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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 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
from torch.optim.optimizer import Optimizer
class LARS(Optimizer):
r"""Extends SGD in PyTorch with LARS scaling from the paper
`Large batch training of Convolutional Networks`__.
.. note::
The application of momentum in the SGD part is modified according to
the PyTorch standards. LARS scaling fits into the equation in the
following fashion.
.. math::
\begin{aligned}
g_{t+1} & = \text{lars_lr} * (\beta * p_{t} + g_{t+1}), \\
v_{t+1} & = \\mu * v_{t} + g_{t+1}, \\
p_{t+1} & = p_{t} - \text{lr} * v_{t+1},
\\end{aligned}
where :math:`p`, :math:`g`, :math:`v`, :math:`\\mu` and :math:`\beta`
denote the parameters, gradient, velocity, momentum, and weight decay
respectively. The :math:`lars_lr` is defined by Eq. 6 in the paper.
The Nesterov version is analogously modified.
.. warning::
Parameters with weight decay set to 0 will automatically be excluded
from layer-wise LR scaling. This is to ensure consistency with papers
like SimCLR and BYOL.
__ https://arxiv.org/pdf/1708.03888.pdf
Note:
Reference code: https://github.com/PyTorchLightning/lightning-bolts/
"""
def __init__(
self,
params,
lr: float = 1e-2,
momentum: float = 0.0,
dampening: float = 0.0,
weight_decay: float = 0.0,
nesterov: bool = False,
trust_coefficient: float = 0.01,
eps: float = 1e-8,
):
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 < 0.0:
raise ValueError(
"Invalid weight_decay value: {}".format(weight_decay)
)
if trust_coefficient < 0.0:
raise ValueError(
"Invalid trust_coefficient value: {}".format(trust_coefficient)
)
defaults = dict(
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
trust_coefficient=trust_coefficient,
eps=eps,
)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError(
"Nesterov momentum requires a momentum and zero dampening"
)
super().__init__(params, defaults)
def __setstate__(self, state) -> None:
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("nesterov", False)
@torch.no_grad()
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:
with torch.enable_grad():
loss = closure()
# exclude scaling for params with 0 weight decay
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
d_p = p.grad
p_norm = torch.norm(p.data)
g_norm = torch.norm(p.grad.data)
# lars scaling + weight decay part
if weight_decay != 0:
if p_norm != 0 and g_norm != 0:
lars_lr = p_norm / (
g_norm + p_norm * weight_decay + group["eps"]
)
lars_lr *= group["trust_coefficient"]
d_p = d_p.add(p, alpha=weight_decay)
d_p *= lars_lr
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"])
return loss |