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Source code for catalyst.contrib.nn.optimizers.radam

from typing import Callable, Optional
import math

import torch
from torch.optim.optimizer import Optimizer


[docs]class RAdam(Optimizer): """Implements RAdam algorithm. It has been proposed in `On the Variance of the Adaptive Learning Rate and Beyond`_. @TODO: Docs (add `Example`). Contribution is welcome Adapted from: https://github.com/LiyuanLucasLiu/RAdam (Apache-2.0 License) .. _On the Variance of the Adaptive Learning Rate and Beyond: https://arxiv.org/abs/1908.03265 """
[docs] def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): """@TODO: Docs. Contribution is welcome.""" defaults = { "lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay, } self.buffer = [[None, None, None] for _ in range(10)] super(RAdam, self).__init__(params, defaults)
def __setstate__(self, state): """@TODO: Docs. Contribution is welcome.""" super(RAdam, self).__setstate__(state) def step(self, closure: Optional[Callable] = None): """Makes optimizer step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. Returns: computed loss Raises: RuntimeError: RAdam does not support sparse gradients """ 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.data.float() if grad.is_sparse: raise RuntimeError("RAdam does not support sparse gradients") p_data_fp32 = p.data.float() state = self.state[p] if len(state) == 0: state["step"] = 0 state["exp_avg"] = torch.zeros_like(p_data_fp32) state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) else: state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32) state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32) exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] beta1, beta2 = group["betas"] exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) exp_avg.mul_(beta1).add_(1 - beta1, grad) state["step"] += 1 buffered = self.buffer[int(state["step"] % 10)] if state["step"] == buffered[0]: n_sma, step_size = buffered[1], buffered[2] else: buffered[0] = state["step"] beta2_t = beta2 ** state["step"] n_sma_max = 2 / (1 - beta2) - 1 n_sma = n_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t) buffered[1] = n_sma # more conservative since it's an approximated value if n_sma >= 5: step_size = ( group["lr"] * math.sqrt( (1 - beta2_t) * (n_sma - 4) / (n_sma_max - 4) * (n_sma - 2) / n_sma * n_sma_max / (n_sma_max - 2) ) / (1 - beta1 ** state["step"]) ) else: step_size = group["lr"] / (1 - beta1 ** state["step"]) buffered[2] = step_size if group["weight_decay"] != 0: p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32) # more conservative since it's an approximated value if n_sma >= 5: denom = exp_avg_sq.sqrt().add_(group["eps"]) p_data_fp32.addcdiv_(-step_size, exp_avg, denom) else: p_data_fp32.add_(-step_size, exp_avg) p.data.copy_(p_data_fp32) return loss
__all__ = ["RAdam"]