers
['adam', 'adamax', 'adamp', 'adamw', 'nadam', 'nadamw', 'radam']
>>> list_optimizers(with_description=True) # Get descriptions
[('adabelief', 'Adapts learning rate based on gradient prediction error'),
('adadelta', 'torch.optim Adadelta, Adapts learning rates based on running windows of gradients'),
('adafactor', 'Memory-efficient implementation of Adam with factored gradients'),
...]
"""
return default_registry.list_optimizers(filter, exclude_filters, with_description)
def get_optimizer_info(name: str) -> OptimInfo:
"""Get the OptimInfo for an optimizer.
Args:
name: Name of the optimizer
Returns:
OptimInfo configuration
Raises:
ValueError: If optimizer is not found
"""
return default_registry.get_optimizer_info(name)
def get_optimizer_class(
name: str,
bind_defaults: bool = True,
) -> Union[OptimType, OptimizerCallable]:
"""Get optimizer class by name with option to bind default arguments.
Retrieves the optimizer class or a partial function with default arguments bound.
This allows direct instantiation of optimizers with their default configurations
without going through the full factory.
Args:
name: Name of the optimizer to retrieve (e.g., 'adam', 'sgd')
bind_defaults: If True, returns a partial function with default arguments from OptimInfo bound.
If False, returns the raw optimizer class.
Returns:
If bind_defaults is False:
The optimizer class (e.g., torch.optim.Adam)
If bind_defaults is True:
A partial function with default arguments bound
Raises:
ValueError: If optimizer name is not found in registry
Examples:
>>> # Get SGD with nesterov momentum default
>>> SGD = get_optimizer_class('sgd') # nesterov=True bound
>>> opt = SGD(model.parameters(), lr=0.1, momentum=0.9)
>>> # Get raw optimizer class
>>> SGD = get_optimizer_class('sgd')
>>> opt = SGD(model.parameters(), lr=1e-3, momentum=0.9)
"""
return default_registry.get_optimizer_class(name, bind_defaults=bind_defaults)
def create_optimizer_v2(
model_or_params: Union[nn.Module, ParamsT],
opt: str = 'sgd',
lr: Optional[float] = None,
weight_decay: float = 0.,
momentum: float = 0.9,
foreach: Optional[bool] = None,
filter_bias_and_bn: bool = True,
layer_decay: Optional[float] = None,
param_group_fn: Optional[Callable[[nn.Module], ParamsT]] = None,
**kwargs: Any,
) -> torch.optim.Optimizer:
"""Create an optimizer instance via timm registry.
Creates and configures an optimizer with appropriate parameter groups and settings.
Supports automatic parameter group creation for weight decay and layer-wise learning
rates, as well as custom parameter grouping.
Args:
model_or_params: A PyTorch model or an iterable of parameters/parameter groups.
If a model is provided, parameters will be automatically extracted and grouped
based on the other arguments.
opt: Name of the optimizer to create (e.g., 'adam', 'adamw', 'sgd').
Use list_optimizers() to see available options.
lr: Learning rate. If None, will use the optimizer's default.
weight_decay: Weight decay factor. Will be used to create param groups if model_or_params is a model.
momentum: Momentum factor for optimizers that support it. Only used if the
chosen optimizer accepts a momentum parameter.
foreach: Enable/disable foreach (multi-tensor) implementation if available.
If None, will use optimizer-specific defaults.
filter_bias_and_bn: If True, bias, norm layer parameters (all 1d params) will not have
weight decay applied. Only used when model_or_params is a model and
weight_decay > 0.
layer_decay: Optional layer-wise learning rate decay factor. If provided,
learning rates will be scaled by layer_decay^(max_depth - layer_depth).
Only used when model_or_params is a model.
param_group_fn: Optional function to create custom parameter groups.
If provided, other parameter grouping options will be ignored.
**kwargs: Additional optimizer-specific arguments (e.g., betas for Adam).
Returns:
Configured optimizer instance.
Examples:
>>> # Basic usage with a model
>>> optimizer = create_optimizer_v2(model, 'adamw', lr=1e-3)
>>> # SGD with momentum and weight decay
>>> optimizer = create_optimizer_v2(
... model, 'sgd', lr=0.1, momentum=0.9, weight_decay=1e-4
... )
>>> # Adam with layer-wise learning rate decay
>>> optimizer = create_optimizer_v2(
... model, 'adam', lr=1e-3, layer_decay=0.7
... )
>>> # Custom parameter groups
>>> def group_fn(model):
... return [
... {'params': model.backbone.parameters(), 'lr': 1e-4},
... {'params': model.head.parameters(), 'lr': 1e-3}
... ]
>>> optimizer = create_optimizer_v2(
... model, 'sgd', param_group_fn=group_fn
... )
Note:
Parameter group handling precedence:
1. If param_group_fn is provided, it will be used exclusively
2. If layer_decay is provided, layer-wise groups will be created
3. If weight_decay > 0 and filter_bias_and_bn is True, weight decay groups will be created
4. Otherwise, all parameters will be in a single group
"""
return default_registry.create_optimizer(
model_or_params,
opt=opt,
lr=lr,
weight_decay=weight_decay,
momentum=momentum,
foreach=foreach,
weight_decay_exclude_1d=filter_bias_and_bn,
layer_decay=layer_decay,
param_group_fn=param_group_fn,
**kwargs
)
def optimizer_kwargs(cfg):
""" cfg/argparse to kwargs helper
Convert optimizer args in argparse args or cfg like object to keyword args for updated create fn.
"""
kwargs = dict(
opt=cfg.opt,
lr=cfg.lr,
weight_decay=cfg.weight_decay,
momentum=cfg.momentum,
)
if getattr(cfg, 'opt_eps', None) is not None:
kwargs['eps'] = cfg.opt_eps
if getattr(cfg, 'opt_betas', None) is not None:
kwargs['betas'] = cfg.opt_betas
if getattr(cfg, 'layer_decay', None) is not None:
kwargs['layer_decay'] = cfg.layer_decay
if getattr(cfg, 'opt_args', None) is not None:
kwargs.update(cfg.opt_args)
if getattr(cfg, 'opt_foreach', None) is not None:
kwargs['foreach'] = cfg.opt_foreach
return kwargs
def create_optimizer(
args,
model: Union[nn.Module, ParamsT],
filter_bias_and_bn: bool = True,
) -> torch.optim.Optimizer:
""" Legacy optimizer factory for backwards compatibility.
NOTE: Use create_optimizer_v2 for new code.
"""
return create_optimizer_v2(
model,
**optimizer_kwargs(cfg=args),
filter_bias_and_bn=filter_bias_and_bn,
)
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