Source code for torch.optim.adamax
import torch
from torch import Tensor
from .optimizer import Optimizer
from typing import List, Optional
[docs]class Adamax(Optimizer):
r"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2
\text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
\: \lambda \text{ (weight decay)}, \\
&\hspace{13mm} \epsilon \text{ (epsilon)} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}if \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 2e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
foreach (bool, optional): whether foreach implementation of optimizer is used (default: None)
maximize (bool, optional): maximize the params based on the objective, instead of
minimizing (default: False)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
"""
def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, foreach: Optional[bool] = None, *, maximize: bool = False):
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,
foreach=foreach, maximize=maximize)
super(Adamax, self).__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault('foreach', None)
group.setdefault('maximize', False)
state_values = list(self.state.values())
step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step'])
if not step_is_tensor:
for s in state_values:
s['step'] = torch.tensor(float(s['step']))
[docs] @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_infs = []
state_steps = []
beta1, beta2 = group['betas']
eps = group['eps']
lr = group['lr']
weight_decay = group['weight_decay']
foreach = group['foreach']
maximize = group['maximize']
for p in group['params']:
if p.grad is None:
continue
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError('Adamax does not support sparse gradients')
grads.append(p.grad)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = torch.tensor(0.)
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['exp_inf'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avgs.append(state['exp_avg'])
exp_infs.append(state['exp_inf'])
state_steps.append(state['step'])
adamax(params_with_grad,
grads,
exp_avgs,
exp_infs,
state_steps,
eps=eps,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
foreach=foreach,
maximize=maximize)
return loss
def adamax(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_infs: List[Tensor],
state_steps: List[Tensor],
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
foreach: bool = None,
maximize: bool = False,
*,
eps: float,
beta1: float,
beta2: float,
lr: float,
weight_decay: float):
r"""Functional API that performs adamax algorithm computation.
See :class:`~torch.optim.Adamax` for details.
"""
if not all([isinstance(t, torch.Tensor) for t in state_steps]):
raise RuntimeError("API has changed, `state_steps` argument must contain a list of singleton tensors")
if foreach is None:
# Placeholder for more complex foreach logic to be added when value is not set
foreach = False
if foreach and torch.jit.is_scripting():
raise RuntimeError('torch.jit.script not supported with foreach optimizers')
if foreach and not torch.jit.is_scripting():
func = _multi_tensor_adamax
else:
func = _single_tensor_adamax
func(params,
grads,
exp_avgs,
exp_infs,
state_steps,
eps=eps,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
maximize=maximize)
def _single_tensor_adamax(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_infs: List[Tensor],
state_steps: List[Tensor],
*,
eps: float,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
maximize: bool):
for i, param in enumerate(params):
grad = grads[i]
grad = grad if not maximize else -grad
exp_avg = exp_avgs[i]
exp_inf = exp_infs[i]
step_t = state_steps[i]
# update step
step_t += 1
step = step_t.item()
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
# Update biased first moment estimate.
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
# Update the exponentially weighted infinity norm.
norm_buf = torch.cat([
exp_inf.mul_(beta2).unsqueeze(0),
grad.abs().add_(eps).unsqueeze_(0)
], 0)
torch.amax(norm_buf, 0, keepdim=False, out=exp_inf)
bias_correction = 1 - beta1 ** step
clr = lr / bias_correction
param.addcdiv_(exp_avg, exp_inf, value=-clr)
def _multi_tensor_adamax(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_infs: List[Tensor],
state_steps: List[Tensor],
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
eps: float,
maximize: bool):
if len(params) == 0:
return
if maximize:
grads = torch._foreach_neg(grads)
# Update steps
torch._foreach_add_(state_steps, 1)
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
# Update biased first moment estimate.
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
# Update the exponentially weighted infinity norm.
torch._foreach_mul_(exp_infs, beta2)
for exp_inf, grad in zip(exp_infs, grads):
norm_buf = torch.cat([
exp_inf.unsqueeze(0),
grad.abs().add_(eps).unsqueeze_(0)
], 0)
torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
bias_corrections = [1 - beta1 ** step.item() for step in state_steps]
clr = [-1 * (lr / bias_correction) for bias_correction in bias_corrections]
torch._foreach_addcdiv_(params, exp_avgs, exp_infs, clr)
