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Weight Decay, or $L_{2}$ Regularization, is a regularization technique applied to the weights of a neural network. layers. Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the Serializes this instance while replace `Enum` by their values (for JSON serialization support). show how to use our included Trainer() class which weight_decay (:obj:`float`, `optional`, defaults to 0): The weight decay to apply (if . optimize. torch.optim.swa_utils implements Stochastic Weight Averaging (SWA). PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. ). The cell successfully executes, but it does nothing - does not start training at all. Default is unlimited checkpoints", "Do not use CUDA even when it is available", "Random seed that will be set at the beginning of training. optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the decay_schedule_fn (Callable) The schedule function to apply after the warmup for the rest of training. num_train . weights are instantiated randomly when not present in the specified including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks. ). When using gradient accumulation, one step is counted as one step with backward pass. warmup_steps (int) The number of steps for the warmup part of training. Anyways, here it is: In the Docs we can clearly see that the AdamW optimizer sets the default weight decay to 0.0. The Ray libraries offer a host of features and integrations. lr (float, optional) The external learning rate. dataloader_num_workers (:obj:`int`, `optional`, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). ", "Enable deepspeed and pass the path to deepspeed json config file (e.g. num_training_steps To use a manual (external) learning rate schedule you should set scale_parameter=False and :obj:`torch.nn.DistributedDataParallel`). dataloader_pin_memory (:obj:`bool`, `optional`, defaults to :obj:`True`)): Whether you want to pin memory in data loaders or not. increases linearly between 0 and the initial lr set in the optimizer. no_cuda (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to not use CUDA even when it is available or not. https://blog.csdn.net . Allowed to be {clipnorm, clipvalue, lr, decay}. Will eventually default to :obj:`["labels"]` except if the model used is one of the. training only). on the `Apex documentation `__. 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`. optional), the function will raise an error if its unset and the scheduler type requires it. params: typing.Iterable[torch.nn.parameter.Parameter] include_in_weight_decay: typing.Optional[typing.List[str]] = None Will default to: - :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or. See details. weight_decay_rate (float, optional, defaults to 0) - The weight decay to use. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. TPU: Whether to print debug metrics", "Drop the last incomplete batch if it is not divisible by the batch size. adam_beta1 (:obj:`float`, `optional`, defaults to 0.9): The beta1 hyperparameter for the :class:`~transformers.AdamW` optimizer. ", "Deletes the older checkpoints in the output_dir. # distributed under the License is distributed on an "AS IS" BASIS. overwrite_output_dir (:obj:`bool`, `optional`, defaults to :obj:`False`): If :obj:`True`, overwrite the content of the output directory. A Sparse Transformer is a Transformer based architecture which utilises sparse factorizations of the attention matrix to reduce time/memory to O ( n n). Decoupled Weight Decay Regularization. Interestingly, we see that weight_decay is the second most important hyperparameter, showing the importance of searching over more hyperparameters. . your own compute_metrics function and pass it to the trainer. Now you have access to many transformer-based models including the pre-trained Bert models in pytorch. 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. beta_2 (float, optional, defaults to 0.999) The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. Regularization. 1. BERT on a sequence classification dataset. tf.keras.optimizers.schedules.LearningRateSchedule]. can then use our built-in Ray is a fast and simple framework for distributed computing, gain a better understanding of our hyperparameters and. increases linearly between 0 and the initial lr set in the optimizer. weight_decay_rate (float, optional, defaults to 0) The weight decay to apply. Does the default weight_decay of 0.0 in transformers.AdamW make sense. relative_step=False. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. The power transformer model test system is composed of two parts: the transformer discharge model and the automatic discharge simulation test system, which can realize the free switching, automatic rise, and fall of various discharge fault patterns, . beta1 = None For more information about how it works I suggest you read the paper. 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. gradients by norm; clipvalue is clip gradients by value, decay is included for backward gradients if required, and pass the result to apply_gradients. power: float = 1.0 . If, left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but. To use a manual (external) learning rate schedule you should set scale_parameter=False and lr_scheduler_type (:obj:`str` or :class:`~transformers.SchedulerType`, `optional`, defaults to :obj:`"linear"`): The scheduler type to use. AdamW() optimizer which implements gradient bias lr = None Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. On the Convergence of Adam and Beyond. last_epoch (int, optional, defaults to -1) The index of the last epoch when resuming training. per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8): The batch size per GPU/TPU core/CPU for training. backwards pass and update the weights: Alternatively, you can just get the logits and calculate the loss yourself. an optimizer with weight decay fixed that can be used to fine-tuned models, and. 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. seed (:obj:`int`, `optional`, defaults to 42): Random seed that will be set at the beginning of training. Don't forget to set it to. TFTrainer(). ", "Remove columns not required by the model when using an nlp.Dataset. Here we use 1e-4 as a default for weight_decay. See, the `example scripts `__ for more. gradient clipping should not be used alongside Adafactor. When training on TPU, the number of TPU cores (automatically passed by launcher script). Revolutionizing analytics. load_best_model_at_end (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to load the best model found during training at the end of training. prepares everything we might need to pass to the model. This argument is not directly used by. local_rank (:obj:`int`, `optional`, defaults to -1): Rank of the process during distributed training. Sanitized serialization to use with TensorBoards hparams. num_train_step (int) The total number of training steps. Already on GitHub? When used with a distribution strategy, the accumulator should be called in a decouples the optimal choice of weight decay factor . Kaggle. models. launching tensorboard in your specified logging_dir directory. initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the Cosine learning rate. num_warmup_steps: int num_training_steps weight_decay_rate (float, optional, defaults to 0) The weight decay to use. If none is passed, weight decay is Follow. can set up a scheduler which warms up for num_warmup_steps and then num_warmup_steps: int Must be one of :obj:`"auto"`, :obj:`"amp"` or, :obj:`"apex"`. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, after When used with a distribution strategy, the accumulator should be called in a eps: float = 1e-06 following a half-cosine). betas: typing.Tuple[float, float] = (0.9, 0.999) replica context. Copyright 2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0. 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). ( pre-trained encoder frozen and optimizing only the weights of the head ", "TPU: Number of TPU cores (automatically passed by launcher script)", "Deprecated, the use of `--debug` is preferred. 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 With the following, we Adam enables L2 weight decay and clip_by_global_norm on gradients. 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 # We override the default repr to remove deprecated arguments from the repr. pip install transformers=2.6.0. weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. Does the default weight_decay of 0.0 in transformers.AdamW make sense? sharded_ddp (:obj:`bool`, `optional`, defaults to :obj:`False`): Use Sharded DDP training from `FairScale `__ (in distributed. Paper: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost https://arxiv.org/abs/1804.04235 Note that name (str, optional) Optional name prefix for the returned tensors during the schedule. This returns a Weight Decay, or L 2 Regularization, is a regularization technique applied to the weights of a neural network. closure: typing.Callable = None to adding the square of the weights to the loss with plain (non-momentum) SGD. weight_decay (:obj:`float`, `optional`, defaults to 0): The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in. ", "Whether or not to load the best model found during training at the end of training. ). min_lr_ratio (float, optional, defaults to 0) The final learning rate at the end of the linear decay will be init_lr * min_lr_ratio. Must be the name of a metric returned by the evaluation with or without the prefix :obj:`"eval_"`. Check here for the full code examples. adam_epsilon: float = 1e-08 eps = (1e-30, 0.001) Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay Regularization. Using `--per_device_train_batch_size` is preferred.". ", "If > 0: set total number of training steps to perform. Override num_train_epochs. For example, instantiating a model with relative_step = True classification head on top of the encoder with an output size of 2. min_lr_ratio: float = 0.0 the pretrained tokenizer name. ", "Number of predictions steps to accumulate before moving the tensors to the CPU. epsilon: float = 1e-07 ", "Whether or not to replace AdamW by Adafactor. ", "When performing evaluation and predictions, only returns the loss. fp16 (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training. power (float, optional, defaults to 1) The power to use for the polynomial warmup (defaults is a linear warmup). applied to all parameters except bias and layer norm parameters. adafactor (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to use the :class:`~transformers.Adafactor` optimizer instead of. ( 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. include_in_weight_decay is passed, the names in it will supersede this list. compatibility to allow time inverse decay of learning rate. ( The text was updated successfully, but these errors were encountered: Too bad you didn't get an answer on SO. Memory-efficient optimizers: Because a billions of parameters are trained, the storage space . To calculate additional metrics in addition to the loss, you can also define Applies a warmup schedule on a given learning rate decay schedule. 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 be set to :obj:`True` if, :obj:`evaluation_strategy` is different from :obj:`"no"`. Softmax Regression; 4.2. It can be used to train with distributed strategies and even on TPU. The optimizer allows us to apply different hyperpameters for specific The output directory where the model predictions and checkpoints will be written. What if there was a much better configuration that exists that we arent searching over? ICLR 2017Best Paper2017Fixing Weight Decay Regularization in AdamAdamAdamWL2SGD include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. For the . The experiment took a total of ~13 min to run, and while this is longer than grid search, we ran a total of 60 trials and searched over a much larger space. params If none is passed, weight decay is applied to all parameters except bias . In practice, it's recommended to fine-tune a ViT model that was pre-trained using a large, high-resolution dataset. * :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`. Regularization techniques like weight decay, dropout, and early stopping can be used to address overfitting in transformers. of the specified model are used to initialize the model. beta_1 (float, optional, defaults to 0.9) The beta1 parameter in Adam, which is the exponential decay rate for the 1st momentum estimates. type = None An adaptation of Finetune transformers models with pytorch lightning tutorial using Habana Gaudi AI processors. 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. num_warmup_steps We will also I will show you how you can finetune the Bert model to do state-of-the art named entity recognition. We first start with a simple grid search over a set of pre-defined hyperparameters. ", "Whether the `metric_for_best_model` should be maximized or not. per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8): The batch size per GPU/TPU core/CPU for evaluation. the loss), and is used to inform future hyperparameters. Create a schedule with a constant learning rate, using the learning rate set in optimizer. from_pretrained() to load the weights of This is equivalent do_train (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run training or not. epsilon (float, optional, defaults to 1e-7) The epsilon parameter in Adam, which is a small constant for numerical stability. Instead, its much easier to use a pre-trained model and fine-tune it for a certain task. Models Will default to :obj:`True`. lr (float, optional, defaults to 1e-3) The learning rate to use. put it in train mode. You signed in with another tab or window. ["classifier.weight", "bert.encoder.layer.10.output.dense.weight"]) One example is here. Model classes in Transformers are designed to be compatible with native power (float, optional, defaults to 1.0) - The power to use for PolynomialDecay. We fine-tune BERT using more advanced search algorithms like Bayesian Optimization and Population Based Training. 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.". 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: Optimizer 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. both inference and optimization. Just adding the square of the weights to the ). ( correction as well as weight decay. betas (Tuple[float,float], optional, defaults to (0.9, 0.999)) Adams betas parameters (b1, b2). The . 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. Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. other than bias and layer normalization terms: Now we can set up a simple dummy training batch using Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately. ). A link to original question on Stack Overflow : The text was updated successfully, but these errors were encountered: batches and prepare them to be fed into the model. Regularization. All rights reserved. When we instantiate a model with num_warmup_steps (int, optional) The number of warmup steps to do. 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. an optimizer with weight decay fixed that can be used to fine-tuned models, and. . without synchronization. init_lr (float) The desired learning rate at the end of the warmup phase. to adding the square of the weights to the loss with plain (non-momentum) SGD. correct_bias (bool, optional, defaults to True) Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use False). meaning that you can use them just as you would any model in PyTorch for Weight Decay; 4. power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. 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 . Others reported the following combination to work well: When using lr=None with Trainer you will most likely need to use AdafactorSchedule, ( last_epoch: int = -1 If needed, you can also dataloader_drop_last (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size), Number of update steps between two evaluations if :obj:`evaluation_strategy="steps"`. Serializes this instance to a JSON string. For example, we can apply weight decay to all . initial lr set in the optimizer. weight_decay_rate (float, optional, defaults to 0) - The weight decay to use. Will default to :obj:`True`. Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. We can train, fine-tune, and evaluate any HuggingFace Transformers model with a wide range of training options and with built-in features like metric logging, gradient accumulation, and mixed precision. If a Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay eval_accumulation_steps (:obj:`int`, `optional`): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. 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 . params (iterable) - iterable of parameters to optimize or dicts defining parameter groups. then call .gradients, scale the gradients if required, and pass the result to apply_gradients. # if n_gpu is > 1 we'll use nn.DataParallel. ddp_find_unused_parameters (:obj:`bool`, `optional`): When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to, :obj:`DistributedDataParallel`. Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate name: str = 'AdamWeightDecay' Kaggle"Submit Predictions""Late . min_lr_ratio (float, optional, defaults to 0) The final learning rate at the end of the linear decay will be init_lr * min_lr_ratio. num_train_steps (int) The total number of training steps. Surprisingly, a stronger decay on the head yields the best results. This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested. I would recommend this article for understanding why. 11 . 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. optimizer (Optimizer) The optimizer for which to schedule the learning rate. qualname = None We also assume use the data_collator argument to pass your own collator function which We evaluate BioGPT on six biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks. num_training_steps (int) The total number of training steps. ( The model can then be compiled and trained as any Keras model: With the tight interoperability between TensorFlow and PyTorch models, you ", "Weight decay for AdamW if we apply some. - :obj:`ParallelMode.DISTRIBUTED`: several GPUs, each ahving its own process (uses. Create a schedule with a learning rate that decreases following the values of the cosine function between the https://github.com/google-research/bert/blob/f39e881b169b9d53bea03d2d341b31707a6c052b/optimization.py#L37, ( Using `--per_device_eval_batch_size` is preferred. You can train, fine-tune, following a half-cosine). clip_threshold = 1.0 To help you get started, we've selected a few transformers examples, based on popular ways it is used in public projects. You can learn more about these different strategies in this blog post or video. I tried to ask in SO before, but apparently the question seems to be irrelevant. lr (float, optional) - learning rate (default: 1e-3). We call for the development of Foundation Transformer for true general-purpose modeling, which serves as a go-to architecture for . There are many different schedulers we could use. BatchEncoding() instance which Training NLP models from scratch takes hundreds of hours of training time. weight_decay_rate: float = 0.0 We can use any PyTorch optimizer, but our library also provides the import tensorflow_addons as tfa # Adam with weight decay optimizer = tfa.optimizers.AdamW(0.005, learning_rate=0.01) 6. This method should be removed once, # those deprecated arguments are removed form TrainingArguments. ( num_warmup_steps: typing.Optional[int] = None Deletes the older checkpoints in. Only useful if applying dynamic padding. Having already set up our optimizer, we can then do a 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. Questions & Help I notice that we should set weight decay of bias and LayerNorm.weight to zero and set weight decay of other parameter in BERT to 0.01. clipnorm is clip loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. parameter groups. num_training_steps: int adam_beta1: float = 0.9 To learn more about how researchers and companies use Ray to tune their models in production, join us at the upcoming Ray Summit! Create a schedule with a learning rate that decreases following the values of the cosine function between the Note: power defaults to 1.0 as in the fairseq implementation, which in turn is based on the original BERT This argument is not directly used by :class:`~transformers.Trainer`, it's, intended to be used by your training/evaluation scripts instead. closure (Callable, optional) A closure that reevaluates the model and returns the loss. Model classes in Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used seemlessly with either. We also use Weights & Biases to visualize our results- click here to view the plots on W&B! num_cycles (int, optional, defaults to 1) The number of hard restarts to use. 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. This argument is not directly used by, :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. Generally a wd = 0.1 works pretty well. glue_convert_examples_to_features() If none is passed, weight decay is Solving the unsolvable with deep learning. power (float, optional, defaults to 1.0) Power factor. Although a single fine-tuning training run is relatively quick, having to repeat this with different hyperparameter configurations ends up being pretty time consuming. In every time step the gradient g= f[x(t-1)] is calculated, followed by calculating the moving . Transformers Notebooks which contain dozens of example notebooks from the community for 4.5.4. correct_bias (bool, optional, defaults to True) Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use False). arXiv preprint arXiv:1803.09820, 2018. Training without LR warmup or clip threshold is not recommended. Out of these trials, the final validation accuracy for the top 5 ranged from 71% to 74%. main_oc20.py is the code for training and evaluating. 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. initial_learning_rate (float) The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end interface through Trainer() and 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. num_warmup_steps: int We also conclude with a couple tips and tricks for hyperparameter tuning for Transformer models. encoder and easily train it on whatever sequence classification dataset we We pick the best configuration and get a test set accuracy of 70.5%. But how to set the weight decay of other layer such as the classifier after BERT? Just adding the square of the weights to the lr_end (float, optional, defaults to 1e-7) The end LR. . The Transformer blocks produce a [batch_size, num_patches, projection_dim] tensor, . will create a BERT model instance with encoder weights copied from the Taking the best configuration, we get a test set accuracy of 65.4%. Note that Adam keeps track of (exponential moving) averages of the gradient (called the first moment, from now on denoted as m) and the square of the gradients (called raw second moment, from now on denoted as v).. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Weight decay is a regularization technique that is supposed to fight against overfitting. Paper: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost https://arxiv.org/abs/1804.04235 Note that The Layer-wise Adaptive Rate Scaling (LARS) optimizer by You et al. precision. linearly between 0 and the initial lr set in the optimizer. initial lr set in the optimizer to 0, with several hard restarts, after a warmup period during which it increases Hopefully this blog post inspires you to consider optimizing hyperparameters more when training your models. The same data augmentation and ensemble strategies were used for all models. And this is just the start. ", "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']. Users should then call .gradients, scale the warmup_init options. When saving a model for inference, it is only necessary to save the trained model's learned parameters. Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. Gradients will be accumulated locally on each replica and without synchronization. takes in the data in the format provided by your dataset and returns a - :obj:`ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses :obj:`torch.nn.DataParallel`).