Source code for torch.optim.adadelta

import torch
from torch import Tensor

from .optimizer import Optimizer
from typing import List, Optional

__all__ = ['Adadelta', 'adadelta']

[docs]class Adadelta(Optimizer): r"""Implements Adadelta algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)}, \: \lambda \text{ (weight decay)} \\ &\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)}, \: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-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} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\ &\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} + \epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\ &\hspace{5mm} u_t \leftarrow u_{t-1} \rho + \Delta x^2_t (1 - \rho) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_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 `ADADELTA: An Adaptive Learning Rate Method`_. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups rho (float, optional): coefficient used for computing a running average of squared gradients (default: 0.9) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-6) lr (float, optional): coefficient that scale delta before it is applied to the parameters (default: 1.0) 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) .. _ADADELTA\: An Adaptive Learning Rate Method: """ def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, 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 <= rho <= 1.0: raise ValueError("Invalid rho value: {}".format(rho)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) defaults = dict(lr=lr, rho=rho, eps=eps, weight_decay=weight_decay, maximize=maximize, foreach=foreach) super(Adadelta, 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)
[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 = [] square_avgs = [] acc_deltas = [] lr, rho, eps, weight_decay, foreach, maximize = (group['lr'], group['rho'], group['eps'], group['weight_decay'], group['foreach'], 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('Adadelta does not support sparse gradients') grads.append(p.grad) state = self.state[p] # Lazy state initialization if len(state) == 0: state['step'] = 0 state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) state['acc_delta'] = torch.zeros_like(p, memory_format=torch.preserve_format) square_avgs.append(state['square_avg']) acc_deltas.append(state['acc_delta']) state['step'] += 1 adadelta(params_with_grad, grads, square_avgs, acc_deltas, lr=lr, rho=rho, eps=eps, weight_decay=weight_decay, foreach=foreach, maximize=maximize) return loss
def adadelta(params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], acc_deltas: 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, *, lr: float, rho: float, eps: float, weight_decay: float, maximize: bool): r"""Functional API that performs Adadelta algorithm computation. See :class:`~torch.optim.Adadelta` for details. """ 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_adadelta else: func = _single_tensor_adadelta func(params, grads, square_avgs, acc_deltas, lr=lr, rho=rho, eps=eps, weight_decay=weight_decay, maximize=maximize) def _single_tensor_adadelta(params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], acc_deltas: List[Tensor], *, lr: float, rho: float, eps: float, weight_decay: float, maximize: bool): for (param, grad, square_avg, acc_delta) in zip(params, grads, square_avgs, acc_deltas): grad = grad if not maximize else -grad if weight_decay != 0: grad = grad.add(param, alpha=weight_decay) if torch.is_complex(param): square_avg = torch.view_as_real(square_avg) acc_delta = torch.view_as_real(acc_delta) grad = torch.view_as_real(grad) square_avg.mul_(rho).addcmul_(grad, grad, value=1 - rho) std = square_avg.add(eps).sqrt_() delta = acc_delta.add(eps).sqrt_().div_(std).mul_(grad) acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho) if torch.is_complex(param): delta = torch.view_as_complex(delta) param.add_(delta, alpha=-lr) def _multi_tensor_adadelta(params: List[Tensor], grads: List[Tensor], square_avgs: List[Tensor], acc_deltas: List[Tensor], *, lr: float, weight_decay: float, rho: float, eps: float, maximize: bool): if len(params) == 0: return if maximize: grads = torch._foreach_neg(grads) if weight_decay != 0: torch._foreach_add_(grads, params, alpha=weight_decay) torch._foreach_mul_(square_avgs, rho) torch._foreach_addcmul_(square_avgs, grads, grads, value=1 - rho) std = torch._foreach_add(square_avgs, eps) torch._foreach_sqrt_(std) deltas = torch._foreach_add(acc_deltas, eps) torch._foreach_sqrt_(deltas) torch._foreach_div_(deltas, std) torch._foreach_mul_(deltas, grads) torch._foreach_add_(params, deltas, alpha=-lr) torch._foreach_mul_(acc_deltas, rho) torch._foreach_addcmul_(acc_deltas, deltas, deltas, value=1 - rho)


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