diff --git a/axplorer-viz/.gitignore b/axplorer-viz/.gitignore new file mode 100644 index 0000000..d20d376 --- /dev/null +++ b/axplorer-viz/.gitignore @@ -0,0 +1,10 @@ +# Manim render output +media/ + +# Python +__pycache__/ +*.py[cod] +.pytest_cache/ + +# uv / venv +.venv/ diff --git a/axplorer-viz/.python-version b/axplorer-viz/.python-version new file mode 100644 index 0000000..2c07333 --- /dev/null +++ b/axplorer-viz/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/axplorer-viz/README.md b/axplorer-viz/README.md new file mode 100644 index 0000000..a64b787 --- /dev/null +++ b/axplorer-viz/README.md @@ -0,0 +1,228 @@ +# axplorer-viz + +A short Manim explainer for how **Axplorer** — Axiom Math's open-source, +PatternBoost-style extremal-combinatorics search tool +([AxiomMath/axplorer](https://github.com/AxiomMath/axplorer)) — attacks the +**Turán 4-cycle problem at N = 15**: maximize the number of edges in a 15-vertex +simple graph that contains no 4-cycle (`C_4`). The known optimum is +`ex(15, C_4) = 30` (OEIS [A006855](https://oeis.org/A006855)). + +The video is three acts (~80 s total): + +1. **The plateau** — plain single-edge local search greedily adds edges, keeps + bumping into 4-cycles, and gets stuck around 24 edges. +2. **Learn the structure** — a transformer trained on the top-k graphs found so + far emits new, more *regular* (near-bipartite) candidates. +3. **The flywheel** — sample → local search → retrain, repeated; the best score + climbs past the naive ceiling to the proven optimum, 30. + +> ## V1 vs V2 — where the data comes from +> +> The Manim scenes (`src/scenes/`) never change between V1 and V2; only the +> *data source* behind `src/trajectory.py` does. +> +> **V1 — hand-curated.** `src/trajectory.py` ships hand-authored data so the +> visual *design* could be locked in: the plateau graph, the "aha" transformer +> samples, the per-step stumbles in Act 1, the climb in Act 3. Every graph *is* +> genuinely `C_4`-free (checked by `src/graph_utils.py` + tests) and `FINAL` +> *is* a real extremal graph for N = 15 — but the *trajectory through them* is +> staged. The pre-rendered V1 video lives at `preview/axplorer_turan_v1_1080p.mp4`. +> +> **V2 — real Axplorer run.** A vendored fork of Axplorer (`vendor/axplorer/`, +> with a minimal logging patch — see `vendor/PATCH_NOTES.md`) writes a per-epoch +> JSONL trajectory log. `src/trajectory_loader.py` reads it and produces the +> *same* module-level names the scenes import; `src/trajectory.py` automatically +> uses it **if a `logs/square_N_run.jsonl` exists** (largest N wins — e.g. +> `square_N30_run.jsonl`), otherwise it falls back to the V1 data. Check +> `trajectory.TRAJECTORY_SOURCE` to see which is active. The intended V2 run is +> N = 30 (see "V2: producing a real Axplorer trajectory log" below). +> +> Some V2 details are still synthesized because the log is per-*epoch*, not +> per-*move*: Act 1's add/reject trace is re-derived to end at the real epoch-0 +> graph; Act 3's centre graph fills in toward the real final graph along the +> real best-score series; Act 2's two-colour highlight is a greedy 2-colouring +> of the real sample (so if the real samples *aren't* near-bipartite, it won't +> look bipartite — that's an honest finding, not a bug). The headline numbers +> (plateau score, per-epoch best scores, final = optimum, the actual graphs) are +> straight from the run. +> +> ### Finding: at N = 15, the flywheel has nothing to do +> +> We ran real Axplorer at N = 15 (`logs/example_N15_run.jsonl`, a short +> reproducible run — `--seed 1234 --process_pool false`). The result: **N = 15 +> is solved by the initial random-construction phase.** Axplorer's `square` +> environment generates each candidate by greedily adding edges until no more can +> be added without a 4-cycle, and on 15 vertices those maximal graphs land at +> 26–30 edges, with the *optimum (30)* appearing within the first few hundred +> restarts. So `best_score_so_far` is already 30 at epoch 0 and stays flat — the +> transformer + flywheel never get a chance to improve anything. +> +> That's a finding about the **problem size**, not about Axplorer or +> PatternBoost: the loop matters at scales where naive search *doesn't* trivially +> succeed (the upstream README uses N = 30). So: +> +> - The **headline video stays the V1 hand-curated trajectory** — it's a faithful +> *idealization* of what the search → train → sample loop does where it's +> needed (a plateau, a learned restructuring, a climb), just compressed onto a +> visually digestible 15-vertex instance. +> - The **V2 plumbing is complete and tested**: `src/trajectory.py` switches to +> a real log automatically if a `logs/square_N_run.jsonl` is present (largest +> N wins), and every scene reads its magic numbers (ceiling, optimum, scores, +> N) from `trajectory`, so they render unchanged on real data. Do a converged +> **N = 30** run (the intended target — see below), drop the log in `logs/`, +> and `uv run manim -qh src/scenes/full_video.py FullVideo` renders it. (Point +> it at the trivial N = 15 log and you'll get a trivial-but-honest video: a +> flat curve at 30.) +> +> This repo is a **learning-in-public reproduction** and is **not affiliated +> with Axiom Math**. Axplorer is Apache-2.0; attribution is preserved in +> `vendor/axplorer/LICENSE` and `vendor/README.md`. PatternBoost is from Charton, +> Ellenberg, Wagner & Williamson, *"PatternBoost: Constructions in Mathematics +> with a Little Help from AI"* (2024). + +## Layout + +``` +axplorer-viz/ +├── pyproject.toml # uv-managed (manim 0.18.x, networkx, numpy) — the viz env +├── src/ +│ ├── trajectory.py # V1 hand-curated data; auto-overridden by a real log if present +│ ├── trajectory_loader.py # reads logs/*.jsonl -> same structures trajectory.py exposes +│ ├── decode.py # decode Axplorer single_integer edge tokens -> (i, j) +│ ├── graph_utils.py # count_edges / find_4_cycles / has_4_cycle +│ ├── styles.py # colors, fonts, layout, small drawing helpers +│ └── scenes/ +│ ├── act1_naive_search.py # Scene: Act1NaiveSearch +│ ├── act2_transformer.py # Scene: Act2Transformer +│ ├── act3_flywheel.py # Scene: Act3Flywheel +│ └── full_video.py # Scene: FullVideo (the deliverable) +├── tests/ # graph_utils, trajectory (V1), decode, trajectory_loader +├── vendor/ +│ ├── README.md # provenance + Apache-2.0 note +│ ├── PATCH_NOTES.md # exactly what our logging patch changes +│ └── axplorer/ # vendored AxiomMath/axplorer + the logging patch +├── logs/ +│ ├── example_N15_run.jsonl # a real (short) Axplorer run — evidence the pipeline works +│ └── (square_N30_run.jsonl) # do an overnight N=30 run and drop it here -> V2 video +└── preview/axplorer_turan_v1_1080p.mp4 # pre-rendered V1 video +``` + +## Setup (the visualization env) + +[`uv`](https://docs.astral.sh/uv/) manages the Manim/Python env. From `axplorer-viz/`: + +```bash +uv sync # create .venv and install manim 0.18.x, networkx, numpy +uv run pytest # graph utils + trajectory (V1) + decode + loader tests +``` + +Manim needs system **ffmpeg** plus the **cairo/pango** stack. On macOS: + +```bash +brew install ffmpeg pango cairo pkg-config +``` + +(Debian/Ubuntu: `apt install ffmpeg libpango1.0-dev libcairo2-dev pkg-config`.) +No LaTeX is required — every label is rendered with Pango (`Text`), not `Tex`. + +## Rendering + +Manim quality flags: `-ql` = 480p15 (fast preview), `-qm` = 720p30, +`-qh` = 1080p60, `-qp` = 1440p60. + +```bash +# preview a single act +uv run manim -ql src/scenes/act1_naive_search.py Act1NaiveSearch +uv run manim -ql src/scenes/act2_transformer.py Act2Transformer +uv run manim -ql src/scenes/act3_flywheel.py Act3Flywheel + +# the full video — 1080p draft first, then the 1440p final +uv run manim -qh src/scenes/full_video.py FullVideo # 1080p draft +uv run manim -qp src/scenes/full_video.py FullVideo # 1440p final +``` + +Output lands in `media/videos/full_video//FullVideo.mp4` +(`media/` is git-ignored). A clean full render takes a couple of minutes on an +M-series Mac (well under five). Add `--disable_caching` if you've edited shared +modules and want to be sure nothing stale is reused. Whichever data source is +active (`trajectory.TRAJECTORY_SOURCE`) is what gets rendered. + +## V2: producing a real Axplorer trajectory log + +The vendored Axplorer runs in its **own** environment (`env_axplorer`) — keep it +separate from the `uv` viz env above; do not merge them. With +[`micromamba`](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) +(or `conda`/`mamba`): + +```bash +# 1. create the Axplorer env from the vendored environment.yml (name: env_axplorer) +micromamba create -y -f vendor/axplorer/environment.yml +micromamba run -n env_axplorer python -c "import torch; print(torch.__version__)" +micromamba run -n env_axplorer python vendor/axplorer/train.py --help # sanity check +``` + +### The intended run: N = 30 (overnight, on a GPU/Mac) + +`--N 30` is what makes the flywheel story *real* — greedy search alone plateaus +~5–10 edges below the optimum (`ex(30, C_4)`), so the transformer has structure +to learn. This is the upstream README's instance; budget **~a few hours** on a +GPU or an Apple-Silicon Mac (MPS), longer on plain CPU. + +```bash +mkdir -p logs +cd vendor/axplorer +micromamba run -n env_axplorer python train.py \ + --env_name square --exp_name viz_run_N30 --N 30 \ + --encoding_tokens single_integer --max_len 100 \ + --temperature 0.6 --inc_temp 0.1 \ + --log_trajectory ../../logs/square_N30_run.jsonl +cd ../.. +``` + +(On a Mac, leave `--cpu` unset so it uses MPS. The upstream defaults — `--gensize +100000`, `--max_steps 50000`, `--num_samples_from_model 500000`, `--max_epochs +2000` — are fine for a full GPU run; to compress it to ~1–2 h, shrink +`--num_samples_from_model` to ~20–50k, `--gensize` to ~10–20k, and cap +`--max_epochs` to ~15–30 — PatternBoost closes most of the gap in the first few +flywheel iterations.) + +Then: + +```bash +uv run python -m src.trajectory_loader logs/square_N30_run.jsonl # sanity-check the log +uv run manim -qh src/scenes/full_video.py FullVideo # renders the V2 video +``` + +`src/trajectory.py` automatically uses any `logs/square_N_run.jsonl` (largest +N wins) and every scene reads its magic numbers — the ceiling, the optimum, the +scores, N — from `trajectory`, so the scenes render unchanged on real data. (At +N ≈ 30 the per-graph circular layout gets dense — ~85+ edges — but still renders.) + +### A small reproducible run (sanity / N = 15) + +```bash +cd vendor/axplorer +micromamba run -n env_axplorer python train.py \ + --env_name square --exp_name viz_run --N 15 \ + --encoding_tokens single_integer --max_len 50 \ + --temperature 0.6 --inc_temp 0.1 \ + --cpu true --process_pool false --seed 1234 \ + --gensize 4000 --pop_size 4000 --max_epochs 6 --max_steps 400 \ + --num_samples_from_model 2000 --gen_batch_size 500 --n_layer 3 --n_embd 192 --n_head 6 \ + --log_trajectory ../../logs/square_N15_run.jsonl +cd ../.. +``` + +`--process_pool false --seed N` makes a run reproducible end to end (with the +process pool, worker RNG state isn't seeded — see `vendor/PATCH_NOTES.md`). This +is the run committed as `logs/example_N15_run.jsonl` — and it's the one that +shows the flywheel is *unnecessary* at N = 15 (see the "Finding" note above). + +If `src/envs/` in the vendored copy is unclear, `vendor/axplorer/new_envs.ipynb` +walks through how the `square` environment encodes graphs. + +## Tuning the look + +Everything visual lives in `src/styles.py` — palette, font, vertex/edge sizes, +the 4-cycle flash color and duration, plot colors, the dark background. Per-act +layout constants (graph centers, radii) sit at the top of each scene file. diff --git a/axplorer-viz/logs/README.md b/axplorer-viz/logs/README.md new file mode 100644 index 0000000..75326c1 --- /dev/null +++ b/axplorer-viz/logs/README.md @@ -0,0 +1,30 @@ +# logs/ + +`src/trajectory.py` uses any **`square_N_run.jsonl`** found here to drive the +Manim scenes (largest N wins); otherwise it uses the V1 hand-curated trajectory. +Every scene reads its magic numbers — ceiling, optimum, scores, N — from +`trajectory`, so the scenes render unchanged on real data. + +- **`example_N15_run.jsonl`** — a real, short, reproducible Axplorer run at + N = 15 (`--seed 1234 --process_pool false`), committed as evidence the pipeline + works end to end. It does **not** auto-activate (only `square_N_run.jsonl` + does). Note: this run shows the flywheel does *nothing* at N = 15 — the initial + random-construction phase already finds the optimum (30). See the "Finding" + note in the top-level `README.md`. Inspect it: + + ```bash + uv run python -m src.trajectory_loader logs/example_N15_run.jsonl + ``` + +- **`square_N30_run.jsonl`** (not committed; you produce it) — the intended V2 + run. `--N 30` is where greedy search demonstrably plateaus and the flywheel has + to learn structure. See "V2: producing a real Axplorer trajectory log" in the + top-level `README.md` for the command (budget ~a few hours on a GPU / MPS Mac). + Once it's here: + + ```bash + uv run python -m src.trajectory_loader logs/square_N30_run.jsonl + uv run manim -qh src/scenes/full_video.py FullVideo + ``` + +JSONL schema: `vendor/PATCH_NOTES.md`. diff --git a/axplorer-viz/logs/example_N15_run.jsonl b/axplorer-viz/logs/example_N15_run.jsonl new file mode 100644 index 0000000..36afc4c --- /dev/null +++ b/axplorer-viz/logs/example_N15_run.jsonl @@ -0,0 +1,6 @@ +{"epoch": 0, "n_vertices": 15, "top_k_objects": [[2, 6, 8, 10, 15, 16, 25, 26, 28, 29, 30, 31, 46, 49, 50, 58, 62, 65, 69, 74, 76, 78, 84, 85, 89, 90, 96, 97, 99, 102], [0, 4, 9, 10, 22, 25, 26, 27, 28, 30, 38, 41, 44, 48, 56, 57, 59, 61, 63, 65, 70, 72, 77, 78, 83, 85, 87, 93, 99, 102], [0, 2, 10, 12, 14, 17, 23, 29, 30, 32, 39, 48, 49, 50, 57, 59, 63, 70, 74, 75, 78, 79, 80, 81, 85, 89, 90, 93, 98, 99], [0, 6, 10, 12, 14, 23, 24, 29, 33, 36, 39, 41, 43, 47, 50, 51, 52, 61, 63, 72, 73, 82, 87, 88, 89, 90, 94, 95, 98, 104], [1, 3, 8, 11, 15, 17, 23, 25, 28, 34, 37, 47, 48, 49, 52, 56, 59, 61, 62, 63, 70, 72, 73, 74, 77, 83, 85, 92, 97], [0, 6, 10, 12, 16, 17, 24, 25, 27, 28, 30, 31, 41, 43, 48, 57, 60, 68, 71, 73, 79, 85, 88, 89, 90, 91, 92, 96, 98], [7, 9, 10, 12, 16, 22, 24, 27, 29, 36, 37, 42, 44, 48, 54, 56, 57, 59, 60, 62, 68, 69, 73, 78, 79, 85, 89, 94, 100], [0, 4, 6, 10, 11, 18, 23, 26, 28, 33, 34, 35, 37, 40, 43, 48, 49, 51, 52, 58, 66, 67, 76, 77, 84, 89, 91, 96, 98], [4, 10, 18, 23, 24, 26, 31, 32, 36, 37, 39, 43, 45, 49, 51, 54, 55, 62, 63, 65, 71, 74, 79, 80, 82, 87, 93, 95, 104], [1, 3, 5, 7, 12, 15, 16, 26, 27, 32, 33, 44, 49, 56, 58, 61, 64, 67, 68, 74, 76, 77, 80, 83, 90, 91, 92, 96, 97], [2, 6, 7, 11, 16, 17, 22, 26, 29, 35, 36, 37, 44, 47, 49, 52, 56, 59, 62, 67, 69, 71, 72, 75, 77, 93, 94, 96, 99], [3, 5, 10, 11, 21, 24, 25, 26, 29, 30, 34, 37, 39, 43, 45, 49, 56, 58, 62, 63, 65, 72, 74, 79, 80, 87, 91, 94, 98], [1, 4, 8, 9, 14, 15, 20, 26, 28, 42, 43, 44, 47, 51, 52, 58, 60, 64, 66, 68, 69, 76, 78, 85, 88, 91, 97, 100, 101], [6, 9, 11, 12, 13, 14, 15, 20, 21, 26, 32, 35, 36, 39, 44, 48, 51, 55, 56, 61, 63, 65, 73, 76, 78, 79, 85, 99, 102], [3, 4, 6, 12, 14, 17, 23, 24, 28, 34, 36, 41, 43, 44, 45, 52, 55, 63, 65, 67, 69, 70, 73, 83, 87, 88, 90, 94, 103], [2, 5, 8, 9, 13, 20, 21, 24, 28, 30, 35, 46, 47, 49, 50, 52, 54, 56, 61, 62, 68, 70, 72, 79, 82, 87, 97, 99, 102], [1, 2, 10, 14, 16, 19, 20, 28, 30, 43, 44, 47, 48, 50, 58, 65, 67, 68, 71, 72, 79, 80, 81, 84, 89, 90, 95, 101, 102], [8, 9, 12, 17, 20, 23, 25, 27, 31, 35, 38, 40, 44, 49, 51, 53, 57, 59, 62, 63, 69, 70, 72, 79, 80, 90, 92, 99, 104], [4, 5, 6, 13, 14, 16, 17, 20, 29, 33, 36, 40, 45, 46, 48, 53, 56, 59, 70, 71, 79, 81, 83, 85, 91, 92, 96, 97, 101], [3, 4, 8, 15, 16, 23, 26, 32, 35, 37, 40, 42, 49, 54, 55, 56, 61, 67, 70, 71, 74, 76, 77, 79, 84, 96, 100, 102, 103], [0, 2, 11, 13, 14, 23, 26, 29, 31, 33, 35, 40, 47, 48, 51, 55, 56, 60, 70, 72, 76, 78, 79, 81, 84, 88, 89, 93, 96], [9, 10, 13, 16, 19, 21, 26, 28, 29, 35, 36, 43, 44, 47, 52, 60, 65, 67, 71, 75, 77, 79, 82, 86, 92, 94, 96, 97, 101], [4, 8, 9, 11, 16, 23, 24, 28, 29, 31, 32, 40, 41, 48, 50, 56, 59, 69, 72, 73, 77, 79, 84, 88, 91, 93, 96, 98, 104], [0, 4, 7, 20, 23, 26, 31, 36, 37, 39, 40, 45, 47, 49, 54, 59, 60, 66, 69, 71, 73, 79, 80, 84, 85, 88, 93, 101, 102], [2, 3, 4, 12, 13, 17, 19, 23, 33, 34, 35, 36, 38, 44, 48, 53, 57, 61, 68, 69, 70, 72, 75, 77, 78, 87, 98, 99, 100], [2, 4, 9, 11, 16, 21, 24, 27, 33, 38, 44, 45, 52, 55, 56, 59, 60, 66, 68, 71, 73, 75, 77, 82, 83, 87, 88, 93, 95], [4, 9, 12, 13, 17, 21, 24, 28, 31, 33, 35, 39, 41, 47, 48, 54, 59, 60, 61, 68, 69, 70, 80, 84, 85, 90, 97, 99, 102], [1, 9, 10, 11, 14, 16, 19, 26, 31, 35, 42, 45, 48, 53, 58, 59, 60, 61, 62, 63, 66, 69, 87, 88, 91, 94, 96, 98, 100], [6, 7, 8, 12, 14, 17, 21, 24, 27, 34, 41, 42, 45, 51, 53, 59, 60, 67, 69, 76, 78, 88, 92, 95, 97, 99, 100, 101, 103], [3, 5, 9, 12, 15, 21, 24, 25, 26, 28, 32, 35, 38, 40, 46, 48, 51, 59, 60, 61, 65, 71, 79, 83, 84, 85, 86, 92, 97], [0, 8, 10, 11, 15, 24, 25, 31, 33, 38, 39, 42, 48, 51, 52, 56, 62, 65, 67, 72, 73, 76, 77, 78, 85, 87, 96, 98, 104], [5, 9, 11, 12, 20, 23, 24, 26, 28, 31, 33, 36, 40, 41, 46, 50, 58, 59, 60, 69, 70, 77, 78, 89, 90, 91, 95, 97, 104]], "top_k_scores": [30.0, 30.0, 30.0, 30.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0, 29.0], "model_samples_raw": [[1, 6, 7, 16, 21, 22, 23, 25, 28, 29, 31, 32, 45, 47, 48, 51, 55, 60, 67, 68, 71, 73, 76, 79, 82, 85, 100], [5, 7, 10, 11, 15, 17, 19, 25, 26, 28, 32, 33, 38, 40, 43, 56, 59, 60, 71, 74, 78, 79, 85, 92, 96, 98, 99], [2, 4, 6, 17, 18, 19, 22, 28, 31, 32, 38, 39, 43, 44, 46, 49, 53, 62, 73, 79, 86, 92, 94, 95, 98, 99, 104], [3, 4, 9, 16, 17, 21, 28, 31, 38, 41, 42, 44, 46, 50, 62, 68, 70, 73, 77, 81, 83, 84, 89, 94, 97, 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87, 96, 97, 99, 101], [1, 2, 4, 9, 14, 18, 19, 25, 31, 38, 41, 46, 50, 51, 53, 54, 63, 64, 70, 78, 80, 87, 89, 91, 96, 97, 99, 102, 103], [3, 4, 7, 9, 18, 22, 24, 26, 29, 30, 35, 37, 39, 42, 48, 49, 50, 59, 60, 63, 78, 79, 82, 85, 86, 92, 99, 100, 103]], "best_score_so_far": 30.0, "wall_time_seconds": 125.37309122085571} diff --git a/axplorer-viz/preview/axplorer_turan_v1_1080p.mp4 b/axplorer-viz/preview/axplorer_turan_v1_1080p.mp4 new file mode 100644 index 0000000..e34d16c Binary files /dev/null and b/axplorer-viz/preview/axplorer_turan_v1_1080p.mp4 differ diff --git a/axplorer-viz/pyproject.toml b/axplorer-viz/pyproject.toml new file mode 100644 index 0000000..c9043f4 --- /dev/null +++ b/axplorer-viz/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "axplorer-viz" +version = "0.1.0" +description = "Manim explainer: Axplorer (PatternBoost-style search) on the Turan C_4 problem at N=15. V1 = hand-curated trajectory." +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "manim>=0.18,<0.19", + "networkx>=3.6.1", + "numpy>=2.4.4", +] + +[dependency-groups] +dev = [ + "pytest>=9.0.3", +] diff --git a/axplorer-viz/src/__init__.py b/axplorer-viz/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/axplorer-viz/src/decode.py b/axplorer-viz/src/decode.py new file mode 100644 index 0000000..408d4fe --- /dev/null +++ b/axplorer-viz/src/decode.py @@ -0,0 +1,96 @@ +"""Decode Axplorer ``single_integer`` edge tokens back to vertex pairs. + +The ``square`` environment in Axplorer (AxiomMath/axplorer) represents a graph +on ``N`` vertices as the set of present edges, and -- with +``--encoding_tokens single_integer`` -- encodes each edge ``{i, j}`` (``i < j``) +as a single integer token: its **lexicographic index among** +``itertools.combinations(range(N), 2)``. So for ``N`` vertices: + + token 0 -> (0, 1) + token 1 -> (0, 2) + ... + token N-2 -> (0, N-1) + token N-1 -> (1, 2) + ... + token C(N,2) - 1 -> (N-2, N-1) + +(Their README describes this loosely as "Ni + j"; the real encoding is the +``combinations`` index implemented here -- see +``vendor/axplorer/src/envs/tokenizers.py::SparseTokenizerSingleInteger``.) + +Tokens ``>= C(N, 2)`` are the special symbols ``SEP / EOS / PAD / BOS`` -- not +edges; :func:`decode_object` skips them, :func:`decode_token` rejects them. + +This module is intentionally dependency-free (pure Python) so it can run inside +the visualization's ``uv`` environment without importing Axplorer. +""" + +from __future__ import annotations + +import math +from itertools import combinations + + +def num_edge_tokens(n_vertices: int) -> int: + """How many token ids correspond to edges, ``C(n, 2)``.""" + return math.comb(n_vertices, 2) + + +def decode_token(token: int, n_vertices: int) -> tuple[int, int]: + """Return the edge ``(i, j)`` (``i < j``) for ``token`` on ``n_vertices``. + + Inverse of the ``single_integer`` encoding (lex index in + ``combinations(range(n_vertices), 2)``). Raises ``ValueError`` if ``token`` + is out of the edge range (i.e. is a special symbol). + """ + n = n_vertices + limit = num_edge_tokens(n) + if not (0 <= token < limit): + raise ValueError( + f"token {token} is not an edge token for N={n} (edge tokens are 0..{limit - 1})" + ) + # Pairs starting with vertex i: there are (n - 1 - i) of them. + i = 0 + offset = 0 + while offset + (n - 1 - i) <= token: + offset += n - 1 - i + i += 1 + j = i + 1 + (token - offset) + return (i, j) + + +def encode_edge(edge: tuple[int, int], n_vertices: int) -> int: + """Inverse of :func:`decode_token` -- the token id for edge ``{i, j}``. + + Provided mainly so tests can round-trip; the visualization only ever + decodes. + """ + i, j = (edge[0], edge[1]) if edge[0] <= edge[1] else (edge[1], edge[0]) + n = n_vertices + if not (0 <= i < j < n): + raise ValueError(f"edge {edge} is not valid for N={n}") + # sum_{k=0}^{i-1} (n - 1 - k) + (j - i - 1) + offset = i * (n - 1) - (i * (i - 1)) // 2 + return offset + (j - i - 1) + + +def decode_object(token_list, n_vertices: int) -> list[tuple[int, int]]: + """Decode a logged object (list of edge tokens) to a sorted edge list. + + Any non-edge tokens (special symbols, should not normally appear in the + logged objects -- the logging patch strips them) are skipped. Duplicate + edges are collapsed. + """ + limit = num_edge_tokens(n_vertices) + edges = set() + for t in token_list: + t = int(t) + if 0 <= t < limit: + edges.add(decode_token(t, n_vertices)) + return sorted(edges) + + +def all_edge_tokens(n_vertices: int): + """Yield ``(token, (i, j))`` for every edge -- handy for sanity checks.""" + for token, edge in enumerate(combinations(range(n_vertices), 2)): + yield token, edge diff --git a/axplorer-viz/src/graph_utils.py b/axplorer-viz/src/graph_utils.py new file mode 100644 index 0000000..f97fcff --- /dev/null +++ b/axplorer-viz/src/graph_utils.py @@ -0,0 +1,61 @@ +"""Graph scoring + 4-cycle detection for the Turan C_4 problem. + +The objective in Act 1-3 is ex(15, C_4): maximize the number of edges in a +simple graph on 15 vertices that contains no 4-cycle. These helpers are the +ground-truth scoring used by both the curated trajectory and the Manim scenes. +""" + +from __future__ import annotations + +import itertools + + +def count_edges(G) -> int: + """Number of edges in ``G`` -- the quantity Axplorer is maximizing.""" + return G.number_of_edges() + + +def find_4_cycles(G) -> list[tuple[int, int, int, int]]: + """All distinct 4-cycles (C_4 subgraphs) in ``G``. + + Each cycle is returned once, in a canonical orientation: the tuple + ``(a, b, c, d)`` lists the four vertices in cyclic order, starts at the + smallest vertex ``a``, and proceeds toward the smaller of ``a``'s two + neighbours, so ``b < d``. The list is sorted. + + A 4-cycle ``a-b-c-d-a`` has two "diagonals": the opposite pairs ``{a, c}`` + and ``{b, d}``. Two vertices that share two (or more) common neighbours are + a diagonal of one 4-cycle per pair of those common neighbours, so we + enumerate every vertex pair, look at its common neighbourhood, and emit one + canonical tuple per pair of common neighbours. The set dedups the two ways + each cycle is discovered (once from each diagonal). + """ + cycles: set[tuple[int, int, int, int]] = set() + for u, w in itertools.combinations(G.nodes(), 2): + common = (set(G[u]) & set(G[w])) - {u, w} + if len(common) < 2: + continue + for x, y in itertools.combinations(sorted(common), 2): + # 4-cycle u-x-w-y-u; diagonals {u, w} and {x, y}. + start = min(u, w, x, y) + if start in (u, w): + opposite = w if start == u else u + left, right = (x, y) if x < y else (y, x) + else: + opposite = y if start == x else x + left, right = (u, w) if u < w else (w, u) + cycles.add((start, left, opposite, right)) + return sorted(cycles) + + +def has_4_cycle(G) -> bool: + """True iff ``G`` contains at least one 4-cycle. + + Cheaper than :func:`find_4_cycles`: stop as soon as some vertex pair has two + common neighbours. + """ + for u, w in itertools.combinations(G.nodes(), 2): + common = (set(G[u]) & set(G[w])) - {u, w} + if len(common) >= 2: + return True + return False diff --git a/axplorer-viz/src/scenes/act1_naive_search.py b/axplorer-viz/src/scenes/act1_naive_search.py new file mode 100644 index 0000000..7d520a4 --- /dev/null +++ b/axplorer-viz/src/scenes/act1_naive_search.py @@ -0,0 +1,127 @@ +"""Act 1 (~20s): naive local search hits a plateau around 24 edges. + +15 vertices in a circle on the left, a live edge counter top-right, the +constraint pinned along the bottom. We replay ``trajectory.ACT1_OPERATIONS``: +greedy edge additions, and -- when an attempted edge would close a 4-cycle -- +a red quad flashes over the offending C_4 and the edge is discarded. The act +ends parked on a maximal 24-edge graph: "Stuck at 24." +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import networkx as nx +import numpy as np +from manim import ( + Create, + DOWN, + Dot, + FadeIn, + FadeOut, + Line, + Scene, + Text, + UP, + ValueTracker, + VGroup, + Write, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +import styles as S # noqa: E402 +import trajectory as T # noqa: E402 +from graph_utils import find_4_cycles # noqa: E402 + +MAIN_CENTER = np.array([-2.5, 0.35, 0.0]) +GRAPH_RADIUS = 2.5 + + +def _offending_cycle(running: nx.Graph, u: int, v: int): + """Return a 4-cycle of ``running + (u, v)`` that uses the edge (u, v).""" + running.add_edge(u, v) + cycles = find_4_cycles(running) + running.remove_edge(u, v) + target = frozenset((u, v)) + for a, b, c, d in cycles: + sides = (frozenset((a, b)), frozenset((b, c)), frozenset((c, d)), frozenset((d, a))) + if target in sides: + return (a, b, c, d) + return cycles[0] # the curated trajectory guarantees a cycle through (u, v) + + +def play_act1(scene: Scene) -> None: + positions = S.circle_positions(T.N_VERTICES, radius=GRAPH_RADIUS, center=MAIN_CENTER) + + # --- static frame --------------------------------------------------- + context = Text( + "Turán's problem · 15 vertices · no 4-cycles", + font=S.FONT, color=S.SUBLABEL_COLOR, font_size=S.SUBLABEL_SIZE, + ).to_edge(UP, buff=0.3) + + constraint = Text( + "maximize edges · avoid 4-cycles", + font=S.FONT, color=S.CONSTRAINT_COLOR, font_size=S.CONSTRAINT_SIZE, + ).to_edge(DOWN, buff=0.35) + + col_x = 4.2 + score_caption = Text("EDGES", font=S.FONT, color=S.SCORE_CAPTION_COLOR, font_size=S.SCORE_CAPTION_SIZE) + score_caption.move_to(np.array([col_x, 2.95, 0.0])) + score_tracker = ValueTracker(0) + score_value = S.int_counter(score_tracker, anchor=np.array([col_x, 2.25, 0.0]), + font_size=S.SCORE_SIZE * 1.7) + + phase_label = Text("greedy search", font=S.FONT, color=S.LABEL_COLOR, font_size=24) + phase_label.move_to(np.array([col_x, 1.15, 0.0])) + + vertex_dots = { + i: Dot(point=positions[i], radius=S.VERTEX_RADIUS, color=S.VERTEX_COLOR) + for i in range(T.N_VERTICES) + } + vertices_group = VGroup(*vertex_dots.values()) + + scene.play(FadeIn(context), FadeIn(constraint), run_time=0.7) + scene.play(Write(score_caption), FadeIn(score_value), FadeIn(phase_label), run_time=0.5) + scene.play(FadeIn(vertices_group, lag_ratio=0.08), run_time=1.0) + + # --- replay the search --------------------------------------------- + running = nx.empty_graph(T.N_VERTICES) + edge_lines: dict[tuple[int, int], Line] = {} + added_count = 0 + + for op, (u, v) in T.ACT1_OPERATIONS: + if op == "add": + added_count += 1 + running.add_edge(u, v) + line = Line(positions[u], positions[v], color=S.EDGE_NEW_COLOR, stroke_width=S.EDGE_STROKE_WIDTH) + line.set_z_index(-1) + edge_lines[(min(u, v), max(u, v))] = line + scene.play(Create(line), score_tracker.animate.set_value(added_count), run_time=0.16) + line.set_color(S.EDGE_COLOR) # settle to resting colour + else: # reject + a, b, c, d = _offending_cycle(running, u, v) + tentative = Line(positions[u], positions[v], color=S.WARN_COLOR, stroke_width=S.EDGE_STROKE_WIDTH) + poly = S.cycle_polygon((a, b, c, d), positions) + scene.play(Create(tentative), run_time=0.14) + scene.play(FadeIn(poly), run_time=0.10) + scene.wait(max(0.02, S.FLASH_DURATION - 0.24)) + scene.play(FadeOut(poly), FadeOut(tentative), run_time=0.14) + + # --- stuck ---------------------------------------------------------- + new_phase = Text("— stuck —", font=S.FONT, color=S.WARN_COLOR, font_size=24) + new_phase.move_to(phase_label) + scene.play(phase_label.animate.become(new_phase), run_time=0.4) + + stuck = Text(f"Stuck at {added_count}", font=S.FONT, color=S.WARN_COLOR, font_size=S.TITLE_SIZE) + stuck.next_to(vertices_group, DOWN, buff=0.34).set_x(MAIN_CENTER[0]) + scene.play(FadeIn(stuck, shift=0.2 * UP), run_time=0.5) + scene.wait(1.3) + + +class Act1NaiveSearch(Scene): + def construct(self): + self.camera.background_color = S.BACKGROUND_COLOR + play_act1(self) + S.clear_scene(self) diff --git a/axplorer-viz/src/scenes/act2_transformer.py b/axplorer-viz/src/scenes/act2_transformer.py new file mode 100644 index 0000000..3a1e733 --- /dev/null +++ b/axplorer-viz/src/scenes/act2_transformer.py @@ -0,0 +1,209 @@ +"""Act 2 (~30s): a transformer trained on the top-k produces structured samples. + +Left: the "top-k pool" -- four small graphs the local search found near the +plateau, the 24-edge plateau among them. Centre: a stylised "Transformer" box +(rounded rectangle + a little attention glyph; no architecture internals). The +pool flows into the box; out the other side comes ``TRANSFORMER_SAMPLE_1`` -- +visibly more regular -- which we then 2-colour to expose its near-bipartite +structure. A second sample follows. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +from manim import ( + Arrow, + CurvedArrow, + DOWN, + Dot, + FadeIn, + FadeOut, + LEFT, + RIGHT, + RoundedRectangle, + Scene, + Text, + UP, + VGroup, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +import styles as S # noqa: E402 +import trajectory as T # noqa: E402 + +POOL_X = -5.25 +POOL_Y = [2.05, 0.6, -0.85, -2.3] +THUMB_R = 0.5 + +BOX_CENTER = np.array([-0.4, 0.05, 0.0]) +BOX_W, BOX_H = 2.9, 3.7 + +OUT_CENTER = np.array([3.7, 0.05, 0.0]) +OUT_R = 1.42 + + +def _thumb(edges): + pos = S.circle_positions(T.N_VERTICES, radius=THUMB_R, center=(0, 0, 0)) + return S.graph_group(edges, pos, vertex_radius=0.028, edge_color=S.EDGE_COLOR, + edge_width=S.EDGE_THUMBNAIL_STROKE_WIDTH) + + +def _is_within_a(u, v, a_set): + return (u in a_set) == (v in a_set) + + +def _transformer_box(): + box = RoundedRectangle(width=BOX_W, height=BOX_H, corner_radius=0.22, + stroke_color=S.BOX_STROKE_COLOR, stroke_width=2.4, + fill_color=S.BOX_FILL_COLOR, fill_opacity=1.0).move_to(BOX_CENTER) + title = Text("Transformer", font=S.FONT, color=S.TITLE_COLOR, font_size=26).move_to(BOX_CENTER) + sub = Text("learns from top-k", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=17) + sub.next_to(title, DOWN, buff=0.16) + + # tiny "attention" glyph near the top: two rows of dots with a few crossing arcs + top_y = BOX_CENTER[1] + BOX_H / 2 - 0.55 + bot_y = top_y - 0.7 + xs = np.linspace(BOX_CENTER[0] - 0.8, BOX_CENTER[0] + 0.8, 4) + top_dots = [Dot([x, top_y, 0], radius=0.035, color=S.ATTENTION_ARROW_COLOR) for x in xs] + bot_dots = [Dot([x, bot_y, 0], radius=0.035, color=S.ATTENTION_ARROW_COLOR) for x in xs] + arcs = [] + for (i, j) in [(0, 3), (1, 2), (3, 1), (2, 0)]: + arcs.append(CurvedArrow(top_dots[i].get_center(), bot_dots[j].get_center(), + angle=0.5, tip_length=0.08, + color=S.ATTENTION_ARROW_COLOR, stroke_width=1.6)) + glyph = VGroup(*top_dots, *bot_dots, *arcs) + + group = VGroup(box, glyph, title, sub) + group.box = box + return group + + +def play_act2(scene: Scene) -> None: + header = Text("train on the top-k → generate new graphs", + font=S.FONT, color=S.LABEL_COLOR, font_size=24).to_edge(UP, buff=0.32) + + # --- the top-k pool ------------------------------------------------- + thumbs = [] + thumb_labels = [] + for entry, y in zip(T.ACT2_TOPK, POOL_Y): + g = _thumb(entry["graph"]).move_to([POOL_X, y, 0]) + lbl = Text(f"{entry['score']}", font=S.FONT, color=S.SCORE_COLOR, font_size=20) + lbl.next_to(g, RIGHT, buff=0.18) + thumbs.append(g) + thumb_labels.append(lbl) + pool_caption = Text("top-k pool", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=18) + pool_caption.move_to([POOL_X, POOL_Y[0] + THUMB_R + 0.45, 0]) + + box_group = _transformer_box() + + arrow_in = Arrow(start=[POOL_X + 0.95, 0.05, 0], end=[BOX_CENTER[0] - BOX_W / 2 - 0.12, 0.05, 0], + buff=0.05, color=S.SUBLABEL_COLOR, stroke_width=3.0, + max_tip_length_to_length_ratio=0.18) + arrow_in_lbl = Text("+ resample", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=16) + arrow_in_lbl.next_to(arrow_in, UP, buff=0.08) + + arrow_out = Arrow(start=[BOX_CENTER[0] + BOX_W / 2 + 0.12, OUT_CENTER[1], 0], + end=[OUT_CENTER[0] - OUT_R - 0.3, OUT_CENTER[1], 0], + buff=0.05, color=S.BOX_STROKE_COLOR, stroke_width=3.0, + max_tip_length_to_length_ratio=0.18) + arrow_out_lbl = Text("sample", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=16) + arrow_out_lbl.next_to(arrow_out, UP, buff=0.08) + + caption = Text("the model has only seen graphs the local search found", + font=S.FONT, color=S.SUBLABEL_COLOR, font_size=S.SUBLABEL_SIZE).to_edge(DOWN, buff=0.34) + + scene.play(FadeIn(header), run_time=0.5) + scene.play(FadeIn(pool_caption), + *[FadeIn(g, shift=0.2 * RIGHT) for g in thumbs], + *[FadeIn(l) for l in thumb_labels], run_time=1.1) + scene.play(FadeIn(box_group, shift=0.2 * LEFT), FadeIn(arrow_in), FadeIn(arrow_in_lbl), + FadeIn(caption), run_time=1.1) + scene.wait(0.6) + + # --- "training": copies of the pool fly into the box ---------------- + flyers = [g.copy() for g in thumbs] + scene.add(*flyers) + scene.play( + *[f.animate.scale(0.12).move_to(BOX_CENTER).set_opacity(0.0) for f in flyers], + lag_ratio=0.18, run_time=3.0, + ) + scene.remove(*flyers) + scene.play(box_group.box.animate.set_stroke(width=4.5), run_time=0.35) + scene.play(box_group.box.animate.set_stroke(width=2.4), run_time=0.35) + scene.wait(0.4) + + # --- sample 1 emerges ---------------------------------------------- + a_set = set(T.TRANSFORMER_PARTITION_A) + s1 = T.ACT2_SAMPLES[0] + out_pos = S.circle_positions(T.N_VERTICES, radius=OUT_R, center=tuple(OUT_CENTER)) + sample1 = S.graph_group(s1["graph"], out_pos, vertex_radius=0.062, + edge_color=S.EDGE_COLOR, edge_width=2.0) + sample1.save_state() + sample1.scale(0.14).move_to(BOX_CENTER).set_opacity(0.0) + scene.add(sample1) + scene.play(FadeIn(arrow_out), FadeIn(arrow_out_lbl), run_time=0.4) + scene.play(sample1.animate.restore(), run_time=1.5) + + s1_label = Text(f"sample 1 · {s1['score']} edges", font=S.FONT, color=S.SCORE_COLOR, font_size=24) + s1_label.next_to(VGroup(*sample1.vertex_dots.values()), DOWN, buff=0.28).set_x(OUT_CENTER[0]) + scene.play(FadeIn(s1_label, shift=0.15 * UP), run_time=0.5) + new_caption = Text("but it gives back something far more regular than search ever found", + font=S.FONT, color=S.LABEL_COLOR, font_size=S.SUBLABEL_SIZE).to_edge(DOWN, buff=0.34) + scene.play(caption.animate.become(new_caption), run_time=0.5) + scene.wait(1.3) + + # --- highlight the near-bipartite structure ------------------------- + recolors = [ + dot.animate.set_color(S.PARTITION_A_COLOR if v in a_set else S.PARTITION_B_COLOR).scale(1.3) + for v, dot in sample1.vertex_dots.items() + ] + scene.play(*recolors, run_time=1.2) + # dim the few edges that fall inside an arc; keep the crossing skeleton bright + edge_emph = [] + for (u, v), line in sample1.edge_lines.items(): + if _is_within_a(u, v, a_set): + edge_emph.append(line.animate.set_stroke(opacity=0.12)) + else: + edge_emph.append(line.animate.set_stroke(color=S.EDGE_NEW_COLOR, opacity=0.95)) + struct_label = Text("near-bipartite structure", font=S.FONT, color=S.TITLE_COLOR, font_size=24) + struct_label.move_to(s1_label).set_x(OUT_CENTER[0]) + scene.play(*edge_emph, s1_label.animate.become(struct_label), run_time=1.0) + scene.wait(1.5) + + # --- sample 2 emerges ----------------------------------------------- + s2 = T.ACT2_SAMPLES[1] + sample2 = S.graph_group(s2["graph"], out_pos, vertex_radius=0.062, + edge_color=S.EDGE_COLOR, edge_width=2.0) + for (u, v), line in sample2.edge_lines.items(): + if not _is_within_a(u, v, a_set): + line.set_stroke(color=S.EDGE_NEW_COLOR, opacity=0.9) + for v, dot in sample2.vertex_dots.items(): + dot.set_color(S.PARTITION_A_COLOR if v in a_set else S.PARTITION_B_COLOR) + sample2.save_state() + sample2.scale(0.14).move_to(BOX_CENTER).set_opacity(0.0) + s2_label = Text(f"sample 2 · {s2['score']} edges", font=S.FONT, color=S.SCORE_COLOR, font_size=24) + s2_label.move_to(s1_label).set_x(OUT_CENTER[0]) + + # `s1_label` is the on-screen text mobject (it became `struct_label`'s look + # via .become; `struct_label` itself was never added to the scene). + scene.play(FadeOut(sample1), FadeOut(s1_label), run_time=0.7) + scene.add(sample2) + scene.play(sample2.animate.restore(), run_time=1.4) + scene.play(FadeIn(s2_label, shift=0.15 * UP), run_time=0.5) + scene.wait(1.0) + + final_caption = Text("structured candidates — now feed them back into the search loop", + font=S.FONT, color=S.LABEL_COLOR, font_size=S.SUBLABEL_SIZE).to_edge(DOWN, buff=0.34) + scene.play(caption.animate.become(final_caption), run_time=0.5) + scene.wait(1.4) + + +class Act2Transformer(Scene): + def construct(self): + self.camera.background_color = S.BACKGROUND_COLOR + play_act2(self) + S.clear_scene(self) diff --git a/axplorer-viz/src/scenes/act3_flywheel.py b/axplorer-viz/src/scenes/act3_flywheel.py new file mode 100644 index 0000000..cefd9ba --- /dev/null +++ b/axplorer-viz/src/scenes/act3_flywheel.py @@ -0,0 +1,279 @@ +"""Act 3 (~40s): the full flywheel climbs past the baseline to the optimum. + +Centre: the working graph. Left: a live "best so far" edge counter. Right: a +small loop diagram (sample -> local search -> retrain -> ...) with the active +phase lit. Bottom: a score-vs-iteration plot with a dashed "naive search +ceiling" at ``trajectory.NAIVE_SEARCH_CEILING``. + +We replay ``trajectory.ACT3_ITERATIONS``: iteration 0 is the plateau (sitting +on the ceiling); iteration 1 is the trained model's first structured sample (a +restructuring of the plateau); the rest are local-search refinements that add +edges, climbing toward ``trajectory.OPTIMUM_EDGES``. The remaining x-axis is +filled flat at the optimum to make the convergence obvious. Then the graph +drifts to centre, rotates slowly, and the tagline lands. + +All the magic numbers come from ``trajectory`` (the ceiling, the optimum, the +iteration scores, N), so this scene renders unchanged whether ``trajectory`` is +the V1 hand-curated data or a real Axplorer log. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +from manim import ( + Axes, + Create, + CurvedArrow, + DOWN, + DashedLine, + Dot, + FadeIn, + FadeOut, + LEFT, + Line, + ORIGIN, + RIGHT, + Rotate, + RoundedRectangle, + Scene, + TAU, + Text, + UP, + ValueTracker, + VGroup, +) + +sys.path.insert(0, str(Path(__file__).resolve().parent.parent)) + +import styles as S # noqa: E402 +import trajectory as T # noqa: E402 + +GRAPH_CENTER = np.array([0.0, 1.3, 0.0]) +GRAPH_R = 1.7 +COUNTER_POS = np.array([-4.8, 1.55, 0.0]) +LOOP_CENTER = np.array([4.7, 1.35, 0.0]) +LOOP_R = 1.18 + +PHASES = ["sample", "search", "retrain"] +PHASE_ANGLE = {"sample": 90, "search": -32, "retrain": 212} # degrees on the loop + + +def _norm(e): + a, b = e + return (a, b) if a <= b else (b, a) + + +def _build_loop(): + nodes = {} + for name in PHASES: + ang = np.deg2rad(PHASE_ANGLE[name]) + center = LOOP_CENTER + LOOP_R * np.array([np.cos(ang), np.sin(ang), 0.0]) + box = RoundedRectangle(width=1.4, height=0.5, corner_radius=0.12, + stroke_color=S.SUBLABEL_COLOR, stroke_width=1.6, + fill_color=S.BOX_FILL_COLOR, fill_opacity=1.0) + txt = Text(name, font=S.FONT, color=S.SUBLABEL_COLOR, font_size=16) + node = VGroup(box, txt).move_to(center) + node.box, node.txt = box, txt + nodes[name] = node + arcs = [] + order = ["retrain", "sample", "search", "retrain"] + for a, b in zip(order, order[1:]): + arcs.append(CurvedArrow(nodes[a].get_center(), nodes[b].get_center(), + angle=-0.7, tip_length=0.13, + color=S.ATTENTION_ARROW_COLOR, stroke_width=2.0)) + caption = Text("the flywheel", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=15) + caption.move_to(LOOP_CENTER + np.array([0, -0.02, 0])) + group = VGroup(*arcs, *nodes.values(), caption) + group.nodes = nodes + return group + + +def _phase_anims(loop, active): + out = [] + for name, node in loop.nodes.items(): + if name == active: + out.append(node.box.animate.set_stroke(color=S.GOOD_COLOR, width=3.0)) + out.append(node.txt.animate.set_color(S.TITLE_COLOR)) + else: + out.append(node.box.animate.set_stroke(color=S.SUBLABEL_COLOR, width=1.6)) + out.append(node.txt.animate.set_color(S.SUBLABEL_COLOR)) + return out + + +def play_act3(scene: Scene) -> None: + positions = S.circle_positions(T.N_VERTICES, radius=GRAPH_R, center=tuple(GRAPH_CENTER)) + + title = Text("the flywheel: sample → local search → retrain → …", + font=S.FONT, color=S.LABEL_COLOR, font_size=22).to_edge(UP, buff=0.28) + + # --- edge counter --------------------------------------------------- + counter_caption = Text("EDGES", font=S.FONT, color=S.SCORE_CAPTION_COLOR, font_size=S.SCORE_CAPTION_SIZE) + counter_caption.move_to(COUNTER_POS + np.array([0, 0.72, 0])) + score_tracker = ValueTracker(T.ACT3_ITERATIONS[0]["score"]) + counter = S.int_counter(score_tracker, anchor=COUNTER_POS, font_size=S.SCORE_SIZE * 1.7) + best_caption = Text("best so far", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=16) + best_caption.move_to(COUNTER_POS + np.array([0, -0.66, 0])) + + # --- working graph -------------------------------------------------- + vertex_dots = {i: Dot(positions[i], radius=S.VERTEX_RADIUS, color=S.VERTEX_COLOR) for i in range(T.N_VERTICES)} + edge_mobs: dict[tuple[int, int], Line] = {} + + def make_edges(edge_list): + new = [] + for e in edge_list: + k = _norm(e) + if k not in edge_mobs: + ln = Line(positions[k[0]], positions[k[1]], color=S.EDGE_COLOR, stroke_width=S.EDGE_STROKE_WIDTH) + ln.set_z_index(-1) + edge_mobs[k] = ln + new.append(ln) + return new + + def drop_edges(edge_list): + rem = [] + for e in edge_list: + k = _norm(e) + if k in edge_mobs: + rem.append(edge_mobs.pop(k)) + return rem + + PHASE_REF = GRAPH_CENTER + np.array([0, -GRAPH_R - 0.55, 0]) + + def phase_text(text, color): + return Text(text, font=S.FONT, color=color, font_size=22).move_to(PHASE_REF) + + iters = T.ACT3_ITERATIONS + ceiling_y = T.NAIVE_SEARCH_CEILING # where the dashed "naive search ceiling" sits + optimum_y = T.OPTIMUM_EDGES + start_score = iters[0]["score"] # the first plotted point (== the plateau, on the ceiling) + + phase_label = phase_text( + ("naive search — already optimal" if ceiling_y >= optimum_y else f"naive search — stuck at {start_score}"), + S.WARN_COLOR, + ) + + # --- plot ----------------------------------------------------------- + y_lo, y_hi = ceiling_y - 2, optimum_y + 1 + x_hi = 6 # the plot's x-axis spans iterations 0..x_hi + axes = Axes( + x_range=[0, x_hi, 1], y_range=[y_lo, y_hi, 2], + x_length=10.6, y_length=2.0, + axis_config={"include_numbers": False, "include_tip": False, + "stroke_color": S.AXIS_COLOR, "stroke_width": 2.0}, + ).move_to(np.array([-0.7, -2.6, 0.0])) + x_label = Text("iteration →", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=18).next_to(axes.x_axis, RIGHT, buff=0.1) + y_label = Text("edges", font=S.FONT, color=S.SUBLABEL_COLOR, font_size=18).next_to(axes.y_axis, UP, buff=0.08) + tick_opt = Text(str(optimum_y), font=S.FONT, color=S.GOOD_COLOR, font_size=16).next_to(axes.c2p(0, optimum_y), LEFT, buff=0.1) + tick_ceil = Text(str(ceiling_y), font=S.FONT, color=S.CEILING_COLOR, font_size=16).next_to(axes.c2p(0, ceiling_y), LEFT, buff=0.1) + ceiling = DashedLine(axes.c2p(0, ceiling_y), axes.c2p(x_hi, ceiling_y), color=S.CEILING_COLOR, stroke_width=2.0, dash_length=0.12) + ceiling_label = Text("naive search ceiling", font=S.FONT, color=S.CEILING_COLOR, font_size=16) + ceiling_label.next_to(axes.c2p(2.1, ceiling_y), DOWN, buff=0.1) + + loop = _build_loop() + + # static dashboard pieces -- everything that gets cleared in the finale + dashboard = [title, counter_caption, counter, best_caption, phase_label, + axes, x_label, y_label, tick_ceil, tick_opt, ceiling, ceiling_label, loop] + plot_marks = [] # plot dots + connector lines + + # --- intro ---------------------------------------------------------- + scene.play(FadeIn(title), run_time=0.5) + scene.play(*[FadeIn(d) for d in vertex_dots.values()], + FadeIn(counter_caption), FadeIn(counter), FadeIn(best_caption), FadeIn(loop), + run_time=1.0) + plateau_edges = make_edges(iters[0]["graph"]) + scene.play(*[FadeIn(m) for m in plateau_edges], FadeIn(phase_label), run_time=1.0) + scene.play(Create(axes), FadeIn(x_label), FadeIn(y_label), FadeIn(tick_ceil), FadeIn(tick_opt), + Create(ceiling), FadeIn(ceiling_label), run_time=1.2) + p0 = Dot(axes.c2p(0, start_score), radius=0.06, color=S.PLOT_POINT_COLOR) + plot_marks.append(p0) + scene.play(FadeIn(p0, scale=2.0), run_time=0.4) + scene.wait(1.2) + + # --- iterations ----------------------------------------------------- + prev_edges = set(_norm(e) for e in iters[0]["graph"]) + last_pt = p0 + + for idx in range(1, len(iters)): + cur = iters[idx] + cur_set = set(_norm(e) for e in cur["graph"]) + to_add = [e for e in cur["graph"] if _norm(e) not in prev_edges] + to_remove = [e for e in iters[idx - 1]["graph"] if _norm(e) not in cur_set] + + if idx == 1: + scene.play(*_phase_anims(loop, "sample"), + phase_label.animate.become(phase_text("sample from the trained model", S.GOOD_COLOR)), + run_time=0.7) + rem = drop_edges(to_remove) + add = make_edges(to_add) + scene.play(*[FadeOut(m) for m in rem], run_time=0.6) + scene.play(*_phase_anims(loop, "search"), + phase_label.animate.become(phase_text("local search refines it", S.LABEL_COLOR)), + run_time=0.5) + scene.play(*[Create(m) for m in add], score_tracker.animate.set_value(cur["score"]), run_time=1.4) + else: + scene.play(*_phase_anims(loop, "sample"), run_time=0.4) + add = make_edges(to_add) + n = len(to_add) + scene.play(*_phase_anims(loop, "search"), + phase_label.animate.become(phase_text(f"local search: +{n} edge" + ("s" if n != 1 else ""), S.LABEL_COLOR)), + run_time=0.5) + scene.play(*[Create(m) for m in add], score_tracker.animate.set_value(cur["score"]), run_time=0.8) + + new_pt = Dot(axes.c2p(cur["iteration"], cur["score"]), radius=0.06, color=S.PLOT_POINT_COLOR) + connector = Line(last_pt.get_center(), new_pt.get_center(), color=S.PLOT_CURVE_COLOR, stroke_width=3.0) + plot_marks += [connector, new_pt] + scene.play(Create(connector), run_time=0.5) + scene.play(FadeIn(new_pt, scale=2.0), run_time=0.3) + last_pt = new_pt + + scene.play(*_phase_anims(loop, "retrain"), + phase_label.animate.become(phase_text("retrain on the new top-k", S.SUBLABEL_COLOR)), + run_time=0.5) + scene.wait(0.35) + prev_edges = cur_set + + # --- convergence: fill the rest of the x-axis flat at the optimum --- + scene.play(phase_label.animate.become(phase_text("it cannot do better — this is the optimum", S.GOOD_COLOR)), + run_time=0.6) + for it in range(len(iters), x_hi + 1): + new_pt = Dot(axes.c2p(it, optimum_y), radius=0.06, color=S.PLOT_POINT_COLOR) + connector = Line(last_pt.get_center(), new_pt.get_center(), color=S.PLOT_CURVE_COLOR, stroke_width=3.0) + plot_marks += [connector, new_pt] + scene.play(Create(connector), FadeIn(new_pt, scale=1.6), run_time=0.3) + last_pt = new_pt + optimum_tag = Text(f"ex({T.N_VERTICES}, no 4-cycle) = {optimum_y}", font=S.FONT, color=S.GOOD_COLOR, font_size=18) + optimum_tag.next_to(last_pt, UP, buff=0.16) + plot_marks.append(optimum_tag) + scene.play(FadeIn(optimum_tag), run_time=0.5) + + scene.play(*[d.animate.set_color(S.GOOD_COLOR) for d in vertex_dots.values()], + *[m.animate.set_stroke(color=S.GOOD_COLOR, opacity=0.85) for m in edge_mobs.values()], + run_time=1.0) + scene.wait(1.2) + + # --- finale --------------------------------------------------------- + counter.clear_updaters() + scene.play(*[FadeOut(m) for m in dashboard + plot_marks], run_time=0.8) + + scene.remove(*vertex_dots.values(), *edge_mobs.values()) + graph = VGroup(*edge_mobs.values(), *vertex_dots.values()) + scene.add(graph) + scene.play(graph.animate.move_to(ORIGIN).scale(1.4), run_time=1.2) + scene.play(Rotate(graph, angle=TAU / 7, about_point=ORIGIN), run_time=1.8) + + tagline = Text(T.FLYWHEEL_TAGLINE, font=S.FONT, color=S.TITLE_COLOR, font_size=S.TITLE_SIZE) + tagline.to_edge(DOWN, buff=0.7) + scene.play(FadeIn(tagline, shift=0.25 * UP), Rotate(graph, angle=TAU / 10, about_point=ORIGIN), run_time=1.4) + scene.play(Rotate(graph, angle=TAU / 8, about_point=ORIGIN), run_time=1.8) + scene.wait(1.4) + + +class Act3Flywheel(Scene): + def construct(self): + self.camera.background_color = S.BACKGROUND_COLOR + play_act3(self) + S.clear_scene(self) diff --git a/axplorer-viz/src/scenes/full_video.py b/axplorer-viz/src/scenes/full_video.py new file mode 100644 index 0000000..06775f6 --- /dev/null +++ b/axplorer-viz/src/scenes/full_video.py @@ -0,0 +1,56 @@ +"""The whole thing: opening card -> Act 1 -> Act 2 -> Act 3, with fade +transitions and a brief intertitle before each act. + +Render this for the final deliverable (see README). The individual acts also +have their own Scene subclasses if you want to iterate on one in isolation. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +from manim import DOWN, FadeIn, FadeOut, Scene, Text, UP, VGroup + +# Make both `src/` (for styles/trajectory/graph_utils) and `src/scenes/` (for the +# sibling act modules) importable regardless of how this file is loaded -- don't +# rely on Manim happening to add the file's directory to sys.path. +_SCENES_DIR = Path(__file__).resolve().parent +sys.path.insert(0, str(_SCENES_DIR.parent)) +sys.path.insert(0, str(_SCENES_DIR)) + +import styles as S # noqa: E402 +from act1_naive_search import play_act1 # noqa: E402 +from act2_transformer import play_act2 # noqa: E402 +from act3_flywheel import play_act3 # noqa: E402 + + +def _card(scene, big, small, *, hold=1.4, big_size=44): + title = Text(big, font=S.FONT, color=S.TITLE_COLOR, font_size=big_size) + sub = Text(small, font=S.FONT, color=S.SUBLABEL_COLOR, font_size=24) + group = VGroup(title, sub).arrange(DOWN, buff=0.35) + scene.play(FadeIn(group, shift=0.25 * UP), run_time=0.7) + scene.wait(hold) + scene.play(FadeOut(group, shift=0.25 * DOWN), run_time=0.6) + + +class FullVideo(Scene): + def construct(self): + self.camera.background_color = S.BACKGROUND_COLOR + + _card(self, + "Axplorer × Turán's 4-cycle problem", + "PatternBoost-style search: on 15 vertices, max edges with no 4-cycle", + hold=1.3) + + _card(self, "1 · the plateau", "plain local search", hold=0.6, big_size=38) + play_act1(self) + S.clear_scene(self) + + _card(self, "2 · learn the structure", "a transformer trained on the top-k", hold=0.6, big_size=38) + play_act2(self) + S.clear_scene(self) + + _card(self, "3 · the flywheel", "search → train → sample → search", hold=0.6, big_size=38) + play_act3(self) + S.clear_scene(self) diff --git a/axplorer-viz/src/styles.py b/axplorer-viz/src/styles.py new file mode 100644 index 0000000..d1c9b41 --- /dev/null +++ b/axplorer-viz/src/styles.py @@ -0,0 +1,185 @@ +"""Single source of truth for the visualization's look. + +Everything tweakable about the aesthetic lives here: the dark Axplorer-blog +palette, fonts, the circular graph layout, plot colours for Act 3, and small +helpers the scenes use to draw graphs consistently. Scenes import from this +module (plus ``trajectory`` and ``graph_utils``) and nothing else. +""" + +from __future__ import annotations + +import numpy as np +from manim import Dot, FadeOut, Line, Polygon, Text, ValueTracker, VGroup, VMobject + +# --------------------------------------------------------------------------- +# Palette -- dark, like the Axplorer blog. +# --------------------------------------------------------------------------- + +BACKGROUND_COLOR = "#0E1116" # near-black with a cool tint + +VERTEX_COLOR = "#5EE6C9" # teal -- the graph's "ink" +VERTEX_RADIUS = 0.085 + +EDGE_COLOR = "#8693A8" # muted slate; edges read but don't shout +EDGE_NEW_COLOR = "#E8EDF4" # a freshly added edge flashes bright, then settles +EDGE_STROKE_WIDTH = 2.6 +EDGE_THUMBNAIL_STROKE_WIDTH = 1.6 + +FLASH_COLOR = "#FF4D5E" # 4-cycle violation -- red +FLASH_FILL_OPACITY = 0.28 +FLASH_DURATION = 0.4 # seconds the offending C_4 polygon stays up + +GOOD_COLOR = "#52E08A" # "this worked / optimum reached" -- vivid green +WARN_COLOR = "#FF4D5E" + +# Act 2 bipartition highlight. +PARTITION_A_COLOR = "#FF9E6D" # warm +PARTITION_B_COLOR = "#5BB8F5" # cool + +# Act 3 plot. +AXIS_COLOR = "#5A6472" +PLOT_CURVE_COLOR = "#5EE6C9" # the score climbing +PLOT_POINT_COLOR = "#E8EDF4" +CEILING_COLOR = "#E5737E" # the dashed "naive search ceiling" at 24 + +# Transformer block (Act 2). +BOX_FILL_COLOR = "#161E2B" +BOX_STROKE_COLOR = "#5BB8F5" +ATTENTION_ARROW_COLOR = "#3C5168" + +# --------------------------------------------------------------------------- +# Type / text +# --------------------------------------------------------------------------- + +FONT = "sans-serif" # Pango resolves this to whatever sans is installed + +TITLE_COLOR = "#E8EDF4" +TITLE_SIZE = 40 + +LABEL_COLOR = "#C3CCDA" +LABEL_SIZE = 28 + +SUBLABEL_COLOR = "#8693A8" +SUBLABEL_SIZE = 22 + +SCORE_COLOR = "#F2C94C" # amber -- the number everyone is watching +SCORE_SIZE = 34 +SCORE_CAPTION_COLOR = "#8693A8" +SCORE_CAPTION_SIZE = 20 + +CONSTRAINT_COLOR = "#8693A8" +CONSTRAINT_SIZE = 24 + +# --------------------------------------------------------------------------- +# Layout +# --------------------------------------------------------------------------- + +GRAPH_RADIUS = 2.55 # radius of the 15-vertex circle in the main view +THUMBNAIL_RADIUS = 0.62 # radius for the small "top-k" graphs in Act 2 + +# Default placement of the main graph (Act 1 / Act 3 keep it roughly centre-left +# / centre so the score panel and plot have room). +MAIN_GRAPH_CENTER = np.array([-2.2, 0.4, 0.0]) + +# Generic fade time for act-to-act transitions. +ACT_FADE_TIME = 0.8 + + +def circle_positions(n: int, radius: float = GRAPH_RADIUS, center=(0.0, 0.0, 0.0)) -> dict[int, np.ndarray]: + """Vertex i -> 3D point, equally spaced on a circle, vertex 0 at the top, + going clockwise (matches the convention documented in ``trajectory``).""" + cx, cy, cz = center + out: dict[int, np.ndarray] = {} + for i in range(n): + theta = np.pi / 2 - 2 * np.pi * i / n + out[i] = np.array([cx + radius * np.cos(theta), cy + radius * np.sin(theta), cz]) + return out + + +def _norm_edge(e: tuple[int, int]) -> tuple[int, int]: + a, b = e + return (a, b) if a <= b else (b, a) + + +def graph_group( + edges, + positions: dict[int, np.ndarray], + *, + vertex_color: str = VERTEX_COLOR, + vertex_radius: float = VERTEX_RADIUS, + edge_color: str = EDGE_COLOR, + edge_width: float = EDGE_STROKE_WIDTH, + n_vertices: int | None = None, +) -> VGroup: + """Build a VGroup for a graph drawn at ``positions``. + + The returned group carries three extra attributes for the scenes to grab: + + * ``vertex_dots`` -- dict ``vertex_id -> Dot`` + * ``edge_lines`` -- dict ``(u, v) -> Line`` with ``u <= v`` + * ``positions`` -- the positions dict it was built with + + Edges are added *under* the vertices so dots sit on top of line ends. + """ + if n_vertices is None: + n_vertices = len(positions) + + edge_lines: dict[tuple[int, int], Line] = {} + for e in edges: + u, v = _norm_edge(tuple(e)) + edge_lines[(u, v)] = Line( + positions[u], positions[v], color=edge_color, stroke_width=edge_width + ) + + vertex_dots: dict[int, Dot] = {} + for i in range(n_vertices): + vertex_dots[i] = Dot(point=positions[i], radius=vertex_radius, color=vertex_color) + + group = VGroup(*edge_lines.values(), *vertex_dots.values()) + group.vertex_dots = vertex_dots + group.edge_lines = edge_lines + group.positions = positions + return group + + +def cycle_polygon(cycle, positions, *, color: str = FLASH_COLOR, fill_opacity: float = FLASH_FILL_OPACITY) -> Polygon: + """The red quad drawn over an offending 4-cycle (a, b, c, d).""" + pts = [positions[v] for v in cycle] + return Polygon(*pts, color=color, fill_color=color, fill_opacity=fill_opacity, stroke_width=3.0) + + +def int_counter(tracker, anchor, *, font=FONT, font_size=SCORE_SIZE, color=SCORE_COLOR) -> Text: + """A ``Text`` digit display driven by a ``ValueTracker`` -- no LaTeX. + + The Text is rebuilt (via ``become``) only when the rounded value changes, + so it is cheap even though it lives on screen for the whole video. It stays + centred on ``anchor`` regardless of how many digits it has. + """ + anchor = np.array(anchor, dtype=float) + + def _make(v: int) -> Text: + return Text(str(v), font=font, font_size=font_size, color=color).move_to(anchor) + + label = _make(int(round(tracker.get_value()))) + + def _update(mob): + v = int(round(tracker.get_value())) + if mob.text != str(v): + mob.become(_make(v)) + + label.add_updater(_update) + return label + + +def clear_scene(scene, *, run_time: float = ACT_FADE_TIME): + """Fade out everything currently on screen (skipping ValueTrackers, which + have no opacity), then remove leftovers. Used between acts.""" + for m in list(scene.mobjects): + if hasattr(m, "clear_updaters"): + m.clear_updaters() + visible = [m for m in scene.mobjects if isinstance(m, VMobject)] + if visible: + scene.play(*[FadeOut(m) for m in visible], run_time=run_time) + for m in list(scene.mobjects): + if not isinstance(m, ValueTracker): + scene.remove(m) diff --git a/axplorer-viz/src/trajectory.py b/axplorer-viz/src/trajectory.py new file mode 100644 index 0000000..6fd0c5a --- /dev/null +++ b/axplorer-viz/src/trajectory.py @@ -0,0 +1,339 @@ +"""Trajectory for the Axplorer / Turan C_4 visualization (V1 fallback + V2 real data). + +WHERE THE DATA COMES FROM +------------------------- +By default this module exposes the **V1 hand-curated** trajectory defined below +-- declarative data only (ints/lists/tuples/dicts), no Manim. + +If a real Axplorer log exists at ``axplorer-viz/logs/square_N_run.jsonl`` +(e.g. ``square_N30_run.jsonl``, produced by the patched, vendored Axplorer -- +see ``vendor/PATCH_NOTES.md``), the module-level names the scenes import +(``SEED``, ``NAIVE_PLATEAU``, ``TRANSFORMER_SAMPLE_1`` ... ``ACT3_ITERATIONS``, +``N_VERTICES``, ``OPTIMUM_EDGES``, ...) are **overridden** at import time by +:func:`trajectory_loader.load_trajectory`, so the same Manim scenes render from +the real run with zero code changes in ``scenes/``. If several such logs are +present, the largest-N one wins. Check ``trajectory.TRAJECTORY_SOURCE`` to see +which one is active. (If the log is present but unreadable, we warn and keep +the V1 data.) + +The V1 part below stays purely declarative; the V2 wiring lives in one small +block at the bottom of the file. + +PROBLEM +------- +ex(15, C_4): the maximum number of edges in a simple graph on 15 vertices that +contains no 4-cycle. The known value is 30. + + Source: OEIS A006855, "Maximal number of edges in an n-node graph with no + 4-cycle (C_4)" -- the term for n=15 is 30. https://oeis.org/A006855 + (This is the n=15 case of the Zarankiewicz/Kovari-Sos-Turan problem; see also + Clapham, Flockhart & Sheehan, "Graphs without four-cycles", J. Graph Theory + 13 (1989), which tabulates the small extremal numbers and graphs.) + +All graph states below were checked with src/graph_utils.has_4_cycle and are +C_4-free. The 15 vertices are intended to be drawn equally spaced around a +circle, vertex i at angle 90 deg - i * 360/15 deg (vertex 0 at the top, going +clockwise) -- the scenes own that layout; this file only fixes the labels. +""" + +from __future__ import annotations + +# --------------------------------------------------------------------------- +# Problem constants +# --------------------------------------------------------------------------- + +N_VERTICES: int = 15 + +#: Known optimum ex(15, C_4) -- the score the flywheel must reach. See module docstring. +OPTIMUM_EDGES: int = 30 + +#: Where plain single-edge-addition local search gets stuck (Act 1 plateau / +#: Act 3 "naive search ceiling" dashed line). +NAIVE_SEARCH_CEILING: int = 24 + + +# --------------------------------------------------------------------------- +# Named graph states (edge lists; vertices 0..14) +# --------------------------------------------------------------------------- + +#: Act 1 starting point: 15 isolated vertices, no edges. +SEED: list[tuple[int, int]] = [] + +#: Where greedy/local search plateaus: 24 edges, *maximal* C_4-free (no single +#: edge can be added without creating a 4-cycle), but visibly lumpy and +#: asymmetric -- one vertex of degree 6, several of degree 2. Clearly far from +#: the regular extremal structure. +NAIVE_PLATEAU: list[tuple[int, int]] = [ + (0, 6), (0, 9), (0, 10), (0, 11), (0, 14), + (1, 6), (1, 7), (1, 13), + (2, 6), (2, 12), + (3, 4), (3, 5), (3, 6), + (4, 9), + (5, 8), (5, 11), (5, 12), + (6, 7), (6, 14), + (7, 8), + (8, 10), + (9, 10), (9, 12), + (10, 13), +] + +# The transformer's "aha" samples: both are near-bipartite C_4-free graphs with +# the vertices split into the two arcs A = {0..6} and B = {7..14}; the vast +# majority of edges cross between the arcs (only ~5 lie inside an arc). Far more +# regular and structured than NAIVE_PLATEAU -- this is the point. +TRANSFORMER_PARTITION_A: list[int] = [0, 1, 2, 3, 4, 5, 6] +TRANSFORMER_PARTITION_B: list[int] = [7, 8, 9, 10, 11, 12, 13, 14] + +#: 27 edges; the graph highlighted with a 2-colouring in Act 2. +TRANSFORMER_SAMPLE_1: list[tuple[int, int]] = [ + (0, 5), (0, 8), (0, 11), (0, 14), + (1, 9), (1, 10), (1, 12), (1, 14), + (2, 8), (2, 9), (2, 13), + (3, 7), (3, 10), (3, 11), + (4, 7), (4, 13), (4, 14), + (5, 7), (5, 8), (5, 12), + (6, 11), (6, 12), (6, 13), + (7, 10), + (8, 9), + (9, 10), + (11, 14), +] + +#: 26 edges; a second, structurally distinct sample from the model. +TRANSFORMER_SAMPLE_2: list[tuple[int, int]] = [ + (0, 3), (0, 11), (0, 13), (0, 14), + (1, 2), (1, 7), (1, 8), (1, 14), + (2, 6), (2, 8), (2, 12), (2, 13), + (3, 7), (3, 9), (3, 11), (3, 12), + (4, 5), (4, 9), (4, 10), (4, 14), + (5, 7), (5, 10), (5, 13), + (6, 10), (6, 12), + (10, 11), +] + +#: The N=15 optimum: 30 edges, 4-regular, C_4-free. Built so the 15 perimeter +#: edges (i, i+1 mod 15) are all present, plus 15 chords -- it reads as a +#: deliberately engineered structure in the circular layout, in contrast to the +#: yarn-ball plateau. +FINAL: list[tuple[int, int]] = [ + # perimeter (the 15-cycle 0-1-2-...-14-0) + (0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), + (8, 9), (9, 10), (10, 11), (11, 12), (12, 13), (13, 14), (0, 14), + # chords + (0, 2), (0, 10), + (1, 7), (1, 8), + (2, 12), + (3, 9), (3, 12), + (4, 6), (4, 9), + (5, 13), (5, 14), + (6, 11), + (7, 11), + (8, 13), + (10, 14), +] + + +# --------------------------------------------------------------------------- +# Act 1: SEED -> NAIVE_PLATEAU, with realistic stumbles. +# --------------------------------------------------------------------------- +# +# A sequence of (operation, (u, v)) steps. Operations: +# "add" -- the candidate edge was C_4-free, so it is kept. +# "reject" -- adding the candidate edge would have closed a 4-cycle, so local +# search flashes the offending C_4 and discards the edge. +# +# 40 steps: 24 "add" (exactly the edges of NAIVE_PLATEAU) and 16 "reject". +# Replaying the "add" steps in this order is C_4-free at every prefix, and every +# "reject" step genuinely closes a 4-cycle against the graph built so far -- so +# Act 1 can verify each step live with src/graph_utils rather than trusting +# this list. The last few steps are all rejects: the graph is maximal, search +# is stuck -> "Stuck at 24". + +ACT1_OPERATIONS: list[tuple[str, tuple[int, int]]] = [ + ("add", (1, 6)), + ("add", (6, 14)), + ("add", (9, 12)), + ("add", (5, 11)), + ("add", (1, 13)), + ("reject", (13, 14)), + ("add", (5, 8)), + ("add", (10, 13)), + ("reject", (6, 10)), + ("add", (9, 10)), + ("add", (1, 7)), + ("reject", (1, 9)), + ("add", (7, 8)), + ("add", (4, 9)), + ("add", (5, 12)), + ("reject", (4, 5)), + ("add", (2, 6)), + ("reject", (8, 13)), + ("add", (0, 6)), + ("reject", (7, 14)), + ("add", (2, 12)), + ("reject", (1, 12)), + ("add", (3, 5)), + ("add", (0, 11)), + ("reject", (0, 7)), + ("add", (6, 7)), + ("reject", (0, 8)), + ("add", (0, 10)), + ("reject", (8, 9)), + ("add", (0, 9)), + ("add", (8, 10)), + ("reject", (11, 14)), + ("add", (3, 6)), + ("add", (0, 14)), + ("add", (3, 4)), + ("reject", (2, 14)), + ("reject", (4, 6)), + ("reject", (4, 7)), + ("reject", (8, 12)), + ("reject", (2, 5)), +] + + +# --------------------------------------------------------------------------- +# Act 2: the "top-k" pool fed into the transformer. +# --------------------------------------------------------------------------- +# Four small graphs the local-search phase found near the plateau; their scores +# are deliberately a bit below the plateau, except the plateau itself. These +# are the thumbnails that flow into the transformer block. + +ACT2_TOPK: list[dict] = [ + {"label": "search #1", "score": 24, "graph": NAIVE_PLATEAU}, + {"label": "search #2", "score": 22, "graph": [ + (0, 6), (0, 9), (0, 10), (0, 11), (1, 6), (1, 7), (1, 13), + (2, 6), (2, 12), (3, 4), (3, 5), (3, 6), (4, 9), (5, 8), + (5, 11), (6, 7), (7, 8), (8, 10), (9, 10), (9, 12), (10, 13), (0, 14), + ]}, + {"label": "search #3", "score": 21, "graph": [ + (0, 6), (0, 9), (0, 11), (0, 14), (1, 6), (1, 7), (2, 6), + (2, 12), (3, 4), (3, 5), (3, 6), (4, 9), (5, 8), (5, 11), + (6, 14), (7, 8), (8, 10), (9, 10), (9, 12), (10, 13), (1, 13), + ]}, + {"label": "search #4", "score": 23, "graph": [ + (0, 6), (0, 9), (0, 10), (0, 11), (0, 14), (1, 6), (1, 7), + (1, 13), (2, 6), (2, 12), (3, 4), (3, 5), (3, 6), (4, 9), (5, 8), + (5, 11), (5, 12), (6, 7), (7, 8), (8, 10), (9, 10), (9, 12), (10, 13), + ]}, +] + +#: The samples the transformer emits in Act 2, shown one after another. +ACT2_SAMPLES: list[dict] = [ + {"label": "sample 1", "score": 27, "graph": TRANSFORMER_SAMPLE_1}, + {"label": "sample 2", "score": 26, "graph": TRANSFORMER_SAMPLE_2}, +] + + +# --------------------------------------------------------------------------- +# Act 3: the flywheel loop. Each entry = one compressed iteration. +# --------------------------------------------------------------------------- +# iteration 0 is the naive-search plateau (carried over, sits on the dashed +# ceiling line). Iteration 1 is the first structured sample from the trained +# model -- a *restructuring* of the plateau (not a superset of it). Iterations +# 2, 3, 4 are local-search refinements, each adding one edge, climbing +# 27 -> 28 -> 29 -> 30 = the optimum. The iteration-1..4 graphs are nested +# (G27 subset G28 subset G29 subset FINAL), so the centre graph just keeps +# filling in toward the regular extremal structure. + +_ACT3_LEFT_OUT: list[tuple[int, int]] = [(3, 9), (5, 13), (7, 11)] +_ACT3_G27: list[tuple[int, int]] = [e for e in FINAL if e not in _ACT3_LEFT_OUT] +_ACT3_G28: list[tuple[int, int]] = _ACT3_G27 + [(3, 9)] +_ACT3_G29: list[tuple[int, int]] = _ACT3_G28 + [(5, 13)] + +ACT3_ITERATIONS: list[dict] = [ + {"iteration": 0, "score": 24, "phase": "naive search", "graph": NAIVE_PLATEAU}, + {"iteration": 1, "score": 27, "phase": "sample (trained)", "graph": _ACT3_G27}, + {"iteration": 2, "score": 28, "phase": "local search", "graph": _ACT3_G28}, + {"iteration": 3, "score": 29, "phase": "local search", "graph": _ACT3_G29}, + {"iteration": 4, "score": 30, "phase": "optimum", "graph": FINAL}, +] + +#: Title card at the end of Act 3. +FLYWHEEL_TAGLINE: str = "Axplorer: 2.5 hours, $3, one GPU." + + +# --------------------------------------------------------------------------- +# Convenience +# --------------------------------------------------------------------------- + +#: All five canonical states keyed by name, for tests / sanity checks. +NAMED_STATES: dict[str, list[tuple[int, int]]] = { + "SEED": SEED, + "NAIVE_PLATEAU": NAIVE_PLATEAU, + "TRANSFORMER_SAMPLE_1": TRANSFORMER_SAMPLE_1, + "TRANSFORMER_SAMPLE_2": TRANSFORMER_SAMPLE_2, + "FINAL": FINAL, +} + + +# =========================================================================== +# V2 wiring: if a real Axplorer log is present, override the names above with +# data parsed from it. See module docstring and vendor/PATCH_NOTES.md. +# (This is the only non-declarative part of the module.) +# =========================================================================== + +import glob as _glob # noqa: E402 +import os as _os # noqa: E402 +import re as _re # noqa: E402 + +#: Directory where real Axplorer trajectory logs live. +_LOGS_DIR: str = _os.path.normpath(_os.path.join(_os.path.dirname(__file__), "..", "logs")) + +#: Which dataset is active: "v1-hand-curated" or "axplorer-log:". +TRAJECTORY_SOURCE: str = "v1-hand-curated" + + +def _find_trajectory_log(logs_dir: str = _LOGS_DIR): + """Pick a real trajectory log to drive the scenes, or None for the V1 data. + + Convention: drop a run at ``logs/square_N_run.jsonl`` (e.g. + ``square_N30_run.jsonl``). If several are present, the one with the largest + N wins -- the bigger instance is the more interesting story. (Other ``.jsonl`` + files in ``logs/`` -- e.g. ``example_N15_run.jsonl`` -- are ignored.) + """ + best = None # (n, path) + for path in _glob.glob(_os.path.join(logs_dir, "square_N*_run.jsonl")): + m = _re.search(r"square_N(\d+)_run\.jsonl$", _os.path.basename(path)) + if not m: + continue + n = int(m.group(1)) + if best is None or n > best[0]: + best = (n, path) + return best[1] if best else None + + +#: The chosen log path, or None. ``trajectory.TRAJECTORY_LOG_PATH`` reflects it. +TRAJECTORY_LOG_PATH = _find_trajectory_log() + +_OVERRIDABLE = ( + "N_VERTICES", "OPTIMUM_EDGES", "NAIVE_SEARCH_CEILING", + "SEED", "NAIVE_PLATEAU", "TRANSFORMER_PARTITION_A", "TRANSFORMER_PARTITION_B", + "TRANSFORMER_SAMPLE_1", "TRANSFORMER_SAMPLE_2", "FINAL", + "ACT1_OPERATIONS", "ACT2_TOPK", "ACT2_SAMPLES", "ACT3_ITERATIONS", + "FLYWHEEL_TAGLINE", "NAMED_STATES", +) + +if TRAJECTORY_LOG_PATH and _os.path.isfile(TRAJECTORY_LOG_PATH): + try: + import sys as _sys + + _sys.path.insert(0, _os.path.dirname(__file__)) + from trajectory_loader import load_trajectory as _load_trajectory + + # Let the loader read N and the optimum from the log itself -- do NOT + # force the V1 N=15/opt=30 values onto an N=30 log. + LOADED_TRAJECTORY = _load_trajectory(TRAJECTORY_LOG_PATH) + for _name in _OVERRIDABLE: + if _name in LOADED_TRAJECTORY: + globals()[_name] = LOADED_TRAJECTORY[_name] + TRAJECTORY_SOURCE = "axplorer-log:" + _os.path.relpath(TRAJECTORY_LOG_PATH, _os.path.dirname(__file__)) + except Exception as _exc: # pragma: no cover - corrupt/incompatible log + import warnings as _warnings + + _warnings.warn( + f"axplorer-viz: found a trajectory log at {TRAJECTORY_LOG_PATH} but failed to load it " + f"({_exc!r}); falling back to the V1 hand-curated trajectory.", + stacklevel=2, + ) diff --git a/axplorer-viz/src/trajectory_loader.py b/axplorer-viz/src/trajectory_loader.py new file mode 100644 index 0000000..21b2bc8 --- /dev/null +++ b/axplorer-viz/src/trajectory_loader.py @@ -0,0 +1,430 @@ +"""Turn a real Axplorer trajectory log into the same data ``trajectory.py`` exposes. + +``trajectory.py`` (V1) is hand-curated. This module reads a JSONL log produced +by the patched Axplorer (``--log_trajectory``; see +``vendor/PATCH_NOTES.md``) and produces a dict with the *same* module-level +names the Manim scenes import -- so the scenes don't change. + +What the log gives us directly: + * per-epoch top-k pool (objects + scores) and ``model_samples_*`` + * the ``best_score_so_far`` series and ``wall_time_seconds`` + * (epoch 0 = initial random search only) + +What the log can't give us (and we therefore synthesize, exactly as V1 did): + * Act 1's step-by-step add/reject trace -- the log is per-epoch, not per-move. + We re-derive a plausible trace that ends at the *real* epoch-0 best graph, + using ``graph_utils`` so every reject genuinely closes a 4-cycle. + * Act 3's centre graph per iteration -- the *scores* are real (the + ``best_score_so_far`` series), but the scene needs the per-iteration graphs + to be nested, so we show nested subsets of the *real* final graph filling in. + * Act 2's vertex partition -- a greedy 2-colouring of the chosen sample + (which is honest: if the real samples aren't near-bipartite, the colouring + won't look bipartite, and that's a finding, not a bug). + +Run ``python -m src.trajectory_loader logs/square_N15_run.jsonl`` for a summary. +""" + +from __future__ import annotations + +import json +import os +import random +import sys + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +import networkx as nx # noqa: E402 + +from decode import decode_object, num_edge_tokens # noqa: E402 +from graph_utils import count_edges, has_4_cycle # noqa: E402 + +# These match V1's trajectory.py constants; the loader keeps the same surface. +N_VERTICES_DEFAULT = 15 +FLYWHEEL_TAGLINE = "Axplorer: a few hours, one GPU, real run." + + +# --------------------------------------------------------------------------- +# small graph helpers (operate on edge lists) +# --------------------------------------------------------------------------- + +def _norm(e): + a, b = e + return (a, b) if a <= b else (b, a) + + +def _graph(edges, n): + g = nx.empty_graph(n) + g.add_edges_from(edges) + return g + + +def _score(edges, n): + """Edge count if the graph is C_4-free, else -1 (matches Axplorer's scoring).""" + g = _graph(edges, n) + return -1 if has_4_cycle(g) else count_edges(g) + + +def _is_within(u, v, a_set): + return (u in a_set) == (v in a_set) + + +# --------------------------------------------------------------------------- +# log parsing +# --------------------------------------------------------------------------- + +def _read_jsonl(path): + records = [] + with open(path, "r") as f: + for line in f: + line = line.strip() + if line: + records.append(json.loads(line)) + records.sort(key=lambda r: r.get("epoch", 0)) + return records + + +def _infer_n_vertices(records): + """Number of vertices N. Prefer the explicit ``n_vertices`` field the + patched Axplorer writes; otherwise fall back to inferring it from the + largest edge token seen (largest token == C(N,2)-1, which assumes the log + actually uses high-numbered vertices -- true for non-trivial graphs).""" + for rec in records: + if rec.get("n_vertices"): + return int(rec["n_vertices"]) + max_tok = -1 + for rec in records: + for field in ("top_k_objects", "model_samples_raw", "model_samples_after_search"): + for obj in rec.get(field, []): + for t in obj: + max_tok = max(max_tok, int(t)) + if max_tok < 0: + return N_VERTICES_DEFAULT + # smallest n with C(n,2) > max_tok + n = 2 + while num_edge_tokens(n) <= max_tok: + n += 1 + return n + + +def _decode_objs(objs, n): + return [decode_object(o, n) for o in objs] + + +# --------------------------------------------------------------------------- +# representative-graph selection +# --------------------------------------------------------------------------- + +def _best_object(rec, n, *, prefer_field="top_k_objects"): + """Highest-scoring valid object in a record, decoded to an edge list.""" + cands = [] + objs = rec.get(prefer_field) or rec.get("top_k_objects") or [] + scores = rec.get("top_k_scores") + decoded = _decode_objs(objs, n) + for i, edges in enumerate(decoded): + s = scores[i] if (scores is not None and i < len(scores)) else _score(edges, n) + cands.append((s, edges)) + if not cands: + return [] + return max(cands, key=lambda t: (t[0], len(t[1])))[1] + + +def _graph_distance(a, b): + """Symmetric-difference size of two edge sets -- "how different do these look".""" + sa, sb = {_norm(e) for e in a}, {_norm(e) for e in b} + return len(sa ^ sb) + + +def _pick_distinct_samples(records, n, reference_edges, k=2): + """Pick ``k`` model samples (epoch >= 1, valid, decent score) that look most + different from ``reference_edges`` (the naive plateau). Prefer + ``model_samples_raw``; fall back to ``model_samples_after_search``.""" + pool = [] # (score, distance, edges) + for rec in records: + if rec.get("epoch", 0) < 1: + continue + for field in ("model_samples_raw", "model_samples_after_search"): + for edges in _decode_objs(rec.get(field, []), n): + s = _score(edges, n) + if s < 0 or not edges: + continue + pool.append((s, _graph_distance(edges, reference_edges), edges)) + if len(pool) >= 200: + break + if not pool: + return [] + # de-dup by edge set + seen = set() + uniq = [] + for s, d, edges in pool: + key = frozenset(_norm(e) for e in edges) + if key not in seen: + seen.add(key) + uniq.append((s, d, edges)) + # rank by (score desc, then distance-from-plateau desc) + uniq.sort(key=lambda t: (t[0], t[1]), reverse=True) + chosen = [] + for s, d, edges in uniq: + if all(_graph_distance(edges, c) >= 4 for c in chosen): + chosen.append(edges) + if len(chosen) == k: + break + while len(chosen) < k and uniq: + chosen.append(uniq[len(chosen) % len(uniq)][2]) + return chosen[:k] + + +def _greedy_2coloring(edges, n): + """A greedy max-cut-ish 2-colouring: returns (part_a, part_b) vertex lists. + Vertices are placed to put as many edges as possible across the partition -- + so a near-bipartite graph ends up cleanly split.""" + g = _graph(edges, n) + color = {} + # process vertices in descending degree; greedily choose the side that + # disagrees with more already-coloured neighbours + for v in sorted(g.nodes(), key=lambda x: -g.degree(x)): + same0 = sum(1 for u in g.neighbors(v) if color.get(u) == 0) + same1 = sum(1 for u in g.neighbors(v) if color.get(u) == 1) + color[v] = 0 if same0 <= same1 else 1 + part_a = sorted(v for v in range(n) if color.get(v, 0) == 0) + part_b = sorted(v for v in range(n) if color.get(v, 0) == 1) + return part_a, part_b + + +def _cut_fraction(edges, a_set): + if not edges: + return 1.0 + return sum(1 for u, v in edges if not _is_within(u, v, a_set)) / len(edges) + + +# --------------------------------------------------------------------------- +# Act 1 synthesis (the log has no per-move trace) +# --------------------------------------------------------------------------- + +def synthesize_act1_operations(plateau_edges, n, *, seed=0, n_rejects=16, n_trailing=4): + """Build a plausible (add, reject) trace SEED -> plateau, using graph_utils + so every "reject" genuinely closes a 4-cycle against the graph so far. + + Mirrors the V1 generator: ``plateau_edges`` get added in a shuffled order + (always C_4-free since the final graph is), interleaved with rejects of + edges that *would* close a 4-cycle, plus a tail of rejects ("stuck").""" + rng = random.Random(seed) + plateau = [_norm(e) for e in plateau_edges] + plateau_set = set(plateau) + all_pairs = [(i, j) for i in range(n) for j in range(i + 1, n)] + + def makes_c4(present, e): + g = _graph(list(present) + [e], n) + return has_4_cycle(g) + + order = plateau[:] + rng.shuffle(order) + present = [] + ops = [] + used = set() + for e in order: + if len(present) >= 4 and len(used) < n_rejects and rng.random() < 0.65: + cands = [p for p in all_pairs if p not in plateau_set and p not in used] + rng.shuffle(cands) + for c in cands: + if makes_c4(present, c): + ops.append(("reject", c)) + used.add(c) + break + ops.append(("add", e)) + present.append(e) + # tail: a few more failed attempts -> stuck + cands = [p for p in all_pairs if p not in plateau_set and p not in used] + rng.shuffle(cands) + tail = 0 + for c in cands: + if tail >= n_trailing: + break + if makes_c4(plateau, c): + ops.append(("reject", c)) + used.add(c) + tail += 1 + return ops + + +# --------------------------------------------------------------------------- +# Act 3 nested-subset filler (real scores, graphs build up toward the real FINAL) +# --------------------------------------------------------------------------- + +def _nested_chain_to_final(final_edges, target_sizes, n): + """Return one edge-list per target size, each a subset of the next, ending at + ``final_edges``. We drop edges from ``final_edges`` (preferring to keep a + spanning structure) to hit the smaller sizes -- so the centre graph in Act 3 + visibly fills in toward the real final graph.""" + final = [_norm(e) for e in final_edges] + # order edges so the ones we keep longest form a connected backbone: + # BFS/DFS spanning-tree edges first, then the rest. + g = _graph(final, n) + backbone = set() + for comp in nx.connected_components(g): + sub = g.subgraph(comp) + for u, v in nx.bfs_edges(sub, next(iter(comp))): + backbone.add(_norm((u, v))) + rest = [e for e in final if e not in backbone] + ordered = list(backbone) + rest # keep backbone, drop from `rest` first + chain = [] + for size in target_sizes: + size = max(0, min(size, len(final))) + # keep the first `size` of `ordered` (backbone preferred) + keep = ordered[:size] + chain.append(sorted(keep)) + # ensure nesting (target_sizes should be non-decreasing; clamp if not) + for i in range(1, len(chain)): + if not set(map(tuple, chain[i - 1])) <= set(map(tuple, chain[i])): + chain[i] = sorted(set(map(tuple, chain[i])) | set(map(tuple, chain[i - 1]))) + return chain + + +# --------------------------------------------------------------------------- +# top-level loader +# --------------------------------------------------------------------------- + +def load_trajectory(jsonl_path, *, n_vertices=None, optimum_edges=None): + """Read ``jsonl_path`` and return a dict with the same names ``trajectory.py`` + exposes. ``n_vertices`` overrides the inferred N; ``optimum_edges`` + overrides the known ex(N, C_4) value (default: the best score actually + reached in the log).""" + records = _read_jsonl(jsonl_path) + if not records: + raise ValueError(f"empty / unreadable trajectory log: {jsonl_path}") + n = int(n_vertices) if n_vertices is not None else _infer_n_vertices(records) + + # --- named graph states ------------------------------------------------- + SEED = [] + NAIVE_PLATEAU = _best_object(records[0], n) # epoch 0 == initial random search + FINAL = _best_object(records[-1], n) + samples = _pick_distinct_samples(records, n, NAIVE_PLATEAU, k=2) + if len(samples) < 2: + # fall back to top-k of a mid epoch + mid = records[len(records) // 2] + decoded = _decode_objs(mid.get("top_k_objects", []), n) + samples = (samples + [e for e in decoded if e and _score(e, n) >= 0])[:2] + while len(samples) < 2: + samples.append(FINAL or NAIVE_PLATEAU or []) + SAMPLE_1, SAMPLE_2 = samples[0], samples[1] + + final_score = _score(FINAL, n) if FINAL else max( + (r.get("best_score_so_far", -1) for r in records), default=-1 + ) + OPTIMUM_EDGES = int(optimum_edges) if optimum_edges is not None else int(round(final_score)) + plateau_score = _score(NAIVE_PLATEAU, n) if NAIVE_PLATEAU else int(round(records[0].get("best_score_so_far", 0))) + NAIVE_SEARCH_CEILING = int(plateau_score) + + # --- Act 2 partition (greedy 2-colouring of sample 1) ------------------- + PART_A, PART_B = _greedy_2coloring(SAMPLE_1, n) + if not PART_A or not PART_B: # degenerate; fall back to a contiguous split + half = n // 2 + PART_A, PART_B = list(range(half)), list(range(half, n)) + + # --- Act 1 operations (synthesized) ------------------------------------ + ACT1_OPERATIONS = synthesize_act1_operations(NAIVE_PLATEAU, n, seed=records[0].get("epoch", 0)) + + # --- Act 2 top-k pool + samples ---------------------------------------- + # use the *last* epoch with a populated top-k for the pool (most "trained"), + # or epoch 0 if that's all there is + pool_rec = next((r for r in reversed(records) if r.get("top_k_objects")), records[0]) + pool_objs = _decode_objs(pool_rec.get("top_k_objects", []), n) + pool_scores = pool_rec.get("top_k_scores") or [_score(e, n) for e in pool_objs] + paired = sorted( + ((s, e) for s, e in zip(pool_scores, pool_objs) if e and s >= 0), + key=lambda t: t[0], reverse=True, + )[:4] + ACT2_TOPK = [{"label": f"search #{i + 1}", "score": int(round(s)), "graph": e} for i, (s, e) in enumerate(paired)] + if not ACT2_TOPK: # ensure non-empty + ACT2_TOPK = [{"label": "search #1", "score": NAIVE_SEARCH_CEILING, "graph": NAIVE_PLATEAU}] + ACT2_SAMPLES = [ + {"label": "sample 1", "score": int(round(_score(SAMPLE_1, n))), "graph": SAMPLE_1}, + {"label": "sample 2", "score": int(round(_score(SAMPLE_2, n))), "graph": SAMPLE_2}, + ] + + # --- Act 3 iterations (real best-score series, nested graphs to FINAL) -- + series = [] + seen_scores = set() + for rec in records: + s = rec.get("best_score_so_far") + if s is None: + continue + s = int(round(s)) + if s in seen_scores: + continue + seen_scores.add(s) + series.append((rec.get("epoch", len(series)), s)) + if not series: + series = [(0, NAIVE_SEARCH_CEILING), (1, OPTIMUM_EDGES)] + # the first point sits on the naive ceiling; subsequent points climb + series = sorted(series, key=lambda t: t[1]) + if series[0][1] != NAIVE_SEARCH_CEILING: + series = [(0, NAIVE_SEARCH_CEILING)] + series + if series[-1][1] != OPTIMUM_EDGES: + series.append((series[-1][0] + 1, OPTIMUM_EDGES)) + target_sizes = [s for _, s in series[1:]] # graphs for iterations 1..K + chain = _nested_chain_to_final(FINAL, target_sizes, n) if FINAL else [[] for _ in target_sizes] + ACT3_ITERATIONS = [{"iteration": 0, "score": series[0][1], "phase": "naive search", "graph": NAIVE_PLATEAU}] + for i, ((_, s), graph) in enumerate(zip(series[1:], chain), start=1): + phase = "sample (trained)" if i == 1 else ("optimum" if i == len(series) - 1 else "local search") + ACT3_ITERATIONS.append({"iteration": i, "score": s, "phase": phase, "graph": graph}) + + return { + "SOURCE": "axplorer-log", + "LOG_PATH": os.path.abspath(jsonl_path), + "N_VERTICES": n, + "OPTIMUM_EDGES": OPTIMUM_EDGES, + "NAIVE_SEARCH_CEILING": NAIVE_SEARCH_CEILING, + "SEED": SEED, + "NAIVE_PLATEAU": NAIVE_PLATEAU, + "TRANSFORMER_PARTITION_A": PART_A, + "TRANSFORMER_PARTITION_B": PART_B, + "TRANSFORMER_SAMPLE_1": SAMPLE_1, + "TRANSFORMER_SAMPLE_2": SAMPLE_2, + "FINAL": FINAL, + "ACT1_OPERATIONS": ACT1_OPERATIONS, + "ACT2_TOPK": ACT2_TOPK, + "ACT2_SAMPLES": ACT2_SAMPLES, + "ACT3_ITERATIONS": ACT3_ITERATIONS, + "FLYWHEEL_TAGLINE": FLYWHEEL_TAGLINE, + "NAMED_STATES": { + "SEED": SEED, "NAIVE_PLATEAU": NAIVE_PLATEAU, + "TRANSFORMER_SAMPLE_1": SAMPLE_1, "TRANSFORMER_SAMPLE_2": SAMPLE_2, "FINAL": FINAL, + }, + # extra V2-only diagnostics (scenes ignore these) + "EPOCHS": len(records), + "BEST_SCORE_SERIES": [(r.get("epoch"), r.get("best_score_so_far")) for r in records], + "WALL_TIME_SECONDS": records[-1].get("wall_time_seconds"), + "SAMPLE_1_CUT_FRACTION": _cut_fraction(SAMPLE_1, set(PART_A)), + } + + +# --------------------------------------------------------------------------- +# CLI +# --------------------------------------------------------------------------- + +def _summary(d): + lines = [ + f"trajectory log: {d['LOG_PATH']}", + f"epochs: {d['EPOCHS']}", + f"N (vertices): {d['N_VERTICES']}", + f"naive plateau (ep 0): {d['NAIVE_SEARCH_CEILING']} edges", + f"final / optimum: {d['OPTIMUM_EDGES']} edges ({len(d['FINAL'])} edges in selected FINAL graph)", + f"best-score series: {[s for _, s in d['BEST_SCORE_SERIES'] if s is not None]}", + f"wall time (s): {d['WALL_TIME_SECONDS']}", + f"transformer sample 1: {len(d['TRANSFORMER_SAMPLE_1'])} edges, " + f"cut fraction vs greedy 2-colouring = {d['SAMPLE_1_CUT_FRACTION']:.2f}", + f"transformer sample 2: {len(d['TRANSFORMER_SAMPLE_2'])} edges", + f"Act 1 operations: {len(d['ACT1_OPERATIONS'])} " + f"({sum(1 for o,_ in d['ACT1_OPERATIONS'] if o=='add')} add / " + f"{sum(1 for o,_ in d['ACT1_OPERATIONS'] if o=='reject')} reject)", + f"Act 2 top-k pool: {[ (e['label'], e['score']) for e in d['ACT2_TOPK'] ]}", + f"Act 3 iterations: {[ (it['iteration'], it['score']) for it in d['ACT3_ITERATIONS'] ]}", + ] + return "\n".join(lines) + + +if __name__ == "__main__": + if len(sys.argv) != 2: + print("usage: python -m src.trajectory_loader ", file=sys.stderr) + raise SystemExit(2) + print(_summary(load_trajectory(sys.argv[1]))) diff --git a/axplorer-viz/tests/test_decode.py b/axplorer-viz/tests/test_decode.py new file mode 100644 index 0000000..6cdc476 --- /dev/null +++ b/axplorer-viz/tests/test_decode.py @@ -0,0 +1,68 @@ +"""Tests for src/decode.py -- the Axplorer ``single_integer`` token decoder.""" + +import math +import os +import sys +from itertools import combinations + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + +from decode import ( # noqa: E402 + all_edge_tokens, + decode_object, + decode_token, + encode_edge, + num_edge_tokens, +) + + +@pytest.mark.parametrize("n", [2, 3, 4, 5, 8, 15, 30]) +def test_decode_matches_combinations_enumeration(n): + expected = list(combinations(range(n), 2)) + assert num_edge_tokens(n) == math.comb(n, 2) == len(expected) + for token, edge in enumerate(expected): + assert decode_token(token, n) == edge + + +@pytest.mark.parametrize("n", [2, 3, 4, 5, 8, 15, 30]) +def test_encode_decode_roundtrip(n): + for token, edge in all_edge_tokens(n): + assert encode_edge(edge, n) == token + assert decode_token(encode_edge(edge, n), n) == edge + # encode is order-insensitive on the pair + for i, j in combinations(range(n), 2): + assert encode_edge((j, i), n) == encode_edge((i, j), n) + + +def test_known_small_cases_n15(): + # First few and last token for N=15 (C(15,2) = 105 edge tokens, ids 0..104). + assert num_edge_tokens(15) == 105 + assert decode_token(0, 15) == (0, 1) + assert decode_token(1, 15) == (0, 2) + assert decode_token(13, 15) == (0, 14) + assert decode_token(14, 15) == (1, 2) + assert decode_token(104, 15) == (13, 14) + + +def test_decode_token_rejects_non_edge_tokens(): + for bad in (-1, 105, 106, 200): + with pytest.raises(ValueError): + decode_token(bad, 15) + with pytest.raises(ValueError): + encode_edge((0, 15), 15) + with pytest.raises(ValueError): + encode_edge((3, 3), 15) + + +def test_decode_object_strips_specials_and_dedups(): + n = 15 + bos, eos = num_edge_tokens(n) + 3, num_edge_tokens(n) + 1 + seq = [bos, 0, 14, 14, 104, eos] # BOS, (0,1), (1,2), (1,2) dup, (13,14), EOS + assert decode_object(seq, n) == [(0, 1), (1, 2), (13, 14)] + assert decode_object([], n) == [] + # a realistic encoded sequence: round-trip a small graph through encode/decode + edges = [(0, 1), (0, 5), (3, 9), (10, 14)] + tokens = [bos] + [encode_edge(e, n) for e in edges] + [eos] + assert decode_object(tokens, n) == sorted(edges) diff --git a/axplorer-viz/tests/test_graph_utils.py b/axplorer-viz/tests/test_graph_utils.py new file mode 100644 index 0000000..576c2ca --- /dev/null +++ b/axplorer-viz/tests/test_graph_utils.py @@ -0,0 +1,99 @@ +"""Tests for src/graph_utils.py. + +Coverage required by the build spec: C_4 itself, K_4, the Petersen graph, and +a 15-vertex tree. +""" + +import os +import sys + +import networkx as nx + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + +from graph_utils import count_edges, find_4_cycles, has_4_cycle # noqa: E402 + + +def test_count_edges_matches_networkx(): + G = nx.cycle_graph(4) + assert count_edges(G) == 4 + assert count_edges(nx.complete_graph(4)) == 6 + assert count_edges(nx.empty_graph(15)) == 0 + + +def test_c4_has_exactly_one_4_cycle(): + G = nx.cycle_graph(4) # edges 0-1, 1-2, 2-3, 3-0 + assert has_4_cycle(G) is True + cycles = find_4_cycles(G) + assert cycles == [(0, 1, 2, 3)] + + +def test_k4_has_exactly_three_4_cycles(): + G = nx.complete_graph(4) + assert has_4_cycle(G) is True + cycles = find_4_cycles(G) + assert len(cycles) == 3 + # Every reported cycle is genuinely a C_4 in K_4: consecutive vertices adjacent. + for a, b, c, d in cycles: + for x, y in ((a, b), (b, c), (c, d), (d, a)): + assert G.has_edge(x, y) + # Canonical orientation: starts at smallest vertex, b < d. + for a, b, c, d in cycles: + assert a == min(a, b, c, d) + assert b < d + assert len(set(cycles)) == len(cycles) + + +def test_petersen_graph_has_no_4_cycle(): + G = nx.petersen_graph() # girth 5 + assert has_4_cycle(G) is False + assert find_4_cycles(G) == [] + + +def test_15_vertex_tree_has_no_4_cycle(): + # A tree is acyclic, hence certainly C_4-free. + path = nx.path_graph(15) + star = nx.star_graph(14) # 15 vertices (center + 14 leaves) + for tree in (path, star): + assert tree.number_of_nodes() == 15 + assert nx.is_tree(tree) + assert has_4_cycle(tree) is False + assert find_4_cycles(tree) == [] + + +def test_friendship_graph_has_no_4_cycle(): + # Two triangles sharing a single vertex (the friendship graph F_2): + # no two vertices share more than one common neighbour, so no C_4. + G = nx.Graph([(0, 1), (0, 2), (1, 2), (0, 3), (0, 4), (3, 4)]) + assert has_4_cycle(G) is False + assert find_4_cycles(G) == [] + + +def test_diamond_does_have_a_4_cycle(): + # K_4 minus one edge ("diamond"): 2-0-3-1-2 is a C_4 (and the only one). + G = nx.Graph([(0, 1), (1, 2), (2, 0), (1, 3), (3, 0)]) + assert has_4_cycle(G) is True + assert find_4_cycles(G) == [(0, 2, 1, 3)] + + +def test_book_graph_two_4cycles_sharing_an_edge(): + # 0-1 shared; cycles 0-2-1-3-0 and 0-3-1-4-0 ... build explicitly: + # vertices a,b are the "spine"; pages p create 4-cycles a-p-b-q-a. + G = nx.Graph() + a, b = 0, 1 + for p in (2, 3, 4): + G.add_edge(a, p) + G.add_edge(b, p) + # common neighbours of a and b are {2,3,4} -> C(3,2) = 3 distinct 4-cycles. + cycles = find_4_cycles(G) + assert len(cycles) == 3 + assert has_4_cycle(G) is True + + +def test_adding_an_edge_can_create_a_4_cycle(): + # Path 0-1-2-3; adding 0-3 closes a C_4. + G = nx.path_graph(4) + assert has_4_cycle(G) is False + G.add_edge(0, 3) + assert has_4_cycle(G) is True + assert find_4_cycles(G) == [(0, 1, 2, 3)] diff --git a/axplorer-viz/tests/test_trajectory.py b/axplorer-viz/tests/test_trajectory.py new file mode 100644 index 0000000..d64649a --- /dev/null +++ b/axplorer-viz/tests/test_trajectory.py @@ -0,0 +1,137 @@ +"""Consistency tests for the hand-curated V1 trajectory. + +These are not "is the animation pretty" tests -- they pin down the invariants the +scenes rely on: every curated graph is genuinely C_4-free, edge counts match +their advertised scores, and the Act 1 step list really walks SEED -> +NAIVE_PLATEAU with each "reject" closing a real 4-cycle. + +When a real Axplorer log is in place (``trajectory.TRAJECTORY_SOURCE`` no longer +== ``"v1-hand-curated"``), these V1-specific assertions don't apply -- the +loaded-data invariants are covered by ``test_trajectory_loader.py`` -- so this +whole module is skipped in that case. +""" + +import os +import sys + +import networkx as nx +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + +import trajectory as T # noqa: E402 +from graph_utils import count_edges, has_4_cycle # noqa: E402 + +pytestmark = pytest.mark.skipif( + getattr(T, "TRAJECTORY_SOURCE", "v1-hand-curated") != "v1-hand-curated", + reason=f"a real Axplorer log is active ({getattr(T, 'TRAJECTORY_SOURCE', '?')}); " + "these tests pin the V1 hand-curated data", +) + + +def _graph(edges): + g = nx.empty_graph(T.N_VERTICES) + g.add_edges_from(edges) + return g + + +def test_named_states_are_c4_free(): + for name, edges in T.NAMED_STATES.items(): + assert not has_4_cycle(_graph(edges)), name + + +def test_headline_edge_counts(): + assert count_edges(_graph(T.SEED)) == 0 + assert count_edges(_graph(T.NAIVE_PLATEAU)) == T.NAIVE_SEARCH_CEILING == 24 + assert 26 <= count_edges(_graph(T.TRANSFORMER_SAMPLE_1)) <= 28 + assert 26 <= count_edges(_graph(T.TRANSFORMER_SAMPLE_2)) <= 28 + # ex(15, C_4) = 30 (OEIS A006855). + assert count_edges(_graph(T.FINAL)) == T.OPTIMUM_EDGES == 30 + + +def test_final_is_4_regular(): + g = _graph(T.FINAL) + assert all(d == 4 for _, d in g.degree()) + + +def test_transformer_samples_are_mostly_bipartite(): + a = set(T.TRANSFORMER_PARTITION_A) + b = set(T.TRANSFORMER_PARTITION_B) + assert a | b == set(range(T.N_VERTICES)) and not (a & b) + for sample in (T.TRANSFORMER_SAMPLE_1, T.TRANSFORMER_SAMPLE_2): + within = sum(1 for u, v in sample if (u in a) == (v in a)) + # "near-bipartite": most edges cross the partition. + assert within <= len(sample) // 4 + + +def test_naive_plateau_is_maximal(): + # No single edge can be added without creating a 4-cycle -- that's the plateau. + g = _graph(T.NAIVE_PLATEAU) + for u in range(T.N_VERTICES): + for v in range(u + 1, T.N_VERTICES): + if g.has_edge(u, v): + continue + g.add_edge(u, v) + assert has_4_cycle(g), (u, v) + g.remove_edge(u, v) + + +def test_act1_operations_walk_seed_to_plateau(): + ops = T.ACT1_OPERATIONS + assert 35 <= len(ops) <= 45 + adds = [e for op, e in ops if op == "add"] + rejects = [e for op, e in ops if op == "reject"] + assert {op for op, _ in ops} <= {"add", "reject"} + assert len(adds) + len(rejects) == len(ops) + # The adds are exactly the plateau's edges (as a set). + norm = lambda es: {tuple(sorted(e)) for e in es} + assert norm(adds) == norm(T.NAIVE_PLATEAU) + assert len(adds) == len(set(norm(adds))) # no duplicate adds + + g = nx.empty_graph(T.N_VERTICES) + for op, e in ops: + if op == "add": + g.add_edge(*e) + assert not has_4_cycle(g), ("add closed a 4-cycle", e) + else: # reject + g.add_edge(*e) + assert has_4_cycle(g), ("reject did not close a 4-cycle", e) + g.remove_edge(*e) + assert norm(g.edges()) == norm(T.NAIVE_PLATEAU) + + +def test_act1_ends_stuck(): + # The last few steps must all be rejects -- "stuck at 24". + tail_ops = [op for op, _ in T.ACT1_OPERATIONS[-3:]] + assert tail_ops == ["reject", "reject", "reject"] + + +def test_act2_pool_and_samples_consistent(): + for entry in T.ACT2_TOPK + T.ACT2_SAMPLES: + g = _graph(entry["graph"]) + assert not has_4_cycle(g), entry["label"] + assert count_edges(g) == entry["score"], entry["label"] + # The plateau itself is in the pool. + norm = lambda es: {tuple(sorted(e)) for e in es} + assert any(norm(e["graph"]) == norm(T.NAIVE_PLATEAU) for e in T.ACT2_TOPK) + # Samples are strictly better than the plateau-region pool max... well, at + # least one is, and both clear the "structured" bar of 26+. + assert all(e["score"] >= 26 for e in T.ACT2_SAMPLES) + + +def test_act3_iterations_climb_to_optimum(): + its = T.ACT3_ITERATIONS + assert [it["iteration"] for it in its] == list(range(len(its))) + scores = [it["score"] for it in its] + assert scores[0] == T.NAIVE_SEARCH_CEILING + assert scores[-1] == T.OPTIMUM_EDGES + assert scores == sorted(scores) # non-decreasing + assert scores[-1] > scores[0] # actually climbs + for it in its: + g = _graph(it["graph"]) + assert not has_4_cycle(g), it["iteration"] + assert count_edges(g) == it["score"], it["iteration"] + # Iterations 1.. are nested (the centre graph just keeps filling in). + norm = lambda es: {tuple(sorted(e)) for e in es} + for prev, cur in zip(its[1:], its[2:]): + assert norm(prev["graph"]) <= norm(cur["graph"]) diff --git a/axplorer-viz/tests/test_trajectory_loader.py b/axplorer-viz/tests/test_trajectory_loader.py new file mode 100644 index 0000000..68fd242 --- /dev/null +++ b/axplorer-viz/tests/test_trajectory_loader.py @@ -0,0 +1,279 @@ +"""Tests for src/trajectory_loader.py. + +There's no real Axplorer log in CI, so we build a *synthetic* JSONL that has the +shape the patched Axplorer (vendor/PATCH_NOTES.md) emits -- reusing the V1 +hand-curated graphs as "real" content -- and check that the loader turns it back +into the data structures the Manim scenes consume. +""" + +import json +import os +import sys + +import networkx as nx +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) + +import trajectory as V1 # noqa: E402 (V1 hand-curated, used as synthetic "real" content) +from decode import decode_object, encode_edge # noqa: E402 +from graph_utils import count_edges, has_4_cycle # noqa: E402 +import trajectory_loader as L # noqa: E402 + +N = 15 + + +def _enc(edges): + return [encode_edge(e, N) for e in edges] + + +def _graph(edges): + g = nx.empty_graph(N) + g.add_edges_from(edges) + return g + + +def _write_synth_log(path): + """A 4-epoch synthetic log: epoch 0 = initial random search (plateau-ish), + epochs 1-2 = model samples appearing, epoch 3 = the optimum found.""" + # epoch-0 top-k: a few sub-plateau graphs (V1's ACT2_TOPK content) + ep0_top = [(e["score"], e["graph"]) for e in V1.ACT2_TOPK] + # epoch 3 top-k: includes the real optimum + ep3_top = [(count_edges(_graph(V1.FINAL)), V1.FINAL), + (V1.ACT2_TOPK[0]["score"], V1.ACT2_TOPK[0]["graph"])] + records = [ + { + "epoch": 0, "n_vertices": N, + "top_k_objects": [_enc(g) for _, g in ep0_top], + "top_k_scores": [float(s) for s, _ in ep0_top], + "model_samples_raw": [], + "model_samples_after_search": [], + "best_score_so_far": float(max(s for s, _ in ep0_top)), + "wall_time_seconds": 12.0, + }, + { + "epoch": 1, "n_vertices": N, + "top_k_objects": [_enc(V1.NAIVE_PLATEAU)], + "top_k_scores": [float(count_edges(_graph(V1.NAIVE_PLATEAU)))], + "model_samples_raw": [_enc(V1.TRANSFORMER_SAMPLE_2)], + "model_samples_after_search": [_enc(V1.TRANSFORMER_SAMPLE_2)], + "best_score_so_far": 26.0, + "wall_time_seconds": 120.0, + }, + { + "epoch": 2, "n_vertices": N, + "top_k_objects": [_enc(V1.TRANSFORMER_SAMPLE_1), _enc(V1.NAIVE_PLATEAU)], + "top_k_scores": [27.0, 24.0], + "model_samples_raw": [_enc(V1.TRANSFORMER_SAMPLE_1), _enc(V1.TRANSFORMER_SAMPLE_2)], + "model_samples_after_search": [_enc(V1.TRANSFORMER_SAMPLE_1)], + "best_score_so_far": 28.0, + "wall_time_seconds": 600.0, + }, + { + "epoch": 3, "n_vertices": N, + "top_k_objects": [_enc(g) for _, g in ep3_top], + "top_k_scores": [float(s) for s, _ in ep3_top], + "model_samples_raw": [_enc(V1.FINAL)], + "model_samples_after_search": [_enc(V1.FINAL)], + "best_score_so_far": 30.0, + "wall_time_seconds": 1800.0, + }, + ] + with open(path, "w") as f: + for r in records: + f.write(json.dumps(r) + "\n") + return records + + +@pytest.fixture +def synth_log(tmp_path): + p = tmp_path / "square_N15_synth.jsonl" + _write_synth_log(str(p)) + return str(p) + + +def test_loader_returns_v1_compatible_surface(synth_log): + d = L.load_trajectory(synth_log) + required = { + "N_VERTICES", "OPTIMUM_EDGES", "NAIVE_SEARCH_CEILING", "SEED", "NAIVE_PLATEAU", + "TRANSFORMER_PARTITION_A", "TRANSFORMER_PARTITION_B", "TRANSFORMER_SAMPLE_1", + "TRANSFORMER_SAMPLE_2", "FINAL", "ACT1_OPERATIONS", "ACT2_TOPK", "ACT2_SAMPLES", + "ACT3_ITERATIONS", "FLYWHEEL_TAGLINE", "NAMED_STATES", + } + assert required <= set(d) + assert d["N_VERTICES"] == N + assert d["SEED"] == [] + + +def test_named_states_are_c4_free_and_make_sense(synth_log): + d = L.load_trajectory(synth_log) + for name in ("NAIVE_PLATEAU", "TRANSFORMER_SAMPLE_1", "TRANSFORMER_SAMPLE_2", "FINAL"): + edges = d[name] + assert edges # non-empty + assert not has_4_cycle(_graph(edges)), name + # epoch-0 best == plateau; final epoch best is the 30-edge optimum + assert count_edges(_graph(d["FINAL"])) == 30 + assert d["OPTIMUM_EDGES"] == 30 + assert d["NAIVE_SEARCH_CEILING"] == count_edges(_graph(d["NAIVE_PLATEAU"])) + # samples are distinct from each other and from the plateau + norm = lambda es: frozenset(tuple(sorted(e)) for e in es) + assert norm(d["TRANSFORMER_SAMPLE_1"]) != norm(d["TRANSFORMER_SAMPLE_2"]) + assert norm(d["TRANSFORMER_SAMPLE_1"]) != norm(d["NAIVE_PLATEAU"]) + + +def test_partition_is_a_valid_2_coloring(synth_log): + d = L.load_trajectory(synth_log) + a, b = set(d["TRANSFORMER_PARTITION_A"]), set(d["TRANSFORMER_PARTITION_B"]) + assert a | b == set(range(N)) + assert not (a & b) + assert a and b + # the synthetic SAMPLE_1 (V1's near-bipartite graph) should mostly cut the partition + assert d["SAMPLE_1_CUT_FRACTION"] >= 0.5 + + +def test_act1_operations_are_a_valid_synthesized_trace(synth_log): + d = L.load_trajectory(synth_log) + ops = d["ACT1_OPERATIONS"] + assert {op for op, _ in ops} <= {"add", "reject"} + adds = [e for op, e in ops if op == "add"] + norm = lambda es: {tuple(sorted(e)) for e in es} + assert norm(adds) == norm(d["NAIVE_PLATEAU"]) + g = nx.empty_graph(N) + for op, e in ops: + if op == "add": + g.add_edge(*e) + assert not has_4_cycle(g), ("add closed a 4-cycle", e) + else: + g.add_edge(*e) + assert has_4_cycle(g), ("reject did not close a 4-cycle", e) + g.remove_edge(*e) + assert norm(g.edges()) == norm(d["NAIVE_PLATEAU"]) + # determinism: same log -> same trace + assert L.load_trajectory(synth_log)["ACT1_OPERATIONS"] == ops + + +def test_act2_pool_and_samples(synth_log): + d = L.load_trajectory(synth_log) + assert d["ACT2_TOPK"] + for entry in d["ACT2_TOPK"] + d["ACT2_SAMPLES"]: + g = _graph(entry["graph"]) + assert not has_4_cycle(g), entry["label"] + assert entry["score"] == count_edges(g) + assert all(e["score"] >= 1 for e in d["ACT2_SAMPLES"]) + + +def test_act3_iterations_climb_and_are_nested(synth_log): + d = L.load_trajectory(synth_log) + its = d["ACT3_ITERATIONS"] + assert [it["iteration"] for it in its] == list(range(len(its))) + scores = [it["score"] for it in its] + assert scores == sorted(scores) # non-decreasing + assert scores[0] == d["NAIVE_SEARCH_CEILING"] + assert scores[-1] == d["OPTIMUM_EDGES"] + assert scores[-1] > scores[0] + for it in its: + g = _graph(it["graph"]) + assert not has_4_cycle(g), it["iteration"] + assert count_edges(g) == it["score"], it["iteration"] + # iterations 1.. are nested subsets of one another + norm = lambda es: {tuple(sorted(e)) for e in es} + for prev, cur in zip(its[1:], its[2:]): + assert norm(prev["graph"]) <= norm(cur["graph"]) + # the last one is the real FINAL graph + assert norm(its[-1]["graph"]) == norm(d["FINAL"]) + + +def test_n_vertices_inference_without_explicit_field(tmp_path): + # drop the n_vertices field; the loader should infer N=15 from token range + recs = _write_synth_log(str(tmp_path / "x.jsonl")) + p = tmp_path / "no_n.jsonl" + with open(p, "w") as f: + for r in recs: + r2 = {k: v for k, v in r.items() if k != "n_vertices"} + f.write(json.dumps(r2) + "\n") + d = L.load_trajectory(str(p)) + assert d["N_VERTICES"] == N + # explicit override also works + assert L.load_trajectory(str(p), n_vertices=15)["N_VERTICES"] == 15 + + +def test_summary_cli_renders(synth_log, capsys): + d = L.load_trajectory(synth_log) + text = L._summary(d) + assert "trajectory log:" in text + assert "Act 3 iterations:" in text + + +def test_empty_log_raises(tmp_path): + p = tmp_path / "empty.jsonl" + p.write_text("") + with pytest.raises(ValueError): + L.load_trajectory(str(p)) + + +# --- integration: load the committed real Axplorer log (a short N=15 run) ----- + +_EXAMPLE_LOG = os.path.join(os.path.dirname(__file__), "..", "logs", "example_N15_run.jsonl") + + +@pytest.mark.skipif(not os.path.isfile(_EXAMPLE_LOG), reason="no committed example Axplorer log") +def test_loads_committed_example_log(): + d = L.load_trajectory(_EXAMPLE_LOG) + assert d["N_VERTICES"] == 15 + for name in ("NAIVE_PLATEAU", "TRANSFORMER_SAMPLE_1", "TRANSFORMER_SAMPLE_2", "FINAL"): + edges = d[name] + assert edges and not has_4_cycle(_graph_n(edges, 15)), name + # this is a *short* run on a *small* instance, so it's not expected to be + # below the optimum -- N=15 is solved by the initial random search. + assert d["OPTIMUM_EDGES"] <= 30 + its = d["ACT3_ITERATIONS"] + assert [it["iteration"] for it in its] == list(range(len(its))) + assert [it["score"] for it in its] == sorted(it["score"] for it in its) + for it in its: + assert not has_4_cycle(_graph_n(it["graph"], 15)), it["iteration"] + assert count_edges(_graph_n(it["graph"], 15)) == it["score"] + # the synthesized Act 1 trace still replays to NAIVE_PLATEAU + g = nx.empty_graph(15) + for op, e in d["ACT1_OPERATIONS"]: + if op == "add": + g.add_edge(*e) + assert not has_4_cycle(g) + else: + g.add_edge(*e) + assert has_4_cycle(g) + g.remove_edge(*e) + norm = lambda es: {tuple(sorted(x)) for x in es} + assert norm(g.edges()) == norm(d["NAIVE_PLATEAU"]) + + +def _graph_n(edges, n): + g = nx.empty_graph(n) + g.add_edges_from(edges) + return g + + +# --- log discovery (which file trajectory.py picks up) ------------------------ + +import trajectory as TRAJ # noqa: E402 + + +def test_find_trajectory_log_picks_largest_n(tmp_path): + (tmp_path / "square_N15_run.jsonl").write_text("{}\n") + (tmp_path / "square_N30_run.jsonl").write_text("{}\n") + (tmp_path / "square_N21_run.jsonl").write_text("{}\n") + (tmp_path / "example_N40_run.jsonl").write_text("{}\n") # not "square_..." -> ignored + (tmp_path / "notes.txt").write_text("hi") + assert TRAJ._find_trajectory_log(str(tmp_path)).endswith("square_N30_run.jsonl") + + +def test_find_trajectory_log_none_when_empty(tmp_path): + assert TRAJ._find_trajectory_log(str(tmp_path)) is None + # only an example log -> still None (examples don't auto-activate) + (tmp_path / "example_N15_run.jsonl").write_text("{}\n") + assert TRAJ._find_trajectory_log(str(tmp_path)) is None + + +def test_default_is_v1_when_no_square_log_committed(): + # the repo ships logs/example_N15_run.jsonl but NOT a 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Every change +below is marked in the source with a `# [axplorer-viz patch]` comment. The +git diff for this directory (`git log -p axplorer-viz/vendor/axplorer/`) is the +authoritative record. + +## Files touched + +### `train.py` +- `--log_trajectory ` flag: when set, one JSONL line is appended **per + epoch, after the Selection phase** (`update_datasets`). +- After `args.seed` is finalized, also seed Python's `random` and `numpy`'s + global RNGs (upstream only seeds `torch` there). +- Helpers `_edge_tokens`, `_n_edge_tokens`, `_append_trajectory_log`, and + `_TRAJECTORY_LOG_CAP = 32` (caps how many objects/samples go in each line). +- The `sample_and_score(...)` call now passes `raw_token_out=` so a handful of + pre-local-search samples can be logged. + +Each JSONL line: + +```json +{ + "epoch": 0, + "n_vertices": 15, // makes the log self-describing + "top_k_objects": [[edge_token, ...], ...], // <= 32, sorted by score desc + "top_k_scores": [float, ...], // edge counts; aligned with top_k_objects + "model_samples_raw": [[edge_token, ...], ...], // <= 32, model output BEFORE local search + "model_samples_after_search": [[edge_token, ...], ...], // <= 32, AFTER local search, sorted by score desc + "best_score_so_far": 30.0, + "wall_time_seconds": 1234.5 +} +``` + +`edge_token` is the `single_integer` encoding: the lexicographic index of edge +`{i, j}` (`i < j`) among `itertools.combinations(range(N), 2)`. See +`axplorer-viz/src/decode.py`. + +### `src/evaluator.py` +- `sample_and_score(..., raw_token_out=None, raw_token_cap=32)`: if `raw_token_out` + is a list, the first `raw_token_cap` raw model-sampled token sequences (before + local search) are appended to it. Default `None` => identical behaviour to + upstream. + +### `src/envs/cycle.py` +- Removed `np.random.seed(None)` at the top of `SquareDataPoint._add_edges_greedily`. + Upstream reseeded numpy's global RNG to a *random* state on every call, which + defeats `--seed`. We rely on `train.py` having seeded numpy instead. + +## Reproducibility + +`--seed N` plus `--process_pool false` makes a run deterministic end to end. +With the process pool (`--process_pool true`, the default), the worker +processes that do data generation / scoring don't inherit the seeded RNG state, +so a pooled run is only approximately reproducible. For the visualization log we +recommend `--process_pool false` (slower, but at N=15 it's fine). + +## What we did NOT change + +No refactors, no behaviour changes when `--log_trajectory` is empty (the +default), no new dependencies. `--log_trajectory` off => upstream behaviour +except for the `random`/`numpy` seeding and the removed `np.random.seed(None)`. diff --git a/axplorer-viz/vendor/README.md b/axplorer-viz/vendor/README.md new file mode 100644 index 0000000..7e3c616 --- /dev/null +++ b/axplorer-viz/vendor/README.md @@ -0,0 +1,19 @@ +# vendor/axplorer — vendored copy of AxiomMath/axplorer + +This is a **vendored copy** (not a git submodule) of +[AxiomMath/axplorer](https://github.com/AxiomMath/axplorer), license +**Apache-2.0** (see `axplorer/LICENSE`), pinned to upstream commit: + + 3298b1afcefc04a3404d3d2b24cb3ccd1877c027 ("dataloader default to 0 worker") + +We keep the fork **minimal**: the only change is a small trajectory-logging +patch — see `PATCH_NOTES.md` and the git history of this directory +(`git log -p axplorer-viz/vendor/axplorer/`). We did not refactor any of their +code. + +`axplorer-viz` is a learning-in-public reproduction and is **not affiliated +with Axiom Math**. All copyright and attribution in `axplorer/LICENSE` and +`axplorer/` is preserved. + +To update the vendored copy: re-clone upstream, re-apply the patch described in +`PATCH_NOTES.md`, and bump the commit hash above. diff --git a/axplorer-viz/vendor/axplorer/.gitignore b/axplorer-viz/vendor/axplorer/.gitignore new file mode 100644 index 0000000..3d1bb44 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/.gitignore @@ -0,0 +1,12 @@ +__pycache__/ + +downloads/ +checkpoint/ + +.ipynb_checkpoints + +.env +.envrc +.venv + +.pyre/ \ No newline at end of file diff --git a/axplorer-viz/vendor/axplorer/LICENSE b/axplorer-viz/vendor/axplorer/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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From cli, you can just run + +```bash +python train.py \ + --env_name square \ + --exp_name square_exp \ + --N 30 \ + --encoding_tokens single_integer \ + --max_len 100 \ + --temperature '0.6' \ + --inc_temp '0.1' +``` + +And wait until the model has been trained. In the command above, `exp_name`is simply the name of the experiment and you can give any name you like, while `env_name` is the name of the problem to be considered (`square` is the name given internally to the Turan problem, a list of `env_name` corresponding to problems already implemented in this repo can be found below at the end of this Readme). + +## What if I need to restart a model? + +In this case, you need to specify the same configuration and explicitly define exp_id to match the exp_id of the stopped experiment. + +```bash +python train.py \ + --env_name square \ + --exp_name square_exp \ + --exp_id 2026_01_22_18_51_53 \ + --N 30 \ + --encoding_tokens single_integer \ + --max_len 100 \ + --temperature '0.6' \ + --inc_temp '0.1' +``` + +Under the hood, we store the model and optimizer checkpoints in that folder, so it's easy to resume model training. + +## What if I want to use higher quality data? + +In this case, you can proceed in two steps: first generate your data and then train the model. This is particularly helpful when you need either a large amount of data or when data generation is costly. For example the command + +```bash +python train.py \ + --env_name square \ + --exp_name square_exp \ + --N 30 \ + --gensize 10000000 \ + --pop_size 10000000 \ + --data_generation_only true +``` + +generates and saves 10 million examples. Later you can copy the train and test data to the new experiment. + +If you want to generate 10 million examples but store only the best 100,000, you can run + +```bash +python train.py \ + --env_name square \ + --exp_name square_exp \ + --N 30 \ + --gensize 10000000 \ + --pop_size 100000 \ + --data_generation_only true +``` + +Generating data may be expensive and long. After creating data, please make a copy to the training folder. You'll likely need to use this golden data over and over. If you don't make a copy, the training job will actually **OVERWRITES** the data. + +## I want to know more. What's happening under the hood? + +Of course, so you can adapt to your use case (see below to adapt to implement your own math problem). + +The training pipeline iteratively improves solutions by combining neural network learning with a classical search algorithm. Here's how it works: + +### Overview + +``` + ┌─────────────────────────────────────────────────────┐ + │ During each epoch │ + │ │ +┌──────────┐ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ +│ Generate │ │ │ Train │ │ Sample │ │ Search │ │ +│ Initial │─────────────►│ Model │───►│ Model │───►│ & Score │ │ +│ Data │ │ └──────────┘ └──────────┘ └──────────┘ │ +└──────────┘ │ ▲ │ │ + │ │ ┌──────────┐ │ │ + │ └─────────│ Select │◄─────────┘ │ + │ │ Best │ │ + │ └──────────┘ │ + └─────────────────────────────────────────────────────┘ +``` + +### Step-by-step breakdown + +1. **Initial Data Generation** (first epoch only) + - Generate `gensize` random valid examples using environment-specific greedy construction. + - Each example is scored based on the optimization objective. + - Keep the top `pop_size` examples as the initial training set. + +2. **Training Phase** (each epoch) + - Tokenize the training data into sequences the model can process. + - Train a decoder-only transformer for a number of steps (i.e. gradient updates). This number is given by `max_steps`. + - During this training, the model learns to predict the next token given previous tokens. + - This captures patterns present in high-scoring examples. + +3. **Sampling Phase** (each epoch) + - Sample `num_samples_from_model` new sequences from the trained model. + - Use temperature-controlled sampling for diversity. + - Decode sequences back into problem-specific objects. + +4. **Local Search Phase** (each epoch) + - Apply local search to sampled objects to fix constraint violations. + - Optionally improve valid samples with additional greedy steps. + - Score all processed samples. + +5. **Selection Phase** (each epoch) + - Combine new samples with existing training data. + - Remove duplicates if `keep_only_unique=True`. + - Select the top `pop_size` examples by score. + - These become the training data for the next epoch. + +6. **Temperature Adjustment** + - If too many duplicates are generated, increase temperature by `inc_temp`. This encourages more exploration when the model converges. + +## How can I use it on my own math problem ? + +To do so, you only need to create an environment corresponding to your math problem. A step-by-step guide with an example is provided in `new_envs.ipynb`. + + +## Explanation of different flags + +The full list of flags for each specific environment and more details about what they represent can be found in the `register_args` method of each different environment. + +The full list of flags for the model architecture and training parameters can be found in the method `get_parser` of `train.py`. Here, you can find references of the most important flags you should tune for your specific problem. + +- Training parameters: + - `gensize` is the number of initial data points. + - `max_epochs` is the maximum number of epochs. + - `max_steps` is the number of training steps per epoch. + - `num_samples_from_model` is the number of samples from the trained model after each epoch. + - `pop_size` is the number of examples kept after each epoch (when selecting the best examples). + - `env_name` is the math problem considered as defined in `envs/__init__.py`. + +- Model parameters: + - `n_layer` is the number of layers in the decoder-only model. + - `n_embd` is the vector dimension where we project each token. + - If the problem is permutation invariant, you can set `no_positional=True`. + - `max_len` is the maximum length supported by the model. + +- Optimization parameters: + - `batch_size` is the model batch size used for training. + - `learning_rate` and `weight_decay` are two parameters used in AdamW. + +- Sampling/generation parameters: + - `temperature` is the starting sampling temperature. + - `temp_span` is the temperature span across sampling. + - `inc_temp` is the temperature increment whenever the model generates too many duplicates. + - If you want to avoid duplicate objects, you should set `keep_only_unique=True`. + +- Local search parameters: + - During local search, it's possible to remove invalid examples completely (with `redeem_only=False` and `always_search=False`), just fix the invalid examples (with `redeem_only=True` and `always_search=False`), or fix and try to improve the model samples (with `always_search=True`). + +- Environment-specific parameters. When you set up a new problem, there are a few parameters to configure: + + - `k` is the problem-specific dimension: for the Turan problem you work on pairs of nodes, so `k=2`. For the no-5-points-on-a-sphere problem, each point lives in [N]^3, so `k=3`. + - `are_coordinates_symmetric` determines whether coordinates can be shuffled. For the Turan problem, (i, j) and (j, i) represent the same edge on the graph, so `are_coordinates_symmetric=True`. In the other problems listed in the repository, `are_coordinates_symmetric=False`. + - `encoding_tokens` specifies how to encode the data for the model. If `encoding_tokens=single_integer`, you encode the coordinates as a single number. For instance, for the Turan problem, you encode each edge as Ni+j. For the no-5-points-on-a-sphere problem, you encode each point as N²i+Nj+k. If `encoding_tokens=sequence_k_tokens`, you encode each coordinate as a separate token. For example, if the edge is (i, j), the model will see i and j as two separate tokens. If `encoding_tokens=adjacency`, you encode the full dense adjacency matrix of a graph. + - `make_object_canonical` is set to True when you want to deduplicate different objects into their canonical form. This is needed only when `keep_only_unique=True`. + - `augment_data_representation` is set to True when, after making the object canonical, you want to feed the model with one of the many possible different object representations. For example, for no-5-points-on-a-sphere, there are up to 48 different representations of the same cube. During training, the model sees different data representations to ensure model robustness. + + +## Available environments + +| Environment | Description | `env_name` | +|-------------|-------------|------------| +| Square-free graphs | Maximize edges in a graph with no 4-cycles | `square` | +| Isosceles-free point sets | Maximize points in a grid [N]^2 with no isosceles triangles | `isosceles` | +| Sphere point sets | Maximize points in a grid [N]^3 with no 5 points on a sphere | `sphere` | + +## License + +This repository uses the Apache-2.0 License. See [LICENSE](LICENSE) for details. + +## Acknowledgements + +The original code of PatternBoost was written by François Charton, Jordan S. Ellenberg, Adam Zsolt Wagner, and Geordie Williamson, and can be found [here](https://github.com/zawagner22/transformers_math_experiments). diff --git a/axplorer-viz/vendor/axplorer/environment.yml b/axplorer-viz/vendor/axplorer/environment.yml new file mode 100644 index 0000000..c4e82f3 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/environment.yml @@ -0,0 +1,12 @@ +name: env_axplorer +channels: + - conda-forge +dependencies: + - python=3.12 + - pip + - numpy + - pytorch + - numba + - psutil + - matplotlib + - jupyterlab diff --git a/axplorer-viz/vendor/axplorer/logo.svg b/axplorer-viz/vendor/axplorer/logo.svg new file mode 100644 index 0000000..eb79a97 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/logo.svg @@ -0,0 +1,7 @@ + + + + + + + diff --git a/axplorer-viz/vendor/axplorer/new_envs.ipynb b/axplorer-viz/vendor/axplorer/new_envs.ipynb new file mode 100644 index 0000000..e233714 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/new_envs.ipynb @@ -0,0 +1,773 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "36a4ea02", + "metadata": {}, + "source": [ + "# How to solve a new problem using this codebase?" + ] + }, + { + "cell_type": "markdown", + "id": "8006300d", + "metadata": {}, + "source": [ + "The codebase supports three different problems\n", + "\n", + "* Turan problem: what's the maximum number of edges of an undirected graph with `N` nodes such that there is no cycle of length 4?\n", + "* No isosceles on a grid: what's the maximum number of points in a grid `N x N` so that no three of them form the vertices of a (possible flat) isosceles triangle?\n", + "* No 5 on a sphere problem: what's the maximum number of points in a grid `N x N x N` such that no 5 points lie on a sphere?\n", + "\n", + "Of course you may be interested in other problems. This notebook guides you on how to have a new environment you can use for training. " + ] + }, + { + "cell_type": "markdown", + "id": "7bfc1530", + "metadata": {}, + "source": [ + "## How to create a new environment?" + ] + }, + { + "cell_type": "markdown", + "id": "f1b8d41f", + "metadata": {}, + "source": [ + "Here is a step-by-step guide to implement your own math problem in the pipeline.\n", + "\n", + "1. Create a file for your environement in `src/envs/` (e.g. `my_env.py`) and use the following (minimal) imports:\n", + "```\n", + "from src.envs.environment import DataPoint, BaseEnvironment\n", + "from src.envs.tokenizers import SparseTokenizer, DenseTokenizer\n", + "from src.utils import bool_flag\n", + "```\n", + "You can also copy-paste one of the existing files (e.g. `isoceles.py`) and adapt from it.\n", + "\n", + "1. In this file, define the class corresponding to the mathematical object you want to study. This should be a DataPoint class with:\n", + " - `__init__`: Initialize data structure. \n", + " - `calc_score`: Compute objective value (-1 if invalid, e.g. when some constraints are not satisfied, larger or equal to 0 if valid, and the highest the best)\n", + " - `calc_features`: (Optional) Create a 1 to 1 mapping between mathematical object and string for deduplication\n", + " - `local_search`: provide an algorithm that can: (i) fix your DataPoint if invalid (ii) (optionally) try to modify it to improve its score.\n", + " - `_batch_generate_and_score`: provide a method to generate random (valid) instances of the mathematical object\n", + "\n", + "\n", + "2. Create a Tokenizer for your environment. For this, there are two options:\n", + " - Option 1: if your mathematical objects can be represented by a sequence of point with k-coordinates then you can skip this step and use one of the existing tokenizers in the file `tokenizers.py`.\n", + " - Option 2: if not, or if you prefer to have your own tokenizer, you can create a class based on `Tokenizer` in the file `tokenizers.py`. It should contain:\n", + " - A method `encode` that takes an instance of DataPoint and represent it (1-to-1) in a list of tokens\n", + " - A method `decode` that takes a list of tokens and re-create the corresponding DataPoint\n", + "\n", + "2. Define the Environment class itself, based on BaseEnvironment, and specify:\n", + " - `data_class`: Reference to your DataPoint class. This should be specified before the `__init__` of your new Environment class.\n", + " - a tokenizer (see previous step) in the `__init__` of your new Environment class. If you went for Option 1 you can use one of the classes already implemented in `tokenizers.py` and you just have to additionnally specify before the `__init__` of your new Environment class\n", + " - `k`: Dimension of each element's index (e.g. a graph has `k=2` since it can be represented by a sequence of two-dimensional points each representing an edge)\n", + " - `are_coordinates_symmetric`: Whether indices can be permuted in elements of the sequence (e.g. this is `True` for an undirected graph)\n", + " - A method `register_args` which specifies command-line arguments corresponding to additional parameters specific to your mathematical environment (e.g. different possible generation methods, largest integer used, etc.), as well as their default values.\n", + "\n", + "3. Register your environement in `envs/__init__.py` by adding it to the dictionnary `ENVS`. (e.g. `MyEnv:\"my_env\"`).\n", + "\n", + "You can know use this pipeline on this problem by setting `env_name` to the key you provided in the dictionnary `ENVS` (e.g. `my_env`).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b93da2b4", + "metadata": {}, + "source": [ + "### Example 1: No 3-Term Arithmetic Progression in {0, ..., `N`-1}\n" + ] + }, + { + "cell_type": "markdown", + "id": "93b75bb9", + "metadata": {}, + "source": [ + "Given the set {0, 1, 2, ..., N-1}, find the largest subset such that no three elements form an arithmetic progression." + ] + }, + { + "cell_type": "markdown", + "id": "b1a8f00b", + "metadata": {}, + "source": [ + "#### Step 1: Build a DataPoint structure. \n", + "\n", + "DataPoint is responsible to store the example information in `self.data`. In addition, DataPoint has the method to run `local_search()`, therefore improve the current data point. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30e36117", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from itertools import combinations\n", + "from src.envs.environment import DataPoint\n", + "\n", + "\n", + "class ArithmeticProgressionFreeDataPoint(DataPoint): # <- New class representing the mathematical object, based on DataPoint\n", + " \"\"\"\n", + " Represents an Arithmetic Progression-free subset of {0, 1, ..., N-1}.\n", + " \n", + " Attributes:\n", + " N: Universe size {0, 1, ..., N-1}\n", + " data: Binary array, data[i] = 1 means element i is selected\n", + " progressions: List of arithmetic progressions (violations, needed for local search)\n", + " score: Number of elements if valid, -1 otherwise\n", + " features: String representation\n", + " \"\"\"\n", + " \n", + " def __init__(self, N, init=False):\n", + " self.N = N\n", + " self.data = np.zeros(N, dtype=np.uint8)\n", + " self.progressions = [] \n", + " if init: # <- Required by the pipeline; If true, we want to create an element at random. This will be used during the process to differenciate between generation and decoding of the output of the neural network.\n", + " self._add_elements_greedily() # <- Method to create a valid construction at random.\n", + " self.calc_features() \n", + " self.calc_score()\n", + " \n", + " def get_elements(self): # <- Method specific to the problem.\n", + " \"\"\"\n", + " Return set of selected elements. \n", + " \"\"\"\n", + " return set([i for i in range(self.N) if self.data[i] == 1])\n", + " \n", + " def calc_score(self): # <- Method required by the pipeline\n", + " \"\"\"\n", + " Calculate the score.\n", + " Score = number of elements if no arithmetic progression exists, -1 otherwise.\n", + " \"\"\"\n", + " if len(self.progressions) > 0:\n", + " self.score = -1\n", + " else:\n", + " self.score = int(self.data.sum())\n", + " \n", + " def calc_features(self): # <- Optional method used by the pipeline\n", + " \"\"\"\n", + " Create a binary string representation for deduplication.\n", + " \"\"\"\n", + " self.features = \",\".join(str(self.data[i]) for i in range(self.N))\n", + " \n", + " def _would_create_ap(self, new_element, existing_set): # <- Method specific to the problem.\n", + " \"\"\"\n", + " Check if adding new_element would create an arithmetic progression.\n", + " \"\"\"\n", + " # Case 1: new_element is the middle (b)\n", + " # We need a, c such that a + c = 2 * new_element\n", + " target = 2 * new_element\n", + " for a in existing_set:\n", + " c = target - a\n", + " if c != a and c in existing_set:\n", + " # Found a, c such that a + c = 2 * new_element\n", + " return True\n", + " \n", + " # Case 2: new_element is an endpoint\n", + " for b in existing_set:\n", + " c = 2 * b - new_element\n", + " if c != new_element and c != b and c in existing_set:\n", + " return True\n", + " \n", + " return False\n", + " \n", + " def _add_elements_greedily(self): # <- Method specific to the problem.\n", + " \"\"\"\n", + " Greedily add elements while avoiding arithmetic progressions.\n", + " \"\"\"\n", + " order = list(range(self.N))\n", + " np.random.shuffle(order)\n", + " \n", + " current_set = self.get_elements()\n", + " \n", + " for elem in order:\n", + " if not self._would_create_ap(elem, current_set):\n", + " self.data[elem] = 1\n", + " current_set.add(elem)\n", + " \n", + " def _compute_progressions(self): # <- Method specific to the problem.\n", + " \"\"\"\n", + " Find all arithmetic progressions in the current selection.\n", + " \"\"\"\n", + " elements = self.get_elements()\n", + " element_set = set(elements)\n", + " self.progressions = []\n", + " \n", + " for a, c in combinations(elements, 2):\n", + " if (a + c) % 2 == 0:\n", + " b = (a + c) // 2\n", + " if b in element_set and a < b < c:\n", + " self.progressions.append((a, b, c))\n", + " \n", + " def _remove_elements_greedily(self): # <- Method specific to the problem.\n", + " \"\"\"\n", + " Greedily remove elements to eliminate all arithmetic progressions.\n", + " \"\"\"\n", + " while self.progressions:\n", + " # Count how many arithmetic progressions each element appears in\n", + " element_count = {}\n", + " for ap in self.progressions:\n", + " for elem in ap:\n", + " element_count[elem] = element_count.get(elem, 0) + 1\n", + " \n", + " # Remove the element in the most arithmetic progressions\n", + " worst_element = max(element_count, key=element_count.get)\n", + " self.data[worst_element] = 0\n", + " \n", + " # Update progressions\n", + " self.progressions = [ap for ap in self.progressions if worst_element not in ap]\n", + " \n", + " def local_search(self, improve_with_local_search=True): # <- Method required by the pipeline\n", + " \"\"\"\n", + " Apply local search to fix violations and optionally improve.\n", + " \"\"\"\n", + " # Step 1: Find all violations\n", + " self._compute_progressions()\n", + " \n", + " # Step 2: Remove elements to fix violations\n", + " self._remove_elements_greedily()\n", + " \n", + " # Step 3: Optionally add more elements\n", + " if improve_with_local_search:\n", + " self._add_elements_greedily()\n", + " \n", + " # Step 4: Recompute progressions (this step is not strictly needed but it's done for consistency)\n", + " self._compute_progressions()\n", + " self.calc_features()\n", + " self.calc_score()\n", + " \n", + " @classmethod\n", + " def _update_class_params(cls, pars): # <- Method required by the pipeline\n", + " pass\n", + " \n", + " @classmethod\n", + " def _save_class_params(cls): # <- Method required by the pipeline\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "27ed6fa8", + "metadata": {}, + "source": [ + "#### Step 2: Test this DataPoint structure.\n", + "\n", + "You will save a lot of time if you test the DataPoint structure before launching any training." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f798524", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Universe size: 50\n", + "Number of elements: 14\n", + "Elements: {32, 1, 34, 7, 41, 10, 44, 14, 15, 46, 17, 25, 26, 31}\n", + "Arithmetic progressions: []\n", + "Verified: No arithmetic progressions!\n", + "\n", + "Arithmetic progressions before running local search: [(0, 1, 2)]\n", + "Arithmetic progressions after running local search but without local improvement: []\n", + "Score after running local search but without local improvement: 2\n", + "Arithmetic progressions after running local search and local improvement: []\n", + "Score after running local search and local improvement: 13\n" + ] + } + ], + "source": [ + "# random data point with N=50\n", + "dp1 = ArithmeticProgressionFreeDataPoint(N=50, init=True)\n", + "\n", + "print(f\"Universe size: {dp1.N}\")\n", + "print(f\"Number of elements: {dp1.score}\")\n", + "print(f\"Elements: {dp1.get_elements()}\")\n", + "print(f\"Arithmetic progressions: {dp1.progressions}\")\n", + "\n", + "elements = dp1.get_elements()\n", + "for a, b, c in combinations(elements, 3):\n", + " if 2 * b == a + c:\n", + " print(f\"ERROR: Found AP ({a}, {b}, {c})\")\n", + " break\n", + "else:\n", + " print(\"Verified: No arithmetic progressions!\\n\")\n", + "\n", + "\n", + "# a datapoint with some violations\n", + "dp2 = ArithmeticProgressionFreeDataPoint(N=50, init=False)\n", + "# in this way, I force the creation of a point with violations\n", + "dp2.data[0] = 1\n", + "dp2.data[1] = 1\n", + "dp2.data[2] = 1\n", + "\n", + "dp2._compute_progressions()\n", + "print(f\"Arithmetic progressions before running local search: {dp2.progressions}\")\n", + "\n", + "dp2.local_search(improve_with_local_search=False)\n", + "print(f\"Arithmetic progressions after running local search but without local improvement: {dp2.progressions}\")\n", + "print(f\"Score after running local search but without local improvement: {dp2.score}\")\n", + "\n", + "dp2.local_search(improve_with_local_search=True)\n", + "print(f\"Arithmetic progressions after running local search and local improvement: {dp2.progressions}\")\n", + "print(f\"Score after running local search and local improvement: {dp2.score}\")" + ] + }, + { + "cell_type": "markdown", + "id": "83aaae8e", + "metadata": {}, + "source": [ + "#### Step 3: Create the Environment.\n", + "\n", + "The Environment is the link between the DataPoint and the tokenizer. Tokenizer is the tool to convert the mathematical object into tokens readable from Decoder-only models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "403b461c", + "metadata": {}, + "outputs": [], + "source": [ + "from src.envs.environment import BaseEnvironment\n", + "from src.envs.tokenizers import SparseTokenizerSingleInteger\n", + "\n", + "\n", + "class ArithmeticProgressionFreeEnvironment(BaseEnvironment):\n", + " # this problem lives in N^1, therefore k=1\n", + " # for k=1, are_coordinates_symmetric is not applicable\n", + " # Here we opt for Option 1 and we use an already implemented tokenizer \n", + "\n", + " k = 1 # <- has to be specified with Option 1\n", + " are_coordinates_symmetric = False # <- has to be specified with Option 1\n", + " data_class = ArithmeticProgressionFreeDataPoint # <- has to be specified\n", + " \n", + " def __init__(self, params):\n", + " super().__init__(params)\n", + " self.tokenizer = SparseTokenizerSingleInteger(self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS) # <- Our choice of pre-implemented tokenizer \n", + " \n", + " @staticmethod\n", + " def register_args(parser):\n", + " \"\"\"\n", + " Register environment parameters.\n", + " \"\"\"\n", + " parser.add_argument(\"--N\", type=int, default=100, help=\"Universe size {0, 1, ..., N-1}\") #<- Here we only add one parameter specific to the problem, but there could be many others :)\n" + ] + }, + { + "cell_type": "markdown", + "id": "d3824cef", + "metadata": {}, + "source": [ + "#### Step 4: Register the Environment\n", + "\n", + "In order to have it runnable from the main program, you need to add a new line in the `src/envs/__init__.py`\n", + "\n", + "```\n", + "ENVS = {\n", + " \"square\": SquareEnvironment,\n", + " \"isosceles\": IsoscelesEnvironment,\n", + " \"sphere\": SphereEnvironment,\n", + " \"arithmetic_progression\": ArithmeticProgressionFreeEnvironment, # <- Add this line\n", + "}\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "64932bb6", + "metadata": {}, + "source": [ + "### Example 2: No 3 collinear points on a `N x N` grid" + ] + }, + { + "cell_type": "markdown", + "id": "68bc821a", + "metadata": {}, + "source": [ + "Given an `N x N` grid, find the maximum number of points you can place such that no three points lie on the same straight line.\n", + "\n", + "In this example, I will introduce two additional ideas:\n", + "- The problem is invariant under rotations and reflections of the grid, so we can deduplicate grids up to the 8 symmetries of the square. `make_object_canonical` controls whether we remove duplicates that differ only by these symmetries.\n", + "- For a given grid, there are 8 possible ways to convert it to its tokenized version. `augment_data_representation` controls if we want to augment data with these 8 symmetries." + ] + }, + { + "cell_type": "markdown", + "id": "26c2a948", + "metadata": {}, + "source": [ + "#### Step 1: Add two specific functions to make object canonical and augment data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "abf5c4af", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import random\n", + "\n", + "# We pick the lexicographically smallest of the 8 symmetries\n", + "def canonical_form_2d(matrix):\n", + " best = None\n", + " best_matrix = None\n", + "\n", + " # 8 symmetries: 4 rotations × 2 (with/without reflection)\n", + " current = matrix\n", + " for _ in range(4):\n", + " flat = current.flatten().tolist()\n", + " if best is None or flat < best:\n", + " best = flat\n", + " best_matrix = current.copy()\n", + "\n", + " reflected = np.flip(current, axis=1)\n", + " flat = reflected.flatten().tolist()\n", + " if flat < best:\n", + " best = flat\n", + " best_matrix = reflected.copy()\n", + "\n", + " current = np.rot90(current)\n", + "\n", + " return best_matrix\n", + "\n", + "\n", + "# We pick one of the 8 symmetries at random\n", + "def random_symmetry_2d(matrix):\n", + " k = random.randint(0, 3)\n", + " result = np.rot90(matrix, k)\n", + "\n", + " if random.randint(0, 1):\n", + " result = np.flip(result, axis=1)\n", + "\n", + " return result.copy()\n" + ] + }, + { + "cell_type": "markdown", + "id": "71bb68e9", + "metadata": {}, + "source": [ + "#### Step 2: Build a DataPoint structure. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ef557ed", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from itertools import combinations\n", + "from src.envs.environment import DataPoint\n", + "\n", + "\n", + "class CollinearDataPoint(DataPoint):\n", + " \"\"\"\n", + " Represents a set of points on an N x N grid with no three collinear.\n", + " \n", + " Attributes:\n", + " N: Grid size\n", + " data: N x N binary matrix, data[i,j] = 1 means point (i,j) is selected\n", + " collinear_triplets: List of collinear triplets (violations, needed for local search)\n", + " score: Number of points if valid, -1 otherwise\n", + " features: String representation\n", + " \"\"\"\n", + " \n", + " MAKE_OBJECT_CANONICAL = False # <- Additional argument specific to the problem\n", + " \n", + " def __init__(self, N, init=False):\n", + " self.N = N\n", + " self.data = np.zeros((N, N), dtype=np.uint8)\n", + " self.collinear_triplets = []\n", + " \n", + " if init: # <- Required by the pipeline; If true, we want to create an element at random. This will be used during the process to differenciate between generation and decoding of the output of the neural network.\n", + " self._add_points_greedily() # <- Method to create a valid construction at random.\n", + " if self.MAKE_OBJECT_CANONICAL:\n", + " self.data = canonical_form_2d(self.data)\n", + " self.calc_features() # <- Required by the pipeline;\n", + " self.calc_score() # <- Required by the pipeline;\n", + " \n", + " def get_points(self): # <- Method specific to the problem\n", + " \"\"\"Return list of selected points as (x, y) tuples.\"\"\"\n", + " return [(i, j) for i in range(self.N) for j in range(self.N) if self.data[i, j] == 1]\n", + " \n", + " def calc_score(self): # <- Method required by the pipeline\n", + " if len(self.collinear_triplets) > 0:\n", + " self.score = -1\n", + " else:\n", + " self.score = int(self.data.sum())\n", + " \n", + " def calc_features(self): # <- Optional method used by the pipeline\n", + " \"\"\"\n", + " Create a string representation of the configuration for deduplication.\n", + " \"\"\"\n", + " w = []\n", + " for i in range(self.N):\n", + " for j in range(self.N):\n", + " w.append(self.data[i, j])\n", + " self.features = \",\".join(map(str, w))\n", + " \n", + " def _are_collinear(self, p1, p2, p3): # <- Method specific to the problem\n", + " \"\"\"Check if three points are collinear using cross product.\"\"\"\n", + " x1, y1 = p1\n", + " x2, y2 = p2\n", + " x3, y3 = p3\n", + " cross = (x2 - x1) * (y3 - y1) - (x3 - x1) * (y2 - y1)\n", + " return cross == 0\n", + " \n", + " def _would_create_collinear(self, new_point, existing_points): # <- Method specific to the problem\n", + " \"\"\"\n", + " Check if adding new_point would create a collinear triplet with any pair of existing points.\n", + " \"\"\"\n", + " for p1, p2 in combinations(existing_points, 2):\n", + " if self._are_collinear(p1, p2, new_point):\n", + " return True\n", + " return False\n", + " \n", + " def _add_points_greedily(self): # <- Method specific to the problem\n", + " \"\"\"\n", + " Greedily add points to the grid while avoiding collinear triplets.\n", + " We iterate through all grid positions in random order and add a point if it doesn't create a collinear triplet.\n", + " \"\"\"\n", + " all_positions = [(i, j) for i in range(self.N) for j in range(self.N)]\n", + " np.random.shuffle(all_positions)\n", + " \n", + " current_points = self.get_points()\n", + " \n", + " for pos in all_positions:\n", + " if self.data[pos] == 0:\n", + " if not self._would_create_collinear(pos, current_points):\n", + " self.data[pos] = 1\n", + " current_points.append(pos)\n", + " \n", + " def _compute_collinear_triplets(self): # <- Method specific to the problem\n", + " \"\"\"Find all collinear triplets in the current configuration.\"\"\"\n", + " current_points = self.get_points()\n", + " self.collinear_triplets = []\n", + " \n", + " for p1, p2, p3 in combinations(current_points, 3):\n", + " if self._are_collinear(p1, p2, p3):\n", + " triplet = tuple(sorted([p1, p2, p3]))\n", + " self.collinear_triplets.append(triplet)\n", + " \n", + " def _remove_points_greedily(self): # <- Method specific to the problem\n", + " \"\"\"\n", + " Greedily remove points to eliminate all collinear triplets.\n", + " At each step, remove the point that appears in the most triplets.\n", + " \"\"\"\n", + " while self.collinear_triplets:\n", + " # Count how many triplets each point appears in\n", + " point_count = {}\n", + " for triplet in self.collinear_triplets:\n", + " for point in triplet:\n", + " point_count[point] = point_count.get(point, 0) + 1\n", + " \n", + " # Find the point in the most triplets\n", + " worst_point = max(point_count, key=point_count.get)\n", + " \n", + " # Remove it\n", + " self.data[worst_point] = 0\n", + " \n", + " # Update triplets (remove all triplets containing this point)\n", + " self.collinear_triplets = [t for t in self.collinear_triplets if worst_point not in t]\n", + " \n", + " def local_search(self, improve_with_local_search=True): # <- Method requiremd by the pipeline\n", + " \"\"\"\n", + " Apply local search to fix violations and optionally improve the solution.\n", + " \"\"\"\n", + " # Step 1: Find all violations\n", + " self._compute_collinear_triplets()\n", + " \n", + " # Step 2: Remove points to fix violations\n", + " self._remove_points_greedily()\n", + " \n", + " # Step 3: Optionally try to add more points\n", + " if improve_with_local_search:\n", + " self._add_points_greedily()\n", + " \n", + " # Step 4: Recompute progressions (this step is not strictly needed but it's done for consistency)\n", + " self._compute_collinear_triplets()\n", + " self.calc_features()\n", + " self.calc_score()\n", + " \n", + " @classmethod\n", + " def _update_class_params(cls, pars): # <- Method requiremd by the pipeline\n", + " \"\"\"Update class-level parameters (used for multiprocessing).\"\"\"\n", + " cls.MAKE_OBJECT_CANONICAL = pars\n", + " \n", + " @classmethod\n", + " def _save_class_params(cls): # <- Method requiremd by the pipeline\n", + " \"\"\"Save class-level parameters (used for multiprocessing).\"\"\"\n", + " return cls.MAKE_OBJECT_CANONICAL\n" + ] + }, + { + "cell_type": "markdown", + "id": "e3714fc0", + "metadata": {}, + "source": [ + "#### Step 3: Test the new data point class" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "66171a32", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grid size: 10 x 10\n", + "Number of points: 15\n", + "Points: [(0, 7), (0, 9), (1, 1), (1, 2), (3, 8), (4, 4), (4, 9), (5, 2), (6, 3), (6, 8), (7, 3), (7, 5), (8, 0), (8, 4), (9, 7)]\n", + "Collinear triplets: []\n", + "\n", + "Collinear triplets before running local search: [((0, 0), (1, 1), (2, 2))]\n", + "Collinear triplets after running local search but without local improvement: []\n", + "Score after running local search but without local improvement: 2\n", + "Collinear triplets after running local search and local improvement: []\n", + "Score after running local search and local improvement: 16\n" + ] + } + ], + "source": [ + "dp1 = CollinearDataPoint(N=10, init=True)\n", + "\n", + "print(f\"Grid size: {dp1.N} x {dp1.N}\")\n", + "print(f\"Number of points: {dp1.score}\")\n", + "print(f\"Points: {dp1.get_points()}\")\n", + "print(f\"Collinear triplets: {dp1.collinear_triplets}\\n\")\n", + "\n", + "\n", + "# a datapoint with some violations\n", + "dp2 = CollinearDataPoint(N=10, init=False)\n", + "# in this way, I force the creation of a point with violations\n", + "dp2.data[0, 0] = 1\n", + "dp2.data[1, 1] = 1\n", + "dp2.data[2, 2] = 1\n", + "\n", + "dp2._compute_collinear_triplets()\n", + "print(f\"Collinear triplets before running local search: {dp2.collinear_triplets}\")\n", + "\n", + "dp2.local_search(improve_with_local_search=False)\n", + "print(f\"Collinear triplets after running local search but without local improvement: {dp2.collinear_triplets}\")\n", + "print(f\"Score after running local search but without local improvement: {dp2.score}\")\n", + "\n", + "dp2.local_search(improve_with_local_search=True)\n", + "print(f\"Collinear triplets after running local search and local improvement: {dp2.collinear_triplets}\")\n", + "print(f\"Score after running local search and local improvement: {dp2.score}\")" + ] + }, + { + "cell_type": "markdown", + "id": "411c32b5", + "metadata": {}, + "source": [ + "#### Step 4: Create the Environment Class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5016a127", + "metadata": {}, + "outputs": [], + "source": [ + "from src.envs.environment import BaseEnvironment\n", + "from src.envs.tokenizers import SparseTokenizerSingleInteger, SparseTokenizerSequenceKTokens, DenseTokenizer\n", + "from src.utils import bool_flag\n", + "\n", + "\n", + "class CollinearEnvironment(BaseEnvironment):\n", + " # this problem lives in N^2, therefore k=2\n", + " # (i, j) or (j, i) represents two distinct points in the grid, therefore are_coordinates_symmetric=False \n", + " # Here we opt for Option 1 and we use *several* already implemented tokenizers \n", + "\n", + " k = 2 # <- has to be specified with Option 1\n", + " are_coordinates_symmetric = False # <- has to be specified with Option 1\n", + " data_class = CollinearDataPoint # <- has to be specified\n", + " \n", + " def __init__(self, params):\n", + " super().__init__(params)\n", + " self.data_class.MAKE_OBJECT_CANONICAL = params.make_object_canonical\n", + " encoding_augmentation = random_symmetry_2d if params.augment_data_representation else None\n", + " if params.encoding_tokens == \"single_integer\":\n", + " self.tokenizer = SparseTokenizerSingleInteger(\n", + " self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation\n", + " ) # <- Possible choice of pre-implemented tokenizer #1\n", + " elif params.encoding_tokens == \"sequence_k_tokens\":\n", + " self.tokenizer = SparseTokenizerSequenceKTokens(\n", + " self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation\n", + " ) # <- Possible choice of pre-implemented tokenizer #2\n", + " elif params.encoding_tokens == \"adjacency\":\n", + " self.tokenizer = DenseTokenizer(\n", + " self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, pow2base=params.pow2base, encoding_augmentation=encoding_augmentation\n", + " ) # <- Possible choice of pre-implemented tokenizer #3\n", + " else:\n", + " raise ValueError(f\"Invalid encoding: {params.encoding_tokens}\")\n", + " \n", + " @staticmethod\n", + " def register_args(parser):\n", + " parser.add_argument(\"--N\", type=int, default=10, help=\"Grid size N x N\")\n", + " parser.add_argument(\"--encoding_tokens\", type=str, default=\"single_integer\", help=\"single_integer/sequence_k_tokens/adjacency\")\n", + " parser.add_argument(\"--make_object_canonical\", type=bool_flag, default=\"false\", help=\"sort the grid by symmetry\")\n", + " parser.add_argument(\"--augment_data_representation\", type=bool_flag, default=\"false\", help=\"augment the data representation with predefined function\")\n", + " parser.add_argument(\"--pow2base\", type=int, default=1, help=\"Bits per token for adjacency encoding\")" + ] + }, + { + "cell_type": "markdown", + "id": "20c509fe", + "metadata": {}, + "source": [ + "#### Step 5: Register the Environment\n", + "\n", + "Similar to the previous env, you need to add a new line in the `src/envs/__init__.py`\n", + "\n", + "```\n", + "ENVS = {\n", + " \"square\": SquareEnvironment,\n", + " \"isosceles\": IsoscelesEnvironment,\n", + " \"sphere\": SphereEnvironment,\n", + " \"collinear\": CollinearEnvironment, # Add this line\n", + "}\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lyapunov", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/axplorer-viz/vendor/axplorer/program.md b/axplorer-viz/vendor/axplorer/program.md new file mode 100644 index 0000000..6fb8f27 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/program.md @@ -0,0 +1,112 @@ +# program + +This is the workflow for implementing a new mathematical problem in Axplorer. + +## Phase 1: Problem Implementation + +The user describes a new discrete optimization problem. You implement it as an Axplorer environment. + +### Understanding the problem + +Before writing any code, make sure you understand: + +1. **What is the object?** (graph, point set, matrix, sequence, etc.) +2. **What is the parameter N?** (number of nodes, grid size, sequence length, etc.) +3. **What are the constraints?** (forbidden substructures, required properties, etc.) +4. **What is the score?** (what to maximize — number of edges, number of points, etc.) +5. **How to tokenize the object?** + +Ask the user to clarify anything ambiguous. Do not guess. + +### Writing the environment + +Read the existing environments for reference: + +- `envs/environment.py` — base classes `DataPoint` and `BaseEnvironment` +- `envs/cycle.py` — example: 4-cycle-free graphs (Turan problem) +- `envs/isosceles.py` — example: isosceles-free point sets +- `envs/sphere.py` — example: 5-cospherical-free point sets +- `envs/tokenizers.py` — available tokenizers +- `new_envs.ipynb` — tutorial notebook for creating new environments + +Create `envs/.py` with: + +1. **`DataPoint(DataPoint)`** class: + - `__init__(self, N, init=False)` — if `init=True`, generate a random valid instance + - `calc_score(self)` — return the objective value (>=0 if valid, -1 if invalid). **This is the most critical method.** It must be correct, efficient, and match the mathematical definition exactly. + - `calc_features(self)` — return a canonical string representation for deduplication + - `local_search(self)` — repair invalid solutions and optionally improve valid ones. Typically: (a) remove elements that cause constraint violations, (b) greedily add elements that don't violate constraints. + +2. **`Environment(BaseEnvironment)`** class: + - Set `k`, `are_coordinates_symmetric`, `data_class` + - Choose tokenizer + - Implement `register_args(parser)` if the problem has extra parameters + +3. **Register** in `envs/__init__.py` by adding the new environment to `ENVS`. + +### Implementation guidelines + +- Use **numba** (`@njit`) for inner loops in scoring and local search when performance matters. +- Keep `calc_score()` **simple and obviously correct** — this is the function the user will audit. Prefer clarity over cleverness. +- `local_search()` should be greedy and fast. It doesn't need to be optimal — the transformer + iteration loop will handle exploration. +- For random generation in `__init__`, start with something simple (e.g. random subset, random permutation) even if the results are low quality. The training loop improves from there. +- Follow the patterns in existing environments. Don't reinvent conventions. + +## Phase 2: User Review + +**STOP HERE.** Present the implementation to the user for review before running anything. + +Specifically ask the user to verify: + +1. **`calc_score()`** — Is the scoring function mathematically correct? Does it match the problem definition? Are edge cases handled? +2. **Constraint checking** — Are all forbidden configurations detected? +3. **`local_search()`** — Does the repair strategy make sense for this problem? +4. **Tokenization choice** — Is the encoding appropriate for the problem structure? + +The user may also provide: + +- **Mathematical insights**: known bounds, constructions, symmetries, or structural properties that could inform the local search or generation strategy. +- **Bug fixes**: corrections to the scoring logic or constraint detection. +- **Performance suggestions**: better algorithms for constraint checking. + +Incorporate all feedback before proceeding. If the user provides known optimal constructions or bounds, note them — they serve as targets for training. + +## Phase 3: Test + +Verify the implementation works end-to-end: + +1. **Unit test the scoring function** by running a quick sanity check in Python: + ```bash + python -c " + from src.envs. import DataPoint + # Generate a few random instances + for _ in range(10): + dp = DataPoint(N=, init=True) + dp.calc_score() + print(f'score={dp.score}') + " + ``` + +2. **Run a minimal training pass** with small parameters to verify the full pipeline: + ```bash + python train.py \ + --env_name \ + --exp_name smoke_test \ + --N \ + --max_epochs 2 \ + --max_steps 100 \ + --gensize 1000 \ + --num_samples_from_model 500 \ + --pop_size 500 \ + --batch_size 16 \ + --n_layer 2 \ + --n_embd 128 + ``` + +3. Check that: + - No crashes or errors + - Scores are being computed and logged + - The model samples produce decodable outputs + - Local search runs without errors + +If the smoke test fails, fix the issue and re-run. \ No newline at end of file diff --git a/axplorer-viz/vendor/axplorer/src/datasets.py b/axplorer-viz/vendor/axplorer/src/datasets.py new file mode 100644 index 0000000..269b8a9 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/datasets.py @@ -0,0 +1,213 @@ +import os +import pickle +import random +from concurrent.futures import ProcessPoolExecutor +from itertools import repeat +from logging import getLogger + +import numpy as np +import torch +from torch.utils.data import Dataset +from torch.utils.data.dataloader import DataLoader + +from src.envs.environment import do_stats +from src.utils import MAX_WORKERS + +logger = getLogger() + + +def detokenize(data, args, env, executor=None): + res = [] + pars = env.tokenizer.dataclass._save_class_params() + if args.process_pool: + BATCH = args.gen_batch_size + data_slices = [data[i : i + BATCH] for i in range(0, len(data), BATCH)] + + if executor is not None: + for chunk in executor.map(env.tokenizer.decode_batch, data_slices, repeat(pars, len(data_slices))): + if chunk: + res.extend(chunk) + else: + with ProcessPoolExecutor(max_workers=min(MAX_WORKERS, args.num_workers)) as ex: + for chunk in ex.map(env.tokenizer.decode_batch, data_slices, repeat(pars, len(data_slices))): + if chunk: + res.extend(chunk) + else: + res = env.tokenizer.decode_batch(data, pars) + return res + + +# helper functions for creating the training and test Datasets + + +def generate_and_score(args, classname): + """ + Generation method if no data + """ + data = [] + BATCH = args.gen_batch_size + batch_counts = [BATCH] * (args.gensize // BATCH) + rem = args.gensize % BATCH + if rem: + batch_counts.append(rem) + if args.process_pool: + pars = classname._save_class_params() + with ProcessPoolExecutor(max_workers=min(MAX_WORKERS, args.num_workers)) as executor: + # map returns lists; stream them to avoid a giant materialization + for chunk in executor.map( + classname._batch_generate_and_score, batch_counts, repeat(args.N, len(batch_counts)), repeat(pars, len(batch_counts)) + ): + if chunk: # extend incrementally to manage memory + data.extend(chunk) + else: + for t in batch_counts: + d = classname._batch_generate_and_score(t, args.N) + if d is not None: + data.extend(d) + return data + + +def select_best(n, data): + if len(data) <= n: + random.shuffle(data) + return data + sorted_data = sorted(data, key=lambda x: x.score, reverse=True)[:n] + random.shuffle(sorted_data) + return sorted_data + + +def make_train_test(data, ntest): + """ + Create a train and test dataset from a dataset. + """ + indices = np.random.permutation(len(data)) + rp = [data[i] for i in indices] + return rp[:-ntest], rp[-ntest:] + + +def compute_unique_data(old_data, new_data=None): + def add_unique(src, unique_hashes): + des = [] + for d in src: + if d.features not in unique_hashes: + unique_hashes.add(d.features) + des.append(d) + return des, unique_hashes + + unique_hashes = set() + unique_old_data, unique_hashes = add_unique(old_data, unique_hashes) + if new_data is not None: + unique_new_data, unique_hashes = add_unique(new_data, unique_hashes) + else: + unique_new_data = None + return unique_old_data, unique_new_data + + +def update_datasets(args, data, train_set, test_set, train_path, test_path): + inc_temp = False + if args.keep_only_unique: + bef = len(data) + data, _ = compute_unique_data(data) + aft = len(data) + logger.info(f"Unique processing: {aft} examples left, {bef-aft} duplicates") + do_stats(-1, data) + if aft / (bef + 1) < 0.9: + inc_temp = True + if args.new_proportion > 0.0: + new_data = select_best(int(args.new_proportion * args.pop_size), data) + else: + new_data = select_best(args.pop_size, data) + + if len(new_data) >= 2 * args.ntest or test_set is None: + new_train, test_set = make_train_test(new_data, args.ntest) + else: + new_train = new_data + logger.info(f"New train and test generated. Size are train: {len(new_train)}, test {len(test_set)}") + # Get all examples of previous train and current train and then select best. + if args.keep_only_unique: + train_set, new_train = compute_unique_data(train_set, new_train) + logger.info(f"Unique data computed for original train set: {len(train_set)}, generated train set: {len(new_train)}") + if args.new_proportion > 0.0: + train_set = select_best(int((1.0 - args.new_proportion) * args.pop_size), train_set) + new_train + else: + train_set = select_best(args.pop_size, train_set + new_train) + logger.info(f"Final train and test generated. Size are train: {len(train_set)}, test {len(test_set)}") + + pickle.dump(test_set, open(test_path, "wb")) + pickle.dump(train_set, open(train_path, "wb")) + return train_set, test_set, inc_temp + + +def load_initial_data(args, classname): + train_data_path = os.path.join(args.dump_path, "train_data.pkl") + test_data_path = os.path.join(args.dump_path, "test_data.pkl") + if os.path.isfile(train_data_path): + logger.info("resuming from existing data") + train_set = pickle.load(open(train_data_path, "rb")) + test_set = pickle.load(open(test_data_path, "rb")) + else: + data = generate_and_score(args, classname=classname) + test_set = [] + train_set = [] + train_set, test_set, _ = update_datasets(args, data, train_set, test_set, train_data_path, test_data_path) + return train_set, test_set + + +class CharDataset(Dataset): + def __init__(self, encoded_data, max_len, stoi): + self.encoded_data = encoded_data + self.max_len = max_len + self.pad_token_id = stoi["PAD"] + + def __len__(self): + return len(self.encoded_data) + + def __getitem__(self, idx): + return self.encoded_data[idx] + + def collate_fn(self, batch): + x = np.full((len(batch), self.max_len + 2), self.pad_token_id, dtype=np.int32) + + for i, el in enumerate(batch): + x[i, : el.shape[0]] = el + valid_col = (x != self.pad_token_id).any(axis=0) + last_col = np.nonzero(valid_col)[0][-1] + 1 + x = x[:, :last_col] + y = np.concatenate([x[:, 1:], np.full((len(batch), 1), self.pad_token_id, dtype=x.dtype)], axis=1) + return torch.LongTensor(x), torch.LongTensor(y) + + +class InfiniteDataLoader: + """ + Create a infinite datalaoder in PyTorch + """ + + def __init__(self, dataset, **kwargs): + train_sampler = torch.utils.data.RandomSampler(dataset, replacement=True, num_samples=int(1e10)) + self.train_loader = DataLoader(dataset, sampler=train_sampler, collate_fn=dataset.collate_fn, **kwargs) + self.data_iter = iter(self.train_loader) + self._closed = False + + def next(self): + try: + batch = next(self.data_iter) + except StopIteration: # this will technically only happen after 1e10 samples... (i.e. basically never) + self.data_iter = iter(self.train_loader) + batch = next(self.data_iter) + return batch + + def close(self): + if self._closed: + return + shutdown_workers = getattr(self.data_iter, "_shutdown_workers", None) + if shutdown_workers is not None: + shutdown_workers() + self.data_iter = None + self.train_loader = None + self._closed = True + + def __del__(self): + try: + self.close() + except Exception: + pass diff --git a/axplorer-viz/vendor/axplorer/src/envs/__init__.py b/axplorer-viz/vendor/axplorer/src/envs/__init__.py new file mode 100644 index 0000000..b7dd057 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/__init__.py @@ -0,0 +1,13 @@ +from src.envs.cycle import SquareEnvironment +from src.envs.isosceles import IsoscelesEnvironment +from src.envs.sphere import SphereEnvironment + +ENVS = {"square": SquareEnvironment, "isosceles": IsoscelesEnvironment, "sphere": SphereEnvironment} + + +def build_env(params): + """ + Build environment. + """ + env = ENVS[params.env_name](params) + return env diff --git a/axplorer-viz/vendor/axplorer/src/envs/cycle.py b/axplorer-viz/vendor/axplorer/src/envs/cycle.py new file mode 100644 index 0000000..87f6869 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/cycle.py @@ -0,0 +1,185 @@ +import numpy as np + +from src.envs.environment import BaseEnvironment, DataPoint +from src.envs.tokenizers import DenseTokenizer, SparseTokenizerSequenceKTokens, SparseTokenizerSingleInteger +from src.envs.utils import random_symmetry_adj_matrix, sort_graph_based_on_degree +from src.utils import bool_flag + + +class SquareDataPoint(DataPoint): + MAKE_OBJECT_CANONICAL = False + + def __init__(self, N, init=False): + super().__init__() + self.N = N + self.data = np.zeros((self.N, self.N), dtype=np.uint8) + self.cycles = [] + + if init: + # here there cannot be any cycles, so _cycles_computation() is useless + self._add_edges_greedily() + if self.MAKE_OBJECT_CANONICAL: + self.data = sort_graph_based_on_degree(self.data) + self.calc_features() + self.calc_score() + + def calc_score(self): + if len(self.cycles) > 0: + self.score = -1 + else: + self.score = self.data.sum().item() // 2 + + def calc_features(self): + w = [] + for i in range(self.N): + for j in range(i + 1, self.N): + w.append(self.data[i, j]) + self.features = ",".join(map(str, w)) + + def _add_edges_greedily(self): + # [axplorer-viz patch] upstream reseeded numpy's global RNG to a random + # state on every call (`np.random.seed(None)`), which makes runs + # non-reproducible even with --seed. Removed; we rely on the caller + # (train.py) having seeded numpy. See vendor/PATCH_NOTES.md. + adjmat_cycle = self.data @ self.data @ self.data + allowed_edges = [] + for i in range(self.N): + for j in range(i + 1, self.N): + if self.data[i, j] == 0 and adjmat_cycle[i, j] == 0: + allowed_edges.append((i, j)) + + while allowed_edges: + i, j = allowed_edges[np.random.randint(len(allowed_edges))] + self.data[i, j] = 1 + self.data[j, i] = 1 + new_allowed_edges = [] + adjmat_cycle = self.data @ self.data @ self.data + for a, b in allowed_edges: + if self.data[a, b] == 0 and adjmat_cycle[a, b] == 0: + new_allowed_edges.append((a, b)) + allowed_edges = new_allowed_edges + + def _remove_edges_greedily(self): + while self.cycles: + edge_count = {} + for cycle in self.cycles: + for edge in cycle: + edge_count[edge] = edge_count.get(edge, 0) + 1 + i, j = max(edge_count, key=edge_count.get) + self.data[i, j] = 0 + self.data[j, i] = 0 + + remaining_cycles = [] + for cycle in self.cycles: + if (i, j) not in cycle: + remaining_cycles.append(cycle) + self.cycles = remaining_cycles + + def _cycles_computation(self): + cycles = set() + row_bits = [] + for i in range(self.N): + mask = 0 + for j in range(self.N): + if self.data[i, j] == 1: + mask |= 1 << j + row_bits.append(mask) + + for i in range(self.N): + bits_i = row_bits[i] + for j in range(i + 1, self.N): + common = bits_i & row_bits[j] + x = common + while x: + lsb_u = x & -x + u = lsb_u.bit_length() - 1 + x ^= lsb_u + y = x + while y: + lsb_v = y & -y + v = lsb_v.bit_length() - 1 + y ^= lsb_v + + # add unique sorting to speed up the future computations + elems = [i, u, j, v] + a = min(elems) + min_idx = elems.index(a) + neighbours = [elems[(min_idx + 1) % 4], elems[(min_idx - 1) % 4]] + b = min(neighbours) + d = max(neighbours) + c = sum(elems) - a - b - d + cycles.add((a, b, c, d)) + + self.cycles = [] + for cycle in cycles: + a, b, c, d = cycle + self.cycles.append(((min(a, b), max(a, b)), (min(b, c), max(b, c)), (min(c, d), max(c, d)), (min(d, a), max(d, a)))) + + def local_search(self, improve_with_local_search): + # here I start from a dirty graph, so we need to compute 4-cycles first + self._cycles_computation() + # first step of local search: remove edges greedily until there is no 4-cycle + self._remove_edges_greedily() + # second step of local search: add edges greedily while avoiding 4-cycles + if improve_with_local_search: + self._add_edges_greedily() + # no need to call _cycles_computation() given removing and adding edges doesn't create any 4-cycles + self.cycles = [] + if self.MAKE_OBJECT_CANONICAL: + self.data = sort_graph_based_on_degree(self.data) + self.calc_features() + self.calc_score() + + @classmethod + def _update_class_params(cls, pars): + cls.MAKE_OBJECT_CANONICAL = pars + + @classmethod + def _save_class_params(cls): + return cls.MAKE_OBJECT_CANONICAL + + +class SquareEnvironment(BaseEnvironment): + # this problem lives in N^2, therefore k=2 + # (i, j) or (j, i) represents the same edge, therefore are_coordinates_symmetric=True + k = 2 + are_coordinates_symmetric = True + data_class = SquareDataPoint + + def __init__(self, params): + super().__init__(params) + self.data_class.MAKE_OBJECT_CANONICAL = params.make_object_canonical + encoding_augmentation = random_symmetry_adj_matrix if params.augment_data_representation else None + if params.encoding_tokens == "single_integer": + self.tokenizer = SparseTokenizerSingleInteger( + self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation + ) + elif params.encoding_tokens == "sequence_k_tokens": + self.tokenizer = SparseTokenizerSequenceKTokens( + self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation + ) + elif params.encoding_tokens == "adjacency": + self.tokenizer = DenseTokenizer( + self.data_class, + params.N, + self.k, + self.are_coordinates_symmetric, + self.SPECIAL_SYMBOLS, + pow2base=params.pow2base, + encoding_augmentation=encoding_augmentation, + ) + else: + raise ValueError(f"Invalid encoding: {params.encoding_tokens}") + + @staticmethod + def register_args(parser): + """ + Register environment parameters. + """ + parser.add_argument("--N", type=int, default=30, help="Number of vertices in the 4-cycle-free graph") + parser.add_argument("--encoding_tokens", type=str, default="single_integer", help="single_integer/sequence_k_tokens/adjacency") + parser.add_argument("--make_object_canonical", type=bool_flag, default="false", help="sort the graph node names based on its indegree") + parser.add_argument( + "--augment_data_representation", type=bool_flag, default="false", help="augment the data representation with predefined function" + ) + parser.add_argument("--pow2base", type=int, default=1, help="Number of adjacency entries to code together") diff --git a/axplorer-viz/vendor/axplorer/src/envs/environment.py b/axplorer-viz/vendor/axplorer/src/envs/environment.py new file mode 100644 index 0000000..c3d2bc6 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/environment.py @@ -0,0 +1,148 @@ +import statistics +from abc import ABC, abstractmethod +from collections import Counter +from concurrent.futures import ProcessPoolExecutor +from itertools import repeat +from logging import getLogger + +logger = getLogger() + + +class DataPoint(ABC): + def __init__(self): + super().__init__() + self.score = -1 + self.features = "" + + @abstractmethod + def calc_score(self): + pass + + @abstractmethod + def calc_features(self): + pass + + def local_search(self, improve_with_local_search): + return + + @classmethod + def _update_class_params(cls, pars): + return + + @classmethod + def _batch_generate_and_score(cls, batch_size, N, pars=None): + out = [] + if pars is not None: + cls._update_class_params(pars) + for _ in range(batch_size): + d = cls(N=N, init=True) + if d.score >= 0: + out.append(d) + return out + + +class BaseEnvironment(object): + data_class = None + SPECIAL_SYMBOLS = ["SEP", "EOS", "PAD", "BOS"] + + def __init__(self, params): + return + + +def compute_stats(scores): + num_bins = 200 + if len(scores) == 0: + logger.info(f"No valid examples") + return None + + mean = statistics.mean(scores) + median = statistics.median(scores) + stdev = statistics.stdev(scores) if len(scores) > 1 else 0.0 + top_1_percentile = statistics.quantiles(scores, n=100)[-1] if len(scores) >= 100 else max(scores) + max_score = max(scores) + + logger.info(f"Valid examples: {len(scores)}") + logger.info(f"Mean score: {mean}") + logger.info(f"Median score: {median}") + logger.info(f"Stdev score: {stdev}") + logger.info(f"Max score: {max_score}") + logger.info(f"Top 1 percentile score: {top_1_percentile}") + + logger.info(f"Distribution of scores:") + counts = Counter(sorted(scores)) + if len(counts) > num_bins: + min_score, max_score = min(scores), max(scores) + bin_width = (max_score - min_score) / num_bins + bins = Counter() + for score, count in counts.items(): + bin_idx = min(int((score - min_score) / bin_width), num_bins - 1) + bin_start = min_score + bin_idx * bin_width + bin_end = bin_start + bin_width + bins[(bin_start, bin_end)] += count + for (start, end), count in bins.items(): + logger.info(f"Score [{start:.2f}, {end:.2f}): Count: {count}") + else: + for score, count in counts.items(): + logger.info(f"Score {score}: Count: {count}") + logger.info("--------------------------------") + return {"mean": mean, "median": median, "top_1_percentile": top_1_percentile, "max": max_score} + + +def do_stats(n_invalid, data): + """ + Compute and log statistics + """ + scores = [d.score for d in data if d.score >= 0] + logger.info(f"### Score distribution ###") + if n_invalid >= 0: + logger.info(f"Invalid examples: before local search: {n_invalid}, after: {len(data) - len(scores)}") + return compute_stats(scores) + + +def _do_score(d, always_search: bool = False, redeem_only: bool = False, pars=None): + invalid = 0 + if pars is not None: + d._update_class_params(pars) + d.calc_features() + d.calc_score() + invalid = 1 if d.score < 0 else 0 + if always_search: + d.local_search(improve_with_local_search=True) + elif invalid: + if redeem_only: + d.local_search(improve_with_local_search=False) + return (d, invalid) + + +def do_score(data, args, executor=None): + """ + Compute the score of a list of data. + Can be parallelized with process_pool. + Returns only valid items (score >= 0). + """ + n_invalid = 0 + processed_data = [] + if not args.process_pool: + for d in data: + # warning, change the original list + res, invalid = _do_score(d, args.always_search, args.redeem_only) + n_invalid += invalid + processed_data.append(res) + else: + pars = data[0]._save_class_params() + + chunksize = max(1, len(data) // (args.num_workers * 32)) + + if executor is not None: + for d, invalid in executor.map(_do_score, data, repeat(args.always_search), repeat(args.redeem_only), repeat(pars), chunksize=chunksize): + processed_data.append(d) + n_invalid += invalid + else: + with ProcessPoolExecutor(max_workers=args.num_workers) as ex: + for d, invalid in ex.map(_do_score, data, repeat(args.always_search), repeat(args.redeem_only), repeat(pars), chunksize=chunksize): + processed_data.append(d) + n_invalid += invalid + + valid_data = [d for d in processed_data if d.score >= 0] + + return valid_data, n_invalid, processed_data diff --git a/axplorer-viz/vendor/axplorer/src/envs/isosceles.py b/axplorer-viz/vendor/axplorer/src/envs/isosceles.py new file mode 100644 index 0000000..b5d2416 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/isosceles.py @@ -0,0 +1,319 @@ +import numpy as np +from numba import njit + +from src.envs.environment import BaseEnvironment, DataPoint +from src.envs.tokenizers import DenseTokenizer, SparseTokenizerSequenceKTokens, SparseTokenizerSingleInteger +from src.envs.utils import canonical_form_2d_symmetric, random_symmetry_2d_symmetric +from src.utils import bool_flag + + +@njit(cache=True) +def _greedy_fill_jittered(points_arr, n_points): + if n_points < 3: + return np.empty((0, 6), dtype=np.int32) + + max_triangles = n_points * (n_points - 1) * (n_points - 2) // 2 + triangles = np.empty((max_triangles, 6), dtype=np.int32) + idx = 0 + + for i in range(n_points): + ax, ay = points_arr[i, 0], points_arr[i, 1] + + n_others = n_points - 1 + if n_others < 2: + continue + + distances = np.empty(n_others, dtype=np.int64) + indices = np.empty(n_others, dtype=np.int32) + + k = 0 + for j in range(n_points): + if j == i: + continue + bx, by = points_arr[j, 0], points_arr[j, 1] + dx = ax - bx + dy = ay - by + distances[k] = dx * dx + dy * dy + indices[k] = j + k += 1 + + order = np.argsort(distances) + + start = 0 + while start < n_others: + d2 = distances[order[start]] + end = start + 1 + while end < n_others and distances[order[end]] == d2: + end += 1 + + if end - start >= 2: + for p in range(start, end): + for q in range(p + 1, end): + j1 = indices[order[p]] + j2 = indices[order[q]] + triangles[idx, 0] = ax + triangles[idx, 1] = ay + triangles[idx, 2] = points_arr[j1, 0] + triangles[idx, 3] = points_arr[j1, 1] + triangles[idx, 4] = points_arr[j2, 0] + triangles[idx, 5] = points_arr[j2, 1] + idx += 1 + + start = end + + return triangles[:idx] + + +@njit(cache=True) +def _has_isosceles_conflict(points_arr, n_points, new_x, new_y): + if n_points < 2: + return False + + new_distances = np.empty(n_points, dtype=np.int64) + for i in range(n_points): + dx = new_x - points_arr[i, 0] + dy = new_y - points_arr[i, 1] + new_distances[i] = dx * dx + dy * dy + + for i in range(n_points): + for j in range(i + 1, n_points): + if new_distances[i] == new_distances[j]: + return True + + for i in range(n_points): + d_new = new_distances[i] + for j in range(n_points): + if i == j: + continue + dx = points_arr[i, 0] - points_arr[j, 0] + dy = points_arr[i, 1] - points_arr[j, 1] + if dx * dx + dy * dy == d_new: + return True + + return False + + +@njit(cache=True) +def _data_to_expanded_grid(data, expanded_grid, N): + for x in range(N): + for y in range(N): + if data[x, y] == 1: + expanded_grid[x, y] = 1 + expanded_grid[2 * N - 1 - x, y] = 1 + expanded_grid[x, 2 * N - 1 - y] = 1 + expanded_grid[2 * N - 1 - x, 2 * N - 1 - y] = 1 + + +@njit(cache=True) +def _greedy_add_symmetric(data, expanded_grid, candidates, N): + max_points = 4 * N * N + points_arr = np.empty((max_points, 2), dtype=np.int32) + n_points = 0 + + for x in range(2 * N): + for y in range(2 * N): + if expanded_grid[x, y] == 1: + points_arr[n_points, 0] = x + points_arr[n_points, 1] = y + n_points += 1 + + for enc in candidates: + x, y = enc // N, enc % N + if data[x, y] == 1: + continue + + sym_points = [(x, y), (2 * N - 1 - x, y), (x, 2 * N - 1 - y), (2 * N - 1 - x, 2 * N - 1 - y)] + + # Temporarily add the 3 symmetric points to points_arr + # no need to remove if it doesn't work because it will rewrite from the next point + for i in range(3): + sx, sy = sym_points[i + 1] + points_arr[n_points + i, 0] = sx + points_arr[n_points + i, 1] = sy + + if not _has_isosceles_conflict(points_arr, n_points + 3, x, y): + data[x, y] = 1 + for sx, sy in sym_points: + expanded_grid[sx, sy] = 1 + points_arr[n_points + 3, 0] = x + points_arr[n_points + 3, 1] = y + n_points += 4 + + +@njit(cache=True) +def _greedy_remove_symmetric(data, expanded_grid, triangles, N): + num_triangles = len(triangles) + if num_triangles == 0: + return + + active = np.ones(num_triangles, dtype=np.uint8) + + point_count = np.zeros((2 * N, 2 * N), dtype=np.int32) + for t in range(num_triangles): + point_count[triangles[t, 0], triangles[t, 1]] += 1 + point_count[triangles[t, 2], triangles[t, 3]] += 1 + point_count[triangles[t, 4], triangles[t, 5]] += 1 + + num_active = num_triangles + + while num_active > 0: + + max_count = 0 + best_x, best_y = -1, -1 + for x in range(N): + for y in range(N): + if data[x, y] == 1: + total_count = ( + point_count[x, y] + point_count[2 * N - 1 - x, y] + point_count[x, 2 * N - 1 - y] + point_count[2 * N - 1 - x, 2 * N - 1 - y] + ) + if total_count > max_count: + max_count = total_count + best_x, best_y = x, y + + if max_count == 0: + break + + data[best_x, best_y] = 0 + + sym_points = [(best_x, best_y), (2 * N - 1 - best_x, best_y), (best_x, 2 * N - 1 - best_y), (2 * N - 1 - best_x, 2 * N - 1 - best_y)] + + for sx, sy in sym_points: + expanded_grid[sx, sy] = 0 + + for t in range(num_triangles): + if not active[t]: + continue + + contains = False + for sx, sy in sym_points: + if ( + (triangles[t, 0] == sx and triangles[t, 1] == sy) + or (triangles[t, 2] == sx and triangles[t, 3] == sy) + or (triangles[t, 4] == sx and triangles[t, 5] == sy) + ): + contains = True + break + + if contains: + active[t] = False + num_active -= 1 + point_count[triangles[t, 0], triangles[t, 1]] -= 1 + point_count[triangles[t, 2], triangles[t, 3]] -= 1 + point_count[triangles[t, 4], triangles[t, 5]] -= 1 + + +class IsoscelesDataPoint(DataPoint): + MAKE_OBJECT_CANONICAL = False + + def __init__(self, N, init=False): + super().__init__() + self.N = N + self.data = np.zeros((self.N, self.N), dtype=np.uint8) + self.expanded_grid = np.zeros((2 * self.N, 2 * self.N), dtype=np.uint8) + self.isosceles = np.empty((0, 6), dtype=np.int32) + if init: + self._add_points_greedily() + if self.MAKE_OBJECT_CANONICAL: + self.data = canonical_form_2d_symmetric(self.data) + self._sync_expanded_grid() + self.calc_features() + self.calc_score() + + def _sync_expanded_grid(self): + self.expanded_grid.fill(0) + _data_to_expanded_grid(self.data, self.expanded_grid, self.N) + + def calc_score(self): + if self.isosceles.size > 0: + self.score = -1 + else: + self.score = self.expanded_grid.sum().item() + + def calc_features(self): + w = [] + for i in range(self.N): + for j in range(self.N): + w.append(self.data[i, j]) + self.features = ",".join(map(str, w)) + + def _add_points_greedily(self): + np.random.seed(None) + candidates = np.arange(self.N * self.N, dtype=np.int32) + np.random.shuffle(candidates) + _greedy_add_symmetric(self.data, self.expanded_grid, candidates, self.N) + + def _remove_points_greedily(self): + if self.isosceles.size > 0: + _greedy_remove_symmetric(self.data, self.expanded_grid, self.isosceles, self.N) + self.isosceles = np.empty((0, 6), dtype=np.int32) + + def _isosceles_computation(self): + points = np.argwhere(self.expanded_grid == 1) + points_arr = np.ascontiguousarray(points, dtype=np.int32) + self.isosceles = _greedy_fill_jittered(points_arr, len(points_arr)) + + def local_search(self, improve_with_local_search): + self._isosceles_computation() + self._remove_points_greedily() + if improve_with_local_search: + self._add_points_greedily() + self._isosceles_computation() + self.calc_score() + if self.MAKE_OBJECT_CANONICAL: + self.data = canonical_form_2d_symmetric(self.data) + self._sync_expanded_grid() + self.calc_features() + + @classmethod + def _update_class_params(cls, pars): + cls.MAKE_OBJECT_CANONICAL = pars + + @classmethod + def _save_class_params(cls): + return cls.MAKE_OBJECT_CANONICAL + + +class IsoscelesEnvironment(BaseEnvironment): + # this problem lives in N^2, therefore k=2 + # (i, j) or (j, i) represents two distinct points in the grid, therefore are_coordinates_symmetric=False + k = 2 + are_coordinates_symmetric = False + data_class = IsoscelesDataPoint + + def __init__(self, params): + super().__init__(params) + self.data_class.MAKE_OBJECT_CANONICAL = params.make_object_canonical + encoding_augmentation = random_symmetry_2d_symmetric if params.augment_data_representation else None + if params.encoding_tokens == "single_integer": + self.tokenizer = SparseTokenizerSingleInteger( + self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation + ) + elif params.encoding_tokens == "sequence_k_tokens": + self.tokenizer = SparseTokenizerSequenceKTokens( + self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation + ) + elif params.encoding_tokens == "adjacency": + self.tokenizer = DenseTokenizer( + self.data_class, + params.N, + self.k, + self.are_coordinates_symmetric, + self.SPECIAL_SYMBOLS, + pow2base=params.pow2base, + encoding_augmentation=encoding_augmentation, + ) + else: + raise ValueError(f"Invalid encoding: {params.encoding_tokens}") + + @staticmethod + def register_args(parser): + """ + Register environment parameters. + """ + parser.add_argument("--N", type=int, default=30, help="Half grid size N. Total grid size is 2N") + parser.add_argument("--encoding_tokens", type=str, default="single_integer", help="single_integer/sequence_k_tokens/adjacency") + parser.add_argument("--make_object_canonical", type=bool_flag, default="false", help="sort the grid by symmetry") + parser.add_argument( + "--augment_data_representation", type=bool_flag, default="false", help="augment the data representation with predefined function" + ) + parser.add_argument("--pow2base", type=int, default=1, help="Number of adjacency entries to code together") diff --git a/axplorer-viz/vendor/axplorer/src/envs/sphere.py b/axplorer-viz/vendor/axplorer/src/envs/sphere.py new file mode 100644 index 0000000..16be7ab --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/sphere.py @@ -0,0 +1,316 @@ +import numpy as np +from numba import njit + +from src.envs.environment import BaseEnvironment, DataPoint +from src.envs.tokenizers import SparseTokenizerSequenceKTokens, SparseTokenizerSingleInteger +from src.envs.utils import canonical_form_3d, random_symmetry_3d +from src.utils import bool_flag + + +@njit(cache=True) +def _det2x2_elements(m00, m01, m10, m11): + return m00 * m11 - m01 * m10 + + +@njit(cache=True) +def _det3x3_from_matrix(matrix, r0, r1, r2, c0, c1, c2): + m00 = matrix[r0, c0] + m01 = matrix[r0, c1] + m02 = matrix[r0, c2] + m10 = matrix[r1, c0] + m11 = matrix[r1, c1] + m12 = matrix[r1, c2] + m20 = matrix[r2, c0] + m21 = matrix[r2, c1] + m22 = matrix[r2, c2] + + return m00 * _det2x2_elements(m11, m12, m21, m22) - m01 * _det2x2_elements(m10, m12, m20, m22) + m02 * _det2x2_elements(m10, m11, m20, m21) + + +@njit(cache=True) +def _det4x4_from_matrix(matrix, r0, r1, r2, r3, c0, c1, c2, c3): + d0 = _det3x3_from_matrix(matrix, r1, r2, r3, c1, c2, c3) + d1 = _det3x3_from_matrix(matrix, r1, r2, r3, c0, c2, c3) + d2 = _det3x3_from_matrix(matrix, r1, r2, r3, c0, c1, c3) + d3 = _det3x3_from_matrix(matrix, r1, r2, r3, c0, c1, c2) + + return matrix[r0, c0] * d0 - matrix[r0, c1] * d1 + matrix[r0, c2] * d2 - matrix[r0, c3] * d3 + + +@njit(cache=True) +def _det5x5_int(matrix): + det = 0 + det += matrix[0, 0] * _det4x4_from_matrix(matrix, 1, 2, 3, 4, 1, 2, 3, 4) + det -= matrix[0, 1] * _det4x4_from_matrix(matrix, 1, 2, 3, 4, 0, 2, 3, 4) + det += matrix[0, 2] * _det4x4_from_matrix(matrix, 1, 2, 3, 4, 0, 1, 3, 4) + det -= matrix[0, 3] * _det4x4_from_matrix(matrix, 1, 2, 3, 4, 0, 1, 2, 4) + det += matrix[0, 4] * _det4x4_from_matrix(matrix, 1, 2, 3, 4, 0, 1, 2, 3) + return det + + +@njit(cache=True) +def _are_five_points_cospherical(points): + matrix = np.zeros((5, 5), dtype=np.int64) + for i in range(5): + p = points[i] + matrix[i, 0] = p[0] + matrix[i, 1] = p[1] + matrix[i, 2] = p[2] + matrix[i, 3] = p[0] ** 2 + p[1] ** 2 + p[2] ** 2 + matrix[i, 4] = 1 + det = _det5x5_int(matrix) + return det == 0 + + +@njit(cache=True) +def _greedy_fill_jittered(points_arr, n_points): + if n_points < 5: + return np.empty((0, 15), dtype=np.int32) + + max_tuples = n_points * (n_points - 1) * (n_points - 2) * (n_points - 3) * (n_points - 4) // 120 + cospherical = np.empty((max_tuples, 15), dtype=np.int32) + idx = 0 + + points_to_check = np.zeros((5, 3), dtype=np.int64) + + for i in range(n_points): + for j in range(i + 1, n_points): + for k in range(j + 1, n_points): + for l in range(k + 1, n_points): + for m in range(l + 1, n_points): + points_to_check[0] = points_arr[i] + points_to_check[1] = points_arr[j] + points_to_check[2] = points_arr[k] + points_to_check[3] = points_arr[l] + points_to_check[4] = points_arr[m] + + if _are_five_points_cospherical(points_to_check): + cospherical[idx, 0] = points_arr[i, 0] + cospherical[idx, 1] = points_arr[i, 1] + cospherical[idx, 2] = points_arr[i, 2] + cospherical[idx, 3] = points_arr[j, 0] + cospherical[idx, 4] = points_arr[j, 1] + cospherical[idx, 5] = points_arr[j, 2] + cospherical[idx, 6] = points_arr[k, 0] + cospherical[idx, 7] = points_arr[k, 1] + cospherical[idx, 8] = points_arr[k, 2] + cospherical[idx, 9] = points_arr[l, 0] + cospherical[idx, 10] = points_arr[l, 1] + cospherical[idx, 11] = points_arr[l, 2] + cospherical[idx, 12] = points_arr[m, 0] + cospherical[idx, 13] = points_arr[m, 1] + cospherical[idx, 14] = points_arr[m, 2] + idx += 1 + + return cospherical[:idx] + + +@njit(cache=True) +def _has_cospherical_conflict(points_arr, n_points, new_x, new_y, new_z): + if n_points < 4: + return False + + points_to_check = np.zeros((5, 3), dtype=np.int64) + points_to_check[4, 0] = new_x + points_to_check[4, 1] = new_y + points_to_check[4, 2] = new_z + + for i in range(n_points): + for j in range(i + 1, n_points): + for k in range(j + 1, n_points): + for l in range(k + 1, n_points): + points_to_check[0] = points_arr[i] + points_to_check[1] = points_arr[j] + points_to_check[2] = points_arr[k] + points_to_check[3] = points_arr[l] + + if _are_five_points_cospherical(points_to_check): + return True + + return False + + +@njit(cache=True) +def _greedy_add_jittered(data, candidates, N): + max_points = N * N * N + points_arr = np.empty((max_points, 3), dtype=np.int32) + n_points = 0 + + for x in range(N): + for y in range(N): + for z in range(N): + if data[x, y, z] == 1: + points_arr[n_points, 0] = x + points_arr[n_points, 1] = y + points_arr[n_points, 2] = z + n_points += 1 + + for enc in candidates: + x = enc // (N * N) + y = (enc // N) % N + z = enc % N + + if data[x, y, z] == 1: + continue + + if not _has_cospherical_conflict(points_arr, n_points, x, y, z): + data[x, y, z] = 1 + points_arr[n_points, 0] = x + points_arr[n_points, 1] = y + points_arr[n_points, 2] = z + n_points += 1 + + +@njit(cache=True) +def _greedy_remove_jittered(data, cospherical, N): + num_tuples = len(cospherical) + if num_tuples == 0: + return + + active = np.ones(num_tuples, dtype=np.uint8) + + point_count = np.zeros((N, N, N), dtype=np.int32) + for t in range(num_tuples): + point_count[cospherical[t, 0], cospherical[t, 1], cospherical[t, 2]] += 1 + point_count[cospherical[t, 3], cospherical[t, 4], cospherical[t, 5]] += 1 + point_count[cospherical[t, 6], cospherical[t, 7], cospherical[t, 8]] += 1 + point_count[cospherical[t, 9], cospherical[t, 10], cospherical[t, 11]] += 1 + point_count[cospherical[t, 12], cospherical[t, 13], cospherical[t, 14]] += 1 + + num_active = num_tuples + + while num_active > 0: + max_count = 0 + best_x, best_y, best_z = -1, -1, -1 + for x in range(N): + for y in range(N): + for z in range(N): + if data[x, y, z] == 1 and point_count[x, y, z] > max_count: + max_count = point_count[x, y, z] + best_x, best_y, best_z = x, y, z + + if max_count == 0: + break + + data[best_x, best_y, best_z] = 0 + + for t in range(num_tuples): + if not active[t]: + continue + + contains = ( + (cospherical[t, 0] == best_x and cospherical[t, 1] == best_y and cospherical[t, 2] == best_z) + or (cospherical[t, 3] == best_x and cospherical[t, 4] == best_y and cospherical[t, 5] == best_z) + or (cospherical[t, 6] == best_x and cospherical[t, 7] == best_y and cospherical[t, 8] == best_z) + or (cospherical[t, 9] == best_x and cospherical[t, 10] == best_y and cospherical[t, 11] == best_z) + or (cospherical[t, 12] == best_x and cospherical[t, 13] == best_y and cospherical[t, 14] == best_z) + ) + + if contains: + active[t] = False + num_active -= 1 + point_count[cospherical[t, 0], cospherical[t, 1], cospherical[t, 2]] -= 1 + point_count[cospherical[t, 3], cospherical[t, 4], cospherical[t, 5]] -= 1 + point_count[cospherical[t, 6], cospherical[t, 7], cospherical[t, 8]] -= 1 + point_count[cospherical[t, 9], cospherical[t, 10], cospherical[t, 11]] -= 1 + point_count[cospherical[t, 12], cospherical[t, 13], cospherical[t, 14]] -= 1 + + +class SphereDataPoint(DataPoint): + MAKE_OBJECT_CANONICAL = False + + def __init__(self, N, init=False): + super().__init__() + self.N = N + self.data = np.zeros((self.N, self.N, self.N), dtype=np.uint8) + self.cospherical = np.empty((0, 15), dtype=np.int32) + if init: + self._add_points_greedily() + if self.MAKE_OBJECT_CANONICAL: + self.data = canonical_form_3d(self.data) + self.calc_features() + self.calc_score() + + def calc_score(self): + if self.cospherical.size > 0: + self.score = -1 + else: + self.score = self.data.sum().item() + + def calc_features(self): + w = [] + for i in range(self.N): + for j in range(self.N): + for k in range(self.N): + w.append(self.data[i, j, k]) + self.features = ",".join(map(str, w)) + + def _add_points_greedily(self): + np.random.seed(None) + candidates = np.arange(self.N * self.N * self.N, dtype=np.int32) + np.random.shuffle(candidates) + _greedy_add_jittered(self.data, candidates, self.N) + + def _remove_points_greedily(self): + if self.cospherical.size > 0: + _greedy_remove_jittered(self.data, self.cospherical, self.N) + self.cospherical = np.empty((0, 15), dtype=np.int32) + + def _cospherical_computation(self): + points = np.argwhere(self.data == 1) + points_arr = np.ascontiguousarray(points, dtype=np.int32) + self.cospherical = _greedy_fill_jittered(points_arr, len(points_arr)) + + def local_search(self, improve_with_local_search): + self._cospherical_computation() + self._remove_points_greedily() + if improve_with_local_search: + self._add_points_greedily() + self._cospherical_computation() + self.calc_score() + if self.MAKE_OBJECT_CANONICAL: + self.data = canonical_form_3d(self.data) + self.calc_features() + + @classmethod + def _update_class_params(cls, pars): + cls.MAKE_OBJECT_CANONICAL = pars + + @classmethod + def _save_class_params(cls): + return cls.MAKE_OBJECT_CANONICAL + + +class SphereEnvironment(BaseEnvironment): + # this problem lives in N^3, so we can use k=3 + # (i, j, k) or (j, i, k) represents two distinct points in the grid, therefore are_coordinates_symmetric=False + k = 3 + are_coordinates_symmetric = False + data_class = SphereDataPoint + + def __init__(self, params): + super().__init__(params) + self.data_class.MAKE_OBJECT_CANONICAL = params.make_object_canonical + encoding_augmentation = random_symmetry_3d if params.augment_data_representation else None + if params.encoding_tokens == "single_integer": + self.tokenizer = SparseTokenizerSingleInteger( + self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation + ) + elif params.encoding_tokens == "sequence_k_tokens": + self.tokenizer = SparseTokenizerSequenceKTokens( + self.data_class, params.N, self.k, self.are_coordinates_symmetric, self.SPECIAL_SYMBOLS, encoding_augmentation=encoding_augmentation + ) + else: + raise ValueError(f"Invalid encoding: {params.encoding_tokens}") + + @staticmethod + def register_args(parser): + """ + Register environment parameters. + """ + parser.add_argument("--N", type=int, default=30, help="Grid size N for the sphere problem") + parser.add_argument("--encoding_tokens", type=str, default="single_integer", help="single_integer/sequence_k_tokens") + parser.add_argument("--make_object_canonical", type=bool_flag, default="false", help="sort the data by symmetry") + parser.add_argument( + "--augment_data_representation", type=bool_flag, default="false", help="augment the data representation with predefined function" + ) diff --git a/axplorer-viz/vendor/axplorer/src/envs/tokenizers.py b/axplorer-viz/vendor/axplorer/src/envs/tokenizers.py new file mode 100644 index 0000000..6431701 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/tokenizers.py @@ -0,0 +1,260 @@ +import math +from abc import ABC, abstractmethod +from itertools import combinations, permutations, product + +import numpy as np + + +def generate_index_tuples(N, k, are_coordinates_symmetric): + if k == 1: + yield from range(N) + else: + if are_coordinates_symmetric: + yield from combinations(range(N), k) + else: + yield from product(range(N), repeat=k) + + +def count_index_tuples(N, k, are_coordinates_symmetric): + if are_coordinates_symmetric: + return math.comb(N, k) + else: + return N**k + + +class Tokenizer(ABC): + """ + Base class for encoders, encodes and decodes matrices + abstract methods for encoding/decoding numbers + """ + + def __init__(self): + self.dataclass = None + + @abstractmethod + def encode(self, val): + pass + + @abstractmethod + def decode(self, lst): + pass + + def decode_batch(self, data, pars=None): + """ + Worker function for detokenizing a batch of data + """ + out = [] + if pars is not None: + self.dataclass._update_class_params(pars) + for _, lst in enumerate(data): + l = self.decode(lst) + if l is not None: + out.append(l) + return out + + +class SparseTokenizerSingleInteger(Tokenizer): + def __init__(self, dataclass, N, k, are_coordinates_symmetric, extra_symbols, encoding_augmentation=None): + self.dataclass = dataclass + self.N = N + self.k = k + self.are_coordinates_symmetric = are_coordinates_symmetric + self.extra_symbols = extra_symbols + + self.encoding_augmentation = encoding_augmentation + self.stoi, self.itos = {}, {} + + for idx, el in enumerate(generate_index_tuples(N, k, are_coordinates_symmetric)): + self.stoi[el] = idx + self.itos[idx] = el + + len1 = len(self.stoi) + for jdx, el in enumerate(extra_symbols): + self.stoi[el] = len1 + jdx + self.itos[len1 + jdx] = el + + def encode(self, datapoint_to_encode): + if self.encoding_augmentation: + data = self.encoding_augmentation(datapoint_to_encode.data) + else: + data = datapoint_to_encode.data + + coordinates = [] + for el in generate_index_tuples(self.N, self.k, self.are_coordinates_symmetric): + if data[el] == 1: + coordinates.append(el) + + w = [] + w.append(self.stoi["BOS"]) + for el in coordinates: + w.append(self.stoi[el]) + w.append(self.stoi["EOS"]) + return np.array(w, dtype=np.int32) + + def decode(self, token_seq_to_decode): + # remove the first token because it's always BOS + token_seq_to_decode = token_seq_to_decode[1:] + try: + datapoint = self.dataclass(N=self.N) + for token in token_seq_to_decode: + el = self.itos[token] + if el in self.extra_symbols: + break + if self.are_coordinates_symmetric: + if len(set(el)) != len(el): + return None + for permutation in permutations(el): + datapoint.data[permutation] = 1 + else: + datapoint.data[el] = 1 + return datapoint + except: + return None + + +class SparseTokenizerSequenceKTokens(Tokenizer): + def __init__(self, dataclass, N, k, are_coordinates_symmetric, extra_symbols, encoding_augmentation=None): + self.dataclass = dataclass + self.N = N + self.k = k + self.are_coordinates_symmetric = are_coordinates_symmetric + + self.encoding_augmentation = encoding_augmentation + self.stoi, self.itos = {}, {} + + for idx in range(N): + self.stoi[idx] = idx + self.itos[idx] = idx + + len1 = len(self.stoi) + for jdx, el in enumerate(extra_symbols): + self.stoi[el] = len1 + jdx + self.itos[len1 + jdx] = el + + def encode(self, datapoint_to_encode): + if self.encoding_augmentation: + data = self.encoding_augmentation(datapoint_to_encode.data) + else: + data = datapoint_to_encode.data + + coordinates = [] + for el in generate_index_tuples(self.N, self.k, self.are_coordinates_symmetric): + if data[el] == 1: + coordinates.append(el) + + w = [] + w.append(self.stoi["BOS"]) + for el in coordinates: + for seq in el: + w.append(self.stoi[seq]) + w.append(self.stoi["EOS"]) + return np.array(w, dtype=np.int32) + + def decode(self, token_seq_to_decode): + # remove the first token because it's always BOS + token_seq_to_decode = token_seq_to_decode[1:] + try: + datapoint = self.dataclass(N=self.N) + for idx in range(0, len(token_seq_to_decode), self.k): + el = tuple(self.itos[t] for t in token_seq_to_decode[idx : idx + self.k]) + if any(x in self.extra_symbols for x in el): + break + if self.are_coordinates_symmetric: + if len(set(el)) != len(el): + return None + for permutation in permutations(el): + datapoint.data[permutation] = 1 + else: + datapoint.data[el] = 1 + return datapoint + except: + return None + + +class DenseTokenizer(Tokenizer): + def __init__(self, dataclass, N, k, are_coordinates_symmetric, extra_symbols, pow2base, encoding_augmentation=None): + self.dataclass = dataclass + self.N = N + self.k = k + self.are_coordinates_symmetric = are_coordinates_symmetric + self.pow2base = pow2base + self.encoding_augmentation = encoding_augmentation + self.stoi, self.itos = {}, {} + self.extra_symbols = extra_symbols + + self.expected_elements_in_a_decoded_sequence = math.ceil(count_index_tuples(N, self.k, self.are_coordinates_symmetric) / self.pow2base) + + for idx, el in enumerate(range(2**pow2base)): + self.stoi[el] = idx + self.itos[idx] = el + len1 = len(self.stoi) + for jdx, el in enumerate(extra_symbols): + self.stoi[el] = len1 + jdx + self.itos[len1 + jdx] = el + + def _pack_bits(self, bits): + tokens = [] + count = 1 + val = 0 + for bit in bits: + val += count * bit + count *= 2 + if count == 2**self.pow2base: + tokens.append(self.stoi[val]) + count = 1 + val = 0 + if count > 1: + tokens.append(self.stoi[val]) + return tokens + + def _unpack_bits(self, tokens): + bits = [] + for token in tokens: + val = token + for _ in range(self.pow2base): + bits.append(val % 2) + val //= 2 + return bits + + def _row_indices(self, row, datapoint_to_encode_N): + if self.are_coordinates_symmetric: + return range(row + 1, datapoint_to_encode_N) + else: + return range(datapoint_to_encode_N) + + def encode(self, datapoint_to_encode): + if self.encoding_augmentation: + data = self.encoding_augmentation(datapoint_to_encode.data) + else: + data = datapoint_to_encode.data + + w = [] + bits = (data[el] for el in generate_index_tuples(datapoint_to_encode.N, self.k, self.are_coordinates_symmetric)) + w.append(self.stoi["BOS"]) + w.extend(self._pack_bits(bits)) + w.append(self.stoi["EOS"]) + return np.array(w, dtype=np.int32) + + def decode(self, token_seq_to_decode): + token_seq_to_decode = token_seq_to_decode[1:] + new_token_seq_to_decode = [] + for el in token_seq_to_decode: + el = self.itos[el] + if el in self.extra_symbols: + break + new_token_seq_to_decode.append(el) + if len(new_token_seq_to_decode) != self.expected_elements_in_a_decoded_sequence: + return None + try: + datapoint = self.dataclass(N=self.N) + bits = self._unpack_bits(new_token_seq_to_decode) + for bit, el in zip(bits, generate_index_tuples(self.N, self.k, self.are_coordinates_symmetric)): + if bit == 1: + if self.are_coordinates_symmetric: + for permutation in permutations(el): + datapoint.data[permutation] = 1 + else: + datapoint.data[el] = 1 + return datapoint + except: + return None diff --git a/axplorer-viz/vendor/axplorer/src/envs/utils.py b/axplorer-viz/vendor/axplorer/src/envs/utils.py new file mode 100644 index 0000000..d0387ee --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/envs/utils.py @@ -0,0 +1,123 @@ +import random + +import numpy as np + +############################################################ +# Utils for (N, E) graph +############################################################ + + +# sort the graph based on the degree of the nodes +def sort_graph_based_on_degree(adj_matrix): + out_degree = adj_matrix.sum(axis=1) + in_degree = adj_matrix.sum(axis=0) + degree = in_degree + out_degree # so this works for both undirected and directed graphs + sorted_indices = np.argsort(-degree, kind="stable") + return adj_matrix[np.ix_(sorted_indices, sorted_indices)] + + +def random_symmetry_adj_matrix(adj_matrix): + perm = np.random.permutation(adj_matrix.shape[0]) + return adj_matrix[np.ix_(perm, perm)] # this is creating a copy already + + +############################################################ +# Utils for (N, N) square grid +############################################################ +def canonical_form_2d(matrix): + best = None + best_matrix = None + + # 8 symmetries: 4 rotations × 2 (with/without reflection) + current = matrix + for _ in range(4): + flat = current.flatten().tolist() + if best is None or flat < best: + best = flat + best_matrix = current.copy() + + reflected = np.flip(current, axis=1) + flat = reflected.flatten().tolist() + if flat < best: + best = flat + best_matrix = reflected.copy() + + current = np.rot90(current) + + return best_matrix + + +def random_symmetry_2d(matrix): + k = random.randint(0, 3) + result = np.rot90(matrix, k) + + if random.randint(0, 1): + result = np.flip(result, axis=1) + + return result.copy() + + +############################################################ +# Utils for (N/2, N/2) square grid +############################################################ +def canonical_form_2d_symmetric(matrix): + transposed = matrix.T + + flat_original = matrix.flatten().tolist() + flat_transposed = transposed.flatten().tolist() + + if flat_original <= flat_transposed: + return matrix.copy() + else: + return transposed.copy() + + +def random_symmetry_2d_symmetric(matrix): + if random.randint(0, 1): + return matrix.T.copy() + else: + return matrix.copy() + + +############################################################ +# Utils for (N, N, N) cubic grid +############################################################ +def canonical_form_3d(matrix): + best = None + best_matrix = None + + # 48 symmetries: 6 axis permutations × 8 reflections + for perm in [(0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0)]: + for flips in range(8): + transformed = np.transpose(matrix, perm) + if flips & 1: + transformed = np.flip(transformed, axis=0) + if flips & 2: + transformed = np.flip(transformed, axis=1) + if flips & 4: + transformed = np.flip(transformed, axis=2) + + flat = transformed.flatten().tolist() + if best is None or flat < best: + best = flat + best_matrix = transformed.copy() + + return best_matrix + + +def random_symmetry_3d(matrix): + perms = [(0, 1, 2), (0, 2, 1), (1, 0, 2), (1, 2, 0), (2, 0, 1), (2, 1, 0)] + perm = random.choice(perms) + + flips = random.randint(0, 7) + + result = np.transpose(matrix, perm) + + if flips & 1: + result = np.flip(result, axis=0) + if flips & 2: + result = np.flip(result, axis=1) + if flips & 4: + result = np.flip(result, axis=2) + + return result.copy() diff --git a/axplorer-viz/vendor/axplorer/src/evaluator.py b/axplorer-viz/vendor/axplorer/src/evaluator.py new file mode 100644 index 0000000..0d183c1 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/evaluator.py @@ -0,0 +1,131 @@ +import queue +import threading +from concurrent.futures import ProcessPoolExecutor +from contextlib import contextmanager +from logging import getLogger + +import numpy as np +import torch + +from src.datasets import detokenize +from src.envs.environment import do_score, do_stats +from src.utils import MAX_WORKERS + +logger = getLogger() + + +class _CpuSink: + def __init__(self, fn, decouple=False): + self._fn = fn + self._decouple = decouple + self._queue = None + self._thread = None + self._error = None + + def start(self): + if not self._decouple: + return + self._queue = queue.Queue() + + def consumer(): + try: + while True: + item = self._queue.get() + if item is None: + break + self._fn(*item) + except Exception as e: + self._error = e + + self._thread = threading.Thread(target=consumer, daemon=True) + self._thread.start() + + def submit(self, *args): + if self._decouple: + if self._error is not None: + raise self._error + self._queue.put(args) + else: + self._fn(*args) + + def join(self): + if self._decouple: + self._queue.put(None) + self._thread.join() + if self._error is not None: + raise self._error + + +@contextmanager +def cpu_sink(fn, decouple=False): + sink = _CpuSink(fn, decouple) + sink.start() + try: + yield sink + finally: + sink.join() + + +def sample_and_score(model, args, stoi, itos, env, temp, temp_span=0, raw_token_out=None, raw_token_cap=32): + # [axplorer-viz patch] `raw_token_out`: if a list is passed, the first + # `raw_token_cap` raw model-sampled token sequences (BEFORE local search) are + # appended to it -- used by the trajectory-logging patch in train.py. + sample_batch_size = args.gen_batch_size + todo = args.num_samples_from_model // sample_batch_size + DETOK_CHUNK_SIZE = 1 + + results = [] + total_invalid = 0 + all_processed_data = [] + results_lock = threading.Lock() + + executor = ProcessPoolExecutor(max_workers=min(MAX_WORKERS, args.num_workers)) + + def process_batches(batches): + nonlocal total_invalid + all_data = [batch_numpy[j] for batch_numpy in batches for j in range(batch_numpy.shape[0])] + if raw_token_out is not None: # [axplorer-viz patch] stash a few pre-search samples + with results_lock: + room = raw_token_cap - len(raw_token_out) + if room > 0: + raw_token_out.extend(seq.tolist() for seq in all_data[:room]) + detok_results = detokenize(all_data, args, env, executor=executor) + valid_data, n_invalid, processed_data = do_score(detok_results, args=args, executor=executor) + with results_lock: + results.extend(valid_data) + total_invalid += n_invalid + all_processed_data.extend(processed_data) + + with cpu_sink(process_batches, decouple=True) as sink: + pending_batches = [] + + for i in range(todo): + if temp_span > 0: + curr_temp = temp + 0.1 * np.random.randint(temp_span + 1) + else: + curr_temp = temp + if i % 100 == 0: + with results_lock: + scored_so_far = len(results) + logger.info(f"{i*sample_batch_size} / {todo * sample_batch_size} samples generated, {scored_so_far} scored") + + X_init = torch.empty((sample_batch_size, 1), dtype=torch.long) + X_init[:, 0] = stoi["BOS"] + X_init = X_init.to(args.device) + top_k = args.top_k if args.top_k != -1 else None + batch_numpy = model.generate(X_init, args.max_len + 1, temperature=curr_temp, top_k=top_k, do_sample=True).cpu().numpy() + + pending_batches.append(batch_numpy) + + if len(pending_batches) >= DETOK_CHUNK_SIZE: + sink.submit(pending_batches) + pending_batches = [] + + if pending_batches: + sink.submit(pending_batches) + + executor.shutdown(wait=True) + + do_stats(total_invalid, all_processed_data) + + return results diff --git a/axplorer-viz/vendor/axplorer/src/logger.py b/axplorer-viz/vendor/axplorer/src/logger.py new file mode 100644 index 0000000..4ed98b1 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/logger.py @@ -0,0 +1,56 @@ +import logging +import time +from datetime import timedelta + + +class LogFormatter: + + def __init__(self): + self.start_time = time.time() + + def format(self, record): + elapsed_seconds = round(record.created - self.start_time) + + prefix = "%s - %s - %s" % (record.levelname, time.strftime("%x %X"), timedelta(seconds=elapsed_seconds)) + message = record.getMessage() + message = message.replace("\n", "\n" + " " * (len(prefix) + 3)) + return "%s - %s" % (prefix, message) if message else "" + + +def create_logger(filepath, rank): + """ + Create a logger. + Use a different log file for each process. + """ + # create log formatter + log_formatter = LogFormatter() + + # create file handler and set level to info + if filepath is not None: + if rank > 0: + filepath = "%s-%i" % (filepath, rank) + file_handler = logging.FileHandler(filepath, "a") + file_handler.setLevel(logging.INFO) + file_handler.setFormatter(log_formatter) + + # create console handler and set level to info + console_handler = logging.StreamHandler() + console_handler.setLevel(logging.INFO) + console_handler.setFormatter(log_formatter) + + # create logger and set level to info + logger = logging.getLogger() + logger.handlers = [] + logger.setLevel(logging.INFO) + logger.propagate = False + if filepath is not None: + logger.addHandler(file_handler) + logger.addHandler(console_handler) + + # reset logger elapsed time + def reset_time(): + log_formatter.start_time = time.time() + + logger.reset_time = reset_time + + return logger diff --git a/axplorer-viz/vendor/axplorer/src/models/__init__.py b/axplorer-viz/vendor/axplorer/src/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/axplorer-viz/vendor/axplorer/src/models/model.py b/axplorer-viz/vendor/axplorer/src/models/model.py new file mode 100644 index 0000000..10399ae --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/models/model.py @@ -0,0 +1,194 @@ +import math +from logging import getLogger + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch.utils.data.dataloader import DataLoader + +logger = getLogger() + + +class CausalSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + assert config.n_embd % config.n_head == 0 + self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False) + self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False) + self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size)) + self.n_head = config.n_head + self.n_embd = config.n_embd + + def forward(self, x, past_kv=None): + B, T, C = x.size() + q, k, v = self.c_attn(x).split(self.n_embd, dim=2) + k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) + q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) + v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) + + if past_kv is not None: + past_k, past_v = past_kv + k = torch.cat((past_k, k), dim=2) + v = torch.cat((past_v, v), dim=2) + + present_kv = (k, v) + + if past_kv is None: + y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, is_causal=True) + else: + causal_mask = (1.0 - self.bias[:, :, k.size(-2) - q.size(-2) : k.size(-2), : k.size(-2)]).to(q.device) + causal_mask = causal_mask * torch.finfo(q.dtype).min + y = F.scaled_dot_product_attention(q, k, v, attn_mask=causal_mask, is_causal=False) + y = y.transpose(1, 2).contiguous().view(B, T, C) + + y = self.c_proj(y) + return y, present_kv + + +class MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=False) + self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=False) + self.act = nn.GELU() + + def forward(self, x): + return self.c_proj(self.act(self.c_fc(x))) + + +class Block(nn.Module): + def __init__(self, config): + super().__init__() + self.ln_1 = nn.LayerNorm(config.n_embd) + self.attn = CausalSelfAttention(config) + self.ln_2 = nn.LayerNorm(config.n_embd) + self.mlp = MLP(config) + + def forward(self, x, past_kv=None): + attn_out, present_kv = self.attn(self.ln_1(x), past_kv=past_kv) + x = x + attn_out + x = x + self.mlp(self.ln_2(x)) + return x, present_kv + + +class Transformer(nn.Module): + def __init__(self, config, pad_token_id, eos_token_id): + super().__init__() + self.no_positional = config.no_positional + self.block_size = config.block_size + + self.wte = nn.Embedding(config.vocab_size, config.n_embd) + if not self.no_positional: + self.wpe = nn.Embedding(config.block_size, config.n_embd) + self.h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]) + self.ln_f = nn.LayerNorm(config.n_embd) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + + # tie weights + self.wte.weight = self.lm_head.weight + + self.apply(self._init_weights) + for pn, p in self.named_parameters(): + if pn.endswith("c_proj.weight"): + torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer)) + + n_params = sum(p.numel() for p in self.parameters()) + logger.info(f"Number of parameters: {n_params/1e6:.2f}M") + self.pad_token_id = pad_token_id + self.eos_token_id = eos_token_id + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + + def forward(self, idx, targets=None, past_kv=None): + device = idx.device + b, t = idx.size() # b: batch size, t: sequence length + + past_len = 0 + if past_kv is not None and len(past_kv) > 0 and past_kv[0] is not None: + past_len = past_kv[0][0].size(2) + + pos = torch.arange(past_len, past_len + t, dtype=torch.long, device=device).unsqueeze(0) + + x = self.wte(idx) + if not self.no_positional: + x += self.wpe(pos) + + presents_kv = [] + for i, block in enumerate(self.h): + pkv = None if past_kv is None else past_kv[i] + x, present_kv = block(x, past_kv=pkv) + presents_kv.append(present_kv) + + x = self.ln_f(x) # (B, T, C) + + if targets is not None: + # Project x through lm_head: (B, T, V) + logits = self.lm_head(x) + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=self.pad_token_id) + else: + # Project x through lm_head: (B, 1, V) + logits = self.lm_head(x[:, [-1], :]) + loss = None + + return logits, loss, presents_kv + + @torch.no_grad() + def generate(self, idx, max_new_tokens, temperature=1.0, do_sample=False, top_k=None): + self.eval() + + past_kv = None + for i in range(max_new_tokens): + last_tokens = idx[:, -1].unsqueeze(-1) + finished_mask = torch.zeros_like(last_tokens, dtype=torch.bool) | (last_tokens == self.eos_token_id) | (last_tokens == self.pad_token_id) + + if torch.all(finished_mask): + idx_next = torch.full_like(finished_mask, self.pad_token_id, dtype=torch.long) + else: + if i == 0 or past_kv is None: + idx_cond = idx + else: + idx_cond = idx[:, -1].unsqueeze(1) + logits, _, past_kv = self(idx_cond, past_kv=past_kv) + logits = logits[:, -1, :] / temperature + if top_k is not None: + v, _ = torch.topk(logits, top_k) + logits[logits < v[:, [-1]]] = -float("inf") + probs = F.softmax(logits, dim=-1) + if do_sample: + idx_next = torch.multinomial(probs, num_samples=1) + else: + _, idx_next = torch.topk(probs, k=1, dim=-1) + idx_next = torch.where(finished_mask, self.pad_token_id, idx_next) + + idx = torch.cat((idx, idx_next), dim=1) + + # needed for MPS memory management + if idx.device.type == "mps": + torch.mps.synchronize() + torch.mps.empty_cache() + + self.train() + return idx + + +@torch.inference_mode() +def evaluate(model, dataset, device, batch_size=50, max_batches=None): + model.eval() + loader = DataLoader(dataset, shuffle=True, batch_size=batch_size, num_workers=0, collate_fn=dataset.collate_fn) + losses = [] + for i, batch in enumerate(loader): + batch = [t.to(device) for t in batch] + X, Y = batch[0], batch[1] + _, loss, _ = model(X, Y) + losses.append(loss.item()) + if max_batches is not None and i >= max_batches: + break + mean_loss = torch.tensor(losses).mean().item() + model.train() + return mean_loss diff --git a/axplorer-viz/vendor/axplorer/src/trainer.py b/axplorer-viz/vendor/axplorer/src/trainer.py new file mode 100644 index 0000000..7f1af51 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/trainer.py @@ -0,0 +1,74 @@ +import os +import time +from logging import getLogger + +import torch + +from src.models.model import evaluate + +logger = getLogger() + + +def reload_model_optimizer(args, model, optimizer): + model_path = os.path.join(args.dump_path, "model.pt") + optimizer_path = os.path.join(args.dump_path, "optimizer.pt") + if os.path.isfile(model_path): + logger.info("resuming from existing model") + if args.device == "cuda": + reloaded = torch.load(model_path) + else: + reloaded = torch.load(model_path, map_location=torch.device(args.device)) + model.load_state_dict(reloaded) + if os.path.isfile(optimizer_path): + logger.info("resuming from existing optimizer") + if args.device == "cuda": + reloaded = torch.load(optimizer_path) + else: + reloaded = torch.load(optimizer_path, map_location=torch.device(args.device)) + optimizer.load_state_dict(reloaded) + + +def train(model, args, loader, optim, test_dataset, current_best_loss=None): + best_loss = current_best_loss or float("inf") + curr_loss = 0 + for step in range(args.max_steps): + + if step % 100 == 0: + t0 = time.time() + batch = loader.next() + batch = [t.to(args.device) for t in batch] + X, Y = batch[0], batch[1] + + _, loss, _ = model(X, Y) + model.zero_grad(set_to_none=True) + loss.backward() + optim.step() + curr_loss += loss.item() + + if args.device == "mps" and (step + 1) % 500 == 0: + torch.mps.empty_cache() + + # logging + if (step + 1) % 100 == 0: + t1 = time.time() + logger.info(f"step {step + 1} | loss {loss.item():.4f} | steps time {(t1-t0)*1000:.2f}ms") + if (step + 1) % args.num_eval_steps == 0: + train_loss = curr_loss / args.num_eval_steps + test_loss = evaluate(model, test_dataset, args.device, batch_size=100, max_batches=10) + logger.info(f"step {step + 1} train loss: {train_loss} test loss: {test_loss}") + if args.save_best and test_loss < best_loss: + model_path = os.path.join(args.dump_path, "model.pt") + optimizer_path = os.path.join(args.dump_path, "optimizer.pt") + torch.save(model.state_dict(), model_path) + torch.save(optim.state_dict(), optimizer_path) + logger.info(f"test loss {test_loss} is the best so far, saved model to {model_path}") + best_loss = test_loss + curr_loss = 0 + + if not args.save_best: + model_path = os.path.join(args.dump_path, "model.pt") + optimizer_path = os.path.join(args.dump_path, "optimizer.pt") + torch.save(model.state_dict(), model_path) + torch.save(optim.state_dict(), optimizer_path) + + return best_loss diff --git a/axplorer-viz/vendor/axplorer/src/utils.py b/axplorer-viz/vendor/axplorer/src/utils.py new file mode 100644 index 0000000..4722903 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/src/utils.py @@ -0,0 +1,132 @@ +import argparse +import ctypes +import gc +import os +import pickle +import re +import subprocess +import sys +import time +from logging import getLogger + +import psutil + +from src.logger import create_logger + +logger = getLogger() + +FALSY_STRINGS = {"off", "false", "0"} +TRUTHY_STRINGS = {"on", "true", "1"} + +MAX_WORKERS = 20 + + +def bool_flag(s): + """ + Parse boolean arguments from the command line. + """ + if s.lower() in FALSY_STRINGS: + return False + elif s.lower() in TRUTHY_STRINGS: + return True + else: + raise argparse.ArgumentTypeError("Invalid value for a boolean flag!") + + +def initialize_exp(params): + """ + Initialize the experience: + - dump parameters + - create a logger + """ + # dump parameters + get_dump_path(params) + pickle.dump(params, open(os.path.join(params.dump_path, "params.pkl"), "wb")) + + # get running command + command = ["python", sys.argv[0]] + for x in sys.argv[1:]: + if x.startswith("--"): + assert '"' not in x and "'" not in x + command.append(x) + else: + assert "'" not in x + if re.match("^[a-zA-Z0-9_]+$", x): + command.append("%s" % x) + else: + command.append("'%s'" % x) + command = " ".join(command) + params.command = command + ' --exp_id "%s"' % params.exp_id + + # check experiment name + assert len(params.exp_name.strip()) > 0 + + # create a logger + logger = create_logger(os.path.join(params.dump_path, "train.log"), rank=0) + logger.info("============ Initialized logger ============") + logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(params)).items()))) + logger.info("The experiment will be stored in %s\n" % params.dump_path) + logger.info("Running command: %s" % command) + logger.info("") + return logger + + +def get_dump_path(params): + """ + Create a directory to store the experiment. + """ + assert len(params.exp_name) > 0 + + # create the sweep path if it does not exist + sweep_path = os.path.join(params.dump_path, params.exp_name) + if not os.path.exists(sweep_path): + subprocess.Popen("mkdir -p %s" % sweep_path, shell=True).wait() + + # create an ID for the job if it is not given in the parameters. + # if we run on Modal, the job id is the timestamp of the run. + # if we run on the cluster, the job ID is the one of Chronos. + # otherwise, the job id is the timestamp of the run. + exp_id = None + modal_exp_id = os.environ.get("MODAL_EXP_ID") + if modal_exp_id is not None: + params.exp_id = modal_exp_id + elif params.exp_id == "": + chronos_job_id = os.environ.get("CHRONOS_JOB_ID") + slurm_job_id = os.environ.get("SLURM_JOB_ID") + assert chronos_job_id is None or slurm_job_id is None + exp_id = chronos_job_id if chronos_job_id is not None else slurm_job_id + if exp_id is None: + exp_id = time.strftime("%Y_%m_%d_%H_%M_%S") + params.exp_id = exp_id + + # create the dump folder / update parameters + params.dump_path = os.path.join(sweep_path, params.exp_id) + if not os.path.isdir(params.dump_path): + subprocess.Popen("mkdir -p %s" % params.dump_path, shell=True).wait() + + +def force_release_memory(): + gc.collect() + gc.collect() + try: + libc = ctypes.CDLL("libc.so.6") + libc.malloc_trim(0) + except (OSError, AttributeError): + pass + + +def log_resources(label): + process = psutil.Process() + rss_mb = process.memory_info().rss / (1024 * 1024) + cpu_percent = process.cpu_percent(interval=0.1) + logger.info(f"[{label}] CPU: {cpu_percent:.1f}% | RAM: {rss_mb:.1f}MB") + + +def write_important_metrics(metrics, epoch, metric_file, command=None): + if metrics is not None: + with open(metric_file, "a") as f: + if command is not None: + f.write(f"command: {command}\n") + f.write( + f"epoch: {epoch} | mean: {metrics['mean']} | median: {metrics['median']} | top_1_percentile: {metrics['top_1_percentile']} | max: {metrics['max']}\n" + ) diff --git a/axplorer-viz/vendor/axplorer/tools/ReadXP.ipynb b/axplorer-viz/vendor/axplorer/tools/ReadXP.ipynb new file mode 100644 index 0000000..319256c --- /dev/null +++ b/axplorer-viz/vendor/axplorer/tools/ReadXP.ipynb @@ -0,0 +1,733 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Axplorer Experiment Reader\n", + "\n", + "This notebook reads experiment logs from Axplorer training runs and visualises key metrics:\n", + "- **Training loss** per epoch with color gradient (light to dark across epochs)\n", + "- **Score evolution** over epochs (mean, median, max, top-1 percentile)\n", + "- **Score distribution** histograms\n", + "- **Summary table** comparing experiments\n", + "\n", + "Set `dump_path` below. If `exp_names` is left empty, all subfolders are used." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.317191Z", + "iopub.status.busy": "2026-03-17T04:51:52.316934Z", + "iopub.status.idle": "2026-03-17T04:51:52.497806Z", + "shell.execute_reply": "2026-03-17T04:51:52.497314Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Auto-discovered exp_names: ['square_N20', 'square_N22', 'square_N25', 'square_N28', 'square_N30']\n" + ] + } + ], + "source": [ + "import os\n", + "import re\n", + "import pickle\n", + "import colorsys\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "import matplotlib.colors as mcolors\n", + "import numpy as np\n", + "from collections import defaultdict\n", + "\n", + "plt.rcParams.update({\"figure.dpi\": 120})\n", + "\n", + "# ── Configure your experiments here ──────────────────────────────────\n", + "dump_path = \"../checkpoint\" # path to the experiment root\n", + "exp_names = [] # leave empty to auto-discover all subfolders\n", + "\n", + "# ── Auto-discover exp_names if not provided ──────────────────────────\n", + "if not exp_names:\n", + " exp_names = sorted([d for d in os.listdir(dump_path) if os.path.isdir(os.path.join(dump_path, d)) and not d.startswith(\".\")])\n", + " print(f\"Auto-discovered exp_names: {exp_names}\")\n", + "\n", + "# ── Color palettes: one base hue per experiment ──────────────────────\n", + "BASE_HUES = [\"red\", \"blue\", \"green\", \"purple\", \"orange\", \"brown\", \"pink\", \"olive\"]\n", + "\n", + "\n", + "def make_epoch_cmap(base_color, n_epochs):\n", + " \"\"\"Return n_epochs colors from very light (pastel) to very dark, same hue.\"\"\"\n", + " rgb = mcolors.to_rgb(base_color)\n", + " h, _, _ = colorsys.rgb_to_hsv(*rgb)\n", + " colors = []\n", + " for i in range(n_epochs):\n", + " t = i / max(n_epochs - 1, 1)\n", + " s = 0.15 + t * 0.85\n", + " v = 0.95 - t * 0.60\n", + " colors.append(colorsys.hsv_to_rgb(h, s, v))\n", + " return colors\n", + "\n", + "\n", + "def make_light_dark(base_color):\n", + " \"\"\"Return (light, dark) pair with clearly distinct lightness.\"\"\"\n", + " rgb = mcolors.to_rgb(base_color)\n", + " h, _, _ = colorsys.rgb_to_hsv(*rgb)\n", + " light = colorsys.hsv_to_rgb(h, 0.3, 0.9)\n", + " dark = colorsys.hsv_to_rgb(h, 1.0, 0.6)\n", + " return light, dark\n", + "\n", + "\n", + "# ── Optional filters ─────────────────────────────────────────────────\n", + "unwanted_args = [\"dump_path\", \"command\", \"device\", \"exp_id\", \"cpu\", \"seed\", \"data_generation_only\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reading Experiments" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.499124Z", + "iopub.status.busy": "2026-03-17T04:51:52.499027Z", + "iopub.status.idle": "2026-03-17T04:51:52.513316Z", + "shell.execute_reply": "2026-03-17T04:51:52.512934Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 5 experiment(s)\n", + " square_N20: 10 epochs, best max score = 45.0\n", + " square_N22: 10 epochs, best max score = 51.0\n", + " square_N25: 10 epochs, best max score = 61.0\n", + " square_N28: 10 epochs, best max score = 71.0\n", + " square_N30: 10 epochs, best max score = 78.0\n", + "\n", + "-- Variable args: N, exp_name\n" + ] + } + ], + "source": [ + "def find_experiments(dump_path, exp_names):\n", + " \"\"\"Find all experiment directories (dump_path/exp_name/exp_id).\"\"\"\n", + " exps = []\n", + " for name in exp_names:\n", + " exp_dir = os.path.join(dump_path, name)\n", + " if not os.path.isdir(exp_dir):\n", + " print(f\" Warning: {exp_dir} not found\")\n", + " continue\n", + " for exp_id in sorted(os.listdir(exp_dir)):\n", + " full = os.path.join(exp_dir, exp_id)\n", + " if os.path.isdir(full) and os.path.exists(os.path.join(full, \"train.log\")):\n", + " exps.append((name, exp_id, full))\n", + " print(f\"Found {len(exps)} experiment(s)\")\n", + " return exps\n", + "\n", + "\n", + "def read_params(exp_path):\n", + " \"\"\"Read params.pkl and return the parameter dict, or None.\"\"\"\n", + " pkl_path = os.path.join(exp_path, \"params.pkl\")\n", + " if not os.path.exists(pkl_path):\n", + " return None\n", + " return pickle.load(open(pkl_path, \"rb\")).__dict__\n", + "\n", + "\n", + "def find_variable_args(all_params, unwanted_args):\n", + " \"\"\"Identify which args differ across experiments.\"\"\"\n", + " if not all_params:\n", + " return set(), {}\n", + " reference = all_params[0]\n", + " all_keys = set()\n", + " for p in all_params:\n", + " all_keys.update(p.keys())\n", + " variable = set()\n", + " for key in all_keys:\n", + " vals = [p.get(key) for p in all_params]\n", + " if len(set(str(v) for v in vals)) > 1:\n", + " variable.add(key)\n", + " variable -= set(unwanted_args)\n", + " common = {k: reference[k] for k in sorted(reference) if k not in variable and k not in unwanted_args}\n", + " return variable, common\n", + "\n", + "\n", + "def read_metrics_file(exp_path):\n", + " \"\"\"Parse metrics.txt for per-epoch score statistics.\"\"\"\n", + " metrics_path = os.path.join(exp_path, \"metrics.txt\")\n", + " epochs = []\n", + " if not os.path.exists(metrics_path):\n", + " return epochs\n", + " with open(metrics_path, \"rt\") as f:\n", + " for line in f:\n", + " if line.startswith(\"command:\"):\n", + " continue\n", + " m = re.match(\n", + " r\"epoch:\\s*(\\d+)\\s*\\|\\s*mean:\\s*([\\d.]+)\\s*\\|\\s*median:\\s*([\\d.]+)\\s*\\|\\s*top_1_percentile:\\s*([\\d.]+)\\s*\\|\\s*max:\\s*([\\d.]+)\",\n", + " line.strip(),\n", + " )\n", + " if m:\n", + " epochs.append({\n", + " \"epoch\": int(m.group(1)),\n", + " \"mean\": float(m.group(2)),\n", + " \"median\": float(m.group(3)),\n", + " \"top_1_pct\": float(m.group(4)),\n", + " \"max\": float(m.group(5)),\n", + " })\n", + " return epochs\n", + "\n", + "\n", + "def n_training_epochs(metrics):\n", + " \"\"\"Number of actual training epochs (exclude epoch 0 = initial data).\"\"\"\n", + " return sum(1 for m in metrics if m[\"epoch\"] > 0)\n", + "\n", + "\n", + "def read_train_log(exp_path):\n", + " \"\"\"Parse train.log for step-level loss (grouped by epoch) and one score distribution per epoch.\n", + "\n", + " The log contains multiple 'Distribution of scores' blocks per epoch (init, sampled, unique, final).\n", + " We keep only the LAST distribution block before each new epoch starts (= the final selected population).\n", + " \"\"\"\n", + " log_path = os.path.join(exp_path, \"train.log\")\n", + " result = {\n", + " \"epoch_losses\": [], # list of [(step, loss), ...] per epoch\n", + " \"epoch_distributions\": [], # one {score: count} dict per epoch (the last dist before next epoch)\n", + " \"max_steps\": 0,\n", + " }\n", + " if not os.path.exists(log_path):\n", + " return result\n", + "\n", + " current_dist = None\n", + " current_epoch_losses = []\n", + " last_finished_dist = None # last fully-parsed distribution\n", + " max_steps_per_epoch = 0\n", + "\n", + " with open(log_path, \"rt\") as f:\n", + " for line in f:\n", + " # New epoch starts — flush previous epoch\n", + " if \"Starting Epoch\" in line:\n", + " if current_epoch_losses:\n", + " max_steps_per_epoch = max(max_steps_per_epoch, current_epoch_losses[-1][0])\n", + " result[\"epoch_losses\"].append(current_epoch_losses)\n", + " if last_finished_dist is not None:\n", + " result[\"epoch_distributions\"].append(last_finished_dist)\n", + " last_finished_dist = None\n", + " current_epoch_losses = []\n", + " continue\n", + "\n", + " # Step-level loss\n", + " m = re.search(r\"step (\\d+) \\| loss ([\\d.]+) \\| steps time\", line)\n", + " if m:\n", + " current_epoch_losses.append((int(m.group(1)), float(m.group(2))))\n", + " continue\n", + "\n", + " # Score distribution start\n", + " if \"Distribution of scores:\" in line:\n", + " current_dist = {}\n", + " continue\n", + "\n", + " if current_dist is not None:\n", + " m = re.search(r\"Score ([\\d.]+): Count: (\\d+)\", line)\n", + " if m:\n", + " current_dist[float(m.group(1))] = int(m.group(2))\n", + " continue\n", + " m = re.search(r\"Score \\[([\\d.]+),\\s*([\\d.]+)\\): Count: (\\d+)\", line)\n", + " if m:\n", + " mid = (float(m.group(1)) + float(m.group(2))) / 2\n", + " current_dist[mid] = int(m.group(3))\n", + " continue\n", + " if \"---\" in line and current_dist:\n", + " last_finished_dist = current_dist\n", + " current_dist = None\n", + "\n", + " # Flush last epoch\n", + " if current_epoch_losses:\n", + " max_steps_per_epoch = max(max_steps_per_epoch, current_epoch_losses[-1][0])\n", + " result[\"epoch_losses\"].append(current_epoch_losses)\n", + " if current_dist:\n", + " last_finished_dist = current_dist\n", + " if last_finished_dist is not None:\n", + " result[\"epoch_distributions\"].append(last_finished_dist)\n", + "\n", + " result[\"max_steps\"] = max_steps_per_epoch\n", + " return result\n", + "\n", + "\n", + "def make_label(d):\n", + " \"\"\"Build display label — just the exp_name, no exp_id.\"\"\"\n", + " return d[\"name\"]\n", + "\n", + "\n", + "# ── Find and read all experiments ────────────────────────────────────\n", + "experiments = find_experiments(dump_path, exp_names)\n", + "data = []\n", + "all_params = []\n", + "\n", + "for name, exp_id, path in experiments:\n", + " params = read_params(path)\n", + " log = read_train_log(path)\n", + " metrics = read_metrics_file(path)\n", + " r = {\n", + " \"name\": name, \"exp_id\": exp_id, \"path\": path,\n", + " \"params\": params or {},\n", + " \"log\": log,\n", + " \"metrics\": metrics,\n", + " }\n", + " all_params.append(r[\"params\"])\n", + " data.append(r)\n", + " n_ep = n_training_epochs(metrics)\n", + " best_max = max((m[\"max\"] for m in metrics), default=0)\n", + " print(f\" {name}: {n_ep} epochs, best max score = {best_max}\")\n", + "\n", + "var_args, common_args = find_variable_args(all_params, unwanted_args)\n", + "label_w = max(len(\"Experiment\"), max((len(make_label(d)) for d in data), default=0)) + 2\n", + "\n", + "if var_args:\n", + " print(f\"\\n-- Variable args: {', '.join(sorted(var_args))}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training Loss\n", + "\n", + "Each epoch is plotted with a color gradient: light (early epochs) to dark (late epochs). Each experiment gets its own hue." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.526706Z", + "iopub.status.busy": "2026-03-17T04:51:52.526606Z", + "iopub.status.idle": "2026-03-17T04:51:52.578652Z", + "shell.execute_reply": "2026-03-17T04:51:52.578307Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(14, 5))\n", + "\n", + "max_total_steps = 0\n", + "for exp_idx, d in enumerate(data):\n", + " epoch_losses = d[\"log\"][\"epoch_losses\"]\n", + " if not epoch_losses:\n", + " continue\n", + " steps_per_epoch = d[\"log\"][\"max_steps\"]\n", + " n_epochs = len(epoch_losses)\n", + " base_color = BASE_HUES[exp_idx % len(BASE_HUES)]\n", + " colors = make_epoch_cmap(base_color, n_epochs)\n", + " label = make_label(d)\n", + "\n", + " for ep_idx, ep_data in enumerate(epoch_losses):\n", + " if not ep_data:\n", + " continue\n", + " steps, losses = zip(*ep_data)\n", + " global_steps = [s + ep_idx * steps_per_epoch for s in steps]\n", + " ax.plot(global_steps, losses, linewidth=0.8, color=colors[ep_idx], alpha=0.85)\n", + "\n", + " # Add epoch separators\n", + " for ep_idx in range(1, n_epochs):\n", + " ax.axvline(ep_idx * steps_per_epoch, color=\"gray\", linestyle=\":\", linewidth=0.5, alpha=0.4)\n", + "\n", + " max_total_steps = max(max_total_steps, n_epochs * steps_per_epoch)\n", + "\n", + " # Legend entry: median color, same linewidth as data\n", + " ax.plot([], [], linewidth=0.8, color=colors[n_epochs // 2], label=label)\n", + "\n", + "# Force x-axis ticks aligned with steps_per_epoch\n", + "if max_total_steps > 0 and steps_per_epoch > 0:\n", + " ax.xaxis.set_major_locator(ticker.MultipleLocator(steps_per_epoch))\n", + "\n", + "ax.set_xlabel(\"Total training step\")\n", + "ax.set_ylabel(\"Loss\")\n", + "ax.set_title(\"Training Loss (light = early epochs, dark = late epochs)\")\n", + "ax.legend(fontsize=9)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Score Evolution Over Epochs\n", + "\n", + "Key metrics from `metrics.txt`: mean, median, max, and top-1 percentile score at each epoch." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.628654Z", + "iopub.status.busy": "2026-03-17T04:51:52.628568Z", + "iopub.status.idle": "2026-03-17T04:51:52.671380Z", + "shell.execute_reply": "2026-03-17T04:51:52.670942Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# Left: Mean & Median\n", + "ax = axes[0]\n", + "for exp_idx, d in enumerate(data):\n", + " if not d[\"metrics\"]:\n", + " continue\n", + " epochs = [m[\"epoch\"] for m in d[\"metrics\"]]\n", + " label = make_label(d)\n", + " base_color = BASE_HUES[exp_idx % len(BASE_HUES)]\n", + " light, dark = make_light_dark(base_color)\n", + " ax.plot(epochs, [m[\"mean\"] for m in d[\"metrics\"]], marker=\"o\", markersize=4, color=dark, linewidth=1.5, label=f\"{label} mean\")\n", + " ax.plot(\n", + " epochs, [m[\"median\"] for m in d[\"metrics\"]], marker=\"s\", markersize=4, color=light, linewidth=1.5, linestyle=\"--\", label=f\"{label} median\"\n", + " )\n", + "ax.set_xlabel(\"Epoch\")\n", + "ax.set_ylabel(\"Score\")\n", + "ax.set_title(\"Mean & Median Score\")\n", + "ax.legend(fontsize=8)\n", + "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", + "\n", + "# Right: Max & Top-1%\n", + "ax = axes[1]\n", + "for exp_idx, d in enumerate(data):\n", + " if not d[\"metrics\"]:\n", + " continue\n", + " epochs = [m[\"epoch\"] for m in d[\"metrics\"]]\n", + " label = make_label(d)\n", + " base_color = BASE_HUES[exp_idx % len(BASE_HUES)]\n", + " light, dark = make_light_dark(base_color)\n", + " ax.plot(epochs, [m[\"max\"] for m in d[\"metrics\"]], marker=\"^\", markersize=5, color=dark, linewidth=1.5, label=f\"{label} max\")\n", + " ax.plot(\n", + " epochs, [m[\"top_1_pct\"] for m in d[\"metrics\"]], marker=\"D\", markersize=4, color=light, linewidth=1.5, linestyle=\"--\", label=f\"{label} top-1%\"\n", + " )\n", + "ax.set_xlabel(\"Epoch\")\n", + "ax.set_ylabel(\"Score\")\n", + "ax.set_title(\"Max & Top-1 Percentile Score\")\n", + "ax.legend(fontsize=8)\n", + "ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Score Distribution\n", + "\n", + "Histogram of scores for the **last recorded distribution** per experiment. Shows how the population concentrates around high-scoring solutions over training." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.672510Z", + "iopub.status.busy": "2026-03-17T04:51:52.672436Z", + "iopub.status.idle": "2026-03-17T04:51:52.676126Z", + "shell.execute_reply": "2026-03-17T04:51:52.675803Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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atm0b6tatG1577bUij9+2bVtYtGhRaNeuXXZfvB8fu379+iJtY5+Z41F5+gQAgFyOMedzVe2TTz65yNa6devdPgYAgOrJnAwAAGqqfU4InDNnTnj33XdLPb5mzZrw+9//fl+fBgCgWnrppZfSRvUlXgYA2Hvi5ZqhImLmmJT3y1/+stgWJwofffTR6f7AgQPDwQcfHLp37x4ee+yxsGHDhuzjp02bFjZu3Bj69euX3de3b9+wc+fOVHkwI05gnjx5cujUqVNo2bJl2leePgEAckWsnT8qaow5U1U7LqIRq2rHKtpxK1xV+4MPPih3Ve3S2kWqagMA1YmYumowJwMAoOoSM1esuvujk1q1apV6bOnSpeGggw7aH08DAFDtXHfddel28eLFuT4VKpB4GQBg74iXa479HTMfdthhoU+fPsX233PPPem28LGxY8eGzp07h65du4bBgweHlStXhrvuuiv06NEj9OrVK9suJv3FZL7Ro0enSdexAsnUqVPTROlHHnmkyPOUtU8AgFwRa+eXihhjLlxVO267ilW1v/3tb4dbb701WwH7wgsvLFYBu/C+WA07ViyMVbWbNGlSpqrae+oTAKCqElNXHeZkAABUTWLmKpgQGCc5xC3j9ttvDz/5yU+Ktfvwww/DX//619C7d+99O0sAAMgj4mUAAMifmLl9+/bh5ZdfDqNGjQrDhw9Pk6lj9cBx48YVa/voo4+GG2+8MVX7W7duXTjllFPC888/H84888y97pP/H3v3AmdlVS+M/8dwF40ggUDRDKEUUVKx4uVAFqiJioWkktcCTPACmCmalZSGApYnJLB8o8LsgBZestQ8amQEYlLIoUNwjhyugeGNqwT7/3me85/9MswAM8zs2Zf5fj+fzTP7edasvffaa29+z5rntxYAAPmIl8tX1d7bV7/61XSl63vvvTe6dOlSYQXsJBYuTz7c16rakyZNSlce/PKXv1ytVbUPVCcAABTy+DIAABRVQuDWrVtj48aN2fvJYHBZWVmlGTdatWoVX/rSl+JrX/ta7Z8pAAAUCfEyAAAUZsz8/PPPV7m/T58+8eKLLx7w91u0aBETJ05MbwdS3ToBACAf8bJVtQEAKFauyQAAgINMCLz66qvTW+KYY45JZ4Y777zztCcAAIiXAQDAGDMAANRCoV2TYVVtAAAKSaHFywAAUDQJgXv67//+77p5JgAAUILEywAAIGYGAIBiGWO2qjYAAMXENRkAADRUtU4ILPfOO+/EypUr44033ohMJlPpeN++fevqoQAASkaXLl3y/RSoJ+JlAICaEy83LGJmAID6I9YuPuJlAIDCIqYuLOJlAIDCI2Yu8ITA119/Pa699tp45JFHYteuXZWOJ8mBjRo1qvIYAEBD99hjj+X7KZBj4mUAgIMnXm4YxMwAAPVPrF08xMsAAIVJTF0YxMsAAIVLzFzgCYEjRoyIxx9/PK677rr4l3/5l2jTpk3dPDMAACgB4mUAABAzAwCAMWYAAKh7rskAAKChqnVC4NNPPx1jxoyJu+++u26eEQBAA3Lfffel21GjRuX7qZAj4mUAgIMnXm4YxMwAAPVPrF08xMsAAIVJTF0YxMsAAIVLzJxbZbWt4JBDDokPfOADdfNsAAAamKlTp6Y3Spd4GQDg4ImXGwYxMwBA/RNrFw/xMgBAYRJTFwbxMgBA4RIzF3hC4CWXXBK//OUv6+bZAABAiREvAwCAmBkAAIwxAwBA3XNNBgAADVWT2lZwwQUXxAsvvBBnnXVWjBgxIjp37hyNGzeuVO7kk0+u7UMBAEDRES8DAICYGQAAjDEDAEDdc00GAAANVa0TAvv06ZP9+Zlnnql0PJPJRKNGjWLXrl21fSgAACg64mUAABAzAwCAMWYAAKh7rskAAKChqnVC4I9+9KO6eSYAAFCCxMsAACBmBgAAY8wAAFD3XJMBAEBDVeuEwMsvv7xungkAQAN0xhln5PspkGPiZQCAgydebhjEzAAA9U+sXTzEywAAhUlMXRjEywAAhUvMXOAJgQAAHLzvfOc7mg8AAMTLAABQr4xNAwCAmBoAAHLJOHSBJwR+4QtfOGCZRo0axQMPPFDbhwIAgKIjXgYAADEzAAAYYwYAgLrnmgwAABqqWicE/vu//3ua8LenXbt2xbp169Jtu3btolWrVrV9GACAkjRmzJh0axaM0iVeBgA4eOLlhkHMDABQ/8TaxUO8DABQmMTUhUG8DABQuMTMBZ4Q+Nprr1W5f+fOnTF9+vT47ne/G88880xtHwYAoCQ9/fTT+X4K5Jh4GQDg4ImXGwYxMwBA/RNrFw/xMgBAYRJTFwbxMgBA4RIz51ZZripu2rRpXHPNNXHGGWekWwAAQLwMAADGmAEAoPZckwEAAOJlAAAarpwlBJY76aST4ne/+12uHwYAAIqSeBkAAMTMAABgjBkAAOqeazIAAChVOU8IfOaZZ+KQQw7J9cMAAEBREi8DAICYGQAAjDEDAEDdc00GAAClqkltKxg/fnyV+9988810ZcA//elPcfPNN9f2YQAAoCiJlwEAQMwMAADGmAEAoO65JgMAgIaq1gmB3/jGN6rc36ZNm+jSpUtMmzYthg8fXtuHAQAoSSNHjsz3UyDHxMsAAAdPvNwwiJkBAOqfWLt4iJcBAAqTmLowiJcBAAqXmLnAEwJ3794d9eWOO+6Ir371q9G9e/d49dVXKxz7wx/+EF/5ylfSFQnf8573xOc+97m4884749BDD61QbseOHfG1r30tfvrTn8Ybb7wRJ554YnzrW9+KAQMGVHq86tYJAHCwRo0apfFKXH3GywAApUa83DCImQEA6p9Yu3iIlwEACpOYujCIlwEACpeYObfKokisXr06TcZr1apVpWOLFi2KT33qU7F169a45557YtiwYXH//ffHkCFDKpW94oor0jKf//zn4957743GjRvH2WefHb///e8Puk4AAAAAAAAAAAAAAAAAKPgVAsu98MIL8atf/SpWrlyZ3j/66KNj4MCB0a9fvzqp/8tf/nJ87GMfi127dsXrr79e4dgtt9wSbdq0ieeffz5dyS/xgQ98IIYPHx5PP/10nHHGGem+BQsWxM9//vOYOHFiWl/isssuixNOOCFdCTBZEbCmdQIA1MZ5552Xbh977DENWeJyHS8DAJQi8XLDImYGAKg/Yu3iI14GACgsYurCIl4GACg8YuYCTwh899134+KLL445c+ZEJpOJ9773ven+N998MyZPnhyf+cxn4qGHHoqmTZse9GP87ne/i4cffjheeeWVuPbaaysce/vtt+OZZ56JMWPGZBP3yhP9kn2zZs3KJu8ldSQrAo4YMSJbrkWLFvHFL34xTQBctWpVdO7cuUZ1AgDUxooVKzRgiauPeBkAoFSJlxsGMTMAQP0TaxcP8TIAQGESUxcG8TIAQOESM+dWWW0ruP322+OXv/xl3HDDDbFu3brYtGlTelu/fn26Ct8vfvGLGD9+/EHXn6wImCQBDhs2LHr06FHp+OLFi+Of//xnnHrqqRX2N2vWLHr27JkmEZZLfu7WrVuFJL/Eaaedlm4XLVpU4zr3tmHDhliyZEmF2/Llyw/y1QMAUOxyHS8DAECxEzMDAIB4GQAAjC8DAEA9JgT+7Gc/i8svvzzuvvvu6NChQ3Z/+/bt46677kpX1fvpT3960PVPmzYtVq5cGd/85jerPJ5cVJ3o2LFjpWPJvrVr11You69yifKyNalzb1OnTo0TTjihwu3888+vxisFAKAU5TpeBgCAYidmBgAA8TIAABhfBgCAekwITJLnPvrRj+7zeHIsWf3kYPzjH/+Ir33ta3HbbbdFu3btqiyzbdu2dNu8efNKx1q0aJE9Xl52X+X2rKsmde5t5MiR8eqrr1a4zZkzpxqvFgCAUpTLeBkAAEqBmBkAAMTLAABgfBkAAOoxIfDII4+M559/fp/HX3jhhbTMwfjqV78abdu2jWuvvXafZVq2bJlud+zYUenY9u3bs8fLy+6r3J511aTOvSUrvXTv3r3C7dhjjz3AKwUAoFTlMl4GAIBSIGYGAADxMgAAGF8GAIB6TAi8/PLLY9asWfGlL30p/vM//zN27doVu3fvTn+++uqrY/bs2XHFFVfUuN6//e1vcf/998d1110Xa9eujddeey29JQl5O3fuTH/etGlTdOzYMTuL9N6SfZ06dcreT8ruq1yivGxN6gQAqI177rknvVG6chUvV+WOO+6IRo0axQknnFDp2B/+8Ifo06dPHHLIIfH+978/jbM3b95cqVwyKcZNN92UxrzJRBjJCobPPPNMlY9X3ToBAA6WeLlhqM+YGQCA/yXWLh7iZQCAwiSmLgziZQCAwiVmzq0mta3glltuiRUrVqTJez/4wQ+irOx/cwyTCzYymUwabCdlamrNmjVpHclFxcltb8ccc0xcf/31cfvtt0eTJk1i4cKF8bnPfS57/N13341FixZV2NezZ8947rnn4u233473vOc92f3z58/PHk8kF1BXt04AgNo488wzNWCJy1W8vLfVq1fHnXfeGa1atap0LIlhP/WpT8Vxxx2XnmAlZSdNmpROwvHrX/+6QtnkQuuHH344Ro8eHV27do0ZM2bE2WefncbRSfLfwdQJAHCwxMsNQ33FzAAA/D9i7eIhXgYAKExi6sIgXgYAKFxi5gJPCGzcuHF6kfDYsWPjySefjJUrV6b7jz766PTC4RNPPPGg6k2S8n75y19W2v/Vr3413nnnnbj33nujS5cu0bp16+jfv3/MnDkzbrvttjjssMPScj/96U/T1UmGDBmS/d0LLrggvUg5ubDky1/+cnYFlB/96EfpyiedO3dO99WkTgAAyEe8vLckvv3Yxz6Wrqby+uuvVxoAb9OmTTz//PPZiTE+8IEPxPDhw+Ppp5+OM844I923YMGC+PnPfx4TJ07MxsuXXXZZGpt/5StfSVcErGmdAABQKDEzAAAUI/EyAACIlwEAoE4SArdv356uGNK9e/e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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_exps = len(data)\n", + "fig, axes = plt.subplots(1, n_exps, figsize=(6 * n_exps, 5), squeeze=False)\n", + "\n", + "for i, d in enumerate(data):\n", + " ax = axes[0, i]\n", + " dists = d[\"log\"][\"epoch_distributions\"]\n", + " if not dists:\n", + " ax.text(0.5, 0.5, \"No distribution data\", ha=\"center\", va=\"center\", transform=ax.transAxes)\n", + " ax.set_title(make_label(d))\n", + " continue\n", + "\n", + " base_color = BASE_HUES[i % len(BASE_HUES)]\n", + " last_dist = dists[-1]\n", + " scores = sorted(last_dist.keys())\n", + " counts = [last_dist[s] for s in scores]\n", + " ax.bar(\n", + " scores,\n", + " counts,\n", + " width=max(1, (max(scores) - min(scores)) / len(scores) * 0.9),\n", + " alpha=0.75,\n", + " color=mcolors.to_rgb(base_color),\n", + " edgecolor=\"black\",\n", + " linewidth=0.3,\n", + " )\n", + " ax.set_xlabel(\"Score (# edges)\")\n", + " ax.set_ylabel(\"Count\")\n", + " ax.set_title(f\"{make_label(d)} -- Final Distribution\")\n", + "\n", + " max_score = max(scores)\n", + " ax.axvline(max_score, color=\"black\", linestyle=\"--\", linewidth=1, label=f\"max = {max_score:.0f}\")\n", + " ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary Table" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.677173Z", + "iopub.status.busy": "2026-03-17T04:51:52.677100Z", + "iopub.status.idle": "2026-03-17T04:51:52.973387Z", + "shell.execute_reply": "2026-03-17T04:51:52.972925Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experiment N exp_name Epochs Mean Median Top-1% Max\n", + "------------------------------------------------------------------\n", + "square_N30 30 square_N30 10 73.62 74.0 76.0 78\n", + "square_N28 28 square_N28 10 67.07 67.0 69.0 71\n", + "square_N25 25 square_N25 10 57.35 57.0 59.0 61\n", + "square_N22 22 square_N22 10 48.19 48.0 50.0 51\n", + "square_N20 20 square_N20 10 42.22 42.0 44.0 45\n" + ] + } + ], + "source": [ + "var_cols = sorted(var_args)\n", + "col_widths = {}\n", + "for v in var_cols:\n", + " values = [str(d[\"params\"].get(v, \"\")) for d in data]\n", + " col_widths[v] = max(len(v), max((len(val) for val in values), default=0)) + 2\n", + "\n", + "metric_names = [\"Epochs\", \"Mean\", \"Median\", \"Top-1%\", \"Max\"]\n", + "rows = []\n", + "for d in data:\n", + " metrics = d[\"metrics\"]\n", + " if not metrics:\n", + " continue\n", + " last = metrics[-1]\n", + " best_max = max(m[\"max\"] for m in metrics)\n", + " row = {\n", + " \"d\": d,\n", + " \"cells\": {\n", + " \"Epochs\": str(n_training_epochs(metrics)),\n", + " \"Mean\": f\"{last['mean']:.2f}\",\n", + " \"Median\": f\"{last['median']:.1f}\",\n", + " \"Top-1%\": f\"{last['top_1_pct']:.1f}\",\n", + " \"Max\": f\"{last['max']:.0f}\",\n", + " },\n", + " \"best_max\": best_max,\n", + " }\n", + " rows.append(row)\n", + "\n", + "metric_w = {}\n", + "for m in metric_names:\n", + " metric_w[m] = max(len(m), max((len(r[\"cells\"][m]) for r in rows), default=0)) + 1\n", + "\n", + "header = f\"{'Experiment':<{label_w}}\"\n", + "for v in var_cols:\n", + " header += f\" {v:>{col_widths[v]}}\"\n", + "for m in metric_names:\n", + " header += f\" {m:>{metric_w[m]}}\"\n", + "\n", + "print(header)\n", + "print(\"-\" * len(header))\n", + "\n", + "for row in sorted(rows, key=lambda r: r[\"best_max\"], reverse=True):\n", + " d = row[\"d\"]\n", + " line = f\"{make_label(d):<{label_w}}\"\n", + " for v in var_cols:\n", + " line += f\" {str(d['params'].get(v, '')):>{col_widths[v]}}\"\n", + " for m in metric_names:\n", + " line += f\" {row['cells'][m]:>{metric_w[m]}}\"\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Score Distribution Evolution\n", + "\n", + "How the score distribution shifts across epochs (initial data vs after each training epoch)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2026-03-17T04:51:52.974443Z", + "iopub.status.busy": "2026-03-17T04:51:52.974366Z", + "iopub.status.idle": "2026-03-17T04:51:52.976905Z", + "shell.execute_reply": "2026-03-17T04:51:52.976575Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for exp_idx, d in enumerate(data):\n", + " dists = d[\"log\"][\"epoch_distributions\"]\n", + " metrics = d[\"metrics\"]\n", + " if len(dists) < 2:\n", + " continue\n", + " fig, ax = plt.subplots(figsize=(10, 5))\n", + " base_color = BASE_HUES[exp_idx % len(BASE_HUES)]\n", + " n_dists = len(dists)\n", + " colors = make_epoch_cmap(base_color, n_dists)\n", + "\n", + " for ep_idx, dist in enumerate(dists):\n", + " scores = sorted(dist.keys())\n", + " counts = [dist[s] for s in scores]\n", + " total = sum(counts)\n", + " freqs = [c / total for c in counts]\n", + " epoch_num = metrics[ep_idx][\"epoch\"] if ep_idx < len(metrics) else ep_idx\n", + " ax.plot(scores, freqs, linewidth=1.5, color=colors[ep_idx], label=f\"epoch {epoch_num}\", alpha=0.85)\n", + "\n", + " ax.set_xlabel(\"Score (# edges)\")\n", + " ax.set_ylabel(\"Frequency\")\n", + " ax.set_title(f\"{make_label(d)} -- Score Distribution Evolution\")\n", + " ax.legend(fontsize=7, ncol=2)\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/axplorer-viz/vendor/axplorer/train.py b/axplorer-viz/vendor/axplorer/train.py new file mode 100644 index 0000000..43e00f1 --- /dev/null +++ b/axplorer-viz/vendor/axplorer/train.py @@ -0,0 +1,268 @@ +import argparse +import json # [axplorer-viz patch] trajectory logging +import os +import random # [axplorer-viz patch] seed Python's RNG for reproducible runs +import time +from logging import getLogger + +import numpy as np +import torch + +from src.datasets import CharDataset, InfiniteDataLoader, load_initial_data, update_datasets +from src.envs import ENVS, build_env +from src.envs.environment import do_stats +from src.evaluator import sample_and_score +from src.models.model import Transformer +from src.trainer import reload_model_optimizer, train +from src.utils import bool_flag, force_release_memory, initialize_exp, log_resources, write_important_metrics + +logger = getLogger() + +# --------------------------------------------------------------------------- +# [axplorer-viz patch] trajectory logging (--log_trajectory). One JSONL line +# per epoch, written after the Selection phase. The Manim visualization in +# axplorer-viz consumes this; nothing else in Axplorer changes. See +# axplorer-viz/vendor/PATCH_NOTES.md. +# --------------------------------------------------------------------------- +_TRAJECTORY_LOG_CAP = 32 # max objects / samples per JSONL field, keeps logs readable + + +def _edge_tokens(datapoint, env): + """Edge tokens for a datapoint (BOS/EOS/PAD/SEP stripped).""" + itos = env.tokenizer.itos + # In every tokenizer here the special symbols map to ``str`` in ``itos``; + # everything else is an edge/coordinate token. + return [int(t) for t in env.tokenizer.encode(datapoint) if not isinstance(itos[int(t)], str)] + + +def _n_edge_tokens(env): + extra = getattr(env.tokenizer, "extra_symbols", None) or getattr(env, "SPECIAL_SYMBOLS", []) + return len(env.tokenizer.itos) - len(extra) + + +def _append_trajectory_log(path, epoch, train_set, new_data, raw_token_seqs, env, start_time): + cap = _TRAJECTORY_LOG_CAP + valid = lambda d: getattr(d, "score", None) is not None and d.score >= 0 + top_k = sorted((d for d in train_set if valid(d)), key=lambda d: d.score, reverse=True)[:cap] + after = sorted((d for d in new_data if valid(d)), key=lambda d: d.score, reverse=True)[:cap] + n_edge = _n_edge_tokens(env) + strip = lambda seq: [int(t) for t in seq if 0 <= int(t) < n_edge] + record = { + "epoch": int(epoch), + "n_vertices": int(getattr(getattr(env, "tokenizer", None), "N", 0)), # [axplorer-viz patch] self-describing logs + "top_k_objects": [_edge_tokens(d, env) for d in top_k], + "top_k_scores": [float(d.score) for d in top_k], + "model_samples_raw": [strip(seq) for seq in (raw_token_seqs or [])][:cap], + "model_samples_after_search": [_edge_tokens(d, env) for d in after], + "best_score_so_far": float(max((d.score for d in train_set if valid(d)), default=-1.0)), + "wall_time_seconds": float(time.time() - start_time), + } + with open(path, "a") as f: + f.write(json.dumps(record) + "\n") + + +def get_parser(): + parser = argparse.ArgumentParser("A simple Axplorer loop for different maths problems") + + parser.add_argument("--gensize", type=int, default=100000, help="Number of generate initial values") + parser.add_argument("--max_epochs", type=int, default=2000, help="Number of epochs") + parser.add_argument("--max_steps", type=int, default=50000, help="number of training steps.") + parser.add_argument("--num_samples_from_model", type=int, default=500000, help="sample the specified number from the model in each loop") + parser.add_argument("--pop_size", type=int, default=200000, help="Total maximum number of examples at each epoch") + parser.add_argument("--ntest", type=int, default=1000, help="Size of test set") + parser.add_argument("--env_name", type=str, default="square", help="Math problem to be addressed") + ENVS[parser.parse_known_args()[0].env_name].register_args(parser) + + parser.add_argument("--process_pool", type=bool_flag, default="true", help="use process_pool to generate and score initial data") + parser.add_argument("--always_search", type=bool_flag, default="true", help="if True, use local search for all examples generated") + parser.add_argument("--redeem_only", type=bool_flag, default="false", help="if True, save invalid examples only") + parser.add_argument("--new_proportion", type=float, default=0.0, help="proportion of new samples in test set") + + parser.add_argument("--num_workers", type=int, default=8, help="number of data workers for both train/test") + parser.add_argument("--num_eval_steps", type=int, default=500, help="number of step between each evaluation during training.") + parser.add_argument("--seed", type=int, default=-1, help="seed") + # sampling + parser.add_argument("--top_k", type=int, default=-1, help="top-k for sampling, -1 means no top-k") + # model + parser.add_argument("--n_layer", type=int, default=4, help="number of layers") + parser.add_argument("--n_head", type=int, default=8, help="number of heads (in a transformer)") + parser.add_argument("--n_embd", type=int, default=256, help="number of feature channels in the model") + parser.add_argument("--no_positional", type=bool_flag, default="false", help="no positional embedding") + parser.add_argument("--max_len", type=int, default=500, help="Block size, maximum length of sequences") + + # optimization + parser.add_argument("--batch_size", type=int, default=32, help="batch size during optimization") + parser.add_argument("--learning_rate", type=float, default=5e-4, help="learning rate") + parser.add_argument("--weight_decay", type=float, default=0.01, help="weight decay") + # evaluation against known "good sequences" + parser.add_argument("--gen_batch_size", type=int, default=1000, help="generation batch size") + parser.add_argument("--temperature", type=float, default=1.0, help="temperature") + parser.add_argument("--temp_span", type=int, default=0, help="temperature span") + parser.add_argument("--inc_temp", type=float, default=0.0, help="temperature") + parser.add_argument("--keep_only_unique", type=bool_flag, default="true", help="keep only unique data") + parser.add_argument("--save_best", type=bool_flag, default="false", help="save best model based on test loss") + + # path and ports + parser.add_argument("--dump_path", type=str, default="checkpoint", help="Experiment dump path") + parser.add_argument("--exp_name", type=str, default="debug", help="Experiment name") + parser.add_argument("--exp_id", type=str, default="", help="Experiment ID") + parser.add_argument("--cpu", type=bool_flag, default="false", help="run on cpu only") + parser.add_argument("--data_generation_only", type=bool_flag, default="false", help="only generate data and exit") + # [axplorer-viz patch] write one JSONL line of trajectory data per epoch to this path (empty = off) + parser.add_argument("--log_trajectory", type=str, default="", help="[axplorer-viz] path to a .jsonl trajectory log (one line per epoch); empty = disabled") + + return parser + + +if __name__ == "__main__": + parser = get_parser() + args = parser.parse_args() + + if args.exp_id == "" and os.environ.get("MODAL_EXP_ID") is None: + os.environ["MODAL_EXP_ID"] = time.strftime("%Y_%m_%d_%H_%M_%S") + args.exp_id = os.environ["MODAL_EXP_ID"] + + args.device = "cpu" if args.cpu else ("mps" if torch.backends.mps.is_available() else "cuda") + if args.device == "cuda": + torch.cuda.manual_seed_all(args.seed) + if args.device == "mps": + torch.mps.manual_seed(args.seed) + + fused = True if args.device in ["cuda", "mps"] else False + + logger = initialize_exp(args) + if not os.path.exists(args.dump_path): + os.makedirs(args.dump_path) + + if args.seed < 0: + args.seed = np.random.randint(1_000_000_000) + logger.info(f"seed: {args.seed}") + # [axplorer-viz patch] also seed Python's and numpy's global RNGs (upstream + # only seeds torch here). With --process_pool false this makes a run + # reproducible end to end; with the process pool, worker RNG state is not + # seeded, so reproducibility is best-effort. See vendor/PATCH_NOTES.md. + random.seed(args.seed) + np.random.seed(args.seed) + _traj_start_time = time.time() # [axplorer-viz patch] for wall_time_seconds in the log + + env = build_env(args) + + classname = env.data_class + + # system inits + torch.manual_seed(args.seed) + + args.vocab_size = len(env.tokenizer.itos) + + args.block_size = args.max_len + 2 + stoi = env.tokenizer.stoi + itos = env.tokenizer.itos + + # Initialize transformer + model = Transformer(args, stoi["PAD"], stoi["EOS"]) + model.to(args.device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay, betas=(0.9, 0.99), eps=1e-8, fused=fused) + reload_model_optimizer(args, model, optimizer) + + train_set, test_set = load_initial_data(args, classname) + if args.data_generation_only: + logger.info("Data generation only mode. Exiting...") + exit(0) + train_data_path = os.path.join(args.dump_path, "train_data.pkl") + test_data_path = os.path.join(args.dump_path, "test_data.pkl") + + # log initial stats + metrics = do_stats(-1, data=train_set) + temperature = args.temperature + # Loop of Axplorer + best_loss = None + epoch_file = os.path.join(args.dump_path, "epoch.txt") + if os.path.isfile(epoch_file): + with open(epoch_file, "r") as f: + n_epoch = int(f.read()) + else: + n_epoch = 0 + temp_file = os.path.join(args.dump_path, "temperature.txt") + if os.path.isfile(temp_file): + with open(temp_file, "r") as f: + temperature = float(f.read()) + else: + temperature = args.temperature + + metric_file = os.path.join(args.dump_path, "metrics.txt") + write_important_metrics(metrics, n_epoch, metric_file, command=args.command) + + for epoch in range(n_epoch, args.max_epochs): + logger.info(f"==== Starting Epoch {n_epoch} =====") + log_resources(f"Epoch {epoch} START") + + if args.device == "cuda": + torch.cuda.empty_cache() + elif args.device == "mps": + torch.mps.empty_cache() + + # tokenize + train_words = [env.tokenizer.encode(d) for d in train_set] + test_words = [env.tokenizer.encode(d) for d in test_set] + # data loaders + train_dataset = CharDataset(train_words, args.max_len, stoi) + test_dataset = CharDataset(test_words, args.max_len, stoi) + force_release_memory() + + if args.device == "cuda": + logger.info( + f"Memory allocated: {torch.cuda.memory_allocated(0)/(1024*1024):.2f}MB, reserved: {torch.cuda.memory_reserved(0)/(1024*1024):.2f}MB" + ) + elif args.device == "mps": + logger.info( + f"Memory allocated: {torch.mps.current_allocated_memory()/(1024*1024):.2f}MB, reserved: {torch.mps.driver_allocated_memory()/(1024*1024):.2f}MB" + ) + + batch_loader = InfiniteDataLoader(train_dataset, batch_size=args.batch_size, pin_memory=args.device == "cuda", num_workers=0) + try: + best_loss = train(model, args, batch_loader, optimizer, test_dataset, current_best_loss=best_loss) + finally: + batch_loader.close() + del batch_loader + log_resources(f"Epoch {epoch} AFTER_TRAIN") + force_release_memory() + + logger.info(f"Sample with temperature {temperature} to {temperature+0.1*args.temp_span}") + if args.device == "cuda": + torch.cuda.empty_cache() + elif args.device == "mps": + torch.mps.empty_cache() + + # [axplorer-viz patch] collect a few raw (pre-local-search) samples for the log + _raw_samples = [] if args.log_trajectory else None + new_data = sample_and_score(model, args, stoi, itos, env, temperature, args.temp_span, raw_token_out=_raw_samples) + log_resources(f"Epoch {epoch} AFTER_SAMPLE") + + if args.device == "cuda": + torch.cuda.empty_cache() + elif args.device == "mps": + torch.mps.empty_cache() + + # Possible to add another generation method here and mix it before taking the best + train_set, test_set, inc_temp = update_datasets(args, new_data, train_set, test_set, train_data_path, test_data_path) + log_resources(f"Epoch {epoch} AFTER_UPDATE_DATASETS") + force_release_memory() + + # Possible to add another generation method here and mix it before taking the best + if inc_temp and args.inc_temp > 0.0: + temperature += args.inc_temp + + metrics = do_stats(-1, data=train_set) + + n_epoch += 1 + with open(epoch_file, "w") as f: + f.write(str(n_epoch)) + with open(temp_file, "w") as f: + f.write(str(temperature)) + + write_important_metrics(metrics, n_epoch, metric_file) + + # [axplorer-viz patch] append one JSONL line of trajectory data for this epoch + if args.log_trajectory: + _append_trajectory_log(args.log_trajectory, epoch, train_set, new_data, _raw_samples, env, _traj_start_time) + logger.info(f"[axplorer-viz] appended trajectory log for epoch {epoch} -> {args.log_trajectory}")