Source code for lmflow.optim.adamax
#!/usr/bin/env python
import torch
from torch.optim.optimizer import Optimizer
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class Adamax(Optimizer):
def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
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))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
super().__init__(params, defaults)
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def __setstate__(self, state):
super().__setstate__(state)
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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
if grad.is_sparse:
raise RuntimeError("Adamax does not support sparse gradients")
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
state["exp_avg"] = torch.zeros_like(p.data)
state["exp_inf"] = torch.zeros_like(p.data)
exp_avg, exp_inf = state["exp_avg"], state["exp_inf"]
beta1, beta2 = group["betas"]
state["step"] += 1
if group["weight_decay"] != 0:
grad = grad.add(group["weight_decay"], p.data)
# Update biased first moment estimate
exp_avg.mul_(beta1).add_(1 - beta1, grad)
# Update the exponentially weighted infinity norm
norm_buf = torch.cat([exp_inf.mul_(beta2).unsqueeze(0), grad.abs().unsqueeze_(0)], 0)
torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
bias_correction = 1 - beta1 ** state["step"]
clr = group["lr"] / bias_correction
p.data.addcdiv_(-clr, exp_avg, exp_inf + group["eps"])
return loss