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https://github.com/zebrajr/pytorch.git
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71d50f4f89
### Description Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function. ### Testing There shouldn't be any testing required. Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487 Approved by: https://github.com/albanD
261 lines
8.4 KiB
Python
261 lines
8.4 KiB
Python
import math
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import torch
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from torch import Tensor
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from .optimizer import Optimizer
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from typing import List, Optional
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__all__ = ['ASGD', 'asgd']
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class ASGD(Optimizer):
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"""Implements Averaged Stochastic Gradient Descent.
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It has been proposed in `Acceleration of stochastic approximation by
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averaging`_.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-2)
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lambd (float, optional): decay term (default: 1e-4)
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alpha (float, optional): power for eta update (default: 0.75)
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t0 (float, optional): point at which to start averaging (default: 1e6)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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foreach (bool, optional): whether foreach implementation of optimizer
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is used (default: None)
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maximize (bool, optional): maximize the params based on the objective, instead of
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minimizing (default: False)
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.. _Acceleration of stochastic approximation by averaging:
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https://dl.acm.org/citation.cfm?id=131098
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"""
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def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0,
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foreach: Optional[bool] = None, maximize: bool = False):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, lambd=lambd, alpha=alpha, t0=t0,
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weight_decay=weight_decay, foreach=foreach, maximize=maximize)
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super(ASGD, self).__init__(params, defaults)
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault('foreach', None)
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group.setdefault('maximize', False)
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state_values = list(self.state.values())
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step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step'])
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if not step_is_tensor:
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for s in state_values:
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s['step'] = torch.tensor(float(s['step']))
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eta_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['eta'])
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if not eta_is_tensor:
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for s in state_values:
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s['eta'] = torch.tensor(s['eta'])
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mu_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['mu'])
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if not mu_is_tensor:
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for s in state_values:
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s['mu'] = torch.tensor(float(s['mu']))
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (Callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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mus = []
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axs = []
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etas = []
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state_steps = []
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for p in group['params']:
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if p.grad is not None:
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('ASGD does not support sparse gradients')
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grads.append(p.grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = torch.tensor(0.)
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state['eta'] = torch.tensor(group['lr'])
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state['mu'] = torch.tensor(1.)
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state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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mus.append(state['mu'])
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axs.append(state['ax'])
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etas.append(state['eta'])
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state_steps.append(state['step'])
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asgd(params_with_grad,
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grads,
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axs,
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mus,
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etas,
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state_steps,
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lambd=group['lambd'],
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lr=group['lr'],
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t0=group['t0'],
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alpha=group['alpha'],
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weight_decay=group['weight_decay'],
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foreach=group['foreach'],
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maximize=group['maximize'])
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return loss
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def asgd(params: List[Tensor],
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grads: List[Tensor],
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axs: List[Tensor],
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mus: List[Tensor],
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etas: List[Tensor],
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state_steps: List[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: bool = None,
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maximize: bool = False,
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float):
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r"""Functional API that performs asgd algorithm computation.
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See :class:`~torch.optim.ASGD` for details.
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"""
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if foreach is None:
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# Placeholder for more complex foreach logic to be added when value is not set
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foreach = False
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if foreach and torch.jit.is_scripting():
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raise RuntimeError('torch.jit.script not supported with foreach optimizers')
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if foreach and not torch.jit.is_scripting():
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func = _multi_tensor_asgd
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else:
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func = _single_tensor_asgd
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func(params,
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grads,
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axs,
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mus,
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etas,
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state_steps,
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lambd=lambd,
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lr=lr,
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t0=t0,
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alpha=alpha,
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weight_decay=weight_decay,
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maximize=maximize)
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def _single_tensor_asgd(params: List[Tensor],
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grads: List[Tensor],
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axs: List[Tensor],
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mus: List[Tensor],
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etas: List[Tensor],
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state_steps: List[Tensor],
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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maximize: bool):
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for i, param in enumerate(params):
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grad = grads[i]
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grad = grad if not maximize else -grad
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mu = mus[i]
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ax = axs[i]
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eta = etas[i]
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step_t = state_steps[i]
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# update step
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step_t += 1
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step = step_t.item()
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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# decay term
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param.mul_(1 - lambd * eta.item())
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# update parameter
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param.add_(grad, alpha=-eta.item())
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# averaging
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if mu.item() != 1:
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ax.add_(param.sub(ax).mul(mu))
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else:
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ax.copy_(param)
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new_eta = torch.tensor(lr / math.pow((1 + lambd * lr * step), alpha))
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eta.copy_(new_eta)
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new_mu = torch.tensor(1 / max(1, step - t0))
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mu.copy_(new_mu)
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def _multi_tensor_asgd(params: List[Tensor],
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grads: List[Tensor],
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axs: List[Tensor],
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mus: List[Tensor],
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etas: List[Tensor],
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state_steps: List[Tensor],
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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maximize: bool):
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if len(params) == 0:
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return
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if maximize:
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grads = torch._foreach_neg(grads)
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# update step
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torch._foreach_add_(state_steps, 1)
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if weight_decay != 0:
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torch._foreach_add_(grads, params, alpha=weight_decay)
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# decay term
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eta = etas[0].item()
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torch._foreach_mul_(params, 1 - lambd * eta)
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# update parameter
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torch._foreach_add_(params, grads, alpha=-eta)
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# averaging
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for i in range(len(axs)):
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if mus[i].item() != 1:
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axs[i].add_(params[i].sub(axs[i]).mul(mus[i]))
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else:
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axs[i].copy_(params[i])
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# update eta and mu
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for i in range(len(mus)):
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new_eta = torch.tensor(lr / math.pow((1 + lambd * lr * state_steps[i].item()), alpha))
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etas[i].copy_(new_eta)
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new_mu = torch.tensor(1 / max(1, state_steps[i].item() - t0))
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mus[i].copy_(new_mu)
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