Source code for torch.optim.asgd

import math
import torch
from torch import Tensor

from .optimizer import Optimizer
from typing import List, Optional

__all__ = ['ASGD', 'asgd']

[docs]class ASGD(Optimizer): """Implements Averaged Stochastic Gradient Descent. It has been proposed in `Acceleration of stochastic approximation by averaging`_. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) lambd (float, optional): decay term (default: 1e-4) alpha (float, optional): power for eta update (default: 0.75) t0 (float, optional): point at which to start averaging (default: 1e6) 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) .. _Acceleration of stochastic approximation by averaging: """ def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, 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 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) defaults = dict(lr=lr, lambd=lambd, alpha=alpha, t0=t0, weight_decay=weight_decay, foreach=foreach, maximize=maximize) super(ASGD, 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'])) eta_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['eta']) if not eta_is_tensor: for s in state_values: s['eta'] = torch.tensor(s['eta']) mu_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['mu']) if not mu_is_tensor: for s in state_values: s['mu'] = torch.tensor(float(s['mu']))
[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 = [] mus = [] axs = [] etas = [] state_steps = [] for p in group['params']: if p.grad is not None: params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError('ASGD 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['eta'] = torch.tensor(group['lr']) state['mu'] = torch.tensor(1.) state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format) mus.append(state['mu']) axs.append(state['ax']) etas.append(state['eta']) state_steps.append(state['step']) asgd(params_with_grad, grads, axs, mus, etas, state_steps, lambd=group['lambd'], lr=group['lr'], t0=group['t0'], alpha=group['alpha'], weight_decay=group['weight_decay'], foreach=group['foreach'], maximize=group['maximize']) return loss
def asgd(params: List[Tensor], grads: List[Tensor], axs: List[Tensor], mus: List[Tensor], etas: 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, *, lambd: float, lr: float, t0: float, alpha: float, weight_decay: float): r"""Functional API that performs asgd algorithm computation. See :class:`~torch.optim.ASGD` 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_asgd else: func = _single_tensor_asgd func(params, grads, axs, mus, etas, state_steps, lambd=lambd, lr=lr, t0=t0, alpha=alpha, weight_decay=weight_decay, maximize=maximize) def _single_tensor_asgd(params: List[Tensor], grads: List[Tensor], axs: List[Tensor], mus: List[Tensor], etas: List[Tensor], state_steps: List[Tensor], *, lambd: float, lr: float, t0: float, alpha: float, weight_decay: float, maximize: bool): for i, param in enumerate(params): grad = grads[i] grad = grad if not maximize else -grad mu = mus[i] ax = axs[i] eta = etas[i] step_t = state_steps[i] if torch.is_complex(param): grad = torch.view_as_real(grad) param = torch.view_as_real(param) ax = torch.view_as_real(ax) # update step step_t += 1 step = step_t.item() if weight_decay != 0: grad = grad.add(param, alpha=weight_decay) # decay term param.mul_(1 - lambd * eta.item()) # update parameter param.add_(grad, alpha=-eta.item()) # averaging if mu.item() != 1: ax.add_(param.sub(ax).mul(mu)) else: ax.copy_(param) new_eta = torch.tensor(lr / math.pow((1 + lambd * lr * step), alpha)) eta.copy_(new_eta) new_mu = torch.tensor(1 / max(1, step - t0)) mu.copy_(new_mu) def _multi_tensor_asgd(params: List[Tensor], grads: List[Tensor], axs: List[Tensor], mus: List[Tensor], etas: List[Tensor], state_steps: List[Tensor], *, lambd: float, lr: float, t0: float, alpha: float, weight_decay: float, maximize: bool): if len(params) == 0: return if maximize: grads = torch._foreach_neg(grads) def _view_complex_as_real(tensor_list): return [torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list] grads = _view_complex_as_real(grads) params = _view_complex_as_real(params) axs = _view_complex_as_real(axs) # update step torch._foreach_add_(state_steps, 1) if weight_decay != 0: grads = torch._foreach_add(grads, params, alpha=weight_decay) # decay term eta = etas[0].item() torch._foreach_mul_(params, 1 - lambd * eta) # update parameter torch._foreach_add_(params, grads, alpha=-eta) # averaging for i in range(len(axs)): if mus[i].item() != 1: axs[i].add_(params[i].sub(axs[i]).mul(mus[i])) else: axs[i].copy_(params[i]) # update eta and mu for i in range(len(mus)): new_eta = torch.tensor(lr / math.pow((1 + lambd * lr * state_steps[i].item()), alpha)) etas[i].copy_(new_eta) new_mu = torch.tensor(1 / max(1, state_steps[i].item() - t0)) mus[i].copy_(new_mu)


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