You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
161 lines
7.7 KiB
161 lines
7.7 KiB
2 years ago
|
# Copyright (c) ByteDance, Inc. and its affiliates.
|
||
|
|
# All rights reserved.
|
||
|
|
#
|
||
|
|
# This source code is licensed under the license found in the
|
||
|
|
# LICENSE file in the root directory of this source tree.
|
||
|
|
#
|
||
|
|
# This file is basically a copy to: https://github.com/rwightman/pytorch-image-models/blob/v0.5.4/timm/optim/lamb.py
|
||
|
|
|
||
|
|
|
||
|
|
""" 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
|
||
|
|
"""
|
||
|
|
import math
|
||
|
|
|
||
|
|
import torch
|
||
|
|
from torch.optim.optimizer import Optimizer
|
||
|
|
|
||
|
|
|
||
|
|
class TimmLAMB(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)
|
||
|
|
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)
|
||
|
|
|
||
|
|
.. _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, grad_averaging=True, max_grad_norm=2.0, 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)
|
||
|
|
print(f'[lamb1] max_grad_norm={max_grad_norm}')
|
||
|
|
self.global_grad_norm = 0
|
||
|
|
|
||
|
|
@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 instad.')
|
||
|
|
global_grad_norm.add_(grad.pow(2).sum())
|
||
|
|
|
||
|
|
global_grad_norm = torch.sqrt(global_grad_norm)
|
||
|
|
self.global_grad_norm = global_grad_norm.item()
|
||
|
|
max_grad_norm = torch.tensor(self.defaults['max_grad_norm'], device=device)
|
||
|
|
clip_global_grad_norm = 1 / 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.mul_(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
|