CustomFromMask¶
- class torch.nn.utils.prune.CustomFromMask(mask)[source]¶
- classmethod apply(module, name, mask)[source]¶
Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and the reparametrization of a tensor in terms of the original tensor and the pruning mask.
- apply_mask(module)¶
Simply handles the multiplication between the parameter being pruned and the generated mask.
Fetches the mask and the original tensor from the module and 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)
- prune(t, default_mask=None, importance_scores=None)¶
Compute and returns a pruned version of input tensor
t
.According to the pruning rule specified in
compute_mask()
.- Parameters
t (torch.Tensor) – tensor to prune (of same dimensions as
default_mask
).importance_scores (torch.Tensor) – tensor of importance scores (of same shape as
t
) used to compute mask for pruningt
. The values in this tensor indicate the importance of the corresponding 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 pruning iteration, if any. To be considered when determining what portion of the tensor that pruning should act on. If None, default to a mask of ones.
- Returns
pruned version of tensor
t
.
- remove(module)¶
Remove the pruning reparameterization from a module.
The pruned parameter named
name
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!