def step(self, gradients: List[Optional[Tensor]]):
params = self.param_group['params']
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
max_exp_avg_sqs = []
state_steps: List[Tensor] = []
if len(params) != len(gradients):
raise ValueError(
"the gradients passed in does not equal to the size of the parameters!"
+ f"Params length: {len(params)}. " +
f"Gradients length: {len(gradients)}")
for param, gradient in zip(self.param_group['params'], gradients):
if gradient is not None:
params_with_grad.append(param)
grads.append(gradient)
# Lazy state initialization
if param not in self.state:
self.state[param] = {}
state = self.state[param]
state['step'] = torch.tensor(0.0)
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(
param, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(
param, memory_format=torch.preserve_format)
if self.amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(
param, memory_format=torch.preserve_format)
state = self.state[param]
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
if self.amsgrad:
max_exp_avg_sqs.append(state['max_exp_avg_sq'])
state_steps.append(state['step'])
with torch.no_grad():
F.adamw(params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=self.amsgrad,
maximize=self.maximize,
beta1=self.defaults['beta1'],
beta2=self.defaults['beta2'],
lr=self.defaults['lr'],
weight_decay=self.defaults['weight_decay'],
eps=self.defaults['eps'],
foreach=self.foreach)
def step_param(self, param: Tensor, grad: Optional[Tensor]):
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
max_exp_avg_sqs = []
state_steps: List[int] = []
if grad is not None:
params_with_grad.append(param)
grads.append(grad)
# Lazy state initialization
if param not in self.state:
self.state[param] = {}
state = self.state[param]
state['step'] = torch.tensor(0.0)
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(
param, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(
param, memory_format=torch.preserve_format)
if self.amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(
param, memory_format=torch.preserve_format)
state = self.state[param]
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
if self.amsgrad:
max_exp_avg_sqs.append(state['max_exp_avg_sq'])
# update the steps for each param group update
state['step'] += 1
# record the step after step update
state_steps.append(state['step'].item())
with torch.no_grad():
F.adamw(params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
amsgrad=self.amsgrad,
maximize=self.maximize,
beta1=self.defaults['beta1'],
beta2=self.defaults['beta2'],
lr=self.defaults['lr'],
weight_decay=self.defaults['weight_decay'],
eps=self.defaults['eps'])