Source code for lmflow.optim.dummy

#!/usr/bin/env python
"""Dummy Optimizer."""

from collections.abc import Iterable
from typing import Callable

import torch
from torch import nn
from torch.optim import Optimizer


[docs] class Dummy(Optimizer): """ An dummy optimizer that does nothing. Parameters: params (:obj:`Iterable[nn.parameter.Parameter]`): Iterable of parameters to optimize or dictionaries defining parameter groups. lr (:obj:`float`, `optional`, defaults to 0): The learning rate to use. """ def __init__( self, params: Iterable[nn.parameter.Parameter], lr: float = 0.0, betas: tuple[float, float] = (0.9, 0.999), weight_decay: float = 0.0, ): if lr < 0.0: raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0") if not 0.0 <= betas[0] < 1.0: raise ValueError(f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0)") if not 0.0 <= betas[1] < 1.0: raise ValueError(f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0)") defaults = {"lr": lr, "betas": betas, "weight_decay": weight_decay} super().__init__(params, defaults) @torch.no_grad()
[docs] def step(self, closure: Callable = None): """ Performs a single optimization step. Arguments: closure (:obj:`Callable`, `optional`): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group["params"]: if p.grad is None: continue grad = p.grad if grad.is_sparse: raise RuntimeError("Dummy does not support sparse gradients yet") state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 state["exp_avg"] = torch.zeros_like(p) state["exp_avg2"] = torch.zeros_like(p) # v := exp_avg # m := double_exp_avg _, m = state["exp_avg"], state["exp_avg2"] # beta1, beta2 = group["betas"] # step_size = group["lr"] state["step"] += 1 p.add_(m, alpha=-0.0) if group["weight_decay"] > 0.0: p.add_(p, alpha=(-group["lr"] * group["weight_decay"])) return loss