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optimizer.py
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import math
#torch
import torch
from torch.optim.optimizer import Optimizer
#credit : https://github.com/Yonghongwei/Gradient-Centralization
def centralized_gradient(x, use_gc=True, gc_conv_only=False):
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
class Ranger(Optimizer): # Radam + LookAhead
def __init__(self, params, lr=1e-3, # lr
alpha=0.5, k=5, N_sma_threshhold=5, # Ranger options
betas=(.95, 0.999), eps=1e-5, weight_decay=0, # Adam options
# Gradient centralization on or off, applied to conv layers only or conv + fc layers
use_gc=True, 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 self.gc_conv_only == False):
print(f"GC applied to both conv and fc layers")
elif (self.use_gc and self.gc_conv_only == True):
print(f"GC applied to conv layers only")
def __setstate__(self, state):
print("set state called")
super(Ranger, self).__setstate__(state)
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 self.gc_loc == False:
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
# https://gist.github.com/colllin/0b146b154c4351f9a40f741a28bff1e3
class AdamW(Optimizer):
"""Implements AdamW algorithm.
It has been proposed in `Fixing Weight Decay Regularization in Adam`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
.. Fixing Weight Decay Regularization in Adam:
https://arxiv.org/abs/1711.05101
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay)
super(AdamW, self).__init__(params, defaults)
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): 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('AdamW does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# according to the paper, this penalty should come after the bias correction
# if group['weight_decay'] != 0:
# grad = grad.add(group['weight_decay'], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
# w = w - wd * lr * w
if group['weight_decay'] != 0:
p.data.add_(-group['weight_decay'] * group['lr'], p.data)
# w = w - lr * w.grad
p.data.addcdiv_(-step_size, exp_avg, denom)
# w = w - wd * lr * w - lr * w.grad
# See http://www.fast.ai/2018/07/02/adam-weight-decay/
return loss