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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import warnings
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
r"""Implements RAdam optimization algorithm.
Note:
Deprecated, please use version provided by PyTorch_.
It has been proposed in `On the Variance of the Adaptive Learning
Rate and Beyond`.
https://arxiv.org/abs/1908.03265
Note:
Reference code: https://github.com/LiyuanLucasLiu/RAdam
"""
def __init__(
self,
params,
lr: float = 1e-3,
betas = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0,
) -> None:
warnings.warn(
"RAdam optimizer is deprecated, since it is included "
"in pytorch natively.",
DeprecationWarning,
stacklevel=2,
)
if lr <= 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if eps < 0.0:
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 weight_decay < 0:
raise ValueError(
"Invalid weight_decay value: {}".format(weight_decay)
)
if (
isinstance(params, (list, tuple))
and len(params) > 0
and isinstance(params[0], dict)
):
for param in params:
if "betas" in param and (
param["betas"][0] != betas[0]
or param["betas"][1] != betas[1]
):
param["buffer"] = [[None, None, None] for _ in range(10)]
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
buffer=[[None, None, None] for _ in range(10)],
)
super(RAdam, self).__init__(params, defaults)
def __setstate__(self, state):
super(RAdam, self).__setstate__(state)
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:
lr = group["lr"]
weight_decay = group["weight_decay"]
beta1, beta2 = group["betas"]
eps = group["eps"]
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data.float()
if grad.is_sparse:
msg = (
"RAdam does not support sparse gradients, "
"please consider SparseAdam instead"
)
raise RuntimeError(msg)
p_data_fp32 = p.data.float()
state = self.state[p]
if len(state) == 0:
state["step"] = 0
state["exp_avg"] = torch.zeros_like(
p_data_fp32, memory_format=torch.preserve_format
)
state["exp_avg_sq"] = torch.zeros_like(
p_data_fp32, memory_format=torch.preserve_format
)
else:
state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
state["exp_avg_sq"] = state["exp_avg_sq"].type_as(
p_data_fp32
)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
state["step"] += 1
buffered = group["buffer"][int(state["step"] % 10)]
if state["step"] == buffered[0]:
N_sma, step_size = buffered[1], buffered[2]
else:
buffered[0] = state["step"]
beta2_t = beta2 ** state["step"]
N_sma_max = 2 / (1 - beta2) - 1
N_sma = N_sma_max - 2 * state["step"] * beta2_t / (
1 - beta2_t
)
buffered[1] = N_sma
# more conservative since it's an approximated value
if N_sma >= 5:
step_size = (
lr
* math.sqrt(
(1 - beta2_t)
* (N_sma - 4)
/ (N_sma_max - 4)
* (N_sma - 2)
/ N_sma
* N_sma_max
/ (N_sma_max - 2)
)
/ (1 - beta1 ** state["step"])
)
else:
step_size = lr / (1 - beta1 ** state["step"])
buffered[2] = step_size
if weight_decay != 0:
p_data_fp32.add_(p_data_fp32, alpha=-weight_decay * lr)
# more conservative since it's an approximated value
if N_sma >= 5:
denom = exp_avg_sq.sqrt().add_(eps)
p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size)
else:
p_data_fp32.add_(exp_avg, alpha=-step_size)
p.data.copy_(p_data_fp32)
return loss |