# composer.algorithms.selective_backprop.selective_backprop#

Core SelectiveBackprop class and functions.

Functions

 select_using_loss Prunes minibatches as a subroutine of SelectiveBackprop. should_selective_backprop Decides if selective backprop should be run based on time in training.

Classes

 SelectiveBackprop Selectively backpropagate gradients from a subset of each batch.
class composer.algorithms.selective_backprop.selective_backprop.SelectiveBackprop(start=0.5, end=0.9, keep=0.5, scale_factor=1.0, interrupt=2)[source]#

Selectively backpropagate gradients from a subset of each batch.

Based on (Jiang et al, 2019), Selective Backprop (SB) prunes minibatches according to the difficulty of the individual training examples, and only computes weight gradients over the pruned subset, reducing iteration time and speeding up training.

The fraction of the minibatch that is kept for gradient computation is specified by the argument 0 <= keep <= 1.

To speed up SB’s selection forward pass, the argument scale_factor can be used to spatially downsample input image tensors. The full-sized inputs will still be used for the weight gradient computation.

To preserve convergence, SB can be interrupted with vanilla minibatch gradient steps every interrupt steps. When interrupt=0, SB will be used at every step during the SB interval. When interrupt=2, SB will alternate with vanilla minibatch steps.

Args:
start (float, optional): SB interval start as fraction of training duration

Default: 0.5.

end (float, optional): SB interval end as fraction of training duration

Default: 0.9.

keep (float, optional): fraction of minibatch to select and keep for gradient computation

Default: 0.5.

scale_factor (float, optional): scale for downsampling input for selection forward pass

Default: 1..

interrupt (int, optional): interrupt SB with a vanilla minibatch step every

interrupt batches. Default: 2.

Example

from composer.algorithms import SelectiveBackprop
algorithm = SelectiveBackprop(start=0.5, end=0.9, keep=0.5)
trainer = Trainer(
model=model,
train_dataloader=train_dataloader,
eval_dataloader=eval_dataloader,
max_duration="1ep",
algorithms=[algorithm],
optimizers=[optimizer]
)

composer.algorithms.selective_backprop.selective_backprop.select_using_loss(input, target, model, loss_fun, keep=0.5, scale_factor=1)[source]#

Prunes minibatches as a subroutine of SelectiveBackprop. Computes the loss function on the provided training examples and runs minibatches according to the difficulty. The fraction of the minibatch that is kept for gradient computation is specified by the argument 0 <= keep <= 1.

To speed up SB’s selection forward pass, the argument scale_factor can be used to spatially downsample input tensors. The full-sized inputs will still be used for the weight gradient computation.

Parameters
• input (Tensor) – Input tensor to prune

• target (Tensor) – Output tensor to prune

• model (Callable) – Model with which to predict outputs

• loss_fun (Callable) – Loss function of the form loss(outputs, targets, reduction='none'). The function must take the keyword argument reduction='none' to ensure that per-sample losses are returned.

• keep (float, optional) – Fraction of examples in the batch to keep. Default: 0.5.

• scale_factor (float, optional) – Multiplier between 0 and 1 for spatial size. Downsampling requires the input tensor to be at least 3D. Default: 1.

Returns

(torch.Tensor, torch.Tensor) – The pruned batch of inputs and targets

Raises
• ValueError – If scale_factor > 1

• TypeError – If loss_fun > 1 has the wrong signature or is not callable

Note

This function runs an extra forward pass through the model on the batch of data. If you are using a non-default precision, ensure that this forward pass runs in your desired precision. For example:

from composer.algorithms.selective_backprop import select_using_loss
with torch.cuda.amp.autocast(True):
X_new, y_new = select_using_loss(X_sb, y_sb, lin_model, loss_fun, keep=0.5, scale_factor=1)

composer.algorithms.selective_backprop.selective_backprop.should_selective_backprop(current_duration, batch_idx, start=0.5, end=0.9, interrupt=2)[source]#

Decides if selective backprop should be run based on time in training.

Returns true if the current_duration is between start and end. It is recommended that SB be applied during the later stages of a training run, once the model has already “learned” easy examples.

To preserve convergence, SB can be interrupted with vanilla minibatch gradient steps every interrupt steps. When interrupt=0, SB will be used at every step during the SB interval. When interrupt=2, SB will alternate with vanilla minibatch steps.

Parameters
• current_duration (float) – The elapsed training duration. Must be within $$[0.0, 1.0)$$.

• batch_idx (int) – The current batch within the epoch

• start (float, optional) – The duration at which selective backprop should be enabled. Default: 0.5.

• end (float, optional) – The duration at which selective backprop should be disabled Default: 0.9.

• interrupt (int, optional) – The number of batches between vanilla minibatch gradient updates Default: 2.

Returns

bool – If selective backprop should be performed on this batch.