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name: pruning_recovery_evaluation description: Evaluate Wanda pruning masks for sparsity, no-update invariants, and linear-output reconstruction error.

Pruning Recovery Evaluation

Use this skill to validate that a pruning experiment actually applied a mask, left unmasked weights unchanged, and produced numeric output-error evidence. It is suitable for reduced Wanda recovery when full LLM perplexity evaluation is not feasible.

Inputs

• Original and pruned weight matrices.
• Evaluation activation matrix shaped examples by input channels.
• Boolean prune mask.
• Optional baseline error for comparison.

Outputs

• Exact sparsity ratio.
• Relative output error ||XW^T - XW_pruned^T||_2 / ||XW^T||_2.
• unmasked_weights_unchanged and masked_weights_zero invariant booleans.

Workflow

1. Check matrix dimensions and mask shape.
2. Compute dense and pruned linear outputs.
3. Compute relative reconstruction error with a safe zero-denominator rule.
4. Verify Wanda's no-update claim: unmasked values must exactly match the original and masked values must be zero.

Validation

Tests cover exact sparsity, unchanged unmasked weights, and a known linear-output error.

Limitations

This skill does not claim full paper reproduction; it supplies mechanism evidence for proxy recovery.