Ross Wightman
3 years ago
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01cb46a9a5
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""" PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb
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This optimizer code was adapted from the following (starting with latest)
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* https://github.com/HabanaAI/Model-References/blob/2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
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* https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
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* https://github.com/cybertronai/pytorch-lamb
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Use FusedLamb if you can. The reason for including this variant of Lamb is to have a version that is
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similar in behaviour to APEX FusedLamb if you aren't using NVIDIA GPUs or cannot install APEX for whatever reason.
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Original copyrights for above sources are below.
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"""
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# Copyright (c) 2021, Habana Labs Ltd. All rights reserved.
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# Copyright (c) 2019-2020, NVIDIA CORPORATION. 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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# MIT License
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#
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# Copyright (c) 2019 cybertronai
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import torch
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from torch.optim import Optimizer
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class NvLamb(Optimizer):
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"""Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer from apex.optimizers.FusedLAMB
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reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
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LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
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Arguments:
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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-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its norm. (default: (0.9, 0.999))
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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): weight decay (L2 penalty) (default: 0)
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grad_averaging (bool, optional): whether apply (1-beta2) to grad when
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calculating running averages of gradient. (default: True)
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set_grad_none (bool, optional): whether set grad to None when zero_grad()
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method is called. (default: True)
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max_grad_norm (float, optional): value used to clip global grad norm
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(default: 1.0)
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use_nvlamb (boolean, optional): Apply adaptive learning rate to 0.0
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weight decay parameter (default: False)
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.. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
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https://arxiv.org/abs/1904.00962
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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def __init__(self, params, lr=1e-3, bias_correction=True,
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betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
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grad_averaging=True, set_grad_none=True,
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max_grad_norm=1.0, use_nvlamb=False):
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defaults = dict(lr=lr, bias_correction=bias_correction,
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betas=betas, eps=eps, weight_decay=weight_decay,
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grad_averaging=grad_averaging,
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max_grad_norm=max_grad_norm)
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super().__init__(params, defaults)
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self.set_grad_none = set_grad_none
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self.use_nvlamb = use_nvlamb
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def zero_grad(self):
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if self.set_grad_none:
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for group in self.param_groups:
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for p in group['params']:
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p.grad = None
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else:
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super(NvLamb, self).zero_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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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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"""
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device = self.param_groups[0]["params"][0].device
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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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global_grad_norm = torch.zeros(1, 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 None:
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continue
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')
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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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max_grad_norm = self.defaults['max_grad_norm']
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if global_grad_norm_ > max_grad_norm:
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clip_global_grad_norm = global_grad_norm_ / max_grad_norm
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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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bias_correction = 1 if group['bias_correction'] else 0
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beta1, beta2 = group['betas']
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grad_averaging = 1 if group['grad_averaging'] else 0
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if grad_averaging:
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beta3 = 1 - beta1
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else:
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beta3 = 1.0
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# assume same step across group now to simplify things
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# per parameter step can be easily support 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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step_size = group['lr']
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if bias_correction:
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bias_correction1 = 1 - beta1 ** group['step']
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bias_correction2 = 1 - beta2 ** group['step']
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else:
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bias_correction1, bias_correction2 = 1.0, 1.0
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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.div_(clip_global_grad_norm)
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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.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg_, exp_avg_sq_ = state['exp_avg'], state['exp_avg_sq']
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# Decay the first and second moment running average coefficient
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# m_t
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exp_avg_.mul_(beta1).add_(grad, alpha=beta3)
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# v_t
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exp_avg_sq_.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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# create clones to avoid modifying runner stats
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exp_avg = exp_avg_.div(bias_correction1)
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exp_avg_sq = exp_avg_sq_.div(bias_correction2)
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# || w_t ||
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weight_norm = p.data.norm(2.0)
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# u_t
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exp_avg_sq_sqrt = torch.sqrt(exp_avg_sq)
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adam_step = exp_avg.div_(exp_avg_sq_sqrt.add_(group['eps']))
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if group['weight_decay'] != 0:
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adam_step.add_(p.data, alpha=group['weight_decay'])
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# || u_t ||
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adam_norm = adam_step.norm(2.0)
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if (group['weight_decay'] != 0 or self.use_nvlamb) and adam_norm > 0 and weight_norm > 0:
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trust_ratio = weight_norm / adam_norm
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trust_ratio = trust_ratio.item()
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else:
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trust_ratio = 1
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state['weight_norm'] = weight_norm
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state['adam_norm'] = adam_norm
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state['trust_ratio'] = trust_ratio
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p.data.add_(adam_step, alpha=-step_size * trust_ratio)
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return loss
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