# K-FAC Memorization Suppression

Reproduction of ["From Memorization to Reasoning in the Spectrum of Loss Curvature"](https://github.com/goodfire-ai/memorization_kfac) with extended experiments on modified importance formulas.

## Overview

This project implements K-FAC (Kronecker-Factored Approximate Curvature) based weight editing to suppress verbatim memorization in language models while preserving general capabilities.

**Key insight:** The Fisher Information Matrix, approximated by K-FAC, reveals directions in weight space associated with memorization (low curvature) vs. generalization (high curvature). By removing low-curvature components, we can suppress memorization.

## Project Goal

1. **Reproduce** the paper's K-FAC method on OLMo-2 1B
2. **Compare** the original importance formula with a modified version:
   - **Original:** $\Pi_{ij} = \lambda_i \cdot \mu_j$
   - **Modified:** $\Pi_{ij} = \lambda_i \cdot \mu_j \cdot |C_{ij}|^2$

## Installation

```bash
pip install -r requirements.txt
```

## Project Structure

```
├── src/
│   ├── kfac_collector.py    # Collect K-FAC statistics (A, G matrices)
│   ├── kfac_editor.py       # Weight editing via eigendecomposition
│   ├── evaluate.py          # Memorization and perplexity metrics
│   └── mine_memorized.py    # Mine memorized sequences from training data
├── notebooks/
│   ├── 01_collect_kfac.ipynb      # Colab: K-FAC collection (~2h on A100)
│   ├── 02_mine_memorized.ipynb    # Colab: Find memorized sequences (~1h)
│   └── 03_experiments.ipynb       # Colab: Run experiments (~2h)
├── plans/
│   └── implementation_plan.md     # Detailed implementation plan
├── context/
│   ├── original_paper/            # Paper sections in markdown
│   └── REPRODUCTION_PLAN.md       # Initial reproduction plan
└── requirements.txt
```

## Quick Start

### Local Development

```python
from src.kfac_collector import KFACCollector, KFACConfig
from src.kfac_editor import KFACEditor, EditConfig
from src.evaluate import memorization_score, perplexity

# Load model
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-7B")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-1124-7B")

# Load pre-collected K-FAC stats
collector = KFACCollector.load("kfac_statistics.pt", model)
kfac_stats = collector.get_statistics()

# Apply K-FAC editing
config = EditConfig(energy_threshold=0.6, formula="original")
editor = KFACEditor(model, kfac_stats, config)
editor.edit_model()

# Evaluate
result = memorization_score(model, tokenizer, prefixes, suffixes)
print(f"Strict accuracy: {result.strict_accuracy*100:.1f}%")
```

### Running on Colab

1. **01_collect_kfac.ipynb** - Collect K-FAC statistics (~20M tokens, ~2h on A100)
2. **02_mine_memorized.ipynb** - Find memorized sequences from training data
3. **03_experiments.ipynb** - Run experiments and compare formulas

## Method

### K-FAC Statistics Collection

For each target MLP layer, we collect:
- **A**: Activation covariance matrix (input side)
- **G**: Gradient covariance matrix (output side)

These approximate the Fisher Information Matrix: $F_W \approx G \otimes A$

### Weight Editing

1. **Eigendecompose** A and G matrices
2. **Transform** weights to curvature basis: $C = U_G^T W U_A$
3. **Compute importance** using either formula
4. **Select** top components by cumulative energy (e.g., 60%)
5. **Reconstruct** edited weights: $W_{edited} = U_G (C \odot M) U_A^T$

### Importance Formulas

| Formula | Definition | Intuition |
|---------|------------|-----------|
| Original | $\Pi_{ij} = \lambda_i \cdot \mu_j$ | Pure curvature |
| Modified | $\Pi_{ij} = \lambda_i \cdot \mu_j \cdot |C_{ij}|^2$ | Curvature weighted by actual weight magnitude |

## Hyperparameters

| Parameter | 7B Model | 1B Model (estimated) |
|-----------|----------|---------------------|
| Target layers | 23, 24, 25 | 11, 12, 13 |
| Projections | gate_proj, up_proj | gate_proj, up_proj |
| Energy threshold | 60% | 60% |
| K-FAC tokens | ~20M | ~20M |

## Expected Results

Based on the paper (OLMo-2 1B):

| Metric | Baseline | After K-FAC |
|--------|----------|-------------|
| Dolma strict accuracy | ~98% | ~3% |
| Perplexity (Pile-10k) | ~23 | ~27 |

## References

- Paper: [From Memorization to Reasoning in the Spectrum of Loss Curvature](https://github.com/goodfire-ai/memorization_kfac)
- Original code: https://github.com/goodfire-ai/memorization_kfac
- Model: [OLMo-2](https://huggingface.co/allenai/OLMo-2-1124-7B)
- K-FAC: [Martens & Grosse, 2015](https://arxiv.org/abs/1503.05671)

## License

MIT