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name: importance_rank_allocator description: Compute AdaLoRA sensitivity-uncertainty triplet scores and globally mask singular values to a target rank budget.

Importance-Aware Rank Allocator

Use this skill when a recovery or implementation needs AdaLoRA's adaptive rank allocation rather than fixed LoRA ranks.

Inputs

• A list of adapted matrices with A, E, B parameters and matching gradients.
• Previous EMA state for sensitivity and uncertainty, or empty state for the first step.
• Hyperparameters beta1, beta2, and target global rank.

Outputs

• Updated EMA state.
• Per-triplet importance scores.
• Mask threshold and masked singular values.
• Rank pattern for every adapted matrix.

Workflow

1. Compute instantaneous sensitivity abs(parameter * gradient) for every AdaLoRA parameter.
2. Update smoothed sensitivity and uncertainty EMAs.
3. Score each entry with smoothed_sensitivity * uncertainty.
4. Combine each triplet as singular-value score plus mean scores from the corresponding A row and B column.
5. Retain the top target-rank triplets across all matrices and set other singular values to zero.
6. Log rank pattern and threshold so downstream recovery can verify budget compliance.

Validation

Run python scripts/rank_allocator.py --self-test or the provided tests.

Limitations

This script is deterministic and standard-library only. It is intended as executable mechanism evidence and a reference contract for tensor implementations.