# Copyright (c) Alibaba, Inc. and its affiliates.
import math
import torch
from torch.optim.optimizer import Optimizer
from easycv.framework.errors import ValueError
[docs]def centralized_gradient(x, use_gc=True, gc_conv_only=False):
'''credit - https://github.com/Yonghongwei/Gradient-Centralization '''
if use_gc:
if gc_conv_only:
if len(list(x.size())) > 3:
x.add_(-x.mean(
dim=tuple(range(1, len(list(x.size())))), keepdim=True))
else:
if len(list(x.size())) > 1:
x.add_(-x.mean(
dim=tuple(range(1, len(list(x.size())))), keepdim=True))
return x
[docs]class Ranger(Optimizer):
"""
Adam+LookAhead: refer to https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
"""
[docs] def __init__(
self,
params,
lr=1e-3, # lr
alpha=0.5,
k=6,
N_sma_threshhold=5, # Ranger options
betas=(.95, 0.999),
eps=1e-5,
weight_decay=0, # Adam options
use_gc=True, # Gradient centralization on or off, applied to conv layers only or conv + fc layers
gc_conv_only=False,
gc_loc=True):
# parameter checks
if not 0.0 <= alpha <= 1.0:
raise ValueError(f'Invalid slow update rate: {alpha}')
if not 1 <= k:
raise ValueError(f'Invalid lookahead steps: {k}')
if not lr > 0:
raise ValueError(f'Invalid Learning Rate: {lr}')
if not eps > 0:
raise ValueError(f'Invalid eps: {eps}')
# parameter comments:
# beta1 (momentum) of .95 seems to work better than .90...
# N_sma_threshold of 5 seems better in testing than 4.
# In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
# prep defaults and init torch.optim base
defaults = dict(
lr=lr,
alpha=alpha,
k=k,
step_counter=0,
betas=betas,
N_sma_threshhold=N_sma_threshhold,
eps=eps,
weight_decay=weight_decay)
super().__init__(params, defaults)
# adjustable threshold
self.N_sma_threshhold = N_sma_threshhold
# look ahead params
self.alpha = alpha
self.k = k
# radam buffer for state
self.radam_buffer = [[None, None, None] for ind in range(10)]
# gc on or off
self.gc_loc = gc_loc
self.use_gc = use_gc
self.gc_conv_only = gc_conv_only
# level of gradient centralization
# self.gc_gradient_threshold = 3 if gc_conv_only else 1
print(
f'Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}'
)
if (self.use_gc and not self.gc_conv_only):
print('GC applied to both conv and fc layers')
elif (self.use_gc and self.gc_conv_only):
print('GC applied to conv layers only')
def __getstate__(self):
state = super(Ranger, self).__getstate__()
state.update({
'N_sma_threshhold': self.N_sma_threshhold,
'alpha': self.alpha,
'k': self.k,
'radam_buffer': self.radam_buffer,
'gc_loc': self.gc_loc,
'use_gc': self.use_gc,
'gc_conv_only': self.gc_conv_only
})
return state
def __setstate__(self, state):
print('set state called')
super(Ranger, self).__setstate__(state)
[docs] def step(self, closure=None):
loss = None
# note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
# Uncomment if you need to use the actual closure...
# if closure is not None:
# loss = closure()
# Evaluate averages and grad, update param tensors
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(
'Ranger optimizer does not support sparse gradients')
p_data_fp32 = p.data.float()
state = self.state[p] # get state dict for this param
if len(
state
) == 0: # if first time to run...init dictionary with our desired entries
# if self.first_run_check==0:
# self.first_run_check=1
# print("Initializing slow buffer...should not see this at load from saved model!")
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p_data_fp32)
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
# look ahead weight storage now in state dict
state['slow_buffer'] = torch.empty_like(p.data)
state['slow_buffer'].copy_(p.data)
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)
# begin computations
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
# GC operation for Conv layers and FC layers
# if grad.dim() > self.gc_gradient_threshold:
# grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
if self.gc_loc:
grad = centralized_gradient(
grad,
use_gc=self.use_gc,
gc_conv_only=self.gc_conv_only)
state['step'] += 1
# compute variance mov avg
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
# compute mean moving avg
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
buffered = self.radam_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
if N_sma > self.N_sma_threshhold:
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'])
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)
# apply lr
if N_sma > self.N_sma_threshhold:
denom = exp_avg_sq.sqrt().add_(group['eps'])
G_grad = exp_avg / denom
else:
G_grad = exp_avg
if group['weight_decay'] != 0:
G_grad.add_(p_data_fp32, alpha=group['weight_decay'])
# GC operation
if not self.gc_loc:
G_grad = centralized_gradient(
G_grad,
use_gc=self.use_gc,
gc_conv_only=self.gc_conv_only)
p_data_fp32.add_(G_grad, alpha=-step_size * group['lr'])
p.data.copy_(p_data_fp32)
# integrated look ahead...
# we do it at the param level instead of group level
if state['step'] % group['k'] == 0:
# get access to slow param tensor
slow_p = state['slow_buffer']
# (fast weights - slow weights) * alpha
slow_p.add_(p.data - slow_p, alpha=self.alpha)
# copy interpolated weights to RAdam param tensor
p.data.copy_(slow_p)
return loss