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import torch | |
from torch import optim | |
class AdamWEMA(optim.Optimizer): | |
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, | |
weight_decay=1e-2, amsgrad=False, ema_decay=0.999, | |
ema_power=1., param_names=()): | |
"""AdamW that saves EMA versions of the parameters.""" | |
if not 0.0 <= lr: | |
raise ValueError("Invalid learning rate: {}".format(lr)) | |
if not 0.0 <= eps: | |
raise ValueError("Invalid epsilon value: {}".format(eps)) | |
if not 0.0 <= betas[0] < 1.0: | |
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) | |
if not 0.0 <= betas[1] < 1.0: | |
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) | |
if not 0.0 <= weight_decay: | |
raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) | |
if not 0.0 <= ema_decay <= 1.0: | |
raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) | |
defaults = dict(lr=lr, betas=betas, eps=eps, | |
weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, | |
ema_power=ema_power, param_names=param_names) | |
super().__init__(params, defaults) | |
def __setstate__(self, state): | |
super().__setstate__(state) | |
for group in self.param_groups: | |
group.setdefault('amsgrad', False) | |
@torch.no_grad() | |
def step(self, closure=None): | |
"""Performs a single optimization step. | |
Args: | |
closure (callable, optional): A closure that reevaluates the model | |
and returns the loss. | |
""" | |
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 = [] | |
ema_params_with_grad = [] | |
state_sums = [] | |
max_exp_avg_sqs = [] | |
state_steps = [] | |
amsgrad = group['amsgrad'] | |
beta1, beta2 = group['betas'] | |
ema_decay = group['ema_decay'] | |
ema_power = group['ema_power'] | |
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'] = 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) | |
# Exponential moving average of parameter values | |
state['param_exp_avg'] = p.detach().float().clone() | |
exp_avgs.append(state['exp_avg']) | |
exp_avg_sqs.append(state['exp_avg_sq']) | |
ema_params_with_grad.append(state['param_exp_avg']) | |
if 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']) | |
optim._functional.adamw(params_with_grad, | |
grads, | |
exp_avgs, | |
exp_avg_sqs, | |
max_exp_avg_sqs, | |
state_steps, | |
amsgrad=amsgrad, | |
beta1=beta1, | |
beta2=beta2, | |
lr=group['lr'], | |
weight_decay=group['weight_decay'], | |
eps=group['eps'], | |
maximize=False) | |
cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) | |
for param, ema_param in zip(params_with_grad, ema_params_with_grad): | |
ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) | |
return loss |
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