🧩 Stochastic Weight Averaging#
[How to Use] - [Suggested Hyperparameters] - [Attribution] - [API Reference]
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Stochastic Weight Averaging (SWA) maintains a running average of the weights towards the end of training. This leads to better generalization than conventional training.
Visualization of SWA from an extensive PyTorch blogpost about SWA.
How to Use#
# Instantiate the algorithm and pass it into the Trainer # The trainer will automatically run it at the appropriate points in the training loop from composer.algorithms import SWA from composer.trainer import Trainer swa_algorithm = SWA( swa_start="1ep", swa_end="2ep", update_interval='5ba', ) trainer = Trainer( model=model, train_dataloader=train_dataloader, eval_dataloader=eval_dataloader, max_duration="3ep", algorithms=[swa_algorithm] ) trainer.fit()
SWA stores an extra copy of the model’s (averaged) weights, and thus doubles the memory required for the model.
EMA also uses a small amount of compute to calculate the average. This does not have an appreciable impact on training speed unless the averaged model is being updated very frequently (e.g. ≤ every ten batches).
swa_start: proportion of training completed before SWA is applied. The default value is
'0.7dur'(0.7 of the duration of training).
swa_end: proportion of training completed before SWA ends. It’s important to have at least one epoch of training after the baseline model is replaced by the SWA model so that the SWA model can have its buffers (most importantly its batch norm statistics) updated. If
swa_endoccurs during the final epoch of training (e.g.
swa_end = 0.9durand training is only 5 epochs, or
swa_end = 1.0dur), the SWA model will not have its buffers updated, which can negatively impact accuracy. The default value is
update_interval- The period at which updates to the moving average are computed. A longer update interval means that updates are computed less frequently.
Our implementation of SWA also has hyperparameters to control an SWA-specific learning rate schedule, but we found that these did not have a substantial impact on training.
Averaging Weights Leads to Wider Optima and Better Generalization by Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Presented at the 2018 Conference on Uncertainty in Artificial Intelligence.