TypeError: get_cosine_schedule_with_warmup() got an unexpected keyword argument 'num_decay_steps' Reinstalling did not solve the problem. kohya-ssadded a commit that referenced this issueSep 29, 2024 fix to work
TL;DR:请大家尽量使用Transformers的lr_scheduler,特别是已经支持在Transformers已经支持get_cosine_with_min_lr_schedule_with_warmup的情况下,使用Deepspeed的lr_scheduler的最后一个理由似乎也已经消失了(Deepspeed仍然有一个优势是资瓷一个额外的参数叫warmup_min_ratio,意思就是说lr先是从从warmup_min_ratio×init_...
importtorchfromtorch.optim.lr_schedulerimportCosineAnnealingLR, CosineAnnealingWarmRestartsimportmatplotlib.pyplotaspltfromtimmimportschedulerastimm_schedulerfromtimm.scheduler.schedulerimportSchedulerastimm_BaseSchedulerfromtorch.optimimportOptimizerfromtorch.optimimportlr_schedulerfromtransformersimportget_cosine_schedule_...
There it's mapped to get_cosine_with_hard_restarts_schedule_with_warmup(), but without a num_cycles argument, defaulting to 1, i.e. it behaves like the cosine option. Probably I could build the scheduler myself and pass it to the Trainer, but then I need to calculate the num_...
warmup_learning_rate=0.0, warmup_steps=0, hold_base_rate_steps=0): """Cosine decay schedule with warm up period. Cosine annealing learning rate as described in: Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts. ...
LearningRateScheduler.WarmupCosine Propriété Référence Commentaires Définition Espace de noms: Azure.ResourceManager.MachineLearning.Models Assembly: Azure.ResourceManager.MachineLearning.dll Paquet: Azure.ResourceManager.MachineLearning v1.2.2 Source: LearningRateScheduler.cs Recuit cosinus ...
1. 概述 在论文《SGDR: Stochastic Gradient Descent with Warm Restarts》中主要介绍了带重启的随机梯度下降算法(SGDR),其中就引入了余弦退火的学习率下降方式。 当我们使用梯度下... 查看原文 深度学习_深度学习基础知识_学习率相关技巧 应该越来越接近loss值的全局最小值。当它逐渐接近这个最小值时,学习率应该变...
optim.lr_schedule == 'cosine': scheduler = CosineRestartAnnealingLR(optimizer, float(max_steps), period_steps, step_steps, eta_min=config.optim.min_lr, use_warmup=use_warmup, warmup_steps=warmup_steps, warmup_startlr=warmup_startlr, warmup_targetlr=warmup_targetlr, use_restart=config...
This function is then passed on to the LearningRateScheduler callback, which applies the function to the learning rate. Now, the tf.keras.callbacks.LearningRateScheduler() passes the epoch number to the function it uses to calculate the learning rate, which is pretty coarse. LR Warmup ...
warmup_learning_rate=0.0, warmup_steps=0, hold_base_rate_steps=0): """Cosine decay schedule with warm up period. Cosine annealing learning rate as described in: Loshchilov and Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts. ...