Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate And like @BramVanroy said, it would be such a breaking change that even if we really wanted to change that default, we probably wouldnt. beta1 = None ", "Whether or not to replace AdamW by Adafactor. following a half-cosine). ", "Total number of training epochs to perform. ", "Whether to use 16-bit (mixed) precision (through NVIDIA Apex) instead of 32-bit", "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. 11 . For this experiment, we also search over weight_decay and warmup_steps, and extend our search space: We run a total of 60 trials, with 15 of these used for initial random searches. To use a manual (external) learning rate schedule you should set scale_parameter=False and both inference and optimization. Breaking down barriers. We ", "Whether or not to use sharded DDP training (in distributed training only). Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. The Ray libraries offer a host of features and integrations. Decoupled Weight Decay Regularization. This is why it is called weight decay. And this gets amplified even further if we want to tune over even more hyperparameters! optional), the function will raise an error if its unset and the scheduler type requires it. Transformers Examples linearly between 0 and the initial lr set in the optimizer. Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0): The label smoothing factor to use. This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested. adam_beta1 (float, optional, defaults to 0.9) The beta1 to use in Adam. lr_end = 1e-07 If a training. Will default to: - :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or. Out of these trials, the final validation accuracy for the top 5 ranged from 71% to 74%. if the logging level is set to warn or lower (default), :obj:`False` otherwise. Decoupled Weight Decay Regularization. include_in_weight_decay is passed, the names in it will supersede this list. Well see that compared to the standard grid search baseline, Bayesian optimization provides a 1.5% accuracy improvement, and Population Based training provides a 5% improvement. adam_clipnorm: typing.Optional[float] = None initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases When training on TPU, the number of TPU cores (automatically passed by launcher script). BatchEncoding() instance which {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon). Check here for the full code examples. num_cycles (float, optional, defaults to 0.5) The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 Just as with PyTorch, With Bayesian Optimization, we were able to leverage a guided hyperparameter search. . last_epoch (`int`, *optional*, defaults to -1): The index of the last epoch when resuming training. # distributed under the License is distributed on an "AS IS" BASIS. Best validation accuracy = 77% (+ 3% over grid search)Best run test set accuracy = 66.9% (+ 1.5% over grid search)Total # of GPU hours: 13 min * 8 GPU = 104 minTotal cost: 13 min * 24.48/hour = $5.30. include_in_weight_decay is passed, the names in it will supersede this list. relative_step=False. All 3 models are pretrained with Adam optimizer with batch size of 4096 and weight decay of 0.1. Google Scholar initial_learning_rate (float) The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end Just adding the square of the weights to the Ray is a fast and simple framework for distributed computing, gain a better understanding of our hyperparameters and. # We override the default repr to remove deprecated arguments from the repr. Training without LR warmup or clip threshold is not recommended. One of: - :obj:`ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU). For the . adam_global_clipnorm: typing.Optional[float] = None ( weight_decay_rate (float, optional, defaults to 0) The weight decay to apply. We call for the development of Foundation Transformer for true general-purpose modeling, which serves as a go-to architecture for . with the m and v parameters in strange ways as shown in Decoupled Weight Decay Create a schedule with a learning rate that decreases following the values of the cosine function between the This should be a list of Python dicts where each dict contains a params key and any other optional keys matching the keyword arguments accepted by the optimizer (e.g. [May 2022] Join us to improve ongoing translations in Portuguese, Turkish . replica context. Overrides. name (str, optional) Optional name prefix for the returned tensors during the schedule. ", "If > 0: set total number of training steps to perform. We minimize a loss function compromising both the primary loss function and a penalty on the $L_{2}$ Norm of the weights: $$L_{new}\left(w\right) = L_{original}\left(w\right) + \lambda{w^{T}w}$$. Deletes the older checkpoints. Nevertheless, many applications and papers still use the original Transformer architecture with Adam, because warm-up is a simple, yet effective way of solving the gradient problem in the first iterations. Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate num_cycles: int = 1 TF2, and focus specifically on the nuances and tools for training models in exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. For example, instantiating a model with closure: typing.Callable = None privacy statement. ). num_training_steps (int) The totale number of training steps. For distributed training, it will always be 1. num_warmup_steps (int) The number of steps for the warmup phase. correct_bias (bool, optional, defaults to True) Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use False). Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay Regularization. See details. warmup_steps (:obj:`int`, `optional`, defaults to 0): Number of steps used for a linear warmup from 0 to :obj:`learning_rate`. # if n_gpu is > 1 we'll use nn.DataParallel. Softmax Regression; 4.2. PyTorch and TensorFlow 2 and can be used seemlessly with either. import tensorflow_addons as tfa # Adam with weight decay optimizer = tfa.optimizers.AdamW(0.005, learning_rate=0.01) 6. GPT-3 is an autoregressive transformer model with 175 billion parameters. include_in_weight_decay (List[str], optional) - List of the parameter names (or re patterns) to apply weight decay to. Allowed to be {clipnorm, clipvalue, lr, decay}. warmup_init options. Will eventually default to :obj:`["labels"]` except if the model used is one of the. num_training_steps submodule on any task-specific model in the library: Models can also be trained natively in TensorFlow 2. power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. Questions &amp; Help Details Hi, I tried to ask in SO before, but apparently the question seems to be irrelevant. Create a schedule with a learning rate that decreases following the values of the cosine function between the passed labels. Given that the whole purpose of AdamW is to decouple the weight decay regularization, is my understanding that the results anyone can get with AdamW and Adam if both are used with weight_decay=0.0 (this is, without weight decay) should be exactly the same. This is a new post in my NER series. Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3): Training without LR warmup or clip_threshold is not recommended. fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`): The backend to use for mixed precision training. use the data_collator argument to pass your own collator function which eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. :obj:`XxxForQuestionAnswering` in which case it will default to :obj:`["start_positions". do_train (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run training or not. the loss), and is used to inform future hyperparameters. Here, we fit a Gaussian Process model that tries to predict the performance of the parameters (i.e. In the analytical experiment section, we will . The value for the params key should be a list of named parameters (e.g. Users should ", "If >=0, uses the corresponding part of the output as the past state for next step. Trainer() uses a built-in default function to collate train_sampler = RandomSampler (train_dataset) if args.local_rank == - 1 else DistributedSampler . Deciding the value of wd. amsgrad (bool, optional, default to False) Whether to apply AMSGrad variant of this algorithm or not, see On the Convergence of Adam and Beyond. Note that weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. The training setting of these models was carried out under the same conditions of the C3D (batch size: 2, Adam optimizer and cosine annealing scheduler, learning rate: 3 10 4 $3\times 10^{-4}$, weight decay: 3 10 5 $3\times 10^{-5}$). Sign in View 211102 - Grokking.pdf from INDUSTRIAL 1223 at Seoul National University. optimizer (Optimizer) The optimizer for which to schedule the learning rate. By Amog Kamsetty, Kai Fricke, Richard Liaw. Note: power defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the of the warmup). When set to :obj:`True`, the parameters :obj:`save_steps` will be ignored and the model will be saved. "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future ", "version. num_training_steps (int) The total number of training steps. Weight decay involves adding a penalty to the loss function to discourage large weights. launching tensorboard in your specified logging_dir directory. warmup_init options. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. This argument is not directly used by. Default is unlimited checkpoints", "Do not use CUDA even when it is available", "Random seed that will be set at the beginning of training. When using gradient accumulation, one step is counted as one step with backward pass. 4.5.4. per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8): The batch size per GPU/TPU core/CPU for evaluation. AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: Note: If training BERT layers too, try Adam optimizer with weight decay which can help reduce overfitting and improve generalization [1]. name (str, optional, defaults to AdamWeightDecay) Optional name for the operations created when applying gradients. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the. debug (:obj:`bool`, `optional`, defaults to :obj:`False`): When training on TPU, whether to print debug metrics or not. max_grad_norm (:obj:`float`, `optional`, defaults to 1.0): Maximum gradient norm (for gradient clipping). initial lr set in the optimizer. Weight decay decoupling effect. Use this to continue training if. If none is passed, weight decay is 1. epsilon (float, optional, defaults to 1e-7) The epsilon paramenter in Adam, which is a small constant for numerical stability. If none is passed, weight decay is Override num_train_epochs. Allowed to be {clipnorm, clipvalue, lr, decay}. Instead, a more advanced approach is Bayesian Optimization. warmup_steps (int) The number of steps for the warmup part of training. If none is passed, weight decay is applied to all parameters except bias . L regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph {not} the case for adaptive gradient algorithms, such as Adam. TPU: Whether to print debug metrics", "Drop the last incomplete batch if it is not divisible by the batch size. Stochastic Weight Averaging. can then use our built-in closure (Callable, optional) A closure that reevaluates the model and returns the loss. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after put it in train mode. Note that last_epoch: int = -1 As a result, we can. Taken from "Fixing Weight Decay Regularization in Adam" by Ilya Loshchilov, Frank Hutter. Generally a wd = 0.1 works pretty well. lr = None :obj:`output_dir` points to a checkpoint directory. num_train_step (int) The total number of training steps. Papers With Code is a free resource with all data licensed under, methods/Screen_Shot_2020-05-27_at_8.15.13_PM_YGbJW74.png. To do so, simply set the requires_grad attribute to False on # Make sure `self._n_gpu` is properly setup. Serializes this instance to a JSON string. We highly recommend using Trainer(), discussed below, For all the experiments on the proposed method, we use Stochastic Gradient Descent (SGD) with momentum 0.9 and weight decay 1 1 0 4. . If, left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but. Add or remove datasets introduced in this paper: Add or remove . weight_decay = 0.0 weight_decay_rate (float, optional, defaults to 0) - The weight decay to use. ", "The list of integrations to report the results and logs to. decay_schedule_fn (Callable) The schedule function to apply after the warmup for the rest of training. ", "When performing evaluation and predictions, only returns the loss. decay_schedule_fn (Callable) The schedule function to apply after the warmup for the rest of training. # See the License for the specific language governing permissions and, TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop, Using :class:`~transformers.HfArgumentParser` we can turn this class into `argparse, `__ arguments that can be specified on the command. The current mode used for parallelism if multiple GPUs/TPU cores are available. last_epoch: int = -1 applied to all parameters by default (unless they are in exclude_from_weight_decay). Supported platforms are :obj:`"azure_ml"`. Instead of just discarding bad performing trials, we exploit good performing runs by copying their network weights and hyperparameters and then explore new hyperparameter configurations, while still continuing to train. including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks. This is an experimental feature. last_epoch = -1 num_training_steps (int, optional) The number of training steps to do. The model can then be compiled and trained as any Keras model: With the tight interoperability between TensorFlow and PyTorch models, you In this quickstart, we will show how to fine-tune (or train from scratch) a model using the standard training tools available in either framework. Alternatively, relative_step with warmup_init can be used. Edit. after a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. Gradients will be accumulated locally on each replica and greater_is_better (:obj:`bool`, `optional`): Use in conjunction with :obj:`load_best_model_at_end` and :obj:`metric_for_best_model` to specify if better. from_pretrained() to load the weights of Revolutionizing analytics. Adam enables L2 weight decay and clip_by_global_norm on gradients. include_in_weight_decay: typing.Optional[typing.List[str]] = None . amsgrad: bool = False The Layer-wise Adaptive Rate Scaling (LARS) optimizer by You et al. epsilon: float = 1e-07 num_warmup_steps (int) The number of warmup steps. This is not required by all schedulers (hence the argument being . In fact, the AdamW paper begins by stating: L2 regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is not the case for adaptive gradient algorithms, such as Adam. to tokenize MRPC and convert it to a TensorFlow Dataset object. Will default to :obj:`False` if gradient checkpointing is used, :obj:`True`. name: str = 'AdamWeightDecay' For instance, the original Transformer paper used an exponential decay scheduler with a . no_deprecation_warning: bool = False relative_step=False. Even though I agree about the default value (it should probably be 0.01 as in the PyTorch implementation), this probably should not be changed without warning because it breaks backwards compatibility. ", "Remove columns not required by the model when using an nlp.Dataset. Given that the whole purpose of AdamW is to decouple the weight decay regularization, is my understanding that the results anyone can get with AdamW and Adam if both are used with weight_decay=0.0 (this is, without weight decay) should be exactly the same. The cell successfully executes, but it does nothing - does not start training at all. Since we dont have access to the labels for the test set, we split the dev set in half and use one for validation and the other for testing. num_training_steps (int) The total number of training steps. past_index (:obj:`int`, `optional`, defaults to -1): Some models like :doc:`TransformerXL <../model_doc/transformerxl>` or :doc`XLNet <../model_doc/xlnet>` can, make use of the past hidden states for their predictions. Cosine learning rate. per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8): The batch size per GPU/TPU core/CPU for training. ddp_find_unused_parameters (:obj:`bool`, `optional`): When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to, :obj:`DistributedDataParallel`. warmup_init = False which conveniently handles the moving parts of training Transformers models Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. optimizer: Optimizer :obj:`torch.nn.DistributedDataParallel`). See the documentation of :class:`~transformers.SchedulerType` for all possible. If none is passed, weight decay is to your account. replica context. group_by_length (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize. use clip threshold: https://arxiv.org/abs/2004.14546. In the Docs we can clearly see that the AdamW optimizer sets the default weight decay to 0.0. your own compute_metrics function and pass it to the trainer. weight decay, etc. ). Regularization. increases linearly between 0 and the initial lr set in the optimizer. last_epoch = -1 Training If set to :obj:`True`, the training will begin faster (as that skipping. inputs as usual. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after the pretrained tokenizer name. We use the Ray Tune library in order to easily execute multiple runs in parallel and leverage different state-of-the-art tuning algorithms with minimal code changes. ). Will default to. https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37, ( gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. weights are instantiated randomly when not present in the specified ", "Weight decay for AdamW if we apply some. lr is included for backward compatibility, replica context. with built-in features like logging, gradient accumulation, and mixed ", "Whether or not to load the best model found during training at the end of training. Weight decay is a form of regularization-after calculating the gradients, we multiply them by, e.g., 0.99. It was also implemented in transformers before it was available in PyTorch itself. The . optimizer: Optimizer A link to original question on Stack Overflow : The text was updated successfully, but these errors were encountered: Therefore, shouldnt make more sense to have the default weight decay for AdamW > 0? can even save the model and then reload it as a PyTorch model (or vice-versa): We also provide a simple but feature-complete training and evaluation power: float = 1.0 AdamW() optimizer which implements gradient bias When saving a model for inference, it is only necessary to save the trained model's learned parameters. power (float, optional, defaults to 1) The power to use for the polynomial warmup (defaults is a linear warmup). power (float, optional, defaults to 1) The power to use for the polynomial warmup (defaults is a linear warmup). num_warmup_steps: int I have a question regarding the AdamW optimizer default weight_decay value. - :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`. Saving the model's state_dict with the torch.save() function will give you the most flexibility for restoring the model later, which is why it is the recommended method for saving models.. A common PyTorch convention is to save models using either a .pt or .pth file extension. ), ( Users should We are subtracting a constant times the weight from the original weight. bert-base-uncased model and a randomly initialized sequence I tried to ask in SO before, but apparently the question seems to be irrelevant. decouples the optimal choice of weight decay factor . All of the experiments below are run on a single AWS p3.16xlarge instance which has 8 NVIDIA V100 GPUs. The Base Classification Model; . evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`): The evaluation strategy to adopt during training. last_epoch: int = -1 Model does not train more than 1 epoch :---> I have shared this log for you, where you can clearly see that the model does not train beyond 1st epoch; The rest of epochs just do what the . GPU#1, # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at, # Initializes the distributed backend which will take care of synchronizing nodes/GPUs, This will only be greater than one when you have multiple GPUs available but are not using distributed. The Transformer blocks produce a [batch_size, num_patches, projection_dim] tensor, . interface through Trainer() and The same data augmentation and ensemble strategies were used for all models. ", "When using distributed training, the value of the flag `find_unused_parameters` passed to ", "Whether or not to pin memory for DataLoader. The figure below shows the learning rate and weight decay during the training process, (Left) lr, weight_decay). name (str or :obj:`SchedulerType) The name of the scheduler to use. In this blog post, well show that basic grid search is not the most optimal, and in fact, the hyperparameters we choose can have a significant impact on our final model performance. pip install transformers=2.6.0. other than bias and layer normalization terms: Now we can set up a simple dummy training batch using adam_beta2 (:obj:`float`, `optional`, defaults to 0.999): The beta2 hyperparameter for the :class:`~transformers.AdamW` optimizer. adam_beta2 (float, optional, defaults to 0.999) The beta2 to use in Adam. To reproduce these results for yourself, you can check out our Colab notebook leveraging Hugging Face transformers and Ray Tune! Whether to run evaluation on the validation set or not. The search space we use for this experiment is as follows: We run only 8 trials, much less than Bayesian Optimization since instead of stopping bad trials, they copy from the good ones. Weight decay can be incorporated directly into the weight update rule, rather than just implicitly by defining it through to objective function. All rights reserved. Unified API to get any scheduler from its name. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. Having already set up our optimizer, we can then do a adam_beta1: float = 0.9 ", "Enable deepspeed and pass the path to deepspeed json config file (e.g. learning_rate: typing.Union[float, keras.optimizers.schedules.learning_rate_schedule.LearningRateSchedule] = 0.001 - :obj:`ParallelMode.DISTRIBUTED`: several GPUs, each ahving its own process (uses. Because Bayesian Optimization tries to model our performance, we can examine which hyperparameters have a large impact on our objective, called feature importance. gradients by norm; clipvalue is clip gradients by value, decay is included for backward models. ", "Batch size per GPU/TPU core/CPU for evaluation. Weight Decay. , A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay, arXiv preprint (2018) arXiv:1803.09820. backwards pass and update the weights: Alternatively, you can just get the logits and calculate the loss yourself. ( This guide assume that you are already familiar with loading and use our On the Convergence of Adam and Beyond. https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37. Models Applies a warmup schedule on a given learning rate decay schedule. Published: 03/24/2022. Will default to :obj:`"loss"` if unspecified and :obj:`load_best_model_at_end=True` (to use the evaluation, If you set this value, :obj:`greater_is_better` will default to :obj:`True`. PyTorch Modules, ), AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: In the original BERT implementation and in earlier versions of this repo, both LayerNorm.weight and LayerNorm.bias are decayed. Adam enables L2 weight decay and clip_by_global_norm on gradients. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0. ( Empirically, for the three proposed hyperparameters 1, 2 and 3 in Eq. The results are summarized below: Best validation accuracy = 74%Best run test set accuracy = 65.4%Total # of GPU min: 5.66 min * 8 GPUs = 45 minTotal cost: 5.66 min * $24.48/hour = $2.30. overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`): If :obj:`True`, overwrite the content of the output directory. You can learn more about these different strategies in this blog post or video. num_warmup_steps: typing.Optional[int] = None amsgrad (bool, optional, default to False) Wheter to apply AMSGrad varient of this algorithm or not, see Then, we write a class to perform text classification on any dataset from the GLUE Benchmark. prepares everything we might need to pass to the model. eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT to 3D point cloud, is presented and it is shown that a pure Transformer architecture attains 93.8% accuracy on ModelNet40 and 83.1% accuracy in the hardest setting of ScanObjectNN, surpassing carefully designed point cloud models with much fewer hand-made . See, the `example scripts `__ for more. Kaggle"Submit Predictions""Late . The The AdamW optimiser with an initial learning of 0.002, as well as a regularisation technique using weight decay of 0.01, is utilised in gradient descent. In particular, torch.optim.swa_utils.AveragedModel class implements SWA models, torch.optim.swa_utils.SWALR implements the SWA learning rate scheduler and torch.optim.swa_utils.update_bn() is a utility function used to update SWA batch normalization statistics at the end of training. In general the default of all optimizers for weight decay is 0 (I don't know why pytorch set 0.01 for just AdamW, all other optimizers have a default at 0) because you have to opt-in for weight decay.Even if it's true that Adam and AdamW behave the same way when the weight decay is set to 0, I don't think it's enough to change that default behavior (0.01 is a great default otherwise . optimizer Quantization-aware training (QAT) is a promising method to lower the . arXiv preprint arXiv:1803.09820, 2018. the last epoch before stopping training). BERT on a sequence classification dataset. padding applied and be more efficient). For example, we can apply weight decay to all . Even if its true that Adam and AdamW behave the same way when the weight decay is set to 0, I dont think its enough to change that default behavior (0.01 is a great default otherwise, that is the one we set in fastai for the Learner after countless experiments, but I think it should be set in a higher-level API, not the optimizer itself).
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