Spaces:
Runtime error
Runtime error
File size: 3,633 Bytes
2b7bf83 |
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 |
# -*- coding: utf-8 -*-
"""RAdam optimizer.
This code is drived from https://github.com/LiyuanLucasLiu/RAdam.
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
"""Rectified Adam optimizer."""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
"""Initilize RAdam optimizer."""
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
self.buffer = [[None, None, None] for ind in range(10)]
super(RAdam, self).__init__(params, defaults)
def __setstate__(self, state):
"""Set state."""
super(RAdam, self).__setstate__(state)
def step(self, closure=None):
"""Run one step."""
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.float()
if grad.is_sparse:
raise RuntimeError("RAdam does not support sparse gradients")
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)
state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
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"]
beta1, beta2 = group["betas"]
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
exp_avg.mul_(beta1).add_(1 - beta1, grad)
state["step"] += 1
buffered = self.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 = 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"]
) # NOQA
else:
step_size = 1.0 / (1 - beta1 ** state["step"])
buffered[2] = step_size
if group["weight_decay"] != 0:
p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
# more conservative since it's an approximated value
if N_sma >= 5:
denom = exp_avg_sq.sqrt().add_(group["eps"])
p_data_fp32.addcdiv_(-step_size * group["lr"], exp_avg, denom)
else:
p_data_fp32.add_(-step_size * group["lr"], exp_avg)
p.data.copy_(p_data_fp32)
return loss
|