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name: activation_calibration description: Estimate per-channel activation norms for Wanda outlier-aware pruning from calibration tensors.

Activation Calibration

Use this skill when implementing Wanda-style pruning and you need input-channel activation scales before computing weight importance. Do not use it for methods that compare weights only by magnitude.

Inputs

• A JSON file containing batches, a list of 2D numeric arrays shaped samples/tokens by input channels, or one 2D array.
• Optional norm type: l2 is the Wanda default.

Outputs

• activation_norms: one non-negative scale per input channel.
• Metadata with channel count, row count, norm type, and outlier-channel ranking.

Workflow

1. Flatten calibration examples across batch/token rows while preserving the final feature dimension.
2. Compute the selected norm independently for every input channel; Wanda uses L2 because it smoothly captures high-magnitude outlier features.
3. Keep zero-norm channels as zero, not NaN or epsilon-inflated values.
4. Save norms and metadata for the row-pruning skill.

Validation

Run python tests/test_activation_calibration.py or validate the skill tree with --run-tests.

Limitations

This skill estimates scales only; it does not choose prune masks or evaluate perplexity.