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[Fix] device mismatch problems #267

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merged 5 commits into from
Aug 13, 2024
Merged

[Fix] device mismatch problems #267

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0b7c53b
update: q to 0.24
kozistr Aug 13, 2024
bc93c1b
fix: device mismatch
kozistr Aug 13, 2024
7811dd2
update: CAME optimizer
kozistr Aug 13, 2024
e8cfb9b
fix: d_numerator device
kozistr Aug 13, 2024
fa94e7c
docs: v3.1.1 changelog
kozistr Aug 13, 2024
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3 changes: 2 additions & 1 deletion docs/changelogs/v3.1.1.md
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Expand Up @@ -10,4 +10,5 @@

### Bug

* Fix to handle the optimizers that only take the `model` instead of the parameters in `create_optimizer()`. (#263)
* Handle the optimizers that only take the `model` instead of the parameters in `create_optimizer()`. (#263)
* Move the variable to the same device with the parameter. (#266, #267)
2 changes: 1 addition & 1 deletion pytorch_optimizer/optimizer/adamg.py
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Expand Up @@ -27,7 +27,7 @@ def __init__(
lr: float = 1e-3,
betas: BETAS = (0.95, 0.999, 0.95),
p: float = 0.5,
q: float = 0.25,
q: float = 0.24,
weight_decay: float = 0.0,
weight_decouple: bool = False,
fixed_decay: bool = False,
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2 changes: 1 addition & 1 deletion pytorch_optimizer/optimizer/adan.py
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Expand Up @@ -83,7 +83,7 @@ def get_global_gradient_norm(self) -> Union[torch.Tensor, float]:
if self.defaults['max_grad_norm'] == 0.0:
return 1.0

global_grad_norm = get_global_gradient_norm(self.param_groups, self.param_groups[0]['params'][0].device)
global_grad_norm = get_global_gradient_norm(self.param_groups)
global_grad_norm.sqrt_().add_(self.defaults['eps'])

return torch.clamp(self.defaults['max_grad_norm'] / global_grad_norm, max=1.0)
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2 changes: 1 addition & 1 deletion pytorch_optimizer/optimizer/alig.py
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Expand Up @@ -52,7 +52,7 @@ def reset(self):
@torch.no_grad()
def compute_step_size(self, loss: float) -> float:
r"""Compute step_size."""
global_grad_norm = get_global_gradient_norm(self.param_groups, torch.device('cpu'))
global_grad_norm = get_global_gradient_norm(self.param_groups)
global_grad_norm.add_(1e-6)

return loss / global_grad_norm.item()
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2 changes: 1 addition & 1 deletion pytorch_optimizer/optimizer/dadapt.py
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Expand Up @@ -473,7 +473,7 @@ def step(self, closure: CLOSURE = None) -> LOSS:
numerator_weighted = group['numerator_weighted']

if group['step'] == 0:
group['g0_norm'] = get_global_gradient_norm(self.param_groups, device).sqrt_().item()
group['g0_norm'] = get_global_gradient_norm(self.param_groups).sqrt_().item()
g0_norm = group['g0_norm']

if g0_norm == 0:
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2 changes: 1 addition & 1 deletion pytorch_optimizer/optimizer/lamb.py
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Expand Up @@ -103,7 +103,7 @@ def get_global_gradient_norm(self) -> Union[torch.Tensor, float]:
if self.defaults['max_grad_norm'] == 0.0:
return 1.0

global_grad_norm = get_global_gradient_norm(self.param_groups, self.param_groups[0]['params'][0].device)
global_grad_norm = get_global_gradient_norm(self.param_groups)
global_grad_norm.sqrt_().add_(self.defaults['eps'])

return torch.clamp(self.defaults['max_grad_norm'] / global_grad_norm, max=1.0)
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2 changes: 2 additions & 0 deletions pytorch_optimizer/optimizer/prodigy.py
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Expand Up @@ -110,6 +110,8 @@ def step(self, closure: CLOSURE = None) -> LOSS:

if 'd_numerator' not in group:
group['d_numerator'] = torch.tensor([0.0], device=device)
elif group['d_numerator'].device != device:
group['d_numerator'] = group['d_numerator'].to(device)

d_numerator = group['d_numerator']
d_numerator.mul_(beta3)
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4 changes: 2 additions & 2 deletions pytorch_optimizer/optimizer/utils.py
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Expand Up @@ -272,9 +272,9 @@ def l2_projection(parameters: PARAMETERS, max_norm: float = 1e2):


@torch.no_grad()
def get_global_gradient_norm(param_groups: List[Dict], device: torch.device) -> torch.Tensor:
def get_global_gradient_norm(param_groups: List[Dict]) -> torch.Tensor:
r"""Get global gradient norm."""
global_grad_norm = torch.zeros(1, dtype=torch.float32, device=device)
global_grad_norm = torch.zeros(1, dtype=torch.float32, device=param_groups[0]['params'][0].device)

for group in param_groups:
for p in group['params']:
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4 changes: 2 additions & 2 deletions tests/constants.py
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Expand Up @@ -428,8 +428,8 @@
(Prodigy, {'lr': 1e0, 'beta3': 0.999, 'weight_decay': 1e-3, 'safeguard_warmup': True}, 15),
(PAdam, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
(Tiger, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
(CAME, {'lr': 7.5e-1, 'weight_decay': 1e-3}, 75),
(CAME, {'lr': 7.5e-1, 'weight_decay': 1e-3, 'ams_bound': True}, 75),
(CAME, {'lr': 7.5e-1, 'weight_decay': 1e-3}, 70),
(CAME, {'lr': 7.5e-1, 'weight_decay': 1e-3, 'ams_bound': True}, 70),
(Aida, {'lr': 1e0, 'weight_decay': 1e-3, 'ams_bound': True}, 5),
(
GaLore,
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