Optimizer

Source: vignettes/optimizer.Rmd

Intro

The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes “out of the box” support for vision, text, tabular, and collab (collaborative filtering) models.

Interesting posts about NN from scratch using R:

Basic steppers

To be able to give examples of optimizer steps, we will need some steppers, like the following:

library(magrittr)
library(fastai)

tst_param = function(val, grad = NULL) {
  "Create a tensor with `val` and a gradient of `grad` for testing"
  res = tensor(val) %>% float()

  if(is.null(grad)) {
    grad = tensor(val / 10)
  } else {
    grad = tensor(grad)
  }

  res$grad = grad %>% float()
  res
}
p = tst_param(1., 0.1)
p
tensor(1.)
sgd_step(p, 1.)
p
tensor(0.9000)
p$grad
tensor(0.1000)

Weight decay 

p = tst_param(1., 0.1)
weight_decay(p, 1., 0.1)
p
tensor(0.9000)

L2 regularization 

p = tst_param(1., 0.1)
l2_reg(p, 1., 0.1)
p$grad
tensor(0.2000)

Making the step

This method will loop over all param groups, then all parameters for which grad is not NULL and call each function in stepper, passing it the parameter p with the hyper-parameters in the corresponding dict in hypers.

params = L(lapply(0:3, function(x) tst_param(x)))

opt = Optimizer(params, sgd_step, lr=0.1)

opt$step()

str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9900)
 $ :tensor(1.9800)
 $ :tensor(2.9700)
params = L(lapply(0:3, function(x) tst_param(x)))

opt = Optimizer(params, list(weight_decay, sgd_step), lr=0.1, wd = 0.1)

opt$step()

str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9800)
 $ :tensor(1.9600)
 $ :tensor(2.9400)
params = L(lapply(0:3, function(x) tst_param(x)))

opt = Optimizer(params, sgd_step, lr=0.1)

try(params[3]$grad <- NULL,
    TRUE)

params[3]$grad

opt$step()

str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9900)
 $ :tensor(1.9800)
 $ :tensor(3.)
params = L(lapply(0:3, function(x) tst_param(x)))

opt = Optimizer(list(params[0:1],params[2:3]), sgd_step, lr=0.1)

opt$hypers$items[[1]][[1]] = 0.01

opt$step()

str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9990)
 $ :tensor(1.9800)
 $ :tensor(2.9700)

Zero grad 

params = L(lapply(0:3, function(x) tst_param(x)))

opt = Optimizer(params, list(weight_decay, sgd_step), lr=0.1, wd = 0.1)

opt$zero_grad()

str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(1.)
 $ :tensor(2.)
 $ :tensor(3.)

Average grad

Keeps track of the avg grads of p in state with mom.

`dampening = FALSE gives the classical formula for momentum in SGD:

  • new_val = old_val * mom + grad

whereas dampening = TRUE makes it an exponential moving average:

p = tst_param(c(1,2,3), c(4,5,6))
state = average_grad(p, mom = 0.9, dampening = FALSE, grad_avg = NULL)
p$grad
# tensor([4., 5., 6.])

state = average_grad(p, mom=0.9, dampening = TRUE)
p$grad*0.1
# tensor([0.4000, 0.5000, 0.6000])
p$grad*(0.1*0.9+0.1)
# tensor([0.7600, 0.9500, 1.1400])

Average sqr_grad

dampening = FALSE gives the classical formula for momentum in SGD:

  • new_val = old_val * mom + grad**2

whereas dampening = TRUE makes it an exponential moving average:

  • new_val = old_val * mom + (grad ** 2) * (1-mom)
p = tst_param(c(1,2,3), c(4,5,6))
state = average_sqr_grad(p, sqr_mom = 0.99, dampening = FALSE)

p$grad$pow(2)
# tensor([16., 25., 36.])

p$grad$pow(2) * 1.99
# tensor([31.8400, 49.7500, 71.6400])
 
state = average_sqr_grad(p, sqr_mom = 0.99)
p$grad$pow(2) * 1e-2
# tensor([0.1600, 0.2500, 0.3600])
state = average_sqr_grad(p, sqr_mom = 0.99)

p$grad$pow(2)*(0.01*0.99+0.01)
# tensor([0.3184, 0.4975, 0.7164])

params = L(lapply(0:3, function(x) tst_param(x)))
opt = Optimizer(params, sgd_step, lr = 0.1)
opt$freeze_to(1L)

SGD

A Optimizer for SGD with lr and mom and params.

Optional weight decay of wd is applied, as true weight decay (decay the weights directly) if decouple_wd = TRUE else as L2 regularization (add the decay to the gradients).

params = L(lapply(0:3, function(x) tst_param(x)))
opt = SGD(params, lr = 0.1)
opt$step()
str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9900)
 $ :tensor(1.9800)
 $ :tensor(2.9700)
params = L(lapply(0:3, function(x) tst_param(x)))
opt = SGD(params, lr = 0.1, mom = 0.9)
opt$step()
str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9900)
 $ :tensor(1.9800)
 $ :tensor(2.9700)

Test weight decay, notice how we can see that L2 regularization is different from weight decay even for simple SGD with momentum.

params =  L(lapply(0:3, function(x) tst_param(x)))
#Weight decay
opt = SGD(params, lr=0.1, mom=0.9, wd=0.1)
opt$step()
str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9800)
 $ :tensor(1.9600)
 $ :tensor(2.9400)
params =  L(lapply(0:3, function(x) tst_param(x)))
#L2 reg
opt = SGD(params, lr=0.1, mom=0.9, wd=0.1, decouple_wd=FALSE)
opt$step()
str(params$items)
List of 4
 $ :tensor(0.)
 $ :tensor(0.9800)
 $ :tensor(1.9600)
 $ :tensor(2.9400)

RMSProp

A Optimizer for RMSProp with lr, sqr_mom, mom and params.

RMSProp was introduced by Geoffrey Hinton in his course. What is named sqr_mom here is the alpha in the course. Optional weight decay of wd is applied, as true weight decay (decay the weights directly) if decouple_wd = TRUE else as L2 regularization (add the decay to the gradients).

params = tst_param(c(1:3), c(0.1,0.2,0.3))
opt = RMSProp(params, lr=0.1)
opt$step()
opt$step()
step = (-0.1 * 0.1) / (sqrt((0.01*0.99+0.01) * 0.1**2) + 1e-8)
params; tensor(c(step, 1+step, 2+step))
tensor([-0.7089,  0.2911,  1.2911])
tensor([-0.7089,  0.2911,  1.2911])
params = tst_param(c(1:3), c(0.1,0.2,0.3))
opt = RMSProp(params, lr=0.1, mom=0.9)
opt$step()
opt$step()
step = (- 0.1 * (0.1 + 0.9*0.1)) / (sqrt((0.01*0.99+0.01) * 0.1**2) + 1e-8)
params; tensor(c(step, 1+step, 2+step))
tensor([-1.3469, -0.3469,  0.6531])
tensor([-1.3469, -0.3469,  0.6531])

Adam

A Optimizer for Adam with lr, mom, sqr_mom, eps and params.

Adam was introduced by Diederik P. Kingma and Jimmy Ba in Adam: A Method for Stochastic Optimization. For consistency across optimizers, we renamed beta1 and beta2 in the paper to mom and sqr_mom. Note that our defaults also differ from the paper (0.99 for sqr_mom or beta2, 1e-5 for eps). Those values seem to be better from our experiments in a wide range of situations.

Optional weight decay of wd is applied, as true weight decay (decay the weights directly) if decouple_wd=TRUE else as L2 regularization (add the decay to the gradients).

params = tst_param(c(1:3), c(0.1,0.2,0.3))
opt = Adam(params, lr=0.1, wd=0)
opt$step()
step = (-0.1 * 0.1) / (sqrt(0.1**2) + 1e-8)
params; tensor(c(1+step, 2+step, 3+step))
tensor([0.9000, 1.9000, 2.9000])
tensor([0.9000, 1.9000, 2.9000])
opt$step()
params;tensor(tensor(c(1+2*step, 2+2*step, 3+2*step)))
tensor([0.8000, 1.8000, 2.8000])
tensor([0.8000, 1.8000, 2.8000])

RAdam 

beta = 0.99
r_inf = 2/(1-beta) - 1
rs = lapply(5:500, function(s) {r_inf - 2*s*beta**s/(1-beta**s)}) %>% as.numeric()
v = sqrt(((rs-4) * (rs-2) * r_inf)/((r_inf-4)*(r_inf-2)*rs))
df_high = data.frame(x = 1:length(v), y = v)

library(highcharter)
hchart(df_high,'line', hcaes(x,y))

QHAdam

An Optimizer for Adam with lr, mom, sqr_mom, nus, eps and params.

params = tst_param(c(1:3), c(0.1,0.2,0.3))
opt = QHAdam(params, lr=0.1)
opt$step()
step = (-0.1 * (((1-0.7) * 0.1) + (0.7 * 0.1)) )/ (
 sqrt(((1-1.0) * 0.1**2) + (1.0 * 0.1**2)) + 1e-8)
params; tensor(c(1+step, 2+step, 3+step))
# tensor([0.9000, 1.9000, 2.9000])
# tensor([0.9000, 1.9000, 2.9000])
opt$step()
params; tensor(c(1+2*step, 2+2*step, 3+2*step))
# tensor([0.8000, 1.8000, 2.8000])
# tensor([0.8000, 1.8000, 2.8000])

Larc

A Optimizer for Adam with lr, mom, sqr_mom, eps and params.

The LARS optimizer was first introduced in Large Batch Training of Convolutional Networks then refined in its LARC variant (original LARS is with clip=FALSE). A learning rate is computed for each individual layer with a certain trust_coefficient, then clipped to be always less than lr.

Optional weight decay of wd is applied, as true weight decay (decay the weights directly) if decouple_wd = TRUE else as L2 regularization (add the decay to the gradients).

params = list(tst_param(c(1:3), c(0.1,0.2,0.3)), tst_param(c(1:3), c(0.01,0.02,0.03)))
opt = Larc(params, lr=0.1)
opt$step()
#First param local lr is 0.02 < lr so it's not clipped
opt$state[params[[1]]]['local_lr']
$local_lr
tensor(0.0200)
opt$state[params[[2]]]['local_lr']
$local_lr
[1] 0.1
params = list(tst_param(c(1:3), c(0.1,0.2,0.3)), tst_param(c(1:3), c(0.01,0.02,0.03)))
opt = Larc(params, lr=0.1, clip = FALSE)
opt$step()
#Second param local lr is 0.2 > lr so it's clipped
opt$state[params[[1]]]['local_lr']
$local_lr
tensor(0.0200)
opt$state[params[[2]]]['local_lr']
$local_lr
tensor(0.2000)

LAMB

A Optimizer for Adam with lr, mom, sqr_mom, eps and params.

LAMB was introduced in Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. Intuitively, it’s LARC applied to Adam. As in Adam, we renamed beta1 and beta2 in the paper to mom and sqr_mom. Note that our defaults also differ from the paper (0.99 for sqr_mom or beta2, 1e-5 for eps). Those values seem to be better from our experiments in a wide range of situations.

Optional weight decay of wd is applied, as true weight decay (decay the weights directly) if decouple_wd=TRUE else as L2 regularization (add the decay to the gradients).

params = tst_param(c(1:3), c(0.1,0.2,0.3))
opt = Lamb(params, lr=0.1)
opt$step()
params
tensor([0.7840, 1.7840, 2.7840])

Lookahead 

params = tst_param(c(1:3), c(0.1,0.2,0.3))
p = params$data$clone()
g = tensor(c(0.1,0.2,0.3))
opt = Lookahead(SGD(params, lr=0.1))

for(i in 1:5) {
  opt$step()
}
#first 5 steps are normal SGD steps
params; p - g * 0.5
# tensor([0.9500, 1.9000, 2.8500])
# tensor([0.9500, 1.9000, 2.8500])

#Since k=6, sixth step is a moving average of the 6 SGD steps with the initial weight
opt$step()
params; p * 0.5 + (p-g*0.6) * 0.5
# tensor([0.9700, 1.9400, 2.9100])
# tensor([0.9700, 1.9400, 2.9100])