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88 lines
3.3 KiB
88 lines
3.3 KiB
""" Lion Optimizer
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Paper: `Symbolic Discovery of Optimization Algorithms` - https://arxiv.org/abs/2302.06675
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Original Impl: https://github.com/google/automl/tree/master/lion
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"""
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# Copyright 2023 Google Research. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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 torch
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from torch.optim.optimizer import Optimizer
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class Lion(Optimizer):
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r"""Implements Lion algorithm."""
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def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
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"""Initialize the hyperparameters.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-4)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.99))
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weight_decay (float, optional): weight decay coefficient (default: 0)
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"""
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if not 0.0 <= lr:
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raise ValueError('Invalid learning rate: {}'.format(lr))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError('Invalid beta parameter at index 0: {}'.format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError('Invalid beta parameter at index 1: {}'.format(betas[1]))
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defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
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super().__init__(params, defaults)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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Returns:
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the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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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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# Perform stepweight decay
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p.data.mul_(1 - group['lr'] * group['weight_decay'])
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grad = p.grad
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p)
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exp_avg = state['exp_avg']
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beta1, beta2 = group['betas']
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# Weight update
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update = exp_avg * beta1 + grad * (1 - beta1)
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p.add_(torch.sign(update), alpha=-group['lr'])
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# Decay the momentum running average coefficient
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exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)
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return loss
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