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adamp.py
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adamp.py
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"""
AdamP
Copyright (c) 2020-present NAVER Corp.
MIT license
Original source code: https://github.com/clovaai/AdamP
"""
import math
import torch
import torch.nn.functional as F
from torch.optim.optimizer import Optimizer
class AdamP(Optimizer):
"""Implements AdamP algorithm.
The original Adam algorithm was proposed in
`Adam: A Method for Stochastic Optimization`_.
The AdamP variant was proposed in
`Slowing Down the Weight Norm Increase in Momentum-based Optimizers`_.
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 coefficient
(default: 0)
delta (float): threshold that determines whether
a set of parameters is scale invariant or not (default: 0.1)
wd_ratio (float): relative weight decay applied on scale-invariant
parameters compared to that applied on scale-variant parameters
(default: 0.1)
nesterov (boolean, optional): enables Nesterov momentum
(default: False)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _Slowing Down the Weight Norm Increase in Momentum-based Optimizers:
https://arxiv.org/abs/2006.08217
"""
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
delta=0.1,
wd_ratio=0.1,
nesterov=False,
):
"""
Args:
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 coefficient
(default: 1e-2)
delta (float): threshold that determines whether
a set of parameters is scale invariant or not (default: 0.1)
wd_ratio (float): relative weight decay applied on scale-invariant
parameters compared to that applied on scale-variant parameters
(default: 0.1)
nesterov (boolean, optional): enables Nesterov momentum
(default: False)
"""
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
delta=delta,
wd_ratio=wd_ratio,
nesterov=nesterov,
)
super(AdamP, self).__init__(params, defaults)
def _channel_view(self, x):
return x.view(x.size(0), -1)
def _layer_view(self, x):
return x.view(1, -1)
def _cosine_similarity(self, x, y, eps, view_func):
x = view_func(x)
y = view_func(y)
return F.cosine_similarity(x, y, dim=1, eps=eps).abs_()
def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
wd = 1
expand_size = [-1] + [1] * (len(p.shape) - 1)
for view_func in [self._channel_view, self._layer_view]:
cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
p_n = p.data / view_func(p.data).norm(dim=1).view(
expand_size
).add_(eps)
perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(
expand_size
)
wd = wd_ratio
return perturb, wd
return perturb, wd
def step(self, closure=None):
"""
Performs a single optimization step (parameter update).
Arguments:
closure (callable): A closure that reevaluates the model and
returns the loss. Optional for most optimizers.
Returns:
computed 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
beta1, beta2 = group["betas"]
nesterov = group["nesterov"]
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
state["exp_avg"] = torch.zeros_like(p.data)
state["exp_avg_sq"] = torch.zeros_like(p.data)
# Adam
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
state["step"] += 1
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(
group["eps"]
)
step_size = group["lr"] / bias_correction1
if nesterov:
perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom
else:
perturb = exp_avg / denom
# Projection
wd_ratio = 1
if len(p.shape) > 1:
perturb, wd_ratio = self._projection(
p,
grad,
perturb,
group["delta"],
group["wd_ratio"],
group["eps"],
)
# Weight decay
if group["weight_decay"] > 0:
p.data.mul_(
1 - group["lr"] * group["weight_decay"] * wd_ratio
)
# Step
p.data.add_(perturb, alpha=-step_size)
return loss
__all__ = ["AdamP"]