Source code for torch.optim.adadelta

import torch
from torch import Tensor

from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
                        _differentiable_doc, _foreach_doc, _maximize_doc)
from typing import List, Optional

__all__ = ["Adadelta", "adadelta"]

[docs]class Adadelta(Optimizer): def __init__( self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0, foreach: Optional[bool] = None, *, maximize: bool = False, differentiable: bool = False, ): if not 0.0 <= lr: raise ValueError(f"Invalid learning rate: {lr}") if not 0.0 <= rho <= 1.0: raise ValueError(f"Invalid rho value: {rho}") if not 0.0 <= eps: raise ValueError(f"Invalid epsilon value: {eps}") if not 0.0 <= weight_decay: raise ValueError(f"Invalid weight_decay value: {weight_decay}") defaults = dict( lr=lr, rho=rho, eps=eps, weight_decay=weight_decay, maximize=maximize, foreach=foreach, differentiable=differentiable, ) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault("foreach", None) group.setdefault("maximize", False) group.setdefault("differentiable", False) def _init_group(self, group, params_with_grad, grads, square_avgs, acc_deltas): 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
[docs] @_use_grad_for_differentiable 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, differentiable = ( group["lr"], group["rho"], group["eps"], group["weight_decay"], group["foreach"], group["maximize"], group["differentiable"], ) self._init_group(group, params_with_grad, grads, square_avgs, acc_deltas) adadelta( params_with_grad, grads, square_avgs, acc_deltas, lr=lr, rho=rho, eps=eps, weight_decay=weight_decay, foreach=foreach, maximize=maximize, differentiable=differentiable, ) return loss
Adadelta.__doc__ = 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`_. """ + fr""" 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_doc} {_maximize_doc} {_differentiable_doc} .. _ADADELTA\: An Adaptive Learning Rate Method: """ 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: Optional[bool] = None, differentiable: bool = False, *, 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. """ # We still respect when the user inputs False for foreach. if foreach is None: _, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=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, differentiable=differentiable, ) 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, differentiable: 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_() if differentiable: delta = delta.clone() delta.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, differentiable: bool, ): assert not differentiable, "_foreach ops don't support autograd" if len(params) == 0: return grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, square_avgs, acc_deltas]) for ((device_params, device_grads, device_square_avgs, device_acc_deltas), _) in grouped_tensors.values(): if maximize: device_grads = torch._foreach_neg(device_grads) if weight_decay != 0: # Re-use the intermediate memory (device_grads) already allocated for maximize if maximize: torch._foreach_add_(device_grads, device_params, alpha=weight_decay) else: device_grads = torch._foreach_add(device_grads, device_params, alpha=weight_decay) torch._foreach_mul_(device_square_avgs, rho) torch._foreach_addcmul_(device_square_avgs, device_grads, device_grads, value=1 - rho) std = torch._foreach_add(device_square_avgs, eps) torch._foreach_sqrt_(std) deltas = torch._foreach_add(device_acc_deltas, eps) torch._foreach_sqrt_(deltas) torch._foreach_div_(deltas, std) torch._foreach_mul_(deltas, device_grads) torch._foreach_add_(device_params, deltas, alpha=-lr) torch._foreach_mul_(device_acc_deltas, rho) torch._foreach_addcmul_(device_acc_deltas, deltas, deltas, value=1 - rho)


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