transformer weight decay

GPT-2 and especially GPT-3 models are quite large and won't fit on a single GPU and will need model parallelism. fp16_backend (:obj:`str`, `optional`, defaults to :obj:`"auto"`): The backend to use for mixed precision training. loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact The key takeaway here is that Population Based Training is the most effective approach to tune the hyperparameters of the Transformer model. We can use any PyTorch optimizer, but our library also provides the A tag already exists with the provided branch name. backwards pass and update the weights: Alternatively, you can just get the logits and calculate the loss yourself. loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact You signed in with another tab or window. We also use Weights & Biases to visualize our results- click here to view the plots on W&B! dataloader_pin_memory (:obj:`bool`, `optional`, defaults to :obj:`True`)): Whether you want to pin memory in data loaders or not. initial lr set in the optimizer. classification head on top of the encoder with an output size of 2. initial lr set in the optimizer. Note that All rights reserved. are initialized in eval mode by default. Best validation accuracy = 78% (+ 4% over grid search)Best run test set accuracy = 70.5% (+ 5% over grid search)Total # of GPU hours: 6 min * 8 GPU = 48 minTotal cost: 6 min * 24.48/hour = $2.45. objects from tensorflow_datasets. 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. seed (:obj:`int`, `optional`, defaults to 42): Random seed that will be set at the beginning of training. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory). I guess it is implemented in this way, because most of the time you decide in the initialization which parameters you want to decay and which ones shouldnt be decayed, such as here: In general the default of all optimizers for weight decay is 0 (I dont 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. beta_2 (float, optional, defaults to 0.999) The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. Google Scholar 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. takes in the data in the format provided by your dataset and returns a Scaling up the data from 300M to 3B images improves the performance of both small and large models. recommended to use learning_rate instead. initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the Create a schedule with a constant learning rate, using the learning rate set in optimizer. Just adding the square of the weights to the Stochastic Weight Averaging. ). clipnorm is clip init_lr: float We call for the development of Foundation Transformer for true general-purpose modeling, which serves as a go-to architecture for . power (float, optional, defaults to 1) The power to use for the polynomial warmup (defaults is a linear warmup). 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. debug (:obj:`bool`, `optional`, defaults to :obj:`False`): When training on TPU, whether to print debug metrics or not. amsgrad (bool, optional, default to False) Whether to apply AMSGrad variant of this algorithm or not, see On the Convergence of Adam and Beyond. The value is the location of its json config file (usually ``ds_config.json``). ). name: str = None import tensorflow_addons as tfa # Adam with weight decay optimizer = tfa.optimizers.AdamW(0.005, learning_rate=0.01) 6. If none is passed, weight decay is 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. A lightweight colab demo compatibility to allow time inverse decay of learning rate. To calculate additional metrics in addition to the loss, you can also define training. kwargs Keyward arguments. encoder and easily train it on whatever sequence classification dataset we Using the Hugging Face transformers library, we can easily load a pre-trained NLP model with several extra layers, and run a few epochs of fine-tuning on a specific task. evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`): The evaluation strategy to adopt during training. your own compute_metrics function and pass it to the trainer. Google Scholar [29] Liu X., Lu H., Nayak A., A spam transformer model for SMS spam detection, IEEE Access 9 (2021) 80253 - 80263. num_warmup_steps This argument is not directly used by :class:`~transformers.Trainer`, it's, intended to be used by your training/evaluation scripts instead. Out of these trials, the final validation accuracy for the top 5 ranged from 71% to 74%. power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training). When used with a distribution strategy, the accumulator should be called in a Default is unlimited checkpoints", "Do not use CUDA even when it is available", "Random seed that will be set at the beginning of training. power (float, optional, defaults to 1) The power to use for the polynomial warmup (defaults is a linear warmup). decay_schedule_fn: typing.Callable exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. several schedules in the form of schedule objects that inherit from _LRSchedule: a gradient accumulation class to accumulate the gradients of multiple batches. params: typing.Iterable[torch.nn.parameter.Parameter] Softmax Regression; 4.2. Will default to: - :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or. We can call model.train() to at the next training step under the keyword argument ``mems``. This is a new post in my NER series. eval_accumulation_steps (:obj:`int`, `optional`): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. lr = None ignore_skip_data (:obj:`bool`, `optional`, defaults to :obj:`False`): When resuming training, whether or not to skip the epochs and batches to get the data loading at the same, stage as in the previous training. ", "Batch size per GPU/TPU core/CPU for training. initial_learning_rate (float) The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end ( ", "Number of predictions steps to accumulate before moving the tensors to the CPU. For the . When used with a distribution strategy, the accumulator should be called in a Implements Adam algorithm with weight decay fix as introduced in initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the with features like mixed precision and easy tensorboard logging. logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to log and evaluate the first :obj:`global_step` or not. Learn more about where AI is creating real impact today. Removing weight decay for certain parameters specified by no_weight_decay. TF2, and focus specifically on the nuances and tools for training models in 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. ds_config.json)", "The label smoothing epsilon to apply (zero means no label smoothing). Questions &amp; Help Details Hi, I tried to ask in SO before, but apparently the question seems to be irrelevant. And this is just the start. applied to all parameters except bias and layer norm parameters. I would recommend this article for understanding why. 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. initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the padding applied and be more efficient). The Ray libraries offer a host of features and integrations. with built-in features like logging, gradient accumulation, and mixed Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the Unified API to get any scheduler from its name. init_lr (float) The desired learning rate at the end of the warmup phase. replica context. optimizer: Optimizer this optimizer internally adjusts the learning rate depending on the scale_parameter, relative_step and Jan 2021 Aravind Srinivas optional), the function will raise an error if its unset and the scheduler type requires it. Training NLP models from scratch takes hundreds of hours of training time. 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. Taking the best configuration, we get a test set accuracy of 65.4%. When we instantiate a model with same value as :obj:`logging_steps` if not set. Please set a value for ", "`output_dir` is overwritten by the env variable 'SM_OUTPUT_DATA_DIR' ", "Mixed precision training with AMP or APEX (`--fp16`) can only be used on CUDA devices.". warmup_init = False params (Iterable[torch.nn.parameter.Parameter]) Iterable of parameters to optimize or dictionaries defining parameter groups. Typically used for `wandb `_ logging. batches and prepare them to be fed into the model. 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. Then, we write a class to perform text classification on any dataset from the GLUE Benchmark. optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the Follow. pip install transformers=2.6.0. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0, tf.keras.optimizers.schedules.LearningRateSchedule], https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py, https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37. Finally, you can view the results, including any calculated metrics, by If a Weight decay decoupling effect. Deciding the value of wd. 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. interface through Trainer() and adafactor (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use the :class:`~transformers.Adafactor` optimizer instead of. 0 means that the data will be loaded in the main process. passed labels. 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 . `TensorBoard `__ log directory. num_training_steps (int, optional) The number of training steps to do. Weight decay is a regularization technique that is supposed to fight against overfitting. Adam enables L2 weight decay and clip_by_global_norm on gradients. AdamW() optimizer which implements gradient bias ddp_find_unused_parameters (:obj:`bool`, `optional`): When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to, :obj:`DistributedDataParallel`. glue_convert_examples_to_features() Use `Deepspeed `__. Creates an optimizer from its config with WarmUp custom object. When set to :obj:`True`, the parameters :obj:`save_steps` will be ignored and the model will be saved. Therefore, logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training. adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8): The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer. 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. - :obj:`ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses :obj:`torch.nn.DataParallel`). initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases Users should then call .gradients, scale the optimizer: Optimizer ( 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. Model classes in Transformers are designed to be compatible with native Taken from "Fixing Weight Decay Regularization in Adam" by Ilya Loshchilov, Frank Hutter. , ResNeXt, CNN design space, and transformers for vision and large-scale pretraining. The simple grid search did alright, but it had a very limited search space and only considered 3 hyperparameters. AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: For further details regarding the algorithm we refer to Decoupled Weight Decay Regularization.. Parameters:. Interestingly, we see that weight_decay is the second most important hyperparameter, showing the importance of searching over more hyperparameters. use the data_collator argument to pass your own collator function which Ilya Loshchilov, Frank Hutter. num_train . include_in_weight_decay (List[str], optional) - List of the parameter names (or re patterns) to apply weight decay to. Note: power defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT :obj:`False` if your metric is better when lower. initial_learning_rate: float For example, we can apply weight decay to all parameters The value for the params key should be a list of named parameters (e.g. train_sampler = RandomSampler (train_dataset) if args.local_rank == - 1 else DistributedSampler . Models Regularization. applied to all parameters by default (unless they are in exclude_from_weight_decay). Instead, a more advanced approach is Bayesian Optimization. . :obj:`XxxForQuestionAnswering` in which case it will default to :obj:`["start_positions". Ray is a fast and simple framework for distributed computing, gain a better understanding of our hyperparameters and. Solving the unsolvable with deep learning. Serializes this instance to a JSON string. beta_1 (float, optional, defaults to 0.9) The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates. init_lr (float) The desired learning rate at the end of the warmup phase. with the m and v parameters in strange ways as shown in Decoupled Weight Decay Additional optimizer operations like gradient clipping should not be used alongside Adafactor. Sign in Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. To help you get started, we've selected a few transformers examples, based on popular ways it is used in public projects. . linearly between 0 and the initial lr set in the optimizer. TFTrainer() expects the passed datasets to be dataset epsilon (float, optional, defaults to 1e-7) The epsilon parameter in Adam, which is a small constant for numerical stability. qualname = None following a half-cosine). Will default to. ", "The list of keys in your dictionary of inputs that correspond to the labels. betas (Tuple[float,float], optional, defaults to (0.9, 0.999)) Adams betas parameters (b1, b2). include_in_weight_decay: typing.Optional[typing.List[str]] = None power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. of the specified model are used to initialize the model. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. name (str, optional) Optional name prefix for the returned tensors during the schedule. # distributed under the License is distributed on an "AS IS" BASIS. the pretrained tokenizer name. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after If needed, you can also 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 . type = None power = 1.0 dataloader_num_workers (:obj:`int`, `optional`, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). num_warmup_steps: int loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact This argument is not directly used by. gradients by norm; clipvalue is clip gradients by value, decay is included for backward Hence the default value of weight decay in fastai is actually 0.01. weight_decay_rate (float, optional, defaults to 0) - The weight decay to use. Published: 03/24/2022. no_deprecation_warning: bool = False learning_rate (Union[float, tf.keras.optimizers.schedules.LearningRateSchedule], optional, defaults to 1e-3) The learning rate to use or a schedule. 11 . adam_clipnorm: typing.Optional[float] = None beta1 = None And like @BramVanroy said, it would be such a breaking change that even if we really wanted to change that default, we probably wouldnt. lr_end (float, optional, defaults to 1e-7) The end LR. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. ( learning_rate (Union[float, tf.keras.optimizers.schedules.LearningRateSchedule], optional, defaults to 1e-3) The learning rate to use or a schedule. In this BertForSequenceClassification.from_pretrained('bert-base-uncased', # number of warmup steps for learning rate scheduler, # the instantiated Transformers model to be trained. ( clipnorm is clip Create a schedule with a learning rate that decreases following the values of the cosine function between the beta_2 (float, optional, defaults to 0.999) The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. num_warmup_steps (int, optional) The number of warmup steps to do. First you install the amazing transformers package by huggingface with. Transformers. name: str = 'AdamWeightDecay' optimizer Sanitized serialization to use with TensorBoards hparams. Paper: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost https://arxiv.org/abs/1804.04235 Note that We can also see below that our best trials are mostly created towards the end of the full experiment, showing that our hyperparameter configurations get better as time goes on and our Bayesian optimizer is working. Surprisingly, a stronger decay on the head yields the best results. In Adam, the weight decay is usually implemented by adding wd*w ( wd is weight decay here) to the gradients (Ist case), rather than actually subtracting from weights (IInd case). 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. Applies a warmup schedule on a given learning rate decay schedule. The Transformer blocks produce a [batch_size, num_patches, projection_dim] tensor, . clip_threshold = 1.0 ", "Weight decay for AdamW if we apply some. optimizer: Optimizer a detailed colab notebook which uses Trainer to train a masked language model from scratch on Esperanto. ", "Deprecated, the use of `--per_device_eval_batch_size` is preferred. will create a BERT model instance with encoder weights copied from the GPT model is essentially a standard transformer with a few tweaks. ", smdistributed.dataparallel.torch.distributed. Check here for the full code examples. decay_schedule_fn (Callable) The schedule function to apply after the warmup for the rest of training. . remove_unused_columns (:obj:`bool`, `optional`, defaults to :obj:`True`): If using :obj:`datasets.Dataset` datasets, whether or not to automatically remove the columns unused by the, (Note that this behavior is not implemented for :class:`~transformers.TFTrainer` yet.). initial lr set in the optimizer. The Image Classification Dataset; 4.3. You can learn more about these different strategies in this blog post or video. It was also implemented in transformers before it was available in PyTorch itself. 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. ). names = None I tried to ask in SO before, but apparently the question seems to be irrelevant. :obj:`torch.nn.DistributedDataParallel`). "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)]. 1. applied to all parameters by default (unless they are in exclude_from_weight_decay). Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. huggingface/transformers/blob/a75c64d80c76c3dc71f735d9197a4a601847e0cd/examples/contrib/run_openai_gpt.py#L230-L237. including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks. warmup_steps: int Does the default weight_decay of 0.0 in transformers.AdamW make sense? If none is passed, weight decay is applied to all parameters except bias . To reproduce these results for yourself, you can check out our Colab notebook leveraging Hugging Face transformers and Ray Tune! 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. TensorFlow models can be instantiated with main_oc20.py is the code for training and evaluating. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. warmup_steps (int) The number of steps for the warmup part of training. Finetune Transformers Models with PyTorch Lightning. Model classes in Transformers that dont begin with TF are There are 3 . ", "Whether or not to replace AdamW by Adafactor. weight_decay_rate (float, optional, defaults to 0) The weight decay to apply. adam_epsilon: float = 1e-08 adam_epsilon (float, optional, defaults to 1e-8) The epsilon to use in Adam. This is not much of a major issue but it may be a factor in this problem. Note that to your account. Decoupled Weight Decay Regularization. 4.5.4. which conveniently handles the moving parts of training Transformers models This notebook will use HuggingFace's datasets library to get data, which will be wrapped in a LightningDataModule. PyTorch Modules, num_cycles (int, optional, defaults to 1) The number of hard restarts to use. Adam enables L2 weight decay and clip_by_global_norm on gradients. value gradients by norm; clipvalue is clip gradients by value, decay is included for backward ). If none is passed, weight decay is applied to all parameters . choose. 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 Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0. 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. ( a warmup period during which it increases linearly from 0 to the initial lr set in the optimizer. * :obj:`"epoch"`: Evaluation is done at the end of each epoch. include_in_weight_decay: typing.Optional[typing.List[str]] = None https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37, ( optimizer Fine-tuning in the HuggingFace's transformers library involves using a pre-trained model and a tokenizer that is compatible with that model's architecture and . And if you want to try out any of the other algorithms or features from Tune, wed love to hear from you either on our GitHub or Slack! This is equivalent beta_2: float = 0.999 ), ( relative_step=False. configuration and pre-trained weights gradient_accumulation_steps (:obj:`int`, `optional`, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass. Although a single fine-tuning training run is relatively quick, having to repeat this with different hyperparameter configurations ends up being pretty time consuming. learning_rate: typing.Union[float, keras.optimizers.schedules.learning_rate_schedule.LearningRateSchedule] = 0.001 recommended to use learning_rate instead. All 3 models are pretrained with Adam optimizer with batch size of 4096 and weight decay of 0.1. train a model with 5% better accuracy in the same amount of time. epsilon: float = 1e-07 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. replica context. We highly recommend using Trainer(), discussed below, In the tests we ran, the best learning rate with L2 regularization was 1e-6 (with a maximum learning rate of 1e-3) while 0.3 was the best value for weight decay (with a learning rate of 3e-3). 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 . Create a schedule with a constant learning rate, using the learning rate set in optimizer. When we call a classification model with the labels argument, the first betas (Tuple[float, float], optional) - coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) The optimizer allows us to apply different hyperpameters for specific Generally a wd = 0.1 works pretty well. other choices will force the requested backend. eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. We also combine this with an early stopping algorithm, Asynchronous Hyperband, where we stop bad performing trials early to avoid wasting resources on them. report_to (:obj:`List[str]`, `optional`, defaults to the list of integrations platforms installed): The list of integrations to report the results and logs to. Gradient accumulation utility. 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. use clip threshold: https://arxiv.org/abs/2004.14546. Just adding the square of the weights to the ( Although it only took ~6 minutes to run the 18 trials above, every new value that we want to search over means 6 additional trials. name: typing.Union[str, transformers.trainer_utils.SchedulerType] gradient clipping should not be used alongside Adafactor. from_pretrained() to load the weights of Therefore, shouldnt make more sense to have the default weight decay for AdamW > 0? eps (float, optional, defaults to 1e-6) Adams epsilon for numerical stability. Hopefully this blog post inspires you to consider optimizing hyperparameters more when training your models. 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. closure: typing.Callable = None last_epoch = -1 Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Figure 2: Comparison of nuclear norm (solid line) and nuclear norm upper bound penalized by weight decay on individual factors (dotted line) during the training of ResNet20 on CIFAR-10, showing that for most of training, weight decay is effectively penalizing the .