PruningContainer#

classtorch.nn.utils.prune.PruningContainer(*args)[source]#

Container holding a sequence of pruning methods for iterative pruning.

Keeps track of the order in which pruning methods are applied and handlescombining successive pruning calls.

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

add_pruning_method(method)[source]#

Add a child pruningmethod to the container.

Parameters

method (subclass ofBasePruningMethod) – child pruning methodto be added to the container.

classmethodapply(module,name,*args,importance_scores=None,**kwargs)[source]#

Add pruning on the fly and reparametrization of a tensor.

Adds the forward pre-hook that enables pruning on the fly andthe reparametrization of a tensor in terms of the original tensorand the pruning mask.

Parameters

• module (nn.Module) – module containing the tensor to prune

• name (str) – parameter name withinmodule on which pruningwill act.

• args – arguments passed on to a subclass ofBasePruningMethod

• importance_scores (torch.Tensor) – tensor of importance scores (ofsame shape as module parameter) used to compute mask for pruning.The values in this tensor indicate the importance of thecorresponding elements in the parameter being pruned.If unspecified or None, the parameter will be used in its place.

• kwargs – keyword arguments passed on to a subclass of aBasePruningMethod

apply_mask(module)[source]#

Simply handles the multiplication between the parameter being pruned and the generated mask.

Fetches the mask and the original tensor from the moduleand returns the pruned version of the tensor.

Parameters

module (nn.Module) – module containing the tensor to prune

Returns

pruned version of the input tensor

Return type

pruned_tensor (torch.Tensor)

compute_mask(t,default_mask)[source]#

Apply the latestmethod by computing the new partial masks and returning its combination with thedefault_mask.

The new partial mask should be computed on the entries or channelsthat were not zeroed out by thedefault_mask.Which portions of the tensort the new mask will be calculated fromdepends on thePRUNING_TYPE (handled by the type handler):

• for ‘unstructured’, the mask will be computed from the raveledlist of nonmasked entries;

• for ‘structured’, the mask will be computed from the nonmaskedchannels in the tensor;

• for ‘global’, the mask will be computed across all entries.

Parameters

• t (torch.Tensor) – tensor representing the parameter to prune(of same dimensions asdefault_mask).

• default_mask (torch.Tensor) – mask from previous pruning iteration.

Returns

new mask that combines the effectsof thedefault_mask and the new mask from the currentpruningmethod (of same dimensions asdefault_mask andt).

Return type

mask (torch.Tensor)

prune(t,default_mask=None,importance_scores=None)[source]#

Compute and returns a pruned version of input tensort.

According to the pruning rule specified incompute_mask().

Parameters

• t (torch.Tensor) – tensor to prune (of same dimensions asdefault_mask).

• importance_scores (torch.Tensor) – tensor of importance scores (ofsame shape ast) used to compute mask for pruningt.The values in this tensor indicate the importance of thecorresponding elements in thet that is being pruned.If unspecified or None, the tensort will be used in its place.

• default_mask (torch.Tensor,optional) – mask from previous pruningiteration, if any. To be considered when determining whatportion of the tensor that pruning should act on. If None,default to a mask of ones.

Returns

pruned version of tensort.

remove(module)[source]#

Remove the pruning reparameterization from a module.

The pruned parameter namedname remains permanently pruned,and the parameter namedname+'_orig' is removed from the parameter list.Similarly, the buffer namedname+'_mask' is removed from the buffers.

Note

Pruning itself is NOT undone or reversed!