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Ralamb optimizer (RAdam + LARS trick)
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class Ralamb(Optimizer): | |
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): | |
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) | |
self.buffer = [[None, None, None] for ind in range(10)] | |
super(Ralamb, self).__init__(params, defaults) | |
def __setstate__(self, state): | |
super(Ralamb, self).__setstate__(state) | |
def step(self, closure=None): | |
loss = None | |
if closure is not None: | |
loss = closure() | |
for group in self.param_groups: | |
for p in group['params']: | |
if p.grad is None: | |
continue | |
grad = p.grad.data.float() | |
if grad.is_sparse: | |
raise RuntimeError('Ralamb does not support sparse gradients') | |
p_data_fp32 = p.data.float() | |
state = self.state[p] | |
if len(state) == 0: | |
state['step'] = 0 | |
state['exp_avg'] = torch.zeros_like(p_data_fp32) | |
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) | |
else: | |
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) | |
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) | |
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
beta1, beta2 = group['betas'] | |
# Decay the first and second moment running average coefficient | |
# m_t | |
exp_avg.mul_(beta1).add_(1 - beta1, grad) | |
# v_t | |
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) | |
state['step'] += 1 | |
buffered = self.buffer[int(state['step'] % 10)] | |
if state['step'] == buffered[0]: | |
N_sma, radam_step_size = buffered[1], buffered[2] | |
else: | |
buffered[0] = state['step'] | |
beta2_t = beta2 ** state['step'] | |
N_sma_max = 2 / (1 - beta2) - 1 | |
N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) | |
buffered[1] = N_sma | |
# more conservative since it's an approximated value | |
if N_sma >= 5: | |
radam_step_size = group['lr'] * math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step']) | |
else: | |
radam_step_size = group['lr'] / (1 - beta1 ** state['step']) | |
buffered[2] = radam_step_size | |
if group['weight_decay'] != 0: | |
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) | |
# more conservative since it's an approximated value | |
radam_step = p_data_fp32.clone() | |
if N_sma >= 5: | |
denom = exp_avg_sq.sqrt().add_(group['eps']) | |
radam_step.addcdiv_(-radam_step_size, exp_avg, denom) | |
else: | |
radam_step.add_(-radam_step_size, exp_avg) | |
radam_norm = radam_step.pow(2).sum().sqrt() | |
weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10) | |
if weight_norm == 0 or radam_norm == 0: | |
trust_ratio = 1 | |
else: | |
trust_ratio = weight_norm / radam_norm | |
state['weight_norm'] = weight_norm | |
state['adam_norm'] = radam_norm | |
state['trust_ratio'] = trust_ratio | |
if N_sma >= 5: | |
p_data_fp32.addcdiv_(-radam_step_size * trust_ratio, exp_avg, denom) | |
else: | |
p_data_fp32.add_(-radam_step_size * trust_ratio, exp_avg) | |
p.data.copy_(p_data_fp32) | |
return loss |
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