# BasePruningMethod

Source: https://www.tensorplay.cn/docs/generated/tensorplay.ao.pruning.BasePruningMethod.html

```python
class tensorplay.ao.pruning.BasePruningMethod
```

Abstract base class for pruning techniques.

Subclasses must override [compute_mask()](#tensorplay.ao.pruning.BasePruningMethod.compute_mask) and declare a
PRUNING_TYPE class attribute (one of 'unstructured',
'structured' or 'global'). The classmethod [apply()](#tensorplay.ao.pruning.BasePruningMethod.apply) installs
the reparameterization on a module and registers an instance of the
subclass as a forward pre-hook.

```python
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](/docs/generated/tensorplay.ao.pruning.PruningContainer.html#tensorplay.ao.pruning.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.

```python
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.

```python
abstractmethod compute_mask(t: Tensor, default_mask: Tensor) → Tensor
```

Compute the pruning mask for the input tensor t.

Starting from default_mask (a mask of ones when t has never
been pruned), derive the new mask according to the recipe of the
concrete method. Entries already zeroed by default_mask must stay
zero.

Parameters:

- t – tensor whose entries or channels are ranked for pruning, typically the importance scores of the parameter.

- default_mask – mask accumulated from previous pruning iterations; must be respected by the new mask. Same shape as t.

Returns:

The mask to apply to t, with the same shape as t.

```python
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()](#tensorplay.ao.pruning.BasePruningMethod.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.

```python
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!