Source code for lmflow.optim.adadelta
#!/usr/bin/env python
import torch
from torch.optim.optimizer import Optimizer
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class Adadelta(Optimizer):
def __init__(self, params, lr=1.0, rho=0.95, eps=1e-6):
defaults = dict(lr=lr, rho=rho, eps=eps)
super().__init__(params, defaults)
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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
state = self.state[p]
if len(state) == 0:
state["step"] = 0
state["square_avg"] = torch.zeros_like(p.data)
state["acc_delta"] = torch.zeros_like(p.data)
square_avg, acc_delta = state["square_avg"], state["acc_delta"]
rho, eps = group["rho"], group["eps"]
state["step"] += 1
square_avg.mul_(rho).addcmul_(1 - rho, grad, grad)
std = square_avg.add(eps).sqrt_()
delta = acc_delta.add(eps).sqrt_().div_(std).mul_(grad)
p.data.add_(-delta)
acc_delta.mul_(rho).addcmul_(1 - rho, delta, delta)
return loss