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3cd1076 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import torch
from torch.optim.optimizer import Optimizer
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
def _channel_view(x):
return x.view(x.size(0), -1)
@staticmethod
def _layer_view(x):
return x.view(1, -1)
@staticmethod
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
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):
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 |