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| # Original source taken from https://github.com/LiyuanLucasLiu/RAdam | |
| # | |
| # Copyright 2019 Liyuan Liu | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import math | |
| import torch | |
| # pylint: disable=no-name-in-module | |
| from torch.optim.optimizer import Optimizer | |
| class RAdam(Optimizer): | |
| """RAdam optimizer""" | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): | |
| """ | |
| Init | |
| :param params: parameters to optimize | |
| :param lr: learning rate | |
| :param betas: beta | |
| :param eps: numerical precision | |
| :param weight_decay: weight decay weight | |
| """ | |
| defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) | |
| self.buffer = [[None, None, None] for _ in range(10)] | |
| super().__init__(params, defaults) | |
| def step(self, closure=None): | |
| 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 | |