Weight decay is a form of regularization-after calculating the gradients, we multiply them by, e.g., 0.99. Image classification with Vision Transformer . module = None padding applied and be more efficient). In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. weight_decay_rate (float, optional, defaults to 0) The weight decay to use. "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)]. 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. transformers.create_optimizer (init_lr: float, num_train_steps: int, . per_device_eval_batch_size (:obj:`int`, `optional`, defaults to 8): The batch size per GPU/TPU core/CPU for evaluation. a detailed colab notebook which uses Trainer to train a masked language model from scratch on Esperanto. bert-base-uncased model and a randomly initialized sequence 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. import tensorflow_addons as tfa # Adam with weight decay optimizer = tfa.optimizers.AdamW(0.005, learning_rate=0.01) 6. 0 means that the data will be loaded in the main process. decay_rate = -0.8 num_training_steps (int) The total number of training steps. WEIGHT DECAY - WORDPIECE - Edit Datasets . privacy statement. names = None . 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 prediction_loss_only (:obj:`bool`, `optional`, defaults to `False`): When performing evaluation and generating predictions, only returns the loss. Create a schedule with a learning rate that decreases following the values of the cosine function between the then call .gradients, scale the gradients if required, and pass the result to apply_gradients. All 3 models are pretrained with Adam optimizer with batch size of 4096 and weight decay of 0.1. evolve in the future. Note that 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. However, under the same name "Transformers", the above areas use different implementations for better performance, e.g., Post-LayerNorm for BERT, and Pre-LayerNorm for GPT and vision Transformers. Anyways, here it is: In the Docs we can clearly see that the AdamW optimizer sets. :obj:`"auto"` will use AMP or APEX depending on the PyTorch version detected, while the. Create a schedule with a learning rate that decreases linearly from the initial lr set in the optimizer to 0, loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact ICLR 2017Best Paper2017Fixing Weight Decay Regularization in AdamAdamAdamWL2SGD If youre inclined to try this out on a multi-node cluster, feel free to give the Ray Cluster Launcher a shot to easily start up a cluster on AWS. layers. Powered by Discourse, best viewed with JavaScript enabled. 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. Will default to :obj:`True`. adam_beta1 (:obj:`float`, `optional`, defaults to 0.9): The beta1 hyperparameter for the :class:`~transformers.AdamW` optimizer. tokenizers are framework-agnostic, so there is no need to prepend TF to When used with a distribution strategy, the accumulator should be called in a can then use our built-in The Transformer blocks produce a [batch_size, num_patches, projection_dim] tensor, . :obj:`"comet_ml"`, :obj:`"mlflow"`, :obj:`"tensorboard"` and :obj:`"wandb"`. On the Convergence of Adam and Beyond. We highly recommend using Trainer(), discussed below, 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).. warmup_steps (int) The number of steps for the warmup part of training. The . where $\lambda$ is a value determining the strength of the penalty (encouraging smaller weights). ", smdistributed.dataparallel.torch.distributed. Follow. applied to all parameters by default (unless they are in exclude_from_weight_decay). The value for the params key should be a list of named parameters (e.g. meaning that you can use them just as you would any model in PyTorch for The figure below shows the learning rate and weight decay during the training process, (Left) lr, weight_decay). # if n_gpu is > 1 we'll use nn.DataParallel. init_lr: float optimizer: Optimizer For distributed training, it will always be 1. ). Trainer() uses a built-in default function to collate TFTrainer(). local_rank (:obj:`int`, `optional`, defaults to -1): Rank of the process during distributed training. power (float, optional, defaults to 1.0) Power factor. Just adding the square of the weights to the This way we can start more runs in parallel and thus test a larger number of hyperparameter configurations. 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. Kaggle. - :obj:`False` if :obj:`metric_for_best_model` is not set, or set to :obj:`"loss"` or :obj:`"eval_loss"`. We AdamW() optimizer which implements gradient bias then call .gradients, scale the gradients if required, and pass the result to apply_gradients. amsgrad: bool = False How to train a language model, Decoupled Weight Decay Regularization. Model classes in Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used seemlessly with either. using the standard training tools available in either framework. Does the default weight_decay of 0.0 in transformers.AdamW make sense. Using `--per_device_eval_batch_size` is preferred. 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. linearly between 0 and the initial lr set in the optimizer. The same data augmentation and ensemble strategies were used for all models. per_device_train_batch_size (:obj:`int`, `optional`, defaults to 8): The batch size per GPU/TPU core/CPU for training. PCT is based on Transformer, which achieves huge success in natural language processing and displays great potential in image processing. . adam_epsilon (:obj:`float`, `optional`, defaults to 1e-8): The epsilon hyperparameter for the :class:`~transformers.AdamW` optimizer. Pretty much everyone (1, 2, 3, 4), including the original BERT authors, either end up disregarding hyperparameter tuning or just doing a simple grid search over just a few different hyperparameters with a very limited search space. 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 . ddp_find_unused_parameters (:obj:`bool`, `optional`): When using distributed training, the value of the flag :obj:`find_unused_parameters` passed to, :obj:`DistributedDataParallel`. Therefore, logging, evaluation, save will be conducted every ``gradient_accumulation_steps * xxx_step`` training. Instead, its much easier to use a pre-trained model and fine-tune it for a certain task. configuration and pre-trained weights of the specified model are used to initialize the model. Weight decay 1 2 0.01: 32: 0.5: 0.0005 . Create a schedule with a constant learning rate, using the learning rate set in optimizer. This is not much of a major issue but it may be a factor in this problem. Hence the default value of weight decay in fastai is actually 0.01. init_lr (float) The desired learning rate at the end of the warmup phase. . Use `Deepspeed `__. 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). num_warmup_steps (int) The number of steps for the warmup phase. Paper: Adafactor: Adaptive Learning Rates with Sublinear Memory Cost https://arxiv.org/abs/1804.04235 Note that 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. TF2, and focus specifically on the nuances and tools for training models in Learn more about where AI is creating real impact today. pip install transformers=2.6.0. To ensure reproducibility across runs, use the, :func:`~transformers.Trainer.model_init` function to instantiate the model if it has some randomly. These terms are often used in transformer architectures, which are out of the scope of this article . which conveniently handles the moving parts of training Transformers models to tokenize MRPC and convert it to a TensorFlow Dataset object. models should have a greater metric or not. In practice, it's recommended to fine-tune a ViT model that was pre-trained using a large, high-resolution dataset. no_cuda (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to not use CUDA even when it is available or not. weight_decay_rate (float, optional, defaults to 0) The weight decay to use. Main differences of this compared to a simple autoregressive transformer are the parameter initialization, weight decay, and learning rate schedule. last_epoch (int, optional, defaults to -1) The index of the last epoch when resuming training. power (float, optional, defaults to 1.0) The power to use for PolynomialDecay. beta_2 (float, optional, defaults to 0.999) The beta2 parameter in Adam, which is the exponential decay rate for the 2nd momentum estimates. ", "The list of keys in your dictionary of inputs that correspond to the labels. I use weight decay and not use weight and surprisingly find that they are the same, why? include_in_weight_decay: typing.Optional[typing.List[str]] = None ( increases linearly between 0 and the initial lr set in the optimizer. increases linearly between 0 and the initial lr set in the optimizer. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. See, the `example scripts `__ for more. num_training_steps: typing.Optional[int] = None Models compatibility to allow time inverse decay of learning rate. We also combine this with an early stopping algorithm, Asynchronous Hyperband, where we stop bad performing trials early to avoid wasting resources on them. include_in_weight_decay (List[str], optional) List of the parameter names (or re patterns) to apply weight decay to. ", "If > 0: set total number of training steps to perform. For further details regarding the algorithm we refer to Decoupled Weight Decay Regularization.. Parameters:. A Sparse Transformer is a Transformer based architecture which utilises sparse factorizations of the attention matrix to reduce time/memory to O ( n n). To use a manual (external) learning rate schedule you should set scale_parameter=False 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! The optimizer allows us to apply different hyperpameters for specific T. Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate Notably used for wandb logging. weight_decay (:obj:`float`, `optional`, defaults to 0): The weight decay to apply (if . weight_decay: The weight decay to apply (if not zero). We can call model.train() to 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. 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. optional), the function will raise an error if its unset and the scheduler type requires it. 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. beta1 = None However, we will show that in rather standard feedforward networks, they need residual connections to be effective (in a sense I will clarify below). If needed, you can also 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. BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2) ", "When using distributed training, the value of the flag `find_unused_parameters` passed to ", "Whether or not to pin memory for DataLoader. loss function is not the correct way of using L2 regularization/weight decay with Adam, since that will interact Instead we want ot decay the weights in a manner that doesnt interact with the m/v parameters. Adamw Adam + weight decate , Adam + L2,,L2loss,,,Adamw,loss. exclude_from_weight_decay (List[str], optional) List of the parameter names (or re patterns) to exclude from applying weight decay to. Have a question about this project? Therefore, shouldnt make more sense to have the default weight decay for AdamW > 0? Now you have access to many transformer-based models including the pre-trained Bert models in pytorch. logging_steps (:obj:`int`, `optional`, defaults to 500): save_steps (:obj:`int`, `optional`, defaults to 500): Number of updates steps before two checkpoint saves. ( name: str = None All rights reserved. ). This is equivalent See details. This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested. Overrides. Just as with PyTorch, num_training_steps: int weight_decay: float = 0.0 Here we use 1e-4 as a default for weight_decay. This is not required by all schedulers (hence the argument being The top few runs get a validation accuracy ranging from 72% to 77%. # You may obtain a copy of the License at, # http://www.apache.org/licenses/LICENSE-2.0, # Unless required by applicable law or agreed to in writing, software. Gradients will be accumulated locally on each replica and correct_bias (bool, optional, defaults to True) Whether ot not to correct bias in Adam (for instance, in Bert TF repository they use False). Anyways, here it is: In the Docs we can clearly see that the AdamW optimizer sets the default weight decay to 0.0. Scaling up the data from 300M to 3B images improves the performance of both small and large models. Implements Adam algorithm with weight decay fix as introduced in Decoupled Weight Decay Ilya Loshchilov, Frank Hutter. dataloader_num_workers (:obj:`int`, `optional`, defaults to 0): Number of subprocesses to use for data loading (PyTorch only). compatibility to allow time inverse decay of learning rate. It was also implemented in transformers before it was available in PyTorch itself. library also includes a number of task-specific final layers or heads whose (14), we set them to 1, 1 and 0.1 in the following comparison experiments. The output directory where the model predictions and checkpoints will be written. initial_learning_rate: float clipnorm is clip adam_beta2 (:obj:`float`, `optional`, defaults to 0.999): The beta2 hyperparameter for the :class:`~transformers.AdamW` optimizer. weight_decay (float, optional, defaults to 0) Decoupled weight decay to apply. AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: label_smoothing_factor (:obj:`float`, `optional`, defaults to 0.0): The label smoothing factor 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. last_epoch: int = -1 Image Source: Deep Learning, Goodfellow et al. Will default to: - :obj:`True` if :obj:`metric_for_best_model` is set to a value that isn't :obj:`"loss"` or. eval_accumulation_steps (:obj:`int`, `optional`): Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. Removing weight decay for certain parameters specified by no_weight_decay. Users should Check here for the full code examples. precision. same value as :obj:`logging_steps` if not set. following a half-cosine). do_predict (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to run predictions on the test set or not. Args: optimizer ( [`~torch.optim.Optimizer`]): The optimizer for which to schedule the learning rate. Creates an optimizer with a learning rate schedule using a warmup phase followed by a linear decay. closure: typing.Callable = None ", "If >=0, uses the corresponding part of the output as the past state for next step. 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. 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.". epsilon: float = 1e-07 Weight Decay. # Copyright 2020 The HuggingFace Team. Then all we have to do is call scheduler.step() after optimizer.step(). 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. initial lr set in the optimizer. arXiv preprint arXiv:1803.09820, 2018. power: float = 1.0 [May 2022] Join us to improve ongoing translations in Portuguese, Turkish . The simple grid search did alright, but it had a very limited search space and only considered 3 hyperparameters. adam_clipnorm: typing.Optional[float] = None Note: If training BERT layers too, try Adam optimizer with weight decay which can help reduce overfitting and improve generalization [1]. if the logging level is set to warn or lower (default), :obj:`False` otherwise. beta_2: float = 0.999 logging_first_step (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to log and evaluate the first :obj:`global_step` or not. parameter groups. The Ray libraries offer a host of features and integrations. ), ( takes in the data in the format provided by your dataset and returns a Gradients will be accumulated locally on each replica and without synchronization. eps (Tuple[float, float], optional, defaults to (1e-30, 1e-3)) Regularization constants for square gradient and parameter scale respectively, clip_threshold (float, optional, defaults 1.0) Threshold of root mean square of final gradient update, decay_rate (float, optional, defaults to -0.8) Coefficient used to compute running averages of square, beta1 (float, optional) Coefficient used for computing running averages of gradient, weight_decay (float, optional, defaults to 0) Weight decay (L2 penalty), scale_parameter (bool, optional, defaults to True) If True, learning rate is scaled by root mean square, relative_step (bool, optional, defaults to True) If True, time-dependent learning rate is computed instead of external learning rate, warmup_init (bool, optional, defaults to False) Time-dependent learning rate computation depends on whether warm-up initialization is being used. This is why it is called weight decay. A lightweight colab demo If a TFTrainer() expects the passed datasets to be dataset Possible values are: * :obj:`"no"`: No evaluation is done during training. lr, weight_decay). Taking the best configuration, we get a test set accuracy of 65.4%. evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.EvaluationStrategy`, `optional`, defaults to :obj:`"no"`): The evaluation strategy to adopt during training. However, the folks at fastai have been a little conservative in this respect. Create a schedule with a constant learning rate, using the learning rate set in optimizer. # 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`.". `__ for more details. to your account. ", "See details at https://nvidia.github.io/apex/amp.html", "The backend to be used for mixed precision. decay_schedule_fn (Callable) The schedule function to apply after the warmup for the rest of training. with features like mixed precision and easy tensorboard logging. num_cycles (int, optional, defaults to 1) The number of hard restarts to use. weight_decay (float, optional) - weight decay (L2 penalty) (default: 0) amsgrad (bool, optional) - whether to use the AMSGrad variant of this algorithm from the paper On the Convergence of Adam and Beyond (default: False) foreach (bool, optional) - whether foreach implementation of optimizer is used (default: None) # distributed under the License is distributed on an "AS IS" BASIS. Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets (2021) A Power, Y Burda, H Edwards, I several schedules in the form of schedule objects that inherit from _LRSchedule: a gradient accumulation class to accumulate the gradients of multiple batches. You signed in with another tab or window. Allowed to be {clipnorm, clipvalue, lr, decay}. Users should correction as well as weight decay. The Base Classification Model; . Gradient accumulation utility. The key takeaway here is that Population Based Training is the most effective approach to tune the hyperparameters of the Transformer model. 4.1. In the analytical experiment section, we will . Supported platforms are :obj:`"azure_ml"`. lr (float, optional) - learning rate (default: 1e-3). ", "Use this to continue training if output_dir points to a checkpoint directory. And this gets amplified even further if we want to tune over even more hyperparameters! torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. Adam enables L2 weight decay and clip_by_global_norm on gradients. torch.optim.lr_scheduler.LambdaLR with the appropriate schedule. 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. All of the experiments below are run on a single AWS p3.16xlarge instance which has 8 NVIDIA V100 GPUs. 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"`. Using `--per_device_train_batch_size` is preferred.". increases linearly between 0 and the initial lr set in the optimizer. 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. The model can then be compiled and trained as any Keras model: With the tight interoperability between TensorFlow and PyTorch models, you start = 1 optimizer to end lr defined by lr_end, after a warmup period during which it increases linearly from 0 to the optimizer: Optimizer If none is passed, weight decay is batch ready to be fed into the model. 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. This is an experimental feature and its API may. The actual batch size for training (may differ from :obj:`per_gpu_train_batch_size` in distributed training). To reproduce these results for yourself, you can check out our Colab notebook leveraging Hugging Face transformers and Ray Tune! Out of these trials, the final validation accuracy for the top 5 ranged from 71% to 74%. Gradients will be accumulated locally on each replica and without synchronization. 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). Although a single fine-tuning training run is relatively quick, having to repeat this with different hyperparameter configurations ends up being pretty time consuming. label_names (:obj:`List[str]`, `optional`): The list of keys in your dictionary of inputs that correspond to the labels. exclude_from_weight_decay: typing.Optional[typing.List[str]] = None To help you get started, we've selected a few transformers examples, based on popular ways it is used in public projects. "The output directory where the model predictions and checkpoints will be written. train_sampler = RandomSampler (train_dataset) if args.local_rank == - 1 else DistributedSampler . epsilon (float, optional, defaults to 1e-7) The epsilon paramenter in Adam, which is a small constant for numerical stability. Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the ", "Deprecated, the use of `--per_device_eval_batch_size` is preferred. num_warmup_steps (int) The number of warmup steps. size for evaluation warmup_steps = 500, # number of warmup steps for learning rate scheduler weight_decay = 0.01, # strength of weight decay logging_dir = './logs', # directory for . decay_schedule_fn: typing.Callable Must be the name of a metric returned by the evaluation with or without the prefix :obj:`"eval_"`. Here, we fit a Gaussian Process model that tries to predict the performance of the parameters (i.e. Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate fp16_opt_level (:obj:`str`, `optional`, defaults to 'O1'): For :obj:`fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. type = None **kwargs Google Scholar [29] Liu X., Lu H., Nayak A., A spam transformer model for SMS spam detection, IEEE Access 9 (2021) 80253 - 80263. # Make sure `self._n_gpu` is properly setup. Pre-trained Transformer models such as BERT have shown great success in a wide range of applications, but at the cost of substantial increases in model complexity. Google Scholar params (Iterable[torch.nn.parameter.Parameter]) Iterable of parameters to optimize or dictionaries defining parameter groups. batches and prepare them to be fed into the model. weight_decay_rate: float = 0.0 applied to all parameters by default (unless they are in exclude_from_weight_decay). launching tensorboard in your specified logging_dir directory. 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. Decoupled Weight Decay Regularization. We use the search space recommended by the BERT authors: We run a total of 18 trials, or full training runs, one for each combination of hyperparameters. num_train_step (int) The total number of training steps. num_training_steps (int) The totale number of training steps. initial_learning_rate (float) The initial learning rate for the schedule after the warmup (so this will be the learning rate at the end When we call a classification model with the labels argument, the first interface through Trainer() and huggingface/transformers/blob/a75c64d80c76c3dc71f735d9197a4a601847e0cd/examples/contrib/run_openai_gpt.py#L230-L237. If none is passed, weight decay is applied to all parameters . {"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). ["classifier.weight", "bert.encoder.layer.10.output.dense.weight"]) weight decay, etc. We evaluate BioGPT on six biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks. Model classes in Transformers that dont begin with TF are ", "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']. lr = None 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.
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