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

from typing import Tuple, Iterable  # isort:skip

import math

import torch
from torch.optim.optimizer import Optimizer


[docs]class Ralamb(Optimizer): """ RAdam optimizer with LARS/LAMB tricks Taken from https://github.com/mgrankin/over9000/blob/master/ralamb.py """
[docs] def __init__( self, params: Iterable, lr: float = 1e-3, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 0 ): """ Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) """ defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) self.buffer = [[None, None, None] for ind in range(10)] super(Ralamb, self).__init__(params, defaults)
def __setstate__(self, state): """Sets state""" super(Ralamb, self).__setstate__(state)
[docs] def step(self, closure=None): """Makes optimizer step""" 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( "Ralamb 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"] # Decay the first and second moment running average coefficient # m_t exp_avg.mul_(beta1).add_(1 - beta1, grad) # v_t exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) state["step"] += 1 buffered = self.buffer[int(state["step"] % 10)] if state["step"] == buffered[0]: N_sma, radam_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: radam_step_size = \ 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: radam_step_size = 1.0 / (1 - beta1**state["step"]) buffered[2] = radam_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 radam_step = p_data_fp32.clone() if N_sma >= 5: denom = exp_avg_sq.sqrt().add_(group["eps"]) radam_step.addcdiv_( -radam_step_size * group["lr"], exp_avg, denom ) else: radam_step.add_(-radam_step_size * group["lr"], exp_avg) radam_norm = radam_step.pow(2).sum().sqrt() weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10) if weight_norm == 0 or radam_norm == 0: trust_ratio = 1 else: trust_ratio = weight_norm / radam_norm state["weight_norm"] = weight_norm state["adam_norm"] = radam_norm state["trust_ratio"] = trust_ratio if N_sma >= 5: p_data_fp32.addcdiv_( -radam_step_size * group["lr"] * trust_ratio, exp_avg, denom ) else: p_data_fp32.add_( -radam_step_size * group["lr"] * trust_ratio, exp_avg ) p.data.copy_(p_data_fp32) return loss