| --- |
| license: cc-by-sa-4.0 |
| pretty_name: corrected-lds banks |
| viewer: false |
| tags: |
| - training-data-attribution |
| - linear-datamodeling-score |
| - datamodels |
| - trak |
| --- |
| |
| # corrected-lds banks |
|
|
| Retrain banks for [`corrected-lds`](https://github.com/simonvary/corrected-lds), which |
| evaluates training-data attribution (TDA) with a seed-noise-corrected Linear Datamodeling |
| Score (LDS). |
|
|
| - A **pool** holds retrain readouts, an R seeds × M masks × Q queries array: cell (r, j) is |
| the per-query margins of a model retrained on random subset j with seed r. |
| - A **tauq** is one attribution method's n_train × Q score matrix, and a **pool_tda** holds |
| the same method one replica at a time. |
| - A **bank** is one folder: a pool and the score files that pair with it. |
| |
| ## Layout |
| |
| ``` |
| cifar10/ CIFAR-10, ResNet-9: R=40 × M=300, Q=2,000, n=50,000 |
| ├── pool.npz |
| ├── tauq_trak1_e{20,100}.npz, tauq_trak10_e{2,10}.npz TRAK |
| ├── pool_tda_trak1.npz, pool_tda_trak10.npz, pool_tda_trak20.npz |
| ├── tauq_ekfac_e30.npz, pool_tda_ekfac.npz EK-FAC, 30 full-set models |
| └── tauq_source_L3_e30.npz, pool_tda_source_L3.npz SOURCE, the same 30 runs |
| |
| qnli/ GLUE QNLI, BERT-base: R=20 × M=400, Q=2,000, n=50,000 |
| ├── pool.npz |
| ├── tauq_trak1_e{10,20,50,100}.npz, tauq_trak5_e{2,4,10,20}.npz TRAK, 100 members |
| ├── pool_tda_trak1.npz, pool_tda_trak5.npz |
| ├── tauq_ekfac_e20.npz, pool_tda_ekfac.npz EK-FAC, 20 full-set models |
| └── tauq_tracin_e20.npz, pool_tda_tracin.npz TracIn, the same 20 runs |
| |
| mnist/ MNIST subset, MLP: R=40 × M=300, Q=2,000, n=6,144 |
| ├── pool.npz |
| └── tauq_trak1_e100.npz, tauq_ekfac_e30.npz, tauq_source_L3_e30.npz |
|
|
| imagenet/ ImageNet-1k, ResNet-18: R=5 × M=100, Q=2,000, n=1,281,167 |
| └── pool.npz |
|
|
| wikitext2_gpt2_bae/ WikiText-2, GPT-2: R=20 × M=400, Q=481, n=4,656 (a companion bank) |
| ├── pool.npz eval-mode readout, the default |
| ├── pool_dropout_readout.npz the same models, dropout on |
| ├── tauq_ekfac_bergson.npz bergson's EK-FAC, one model: a check |
| ├── tauq_ekfac_e{5,10}.npz, pool_tda_ekfac.npz our EK-FAC, 10 full-set runs: the main one |
| ├── tauq_tracin_e10.npz, pool_tda_tracin.npz our TracIn, the same runs |
| ├── tauq_source_L3_e1.npz SOURCE (L=3, Adam), one model |
| ├── tauq_trak1_e{10,20,50}.npz, tauq_trak5_e{2,4,10}.npz TRAK, 50 members |
| └── pool_tda_trak1.npz, pool_tda_trak5.npz |
| ``` |
| |
| ## Names and protocols |
| |
| - `tauq_<method>_e<E>.npz` averages **E replicas**, independent estimates of the scores. |
| TRAK forms one replica from `e_features` members (one Eq. 15 unit), so `trak10_e2` is |
| 2 × 10 = 20 trained models and `trak1_e100` is 100. SOURCE names its segment count |
| (`source_L3`). |
| - `pool_tda_<method>.npz` keeps one slice per replica, so any smaller ensemble is an exact |
| average of slices: the whole ensemble-size curve without rescoring. |
| - TRAK members train on random halves of the data, as TRAK intends. EK-FAC (damping 1e-8, |
| final checkpoint), TracIn and SOURCE (six checkpoints in L = 3 segments, Bae et al. 2024) |
| score models trained on the full set. |
| - Recipes: cifar10 and qnli follow TRAK's App. A.1, mnist SOURCE's App. B.1, and imagenet |
| a 15-epoch ResNet-18. Every file stamps its recipe, data hashes, index draws and mask draw |
| inside, and the loaders refuse a pool and a tauq that do not pair. |
|
|
| ## The WikiText-2 companion bank |
|
|
| Built with EleutherAI's [bergson](https://github.com/EleutherAI/bergson) (commit |
| `0ad2e5b`) in the setup of Bae et al. 2024 (GPT-2 124M, AdamW at a constant 3e-5, batch 8, |
| 3 epochs, dropout on in training). Its numbers do not mix with the other banks': |
|
|
| - A mask zeroes the excluded documents' loss weights rather than retraining on the subset, |
| and a replica's masks share one seed. The pool is stamped `shared-per-replica`, and |
| `evaluate` uses the matching seed-noise estimator. |
| - `pool.npz` reads every model in eval mode, as bergson's published number does; |
| `pool_dropout_readout.npz` reads the same models with dropout on, as bergson's `validate` |
| loop logs them. |
| - Two EK-FACs: ours, over 10 full-set runs, is the bank's EK-FAC; bergson's own, on one |
| model, checks it (they agree to within 0.001) and the replication: on Bae et al.'s |
| protocol (5 seeds, the first 100 masks) it scores 0.456 in mean per-query Spearman, against |
| 0.468 ± 0.015 in bergson's example. Every file's numbers are in |
| `wikitext2_gpt2_bae/README.md`. |
|
|
| ## Use |
|
|
| These are raw npz files, not a `load_dataset` dataset; read them with the corrected-lds |
| tooling: |
|
|
| ```bash |
| git clone https://github.com/simonvary/corrected-lds && cd corrected-lds |
| pip install -e . |
| corrected-lds fetch-banks # every file checked against banks-manifest.json |
| corrected-lds evaluate banks/cifar10 --tauq source_L3_e30 |
| ``` |
|
|
| `banks-manifest.json` in the code repo pins every file's sha256 at one commit of this |
| dataset. To score your own method, save its scores with `corrected_lds.write_tauq` and pass |
| the file to `evaluate`. |
|
|
| ## Licence |
|
|
| The banks are released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). |
| They hold margins, masks, indices and scores only, no dataset content, but they derive from |
| datasets with their own terms: QNLI (from SQuAD, CC BY-SA 4.0), WikiText-2 (CC BY-SA 3.0) and |
| MNIST (CC BY-SA 3.0) are share-alike, and the ImageNet pool was built from the license-gated |
| [`ILSVRC/imagenet-1k`](https://huggingface.co/datasets/ILSVRC/imagenet-1k), so its use is also |
| subject to the ImageNet terms of access. The code is MIT-licensed. |
|
|
