import torch
import math
from torch.optim.optimizer import required
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class SOAP(torch.optim.Optimizer):
r"""
Stochastic Optimization of AP (SOAP) is used for optimizing :obj:`~libauc.losses.AveragePrecisionLoss`. The key update steps are summarized as follows:
1. Initialize :math:`\mathbf u=0, \mathbf w_0`
2. For :math:`t=1, \ldots, T`:
3. :math:`\hspace{5mm}` For each :math:`\mathbf{x}_i\in \mathcal B_{+}`, update
:math:`\mathbf{u}^1_{\mathbf{x}_i} = (1-\gamma)\mathbf{u}^1_{\mathbf{x}_i} + \gamma \frac{1}{|\mathcal B|}\sum\limits_{x_j\in\mathcal B} \ell(\mathbf{w}_t;\mathbf{x}_j,\mathbf{x}_i)\mathbb{I}(y_j=1)`
:math:`\mathbf{u}^2_{\mathbf{x}_i} = (1-\gamma)\mathbf{u}^2_{\mathbf{x}_i} + \gamma \frac{1}{|\mathcal B|}\sum\limits_{\mathbf{x}_j\in\mathcal B} \ell(\mathbf{w}_t;\mathbf{x}_j,\mathbf{x}_i)`
4. :math:`\hspace{5mm}` Compute (biased) Stochastic Gradient Estimator:
:math:`G(\mathbf{w}_t) = \frac{1}{|B_+|}\sum\limits_{\mathbf{x}_i\in\mathcal B_+} \sum\limits_{\mathbf{x}_j\in\mathcal B}\frac{(\mathbf{u}_{\mathbf{x}_i}^1 - \mathbf{u}_{\mathbf{x}_i}^2\mathbf{I}(\mathbf{y}_j=1))\nabla \ell(\mathbf{w};\mathbf{x}_j,\mathbf{x}_i) }{|B|(\mathbf{u}_{\mathbf{x}_i}^2)^2}`
7. :math:`\hspace{5mm}` Update :math:`\mathbf w_{t+1} =\mathbf w_t - \eta G(\mathbf{w}_t)` (or Momentum/Adam style)
For more details, please refer to `Stochastic optimization of areas under precision-recall curves with provable convergence <https://proceedings.neurips.cc/paper/2021/file/0dd1bc593a91620daecf7723d2235624-Paper.pdf>`__.
Args:
params (iterable): iterable of parameters to optimize
lr (float): learning rate (default: ``1e-3``)
mode (str, optional): optimization mode, 'sgd' or 'adam' (default: ``'adam'``)
clip_value (float, optional): gradient clipping value (default: ``1.0``)
weight_decay (float, optional): weight decay (L2 penalty) (default: ``1e-5``)
epoch_decay (float, optional): epoch decay (epoch-wise l2 penalty) (default: ``0.0``)
momentum (float, optional): momentum factor (default: ``0.9``)
dampening (float, optional): dampening for momentum (default: ``0.1``)
nesterov (bool, optional): enables Nesterov momentum (default: ``False``)
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``)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: ``False``)
verbose (bool, optional): whether to print optimization progress (default: ``True``)
device (torch.device, optional): the device used for optimization, e.g., 'cpu' or 'cuda' (default: ``None``)
Example:
>>> optimizer = libauc.optimizers.SOAP(model.parameters(), lr=1e-3, mode='adam')
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
Reference:
.. [1] Qi, Qi, Youzhi Luo, Zhao Xu, Shuiwang Ji, and Tianbao Yang.
"Stochastic optimization of areas under precision-recall curves with provable convergence."
In Advances in Neural Information Processing Systems 34 (2021): 1752-1765.
https://proceedings.neurips.cc/paper/2021/file/0dd1bc593a91620daecf7723d2235624-Paper.pdf
"""
def __init__(self,
params,
lr=1e-3,
mode='adam',
clip_value=1.0,
weight_decay=1e-5,
epoch_decay=0,
momentum=0.9,
nesterov=False,
dampening=0.1,
betas=(0.9, 0.999),
eps=1e-8,
amsgrad=False,
verbose=True,
device=None,
**kwargs):
if not device:
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
else:
self.device = device
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {}".format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= epoch_decay:
raise ValueError("Invalid epoch_decay value: {}".format(epoch_decay))
if not isinstance(mode, str):
raise ValueError("Invalid mode type: {}".format(mode))
self.params = list(params)
self.lr = lr
self.mode = mode.lower()
self.model_ref = self.__init_model_ref__(self.params) if epoch_decay > 0 else None
self.model_acc = self.__init_model_acc__(self.params) if epoch_decay > 0 else None
self.T = 0 # for epoch_decay
self.steps = 0 # total optimization steps
self.verbose = verbose # print updates for lr/regularizer
assert self.mode in ['adam', 'sgd'], "Keyword is not found in [`adam`, `sgd`]!"
defaults = dict(lr=lr, weight_decay=weight_decay, epoch_decay=epoch_decay,
momentum=momentum, dampening=dampening, nesterov=nesterov,
betas=betas, eps=eps, amsgrad=amsgrad,
clip_value=clip_value, model_ref=self.model_ref, model_acc=self.model_acc)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super(SOAP, self).__init__(self.params, defaults)
def __setstate__(self, state):
super(SOAP, self).__setstate__(state)
for group in self.param_groups:
if self.mode == 'sgd':
group.setdefault('nesterov', False)
elif self.mode == 'adam':
group.setdefault('amsgrad', False)
else:
NotImplementedError
def __init_model_ref__(self, params):
model_ref = []
if not isinstance(params, list):
params = list(params)
for var in params:
if var is not None:
model_ref.append(torch.empty(var.shape).normal_(mean=0, std=0.01).to(self.device))
return model_ref
def __init_model_acc__(self, params):
model_acc = []
if not isinstance(params, list):
params = list(params)
for var in params:
if var is not None:
model_acc.append(torch.zeros(var.shape, dtype=torch.float32, device=self.device, requires_grad=False).to(self.device))
return model_acc
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@torch.no_grad()
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:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
clip_value = group['clip_value']
weight_decay = group['weight_decay']
epoch_decay = group['epoch_decay']
model_ref = group['model_ref']
model_acc = group['model_acc']
self.lr = group['lr']
if self.mode == 'sgd':
for i, p in enumerate(group['params']):
if p.grad is None:
continue
if epoch_decay > 0:
d_p = torch.clamp(p.grad.data , -clip_value, clip_value) + epoch_decay*(p.data - model_ref[i].data) + weight_decay*p.data
else:
d_p = torch.clamp(p.grad.data , -clip_value, clip_value) + weight_decay*p.data
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
if nesterov:
d_p = d_p.add(buf, alpha=momentum)
else:
d_p = buf
p.add_(d_p, alpha=-group['lr'])
if epoch_decay > 0:
model_acc[i].data = model_acc[i].data + p.data
elif self.mode == 'adam':
for i, p in enumerate(group['params']):
if p.grad is None:
continue
if epoch_decay > 0:
grad = torch.clamp(p.grad.data , -clip_value, clip_value) + epoch_decay*(p.data - model_ref[i].data) + weight_decay*p.data
else:
grad = torch.clamp(p.grad.data , -clip_value, clip_value) + weight_decay*p.data
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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() / math.sqrt(bias_correction2)).add_(group['eps'])
else:
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
step_size = group['lr'] / bias_correction1
p.addcdiv_(exp_avg, denom, value=-step_size)
if epoch_decay > 0:
model_acc[i].data = model_acc[i].data + p.data
self.steps += 1
self.T += 1
return loss
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def update_lr(self, decay_factor=None):
if decay_factor != None:
self.param_groups[0]['lr'] = self.param_groups[0]['lr']/decay_factor
print ('Reducing learning rate to %.5f @ T=%s!'%(self.param_groups[0]['lr'], self.steps))
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def update_regularizer(self, decay_factor=None):
if decay_factor != None:
self.param_groups[0]['lr'] = self.param_groups[0]['lr']/decay_factor
if self.verbose:
print ('Reducing learning rate to %.5f @ T=%s!'%(self.param_groups[0]['lr'], self.steps))
if self.verbose:
print ('Updating regularizer @ T=%s!'%(self.steps))
for i, param in enumerate(self.model_ref):
self.model_ref[i].data = self.model_acc[i].data/self.T
for i, param in enumerate(self.model_acc):
self.model_acc[i].data = torch.zeros(param.shape, dtype=torch.float32, device=self.device, requires_grad=False).to(self.device)
self.T = 0