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Guocheng Qian
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optimizer.py
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325
optimizer.py
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# Copyright 2022 Garena Online Private Limited
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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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import math
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from typing import List
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import torch
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from torch import Tensor
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from torch.optim.optimizer import Optimizer
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class Adan(Optimizer):
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"""
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Implements a pytorch variant of Adan
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Adan was proposed in
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Adan: Adaptive Nesterov Momentum Algorithm for
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Faster Optimizing Deep Models[J].arXiv preprint arXiv:2208.06677, 2022.
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https://arxiv.org/abs/2208.06677
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Arguments:
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params (iterable): iterable of parameters to optimize or
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dicts defining parameter groups.
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lr (float, optional): learning rate. (default: 1e-3)
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betas (Tuple[float, float, flot], optional): coefficients used for
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first- and second-order moments. (default: (0.98, 0.92, 0.99))
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eps (float, optional): term added to the denominator to improve
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numerical stability. (default: 1e-8)
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weight_decay (float, optional): decoupled weight decay
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(L2 penalty) (default: 0)
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max_grad_norm (float, optional): value used to clip
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global grad norm (default: 0.0 no clip)
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no_prox (bool): how to perform the decoupled weight decay
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(default: False)
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foreach (bool): if True would use torch._foreach implementation.
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It's faster but uses slightly more memory. (default: True)
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"""
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def __init__(self,
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params,
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lr=1e-3,
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betas=(0.98, 0.92, 0.99),
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eps=1e-8,
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weight_decay=0.0,
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max_grad_norm=0.0,
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no_prox=False,
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foreach: bool = True):
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if not 0.0 <= max_grad_norm:
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raise ValueError('Invalid Max grad norm: {}'.format(max_grad_norm))
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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 <= eps:
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raise ValueError('Invalid epsilon value: {}'.format(eps))
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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(
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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(
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betas[1]))
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if not 0.0 <= betas[2] < 1.0:
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raise ValueError('Invalid beta parameter at index 2: {}'.format(
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betas[2]))
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defaults = dict(lr=lr,
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betas=betas,
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eps=eps,
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weight_decay=weight_decay,
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max_grad_norm=max_grad_norm,
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no_prox=no_prox,
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foreach=foreach)
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super().__init__(params, defaults)
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def __setstate__(self, state):
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super(Adan, self).__setstate__(state)
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for group in self.param_groups:
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group.setdefault('no_prox', False)
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@torch.no_grad()
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def restart_opt(self):
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for group in self.param_groups:
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group['step'] = 0
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for p in group['params']:
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if p.requires_grad:
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state = self.state[p]
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# State initialization
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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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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p)
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# Exponential moving average of gradient difference
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state['exp_avg_diff'] = torch.zeros_like(p)
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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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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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if self.defaults['max_grad_norm'] > 0:
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device = self.param_groups[0]['params'][0].device
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global_grad_norm = torch.zeros(1, device=device)
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max_grad_norm = torch.tensor(self.defaults['max_grad_norm'],
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device=device)
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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 not None:
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grad = p.grad
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global_grad_norm.add_(grad.pow(2).sum())
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global_grad_norm = torch.sqrt(global_grad_norm)
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clip_global_grad_norm = torch.clamp(
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max_grad_norm / (global_grad_norm + group['eps']),
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max=1.0).item()
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else:
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clip_global_grad_norm = 1.0
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_avg_sqs = []
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exp_avg_diffs = []
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neg_pre_grads = []
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beta1, beta2, beta3 = group['betas']
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# assume same step across group now to simplify things
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# per parameter step can be easily support
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# by making it tensor, or pass list into kernel
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if 'step' in group:
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group['step'] += 1
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else:
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group['step'] = 1
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bias_correction1 = 1.0 - beta1**group['step']
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bias_correction2 = 1.0 - beta2**group['step']
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bias_correction3 = 1.0 - beta3**group['step']
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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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params_with_grad.append(p)
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grads.append(p.grad)
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state = self.state[p]
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if len(state) == 0:
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state['exp_avg'] = torch.zeros_like(p)
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state['exp_avg_sq'] = torch.zeros_like(p)
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state['exp_avg_diff'] = torch.zeros_like(p)
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if 'neg_pre_grad' not in state or group['step'] == 1:
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state['neg_pre_grad'] = p.grad.clone().mul_(
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-clip_global_grad_norm)
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exp_avgs.append(state['exp_avg'])
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exp_avg_sqs.append(state['exp_avg_sq'])
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exp_avg_diffs.append(state['exp_avg_diff'])
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neg_pre_grads.append(state['neg_pre_grad'])
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kwargs = dict(
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params=params_with_grad,
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grads=grads,
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exp_avgs=exp_avgs,
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exp_avg_sqs=exp_avg_sqs,
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exp_avg_diffs=exp_avg_diffs,
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neg_pre_grads=neg_pre_grads,
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beta1=beta1,
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beta2=beta2,
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beta3=beta3,
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bias_correction1=bias_correction1,
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bias_correction2=bias_correction2,
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bias_correction3_sqrt=math.sqrt(bias_correction3),
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lr=group['lr'],
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weight_decay=group['weight_decay'],
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eps=group['eps'],
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no_prox=group['no_prox'],
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clip_global_grad_norm=clip_global_grad_norm,
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)
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if group['foreach']:
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_multi_tensor_adan(**kwargs)
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else:
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_single_tensor_adan(**kwargs)
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return loss
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def _single_tensor_adan(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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exp_avg_diffs: List[Tensor],
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neg_pre_grads: List[Tensor],
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*,
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beta1: float,
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beta2: float,
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beta3: float,
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bias_correction1: float,
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bias_correction2: float,
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bias_correction3_sqrt: float,
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lr: float,
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weight_decay: float,
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eps: float,
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no_prox: bool,
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clip_global_grad_norm: Tensor,
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):
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for i, param in enumerate(params):
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grad = grads[i]
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exp_avg = exp_avgs[i]
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exp_avg_sq = exp_avg_sqs[i]
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exp_avg_diff = exp_avg_diffs[i]
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neg_grad_or_diff = neg_pre_grads[i]
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grad.mul_(clip_global_grad_norm)
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# for memory saving, we use `neg_grad_or_diff`
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# to get some temp variable in a inplace way
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neg_grad_or_diff.add_(grad)
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # m_t
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exp_avg_diff.mul_(beta2).add_(neg_grad_or_diff,
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alpha=1 - beta2) # diff_t
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neg_grad_or_diff.mul_(beta2).add_(grad)
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exp_avg_sq.mul_(beta3).addcmul_(neg_grad_or_diff,
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neg_grad_or_diff,
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value=1 - beta3) # n_t
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denom = ((exp_avg_sq).sqrt() / bias_correction3_sqrt).add_(eps)
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step_size_diff = lr * beta2 / bias_correction2
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step_size = lr / bias_correction1
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if no_prox:
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param.mul_(1 - lr * weight_decay)
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param.addcdiv_(exp_avg, denom, value=-step_size)
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param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff)
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else:
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param.addcdiv_(exp_avg, denom, value=-step_size)
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param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff)
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param.div_(1 + lr * weight_decay)
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neg_grad_or_diff.zero_().add_(grad, alpha=-1.0)
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def _multi_tensor_adan(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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exp_avg_diffs: List[Tensor],
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neg_pre_grads: List[Tensor],
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*,
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beta1: float,
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beta2: float,
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beta3: float,
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bias_correction1: float,
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bias_correction2: float,
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bias_correction3_sqrt: float,
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lr: float,
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weight_decay: float,
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eps: float,
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no_prox: bool,
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clip_global_grad_norm: Tensor,
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):
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if len(params) == 0:
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return
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torch._foreach_mul_(grads, clip_global_grad_norm)
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# for memory saving, we use `neg_pre_grads`
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# to get some temp variable in a inplace way
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torch._foreach_add_(neg_pre_grads, grads)
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torch._foreach_mul_(exp_avgs, beta1)
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torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1) # m_t
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torch._foreach_mul_(exp_avg_diffs, beta2)
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torch._foreach_add_(exp_avg_diffs, neg_pre_grads,
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alpha=1 - beta2) # diff_t
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torch._foreach_mul_(neg_pre_grads, beta2)
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torch._foreach_add_(neg_pre_grads, grads)
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torch._foreach_mul_(exp_avg_sqs, beta3)
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torch._foreach_addcmul_(exp_avg_sqs,
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neg_pre_grads,
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neg_pre_grads,
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value=1 - beta3) # n_t
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denom = torch._foreach_sqrt(exp_avg_sqs)
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torch._foreach_div_(denom, bias_correction3_sqrt)
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torch._foreach_add_(denom, eps)
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step_size_diff = lr * beta2 / bias_correction2
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step_size = lr / bias_correction1
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if no_prox:
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torch._foreach_mul_(params, 1 - lr * weight_decay)
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torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size)
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torch._foreach_addcdiv_(params,
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exp_avg_diffs,
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denom,
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value=-step_size_diff)
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else:
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torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size)
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torch._foreach_addcdiv_(params,
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exp_avg_diffs,
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denom,
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value=-step_size_diff)
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torch._foreach_div_(params, 1 + lr * weight_decay)
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torch._foreach_zero_(neg_pre_grads)
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torch._foreach_add_(neg_pre_grads, grads, alpha=-1.0)
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