PruningContainer

class tensorplay.ao.pruning.PruningContainer(*args: BasePruningMethod)[source]

Sequence of pruning methods applied iteratively to the same tensor.

Tracks the order in which methods were added and combines successive pruning calls: each new method only ranks the entries or channels that the previous masks left unpruned.

Accepts as argument an instance of a BasePruningMethod or an iterable of them.

add_pruning_method(method: BasePruningMethod | None) → None[source]

Add a child pruning method to the container.

Parameters:

method – child pruning method to be added to the container.

Raises:

• TypeError – if method is neither None nor a BasePruningMethod instance.

• ValueError – if method acts on a different tensor name than the methods already held by the container.

classmethod apply(module: Module, name: str, *args: Any, importance_scores: Tensor | None = None, **kwargs: Any) → BasePruningMethod

Install the pruning reparameterization for module[name].

Moves the original parameter to name + '_orig', registers the computed mask as the buffer name + '_mask', stores the masked values under name and adds a forward pre-hook that re-applies the mask on every forward pass. If the tensor is already pruned by another method, the new method is composed with the existing one through a PruningContainer.

Parameters:

• module – module containing the tensor to prune.

• name – parameter name within module on which pruning acts.

• args – positional arguments forwarded to the subclass constructor.

• importance_scores – tensor of importance scores with the same shape as module[name]; each entry ranks the corresponding element of the parameter. When unspecified, the parameter itself is used as its own importance scores.

• kwargs – keyword arguments forwarded to the subclass constructor.

Returns:

The pruning method (or container of methods) now attached to the module.

apply_mask(module: Module) → Tensor

Return the pruned version of the tensor held by module.

Fetches the mask and the original values from the module and returns their elementwise product.

Parameters:

module – module holding the pruned tensor.

Returns:

The product of the mask and the original values.

compute_mask(t: Tensor, default_mask: Tensor) → Tensor[source]

Apply the latest method and merge its mask into default_mask.

The new partial mask is computed on the entries or channels that default_mask has not zeroed out. Which portion of t the new mask is derived from depends on the PRUNING_TYPE of the last method:

• 'unstructured': the mask is computed from the flattened list of entries not yet masked;

• 'structured': the mask is computed from the channels that still hold at least one unmasked entry;

• 'global': the mask is computed across all entries.

Parameters:

• t – tensor representing the parameter to prune (same shape as default_mask).

• default_mask – mask accumulated from previous pruning iterations.

Returns:

The mask combining the effects of default_mask and of the latest method, with the same shape as default_mask and t.

prune(t: Tensor, default_mask: Tensor | None = None, importance_scores: Tensor | None = None) → Tensor

Return a pruned copy of the input tensor t.

Applies the rule implemented by compute_mask() without any module-side reparameterization.

Parameters:

• t – tensor to prune (same shape as default_mask).

• importance_scores – tensor of importance scores with the same shape as t; each entry ranks the corresponding element of t. When unspecified, t itself is used.

• default_mask – mask from a previous pruning iteration, if any. Pruning must respect the entries it already zeroes. When unspecified, a mask of ones is used.

Returns:

The pruned version of t.

remove(module: Module) → None

Make the current pruning of module permanent.

The pruned values remain pruned: the product of mask and original values is written back into the parameter name, and the auxiliary parameter name + '_orig' and buffer name + '_mask' are dropped.

Note

Pruning itself is NOT undone or reversed!