---
license: apache-2.0
tags:
- diffusiongemma
- gemma4
- code-graphs
- code-refactoring
- latent-diffusion
- ast
pretty_name: DiffusionGemma 2 Latent Code Graph Benchmarks & Raw Predictions
size_categories:
- 1K<n<10K
---

# DiffusionGemma 2: Latent Code-Graph Benchmarks & Raw Predictions

Standardized AST code-graph benchmark packs, continuous symbol embeddings, pre-registration (`PREREG.json`), linear evaluation probes, and **raw `.npy` and `.npz` model prediction arrays** for independent CPU rescoring of ***Latent Roadmaps: Soft-Token Re-Feeding in DiffusionGemma for Code Graphs***.

- **Author:** `bumblebeers` ([GitHub](https://github.com/bumblebeers))
- **License:** Apache-2.0
- **Code & Scoring Harness:** [bumblebeers/dgg2-latent-code-graphs](https://github.com/bumblebeers/dgg2-latent-code-graphs)
- **Trained Two-Seed LoRA Adapters & Readout Heads:** [bumblebeer/dgg2-latent-code-graphs-adapters](https://huggingface.co/bumblebeer/dgg2-latent-code-graphs-adapters)
- **Standalone Libraries (`v0.1.0`):** [dgg-latent](https://github.com/bumblebeers/dgg-latent) | [dgg-codebook](https://github.com/bumblebeers/dgg-codebook) | [dgg-vit-decoder](https://github.com/bumblebeers/dgg-vit-decoder)
- **Upstream `transformers` Multimodal Mask Fix:** [huggingface/transformers#50684](https://github.com/huggingface/transformers/pull/50684)

---

## Repository Contents

### 1. Experiment A: 16x16 Visual Adjacency Grid Benchmark Packs (`data/gridA_*`)
- **`data/gridA_train1608.npz`**: 1,608 16-symbol Python AST subgraphs extracted across 3 training repositories (`fastapi`, `requests`, `rich`) with 22 fixed candidate states and 3 open-ended edit families.
- **`data/gridA_test360_httpx.npz`**: 360 held-out 16-symbol AST subgraphs from `encode/httpx` across seen, shuffled, unseen paraphrase, indirect intent, held-out single-pass, 2-step re-feeding, 3-step chain, and open-ended manifests.
- **`data/gridA_test360_click.npz`**: 360 held-out 16-symbol AST subgraphs from `pallets/click` for zero-shot cross-repository transfer.
- **`data/gridA_n32_*.npz` & `data/gridA_n48_*.npz`**: Multi-resolution 32x32 and 48x48 subgraph packs testing sub-patch packing limits (Gate A1).
- **`data/*.nodes.json`**: Per-subgraph AST symbol and line metadata.
- **`reader.npz`** (`outputs/m1/reader.npz`): Gate A1 closed-form Ridge probe mapping clean SigLIP latents `h_v` (`256 x 1152`) to the 8 discrete palette classes (`1.000` clean cell accuracy across all 8 classes).

### 2. Experiment B: 256-Symbol Continuous Embedding Packs (`data/nodeB_*`)
- **`data/nodeB_train1608.json.gz`**: 1,608 256-symbol AST windows for single-site and multi-site symbol refactoring.
- **`data/nodeB_test360_httpx.json.gz`** & **`data/nodeB_test360_click.json.gz`**: Held-out `httpx` (`N=360`) and zero-shot `click` (`N=360`) evaluation packs (`chance = 12.5%`, `mean-vector floor = 25.0%`).
- **`data/nodeB_test360_httpx_unseenargs.json.gz`** & **`data/nodeB_test180_httpx_renameclass.json.gz`**: Unseen argument and class-rename splits.
- **`data/nodeB_multi_test360_httpx_k2.json.gz`** & **`k4.json.gz`**: 2-site and 4-site simultaneous and re-fed symbol-editing splits.
- **`emb.npz`** & **`emb_train.npz`**: Frozen `sentence-transformers/all-MiniLM-L6-v2` (`d=384`) symbol embeddings and train-only standardization statistics.

### 3. Raw Model Prediction Arrays (`predictions/` & `outputs/m2/decoded_grids_all_arms.npz`)
To enable full independent verification without requiring a 26B GPU, this dataset hosts **both** continuous latent `.npy` outputs and Ridge-decoded discrete `(M, 16, 16)` `uint8` grid `.npy`/`.npz` files:
- **`predictions/expA_hv/<arm>/preds_*.npy`**: Raw continuous predicted SigLIP latent tensors (`360 x 256 x 1152`, `float32`) emitted by `stock_generate_with_readout` for two-seed LoRA DG (`cg_lora_dg_seed42_ep3`, `cg_lora_dg_seed1337_ep3`), `cg_frozen_dg_ep3`, and Non-DG baselines.
- **`predictions/expA_grids/<arm>/*.npy`** & **`outputs/m2/decoded_grids_all_arms.npz`**: Decoded `(360, 16, 16)` `uint8` palette grids (`0..7`) across all evaluated arms — two-seed LoRA DG (`httpx` and two-seed zero-shot `click`), Frozen DG, two-seed Non-DG (`Frozen Instr` and `Enc LoRA`), and preliminary `google/gemma-4-26B-A4B-it` autoregressive references (2-shot `gemma4_ar_prompted` and 354-of-4,824-step single-seed `gemma4_ar_lora_r64_s42` across all 4 primary splits).
- **`predictions/expB_vec/<arm>/*.npy`**: Raw standardized continuous symbol-vector predictions (`M x 256 x 384`, `float32`) for two-seed Experiment B LoRA DG (`cg_expB_lora_dg_seed42_ep3`, `cg_expB_lora_dg_seed1337_ep3`) and two-seed Non-DG baselines.

---

## Independent CPU Rescoring Quickstart

```bash
git clone https://github.com/bumblebeers/dgg2-latent-code-graphs.git
cd dgg2-latent-code-graphs

# Rescore Experiment A directly from raw continuous h_v (.npy, shape 360x256x1152) + Ridge probe:
python3 cg/cg_score_preds.py \
  --pack data/gridA_test360_httpx.npz \
  --reader reader.npz \
  --manifest seen_bal360 \
  --pred predictions/expA_hv/cg_lora_dg_seed42_ep3/preds_cg_lora_dg_seed42_ep3_seen_bal360.npy

# Rescore decoded (360, 16, 16) uint8 palette grids (.npy), including Gemma 4 AR LoRA:
python3 cg/cg_score_preds.py \
  --pack data/gridA_test360_httpx.npz \
  --manifest refeed_heldout_comp_bal360 \
  --grids predictions/expA_grids/gemma4_ar_lora_r64_s42/refeed_heldout_comp_bal360.npy
```