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name: dependency_group_pruning description: Aggregate LoRAPrune element importances into structured groups and create deterministic channel or head pruning masks.

Dependency Group Pruning

Use this skill after a criterion has produced elementwise importance and before applying LoRAPrune masks. It is for structured row, column, channel, or head pruning, not unstructured element sparsity.

Inputs

• An importance matrix or tensor slice.
• A grouping convention, such as row groups for output channels or column groups for input channels.
• A prune count or equivalent target sparsity converted to a group count.

Outputs

• Group importance scores computed by summing member weights.
• A binary group mask: 1 keeps a group and 0 prunes it.
• A broadcastable element mask for the represented weight matrix.

Workflow

1. Validate the importance shape and group specification.
2. Accumulate group scores following LoRAPrune Eq. 4.
3. Sort scores ascending with stable index tie-breaking.
4. Mark the lowest-scoring groups as pruned according to the requested sparsity.
5. Broadcast the group mask to the element shape for the masked forward pass.

Validation

Run the skill tree validator with tests. The deterministic tests cover row grouping, column grouping, broadcast masks, and stable tie handling.

python /share/project/yuyang/workspace/Paper2Skills/Distiller/skills/module-to-skill/scripts/validate_skill_tree.py <this_skill_dir> --run-tests

Limitations

The supplied script implements row and column groups for reduced recovery. Full transformer pruning should map attention heads and MLP channels into equivalent groups while preserving this contract: group scores are accumulated before thresholding, and masks prune whole structures.