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175 lines
6.5 KiB
175 lines
6.5 KiB
""" Optimizer Factory w/ Custom Weight Decay
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from .adafactor import Adafactor
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from .adahessian import Adahessian
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from .adamp import AdamP
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from .lookahead import Lookahead
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from .nadam import Nadam
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from .novograd import NovoGrad
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from .nvnovograd import NvNovoGrad
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from .radam import RAdam
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from .rmsprop_tf import RMSpropTF
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from .sgdp import SGDP
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from .adabelief import AdaBelief
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try:
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from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD
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has_apex = True
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except ImportError:
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has_apex = False
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def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
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decay = []
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no_decay = []
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for name, param in model.named_parameters():
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if not param.requires_grad:
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continue # frozen weights
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if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
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no_decay.append(param)
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else:
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decay.append(param)
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return [
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{'params': no_decay, 'weight_decay': 0.},
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{'params': decay, 'weight_decay': weight_decay}]
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def optimizer_kwargs(cfg):
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""" cfg/argparse to kwargs helper
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Convert optimizer args in argparse args or cfg like object to keyword args for updated create fn.
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"""
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kwargs = dict(
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optimizer_name=cfg.opt,
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learning_rate=cfg.lr,
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weight_decay=cfg.weight_decay,
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momentum=cfg.momentum)
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if getattr(cfg, 'opt_eps', None) is not None:
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kwargs['eps'] = cfg.opt_eps
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if getattr(cfg, 'opt_betas', None) is not None:
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kwargs['betas'] = cfg.opt_betas
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if getattr(cfg, 'opt_args', None) is not None:
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kwargs.update(cfg.opt_args)
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return kwargs
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def create_optimizer(args, model, filter_bias_and_bn=True):
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""" Legacy optimizer factory for backwards compatibility.
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NOTE: Use create_optimizer_v2 for new code.
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"""
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return create_optimizer_v2(
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model,
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**optimizer_kwargs(cfg=args),
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filter_bias_and_bn=filter_bias_and_bn,
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)
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def create_optimizer_v2(
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model: nn.Module,
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optimizer_name: str = 'sgd',
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learning_rate: Optional[float] = None,
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weight_decay: float = 0.,
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momentum: float = 0.9,
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filter_bias_and_bn: bool = True,
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**kwargs):
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""" Create an optimizer.
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TODO currently the model is passed in and all parameters are selected for optimization.
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For more general use an interface that allows selection of parameters to optimize and lr groups, one of:
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* a filter fn interface that further breaks params into groups in a weight_decay compatible fashion
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* expose the parameters interface and leave it up to caller
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Args:
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model (nn.Module): model containing parameters to optimize
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optimizer_name: name of optimizer to create
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learning_rate: initial learning rate
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weight_decay: weight decay to apply in optimizer
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momentum: momentum for momentum based optimizers (others may use betas via kwargs)
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filter_bias_and_bn: filter out bias, bn and other 1d params from weight decay
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**kwargs: extra optimizer specific kwargs to pass through
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Returns:
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Optimizer
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"""
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opt_lower = optimizer_name.lower()
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if weight_decay and filter_bias_and_bn:
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skip = {}
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if hasattr(model, 'no_weight_decay'):
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skip = model.no_weight_decay()
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parameters = add_weight_decay(model, weight_decay, skip)
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weight_decay = 0.
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else:
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parameters = model.parameters()
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if 'fused' in opt_lower:
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assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
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opt_args = dict(lr=learning_rate, weight_decay=weight_decay, **kwargs)
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opt_split = opt_lower.split('_')
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opt_lower = opt_split[-1]
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if opt_lower == 'sgd' or opt_lower == 'nesterov':
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opt_args.pop('eps', None)
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optimizer = optim.SGD(parameters, momentum=momentum, nesterov=True, **opt_args)
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elif opt_lower == 'momentum':
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opt_args.pop('eps', None)
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optimizer = optim.SGD(parameters, momentum=momentum, nesterov=False, **opt_args)
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elif opt_lower == 'adam':
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optimizer = optim.Adam(parameters, **opt_args)
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elif opt_lower == 'adabelief':
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optimizer = AdaBelief(parameters, rectify=False, **opt_args)
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elif opt_lower == 'adamw':
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optimizer = optim.AdamW(parameters, **opt_args)
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elif opt_lower == 'nadam':
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optimizer = Nadam(parameters, **opt_args)
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elif opt_lower == 'radam':
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optimizer = RAdam(parameters, **opt_args)
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elif opt_lower == 'adamp':
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optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
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elif opt_lower == 'sgdp':
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optimizer = SGDP(parameters, momentum=momentum, nesterov=True, **opt_args)
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elif opt_lower == 'adadelta':
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optimizer = optim.Adadelta(parameters, **opt_args)
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elif opt_lower == 'adafactor':
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if not learning_rate:
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opt_args['lr'] = None
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optimizer = Adafactor(parameters, **opt_args)
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elif opt_lower == 'adahessian':
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optimizer = Adahessian(parameters, **opt_args)
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elif opt_lower == 'rmsprop':
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optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=momentum, **opt_args)
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elif opt_lower == 'rmsproptf':
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optimizer = RMSpropTF(parameters, alpha=0.9, momentum=momentum, **opt_args)
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elif opt_lower == 'novograd':
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optimizer = NovoGrad(parameters, **opt_args)
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elif opt_lower == 'nvnovograd':
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optimizer = NvNovoGrad(parameters, **opt_args)
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elif opt_lower == 'fusedsgd':
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opt_args.pop('eps', None)
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optimizer = FusedSGD(parameters, momentum=momentum, nesterov=True, **opt_args)
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elif opt_lower == 'fusedmomentum':
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opt_args.pop('eps', None)
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optimizer = FusedSGD(parameters, momentum=momentum, nesterov=False, **opt_args)
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elif opt_lower == 'fusedadam':
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optimizer = FusedAdam(parameters, adam_w_mode=False, **opt_args)
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elif opt_lower == 'fusedadamw':
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optimizer = FusedAdam(parameters, adam_w_mode=True, **opt_args)
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elif opt_lower == 'fusedlamb':
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optimizer = FusedLAMB(parameters, **opt_args)
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elif opt_lower == 'fusednovograd':
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opt_args.setdefault('betas', (0.95, 0.98))
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optimizer = FusedNovoGrad(parameters, **opt_args)
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else:
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assert False and "Invalid optimizer"
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raise ValueError
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if len(opt_split) > 1:
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if opt_split[0] == 'lookahead':
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optimizer = Lookahead(optimizer)
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return optimizer
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