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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 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
from torch.optim.optimizer import Optimizer
class Yogi(Optimizer):
r"""Implements Yogi Optimizer Algorithm.
It has been proposed in `Adaptive methods for Nonconvex Optimization`.
https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization # noqa
Note:
Reference code: https://github.com/4rtemi5/Yogi-Optimizer_Keras
"""
def __init__(
self,
params,
lr: float = 1e-2,
betas = (0.9, 0.999),
eps: float = 1e-3,
initial_accumulator: float = 1e-6,
weight_decay: float = 0,
) -> None:
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)
)
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
initial_accumulator=initial_accumulator,
weight_decay=weight_decay,
)
super(Yogi, self).__init__(params, defaults)
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:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"Yogi does not support sparse gradients, "
"please consider SparseAdam instead"
)
state = self.state[p]
# State initialization
# Followed from official implementation in tensorflow addons:
# https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/yogi.py#L118 # noqa
# For more details refer to the discussion:
# https://github.com/jettify/pytorch-optimizer/issues/77
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = nn.init.constant_(
torch.empty_like(
p.data, memory_format=torch.preserve_format
),
group["initial_accumulator"],
)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = nn.init.constant_(
torch.empty_like(
p.data, memory_format=torch.preserve_format
),
group["initial_accumulator"],
)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
if group["weight_decay"] != 0:
grad = grad.add(p.data, alpha=group["weight_decay"])
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
grad_squared = grad.mul(grad)
exp_avg_sq.addcmul_(
torch.sign(exp_avg_sq - grad_squared),
grad_squared,
value=-(1 - beta2),
)
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(
group["eps"]
)
step_size = group["lr"] / bias_correction1
p.data.addcdiv_(exp_avg, denom, value=-step_size)
return loss |