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"""RAdam |
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Original source taken from https://github.com/LiyuanLucasLiu/RAdam |
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Copyright 2019 Liyuan Liu |
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Licensed under the Apache License, Version 2.0 (the "License"); |
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you may not use this file except in compliance with the License. |
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You may obtain a copy of the License at |
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http://www.apache.org/licenses/LICENSE-2.0 |
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Unless required by applicable law or agreed to in writing, software |
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distributed under the License is distributed on an "AS IS" BASIS, |
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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See the License for the specific language governing permissions and |
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limitations under the License. |
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""" |
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import math |
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import torch |
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from torch.optim.optimizer import Optimizer |
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class RAdam(Optimizer): |
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"""RAdam optimizer""" |
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): |
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""" |
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Init |
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:param params: parameters to optimize |
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:param lr: learning rate |
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:param betas: beta |
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:param eps: numerical precision |
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:param weight_decay: weight decay weight |
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""" |
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) |
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self.buffer = [[None, None, None] for _ in range(10)] |
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super().__init__(params, defaults) |
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def step(self, closure=None): |
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loss = None |
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if closure is not None: |
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loss = closure() |
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for group in self.param_groups: |
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for p in group['params']: |
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if p.grad is None: |
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continue |
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grad = p.grad.data.float() |
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if grad.is_sparse: |
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raise RuntimeError('RAdam does not support sparse gradients') |
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p_data_fp32 = p.data.float() |
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state = self.state[p] |
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if len(state) == 0: |
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state['step'] = 0 |
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state['exp_avg'] = torch.zeros_like(p_data_fp32) |
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state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) |
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else: |
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state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) |
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state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) |
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] |
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beta1, beta2 = group['betas'] |
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1.0 - beta2)) |
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exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1)) |
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state['step'] += 1 |
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buffered = self.buffer[int(state['step'] % 10)] |
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if state['step'] == buffered[0]: |
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N_sma, step_size = buffered[1], buffered[2] |
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else: |
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buffered[0] = state['step'] |
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beta2_t = beta2 ** state['step'] |
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N_sma_max = 2 / (1 - beta2) - 1 |
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) |
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buffered[1] = N_sma |
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if N_sma >= 5: |
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step_size = ( |
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group['lr'] |
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* math.sqrt( |
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(1 - beta2_t) |
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* (N_sma - 4) |
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/ (N_sma_max - 4) |
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* (N_sma - 2) |
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/ N_sma |
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* N_sma_max |
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/ (N_sma_max - 2) |
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) |
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/ (1 - beta1 ** state['step']) |
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) |
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else: |
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step_size = group['lr'] / (1 - beta1 ** state['step']) |
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buffered[2] = step_size |
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if group['weight_decay'] != 0: |
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p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) |
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if N_sma >= 5: |
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denom = exp_avg_sq.sqrt().add_(group['eps']) |
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p_data_fp32.addcdiv_(-step_size, exp_avg, denom) |
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else: |
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p_data_fp32.add_(-step_size, exp_avg) |
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p.data.copy_(p_data_fp32) |
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return loss |
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