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import math
import torch
import torch.optim as optim
from torch.optim.optimizer import Optimizer
from torchtask.utils import cmd
from torchtask.nn.func import pytorch_support
""" This file wraps the optimizers used in the script.
"""
VALID_OPTIMIZER = ['sgd', 'rmsprop', 'adam', 'wdadam']
def add_parser_arguments(parser):
""" Add the arguments related to the optimizer.
This 'add_parser_arguments' function will be called every time.
Please do not use the argument's name that are already defined in is function.
The default value '-1' means that the default value corresponding to
different LR schedulers will be used.
"""
parser.add_argument('--lr', type=float, default=-1, metavar='',
help='optimizer - learning rate (required by [all])')
parser.add_argument('--dampening', type=float, default=-1, metavar='',
help='optimizer - dampening for momentum (required by [sgd])')
parser.add_argument('--nesterov', type=cmd.str2bool, default=False, metavar='',
help='optimizer - enables Nesterov momentum if True (required by [sgd])')
parser.add_argument('--weight-decay', type=float, default=-1, metavar='',
help='optimizer - weight decay (L2 penalty) (required by [sgd, rmsprop, adam, wdadam])')
parser.add_argument('--momentum', type=float, default=-1, metavar='',
help='optimizer - momentum factor (required by [sgd, rmsprop])')
parser.add_argument('--alpha', type=float, default=-1, metavar='',
help='smoothing constant (required by [rmsprop])')
parser.add_argument('--centered', type=cmd.str2bool, default=False, metavar='',
help='if True, compute the centered RMSProp, the gradient is normalized by an estimation of its variance ( required by [rmsprop])')
parser.add_argument('--eps', type=float, default=-1, metavar='',
help='optimizer - term added to the denominator to improve numerical stability (required by [rmsprop, adam, wdadam])')
parser.add_argument('--beta1', type=float, default=-1, metavar='',
help='optimizer - coefficients used for computing running averages of gradient and its square (required by [adam, wdadam])')
parser.add_argument('--beta2', type=float, default=-1, metavar='',
help='optimizer - coefficients used for computing running averages of gradient and its square (required by [adam, wdadam])')
parser.add_argument('--amsgrad', type=cmd.str2bool, default=False, metavar='',
help='optimizer - use the AMSGrad variant if True (required by [wdadam])')
# ---------------------------------------------------------------------
# Wrapper of Optimizer
# ---------------------------------------------------------------------
def sgd(args):
""" Wrapper of torch.optim.SGD (PyTorch >= 1.0.0).
Implements stochastic gradient descent (optionally with momentum).
"""
args.lr = 0.01 if args.lr == -1 else args.lr
args.weight_decay = 0 if args.weight_decay == -1 else args.weight_decay
args.momentum = 0 if args.momentum == -1 else args.momentum
args.dampening = 0 if args.dampening == -1 else args.dampening
args.nesterov = False if args.nesterov == False else args.nesterov
def sgd_wrapper(param_groups):
pytorch_support(required_version='1.0.0', info_str='Optimizer - SGD')
return optim.SGD(
param_groups,
lr=args.lr, momentum=args.momentum, dampening=args.dampening,
weight_decay=args.weight_decay, nesterov=args.nesterov)
return sgd_wrapper
def rmsprop(args):
""" Wrapper of torch.optim.RMSprop (PyTorch >= 1.0.0).
Implements RMSprop algorithm.
Proposed by G. Hinton in his course.
The centered version first appears in Generating Sequences With Recurrent Neural Networks.
"""
args.lr = 0.01 if args.lr == -1 else args.lr
args.alpha = 0.99 if args.alpha == -1 else args.alpha
args.eps = 1e-08 if args.eps == -1 else args.eps
args.weight_decay = 0 if args.weight_decay == -1 else args.weight_decay
args.momentum = 0 if args.momentum == -1 else args.momentum
args.centered = False if args.centered == False else args.centered
def rmsprop_wrapper(param_groups):
pytorch_support(required_version='1.0.0', info_str='Optimizer - RMSprop')
return optim.RMSprop(
param_groups,
lr=args.lr, alpha=args.alpha, eps=args.eps, weight_decay=args.weight_decay,
momentum=args.momentum, centered=args.centered)
return rmsprop_wrapper
def adam(args):
""" Wrapper of torch.optim.Adam (PyTorch >= 1.0.0).
Implements Adam algorithm.
It has been proposed in 'Adam: A Method for Stochastic Optimization'.
"""
args.lr = 0.001 if args.lr == -1 else args.lr
args.beta1 = 0.9 if args.beta1 == -1 else args.beta1
args.beta2 = 0.999 if args.beta2 == -1 else args.beta2
args.eps = 1e-08 if args.eps == -1 else args.eps
args.weight_decay = 0.0 if args.weight_decay == -1 else args.weight_decay
def adam_wrapper(param_groups):
pytorch_support(required_version='1.0.0', info_str='Optimizer - Adam')
return optim.Adam(
param_groups,
lr=args.lr, betas=(args.beta1, args.beta2), eps=args.eps,
weight_decay=args.weight_decay)
return adam_wrapper
def wdadam(args):
""" Wrapper of torchtask.nn.optimizer.WDAdam (PyTorch >= 1.0.0).
Implements Adam algorithm with weight decay and AMSGrad.
"""
args.lr = 0.001 if args.lr == -1 else args.lr
args.beta1 = 0.9 if args.beta1 == -1 else args.beta1
args.beta2 = 0.999 if args.beta2 == -1 else args.beta2
args.eps = 1e-08 if args.eps == -1 else args.eps
args.weight_decay = 0.0 if args.weight_decay == -1 else args.weight_decay
args.amsgrad = False if args.amsgrad == False else args.amsgrad
def wdadam_wrapper(param_groups):
pytorch_support(required_version='1.0.0', info_str='Optimizer - WDAdam')
return WDAdam(
param_groups,
lr=args.lr, betas=(args.beta1, args.beta2), eps=args.eps,
weight_decay=args.weight_decay, amsgrad=args.amsgrad)
return wdadam_wrapper
# ---------------------------------------------------------------------
# Implementation of Optimizer
# ---------------------------------------------------------------------
class WDAdam(Optimizer):
""" Implements Adam algorithm with weight decay and AMSGrad.
It has been proposed in `Adam: A Method for Stochastic Optimization`.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay using the method from
the paper `Fixing Weight Decay Regularization in Adam` (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, amsgrad=False):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {0}".format(lr))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {0}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {0}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {0}".format(betas[1]))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay / lr, amsgrad=amsgrad)
super(WDAdam, self).__init__(params, defaults)
def __setstate__(self, state):
super(WDAdam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
def step(self, closure=None):
""" Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients')
amsgrad = group['amsgrad']
# State initialization
state = self.state[p]
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
# Maintains max of all exp. moving avg. of sq. grad. values
if amsgrad:
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
if group['weight_decay'] != 0:
p.data.add_(-group['weight_decay'] * group['lr'], p.data)
p.data.addcdiv_(-step_size, exp_avg, denom)
return loss
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