Source code for mmpretrain.engine.optimizers.lamb
"""PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb.
This optimizer code was adapted from the following (starting with latest)
* https://github.com/HabanaAI/Model-References/blob/
2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
* https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/
LanguageModeling/Transformer-XL/pytorch/lamb.py
* https://github.com/cybertronai/pytorch-lamb
Use FusedLamb if you can (GPU). The reason for including this variant of Lamb
is to have a version that is
similar in behaviour to APEX FusedLamb if you aren't using NVIDIA GPUs or
cannot install/use APEX.
In addition to some cleanup, this Lamb impl has been modified to support
PyTorch XLA and has been tested on TPU.
Original copyrights for above sources are below.
Modifications Copyright 2021 Ross Wightman
"""
# Copyright (c) 2021, Habana Labs Ltd. All rights reserved.
# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# MIT License
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# Copyright (c) 2019 cybertronai
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# SOFTWARE.
import math
import torch
from torch.optim import Optimizer
from mmpretrain.registry import OPTIMIZERS
[docs]@OPTIMIZERS.register_module()
class Lamb(Optimizer):
"""A pure pytorch variant of FuseLAMB (NvLamb variant) optimizer.
This class is copied from `timm`_. The LAMB was proposed in `Large Batch
Optimization for Deep Learning - Training BERT in 76 minutes`_.
.. _timm:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/lamb.py
.. _Large Batch Optimization for Deep Learning - Training BERT in 76 minutes:
https://arxiv.org/abs/1904.00962
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)
grad_averaging (bool, optional): whether apply (1-beta2) to grad when
calculating running averages of gradient. (default: True)
max_grad_norm (float, optional): value used to clip global grad norm
(default: 1.0)
trust_clip (bool): enable LAMBC trust ratio clipping (default: False)
always_adapt (boolean, optional): Apply adaptive learning rate to 0.0
weight decay parameter (default: False)
""" # noqa: E501
def __init__(self,
params,
lr=1e-3,
bias_correction=True,
betas=(0.9, 0.999),
eps=1e-6,
weight_decay=0.01,
grad_averaging=True,
max_grad_norm=1.0,
trust_clip=False,
always_adapt=False):
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,
trust_clip=trust_clip,
always_adapt=always_adapt)
super().__init__(params, defaults)
[docs] @torch.no_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.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
device = self.param_groups[0]['params'][0].device
one_tensor = torch.tensor(
1.0, device=device
) # because torch.where doesn't handle scalars correctly
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
if grad.is_sparse:
raise RuntimeError(
'Lamb does not support sparse gradients, consider '
'SparseAdam instead.')
global_grad_norm.add_(grad.pow(2).sum())
global_grad_norm = torch.sqrt(global_grad_norm)
# FIXME it'd be nice to remove explicit tensor conversion of scalars
# when torch.where promotes
# scalar types properly https://github.com/pytorch/pytorch/issues/9190
max_grad_norm = torch.tensor(
self.defaults['max_grad_norm'], device=device)
clip_global_grad_norm = torch.where(global_grad_norm > max_grad_norm,
global_grad_norm / max_grad_norm,
one_tensor)
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
beta3 = 1 - beta1 if grad_averaging else 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
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.div_(clip_global_grad_norm)
state = self.state[p]
# State initialization
if len(state) == 0:
# Exponential moving average of gradient valuesa
state['exp_avg'] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=beta3) # m_t
exp_avg_sq.mul_(beta2).addcmul_(
grad, grad, value=1 - beta2) # v_t
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(
group['eps'])
update = (exp_avg / bias_correction1).div_(denom)
weight_decay = group['weight_decay']
if weight_decay != 0:
update.add_(p, alpha=weight_decay)
if weight_decay != 0 or group['always_adapt']:
# Layer-wise LR adaptation. By default, skip adaptation on
# parameters that are
# excluded from weight decay, unless always_adapt == True,
# then always enabled.
w_norm = p.norm(2.0)
g_norm = update.norm(2.0)
# FIXME nested where required since logical and/or not
# working in PT XLA
trust_ratio = torch.where(
w_norm > 0,
torch.where(g_norm > 0, w_norm / g_norm, one_tensor),
one_tensor,
)
if group['trust_clip']:
# LAMBC trust clipping, upper bound fixed at one
trust_ratio = torch.minimum(trust_ratio, one_tensor)
update.mul_(trust_ratio)
p.add_(update, alpha=-group['lr'])
return loss