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lamb.py
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lamb.py
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# Copyright (c) 2021, Habana Labs Ltd. All rights reserved.
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
#
# 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.
# MIT License
#
# Copyright (c) 2019 cybertronai
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import torch
from torch.optim import Optimizer
class NVLAMB(Optimizer):
"""Implements a pure pytorch variant of FuseLAMB (NVLAMB variant) optimizer from apex.optimizers.FusedLAMB
reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
LAMB was proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its norm. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
NOT SUPPORTED now! (default: False)
adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
True for decoupled weight decay(also known as AdamW) (default: True)
grad_averaging (bool, optional): whether apply (1-beta2) to grad when
calculating running averages of gradient. (default: True)
set_grad_none (bool, optional): whether set grad to None when zero_grad()
method is called. (default: True)
max_grad_norm (float, optional): value used to clip global grad norm
(default: 1.0)
use_nvlamb (boolean, optional): Apply adaptive learning rate to 0.0
weight decay parameter (default: False)
.. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
https://arxiv.org/abs/1904.00962
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, bias_correction=True,
betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
amsgrad=False, adam_w_mode=True,
grad_averaging=True, set_grad_none=True,
max_grad_norm=1.0, use_nvlamb=False,fused=False):
if amsgrad:
raise RuntimeError('NVLAMB does not support the AMSGrad variant.')
defaults = dict(lr=lr, bias_correction=bias_correction,
betas=betas, eps=eps, weight_decay=weight_decay,
grad_averaging=grad_averaging,
max_grad_norm=max_grad_norm)
super().__init__(params, defaults)
self.fused = fused
self.adam_w_mode = 1 if adam_w_mode else 0 # dummy for now, always use adam_w mode (wd is excluded from EMA)
self.set_grad_none = set_grad_none
self.use_nvlamb = use_nvlamb
def zero_grad(self):
if self.set_grad_none:
for group in self.param_groups:
for p in group['params']:
p.grad = None
else:
super(NVLAMB, self).zero_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
device = self.param_groups[0]["params"][0].device
loss = None
if closure is not None:
loss = closure()
global_grad_norm = torch.zeros(1, device=device)
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')
global_grad_norm.add_(grad.pow(2).sum())
global_grad_norm_ = torch.sqrt(global_grad_norm)
max_grad_norm = self.defaults['max_grad_norm']
if global_grad_norm_ > max_grad_norm:
clip_global_grad_norm = global_grad_norm_ / max_grad_norm
else:
clip_global_grad_norm = 1.0
for group in self.param_groups:
bias_correction = 1 if group['bias_correction'] else 0
beta1, beta2 = group['betas']
grad_averaging = 1 if group['grad_averaging'] else 0
if grad_averaging:
beta3 = 1 - beta1
else:
beta3 = 1.0
# assume same step across group now to simplify things
# per parameter step can be easily support by making it tensor, or pass list into kernel
if 'step' in group:
group['step'] += 1
else:
group['step'] = 1
step_size = group['lr']
if bias_correction:
bias_correction1 = 1 - beta1 ** group['step']
bias_correction2 = 1 - beta2 ** group['step']
else:
bias_correction1, bias_correction2 = 1.0, 1.0
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data.div_(clip_global_grad_norm)
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
exp_avg_, exp_avg_sq_ = state['exp_avg'], state['exp_avg_sq']
# Decay the first and second moment running average coefficient
# m_t
exp_avg_.mul_(beta1).add_(grad, alpha=beta3)
# v_t
exp_avg_sq_.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
# create clones to avoid modifying runner stats
exp_avg = exp_avg_.div(bias_correction1)
exp_avg_sq = exp_avg_sq_.div(bias_correction2)
# || w_t ||
weight_norm = p.data.norm(2.0)
# u_t
exp_avg_sq_sqrt = torch.sqrt(exp_avg_sq)
adam_step = exp_avg.div_(exp_avg_sq_sqrt.add_(group['eps']))
if group['weight_decay'] != 0:
adam_step.add_(p.data, alpha=group['weight_decay'])
# || u_t ||
adam_norm = adam_step.norm(2.0)
if (group['weight_decay'] != 0 or self.use_nvlamb) and adam_norm > 0 and weight_norm > 0:
trust_ratio = weight_norm / adam_norm
trust_ratio = trust_ratio.item()
else:
trust_ratio = 1
state['weight_norm'] = weight_norm
state['adam_norm'] = adam_norm
state['trust_ratio'] = trust_ratio
#p.data.add_(adam_step, alpha=-step_size * trust_ratio)
alpha = -step_size * trust_ratio
adam_step2 = adam_step * alpha
p.data.add_(adam_step2)