composer.algorithms.selective_backprop.selective_backprop#
composer.algorithms.selective_backprop.selective_backprop
Functions
Selectively backpropagate gradients from a subset of each batch (Jiang et al, 2019). |
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Decide if selective backprop should be run based on time in training. |
Classes
Base class for algorithms. |
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The interface needed to make a PyTorch model compatible with |
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Enum to represent events in the training loop. |
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Logger routes metrics to the |
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Selectively backpropagate gradients from a subset of each batch (Jiang et al, 2019). |
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The state of the trainer. |
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composer.algorithms.selective_backprop.selective_backprop.torch.Tensor |
Attributes
Callable
Optional
Tensors
Tuple
annotations
- class composer.algorithms.selective_backprop.selective_backprop.SelectiveBackprop(start=0.5, end=0.9, keep=0.5, scale_factor=0.5, interrupt=2)[source]#
Bases:
composer.core.algorithm.Algorithm
Selectively backpropagate gradients from a subset of each batch (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 SBโs 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. Wheninterrupt=0
, SB will be used at every step during the SB interval. Wheninterrupt=2
, SB will alternate with vanilla minibatch steps.- Parameters
start (float, optional) โ SB interval start as fraction of training duration Default:
0.5
.end (float, optional) โ SB interval end as fraction of training duration Default:
0.9
.keep (float, optional) โ fraction of minibatch to select and keep for gradient computation Default:
0.5
.scale_factor (float, optional) โ scale for downsampling input for selection forward pass Default:
0.5
.interrupt (int, optional) โ interrupt SB with a vanilla minibatch step every
interrupt
batches. Default:2
.
- composer.algorithms.selective_backprop.selective_backprop.select_using_loss(input, target, model, loss_fun, keep=0.5, scale_factor=1)[source]#
Selectively backpropagate gradients from a subset of each batch (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 selected subset. This reduces iteration time and speeds up training. The fraction of the minibatch that is kept for gradient computation is specified by the argument
0 <= keep <= 1
.To speed up SBโs 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) โ Input tensor to prune
target (Tensor) โ Output tensor to prune
model (Callable) โ Model with which to predict outputs
loss_fun (Callable) โ Loss function of the form
loss(outputs, targets, reduction='none')
. The function must take the keyword argumentreduction='none'
to ensure that per-sample losses are returned.keep (float, optional) โ Fraction of examples in the batch to keep. Default:
0.5
.scale_factor (float, optional) โ 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) โ The pruned batch of inputs and targets
- Raises
ValueError โ If
scale_factor > 1
TypeError โ 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:
with torch.cuda.amp.autocast(True): X_new, y_new = selective_backprop(X, y, model, loss_fun, keep, scale_factor)
- composer.algorithms.selective_backprop.selective_backprop.should_selective_backprop(current_duration, batch_idx, start=0.5, end=0.9, interrupt=2)[source]#
Decide if selective backprop should be run based on time in training.
Returns true if the
current_duration
is betweenstart
andend
. Recommend that SB be applied during the later stages of a training run, once the model has already โlearnedโ easy examples.To preserve convergence, SB can be interrupted with vanilla minibatch gradient steps every
interrupt
steps. Wheninterrupt=0
, SB will be used at every step during the SB interval. Wheninterrupt=2
, SB will alternate with vanilla minibatch steps.- Parameters
current_duration (float) โ The elapsed training duration. Must be within \([0.0, 1.0)\).
batch_idx (int) โ The current batch within the epoch
start (float, optional) โ The duration at which selective backprop should be enabled. Default:
0.5
.end (float, optional) โ The duration at which selective backprop should be disabled Default:
0.9
.interrupt (int, optional) โ The number of batches between vanilla minibatch gradient updates Default:
2
.
- Returns
bool โ If selective backprop should be performed on this batch.