---

name: wanda_row_pruning description: Compute row-wise Wanda pruning scores, masks, and pruned weights without any weight update.

Wanda Row Pruning

Use this skill to prune a linear layer with the Wanda rule: score each weight by abs(W_ij) * activation_norm_j, compare scores only within the same output row, and zero the lowest-scoring weights. Do not use it when the experiment requires global layer-wise magnitude thresholds.

Inputs

• Weight matrix shaped [output_channels][input_channels].
• Activation norm vector with one entry per input channel.
• Unstructured sparsity ratio in [0, 1).

Outputs

• Boolean prune mask where true means the weight is removed.
• Pruned weight matrix with masked entries set to zero.
• Score matrix and exact sparsity metadata.

Workflow

1. Validate the activation vector length against the matrix width.
2. Compute scores by multiplying every row of abs(W) by the activation norms.
3. For each output row, deterministically select the lowest floor(width * sparsity_ratio) scores.
4. Set only masked weights to zero; never alter unmasked values.

Validation

The tests include an outlier-channel case where Wanda keeps a low-magnitude but high-activation weight that magnitude pruning removes.

Limitations

This skill handles one dense matrix. Model traversal and calibration capture are separate modules.