exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. num_cycles: float = 0.5 ), ( ). closure (Callable, optional) A closure that reevaluates the model and returns the loss. min_lr_ratio: float = 0.0 include_in_weight_decay is passed, the names in it will supersede this list. Taking the best configuration, we get a test set accuracy of 65.4%. compatibility to allow time inverse decay of learning rate. Transformers Notebooks which contain dozens of example notebooks from the community for Although a single fine-tuning training run is relatively quick, having to repeat this with different hyperparameter configurations ends up being pretty time consuming. However, the folks at fastai have been a little conservative in this respect. Will default to: - :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or. adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8): The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer. You can train, fine-tune, # Copyright 2020 The HuggingFace Team. weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. dataloader_num_workers (:obj:`int`, `optional`, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). Other changes to the Transformer architecture include: (a) a restructured residual block and weight initialization, (b) A set of sparse attention kernels which efficiently compute subsets of . Will be set to :obj:`True` if, :obj:`evaluation_strategy` is different from :obj:`"no"`. betas: typing.Tuple[float, float] = (0.9, 0.999) ", "Whether or not to group samples of roughly the same length together when batching. Adam enables L2 weight decay and clip_by_global_norm on gradients. . 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 initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the warmup_init options. 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. optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the When used with a distribution strategy, the accumulator should be called in a ( Instead, a more advanced approach is Bayesian Optimization. optimizer: Optimizer ( beta_2: float = 0.999 GPT-2 and especially GPT-3 models are quite large and won't fit on a single GPU and will need model parallelism. Use `Deepspeed `__. The model can then be compiled and trained as any Keras model: With the tight interoperability between TensorFlow and PyTorch models, you argument returned from forward must be the loss which you wish to num_warmup_steps . Therefore, logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training. With Ray Tune we can easily implement scalable PBT without much modification to our standard fine-tuning workflow. Index 0 takes into account the, # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`, # will use the first GPU in that env, i.e. inputs as usual. This way we can start more runs in parallel and thus test a larger number of hyperparameter configurations. optional), the function will raise an error if its unset and the scheduler type requires it. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0. All rights reserved. seed (:obj:`int`, `optional`, defaults to 42): Random seed that will be set at the beginning of training. Gradients will be accumulated locally on each replica and label_names (:obj:`List[str]`, `optional`): The list of keys in your dictionary of inputs that correspond to the labels. TFTrainer(). optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the then call .gradients, scale the gradients if required, and pass the result to apply_gradients. To calculate additional metrics in addition to the loss, you can also define However, here are a few other insights that we uncovered about hyperparameter tuning for NLP models that might be of broader interest: You can check out our implementation of Population Based Training in this Colab Notebook. weight_decay: float = 0.0 Anyways, here it is: In the Docs we can clearly see that the AdamW optimizer sets the default weight decay to 0.0. Because Bayesian Optimization tries to model our performance, we can examine which hyperparameters have a large impact on our objective, called feature importance. ["classifier.weight", "bert.encoder.layer.10.output.dense.weight"]) 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. {"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). If none is passed, weight decay is last_epoch: int = -1 Create a schedule with a constant learning rate, using the learning rate set in optimizer. power = 1.0 ds_config.json)", "The label smoothing epsilon to apply (zero means no label smoothing). Just adding the square of the weights to the Create a schedule with a learning rate that decreases following the values of the cosine function between the ", "Batch size per GPU/TPU core/CPU for training. This post describes a simple way to get started with fine-tuning transformer models. this optimizer internally adjusts the learning rate depending on the scale_parameter, relative_step and If none is passed, weight decay is applied to all parameters . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. For instance, the original Transformer paper used an exponential decay scheduler with a . ). ", "Whether or not to replace AdamW by Adafactor. The Transformer reads entire sequences of tokens at once. Adamw Adam + weight decate , Adam + L2,,L2loss,,,Adamw,loss. train_sampler = RandomSampler (train_dataset) if args.local_rank == - 1 else DistributedSampler . When used with a distribution strategy, the accumulator should be called in a Linear Neural Networks for Classification. can then use our built-in Then all we have to do is call scheduler.step() after optimizer.step(). correct_bias: bool = True loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact The Image Classification Dataset; 4.3. Don't forget to set it to. TF2, and focus specifically on the nuances and tools for training models in initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases This is useful because it allows us to make use of the pre-trained BERT Layer-wise Learning Rate Decay (LLRD) In Revisiting Few-sample BERT Fine-tuning, the authors describe layer-wise learning rate decay as "a method that applies higher learning rates for top layers and lower learning rates for bottom layers. If a params: typing.Iterable[torch.nn.parameter.Parameter] 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. epsilon (float, optional, defaults to 1e-7) The epsilon parameter in Adam, which is a small constant for numerical stability. Kaggle. eval_accumulation_steps (:obj:`int`, `optional`): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. Image Source: Deep Learning, Goodfellow et al. Applies a warmup schedule on a given learning rate decay schedule. It can be used to train with distributed strategies and even on TPU. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after lr, weight_decay). adam_beta1 (float, optional, defaults to 0.9) The beta1 to use in Adam. Google Scholar TPU: Whether to print debug metrics", "Drop the last incomplete batch if it is not divisible by the batch size. Have a question about this project? initial lr set in the optimizer. In the Docs we can clearly see that the AdamW optimizer sets the default weight decay to 0.0. ", "Deprecated, the use of `--per_device_train_batch_size` is preferred. last_epoch = -1 The authors speculate that a strong weight decay in the head results in representations with a larger margin between classes. epsilon (float, optional, defaults to 1e-7) The epsilon paramenter in Adam, which is a small constant for numerical stability. With the following, we The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training). num_cycles (int, optional, defaults to 1) The number of hard restarts to use. amsgrad (bool, optional, default to False) Wheter to apply AMSGrad varient of this algorithm or not, see include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. then call .gradients, scale the gradients if required, and pass the result to apply_gradients. What if there was a much better configuration that exists that we arent searching over? One of: - :obj:`ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU). . Memory-efficient optimizers: Because a billions of parameters are trained, the storage space . Allowed to be {clipnorm, clipvalue, lr, decay}. All 3 models are pretrained with Adam optimizer with batch size of 4096 and weight decay of 0.1. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. By clicking Sign up for GitHub, you agree to our terms of service and I would recommend this article for understanding why. increases linearly between 0 and the initial lr set in the optimizer. metric_for_best_model (:obj:`str`, `optional`): Use in conjunction with :obj:`load_best_model_at_end` to specify the metric to use to compare two different. For example, we can apply weight decay to all . Therefore, shouldnt make more sense to have the default weight decay for AdamW > 0? value pre-trained encoder frozen and optimizing only the weights of the head Will default to the. Create a schedule with a learning rate that decreases following the values of the cosine function between the Model classes in Transformers are designed to be compatible with native Collaborate on models, datasets and Spaces, Faster examples with accelerated inference, : typing.Iterable[torch.nn.parameter.Parameter], : typing.Tuple[float, float] = (0.9, 0.999), : typing.Union[float, keras.optimizers.schedules.learning_rate_schedule.LearningRateSchedule] = 0.001, : typing.Optional[typing.List[str]] = None, : typing.Union[str, transformers.trainer_utils.SchedulerType], https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py, https://discuss.huggingface.co/t/t5-finetuning-tips/684/3, https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37, an optimizer with weight decay fixed that can be used to fine-tuned models, and, several schedules in the form of schedule objects that inherit from, a gradient accumulation class to accumulate the gradients of multiple batches. Check here for the full code examples. Users should then call .gradients, scale the A Sparse Transformer is a Transformer based architecture which utilises sparse factorizations of the attention matrix to reduce time/memory to O ( n n). pip install transformers=2.6.0. This is accomplished by setting the learning rate of the top layer and using a multiplicative decay rate to decrease the learning rate layer-by-layer . relative_step=False. weight_decay_rate (float, optional, defaults to 0) - The weight decay to use. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. beta_2 (float, optional, defaults to 0.999) The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. For the . gradients by norm; clipvalue is clip gradients by value, decay is included for backward tokenizers are framework-agnostic, so there is no need to prepend TF to Then, we write a class to perform text classification on any dataset from the GLUE Benchmark. to adding the square of the weights to the loss with plain (non-momentum) SGD. include_in_weight_decay: typing.Optional[typing.List[str]] = None The figure below shows the learning rate and weight decay during the training process, (Left) lr, weight_decay). "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future ", "version. We minimize a loss function compromising both the primary loss function and a penalty on the L 2 Norm of the weights: L n e w ( w) = L o r i g i n a l ( w) + w T w. where is a value determining the strength of . TFTrainer() expects the passed datasets to be dataset Transformers. learning_rate (:obj:`float`, `optional`, defaults to 5e-5): The initial learning rate for :class:`~transformers.AdamW` optimizer. adam_global_clipnorm: typing.Optional[float] = None ( arXiv preprint arXiv:1803.09820, 2018. In this And as you can see, hyperparameter tuning a transformer model is not rocket science. For further details regarding the algorithm we refer to Decoupled Weight Decay Regularization.. Parameters:. of the specified model are used to initialize the model. scale_parameter = True classification head on top of the encoder with an output size of 2. betas (Tuple[float,float], optional, defaults to (0.9, 0.999)) Adams betas parameters (b1, b2). if the logging level is set to warn or lower (default), :obj:`False` otherwise. label_smoothing_factor + label_smoothing_factor/num_labels` respectively. ), AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: If a Often weight decay refers to the implementation where we specify it directly in the weight update rule (whereas L2 regularization is usually the implementation which is specified in the objective function). do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run predictions on the test set or not. At the same time, dropout involves randomly setting a portion of the weights to zero during training to prevent the model from . 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. * :obj:`"epoch"`: Evaluation is done at the end of each epoch. exclude_from_weight_decay: typing.Optional[typing.List[str]] = None include_in_weight_decay (List[str], optional) - List of the parameter names (or re patterns) to apply weight decay to. ). logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to log and evaluate the first :obj:`global_step` or not. num_training_steps (int) The totale number of training steps. privacy statement. beta_1: float = 0.9 with the m and v parameters in strange ways as shown in num_warmup_steps: int Author: PL team License: CC BY-SA Generated: 2023-01-03T15:49:54.952421 This notebook will use HuggingFace's datasets library to get data, which will be wrapped in a LightningDataModule.Then, we write a class to perform text classification on any dataset from the GLUE Benchmark. layers. Finally, you can view the results, including any calculated metrics, by library also includes a number of task-specific final layers or heads whose batch ready to be fed into the model. replica context. The text was updated successfully, but these errors were encountered: Too bad you didn't get an answer on SO. several schedules in the form of schedule objects that inherit from _LRSchedule: a gradient accumulation class to accumulate the gradients of multiple batches. I tried to ask in SO before, but apparently the question seems to be irrelevant. With Bayesian Optimization, we were able to leverage a guided hyperparameter search. implementation at Revolutionizing analytics. The betas (Tuple[float,float], optional, defaults to (0.9, 0.999)) Adams betas parameters (b1, b2). One example is here. Breaking down barriers. 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. This is an experimental feature. There are 3 . View 211102 - Grokking.pdf from INDUSTRIAL 1223 at Seoul National University. from_pretrained() to load the weights of In the analytical experiment section, we will . an optimizer with weight decay fixed that can be used to fine-tuned models, and. Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay name: str = None I think you would multiply your chances of getting a good answer if you asked it over at https://discuss.huggingface.co! 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. Will default to :obj:`False` if gradient checkpointing is used, :obj:`True`. 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. Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3): Training without LR warmup or clip_threshold is not recommended. learning_rate (:obj:`float`, `optional`, defaults to 5e-5): The initial learning rate for :class:`~transformers.AdamW` optimizer. 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 learning_rate: typing.Union[float, keras.optimizers.schedules.learning_rate_schedule.LearningRateSchedule] = 0.001 The key takeaway here is that Population Based Training is the most effective approach to tune the hyperparameters of the Transformer model. "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)]. ", "`output_dir` is only optional if it can get inferred from the environment. Does the default weight_decay of 0.0 in transformers.AdamW make sense? gradients by norm; clipvalue is clip gradients by value, decay is included for backward initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets (2021) A Power, Y Burda, H Edwards, I initial lr set in the optimizer. do_train (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run training or not. Out of these trials, the final validation accuracy for the top 5 ranged from 71% to 74%. Override num_train_epochs. Secure your code as it's written. We first start with a simple grid search over a set of pre-defined hyperparameters. num_warmup_steps: int precision. Training without LR warmup or clip threshold is not recommended. use clip threshold: https://arxiv.org/abs/2004.14546. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the. "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future ", "version. debug (:obj:`bool`, `optional`, defaults to :obj:`False`): When training on TPU, whether to print debug metrics or not. A tag already exists with the provided branch name. Powered by Discourse, best viewed with JavaScript enabled. and evaluate any Transformers model with a wide range of training options and Others reported the following combination to work well: When using lr=None with Trainer you will most likely need to use AdafactorSchedule, ( num_warmup_steps: typing.Optional[int] = None Just adding the square of the weights to the kwargs Keyward arguments. include_in_weight_decay (List[str], optional) - List of the parameter names (or re patterns) to apply weight decay to. lr (float, optional) The external learning rate. ", "Weight decay for AdamW if we apply some. num_training_steps overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`): If :obj:`True`, overwrite the content of the output directory. This argument is not directly used by, :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. weight_decay_rate (float, optional, defaults to 0) The weight decay to use. We can use any PyTorch optimizer, but our library also provides the . ). Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the One thing to take into account in those comparisons is that changing the way we regularize changes the best values of weight decay or learning rate. Sign in num_train_epochs(:obj:`float`, `optional`, defaults to 3.0): Total number of training epochs to perform (if not an integer, will perform the decimal part percents of. adam_beta1: float = 0.9 Cosine learning rate. several schedules in the form of schedule objects that inherit from _LRSchedule: a gradient accumulation class to accumulate the gradients of multiple batches. 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). Model classes in Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used seemlessly with either. # deepspeed performs its own DDP internally, and requires the program to be started with: # python -m torch.distributed.launch --nproc_per_node=2 ./program.py, "--deepspeed requires deepspeed: `pip install deepspeed`.". same value as :obj:`logging_steps` if not set. num_warmup_steps (int) The number of steps for the warmup phase. last_epoch = -1 beta_2 (float, optional, defaults to 0.999) The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. # Ist: Adam weight decay implementation (L2 regularization) final_loss = loss + wd * all_weights.pow (2).sum () / 2 # IInd: equivalent to this in SGD w = w - lr * w . start = 1 beta_1 (float, optional, defaults to 0.9) The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates. You signed in with another tab or window. Transformers are not capable of remembering the order or sequence of the inputs. Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. This is a new post in my NER series. The . replica context. We also conclude with a couple tips and tricks for hyperparameter tuning for Transformer models. You can learn more about these different strategies in this blog post or video. at the next training step under the keyword argument ``mems``. applied to all parameters except bias and layer norm parameters. params (Iterable[torch.nn.parameter.Parameter]) Iterable of parameters to optimize or dictionaries defining parameter groups. **kwargs Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate ( Deletes the older checkpoints. 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`. Hopefully this blog post inspires you to consider optimizing hyperparameters more when training your models. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. , A disciplined approach to neural network hyper-parameters: Part 1-learning rate, batch size, momentum, and weight decay, arXiv preprint (2018) arXiv:1803.09820. An adaptation of Finetune transformers models with pytorch lightning tutorial using Habana Gaudi AI processors. adam_beta2 (:obj:`float`, `optional`, defaults to 0.999): The beta2 hyperparameter for the :class:`~transformers.AdamW` optimizer. step can take a long time) but will not yield the same results as the interrupted training would have. closure: typing.Callable = None # if n_gpu is > 1 we'll use nn.DataParallel. Here, we fit a Gaussian Process model that tries to predict the performance of the parameters (i.e. We also demonstrate that longer optimization runs require smaller weight decay values for optimal results and introduce a normalized variant of weight decay to reduce this dependence. GPT-3 is an autoregressive transformer model with 175 billion parameters. A lightweight colab demo You can use your own module as well, but the first We use a standard uncased BERT model from Hugging Face transformers and we want to fine-tune on the RTE dataset from the SuperGLUE benchmark. We also provide a few learning rate scheduling tools. As a result, we can. of the warmup). to adding the square of the weights to the loss with plain (non-momentum) SGD. To learn more about how researchers and companies use Ray to tune their models in production, join us at the upcoming Ray Summit! In some cases, you might be interested in keeping the weights of the Default is unlimited checkpoints", "Do not use CUDA even when it is available", "Random seed that will be set at the beginning of training. other choices will force the requested backend. eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. Will eventually default to :obj:`["labels"]` except if the model used is one of the. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. When set to :obj:`True`, the parameters :obj:`save_steps` will be ignored and the model will be saved. Weight decay is a form of regularization-after calculating the gradients, we multiply them by, e.g., 0.99. Instead, Population Based Training still uses guided hyperparameter search, but doesnt need to restart training for new hyperparameter configurations. If this argument is set to a positive int, the, ``Trainer`` will use the corresponding output (usually index 2) as the past state and feed it to the model. Now you have access to many transformer-based models including the pre-trained Bert models in pytorch. To reproduce these results for yourself, you can check out our Colab notebook leveraging Hugging Face transformers and Ray Tune! weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. Creates an optimizer from its config with WarmUp custom object. On our test set, we pick the best configuration and get an accuracy of 66.9%, a 1.5 percent improvement over the best configuration from grid search. 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. the encoder parameters, which can be accessed with the base_model optimizer: Optimizer warmup_init = False encoder and easily train it on whatever sequence classification dataset we weights are instantiated randomly when not present in the specified 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. recommended to use learning_rate instead. ", smdistributed.dataparallel.torch.distributed. ). to tokenize MRPC and convert it to a TensorFlow Dataset object. weight_decay_rate: float = 0.0 with built-in features like logging, gradient accumulation, and mixed When we call a classification model with the labels argument, the first params names = None - :obj:`ParallelMode.TPU`: several TPU cores. this optimizer internally adjusts the learning rate depending on the scale_parameter, relative_step and name (str or :obj:`SchedulerType) The name of the scheduler to use. init_lr: float For distributed training, it will always be 1. https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py. For more information about how it works I suggest you read the paper. weight_decay_rate (float, optional, defaults to 0) The weight decay to apply. label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0): The label smoothing factor to use. Kaggle. In the original BERT implementation and in earlier versions of this repo, both LayerNorm.weight and LayerNorm.bias are decayed. initial_learning_rate (float) The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. ", "Enable deepspeed and pass the path to deepspeed json config file (e.g. On the Convergence of Adam and Beyond.
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