#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import torch
from torch.optim.optimizer import Optimizer
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class SGDP(Optimizer):
r"""Implements SGDP algorithm.
It has been proposed in `Slowing Down the Weight Norm Increase in
Momentum-based Optimizers`.
https://arxiv.org/abs/2006.08217
Note:
Reference code: https://github.com/clovaai/AdamP
"""
def __init__(
self,
params,
lr: float = 1e-3,
momentum: float = 0,
dampening: float = 0,
eps: float = 1e-8,
weight_decay: float = 0,
delta: float = 0.1,
wd_ratio: float = 0.1,
nesterov: bool = False,
) -> None:
if lr <= 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if eps < 0.0:
raise ValueError("Invalid epsilon value: {}".format(eps))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if dampening < 0.0:
raise ValueError("Invalid dampening value: {}".format(dampening))
if weight_decay < 0:
raise ValueError(
"Invalid weight_decay value: {}".format(weight_decay)
)
if delta < 0:
raise ValueError("Invalid delta value: {}".format(delta))
if wd_ratio < 0:
raise ValueError("Invalid wd_ratio value: {}".format(wd_ratio))
defaults = dict(
lr=lr,
momentum=momentum,
dampening=dampening,
eps=eps,
weight_decay=weight_decay,
delta=delta,
wd_ratio=wd_ratio,
nesterov=nesterov,
)
super(SGDP, self).__init__(params, defaults)
@staticmethod
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def _channel_view(x):
return x.view(x.size(0), -1)
@staticmethod
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def _layer_view(x):
return x.view(1, -1)
@staticmethod
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def _cosine_similarity(x, y, eps, view_func):
x = view_func(x)
y = view_func(y)
x_norm = x.norm(dim=1).add_(eps)
y_norm = y.norm(dim=1).add_(eps)
dot = (x * y).sum(dim=1)
return dot.abs() / x_norm / y_norm
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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
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def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: 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:
weight_decay = group["weight_decay"]
momentum = group["momentum"]
dampening = group["dampening"]
nesterov = group["nesterov"]
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
state = self.state[p]
# State initialization
if len(state) == 0:
state["momentum"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# SGD
buf = state["momentum"]
buf.mul_(momentum).add_(grad, alpha=1 - dampening)
if nesterov:
d_p = grad + momentum * buf
else:
d_p = buf
# Projection
wd_ratio = 1
if len(p.shape) > 1:
d_p, wd_ratio = self._projection(
p,
grad,
d_p,
group["delta"],
group["wd_ratio"],
group["eps"],
)
# Weight decay
if weight_decay != 0:
p.data.mul_(
1
- group["lr"]
* group["weight_decay"]
* wd_ratio
/ (1 - momentum)
)
# Step
p.data.add_(d_p, alpha=-group["lr"])
return loss
