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[Fix] schedulefree #351

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[Fix] schedulefree #351

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3d54dfb
fix: schedulefree
Feb 17, 2025
63497b8
fix: restore weight_decouple argument in ScheduleFreeSGD
Feb 19, 2025
5ba1182
fix: palm betas scheduling in ScheduleFreeAdamW/ScheduleFreeRAdam
Feb 19, 2025
10dd63e
fix: skip updates when lr is 0 in ScheduleFreeRAdam
Feb 19, 2025
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83 changes: 35 additions & 48 deletions pytorch_optimizer/optimizer/schedulefree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@ class ScheduleFreeSGD(BaseOptimizer):
:param lr: float. learning rate.
:param momentum: float. momentum factor, must be between 0 and 1 exclusive.
:param weight_decay: float. weight decay (L2 penalty).
:param weight_decouple: bool. the optimizer uses decoupled weight decay as in AdamW.
:param fixed_decay: bool. fix weight decay.
:param r: float. use polynomial weighting in the average with power r.
:param weight_lr_power: float. during warmup, the weights in the average will be equal to lr raised to this power.
Expand All @@ -30,7 +29,6 @@ def __init__(
lr: float = 1.0,
momentum: float = 0.9,
weight_decay: float = 0.0,
weight_decouple: bool = True,
fixed_decay: bool = False,
r: float = 0.0,
weight_lr_power: float = 2.0,
Expand All @@ -47,7 +45,6 @@ def __init__(
'lr': lr,
'momentum': momentum,
'weight_decay': weight_decay,
'weight_decouple': weight_decouple,
'fixed_decay': fixed_decay,
'r': r,
'weight_lr_power': weight_lr_power,
Expand Down Expand Up @@ -132,21 +129,15 @@ def step(self, closure: CLOSURE = None) -> LOSS:
if len(state) == 0:
state['z'] = p.clone()

self.apply_weight_decay(
p=p,
grad=grad,
lr=lr,
weight_decay=group['weight_decay'],
weight_decouple=group['weight_decouple'],
fixed_decay=group['fixed_decay'],
)

z = state['z']

grad.mul_(lr)
grad.add_(p, alpha=group['weight_decay'] * (1.0 if group['fixed_decay'] else lr))

p.lerp_(z, weight=checkpoint)
p.add_(grad, alpha=lr * (momentum * (1.0 - checkpoint) - 1))
p.add_(grad, alpha=momentum * (1.0 - checkpoint) - 1)

z.sub_(grad, alpha=lr)
z.sub_(grad)

return loss

Expand Down Expand Up @@ -259,9 +250,9 @@ def step(self, closure: CLOSURE = None) -> LOSS:

beta1, beta2 = group['betas']

bias_correction2_sq: float = math.sqrt(1.0 - beta2 ** group['step'])
bias_correction2: float = 1.0 - beta2 ** group['step']

lr: float = group['lr'] * schedule * bias_correction2_sq
lr: float = group['lr'] * schedule
lr_max = group['lr_max'] = max(lr, group['lr_max'])

weight = (group['step'] ** group['r']) * (lr_max ** group['weight_lr_power'])
Expand All @@ -271,9 +262,8 @@ def step(self, closure: CLOSURE = None) -> LOSS:

if group['use_palm']:
beta2: float = 1.0 - group['step'] ** -0.8
debias: float = (1.0 - beta2) / (1.0 - beta2 ** group['step'])
else:
debias: float = beta2
# unnecessary bias correction when PaLM beta2 scheduling
bias_correction2 = 1.0

for p in group['params']:
if p.grad is None:
Expand All @@ -289,31 +279,27 @@ def step(self, closure: CLOSURE = None) -> LOSS:
state['z'] = p.clone()
state['exp_avg_sq'] = torch.zeros_like(p)

self.apply_weight_decay(
p=p,
grad=grad,
lr=lr,
weight_decay=group['weight_decay'],
weight_decouple=group['weight_decouple'],
fixed_decay=group['fixed_decay'],
)
if not group['weight_decouple']:
grad.add_(p, alpha=group['weight_decay'])

z, exp_avg_sq = state['z'], state['exp_avg_sq']
exp_avg_sq.mul_(debias).addcmul_(grad, grad, value=1.0 - debias)

exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
de_nom = self.apply_ams_bound(
ams_bound=group['ams_bound'],
exp_avg_sq=exp_avg_sq,
exp_avg_sq=exp_avg_sq.div(bias_correction2),
max_exp_avg_sq=state.get('max_exp_avg_sq', None),
eps=group['eps'],
)

grad.div_(de_nom)
grad.mul_(lr)
if group['weight_decouple']:
grad.add_(p, alpha=group['weight_decay'] * (1.0 if group['fixed_decay'] else lr))

p.lerp_(z, weight=checkpoint)
p.add_(grad, alpha=lr * (beta1 * (1.0 - checkpoint) - 1))
p.add_(grad, alpha=beta1 * (1.0 - checkpoint) - 1)

z.sub_(grad, alpha=lr)
z.sub_(grad)

return loss

Expand Down Expand Up @@ -428,6 +414,12 @@ def step(self, closure: CLOSURE = None) -> LOSS:
n_sma_threshold=4,
degenerated_to_sgd=group['degenerated_to_sgd'],
)
if n_sma > 4:
# cancel bias correction2
lr = lr / bias_correction2_sq
elif lr < 0.:
# n_sma < 4.0 and degenerated_to_sgd is False
lr = 0.0

lr_max = group['lr_max'] = max(lr, group['lr_max'])

Expand All @@ -436,13 +428,10 @@ def step(self, closure: CLOSURE = None) -> LOSS:

checkpoint: float = weight / weight_sum if weight_sum != 0.0 else 0.0

adaptive_y_lr: float = lr * (beta1 * (1.0 - checkpoint) - 1.0)

if group['use_palm']:
beta2: float = 1.0 - group['step'] ** -0.8
debias: float = (1.0 - beta2) / (1.0 - beta2 ** group['step'])
else:
debias: float = beta2
# unnecessary bias correction when PaLM beta2 scheduling
bias_correction2_sq = 1.0

for p in group['params']:
if p.grad is None:
Expand All @@ -458,25 +447,23 @@ def step(self, closure: CLOSURE = None) -> LOSS:
state['z'] = p.clone()
state['exp_avg_sq'] = torch.zeros_like(p)

if not group['weight_decouple']:
grad.add_(p, alpha=group['weight_decay'])

z, exp_avg_sq = state['z'], state['exp_avg_sq']
exp_avg_sq.mul_(debias).addcmul_(grad, grad, value=1.0 - debias)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)

if n_sma > 4.0:
de_nom = exp_avg_sq.sqrt().div_(bias_correction2_sq).add_(group['eps'])
grad.div_(de_nom)

self.apply_weight_decay(
p=p,
grad=grad,
lr=lr,
weight_decay=group['weight_decay'],
weight_decouple=group['weight_decouple'],
fixed_decay=group['fixed_decay'],
)
grad.mul_(lr)
if group['weight_decouple']:
grad.add_(p, alpha=group['weight_decay'] * (1.0 if group['fixed_decay'] else lr))

p.lerp_(z, weight=checkpoint)
p.add_(grad, alpha=adaptive_y_lr)
p.add_(grad, alpha=beta1 * (1.0 - checkpoint) - 1.0)

z.sub_(grad, alpha=lr)
z.sub_(grad)

return loss
4 changes: 3 additions & 1 deletion tests/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -502,7 +502,9 @@
(Adalite, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
(ScheduleFreeSGD, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
(ScheduleFreeAdamW, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
(ScheduleFreeAdamW, {'lr': 1e-2, 'weight_decay': 1e-3, 'use_palm': True}, 5),
(ScheduleFreeAdamW, {'lr': 1e0, 'weight_decay': 1e-3, 'use_palm': True}, 5),
(ScheduleFreeRAdam, {'lr': 5e0, 'weight_decay': 1e-3}, 10),
(ScheduleFreeRAdam, {'lr': 5e0, 'weight_decay': 1e-3, 'use_palm': True}, 10),
(ScheduleFreeRAdam, {'lr': 1e0, 'weight_decay': 1e-3, 'degenerated_to_sgd': True}, 5),
(ScheduleFreeRAdam, {'lr': 1e0, 'weight_decay': 1e-3, 'use_palm': True, 'degenerated_to_sgd': True}, 5),
(FAdam, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
Expand Down