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UnstructuredPruner

oxigdal_ml::optimization::pruning

Struct UnstructuredPruner 

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pub struct UnstructuredPruner { /* private fields */ }
Expand description

Unstructured pruner for element-wise weight removal

This is the main orchestrator for unstructured pruning operations, supporting various importance methods, mask creation modes, and advanced features like lottery ticket rewinding.

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impl UnstructuredPruner

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pub fn new(config: PruningConfig, importance_method: ImportanceMethod) -> Self

Creates a new unstructured pruner

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pub fn with_mask_mode(self, mode: MaskCreationMode) -> Self

Sets the mask creation mode

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pub fn enable_lottery_ticket(&mut self, initial_weights: Vec<WeightTensor>)

Enables lottery ticket hypothesis support

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pub fn disable_lottery_ticket(&mut self)

Disables lottery ticket support

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pub fn masks(&self) -> &[PruningMask]

Returns the current masks

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pub fn current_iteration(&self) -> usize

Returns the current iteration

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pub fn lottery_ticket_state(&self) -> Option<&LotteryTicketState>

Returns the lottery ticket state if enabled

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pub fn rewind_to_initial(&self) -> Option<Vec<WeightTensor>>

Rewinds to initial weights with current masks (lottery ticket)

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pub fn compute_importance( &mut self, weights: &WeightTensor, gradient_info: Option<&GradientInfo>, ) -> Vec<f32>

Computes importance scores for a weight tensor

§Arguments
  • weights - The weight tensor to compute importance for
  • gradient_info - Optional gradient information for gradient-based methods
§Returns

Vector of importance scores (same length as weights)

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pub fn create_mask(&self, importance: &[f32], shape: &[usize]) -> PruningMask

Creates a pruning mask based on importance scores and mask mode

§Arguments
  • importance - Importance scores for each weight
  • shape - Shape of the weight tensor
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pub fn prune_tensor( &mut self, weights: &WeightTensor, ) -> Result<(WeightTensor, PruningMask)>

Prunes a single weight tensor

§Arguments
  • weights - The weight tensor to prune
§Returns

Tuple of (pruned weights, mask)

§Errors

Returns an error if pruning fails

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pub fn prune_tensor_with_gradients( &mut self, weights: &WeightTensor, gradient_info: Option<&GradientInfo>, ) -> Result<(WeightTensor, PruningMask)>

Prunes a single weight tensor with gradient information

§Arguments
  • weights - The weight tensor to prune
  • gradient_info - Optional gradient information
§Returns

Tuple of (pruned weights, mask)

§Errors

Returns an error if pruning fails

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pub fn prune_tensors_global( &mut self, tensors: &[WeightTensor], ) -> Result<(Vec<WeightTensor>, Vec<PruningMask>)>

Prunes multiple weight tensors globally

For global pruning, importance scores are computed across all tensors and a single threshold is applied.

§Arguments
  • tensors - Weight tensors to prune
§Returns

Tuple of (pruned tensors, masks)

§Errors

Returns an error if pruning fails

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pub fn prune_tensors_global_with_gradients( &mut self, tensors: &[WeightTensor], gradient_infos: &[GradientInfo], ) -> Result<(Vec<WeightTensor>, Vec<PruningMask>)>

Prunes multiple weight tensors globally with gradient information

§Arguments
  • tensors - Weight tensors to prune
  • gradient_infos - Gradient information for each tensor
§Returns

Tuple of (pruned tensors, masks)

§Errors

Returns an error if pruning fails

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pub fn prune_tensors_layerwise( &mut self, tensors: &[WeightTensor], ) -> Result<(Vec<WeightTensor>, Vec<PruningMask>)>

Prunes multiple weight tensors layer-wise

Each tensor is pruned independently with the same sparsity target.

§Arguments
  • tensors - Weight tensors to prune
§Returns

Tuple of (pruned tensors, masks)

§Errors

Returns an error if pruning fails

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pub fn prune_tensors_layerwise_with_gradients( &mut self, tensors: &[WeightTensor], gradient_infos: &[GradientInfo], ) -> Result<(Vec<WeightTensor>, Vec<PruningMask>)>

Prunes multiple weight tensors layer-wise with gradient information

§Arguments
  • tensors - Weight tensors to prune
  • gradient_infos - Gradient information for each tensor
§Returns

Tuple of (pruned tensors, masks)

§Errors

Returns an error if pruning fails

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pub fn current_sparsity(&self) -> f32

Returns the current overall sparsity

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pub fn iterative_prune<F: FineTuneCallback>( &mut self, initial_weights: Vec<WeightTensor>, callback: &mut F, ) -> Result<(Vec<WeightTensor>, Vec<PruningMask>)>

Performs iterative pruning with fine-tuning

§Arguments
  • initial_weights - Initial weight tensors
  • callback - Fine-tuning callback
§Returns

Final pruned weights and masks after all iterations

§Errors

Returns an error if pruning or fine-tuning fails

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pub fn compute_stats(&self, original_tensors: &[WeightTensor]) -> PruningStats

Computes pruning statistics

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