| # `gun` unlearning data |
|
|
| Target concept: **gun** (29 firearm tags: `gun`, `pistol`, `rifle`, `revolver`, |
| `shotgun`, `handgun`, `sniper rifle`, `uzi submachine gun`, `mp5 submachine gun`, |
| `bullet`, `ammunition`, `gun barrel`, … — full list in `manifest_curated.json`). |
|
|
| ## What's on disk |
|
|
| | Path | Size | Shards / rows | What it is | |
| |------|------|---------------|------------| |
| | `forget/` | 37 GB | 90 tars | **Forget set** — images containing the gun concept. `447,370` samples. | |
| | `retain/` | 92 GB | 269 tars | **Retain set** — everything else, ~3× the forget set. `1,342,021` samples (of `1,357,557` requested; seed `42`). | |
| | `pairs/` | 433 MB | 69 files | Counterfactual **pair tables** (parquet). `pairs_train_clean.parquet` = `1,823,249` rows. | |
| | `pair_shards_seq/` | 52 GB | 180 tars | Pairs pre-materialized into sequential tar shards (used by the `seq_fast` recipe). | |
| | `concept_table/` | 30 GB | — | Per-concept lookup table from Stage 2/3. | |
| | `manifest_curated.json` | 5.5 MB | — | Authoritative counts + shard list. Read this, not the config, for exact numbers. | |
| | `results/` | — | — | Training output dir referenced by the 1M `juwels_gun.yaml` preset. | |
|
|
| ### Counts (from `manifest_curated.json`) |
| |
| ``` |
| n_forget_total: 447,370 |
| n_retain_total: 1,342,021 (retain_size_requested 1,357,557, seed 42) |
| n_shards: 22,021 |
| ``` |
| |
| ### The counterfactual pairs |
| |
| Each row of `pairs_train_clean.parquet` is a matched pair: |
| |
| - `image_pos` — image **with** the gun concept |
| - `image_neg` — near-duplicate image **without** it |
| - `pair_id`, `target_concept`, `jaccard`, `similarity_score`, `match_weight`, |
| `block_label`, `tags_pos/neg`, `context_pos/neg`, `split` |
| |
| > **Use `pairs_train_clean.parquet`, not `pairs_train.parquet`** — ~19.4% of the |
| > unclean file's rows have invalid 10-digit keys. |
| |
| ## How it's used in training (current sequential recipe, where we need KLD anchor) |
| |
| Config: `configs/unlearn/juwels_bf16_l2dist_kl_v2_seq_fast.yaml` |
| (`experiment_name: bf16_l2dist_kl_v2_seq_fast_gun`, `max_train_steps: 7000`). |
| |
| ### Two streams, merged per batch |
| |
| - **Pair stream** — `pairs_train_clean.parquet` rows, images pulled from the |
| DataComp tars at `pair_tar_dir: /p/data1/mmlaion/cabs/dataconcept_128m` |
| (`pair_source: "tar"`, 48-shard cache, `pair_shuffle: false`). |
| `PairDataset` → `PairCollator` → `pixel_values_pos [B,C,H,W]` + |
| `pixel_values_neg [B,C,H,W]`, prefixed `pair_*`. |
| - **Retain stream** — retain SFT examples. `RetainDataCollator` → |
| `input_ids`, `labels`, `images`, `image_sizes`, `modalities` |
| (a normal LLaVA next-token batch; no `attention_mask` — LLaVA rebuilds it |
| after `<image>`-token expansion). |
| |
| Batch mix: `pair_batch_size: 2`, `retain_batch_size: 2` (1:1). Bad/corrupt |
| images are replaced by zero tensors rather than crashing a rank. |
| |
| ### Sequential phase schedule |
| |
| Unlike the interleaved presets, `seq_fast` runs `phase_mode: "sequential"`, |
| `phase_forget_frac: 0.5` — **two blocks** over the 7000 steps: |
| |
| | Block (steps) | Data used | Active losses | |
| |---|---|---| |
| | **FORGET** — first 50% (0–3500) | pair stream only | visual invariance (pos≈neg) + L2 param-locality | |
| | **RETAIN** — last 50% (3500–7000) | retain stream | retain-SFT CE + KL-locality (student≈teacher logits) + L2 param-locality | |
| |
| L2 locality is on throughout (it anchors params, needs no data); the retain |
| forward is skipped entirely during the FORGET block. |
| |
| ### Loss weights (unlearning_loss_adapter.py) |
| |
| | Term | Data it consumes | Weight | |
| |---|---|---| |
| | retain-SFT cross-entropy | retain `input_ids/labels/images` | `1.0` | |
| | pooled visual invariance (l2, squared-dist, mean-pool) | `pair_pixel_values_pos/neg` | `1.0` | |
| | KL locality | retain batch + frozen teacher | `0.5` | |
| | L2 parameter locality | trainable params vs start snapshot | `0.1` | |
| |
| ### Model / optimisation |
| |
| - Base: `llava-onevision-qwen2-7b-ov`, `model_max_length: 1024`, `image_aspect_ratio: "pad"`. |
| - Trainable: **projector only**. bf16, FSDP, 8 dataloader workers, no grad checkpointing. |
| - LR `1e-5` cosine, warmup `0.1`, `per_device_train_batch_size: 2`, grad-accum `1`, `max_grad_norm 1.0`. |
| |
| ## Net effect on data |
| |
| Forget pairs teach the model to **stop distinguishing gun-present from |
| gun-absent** images (invariance); the retain block **re-anchors normal |
| behaviour** via SFT + KL-to-teacher; L2 locality keeps weights close to the |
| original throughout so retain performance doesn't drift. |
| |
