in timm/optim/adamw.py [0:0]
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
self._cuda_graph_capture_health_check()
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
max_exp_avg_sqs = []
state_steps = []
beta1, beta2 = group['betas']
amsgrad = group['amsgrad']
for p in group['params']:
if p.grad is None:
continue
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError('AdamW does not support sparse gradients')
grads.append(p.grad)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = torch.tensor(0.)
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
if amsgrad:
max_exp_avg_sqs.append(state.get('max_exp_avg_sq', None))
state_steps.append(state['step'])
adamw(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
foreach=group['foreach'],
amsgrad=amsgrad,
beta1=beta1,
beta2=beta2,
lr=group['lr'],
weight_decay=group['weight_decay'],
eps=group['eps'],
caution=group['caution'],
maximize=group['maximize'],
capturable=group['capturable'],
max_lr=self.defaults['lr'] if group['corrected_weight_decay'] else None,
)
return loss