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_lr值warmup爬到init_lr,然后再用cosine降低到cos_min_ratio×init_lr值,并且额外资瓷一...
代码实现 from paddle.optimizer.lr import LinearWarmup from paddle.optimizer.lr import CosineAnnealingDecay class Cosine(CosineAnnealingDecay): """ Cosine learning rate decay lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1) Args: lr(float): initial learning rate ...
from paddle.optimizer.lr import LinearWarmup from paddle.optimizer.lr import CosineAnnealingDecay class Cosine(CosineAnnealingDecay): """ Cosine learning rate decay lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1) Args: lr(float): initial learning rate step_each_epoch(int): steps...
Describe the bug It's unclear if this is a bug, an intentional design decision, or part of a design trade-off I don't fully understand. Let me explain with an example. I'm using the cosine LR scheduler and my script uses a warm up LR (1e-5), number of warm up epochs (20), ...
其中self.last_epoch之前在基类_LRScheduler中已经被赋值了self.last_epoch = epoch ,所以直接根据学习率变化公式计算处理由上可知,get_lr()和_get_closed_form_lr()就是具体的学习率计算方法这样,我们就可以根据不同的学习率计算方式设计自己的scheduler类了。
'trainable_params': 159498}# 配置模型from paddle.metric import Accuracyscheduler = CosineWarmup(lr=...
case'cosineTimm': steps_per_epoch =1scheduler = timm_scheduler.CosineLRScheduler(optimizer=optimizer, t_initial=max_epoch, lr_min=4.5e-6, warmup_t=1, warmup_lr_init=4.5e-6) case'cosineTorchLambda': warmup_epoch =2warmup_factor =1e-3steps_per_epoch =1deff(current_epoch):""" ...
raiseValueError("Unknown scheduler {}".format(scheduler)) 「注意」:当num_warmup_steps参数设置为0时,learning rate没有预热的上升过程,只有从初始设定的learning rate 逐渐衰减到0的过程 图2. warmupcosine 4. 实验 deftrain(trainset, evalset, model, tokenizer, model_dir, lr, epochs, device): ...
classWarmUpCosineDecayScheduler(keras.callbacks.Callback): """Cosine decay with warmup learning rate scheduler """ def__init__(self, learning_rate_base, total_steps, global_step_init=0, warmup_learning_rate=0.0, warmup_steps=0, hold_base_rate_steps=0, ...
Returns the correct learning rate scheduler. Available scheduler: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts """ scheduler = scheduler.lower() if scheduler == 'constantlr': return transformers.get_constant_schedule(optimizer) ...