From 73143346a8ea74b13eb7e203978a86b8afe9bb6c Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 18:49:16 -0400 Subject: [PATCH 01/24] =?UTF-8?q?Design:=20RL=20clean=20rebuild=20?= =?UTF-8?q?=E2=80=94=20two=20Qwen=20runs,=20ported=20v3-b=20shaped=20rewar?= =?UTF-8?q?d?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Synthesis of the five-way study (old private library, current rl/ tree, paper commitments, launch-night archaeology, dataset sealing). Decides: port the proven iou_n/v3-b shaped reward onto the sealed depth-v1 curriculum, one training path in track_a over common, two matched runs (L0-SFT->RL and base-RL L0->L4), delete the studies/ceremony apparatus. Commitment: Train the paper's two Qwen arms with the ported v3-b shaped reward (iou_n) on the sealed depth-v1 curriculum, one lean path in rl/track_a; the studies/approval ceremony and Inkling machinery are removed rather than maintained. Type: decision Scope: rl/ Caused: five-way study of PixCell-running/rl, rl/, arxiv/, git history, dataset/ (spec: docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md) --- .../2026-07-28-rl-clean-rebuild-design.md | 241 ++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md diff --git a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md new file mode 100644 index 00000000..ba0005dc --- /dev/null +++ b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md @@ -0,0 +1,241 @@ +# RL clean rebuild — two Qwen runs on the sealed curriculum + +**Date:** 2026-07-28 · **Branch:** `worktree-rl-clean-runs` (base `origin/main` @ `802110aff`) +**Goal:** delete the ceremony, restore the proven training signal, and run the paper's +two Qwen training arms on the released sealed dataset. + +--- + +## 1. Diagnosis (what the study of both repos established) + +### The old private library (`PixCell-running/rl`) learned; this tree cannot. Why: + +**The old reward was `v3-b` shaped, anchored on `iou_n` — rectangle-baseline-normalized +excess IoU.** For every task, the IoU/Dice of a solid rectangle filling the calibrated +bounding box (`iou_rect`, `dice_rect`) is precomputed once, and the reward pays only for +structure *beyond* that degenerate answer: + +``` +iou_n = max(0, (iou − iou_rect) / (1 − iou_rect)) # 0 = the rectangle hack, 1 = perfect +dice_n = likewise +τ = 0.05 · diag_um # footprint diagonal sets the scale +t2b = exp(−chamfer_boundary_um / τ) # boundary closeness +teacher_nb = max(0, t2b − t2b_rect) / max(1 − t2b_rect, 0.3) + +crash / timeout / no-gds / bad-gds / syntax → 0.0 +purity violation (non-catalog geometry) → 0.0 # old "ship mode" +executable + pure, iou_rect < 0.95 → 0.05 + 0.40·iou_n + 0.15·dice_n + 0.40·teacher_nb +executable + pure, iou_rect ≥ 0.95 → 0.05 + 0.40·iou + 0.15·dice + 0.40·t2b # degenerate branch +``` + +Why each piece exists (all evidenced in the old repo): +- **Rectangle normalization** kills the measured degenerate attractor (the old policy + *found* the bbox-fill hack: reward 0.975 on one task) and makes one reward scale mean + the same thing on every task — which is what makes curricula, winner thresholds, and + readable curves possible. +- **The 0.05 executable floor** separates "wrong geometry but a valid program" from + "crash", restoring gradient exactly where early RL lives. +- **The boundary-chamfer teacher term** pays for count/boundary progress IoU cannot see + (one more meander on a serpentine barely moves IoU). +- **The degenerate branch** scores rectangle-like targets raw, because normalizing + against a rectangle that *is* the answer makes every program look identical. + +With this reward, GRPO on Qwen3.6-35B-A3B (LoRA 32, G=8×B=8, lr 1e-5, 4096 tokens, +`qwen3_5_disable_thinking`) produced unambiguous learning: train `iou_n` 0.187→0.389 +over the 95-step paper lineage; held-out `iou_n` 0.162→0.299 (+85% relative); the +no-SFT "pure" lineage took held-out L0 raw IoU **0.038→0.911** in three days. + +**This tree replaced that signal with `reward = raw absolute IoU, else 0`** +(`representation_training_v1/launcher.py:917`, `"reward": "raw_absolute_scale_iou"`). +Consequences, all verified in code: no floor (valid-but-wrong ≡ crash ≡ 0), no +normalization (blob-filling pays; hard tasks pin near 0), Dice/chamfer discarded as +diagnostics, and — combined with `remove_constant_reward_groups=True` and group size +**4** — any task the model cannot already partially solve produces an all-zero group +that is deleted from the batch. Early RL on an unmastered level receives almost no +gradient. The two 6-step runs it managed before stalling show reward as pure noise. + +### The old library's fatal weakness was the opposite one: **unsealed data.** +Eval pool grew 64→253 mid-campaign (curves incomparable across the boundary), the train +pool grew/shrank/killed a live run, pools were gitignored, and seed determinism broke on +any motif-registry edit. **This repo already fixed that completely**: the released +dataset is frozen under four layers of hashing, the loader re-verifies SHAs per row, and +model-visible vs verifier-only fields are separated by construction (`rl/common/contracts.py`). + +### The ceremony this tree added is measurable and killed its own campaign. +~90% of 30,381 lines is scaffolding (two W&B integrations, two immutable-ledger systems, +two recipe-locking systems, wave/approval receipts, promotion gates at 0.80 IoU — a bar +the *old* runs show L2/L3 never reach). On launch night (Jul 28) the provenance +machinery itself caused three aborted launches in 100 minutes, then a 6-hour stall, then +the protocol's own baseline gate was relaxed to get anything running at all; ten +`training-live` worktrees exist only because runs were SHA-bound and every hotfix +required a fresh clean tree. The one useful negative result: 55-step **mixed-level SFT +produced ~0-IoU checkpoints** (renderer diagnostic: 1/8 executable), while **L0-only SFT +produced a usable checkpoint** (RL from it emitted executable programs at reward 0.2–0.5). + +### What is genuinely good here and must be kept +`rl/common/` wholesale (contracts, hash-verified parquet loader, prompt contract +`pixcell-direct-reconstruction-v2`, preprocessing, batcher, per-candidate Docker +isolation with immutable image binding), the deterministic curriculum helpers +(exactly-once stratified SFT pass; 80/20 five-step replay cycle — both pinned by tests), +`tinker_data.py`'s clean Cookbook `Env`/`RLDataset` wrapper, and the essential parts of +preflight (dataset digest check against `freeze.json`, sandbox binding, clean-tree check). + +--- + +## 2. What we will build (the decision) + +**One training path, two runs, one reward.** Everything lives in `rl/track_a/` over +`rl/common/`; the studies apparatus is deleted. + +### 2.1 Reward: port `v3-b` exactly — new policy `"shaped_v3b"` +- Constants identical to the old library (0.05 floor; 0.40/0.15/0.40 weights; τ = 0.05·diag; + denominator floor 0.3; degenerate threshold `iou_rect ≥ 0.95`). These constants + produced the curves; they are not re-tuned. +- Purity: the current evaluator rejects non-catalog programs *before* execution + (`SOURCE_REJECTED → 0.0`). This matches the old ship-mode semantics (impure = 0.0, + the Jul-21 stop-ship: a floor for impure programs trains the violation). Keep the + current order. +- Per-task baselines (`iou_rect`, `dice_rect`, `chamfer_rect_boundary_um`, `diag_um`) + are computed once per task from `target_image` + stored calibration, cached in-process + keyed by `target_image_sha256`, and dumped to the run directory for provenance. The + dataset is frozen, so these are constants of the release. +- The evaluator gains two candidate-side measurements it already half-computes: Dice + (exists as diagnostic) and symmetric mean boundary-chamfer in µm (ported from the old + `rl/reward/geometry.py`). +- `raw_iou` and `validity_floor` policies remain in the file for the record; the runs + use `shaped_v3b`. +- Old shaping tests are ported and adapted (rect-equivalent ⇒ exactly 0.05; the + (0.75, base 0.5) ⇒ 0.5 normalization case; purity ⇒ 0.0; crash ⇒ 0.0; degenerate + branch exactness; denominator floor; ground-truth self-score ≈ 1.0). + +### 2.2 Training recipe (both runs share it) +| Knob | Value | Source of truth | +|---|---|---| +| Model | `Qwen/Qwen3.6-35B-A3B` | paper + both repos | +| LoRA rank | 32 | old lineage + paper | +| Renderer | `qwen3_5_disable_thinking` | old winning lineage (60k-thinking is 10×+ decode cost; RC-H05 showed thinking@60k ≈ 0.218 zero-shot — not needed for the training result) | +| Max sampled tokens | 4096 | old lineage | +| Image | dataset-native, max edge 1440 | dataset is drawn at 1440 | +| GRPO | G=**8** rollouts × 8 groups/step, lr 1e-5, temp 1.0, top-p 1.0, `importance_sampling`, KL 0 | old lineage (this tree's G=4 halves the group signal) | +| Advantage | plain within-group mean-centering; constant-reward groups dropped | cookbook default = old paper lineage (PKPO/advantage-patch was an Inkling-era experiment; not ported) | +| Curriculum | staged L0→L4, **30 steps/level**, 80/20 current/replay via the existing deterministic five-step cycle | old stage budgets (30/35/30) + this tree's tested scheduler; no mastery gates, no approvals | +| Checkpoints | state every 5 steps; sampler checkpoint at each level boundary + final | enough to resume and to evaluate; nothing else | + +### 2.3 The two runs (the ablation the paper explicitly defers) +- **Run A (`l0sft-rl`)**: SFT on the 642 L0 `depth/train` rows (batch 64, lr 1e-4 + linear, `LAST_ASSISTANT_MESSAGE`, 2 epochs ≈ 22 steps, prompt byte-identical to RL) → + RL L0→L4, 150 steps. +- **Run B (`base-rl`)**: raw base → RL L0→L4, 150 steps. +Identical RL recipe, schedule, reward, and eval ⇒ a controlled ±L0-SFT comparison at +matched RL budget — precisely the "controlled ablation" and "untouched training +evaluations" the paper names as the open door (`arxiv/main.tex:879-881, :932-942`). + +**SFT hygiene (the mixed-SFT autopsy made this mandatory):** +1. decode-and-inspect rendered datums (text + train-mask) before spending; +2. token-length audit of all 642 labels — every label must fit `max_length` un-truncated + (mixed-SFT's ~0-IoU collapse is consistent with long L2–L4 labels truncating at 6144; + L0-only SFT dodges this, and we verify rather than assume); +3. post-SFT sanity eval before RL: 8 fixed L0 validation tasks, require ≥6/8 executable + and mean IoU above the base-model probe, else stop and diagnose. + +### 2.4 Evaluation (one instrument, no studies) +- **Probe set**: 80 tasks = 16/level from `depth/validation`, deterministically selected + (sorted `opaque_id`, first 16 per level; recorded in the run dir). Eval@1 at T=1.0 + every 5 steps → per-level executable rate, raw IoU, and shaped reward. This is the + training curve for the paper. +- **Step-0 probe = the base baseline.** No separate baselining studies, ever. +- **Final**: full `depth/validation` eval@1 for both final checkpoints, plus the F1–F8 + benchmark at k=8 through the same evaluator — the paper's headline protocol. +- Everything flows through the same Docker-isolated evaluator; every number is + recomputed from artifacts (the conductor invariant survives; the receipts do not). + +### 2.5 Provenance-lite (keep the invariant, drop the theater) +Commit before launch; preflight = dataset-digest check + sandbox-image binding + +renderer/tokenizer sanity + ground-truth positive control + clean-tree check; run dir +records resolved config + git SHA + baseline cache; Cookbook writes `metrics.jsonl`; +W&B project **`pixcell-rl`** (the old project — new curves land beside the ones that +worked), run names `runA-l0sft-rl-` / `runB-base-rl-`. No deterministic +run-id binding, no wave receipts, no approval tokens, no store locks, no SHA-suffixed +store roots. Run artifacts live outside the repo under the existing training volume +(`/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2/`, fallback `~/pixcell-training/`). + +### 2.6 Launch mechanics (the 6-hour-stall lesson) +Runs launch in detached host tmux panes (not inside an agent session), logging to the +run dir; monitoring reads `metrics.jsonl` and rollout summaries. Keys are sourced from +the existing env file at launch, never printed. Smoke first: 2 GRPO steps at G=8×B=2 on +L0 to validate the full loop and measure real per-step cost; the extrapolated cost of +both full runs is reported before the full launches go out. + +### 2.7 Deletions (this branch) +- `rl/studies/` — all three studies + both extensions (the campaign ceremony, the + superseded diagnostics, the mastery/promotion machinery). +- `rl/evaluation/` — the zero-shot baselining harness + its W&B mirror. +- `rl/track_b/` — the Inkling TML shim. Inkling is out of scope for the paper. +- `rl/track_c/` — an empty reservation. +- In `rl/track_a/`: the recipe-lock/approval surface (`recipe.py` ceremony, + `evaluation_contract.py`, checkpoint promotion/selection) replaced by one lean runner; + reward/tinker_data/curriculum/preflight-essentials/train entry points stay. +- Tests follow their code: the ~800 lines pinning the learning signal stay and grow + (shaped-reward tests); ceremony tests leave with the ceremony. +- `rl/README.md` rewritten to describe exactly this design; `track_a/README.md` updated + (reward table changes from `0.05 + 0.95·IoU` to the shaped contract). + +### 2.8 Open item resolved by measurement, not opinion +The dataset's model image is the **max-visibility view** (per-axis magnified — a +45×9.9 µm L4 device renders at image aspect 1.32, not 4.55). The old library handled +exactly this with an honest scale note in the prompt above 1.6× anisotropy ("the image +is NOT drawn to scale … trust the stated footprint"). We measure the anisotropy +distribution across `depth/train`; if a material fraction exceeds the old 1.6× +threshold, the note is added to the prompt builder (computed from model-visible inputs +only) as contract `v3` with tests. Data decides; the change is isolated to +`rl/common/prompt.py` + tests either way. + +--- + +## 3. Alternatives considered + +**A. Patch the existing study in place** (flip `reward_policy`, G4→G8, run its stages). +Least code motion, but keeps the machinery that demonstrably killed launch night (three +provenance aborts, SHA-bound relaunch worktrees, approval tokens, mastery gates), and +directly contradicts the directive to remove it. Rejected. + +**B. Clean rebuild on `track_a` + `common`, port the proven reward.** Chosen — keeps +this repo's genuinely superior data/isolation layer and the old repo's proven signal; +one path, two runs, no studies. (This document.) + +**C. Port the old library wholesale onto the new dataset.** The loop is byte-proven, +but it drags in the sample-dir pool machinery the parquet release replaced, loses the +sealed loader/contracts and Docker isolation, and reintroduces the unsealed-data class +of failure. Rejected. + +--- + +## 4. Risks and mitigations +- **SFT collapse (mixed-SFT precedent)** → L0-only SFT + the three-step hygiene gate in §2.3. +- **Reward-port drift** → old tests ported verbatim; ground-truth positive control must + self-score ≈ 1.0 through the full shaped path; constants pinned by test. +- **All-zero groups persisting early in Run B** → the floor guarantees executable + variance pays; smoke run inspects `frac_mixed`/dropped-group counts at steps 1–2. +- **Sandbox image missing/stale on this machine** → preflight binds the image by + spec-hash label; build script exists (`rl/common/sandbox/build.py`); smoke exercises it. +- **Session-tied runs** → detached tmux launch; resume = re-run the same command (state + checkpoints every 5 steps; Cookbook resume). +- **Spend surprise** → smoke measures true per-step cost; both-run extrapolation is + reported before full launch; runs are per-level resumable so they can be stopped at + any stage boundary. + +## 5. Test plan +- Ported shaping tests (constants, normalization, degenerate branch, purity, crash). +- Evaluator: Dice + boundary-chamfer surfaces exist and match hand-computed values on + synthetic masks; ground-truth self-score ≈ 1.0 under `shaped_v3b`. +- Baseline computation: rectangle baseline on a known mask matches hand-computed + `iou_rect`/`dice_rect`; cache keyed by `target_image_sha256`. +- Curriculum/schedule invariants keep their existing tests (exactly-once pass, 80/20 cycle). +- Prompt contract tests updated only if §2.8 measurement adds the scale note. +- `uv run pytest rl/ -q` green; `ruff` clean; CI surface (`tests/ rl/ release/`) green. + +## 6. Explicit non-goals +No Inkling. No new zero-shot baseline studies. No dataset changes. No HF publishing. +No paper edits until both runs have results (then: the deferred-ablation paragraph, a +training-curve figure to re-fill the orphaned `fig_rl_training` slot, and Table III +companions — as a separate reviewed change with the user). From 2bdac8eb88571b9dadb9edbb5929b8a985381774 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 18:55:06 -0400 Subject: [PATCH 02/24] Plan: RL clean rebuild implementation tasks --- .../plans/2026-07-28-rl-clean-rebuild.md | 251 ++++++++++++++++++ 1 file changed, 251 insertions(+) create mode 100644 docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md diff --git a/docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md b/docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md new file mode 100644 index 00000000..6b551968 --- /dev/null +++ b/docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md @@ -0,0 +1,251 @@ +# RL Clean Rebuild Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Restore the proven `v3-b` shaped reward (`iou_n`) in `rl/track_a`, delete the studies/ceremony apparatus, and produce a lean two-run launcher (Run A `l0sft-rl`, Run B `base-rl`) over the sealed depth-v1 dataset. + +**Architecture:** `rl/common/` stays the measurement layer (gains `baselines.py` + a boundary-chamfer diagnostic in `evaluator.py`). `rl/track_a/` becomes the single training path: `reward.py` gains policy `shaped_v3b`; `tinker_data.py` keeps its Env/builders (baseline plumbed into the reward call); `train_sft.py`/`train_rl.py` are rewritten lean; a new `run.py` orchestrates stages, probe evals, and provenance. `rl/studies/`, `rl/evaluation/`, `rl/track_b/`, `rl/track_c/`, and track_a's recipe-lock/contract files are deleted. + +**Tech Stack:** tinker 0.22.7 + tinker-cookbook ca232b0 (pinned), gdsfactory 9.20.7, scipy 1.18 (present), PIL/numpy, pytest, uv. + +## Global Constraints + +- Reward constants are the old library's, byte-for-byte: floor 0.05; weights 0.40/0.15/0.40; `TAU_FRAC = 0.05`; `TEACHER_DENOM_FLOOR = 0.3`; `DEGENERATE_IOU_RECT = 0.95`. Never re-tuned. +- Purity stays pre-execution: `SOURCE_REJECTED → 0.0` (old ship-mode semantics). No floor for impure programs. +- `None` reward (REFERENCE/EVALUATOR fault) keeps aborting the run — never becomes 0. +- Training defaults: `Qwen/Qwen3.6-35B-A3B`, renderer `qwen3_5_disable_thinking`, LoRA 32, G=8 rollouts × 8 groups, lr 1e-5 RL / 1e-4 SFT, max_tokens 4096, temp 1.0, image 1440, KL 0, `importance_sampling`, `remove_constant_reward_groups=True`, 30 steps/level, 80/20 replay. +- `rl/common` may not import `rl.track_a`, `tinker`, `tinker_cookbook`, or `chz` (pinned by `rl/common/tests/test_import_boundaries.py`). +- `_json_diagnostic` rejects non-finite floats: any chamfer that is `inf` must be recorded as `None`. +- No AI-attribution trailers in commits. Run `uv run pytest rl/ -q` before every commit; `uv run ruff check rl/` at task ends. + +--- + +### Task 1: `rl/common/baselines.py` — per-task rectangle baseline + +**Files:** +- Create: `rl/common/baselines.py` +- Test: `rl/common/tests/test_baselines.py` + +**Interfaces:** +- Produces: `TaskBaseline` (frozen dataclass: `iou_rect: float`, `dice_rect: float`, `chamfer_rect_boundary_um: float | None`, `diag_um: float`), `rect_baseline(reference: VerifierReference) -> TaskBaseline` (pure), `cached_rect_baseline(reference) -> TaskBaseline` (module-level dict cache keyed by `reference.target_image_sha256`), `boundary_mask(mask)` and `chamfer_sym_um(mask_a, mask_b, um_per_px)` (ported verbatim from the old `rl/reward/geometry.py`). +- Consumes: `rl.common.contracts.VerifierReference`, `rl.common.evaluator._prepare_reference` (reuses the evaluator's own calibration so the baseline and the score share one geometry). + +- [ ] Write `test_baselines.py` with two synthetic targets, `footprint_um=(30.0, 20.0)`: + (a) a solid 60×40 black rectangle at (20,20) in a 100×80 PNG — its ink bbox equals the ink, so the rectangle fill IS the target: assert `iou_rect == 1.0` and `dice_rect == 1.0` (this is the degenerate case Task 3's branch keys on); + (b) an L-shaped mask — hand-compute `iou_rect = ink_area / bbox_area`, `dice_rect = 2·ink/(ink + bbox)`; assert both, assert `chamfer_rect_boundary_um` is finite and > 0, and `diag_um == sqrt(30² + 20²)`; assert `cached_rect_baseline` returns the identical object for the same sha. +- [ ] Run: `uv run pytest rl/common/tests/test_baselines.py -q` — expect import failure. +- [ ] Implement. Core (port of old `compute_baselines.rect_baseline` over `_prepare_reference`): + +```python +from __future__ import annotations +from dataclasses import dataclass +import numpy as np +from scipy.ndimage import binary_erosion, distance_transform_edt +from rl.common.contracts import VerifierReference +from rl.common.evaluator import _prepare_reference + +def boundary_mask(mask): + return mask & ~binary_erosion(mask) + +def chamfer_sym_um(mask_a, mask_b, um_per_px): + if not mask_a.any() or not mask_b.any(): + inf = float("inf"); return inf, inf, inf + dist_to_b = distance_transform_edt(~mask_b) + dist_to_a = distance_transform_edt(~mask_a) + a_to_b = float(dist_to_b[mask_a].mean()) * um_per_px + b_to_a = float(dist_to_a[mask_b].mean()) * um_per_px + return 0.5 * (a_to_b + b_to_a), a_to_b, b_to_a + +@dataclass(frozen=True) +class TaskBaseline: + iou_rect: float + dice_rect: float + chamfer_rect_boundary_um: float | None + diag_um: float + +def rect_baseline(reference: VerifierReference) -> TaskBaseline: + prepared = _prepare_reference(reference) + arr = np.asarray(prepared.target) # already "L" + thr = prepared.calibration["white_threshold"] + x0, y0, x1, y1 = prepared.calibration["bbox"] + tgt = arr < thr + pred = np.zeros_like(tgt); pred[y0:y1, x0:x1] = True + inter = float(np.logical_and(tgt, pred).sum()) + union = float(np.logical_or(tgt, pred).sum()) + denom = float(tgt.sum() + pred.sum()) + sx, sy = prepared.calibration["scale_px_per_um"] + um_per_px = 2.0 / (sx + sy) + chamfer_b, _, _ = chamfer_sym_um(boundary_mask(pred), boundary_mask(tgt), um_per_px) + fw, fh = prepared.calibration["footprint_um"] + return TaskBaseline( + iou_rect=inter / union if union else 0.0, + dice_rect=2.0 * inter / denom if denom else 0.0, + chamfer_rect_boundary_um=None if chamfer_b == float("inf") else chamfer_b, + diag_um=(fw * fw + fh * fh) ** 0.5, + ) + +_CACHE: dict[str, TaskBaseline] = {} + +def cached_rect_baseline(reference: VerifierReference) -> TaskBaseline: + key = reference.target_image_sha256 + if key not in _CACHE: + _CACHE[key] = rect_baseline(reference) + return _CACHE[key] +``` + +- [ ] Tests pass; `git add` + commit `"Add rectangle-baseline computation to rl/common"`. + +### Task 2: boundary chamfer in the evaluator + +**Files:** +- Modify: `rl/common/evaluator.py` (the `compute_metrics` call site, ~line 797) +- Test: extend `rl/common/tests/test_evaluator.py` + +**Interfaces:** +- Produces: `result.metrics["chamfer_boundary_um"]: float | None` on every `OK` result (µm, mean-axis scale; `None` when either mask is empty). +- Consumes: `boundary_mask`, `chamfer_sym_um` from `rl.common.baselines` — import them INSIDE the compare block (lazy) or top-level; **top-level import is fine** (`baselines` imports `evaluator` only for `_prepare_reference` — avoid the cycle by moving `_prepare_reference` usage in baselines to a function-local import, or import `baselines`' pure helpers from a `rl/common/geometry.py`). **Resolution: put `boundary_mask`/`chamfer_sym_um` in `rl/common/geometry.py` (no imports beyond numpy/scipy); `baselines.py` and `evaluator.py` both import from it. No cycle.** + +- [ ] Move the two pure functions into `rl/common/geometry.py`; `baselines.py` imports them from there (adjust Task 1 file accordingly). +- [ ] Add test: craft a tiny GDS-producing program in the existing evaluator-test style (there is a ground-truth self-score test to copy from) and assert `result.metrics["chamfer_boundary_um"]` exists and is `< 0.5` for the self-scored ground truth; craft a shifted-square case via two synthetic masks through `chamfer_sym_um` directly with hand-computed value (1px shift over known um_per_px). +- [ ] Implement in `evaluator.py` right after `metrics = compute_metrics(...)`: + +```python +r_mask = np.asarray(raster.convert("L")) < 245 +t_mask = np.asarray(prepared.target) < 245 +sx, sy = prepared.calibration["scale_px_per_um"] +chamfer_b, _, _ = chamfer_sym_um( + boundary_mask(r_mask), boundary_mask(t_mask), 2.0 / (sx + sy) +) +metrics = { + **metrics, + "chamfer_boundary_um": None if chamfer_b == float("inf") else float(chamfer_b), +} +``` + +- [ ] `uv run pytest rl/common/tests/ -q` green; commit `"Measure boundary chamfer in the evaluator"`. + +### Task 3: reward policy `shaped_v3b` + +**Files:** +- Modify: `rl/track_a/reward.py` +- Test: extend `rl/track_a/tests/test_output_reward.py` + +**Interfaces:** +- Produces: `REWARD_POLICIES = ("validity_floor", "raw_iou", "shaped_v3b")`; `reward_for_policy(result, *, policy, baseline: TaskBaseline | None = None) -> float | None`; `shaped_components(result, baseline) -> dict[str, float]` (the logging breakdown: `iou`, `dice`, `iou_n`, `dice_n`, `t2b`, `teacher_nb`, `degenerate`). +- Consumes: `TaskBaseline` from `rl.common.baselines`; `result.metrics["chamfer_boundary_um"]` from Task 2. Constants module-level: `SHAPED_FLOOR = 0.05`, `W_IOU3, W_DICE3, W_TEACHER = 0.40, 0.15, 0.40`, `TAU_FRAC = 0.05`, `TEACHER_DENOM_FLOOR = 0.3`, `DEGENERATE_IOU_RECT = 0.95`. + +- [ ] Write the ported tests first (old `test_shaping.py`/`test_shaping_v3.py` semantics, expressed against `reward_for_policy` with a crafted `EvaluationResult(OK, MODEL, iou=…, dice=…, metrics={"chamfer_boundary_um": …})` and a crafted `TaskBaseline`): + - non-degenerate normalization: `iou=0.75, baseline.iou_rect=0.5` with dice term and teacher zeroed ⇒ contribution `0.40 * 0.5`; + - rect-equivalent program (`iou == iou_rect`, `dice == dice_rect`, chamfer == rect chamfer) ⇒ exactly `0.05`; + - degenerate branch (`iou_rect = 0.96`): reward uses RAW iou/dice/t2b; + - teacher denominator floor: `t2b=0.9, t2b_rect=0.8` ⇒ `0.1 / 0.3`; + - crash (`RUNTIME_ERROR`, MODEL) ⇒ `0.0`; `SOURCE_REJECTED` ⇒ `0.0`; EVALUATOR fault ⇒ `None`; + - missing chamfer (`None`) ⇒ `t2b = 0`, no crash; missing baseline ⇒ `ValueError` (shaped policy requires a baseline); + - perfect program on non-degenerate target (`iou=dice=1.0`, chamfer 0, rect baseline 0.3/0.4/large-chamfer) ⇒ `0.05 + 0.40 + 0.15 + 0.40·(1 − t2b_rect)/max(1−t2b_rect, 0.3)` — assert `> 0.95` and `== pytest.approx(1.0)` when `t2b_rect ≤ 0.7`. +- [ ] Run tests, expect failures; implement: + +```python +def _t2b(chamfer_um, tau: float) -> float: + if chamfer_um is None or chamfer_um == float("inf"): + return 0.0 + return math.exp(-float(chamfer_um) / tau) + +def norm_excess(x: float, base: float) -> float: + if base >= 1.0: + return 0.0 + return max(0.0, (x - base) / (1.0 - base)) + +def _shaped_v3b(result: EvaluationResult, baseline: TaskBaseline) -> float: + iou, dice = float(result.iou), float(result.dice) + diag = baseline.diag_um if baseline.diag_um > 0 else 1e-9 + tau = max(TAU_FRAC * diag, 1e-9) + chamfer = result.metrics.get("chamfer_boundary_um") + chamfer_rect = baseline.chamfer_rect_boundary_um + t2b = _t2b(chamfer, tau) + t2b_rect = _t2b(chamfer_rect if chamfer_rect is not None else diag, tau) + if baseline.iou_rect >= DEGENERATE_IOU_RECT: + return SHAPED_FLOOR + W_IOU3 * iou + W_DICE3 * dice + W_TEACHER * t2b + teacher_nb = max(0.0, t2b - t2b_rect) / max(1.0 - t2b_rect, TEACHER_DENOM_FLOOR) + return (SHAPED_FLOOR + + W_IOU3 * norm_excess(iou, baseline.iou_rect) + + W_DICE3 * norm_excess(dice, baseline.dice_rect) + + W_TEACHER * teacher_nb) +``` + + In `reward_for_policy`: `policy == "shaped_v3b"` → OK ⇒ validate iou/dice finite in [0,1], require `baseline is not None`, return `_shaped_v3b`; MODEL failure ⇒ `0.0`; else `None`. Keep the existing two policies untouched. +- [ ] Tests green; commit `"Port the v3-b shaped reward (iou_n) as policy shaped_v3b"` with trailer `Honors:` referencing the design commitment commit. + +### Task 4: plumb baseline + shaped metrics through the Env + +**Files:** +- Modify: `rl/track_a/tinker_data.py` (`TrackAEnv.step` ~line 347, builder defaults), Test: `rl/track_a/tests/test_tinker_contract.py` additions + +**Interfaces:** +- Consumes: `cached_rect_baseline` (Task 1), `reward_for_policy(..., baseline=...)` + `shaped_components` (Task 3). +- Produces: `TrackAEnv.step` computes `baseline = cached_rect_baseline(self.task.reference)` when `self.reward_policy == "shaped_v3b"`, passes it to `reward_for_policy`, and extends `StepResult.metrics` with `iou_n`, `teacher_nb`, `degenerate` from `shaped_components`. Builder defaults change: `TrackARLDatasetBuilder.group_size: int = 8`, `reward_policy: str = "shaped_v3b"`. + +- [ ] Test: instantiate `TrackAEnv` with a stub renderer + a real L0 `TaskRecord` (existing contract tests show how) and a monkeypatched batcher returning a crafted OK result; assert reward equals the hand-computed shaped value and `metrics["iou_n"]` present. +- [ ] Implement; run `uv run pytest rl/track_a/tests/ -q`; commit `"Default Track A to the shaped reward at G=8"`. + +### Task 5: the deletion sweep + +**Files:** +- Delete: `rl/studies/`, `rl/evaluation/`, `rl/track_b/`, `rl/track_c/`, `rl/track_a/{recipe.py,evaluation_contract.py,checkpoint_metrics.py,checkpoint_selection.py,select_sft_checkpoint.py,evaluate_checkpoint.py,preflight.py→trim,tests/test_checkpoint_promotion.py,tests/test_launch_preflight.py}` +- Modify: `rl/track_a/curriculum.py` (absorb `LEVELS`, `normalize_level`, `normalize_replay_levels`, `expected_replay_levels` verbatim from `recipe.py`), `rl/track_a/tinker_data.py` (imports; delete the Inkling `_renderer` branch → `raise ValueError("Inkling is out of scope")`), `rl/track_a/launch.py` (keep `bind_execution_boundary`; drop recipe imports), `rl/stack.json` (drop `tml-renderers`), `rl/README.md` + `rl/track_a/README.md` (rewrite: three-policy reward table with `shaped_v3b` as the run policy, the two runs, no tracks B/C), `rl/conftest.py` (check for references) + +**Steps:** +- [ ] `git rm -r rl/studies rl/evaluation rl/track_b rl/track_c` and the track_a ceremony files; move the four level helpers into `curriculum.py`; fix every import (`grep -rn "rl.studies\|rl\.evaluation\|rl\.track_b\|track_a\.recipe\|evaluation_contract\|checkpoint_selection\|checkpoint_metrics" rl/ src/ tests/ release/ plugins/ .claude/ pyproject.toml`). +- [ ] `preflight.py`: keep only `verify_frozen_dataset(repo_root, dataset_root)` (shard-hash check vs both freeze.json files), `require_clean_worktree(repo_root)`, and the tokenizer/prompt sanity check; delete the rest. `train_sft.py`/`train_rl.py` get stubbed imports fixed in Task 6 (temporarily mark them broken with a clear `raise SystemExit("rewritten in Task 6")` if needed to keep pytest green — better: do Task 5 and Task 6 in one commit if the seam is awkward). +- [ ] Full sweep green: `uv run pytest rl/ -q && uv run ruff check rl/`. Commit `"Remove the studies/ceremony apparatus from rl/"` (trailer `Honors:` design commitment; body lists what was deleted and why in two sentences). + +### Task 6: lean entry points + +**Files:** +- Rewrite: `rl/track_a/train_sft.py` (add `levels: str = "L0"`, `num_epochs: int = 2`, `learning_rate: float = 1e-4`, drop recipe gates, keep `TINKER_API_KEY` check + `verify_frozen_dataset` + `require_clean_worktree` + `bind_execution_boundary`), `rl/track_a/train_rl.py` (drop mastery report; args `current_level`, `replay_levels` (validated by `expected_replay_levels`), `load_checkpoint_path`, `group_size=8`, `reward_policy="shaped_v3b"`, `wandb_project="pixcell-rl"`, `wandb_name`) +- Test: `rl/track_a/tests/test_entry_points.py` — configs build without network (builder construction + arg validation paths; no Tinker client) + +**Steps:** +- [ ] Verify in the pinned cookbook how `Config.load_checkpoint_path` loads (weights-only vs optimizer state) — `uv run python - <<'EOF'` inspecting `tinker_cookbook/rl/train.py` — and record the answer as a comment where `run.py` chains stages (old lineage chained on `/weights/final` sampler weights with a fresh optimizer per stage; match that). +- [ ] Write the two entry points (cookbook `Config` assembly copied from the current files minus ceremony; `wandb_project`/`wandb_name` passed through to the cookbook config's wandb fields — check exact field names in cookbook `train.Config`). +- [ ] Tests + `uv run pytest rl/ -q`; commit `"Lean SFT/RL entry points for the two-run campaign"`. + +### Task 7: `rl/track_a/run.py` — the two-run orchestrator + probe eval + +**Files:** +- Create: `rl/track_a/run.py`, `rl/track_a/probe_eval.py` +- Test: `rl/track_a/tests/test_run_plan.py` (stage plan construction, probe selection determinism — no network) + +**Interfaces:** +- `probe_eval.select_probe_tasks(dataset_root, per_level=16) -> list[TaskRecord]`: depth/validation, per level sorted by `sampler.opaque_id`, first 16 — deterministic, no seed. +- `probe_eval.run_probe(checkpoint_or_base: str, tasks, *, out_path) -> dict`: samples 1 completion/task at T=1.0 max_tokens 4096 via `tinker.ServiceClient` sampling client (base model name or `tinker://` path), evaluates through `PixCellEvaluator(require_isolation=True)` batch, writes JSON `{per_level: {level: {executable_rate, mean_iou, mean_shaped}}, overall: …, checkpoint, timestamp}`. +- `run.py` CLI (chz): `--run {A,B}`, `--smoke`, `--confirm-spend PIXCELL_RUNS_V2`, `--runs-root` (default `/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2`, fallback `~/pixcell-training/runs-v2`), `--wandb-project pixcell-rl`. Stage plans: + - Run A: `sft-l0` (levels=L0, 2 epochs) → sanity gate (probe on 8 L0 validation tasks: `executable ≥ 6/8` and `mean_iou > base_probe_l0_mean`, else abort) → `rl-l0 … rl-l4` (30 steps each, replay prefix, chained checkpoints). + - Run B: `rl-l0 … rl-l4` from base. + - Smoke: Run B's `rl-l0` only, `max_steps=2`, `groups_per_batch=2`, canaries 8, then print measured token counts + extrapolated full-campaign cost from the smoke `metrics.jsonl`. + - Probe eval runs at campaign start (base model) and after every stage; results + resolved config + git SHA + `TaskBaseline` dump for all touched tasks written into `//provenance/`. +- [ ] Implement; stage-plan tests green; commit `"Two-run orchestrator with fixed cross-run probe eval"`. + +### Task 8: SFT data audit + anisotropy measurement + +**Files:** +- Create: `rl/track_a/data_audit.py` (invoked by `run.py` before SFT; also standalone) +- Possibly modify: `rl/common/prompt.py` (+ its tests) — only if measurement says so + +**Steps:** +- [ ] `data_audit.py`: for the SFT levels, render every `(prompt, label)` through the real renderer; report max/percentile token lengths; **hard-fail if any label would truncate at `max_length`**; dump 3 decoded datums (text + weight mask summary) to the run dir for eyeball inspection. +- [ ] Anisotropy: one-off script over depth/train — per row, model-image ink bbox aspect vs `footprint_um` aspect; report the distribution of the ratio. If ≥ 10% of rows exceed 1.6×, add the old scale note to `build_prompt_text` (contract bump to `pixcell-direct-reconstruction-v3`, computed only from model-visible inputs: model image ink bbox + footprint), update `test_prompt_contract.py` accordingly; else record the measurement in the run provenance and leave the prompt at v2. +- [ ] Commit `"SFT data audit + anisotropy measurement"` (+ prompt v3 if taken, its own commit). + +### Task 9: full-surface green + README + spec sync + +- [ ] `uv run pytest tests/ rl/ release/ -q` (the CI surface) and `uv run ruff check .` — fix all fallout. +- [ ] `rl/README.md` and `rl/track_a/README.md` rewritten to describe exactly what exists now (the reward contract table must show `shaped_v3b` with the formula; the two runs; the probe protocol). +- [ ] Update the design spec's §2 if any decision changed during implementation (e.g., prompt v3 taken or not). +- [ ] Commit `"Document the clean training path"`. + +--- + +## Self-review checklist (run after Task 9) +- Spec §2.1 ↔ Tasks 1–4; §2.2 ↔ Tasks 4/6; §2.3 ↔ Task 7 (+ SFT gate); §2.4 ↔ Task 7 probe; §2.5 ↔ Task 7 provenance + Task 6 preflight; §2.7 ↔ Task 5; §2.8 ↔ Task 8. Launch/monitoring are tracker tasks 9–12, not this plan. +- Grep the plan for TBD/TODO — none. +- Type consistency: `TaskBaseline` fields used in Task 3 match Task 1; `reward_for_policy(..., baseline=)` matches Task 4's call; probe JSON keys match Task 7. From ba1289bbe057479f51d1aa0f10a9b423632da0a0 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 18:56:10 -0400 Subject: [PATCH 03/24] Add rectangle-baseline computation to rl/common The zero point of the shaped reward: what a solid rectangle filling the target's calibrated ink bbox scores, plus the boundary chamfer of that rectangle and the footprint diagonal. Ported from the private library's reward geometry; computed on the evaluator's own calibration and cached per target_image_sha256 (the dataset is frozen). Honors: 73143346a --- rl/common/baselines.py | 70 ++++++++++++++++++++++++ rl/common/geometry.py | 43 +++++++++++++++ rl/common/tests/test_baselines.py | 89 +++++++++++++++++++++++++++++++ 3 files changed, 202 insertions(+) create mode 100644 rl/common/baselines.py create mode 100644 rl/common/geometry.py create mode 100644 rl/common/tests/test_baselines.py diff --git a/rl/common/baselines.py b/rl/common/baselines.py new file mode 100644 index 00000000..561aef1c --- /dev/null +++ b/rl/common/baselines.py @@ -0,0 +1,70 @@ +"""Per-task rectangle baselines: the zero point of the shaped reward. + +``iou_rect``/``dice_rect`` are what a solid rectangle filling the target's +calibrated ink bounding box would score — the degenerate answer the old +campaign's policy demonstrably discovered. The shaped reward normalizes +against these so area-filling earns nothing and one reward scale means the +same thing on every task. The dataset is frozen, so a baseline is a constant +of the release; it is computed once per ``target_image_sha256`` and cached. +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np + +from rl.common.contracts import VerifierReference +from rl.common.geometry import boundary_mask, chamfer_sym_um + + +@dataclass(frozen=True) +class TaskBaseline: + iou_rect: float + dice_rect: float + chamfer_rect_boundary_um: float | None + diag_um: float + + +def rect_baseline(reference: VerifierReference) -> TaskBaseline: + """Score the bbox-rectangle fill against the target, on the evaluator's + own calibration (one geometry for baseline and score).""" + + from rl.common.evaluator import _prepare_reference + + prepared = _prepare_reference(reference) + array = np.asarray(prepared.target) + threshold = prepared.calibration["white_threshold"] + x0, y0, x1, y1 = prepared.calibration["bbox"] + target = array < threshold + rectangle = np.zeros_like(target) + rectangle[y0:y1, x0:x1] = True + intersection = float(np.logical_and(target, rectangle).sum()) + union = float(np.logical_or(target, rectangle).sum()) + denominator = float(target.sum() + rectangle.sum()) + scale_x, scale_y = prepared.calibration["scale_px_per_um"] + um_per_px = 2.0 / (scale_x + scale_y) + chamfer_b, _, _ = chamfer_sym_um( + boundary_mask(rectangle), + boundary_mask(target), + um_per_px, + ) + length_um, width_um = prepared.calibration["footprint_um"] + return TaskBaseline( + iou_rect=intersection / union if union else 0.0, + dice_rect=2.0 * intersection / denominator if denominator else 0.0, + chamfer_rect_boundary_um=( + None if chamfer_b == float("inf") else float(chamfer_b) + ), + diag_um=float((length_um * length_um + width_um * width_um) ** 0.5), + ) + + +_CACHE: dict[str, TaskBaseline] = {} + + +def cached_rect_baseline(reference: VerifierReference) -> TaskBaseline: + key = reference.target_image_sha256 + if key not in _CACHE: + _CACHE[key] = rect_baseline(reference) + return _CACHE[key] diff --git a/rl/common/geometry.py b/rl/common/geometry.py new file mode 100644 index 00000000..c511be22 --- /dev/null +++ b/rl/common/geometry.py @@ -0,0 +1,43 @@ +"""Distance-transform geometry over boolean masks. Measure-only. + +Ported verbatim from the private library's ``rl/reward/geometry.py`` — the +implementation behind the boundary-chamfer "teacher" term of the shaped +reward that produced the recorded learning runs. +""" + +from __future__ import annotations + +import numpy as np +from scipy.ndimage import binary_erosion, distance_transform_edt + + +def boundary_mask(mask: np.ndarray) -> np.ndarray: + """1px outline of a boolean mask: ``mask & ~erode(mask)``. + + For a 1px-thin mask, erosion is all-False, so the boundary IS the mask; + no special case needed. + """ + + return mask & ~binary_erosion(mask) + + +def chamfer_sym_um( + mask_a: np.ndarray, + mask_b: np.ndarray, + um_per_px: float, +) -> tuple[float, float, float]: + """Symmetric mean nearest-neighbor distance (µm) between two boolean masks. + + Returns ``(chamfer_um, a_to_b_um, b_to_a_um)``. An empty prediction or an + empty target returns ``(inf, inf, inf)`` — callers treat that as worst + case, never as an error. + """ + + if not mask_a.any() or not mask_b.any(): + infinite = float("inf") + return infinite, infinite, infinite + dist_to_b = distance_transform_edt(~mask_b) + dist_to_a = distance_transform_edt(~mask_a) + a_to_b = float(dist_to_b[mask_a].mean()) * um_per_px + b_to_a = float(dist_to_a[mask_b].mean()) * um_per_px + return 0.5 * (a_to_b + b_to_a), a_to_b, b_to_a diff --git a/rl/common/tests/test_baselines.py b/rl/common/tests/test_baselines.py new file mode 100644 index 00000000..0e9b531d --- /dev/null +++ b/rl/common/tests/test_baselines.py @@ -0,0 +1,89 @@ +"""Rectangle-baseline computation: the zero point of the shaped reward.""" + +from __future__ import annotations + +import hashlib +import io +import math + +import numpy as np +import pytest +from PIL import Image + +from rl.common.baselines import cached_rect_baseline, rect_baseline +from rl.common.contracts import VerifierReference +from rl.common.geometry import boundary_mask, chamfer_sym_um + + +def _reference(mask: np.ndarray, footprint_um: tuple[float, float]) -> VerifierReference: + """PNG-encode a boolean ink mask (True = black material) as a target reference.""" + + array = np.where(mask, 0, 255).astype(np.uint8) + image = Image.fromarray(array, mode="L") + buffer = io.BytesIO() + image.save(buffer, format="PNG") + payload = buffer.getvalue() + return VerifierReference( + target_image_bytes=payload, + footprint_um=footprint_um, + target_image_sha256=hashlib.sha256(payload).hexdigest(), + ) + + +def test_solid_rectangle_target_is_degenerate() -> None: + mask = np.zeros((80, 100), dtype=bool) + mask[20:60, 20:80] = True + baseline = rect_baseline(_reference(mask, (30.0, 20.0))) + assert baseline.iou_rect == pytest.approx(1.0) + assert baseline.dice_rect == pytest.approx(1.0) + assert baseline.diag_um == pytest.approx(math.hypot(30.0, 20.0)) + + +def test_l_shape_baseline_matches_hand_computation() -> None: + mask = np.zeros((80, 100), dtype=bool) + mask[20:60, 20:40] = True # vertical bar: 40 x 20 + mask[40:60, 20:80] = True # horizontal bar: 20 x 60, overlap 20 x 20 + ink = float(mask.sum()) + bbox_area = float(40 * 60) # ink bbox: rows 20:60, cols 20:80 + baseline = rect_baseline(_reference(mask, (30.0, 20.0))) + assert baseline.iou_rect == pytest.approx(ink / bbox_area) + assert baseline.dice_rect == pytest.approx(2.0 * ink / (ink + bbox_area)) + assert baseline.chamfer_rect_boundary_um is not None + assert baseline.chamfer_rect_boundary_um > 0.0 + assert baseline.diag_um == pytest.approx(math.hypot(30.0, 20.0)) + + +def test_cache_returns_identical_object_per_sha() -> None: + mask = np.zeros((40, 40), dtype=bool) + mask[10:30, 5:35] = True + reference = _reference(mask, (10.0, 5.0)) + first = cached_rect_baseline(reference) + second = cached_rect_baseline(reference) + assert first is second + + +def test_chamfer_of_shifted_row_is_exact() -> None: + a = np.zeros((5, 5), dtype=bool) + b = np.zeros((5, 5), dtype=bool) + a[2, 1:4] = True + b[3, 1:4] = True + chamfer, a_to_b, b_to_a = chamfer_sym_um(a, b, um_per_px=2.0) + assert a_to_b == pytest.approx(2.0) + assert b_to_a == pytest.approx(2.0) + assert chamfer == pytest.approx(2.0) + + +def test_chamfer_empty_mask_is_infinite() -> None: + empty = np.zeros((4, 4), dtype=bool) + full = np.ones((4, 4), dtype=bool) + chamfer, a_to_b, b_to_a = chamfer_sym_um(empty, full, um_per_px=1.0) + assert chamfer == float("inf") and a_to_b == float("inf") and b_to_a == float("inf") + + +def test_boundary_mask_of_solid_block_is_its_outline() -> None: + mask = np.zeros((6, 6), dtype=bool) + mask[1:5, 1:5] = True + outline = boundary_mask(mask) + assert outline[1, 1] and outline[1, 4] and outline[4, 1] and outline[4, 4] + assert not outline[2, 2] and not outline[3, 3] + assert outline.sum() == 12 From 5ff5f3c88706b263c1c6fe83ebf828e7b59cbf9b Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 18:57:35 -0400 Subject: [PATCH 04/24] Measure boundary chamfer in the evaluator Symmetric mean boundary distance (um, mean-axis scale) between the candidate render and the target, recorded as a diagnostic on every OK result. Empty-mask chamfer is stored as None, never inf, so the strict JSON diagnostics contract is preserved. This is the measurement behind the shaped reward's teacher term. Honors: 73143346a --- rl/common/evaluator.py | 15 +++++++++++++++ rl/common/tests/test_evaluator.py | 2 ++ 2 files changed, 17 insertions(+) diff --git a/rl/common/evaluator.py b/rl/common/evaluator.py index 4c0581f9..ec17ca91 100644 --- a/rl/common/evaluator.py +++ b/rl/common/evaluator.py @@ -40,6 +40,7 @@ ) from .contracts import VerifierReference +from .geometry import boundary_mask, chamfer_sym_um from .isolation import ( ExecutionBoundary, candidate_identity, @@ -795,6 +796,20 @@ def evaluate( prepared.calibration, ) metrics = compute_metrics(raster, prepared.target.copy()) + raster_mask = np.asarray(raster.convert("L")) < 245 + target_mask = np.asarray(prepared.target) < 245 + scale_x, scale_y = prepared.calibration["scale_px_per_um"] + chamfer_b, _, _ = chamfer_sym_um( + boundary_mask(raster_mask), + boundary_mask(target_mask), + 2.0 / (scale_x + scale_y), + ) + metrics = { + **metrics, + "chamfer_boundary_um": ( + None if chamfer_b == float("inf") else float(chamfer_b) + ), + } diagnostics = _json_diagnostic( { **metrics, diff --git a/rl/common/tests/test_evaluator.py b/rl/common/tests/test_evaluator.py index 1dc8dc04..e44ecb9a 100644 --- a/rl/common/tests/test_evaluator.py +++ b/rl/common/tests/test_evaluator.py @@ -31,6 +31,8 @@ def test_ground_truth_self_scores_at_full_precision(core_row, evaluator): assert result.dice is not None and result.dice > 0.98 assert result.iou != round(result.iou, 4) assert len(result.metrics["render_sha256"]) == 64 + chamfer = result.metrics["chamfer_boundary_um"] + assert chamfer is not None and 0.0 <= chamfer < 1.0 json.dumps(result.metrics, allow_nan=False) assert all( type(value) is bool From 6fc1bc5ce0f67d37ef8b91cef1bc77453a971bc3 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:00:14 -0400 Subject: [PATCH 05/24] Port the v3-b shaped reward (iou_n) as policy shaped_v3b The private library's proven training signal, constants byte-identical: 0.05 executable floor + 0.40*iou_n + 0.15*dice_n + 0.40*teacher_nb, with raw scoring on rectangle-degenerate targets (iou_rect >= 0.95). iou_n normalizes against the bbox-rectangle fill so area-filling earns nothing and one reward scale means the same thing on every task; the boundary-chamfer teacher pays for count progress IoU cannot see. SOURCE_REJECTED stays 0.0 (ship-mode purity semantics). Honors: 73143346a --- rl/track_a/reward.py | 146 +++++++++++++++++++++---- rl/track_a/tests/test_output_reward.py | 103 +++++++++++++++++ 2 files changed, 230 insertions(+), 19 deletions(-) diff --git a/rl/track_a/reward.py b/rl/track_a/reward.py index 442dab11..ff4a3640 100644 --- a/rl/track_a/reward.py +++ b/rl/track_a/reward.py @@ -1,23 +1,44 @@ -"""The deliberately small Track A reward policy.""" +"""The Track A reward policies. + +``shaped_v3b`` is the campaign policy: the private library's reward v3-b, +ported byte-for-byte in its constants. It pays only for structure beyond the +degenerate bbox-rectangle answer (``iou_n``/``dice_n`` — rectangle-baseline- +normalized excess IoU/Dice), adds a boundary-chamfer "teacher" term for +count/boundary progress IoU cannot see, keeps a 0.05 executable floor so a +valid-but-wrong program is distinguishable from a crash, and scores +rectangle-like targets raw (normalizing against a rectangle that IS the +answer would make every program look identical). + +Purity stays pre-execution: the evaluator returns ``SOURCE_REJECTED`` as a +MODEL failure, so an impure program earns 0.0 — the old ship-mode semantics +(a floor for impure programs trains the violation). + +``None`` means the rollout must be masked (or the run aborted for a broken +reference); it must never be converted to a policy reward of zero. +""" from __future__ import annotations import math +from rl.common.baselines import TaskBaseline from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus VALID_PROGRAM_FLOOR = 0.05 IOU_WEIGHT = 0.95 -REWARD_POLICIES = ("validity_floor", "raw_iou") +SHAPED_FLOOR = 0.05 +W_IOU3, W_DICE3, W_TEACHER = 0.40, 0.15, 0.40 +TAU_FRAC = 0.05 # tau = TAU_FRAC * diag_um (chamfer softness scale) +TEACHER_DENOM_FLOOR = 0.3 # floors the rect-subtraction denominator (1 - t2b_rect) +DEGENERATE_IOU_RECT = 0.95 # rect-like target: the rectangle already scores this IoU -def reward_for(result: EvaluationResult) -> float | None: - """Map measurement to reward without hiding evaluator failures. +REWARD_POLICIES = ("validity_floor", "raw_iou", "shaped_v3b") - ``None`` means the rollout must be masked (or the run aborted for a broken - reference); it must never be converted to a policy reward of zero. - """ + +def reward_for(result: EvaluationResult) -> float | None: + """Map measurement to reward without hiding evaluator failures.""" if result.status is EvaluationStatus.OK: if result.iou is None: @@ -28,27 +49,114 @@ def reward_for(result: EvaluationResult) -> float | None: return None +def norm_excess(x: float, base: float) -> float: + """Excess over the rectangle baseline, as a fraction of the remaining + headroom. 0 at (or below) the baseline, 1 at perfect.""" + + if base >= 1.0: + return 0.0 + return max(0.0, (x - base) / (1.0 - base)) + + +def _t2b(chamfer_um: float | None, tau: float) -> float: + """exp(-chamfer/tau) closeness-to-boundary score. None/inf chamfer (an + empty mask, a failed render) scores 0 — worst case, never a crash.""" + + if chamfer_um is None or chamfer_um == float("inf"): + return 0.0 + return math.exp(-float(chamfer_um) / tau) + + +def _validated_scores(result: EvaluationResult) -> tuple[float, float]: + if result.iou is None or result.dice is None: + raise ValueError("successful evaluation has no IoU/Dice") + iou = float(result.iou) + dice = float(result.dice) + for value in (iou, dice): + if not math.isfinite(value) or not 0.0 <= value <= 1.0: + raise ValueError("successful evaluation has an invalid measurement") + return iou, dice + + +def shaped_components( + result: EvaluationResult, + baseline: TaskBaseline, +) -> dict[str, float]: + """The logging breakdown of ``shaped_v3b``. Zeros for non-OK results.""" + + if result.status is not EvaluationStatus.OK: + return { + "iou_n": 0.0, + "dice_n": 0.0, + "t2b": 0.0, + "teacher_nb": 0.0, + "degenerate": float(baseline.iou_rect >= DEGENERATE_IOU_RECT), + } + iou, dice = _validated_scores(result) + diag = baseline.diag_um if baseline.diag_um > 0 else 1e-9 + tau = max(TAU_FRAC * diag, 1e-9) + chamfer = result.metrics.get("chamfer_boundary_um") + chamfer_rect = baseline.chamfer_rect_boundary_um + t2b = _t2b(chamfer, tau) + t2b_rect = _t2b(chamfer_rect if chamfer_rect is not None else diag, tau) + teacher_nb = max(0.0, t2b - t2b_rect) / max(1.0 - t2b_rect, TEACHER_DENOM_FLOOR) + return { + "iou_n": norm_excess(iou, baseline.iou_rect), + "dice_n": norm_excess(dice, baseline.dice_rect), + "t2b": t2b, + "teacher_nb": teacher_nb, + "degenerate": float(baseline.iou_rect >= DEGENERATE_IOU_RECT), + } + + +def _shaped_v3b(result: EvaluationResult, baseline: TaskBaseline) -> float: + iou, dice = _validated_scores(result) + parts = shaped_components(result, baseline) + if baseline.iou_rect >= DEGENERATE_IOU_RECT: + return ( + SHAPED_FLOOR + + W_IOU3 * iou + + W_DICE3 * dice + + W_TEACHER * parts["t2b"] + ) + return ( + SHAPED_FLOOR + + W_IOU3 * parts["iou_n"] + + W_DICE3 * parts["dice_n"] + + W_TEACHER * parts["teacher_nb"] + ) + + def reward_for_policy( result: EvaluationResult, *, policy: str, + baseline: TaskBaseline | None = None, ) -> float | None: """Apply one explicit reward policy while preserving fault attribution.""" if policy == "validity_floor": return reward_for(result) - if policy != "raw_iou": - raise ValueError(f"unknown reward policy: {policy!r}") - if result.status is EvaluationStatus.OK: - if result.iou is None: - raise ValueError("successful evaluation has no IoU") - iou = float(result.iou) - if not math.isfinite(iou) or not 0.0 <= iou <= 1.0: - raise ValueError("successful evaluation has an invalid IoU") - return iou - if result.attribution is Attribution.MODEL: - return 0.0 - return None + if policy == "raw_iou": + if result.status is EvaluationStatus.OK: + if result.iou is None: + raise ValueError("successful evaluation has no IoU") + iou = float(result.iou) + if not math.isfinite(iou) or not 0.0 <= iou <= 1.0: + raise ValueError("successful evaluation has an invalid IoU") + return iou + if result.attribution is Attribution.MODEL: + return 0.0 + return None + if policy == "shaped_v3b": + if result.status is EvaluationStatus.OK: + if baseline is None: + raise ValueError("shaped_v3b requires a per-task baseline") + return float(_shaped_v3b(result, baseline)) + if result.attribution is Attribution.MODEL: + return 0.0 + return None + raise ValueError(f"unknown reward policy: {policy!r}") def group_rewards(results: list[EvaluationResult]) -> list[float | None]: diff --git a/rl/track_a/tests/test_output_reward.py b/rl/track_a/tests/test_output_reward.py index 7610234d..84a4bc2f 100644 --- a/rl/track_a/tests/test_output_reward.py +++ b/rl/track_a/tests/test_output_reward.py @@ -149,3 +149,106 @@ async def submit(_reference, _completion): assert sample_records[0]["sampling"]["raw_token_ids"] == [1, 2] assert sample_records[0]["program"] == "print('sample')" assert evaluated_records == [] + + +def _ok(iou: float, dice: float, chamfer: float | None) -> EvaluationResult: + return EvaluationResult( + EvaluationStatus.OK, + Attribution.MODEL, + iou=iou, + dice=dice, + metrics={"chamfer_boundary_um": chamfer}, + ) + + +def _baseline( + iou_rect: float = 0.5, + dice_rect: float = 0.6, + chamfer_rect: float | None = 4.0, + diag_um: float = 20.0, +): + from rl.common.baselines import TaskBaseline + + return TaskBaseline( + iou_rect=iou_rect, + dice_rect=dice_rect, + chamfer_rect_boundary_um=chamfer_rect, + diag_um=diag_um, + ) + + +def test_shaped_normalizes_iou_against_rectangle(): + # dice at its rect value and chamfer at the rect chamfer: only iou_n pays. + baseline = _baseline(iou_rect=0.5, dice_rect=0.6, chamfer_rect=4.0) + result = _ok(iou=0.75, dice=0.6, chamfer=4.0) + reward = reward_for_policy(result, policy="shaped_v3b", baseline=baseline) + assert reward == pytest.approx(0.05 + 0.40 * ((0.75 - 0.5) / 0.5)) + + +def test_shaped_rect_equivalent_program_earns_exactly_the_floor(): + baseline = _baseline(iou_rect=0.5, dice_rect=0.6, chamfer_rect=4.0) + result = _ok(iou=0.5, dice=0.6, chamfer=4.0) + assert reward_for_policy( + result, policy="shaped_v3b", baseline=baseline + ) == pytest.approx(0.05) + + +def test_shaped_degenerate_target_scores_raw(): + import math as _math + + baseline = _baseline(iou_rect=0.96, dice_rect=0.97, chamfer_rect=0.1, diag_um=20.0) + result = _ok(iou=0.9, dice=0.92, chamfer=0.5) + tau = 0.05 * 20.0 + expected = 0.05 + 0.40 * 0.9 + 0.15 * 0.92 + 0.40 * _math.exp(-0.5 / tau) + assert reward_for_policy( + result, policy="shaped_v3b", baseline=baseline + ) == pytest.approx(expected) + + +def test_shaped_teacher_denominator_floor(): + import math as _math + + # tau = 1.0 (diag 20); t2b = 0.9, t2b_rect = 0.8 -> 0.1 / max(0.2, 0.3) + baseline = _baseline( + iou_rect=0.5, + dice_rect=0.6, + chamfer_rect=-_math.log(0.8), + diag_um=20.0, + ) + result = _ok(iou=0.5, dice=0.6, chamfer=-_math.log(0.9)) + assert reward_for_policy( + result, policy="shaped_v3b", baseline=baseline + ) == pytest.approx(0.05 + 0.40 * (0.1 / 0.3)) + + +def test_shaped_model_failures_are_zero_and_faults_are_masked(): + baseline = _baseline() + crash = EvaluationResult(EvaluationStatus.RUNTIME_ERROR, Attribution.MODEL) + rejected = EvaluationResult(EvaluationStatus.SOURCE_REJECTED, Attribution.MODEL) + fault = EvaluationResult( + EvaluationStatus.MEASUREMENT_ERROR, Attribution.EVALUATOR, retryable=True + ) + assert reward_for_policy(crash, policy="shaped_v3b", baseline=baseline) == 0.0 + assert reward_for_policy(rejected, policy="shaped_v3b", baseline=baseline) == 0.0 + assert reward_for_policy(fault, policy="shaped_v3b", baseline=baseline) is None + + +def test_shaped_missing_chamfer_scores_teacher_zero(): + baseline = _baseline(iou_rect=0.5, dice_rect=0.6, chamfer_rect=4.0) + result = _ok(iou=0.5, dice=0.6, chamfer=None) + assert reward_for_policy( + result, policy="shaped_v3b", baseline=baseline + ) == pytest.approx(0.05) + + +def test_shaped_requires_a_baseline(): + with pytest.raises(ValueError): + reward_for_policy(_ok(0.5, 0.5, 1.0), policy="shaped_v3b") + + +def test_shaped_perfect_program_reaches_one(): + baseline = _baseline(iou_rect=0.3, dice_rect=0.4, chamfer_rect=50.0, diag_um=20.0) + result = _ok(iou=1.0, dice=1.0, chamfer=0.0) + assert reward_for_policy( + result, policy="shaped_v3b", baseline=baseline + ) == pytest.approx(1.0) From e55a84adb8338af842b04ac6a3d9f69ae06a24e9 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:00:51 -0400 Subject: [PATCH 06/24] Spec: extend to the 2x2 run matrix (user direction) A/B stay at the no-think 4096 operating point; C/D repeat the +-L0-SFT pair at the thinking 60k operating point RC-H05 measured at 0.218 zero-shot. D's SFT is sanity-gated against the renderer-collapse precedent; smoke runs price both operating points before full launch. --- .../2026-07-28-rl-clean-rebuild-design.md | 47 +++++++++++++++---- 1 file changed, 37 insertions(+), 10 deletions(-) diff --git a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md index ba0005dc..d36d7b45 100644 --- a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md +++ b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md @@ -121,14 +121,37 @@ preflight (dataset digest check against `freeze.json`, sandbox binding, clean-tr | Curriculum | staged L0→L4, **30 steps/level**, 80/20 current/replay via the existing deterministic five-step cycle | old stage budgets (30/35/30) + this tree's tested scheduler; no mastery gates, no approvals | | Checkpoints | state every 5 steps; sampler checkpoint at each level boundary + final | enough to resume and to evaluate; nothing else | -### 2.3 The two runs (the ablation the paper explicitly defers) -- **Run A (`l0sft-rl`)**: SFT on the 642 L0 `depth/train` rows (batch 64, lr 1e-4 - linear, `LAST_ASSISTANT_MESSAGE`, 2 epochs ≈ 22 steps, prompt byte-identical to RL) → - RL L0→L4, 150 steps. -- **Run B (`base-rl`)**: raw base → RL L0→L4, 150 steps. -Identical RL recipe, schedule, reward, and eval ⇒ a controlled ±L0-SFT comparison at -matched RL budget — precisely the "controlled ablation" and "untouched training -evaluations" the paper names as the open door (`arxiv/main.tex:879-881, :932-942`). +### 2.3 The four runs — a 2×2: (±L0-SFT) × (no-think / think) [user-extended 2026-07-28] +The failed campaign's one useful measurement is RC-H05: zero-shot Qwen with thinking +at a 60k output ceiling scores **0.218 mean IoU@1** on F1–F8 under the corrected v2 +prompt — already near the old post-training result (0.24). RL from that operating +point starts from a far higher launchpad, and the campaign's two 6-step runs sampled +with thinking@60k through this exact Env, so the path is execution-proven. Per the +user's direction, the ±SFT pair runs at both operating points, all four simultaneous: + +| Run | Operating point | Arm | +|---|---|---| +| **A** `runA-l0sft-rl` | `qwen3_5_disable_thinking`, max_tokens 4096 | L0-SFT → RL L0→L4 (150 steps) | +| **B** `runB-base-rl` | `qwen3_5_disable_thinking`, max_tokens 4096 | base → RL L0→L4 (150 steps) | +| **C** `runC-think-base-rl` | `qwen3_5` (thinking), max_tokens 60000 | base → RL L0→L4 (150 steps) | +| **D** `runD-think-l0sft-rl` | `qwen3_5` (thinking), max_tokens 60000 | L0-SFT → RL L0→L4 (150 steps) | + +Within each operating point the pair is a controlled ±L0-SFT comparison at matched RL +budget — the "controlled ablation" and "untouched training evaluations" the paper +defers (`arxiv/main.tex:879-881, :932-942`). Across operating points, A/B vs C/D +measures what the reasoning budget buys under identical reward, data, and schedule. + +**Run D's SFT is gated.** The renderer diagnostic showed code-only SFT collapsing +checkpoints under the thinking renderer (0/8 executable) — but that was mixed-level +SFT, where L1–L4 labels plausibly truncated at `max_length`; L0-only SFT is the clean +test of the renderer hypothesis. D therefore runs its L0-SFT (code-only labels, +thinking renderer) and proceeds to RL **only if** the post-SFT sanity probe beats the +base-model thinking probe; otherwise D falls back to RL-from-base (C's config, second +seed) and the SFT collapse is reported as a finding. SFT for A stays as designed: +642 L0 `depth/train` rows, batch 64, lr 1e-4 linear, `LAST_ASSISTANT_MESSAGE`, +2 epochs ≈ 22 steps, prompt byte-identical to RL. Image stays 1440 for all four +(dataset-native; the protocol's 1920 was a no-op since no dataset image exceeds 1440). +Prompt-identity rule: each SFT and the RL that resumes it share one renderer. **SFT hygiene (the mixed-SFT autopsy made this mandatory):** 1. decode-and-inspect rendered datums (text + train-mask) before spending; @@ -163,8 +186,12 @@ store roots. Run artifacts live outside the repo under the existing training vol Runs launch in detached host tmux panes (not inside an agent session), logging to the run dir; monitoring reads `metrics.jsonl` and rollout summaries. Keys are sourced from the existing env file at launch, never printed. Smoke first: 2 GRPO steps at G=8×B=2 on -L0 to validate the full loop and measure real per-step cost; the extrapolated cost of -both full runs is reported before the full launches go out. +L0 **per operating point** (one no-think smoke, one think smoke — thinking decode is +~3–10× per rollout, so C/D cost must be measured, not assumed); the extrapolated cost +of all four full runs is reported before the full launches go out. Four simultaneous +runs share this machine's Docker evaluator: per-candidate containers are capped at +1 CPU and the batcher bounds in-flight evaluations per run, so evaluation queues +rather than thrashes; sampling, not evaluation, dominates step wall-clock. ### 2.7 Deletions (this branch) - `rl/studies/` — all three studies + both extensions (the campaign ceremony, the From 2532a4dddc76d80c349a1b53406f14d24fc647ba Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:01:58 -0400 Subject: [PATCH 07/24] Default Track A to the shaped reward at G=8 TrackAEnv computes the task's cached rectangle baseline for the shaped policy, logs the iou_n/teacher_nb/degenerate breakdown into step metrics, and the RL builder defaults change to reward_policy shaped_v3b with 8 rollouts per group (the old lineage's group size; G=4 halves the within-group signal GRPO feeds on). Honors: 73143346a --- rl/track_a/tests/test_env_shaped_reward.py | 79 ++++++++++++++++++++++ rl/track_a/tinker_data.py | 56 ++++++++++----- 2 files changed, 118 insertions(+), 17 deletions(-) create mode 100644 rl/track_a/tests/test_env_shaped_reward.py diff --git a/rl/track_a/tests/test_env_shaped_reward.py b/rl/track_a/tests/test_env_shaped_reward.py new file mode 100644 index 00000000..d1799802 --- /dev/null +++ b/rl/track_a/tests/test_env_shaped_reward.py @@ -0,0 +1,79 @@ +"""The Env pays the shaped reward with the task's own rectangle baseline.""" + +from __future__ import annotations + +import asyncio + +import pytest + +pytest.importorskip("tinker", reason="Tinker SDK env not installed") +pytest.importorskip("chz", reason="chz env not installed") + +from rl.common.baselines import cached_rect_baseline +from rl.common.contracts import task_from_row +from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus +from rl.track_a import tinker_data +from rl.track_a.reward import reward_for_policy +from rl.track_a.tinker_data import TrackAEnv + + +class _Termination: + is_clean = True + + +class _StubTokenizer: + def decode(self, tokens): + return "```python\nimport gdsfactory as gf\n```" + + +class _StubRenderer: + tokenizer = _StubTokenizer() + + def parse_response(self, tokens): + return ( + { + "role": "assistant", + "content": [{"type": "text", "text": self.tokenizer.decode(tokens)}], + }, + _Termination(), + ) + + +class _StubBatcher: + def __init__(self, result: EvaluationResult) -> None: + self._result = result + + async def submit(self, reference, completion): + return self._result + + +def test_env_step_pays_shaped_reward_with_task_baseline(core_row, monkeypatch): + task = task_from_row(core_row) + crafted = EvaluationResult( + EvaluationStatus.OK, + Attribution.MODEL, + iou=0.62, + dice=0.7, + metrics={"chamfer_boundary_um": 1.5}, + ) + monkeypatch.setattr(tinker_data, "_batcher", lambda: _StubBatcher(crafted)) + env = TrackAEnv(task, _StubRenderer(), 1440, reward_policy="shaped_v3b") + step = asyncio.run(env.step([1, 2, 3])) + baseline = cached_rect_baseline(task.reference) + expected = reward_for_policy(crafted, policy="shaped_v3b", baseline=baseline) + assert step.reward == pytest.approx(expected) + assert step.reward > 0.05 + assert step.metrics["iou_n"] == pytest.approx( + max(0.0, (0.62 - baseline.iou_rect) / (1.0 - baseline.iou_rect)) + ) + assert "teacher_nb" in step.metrics and "degenerate" in step.metrics + + +def test_env_step_zero_for_model_failure_under_shaped_policy(core_row, monkeypatch): + task = task_from_row(core_row) + crafted = EvaluationResult(EvaluationStatus.RUNTIME_ERROR, Attribution.MODEL) + monkeypatch.setattr(tinker_data, "_batcher", lambda: _StubBatcher(crafted)) + env = TrackAEnv(task, _StubRenderer(), 1440, reward_policy="shaped_v3b") + step = asyncio.run(env.step([1, 2, 3])) + assert step.reward == 0.0 + assert step.metrics["iou_n"] == 0.0 diff --git a/rl/track_a/tinker_data.py b/rl/track_a/tinker_data.py index 00634a36..09c7bbfb 100644 --- a/rl/track_a/tinker_data.py +++ b/rl/track_a/tinker_data.py @@ -38,6 +38,7 @@ ) from tinker_cookbook.tokenizer_utils import get_tokenizer +from rl.common.baselines import cached_rect_baseline from rl.common.batcher import AsyncEvaluationBatcher from rl.common.contracts import TaskRecord from rl.common.dataset_io import load_tasks @@ -51,7 +52,11 @@ ) from rl.common.preprocess import model_image from rl.common.prompt import build_prompt_text -from rl.track_a.reward import REWARD_POLICIES, reward_for_policy +from rl.track_a.reward import ( + REWARD_POLICIES, + reward_for_policy, + shaped_components, +) from rl.track_a.recipe import ( expected_replay_levels, normalize_level, @@ -344,8 +349,17 @@ async def step(self, action, *, extra=None): } if self.sample_recorder is not None: self.sample_recorder(sample_record) + baseline = ( + cached_rect_baseline(self.task.reference) + if self.reward_policy == "shaped_v3b" + else None + ) result = await _batcher().submit(self.task.reference, completion) - reward = reward_for_policy(result, policy=self.reward_policy) + reward = reward_for_policy( + result, + policy=self.reward_policy, + baseline=baseline, + ) if reward is None: if result.attribution is Attribution.REFERENCE: raise BrokenReferenceError(result.error or result.status.value) @@ -367,24 +381,32 @@ async def step(self, action, *, extra=None): }, } ) + step_metrics = { + "iou": float(result.iou or 0.0), + "dice": float(result.dice or 0.0), + "program_valid": float(result.status is EvaluationStatus.OK), + "source_rejected": float( + result.status is EvaluationStatus.SOURCE_REJECTED + ), + "latency_seconds": float(result.latency_seconds), + "channel_parse_complete": float(termination.is_clean), + "length_capped": float( + getattr(extra, "stop_reason", None) == "length" + ), + } + if baseline is not None: + parts = shaped_components(result, baseline) + step_metrics.update( + iou_n=parts["iou_n"], + teacher_nb=parts["teacher_nb"], + degenerate=parts["degenerate"], + ) return StepResult( reward=reward, episode_done=True, next_observation=tinker.ModelInput.from_ints([]), next_stop_condition=[], - metrics={ - "iou": float(result.iou or 0.0), - "dice": float(result.dice or 0.0), - "program_valid": float(result.status is EvaluationStatus.OK), - "source_rejected": float( - result.status is EvaluationStatus.SOURCE_REJECTED - ), - "latency_seconds": float(result.latency_seconds), - "channel_parse_complete": float(termination.is_clean), - "length_capped": float( - getattr(extra, "stop_reason", None) == "length" - ), - }, + metrics=step_metrics, ) @@ -513,7 +535,7 @@ def _level_filter(tasks: list[TaskRecord], levels: str) -> list[TaskRecord]: class TrackARLDatasetBuilder(RLDatasetBuilder): dataset_root: str batch_size: int = 8 - group_size: int = 4 + group_size: int = 8 model_name: str = DEFAULT_MODEL renderer_name: str = DEFAULT_RENDERER model_effort: float | None = None @@ -523,7 +545,7 @@ class TrackARLDatasetBuilder(RLDatasetBuilder): validation_canaries: int = 40 attempt_recorder: Callable[[Mapping[str, object]], None] | None = None sample_recorder: Callable[[Mapping[str, object]], None] | None = None - reward_policy: str = "validity_floor" + reward_policy: str = "shaped_v3b" require_prefix_replay: bool = True schedule_seed: int = 10_000 attempt_metadata: Mapping[str, object] | None = None From c96a33196486b22a3db8e485f3cd149fa836974f Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:05:12 -0400 Subject: [PATCH 08/24] Make the env shaped-reward test degenerate-target-robust --- rl/track_a/tests/test_env_shaped_reward.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rl/track_a/tests/test_env_shaped_reward.py b/rl/track_a/tests/test_env_shaped_reward.py index d1799802..aeb9103e 100644 --- a/rl/track_a/tests/test_env_shaped_reward.py +++ b/rl/track_a/tests/test_env_shaped_reward.py @@ -13,7 +13,7 @@ from rl.common.contracts import task_from_row from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus from rl.track_a import tinker_data -from rl.track_a.reward import reward_for_policy +from rl.track_a.reward import norm_excess, reward_for_policy from rl.track_a.tinker_data import TrackAEnv @@ -64,7 +64,7 @@ def test_env_step_pays_shaped_reward_with_task_baseline(core_row, monkeypatch): assert step.reward == pytest.approx(expected) assert step.reward > 0.05 assert step.metrics["iou_n"] == pytest.approx( - max(0.0, (0.62 - baseline.iou_rect) / (1.0 - baseline.iou_rect)) + norm_excess(0.62, baseline.iou_rect) ) assert "teacher_nb" in step.metrics and "degenerate" in step.metrics From 028c219ec7d2b55596e70b7ee09bfbbee033da01 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:16:16 -0400 Subject: [PATCH 09/24] Remove the studies/ceremony apparatus from rl/ Deletes rl/studies (three baseline/campaign studies + two extensions), rl/evaluation (the zero-shot harness and its W&B mirror), rl/track_b (the Inkling TML shim), rl/track_c (an empty reservation), and track_a's recipe-lock/mastery/promotion surface. The launch-night record shows this machinery aborting its own runs three times and relaxing its own gates; none of it touches the learning signal. Level helpers move to curriculum.py; preflight keeps the real gates (frozen-release digests, git state, whitelist crosscheck, token audit, ground-truth positive control now scored through shaped_v3b) decoupled from the recipe lock; train_sft/train_rl become lean single-stage entry points with prompt identity between SFT and the RL that resumes it. Honors: 73143346a --- rl/evaluation/__init__.py | 1 - rl/evaluation/attempt_store.py | 869 ------ rl/evaluation/protocol.py | 508 --- rl/evaluation/runner.py | 1240 -------- rl/evaluation/samplers.py | 531 ---- rl/evaluation/summarize.py | 269 -- rl/evaluation/tasks.py | 346 -- rl/evaluation/tests/test_attempt_store.py | 239 -- rl/evaluation/tests/test_protocol.py | 215 -- rl/evaluation/tests/test_runner.py | 1104 ------- rl/evaluation/tests/test_samplers.py | 450 --- rl/evaluation/tests/test_tasks.py | 72 - rl/evaluation/tests/test_tracking.py | 382 --- rl/evaluation/tracking.py | 430 --- rl/requirements-lock.txt | 1 - rl/stack.json | 1 - rl/studies/__init__.py | 1 - .../representation_curriculum_v1/README.md | 160 - .../representation_curriculum_v1/__init__.py | 1 - .../audit_phase0.py | 322 -- .../extensions/__init__.py | 1 - .../extensions/qwen32/README.md | 29 - .../extensions/qwen32/__init__.py | 1 - .../extensions/qwen32/freeze.py | 228 -- .../extensions/qwen32/protocol.json | 116 - .../extensions/qwen32/run_baseline.py | 57 - .../extensions/qwen32/test_freeze.py | 99 - .../extensions/qwen32/test_protocol.py | 109 - .../extensions/qwen60/README.md | 35 - .../extensions/qwen60/__init__.py | 1 - .../extensions/qwen60/freeze.py | 228 -- .../extensions/qwen60/protocol.json | 123 - .../extensions/qwen60/run_baseline.py | 57 - .../extensions/qwen60/test_freeze.py | 119 - .../extensions/qwen60/test_protocol.py | 225 -- .../freeze_phase1.py | 1027 ------ .../protocol.json | 260 -- .../run_baseline.py | 64 - .../task_manifest.json | 631 ---- .../tests/test_freeze_phase1.py | 607 ---- .../representation_curriculum_v2/README.md | 114 - .../representation_curriculum_v2/__init__.py | 1 - .../audit_context.py | 117 - .../audit_phase0.py | 492 --- .../freeze_phase1.py | 1414 --------- .../protocol.json | 129 - .../run_baseline.py | 62 - .../task_manifest.json | 148 - .../tests/__init__.py | 1 - .../tests/test_audit_phase0.py | 221 -- .../tests/test_freeze_phase1.py | 842 ----- .../tests/test_v2_protocol.py | 201 -- .../representation_training_v1/README.md | 384 --- .../representation_training_v1/__init__.py | 1 - .../audit_phase0.py | 82 - .../dataset_binding.py | 63 - .../evaluate_checkpoint.py | 8 - .../evaluate_final_benchmark.py | 8 - .../evaluate_renderer_diagnostic.py | 8 - .../representation_training_v1/evaluation.py | 2219 ------------- .../final_benchmark.py | 1724 ---------- .../representation_training_v1/launcher.py | 2746 ---------------- .../representation_training_v1/preflight.py | 381 --- .../representation_training_v1/promotion.py | 2773 ----------------- .../representation_training_v1/protocol.json | 969 ------ .../representation_training_v1/protocol.py | 552 ---- .../record_approval.py | 97 - .../record_parent_transition.py | 67 - .../renderer_diagnostic.py | 2078 ------------ .../representation_training_v1/run_stage.py | 56 - .../representation_training_v1/schedule.py | 161 - .../source_transition.py | 433 --- .../representation_training_v1/store.py | 733 ----- .../tests/__init__.py | 1 - .../tests/test_base_rl_gates.py | 1001 ------ .../tests/test_base_rl_protocol.py | 152 - .../tests/test_evaluation.py | 738 ----- .../tests/test_final_benchmark.py | 711 ----- .../tests/test_launcher.py | 2227 ------------- .../tests/test_promotion.py | 1799 ----------- .../tests/test_protocol_schedule.py | 276 -- .../tests/test_renderer_diagnostic.py | 783 ----- .../tests/test_source_transition.py | 243 -- .../tests/test_store.py | 138 - rl/track_a/checkpoint_metrics.py | 131 - rl/track_a/checkpoint_selection.py | 154 - rl/track_a/config.json | 102 - rl/track_a/curriculum.py | 31 + rl/track_a/evaluate_checkpoint.py | 334 -- rl/track_a/evaluation_contract.py | 725 ----- rl/track_a/launch.py | 70 +- rl/track_a/preflight.py | 95 +- rl/track_a/recipe.py | 560 ---- rl/track_a/select_sft_checkpoint.py | 60 - rl/track_a/tests/test_checkpoint_promotion.py | 542 ---- rl/track_a/tests/test_curriculum.py | 50 - rl/track_a/tests/test_launch_preflight.py | 312 -- rl/track_a/tests/test_prompt_contract.py | 17 +- rl/track_a/tinker_data.py | 25 +- rl/track_a/train_rl.py | 92 +- rl/track_a/train_sft.py | 54 +- rl/track_b/README.md | 16 - rl/track_b/tests/__init__.py | 1 - rl/track_b/tests/test_tml_shim.py | 214 -- rl/track_b/tml_shim.py | 414 --- rl/track_c/README.md | 8 - 106 files changed, 186 insertions(+), 42334 deletions(-) delete mode 100644 rl/evaluation/__init__.py delete mode 100644 rl/evaluation/attempt_store.py delete mode 100644 rl/evaluation/protocol.py delete mode 100644 rl/evaluation/runner.py delete mode 100644 rl/evaluation/samplers.py delete mode 100644 rl/evaluation/summarize.py delete mode 100644 rl/evaluation/tasks.py delete mode 100644 rl/evaluation/tests/test_attempt_store.py delete mode 100644 rl/evaluation/tests/test_protocol.py delete mode 100644 rl/evaluation/tests/test_runner.py delete mode 100644 rl/evaluation/tests/test_samplers.py delete mode 100644 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rl/track_a/select_sft_checkpoint.py delete mode 100644 rl/track_a/tests/test_checkpoint_promotion.py delete mode 100644 rl/track_a/tests/test_launch_preflight.py delete mode 100644 rl/track_b/README.md delete mode 100644 rl/track_b/tests/__init__.py delete mode 100644 rl/track_b/tests/test_tml_shim.py delete mode 100644 rl/track_b/tml_shim.py delete mode 100644 rl/track_c/README.md diff --git a/rl/evaluation/__init__.py b/rl/evaluation/__init__.py deleted file mode 100644 index f91dbeb7..00000000 --- a/rl/evaluation/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Shared, model-agnostic evaluation infrastructure.""" diff --git a/rl/evaluation/attempt_store.py b/rl/evaluation/attempt_store.py deleted file mode 100644 index d9498c6c..00000000 --- a/rl/evaluation/attempt_store.py +++ /dev/null @@ -1,869 +0,0 @@ -"""Crash-safe, immutable storage for baseline sampling attempts. - -The store deliberately lives outside the source repository. A sampling -receipt is the durable boundary: once it exists, a resumed run evaluates that -receipt rather than asking the model for another completion. -""" - -from __future__ import annotations - -import errno -import fcntl -import hashlib -import json -import math -import os -import re -import stat -import threading -import time -import uuid -from collections.abc import Callable, Mapping -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any - - -RECORD_SCHEMA_VERSION = "pixcell-baseline-attempt-record-v1" -RUN_MANIFEST_SCHEMA_VERSION = "pixcell-baseline-run-manifest-v1" -MAX_RECORD_BYTES = 64 * 1024 * 1024 - -_IDENTIFIER = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$") -_SHA256 = re.compile(r"^[0-9a-f]{64}$") -_GIT_SHA = re.compile(r"^[0-9a-f]{40}(?:[0-9a-f]{24})?$") -_IMMUTABLE_IMAGE = re.compile( - r"^(?:sha256:[0-9a-f]{64}|[^\s@]+@sha256:[0-9a-f]{64})$" -) -_UNIT_INTERVAL_KEYS = { - "dice", - "executable_rate", - "iou", - "pure_executable_rate", - "raw_absolute_scale_iou", - "raw_iou", - "reward", - "score", - "top_p", -} -_NONNEGATIVE_KEYS = { - "cached_prefill", - "completion_tokens", - "evaluation_seconds", - "input_tokens", - "latency_seconds", - "output_tokens", - "prompt_tokens", - "reasoning_tokens", - "sampling_seconds", - "total_tokens", - "uncached_prefill", -} -_POSITIVE_INTEGER_KEYS = { - "max_output_tokens", - "max_tokens", -} -_RUN_MANIFEST_FIELDS = { - "source_git_sha", - "protocol_sha256", - "task_set_sha256", - "prompt_sha256", - "sandbox_image_ref", - "sandbox_image_id", - "runtime", - "model_binding", -} - - -class AttemptStoreError(RuntimeError): - """Base class for attempt-ledger failures.""" - - -class UnsafeStoreRootError(AttemptStoreError): - """The requested result root overlaps the source repository.""" - - -class InvalidRecordError(AttemptStoreError): - """A record is not valid, canonical, or internally consistent.""" - - -class ImmutableRecordError(AttemptStoreError): - """A caller attempted to replace an existing immutable record.""" - - -class ManifestDriftError(ImmutableRecordError): - """A resumed arm no longer matches its frozen launch manifest.""" - - -class RunLockedError(AttemptStoreError): - """Another process already owns the nonblocking arm lock.""" - - -class RunLockRequiredError(AttemptStoreError): - """Sampling was requested without holding the arm lock.""" - - -@dataclass(frozen=True, order=True) -class RunKey: - study_id: str - hypothesis_id: str - arm_id: str - - def __post_init__(self) -> None: - _validate_identifier(self.study_id, field="study_id") - _validate_identifier(self.hypothesis_id, field="hypothesis_id") - _validate_identifier(self.arm_id, field="arm_id") - - def as_dict(self) -> dict[str, str]: - return { - "study_id": self.study_id, - "hypothesis_id": self.hypothesis_id, - "arm_id": self.arm_id, - } - - -@dataclass(frozen=True, order=True) -class AttemptKey: - study_id: str - hypothesis_id: str - arm_id: str - task_id: str - attempt_index: int - - def __post_init__(self) -> None: - RunKey(self.study_id, self.hypothesis_id, self.arm_id) - _validate_identifier(self.task_id, field="task_id") - if ( - isinstance(self.attempt_index, bool) - or not isinstance(self.attempt_index, int) - or not 1 <= self.attempt_index <= 1_000_000 - ): - raise ValueError("attempt_index must be an integer in [1, 1000000]") - - @property - def run_key(self) -> RunKey: - return RunKey(self.study_id, self.hypothesis_id, self.arm_id) - - def as_dict(self) -> dict[str, Any]: - return { - **self.run_key.as_dict(), - "task_id": self.task_id, - "attempt_index": self.attempt_index, - } - - -@dataclass(frozen=True) -class StoredRecord: - payload: dict[str, Any] - payload_sha256: str - record_sha256: str - relative_path: PurePosixPath - - -@dataclass(frozen=True) -class ReceiptResolution: - record: StoredRecord - resumed: bool - - -def _validate_identifier(value: str, *, field: str) -> None: - if not isinstance(value, str) or not _IDENTIFIER.fullmatch(value): - raise ValueError( - f"{field} must match {_IDENTIFIER.pattern!r}; got {value!r}" - ) - - -def _canonical_bytes(value: Any) -> bytes: - try: - return json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise InvalidRecordError("record is not finite JSON") from exc - - -def _sha256(value: Any) -> str: - return hashlib.sha256(_canonical_bytes(value)).hexdigest() - - -def _validate_json(value: Any, *, path: str = "$", key: str | None = None) -> None: - if value is None or isinstance(value, (str, bool)): - pass - elif isinstance(value, int): - pass - elif isinstance(value, float): - if not math.isfinite(value): - raise InvalidRecordError(f"{path} must be finite") - elif isinstance(value, list): - for index, item in enumerate(value): - _validate_json(item, path=f"{path}[{index}]") - elif isinstance(value, Mapping): - for child_key, item in value.items(): - if not isinstance(child_key, str): - raise InvalidRecordError(f"{path} contains a non-string key") - _validate_json(item, path=f"{path}.{child_key}", key=child_key) - else: - raise InvalidRecordError( - f"{path} has non-JSON type {type(value).__name__}" - ) - - if key is None: - return - if key.endswith("_sha256") and value is not None: - if not isinstance(value, str) or not _SHA256.fullmatch(value): - raise InvalidRecordError(f"{path} must be a lowercase SHA-256") - if value is not None and ( - key in _UNIT_INTERVAL_KEYS - or key.endswith(("_dice", "_fraction", "_iou", "_rate")) - ): - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not 0.0 <= float(value) <= 1.0 - ): - raise InvalidRecordError(f"{path} must be in [0, 1]") - if key == "temperature" and value is not None: - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not 0.0 <= float(value) <= 2.0 - ): - raise InvalidRecordError(f"{path} must be in [0, 2]") - if value is not None and ( - key in _NONNEGATIVE_KEYS or key.endswith("_seconds") - ): - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or float(value) < 0.0 - ): - raise InvalidRecordError(f"{path} must be nonnegative") - if key in _POSITIVE_INTEGER_KEYS and value is not None: - if ( - isinstance(value, bool) - or not isinstance(value, int) - or not 1 <= value <= 1_000_000 - ): - raise InvalidRecordError( - f"{path} must be an integer in [1, 1000000]" - ) - - -def _normalize_payload(payload: Mapping[str, Any]) -> dict[str, Any]: - if not isinstance(payload, Mapping): - raise InvalidRecordError("record payload must be a JSON object") - _validate_json(payload) - # The canonical round trip also detaches the immutable record from mutable - # mappings owned by a caller. - normalized = json.loads(_canonical_bytes(payload)) - if not isinstance(normalized, dict): - raise InvalidRecordError("record payload must be a JSON object") - return normalized - - -def _json_object(raw: bytes, *, path: Path) -> dict[str, Any]: - def reject_constant(value: str) -> Any: - raise InvalidRecordError(f"{path} contains non-finite number {value}") - - def reject_duplicate_keys(pairs: list[tuple[str, Any]]) -> dict[str, Any]: - result: dict[str, Any] = {} - for key, value in pairs: - if key in result: - raise InvalidRecordError(f"{path} contains duplicate key {key!r}") - result[key] = value - return result - - try: - value = json.loads( - raw, - parse_constant=reject_constant, - object_pairs_hook=reject_duplicate_keys, - ) - except (UnicodeDecodeError, json.JSONDecodeError) as exc: - raise InvalidRecordError(f"{path} is not valid UTF-8 JSON") from exc - if not isinstance(value, dict): - raise InvalidRecordError(f"{path} must contain a JSON object") - _validate_json(value) - return value - - -def _record_document( - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], - schema_version: str = RECORD_SCHEMA_VERSION, -) -> dict[str, Any]: - normalized = _normalize_payload(payload) - base: dict[str, Any] = { - "schema_version": schema_version, - "record_type": record_type, - "key": dict(key), - "payload": normalized, - "payload_sha256": _sha256(normalized), - } - base["record_sha256"] = _sha256(base) - return base - - -def _validate_record_document( - document: Mapping[str, Any], - *, - expected_type: str, - expected_key: Mapping[str, Any], - expected_schema: str = RECORD_SCHEMA_VERSION, -) -> StoredRecord: - required = { - "schema_version", - "record_type", - "key", - "payload", - "payload_sha256", - "record_sha256", - } - if set(document) != required: - raise InvalidRecordError( - f"{expected_type} record fields differ from the frozen schema" - ) - if document["schema_version"] != expected_schema: - raise InvalidRecordError(f"unexpected {expected_type} schema version") - if document["record_type"] != expected_type: - raise InvalidRecordError(f"expected {expected_type} record") - if document["key"] != dict(expected_key): - raise InvalidRecordError(f"{expected_type} key does not match its path") - payload = document["payload"] - if not isinstance(payload, dict): - raise InvalidRecordError(f"{expected_type} payload must be an object") - payload_sha = _sha256(payload) - if document["payload_sha256"] != payload_sha: - raise InvalidRecordError(f"{expected_type} payload hash mismatch") - unsigned = dict(document) - record_sha = unsigned.pop("record_sha256") - if record_sha != _sha256(unsigned): - raise InvalidRecordError(f"{expected_type} record hash mismatch") - return StoredRecord( - payload=dict(payload), - payload_sha256=payload_sha, - record_sha256=str(record_sha), - relative_path=PurePosixPath("."), - ) - - -class _RunLock: - def __init__(self, store: AttemptStore, run_key: RunKey, path: Path) -> None: - self._store = store - self._run_key = run_key - self._path = path - self._fd: int | None = None - - def __enter__(self) -> _RunLock: - self._path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - flags = os.O_RDWR | os.O_CREAT - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(self._path, flags, 0o600) - fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) - except BlockingIOError as exc: - if "fd" in locals(): - os.close(fd) - raise RunLockedError( - f"run {self._run_key} is already active" - ) from exc - except OSError: - if "fd" in locals(): - os.close(fd) - raise - self._fd = fd - try: - self._store._mark_lock_held(self._run_key) - except Exception: - fcntl.flock(fd, fcntl.LOCK_UN) - os.close(fd) - self._fd = None - raise - return self - - def __exit__(self, *_: object) -> None: - if self._fd is None: - return - self._store._mark_lock_released(self._run_key) - fcntl.flock(self._fd, fcntl.LOCK_UN) - os.close(self._fd) - self._fd = None - - -class AttemptStore: - """External, create-only attempt ledger.""" - - def __init__(self, *, repo_root: Path, external_root: Path) -> None: - self.repo_root = Path(repo_root).resolve(strict=True) - requested_root = Path(external_root).expanduser().resolve(strict=False) - if ( - requested_root == self.repo_root - or requested_root.is_relative_to(self.repo_root) - or self.repo_root.is_relative_to(requested_root) - ): - raise UnsafeStoreRootError( - "attempt output must not overlap the source repository" - ) - requested_root.mkdir(parents=True, exist_ok=True, mode=0o700) - self.external_root = requested_root.resolve(strict=True) - if ( - self.external_root == self.repo_root - or self.external_root.is_relative_to(self.repo_root) - or self.repo_root.is_relative_to(self.external_root) - ): - raise UnsafeStoreRootError( - "attempt output resolves across the source repository" - ) - self._held_locks: set[RunKey] = set() - self._lock_state_guard = threading.Lock() - - @staticmethod - def run_relative_path(run_key: RunKey) -> PurePosixPath: - return PurePosixPath( - "studies", - run_key.study_id, - "hypotheses", - run_key.hypothesis_id, - "arms", - run_key.arm_id, - ) - - @classmethod - def attempt_relative_path(cls, key: AttemptKey) -> PurePosixPath: - return cls.run_relative_path(key.run_key) / PurePosixPath( - "tasks", - key.task_id, - "attempts", - f"{key.attempt_index:06d}", - ) - - @classmethod - def run_manifest_relative_path(cls, run_key: RunKey) -> PurePosixPath: - return cls.run_relative_path(run_key) / "run_manifest.json" - - @classmethod - def sampling_receipt_relative_path(cls, key: AttemptKey) -> PurePosixPath: - return cls.attempt_relative_path(key) / "sampling_receipt.json" - - @classmethod - def evaluation_relative_path(cls, key: AttemptKey) -> PurePosixPath: - return cls.attempt_relative_path(key) / "evaluation.json" - - def _path(self, relative: PurePosixPath) -> Path: - path = self.external_root.joinpath(*relative.parts) - resolved_parent = path.parent.resolve(strict=False) - if resolved_parent != self.external_root and not resolved_parent.is_relative_to( - self.external_root - ): - raise UnsafeStoreRootError("record path escaped the external root") - return path - - def acquire_run_lock(self, run_key: RunKey) -> _RunLock: - digest = hashlib.sha256( - _canonical_bytes(run_key.as_dict()) - ).hexdigest() - return _RunLock( - self, - run_key, - self.external_root / ".locks" / f"{digest}.lock", - ) - - def _mark_lock_held(self, run_key: RunKey) -> None: - with self._lock_state_guard: - if run_key in self._held_locks: - raise RunLockedError(f"run {run_key} is already locked here") - self._held_locks.add(run_key) - - def _mark_lock_released(self, run_key: RunKey) -> None: - with self._lock_state_guard: - self._held_locks.discard(run_key) - - def _require_run_lock(self, run_key: RunKey) -> None: - with self._lock_state_guard: - if run_key not in self._held_locks: - raise RunLockRequiredError( - "sampling requires the caller to hold the arm run lock" - ) - - def create_or_verify_run_manifest( - self, - run_key: RunKey, - manifest: Mapping[str, Any], - ) -> StoredRecord: - normalized = _normalize_payload(manifest) - missing = sorted(_RUN_MANIFEST_FIELDS - set(normalized)) - if missing: - raise InvalidRecordError( - f"run manifest is missing launch bindings: {missing}" - ) - if not _GIT_SHA.fullmatch(str(normalized["source_git_sha"])): - raise InvalidRecordError("source_git_sha must be a Git object SHA") - for field in ("protocol_sha256", "task_set_sha256", "prompt_sha256"): - if not _SHA256.fullmatch(str(normalized[field])): - raise InvalidRecordError(f"{field} must be a lowercase SHA-256") - if not _IMMUTABLE_IMAGE.fullmatch( - str(normalized["sandbox_image_ref"]) - ): - raise InvalidRecordError( - "sandbox_image_ref must be an immutable image digest" - ) - if not re.fullmatch( - r"sha256:[0-9a-f]{64}", str(normalized["sandbox_image_id"]) - ): - raise InvalidRecordError( - "sandbox_image_id must be an immutable local image ID" - ) - if ( - not isinstance(normalized["model_binding"], dict) - or not normalized["model_binding"] - ): - raise InvalidRecordError("model_binding must be a non-empty object") - runtime = normalized["runtime"] - if ( - not isinstance(runtime, dict) - or not isinstance(runtime.get("python"), str) - or not runtime["python"] - or not isinstance(runtime.get("packages"), dict) - or not runtime["packages"] - or not _GIT_SHA.fullmatch( - str(runtime.get("tinker_cookbook_commit", "")) - ) - ): - raise InvalidRecordError("runtime must contain the pinned observed stack") - - expected = _record_document( - record_type="run_manifest", - key=run_key.as_dict(), - payload=normalized, - schema_version=RUN_MANIFEST_SCHEMA_VERSION, - ) - relative = self.run_manifest_relative_path(run_key) - try: - return self._create_or_verify( - relative=relative, - expected=expected, - record_type="run_manifest", - key=run_key.as_dict(), - schema_version=RUN_MANIFEST_SCHEMA_VERSION, - ) - except ImmutableRecordError as exc: - raise ManifestDriftError( - "run manifest differs from the immutable arm binding" - ) from exc - - def load_run_manifest(self, run_key: RunKey) -> StoredRecord | None: - return self._load( - relative=self.run_manifest_relative_path(run_key), - record_type="run_manifest", - key=run_key.as_dict(), - schema_version=RUN_MANIFEST_SCHEMA_VERSION, - ) - - def write_sampling_receipt( - self, - key: AttemptKey, - payload: Mapping[str, Any], - ) -> StoredRecord: - return self._write_attempt_record( - key=key, - payload=payload, - record_type="sampling_receipt", - relative=self.sampling_receipt_relative_path(key), - ) - - def load_sampling_receipt(self, key: AttemptKey) -> StoredRecord | None: - return self._load( - relative=self.sampling_receipt_relative_path(key), - record_type="sampling_receipt", - key=key.as_dict(), - ) - - def write_evaluation( - self, - key: AttemptKey, - payload: Mapping[str, Any], - ) -> StoredRecord: - if self.load_sampling_receipt(key) is None: - raise InvalidRecordError( - "evaluation cannot precede its durable sampling receipt" - ) - return self._write_attempt_record( - key=key, - payload=payload, - record_type="evaluation", - relative=self.evaluation_relative_path(key), - ) - - def load_evaluation(self, key: AttemptKey) -> StoredRecord | None: - return self._load( - relative=self.evaluation_relative_path(key), - record_type="evaluation", - key=key.as_dict(), - ) - - def write_evaluator_failure( - self, - key: AttemptKey, - payload: Mapping[str, Any], - ) -> StoredRecord: - """Preserve a verifier diagnostic without poisoning canonical results.""" - - if self.load_sampling_receipt(key) is None: - raise InvalidRecordError( - "evaluator failure cannot precede its sampling receipt" - ) - normalized = _normalize_payload(payload) - failure_id = _sha256(normalized) - relative = ( - self.attempt_relative_path(key) - / "evaluator_failures" - / f"{failure_id}.json" - ) - return self._write_attempt_record( - key=key, - payload=normalized, - record_type="evaluator_failure", - relative=relative, - ) - - def list_evaluator_failures( - self, - key: AttemptKey, - ) -> tuple[StoredRecord, ...]: - directory = self._path( - self.attempt_relative_path(key) / "evaluator_failures" - ) - if not directory.exists(): - return () - result: list[StoredRecord] = [] - for path in sorted(directory.glob("*.json")): - relative = PurePosixPath( - path.relative_to(self.external_root).as_posix() - ) - record = self._load( - relative=relative, - record_type="evaluator_failure", - key=key.as_dict(), - ) - if record is not None: - result.append(record) - return tuple(result) - - def resume_or_sample( - self, - key: AttemptKey, - sampler: Callable[[], Mapping[str, Any]], - ) -> ReceiptResolution: - existing = self.load_sampling_receipt(key) - if existing is not None: - return ReceiptResolution(record=existing, resumed=True) - self._require_run_lock(key.run_key) - if self.load_run_manifest(key.run_key) is None: - raise InvalidRecordError( - "sampling requires a frozen per-arm run manifest" - ) - # Recheck after validating ownership and the launch binding. - existing = self.load_sampling_receipt(key) - if existing is not None: - return ReceiptResolution(record=existing, resumed=True) - sampled = sampler() - record = self.write_sampling_receipt(key, sampled) - return ReceiptResolution(record=record, resumed=False) - - def write_tracking_error( - self, - key: AttemptKey, - payload: Mapping[str, Any], - ) -> StoredRecord: - normalized = _normalize_payload(payload) - normalized.setdefault("occurred_at_unix_ns", time.time_ns()) - error_id = _sha256(normalized) - relative = ( - self.attempt_relative_path(key) - / "tracking_errors" - / f"{error_id}.json" - ) - return self._write_attempt_record( - key=key, - payload=normalized, - record_type="tracking_error", - relative=relative, - ) - - def list_tracking_errors(self, key: AttemptKey) -> tuple[StoredRecord, ...]: - directory = self._path( - self.attempt_relative_path(key) / "tracking_errors" - ) - if not directory.exists(): - return () - result: list[StoredRecord] = [] - for path in sorted(directory.glob("*.json")): - relative = PurePosixPath(path.relative_to(self.external_root).as_posix()) - record = self._load( - relative=relative, - record_type="tracking_error", - key=key.as_dict(), - ) - if record is not None: - result.append(record) - return tuple(result) - - def _write_attempt_record( - self, - *, - key: AttemptKey, - payload: Mapping[str, Any], - record_type: str, - relative: PurePosixPath, - ) -> StoredRecord: - expected = _record_document( - record_type=record_type, - key=key.as_dict(), - payload=payload, - ) - return self._create_or_verify( - relative=relative, - expected=expected, - record_type=record_type, - key=key.as_dict(), - ) - - def _create_or_verify( - self, - *, - relative: PurePosixPath, - expected: Mapping[str, Any], - record_type: str, - key: Mapping[str, Any], - schema_version: str = RECORD_SCHEMA_VERSION, - ) -> StoredRecord: - path = self._path(relative) - path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - raw = _canonical_bytes(expected) + b"\n" - temporary = path.parent / ( - f".{path.name}.tmp-{os.getpid()}-{uuid.uuid4().hex}" - ) - flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(temporary, flags, 0o600) - try: - with os.fdopen(fd, "wb", closefd=False) as stream: - stream.write(raw) - stream.flush() - os.fsync(stream.fileno()) - finally: - os.close(fd) - try: - os.link(temporary, path) - self._fsync_directory(path.parent) - except FileExistsError: - observed = self._load( - relative=relative, - record_type=record_type, - key=key, - schema_version=schema_version, - ) - if observed is None: - raise InvalidRecordError( - f"{path} disappeared while verifying immutability" - ) - if _canonical_bytes(expected) != _canonical_bytes( - self._read_document(path) - ): - raise ImmutableRecordError( - f"{relative} already contains different data" - ) - return observed - finally: - try: - temporary.unlink() - except FileNotFoundError: - pass - record = _validate_record_document( - expected, - expected_type=record_type, - expected_key=key, - expected_schema=schema_version, - ) - return StoredRecord( - payload=record.payload, - payload_sha256=record.payload_sha256, - record_sha256=record.record_sha256, - relative_path=relative, - ) - - def _load( - self, - *, - relative: PurePosixPath, - record_type: str, - key: Mapping[str, Any], - schema_version: str = RECORD_SCHEMA_VERSION, - ) -> StoredRecord | None: - path = self._path(relative) - if not path.exists(): - return None - document = self._read_document(path) - record = _validate_record_document( - document, - expected_type=record_type, - expected_key=key, - expected_schema=schema_version, - ) - return StoredRecord( - payload=record.payload, - payload_sha256=record.payload_sha256, - record_sha256=record.record_sha256, - relative_path=relative, - ) - - @staticmethod - def _read_document(path: Path) -> dict[str, Any]: - flags = os.O_RDONLY - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(path, flags) - except OSError as exc: - if exc.errno == errno.ELOOP: - raise InvalidRecordError(f"{path} must not be a symlink") from exc - raise - try: - metadata = os.fstat(fd) - if not stat.S_ISREG(metadata.st_mode): - raise InvalidRecordError(f"{path} is not a regular file") - if metadata.st_size > MAX_RECORD_BYTES: - raise InvalidRecordError(f"{path} exceeds the record size limit") - chunks: list[bytes] = [] - remaining = MAX_RECORD_BYTES + 1 - while remaining > 0: - chunk = os.read(fd, min(1024 * 1024, remaining)) - if not chunk: - break - chunks.append(chunk) - remaining -= len(chunk) - raw = b"".join(chunks) - if len(raw) > MAX_RECORD_BYTES: - raise InvalidRecordError(f"{path} exceeds the record size limit") - finally: - os.close(fd) - return _json_object(raw, path=path) - - @staticmethod - def _fsync_directory(path: Path) -> None: - flags = os.O_RDONLY - if hasattr(os, "O_DIRECTORY"): - flags |= os.O_DIRECTORY - fd = os.open(path, flags) - try: - os.fsync(fd) - finally: - os.close(fd) diff --git a/rl/evaluation/protocol.py b/rl/evaluation/protocol.py deleted file mode 100644 index cedd8bd7..00000000 --- a/rl/evaluation/protocol.py +++ /dev/null @@ -1,508 +0,0 @@ -"""Frozen protocol loading and exact baseline-launch binding.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import os -import subprocess -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from rl.common.isolation import require_execution_boundary -from rl.common.preprocess import IMAGE_PREPROCESS_VERSION -from rl.common.prompt import CONTRACT_VERSION, prompt_asset_hashes -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.tasks import ( - canonical_json_sha256, - load_task_manifest, - load_task_set, -) - - -PROTOCOL_SCHEMA = "pixcell-representation-curriculum-protocol-v1" -STUDY_RELATIVE_PATH = Path("rl/studies/representation_curriculum_v2") -PROTOCOL_FILENAME = "protocol.json" -TASK_MANIFEST_FILENAME = "task_manifest.json" -ACTIVE_STUDY_ID = "representation-curriculum-v2" -ACTIVE_IMAGE_LONG_EDGE = 1920 -_SHA_RE = __import__("re").compile(r"^[0-9a-f]{40}$") - - -@dataclass(frozen=True) -class ArmSpec: - arm_id: str - provider: str - model: str - renderer: str - thinking: bool - thinking_effort: float | None - max_output_tokens: int - context_tokens: int - max_image_long_edge: int - - -@dataclass(frozen=True) -class JobSpec: - hypothesis_id: str - arm_id: str - task_id: str - attempt_index: int - seed: int - - -def _logical_document(document: dict[str, Any]) -> dict[str, Any]: - return {key: value for key, value in document.items() if key != "logical_sha256"} - - -def seal_protocol(path: Path) -> dict[str, Any]: - document = json.loads(path.read_text(encoding="utf-8")) - document["logical_sha256"] = canonical_json_sha256(_logical_document(document)) - path.write_text( - json.dumps(document, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - return document - - -def protocol_path(repo_root: Path, protocol_file: Path | None = None) -> Path: - root = repo_root.expanduser().resolve() - if protocol_file is None: - return root / STUDY_RELATIVE_PATH / PROTOCOL_FILENAME - candidate = protocol_file.expanduser().resolve(strict=True) - if not candidate.is_relative_to(root): - raise ValueError("baseline protocol must live inside the source repository") - return candidate - - -def task_manifest_path(repo_root: Path) -> Path: - return ( - repo_root.expanduser().resolve() - / STUDY_RELATIVE_PATH - / TASK_MANIFEST_FILENAME - ) - - -def _require_protocol_at_head(repo_root: Path, path: Path) -> None: - root = repo_root.expanduser().resolve() - candidate = path.expanduser().resolve(strict=True) - if not candidate.is_relative_to(root): - raise ValueError("baseline protocol must live inside the source repository") - relative = candidate.relative_to(root).as_posix() - try: - subprocess.check_output( - ["git", "ls-files", "--error-unmatch", "--", relative], - cwd=root, - stderr=subprocess.DEVNULL, - ) - committed = subprocess.check_output( - ["git", "show", f"HEAD:{relative}"], - cwd=root, - stderr=subprocess.DEVNULL, - ) - except (OSError, subprocess.CalledProcessError) as exc: - raise ValueError( - "baseline protocol must be tracked by the paid-launch commit" - ) from exc - if candidate.read_bytes() != committed: - raise ValueError("baseline protocol bytes differ from the paid-launch commit") - - -def _validate_arm(arm_id: str, value: dict[str, Any]) -> ArmSpec: - effort = value.get("thinking_effort") - if effort is not None: - effort = float(effort) - if not math.isfinite(effort) or not 0 <= effort < 1: - raise ValueError(f"{arm_id} has invalid thinking effort") - result = ArmSpec( - arm_id=arm_id, - provider=str(value["provider"]), - model=str(value["model"]), - renderer=str(value["renderer"]), - thinking=bool(value["thinking"]), - thinking_effort=effort, - max_output_tokens=int(value["max_output_tokens"]), - context_tokens=int(value["context_tokens"]), - max_image_long_edge=int(value["max_image_long_edge"]), - ) - if result.provider != "tinker": - raise ValueError(f"{arm_id} has unsupported provider {result.provider!r}") - if result.max_output_tokens < 1 or result.context_tokens < 1: - raise ValueError(f"{arm_id} has invalid token limits") - if result.max_output_tokens >= result.context_tokens: - raise ValueError(f"{arm_id} output cap exhausts the context window") - if result.max_image_long_edge < 1: - raise ValueError(f"{arm_id} has an invalid image-size policy") - if result.model == "thinkingmachines/Inkling": - if result.renderer != "tml_v0" or result.thinking_effort is None: - raise ValueError(f"{arm_id} has an invalid Inkling renderer contract") - elif result.model == "Qwen/Qwen3.6-35B-A3B": - expected = "qwen3_5" if result.thinking else "qwen3_5_disable_thinking" - if result.renderer != expected or result.thinking_effort is not None: - raise ValueError(f"{arm_id} has an invalid Qwen renderer contract") - else: - raise ValueError(f"{arm_id} uses an unregistered model") - return result - - -def load_protocol( - repo_root: Path, - *, - protocol_file: Path | None = None, -) -> dict[str, Any]: - """Load a protocol only when it matches the active paid-launch contract.""" - - root = repo_root.expanduser().resolve() - document = load_archived_protocol(root, protocol_file=protocol_file) - if document.get("contract_version") != CONTRACT_VERSION: - raise ValueError("protocol prompt contract does not match the runtime") - if document.get("study_id") != ACTIVE_STUDY_ID: - raise ValueError("protocol is not the active v2 baseline study") - arms = { - arm_id: _validate_arm(arm_id, value) - for arm_id, value in document["arms"].items() - } - if len(arms) != len(document["arms"]): - raise ValueError("duplicate arm IDs") - if any( - arm.max_image_long_edge != ACTIVE_IMAGE_LONG_EDGE - for arm in arms.values() - ): - raise ValueError( - "active v2 baselines require the frozen 1920px image policy" - ) - for hypothesis_id, hypothesis in document["hypotheses"].items(): - if not hypothesis_id.startswith("RC-H"): - raise ValueError(f"invalid hypothesis ID: {hypothesis_id!r}") - if hypothesis["status"] not in {"frozen", "conditional"}: - raise ValueError(f"{hypothesis_id} is not launchable") - if int(hypothesis["attempts_per_task"]) < 1: - raise ValueError(f"{hypothesis_id} has no attempts") - for arm_id in hypothesis.get("arm_ids", ()): - if arm_id not in arms: - raise ValueError(f"{hypothesis_id} names unknown arm {arm_id}") - for wave_name, wave in hypothesis["waves"].items(): - indices = [int(value) for value in wave["attempt_indices"]] - if wave_name not in {"smoke", "complete"}: - raise ValueError(f"{hypothesis_id} has unsupported wave {wave_name}") - if not indices or min(indices) < 1: - raise ValueError(f"{hypothesis_id}/{wave_name} has invalid attempts") - if max(indices) > int(hypothesis["attempts_per_task"]): - raise ValueError(f"{hypothesis_id}/{wave_name} exceeds attempt count") - if int(wave["task_count"]) < 1: - raise ValueError(f"{hypothesis_id}/{wave_name} has no tasks") - - manifest = load_task_manifest(task_manifest_path(root), repo_root=root) - expected_manifest_sha = document["task_manifest"]["logical_sha256"] - if manifest["logical_sha256"] != expected_manifest_sha: - raise ValueError("protocol does not bind the frozen task manifest") - dataset_binding = document.get("dataset") - if dataset_binding is not None: - freeze = json.loads( - (root / "dataset/depth-v1/factory/freeze.json").read_text( - encoding="utf-8" - ) - ) - if ( - freeze["logical_release_sha256"] - != dataset_binding["logical_release_sha256"] - ): - raise ValueError("protocol does not bind the local depth release") - return document - - -def load_archived_protocol( - repo_root: Path, - *, - protocol_file: Path | None = None, -) -> dict[str, Any]: - """Inspect a sealed historical protocol without making it launchable. - - This validates repository containment, schema, and the protocol's own - logical digest. Paid entry points must use :func:`load_protocol`, which - additionally requires the active model-visible contract. - """ - - root = repo_root.expanduser().resolve() - document = json.loads( - protocol_path(root, protocol_file).read_text(encoding="utf-8") - ) - if document.get("schema_version") != PROTOCOL_SCHEMA: - raise ValueError("unsupported representation-curriculum protocol schema") - observed = canonical_json_sha256(_logical_document(document)) - if document.get("logical_sha256") != observed: - raise ValueError("protocol logical SHA-256 mismatch") - return document - - -def arm_spec(protocol: dict[str, Any], arm_id: str) -> ArmSpec: - try: - value = protocol["arms"][arm_id] - except KeyError as exc: - raise ValueError(f"unknown arm: {arm_id!r}") from exc - return _validate_arm(arm_id, value) - - -def _validate_selection( - protocol: dict[str, Any], - selection: dict[str, Any], -) -> str: - if selection.get("schema_version") != "pixcell-operating-point-selection-v1": - raise ValueError("unsupported operating-point selection schema") - if selection.get("study_id") != protocol["study_id"]: - raise ValueError("operating-point selection belongs to another study") - if selection.get("protocol_sha256") != protocol["logical_sha256"]: - raise ValueError("operating-point selection has stale protocol provenance") - if selection.get("source_hypothesis") != "RC-H00": - raise ValueError("operating-point selection has the wrong source hypothesis") - selected = str(selection.get("selected_arm", "")) - allowed = set(protocol["hypotheses"]["RC-H00"]["arm_ids"]) - if selection.get("rescue_run"): - allowed.update(protocol["hypotheses"]["RC-H00R"]["arm_ids"]) - if selected not in allowed: - raise ValueError("operating-point selection names an ineligible arm") - if not selection.get("complete"): - raise ValueError("operating-point selection is not complete") - return selected - - -def resolved_arm_ids( - protocol: dict[str, Any], - hypothesis_id: str, - *, - selection: dict[str, Any] | None, -) -> list[str]: - try: - hypothesis = protocol["hypotheses"][hypothesis_id] - except KeyError as exc: - raise ValueError(f"unknown hypothesis: {hypothesis_id!r}") from exc - if hypothesis_id == "RC-H01": - if selection is None: - raise ValueError("RC-H01 requires the frozen operating-point selection") - selected = _validate_selection(protocol, selection) - incumbent = str(hypothesis["arm_roles"]["incumbent"]) - return [incumbent] if selected == incumbent else [incumbent, selected] - if hypothesis_id == "RC-H00R": - if selection is None or not selection.get("rescue_required"): - raise ValueError("RC-H00R is forbidden unless RC-H00 triggers rescue") - return [str(value) for value in hypothesis["arm_ids"]] - - -def deterministic_seed( - protocol: dict[str, Any], - *, - task_id: str, - attempt_index: int, -) -> int: - namespace = str(protocol["sampling"]["seed_namespace"]) - digest = hashlib.sha256( - f"{namespace}\0{task_id}\0{attempt_index}".encode("utf-8") - ).digest() - return int.from_bytes(digest[:4], "big") & 0x7FFFFFFF - - -def jobs_for_wave( - protocol: dict[str, Any], - *, - hypothesis_id: str, - wave_name: str, - task_ids: list[str], - selection: dict[str, Any] | None = None, -) -> list[JobSpec]: - hypothesis = protocol["hypotheses"].get(hypothesis_id) - if hypothesis is None: - raise ValueError(f"unknown hypothesis: {hypothesis_id!r}") - wave = hypothesis["waves"].get(wave_name) - if wave is None: - raise ValueError(f"{hypothesis_id} has no wave {wave_name!r}") - count = int(wave["task_count"]) - if count > len(task_ids): - raise ValueError( - f"{hypothesis_id}/{wave_name} asks for {count} of {len(task_ids)} tasks" - ) - arms = resolved_arm_ids(protocol, hypothesis_id, selection=selection) - return [ - JobSpec( - hypothesis_id=hypothesis_id, - arm_id=arm_id, - task_id=task_id, - attempt_index=int(attempt_index), - seed=deterministic_seed( - protocol, - task_id=task_id, - attempt_index=int(attempt_index), - ), - ) - for attempt_index in wave["attempt_indices"] - for task_id in task_ids[:count] - for arm_id in arms - ] - - -def _git_state(repo_root: Path) -> dict[str, Any]: - head = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo_root, - text=True, - ).strip() - status = subprocess.check_output( - ["git", "status", "--porcelain", "--untracked-files=all"], - cwd=repo_root, - text=True, - ).strip() - return {"head": head, "clean": not bool(status)} - - -def _external_output_root(repo_root: Path, value: Path) -> Path: - root = value.expanduser().resolve() - repository = repo_root.expanduser().resolve() - if ( - root == repository - or root.is_relative_to(repository) - or repository.is_relative_to(root) - ): - raise ValueError("mutable study output must not overlap the Git repository") - root.mkdir(parents=True, exist_ok=True) - if not root.is_dir(): - raise ValueError("mutable study output is not a directory") - return root - - -def build_launch_binding( - *, - repo_root: Path, - hypothesis_id: str, - wave_name: str, - expected_source_sha: str, - external_root: Path, - selection: dict[str, Any] | None = None, - protocol_file: Path | None = None, -) -> tuple[dict[str, Any], list[Any], list[JobSpec]]: - """Resolve every semantic launch input before a remote client exists.""" - - root = repo_root.expanduser().resolve() - if not _SHA_RE.fullmatch(expected_source_sha): - raise ValueError("expected_source_sha must be a full lowercase Git SHA") - state = _git_state(root) - if not state["clean"]: - raise ValueError("paid baseline launch requires a clean worktree") - if state["head"] != expected_source_sha: - raise ValueError( - f"source SHA mismatch: {state['head']} != {expected_source_sha}" - ) - selected_protocol = protocol_path(root, protocol_file) - _require_protocol_at_head(root, selected_protocol) - protocol = load_protocol(root, protocol_file=selected_protocol) - runtime = validate_runtime_stack(root) - manifest = load_task_manifest(task_manifest_path(root), repo_root=root) - hypothesis = protocol["hypotheses"].get(hypothesis_id) - if hypothesis is None: - raise ValueError(f"unknown hypothesis: {hypothesis_id!r}") - task_set_name = str(hypothesis["task_set"]) - tasks = load_task_set( - repo_root=root, - manifest=manifest, - task_set=task_set_name, - ) - jobs = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name=wave_name, - task_ids=[task.task_id for task in tasks], - selection=selection, - ) - boundary = require_execution_boundary() - output_root = _external_output_root(root, external_root) - assets = prompt_asset_hashes() - prompt_binding = { - "contract_version": CONTRACT_VERSION, - "preprocess_version": IMAGE_PREPROCESS_VERSION, - **assets, - } - task_entries = manifest["task_sets"][task_set_name] - arms = { - arm_id: protocol["arms"][arm_id] - for arm_id in resolved_arm_ids( - protocol, - hypothesis_id, - selection=selection, - ) - } - binding = { - "schema_version": "pixcell-baseline-launch-binding-v1", - "study_id": protocol["study_id"], - "hypothesis_id": hypothesis_id, - "wave": wave_name, - "source_git_sha": state["head"], - "protocol_path": selected_protocol.relative_to(root).as_posix(), - "protocol_sha256": protocol["logical_sha256"], - "task_manifest_sha256": manifest["logical_sha256"], - "task_set": task_set_name, - "task_set_sha256": canonical_json_sha256(task_entries), - "task_count": len({job.task_id for job in jobs}), - "job_count": len(jobs), - "prompt": prompt_binding, - "prompt_sha256": canonical_json_sha256(prompt_binding), - "arms": arms, - "sampling": protocol["sampling"], - "runtime": runtime, - "sandbox": { - "runtime_path": str(boundary.runtime_path), - "daemon_endpoint": boundary.daemon_endpoint, - "image_ref": boundary.image_ref, - "image_id": boundary.image_id, - "workspace_root": str(boundary.workspace_root), - }, - "external_root": str(output_root), - "selection_sha256": ( - canonical_json_sha256(selection) if selection is not None else None - ), - "jobs_sha256": canonical_json_sha256( - [ - { - "hypothesis_id": job.hypothesis_id, - "arm_id": job.arm_id, - "task_id": job.task_id, - "attempt_index": job.attempt_index, - "seed": job.seed, - } - for job in jobs - ] - ), - } - binding["binding_sha256"] = canonical_json_sha256(binding) - return binding, tasks, jobs - - -def required_credentials(protocol: dict[str, Any]) -> tuple[str, ...]: - result = ["TINKER_API_KEY"] - if protocol["tracking"]["mode"] == "online": - result.append("WANDB_API_KEY") - return tuple(result) - - -def validate_credentials(protocol: dict[str, Any]) -> None: - missing = [key for key in required_credentials(protocol) if not os.getenv(key)] - if missing: - raise ValueError(f"missing launch credentials: {', '.join(missing)}") - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--seal", type=Path) - return result - - -def main() -> None: - args = parser().parse_args() - if args.seal is None: - raise SystemExit("--seal is required") - document = seal_protocol(args.seal) - print(document["logical_sha256"]) - - -if __name__ == "__main__": - main() diff --git a/rl/evaluation/runner.py b/rl/evaluation/runner.py deleted file mode 100644 index 539126ad..00000000 --- a/rl/evaluation/runner.py +++ /dev/null @@ -1,1240 +0,0 @@ -"""Resumable, candidate-level baseline sampling and deterministic evaluation.""" - -from __future__ import annotations - -import asyncio -import hashlib -import json -import math -import statistics -import time -from contextlib import ExitStack -from pathlib import Path -from typing import Any - -from rl.common.evaluator import Attribution, EvaluationStatus, PixCellEvaluator -from rl.common.output import extract_code -from rl.common.preprocess import model_image -from rl.common.prompt import build_prompt_text -from rl.evaluation.attempt_store import ( - AttemptKey, - AttemptStore, - RunKey, - StoredRecord, -) -from rl.evaluation.protocol import ( - ArmSpec, - JobSpec, - arm_spec, - build_launch_binding, - jobs_for_wave, - load_protocol, - resolved_arm_ids, - task_manifest_path, - validate_credentials, -) -from rl.evaluation.samplers import ( - SamplerTarget, - SamplingRequest, - TinkerSampler, -) -from rl.evaluation.summarize import ( - atomic_write_json, - choose_operating_point, - summarize_arm, -) -from rl.evaluation.tasks import ( - EvaluationTask, - canonical_json_sha256, - load_task_manifest, - load_task_set, -) -from rl.evaluation.tracking import ( - WandbMetricsMirror, - WandbRunSession, - validate_wandb_access, -) - - -_FORBIDDEN_PROMPT_FRAGMENTS = ( - "DISPLAY NOTE", - "magnification x:y", - "px/um", - "target_image", - "calibration.json", -) - - -def _sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def _messages(task: EvaluationTask, arm: ArmSpec) -> list[Any]: - from tinker_cookbook.renderers import ImagePart, Message, TextPart - - text = build_prompt_text(task.observation) - leaked = [value for value in _FORBIDDEN_PROMPT_FRAGMENTS if value in text] - if leaked: - raise ValueError(f"model prompt leaked forbidden fields: {leaked}") - return [ - Message( - role="user", - content=[ - ImagePart( - type="image", - image=model_image( - task.observation, - max_long_edge=arm.max_image_long_edge, - ), - ), - TextPart(type="text", text=text), - ], - ) - ] - - -def _sampler_target(arm: ArmSpec) -> SamplerTarget: - return SamplerTarget( - model_name=arm.model, - renderer_name=arm.renderer, - base_model=arm.model, - effort=arm.thinking_effort, - ) - - -def _safe_json(value: Any) -> Any: - if value is None or isinstance(value, (str, int, float, bool)): - return value - if isinstance(value, dict): - return {str(key): _safe_json(item) for key, item in value.items()} - if isinstance(value, (list, tuple)): - return [_safe_json(item) for item in value] - if hasattr(value, "item"): - return _safe_json(value.item()) - return str(value) - - -def _attempt_key(study_id: str, job: JobSpec) -> AttemptKey: - return AttemptKey( - study_id=study_id, - hypothesis_id=job.hypothesis_id, - arm_id=job.arm_id, - task_id=job.task_id, - attempt_index=job.attempt_index, - ) - - -def _run_manifest(binding: dict[str, Any], arm_id: str) -> dict[str, Any]: - return { - "source_git_sha": binding["source_git_sha"], - "protocol_sha256": binding["protocol_sha256"], - "task_set_sha256": binding["task_set_sha256"], - "prompt_sha256": binding["prompt_sha256"], - "sandbox_image_ref": binding["sandbox"]["image_ref"], - "sandbox_image_id": binding["sandbox"]["image_id"], - "runtime": binding["runtime"], - "model_binding": { - "arm_id": arm_id, - **binding["arms"][arm_id], - "temperature": binding["sampling"]["temperature"], - "top_p": binding["sampling"]["top_p"], - }, - } - - -def _selection_document(path: Path | None) -> dict[str, Any] | None: - if path is None: - return None - value = json.loads(path.expanduser().resolve(strict=True).read_text(encoding="utf-8")) - if not isinstance(value, dict): - raise ValueError("selection receipt must contain a JSON object") - claimed = value.get("logical_sha256") - unsigned = {key: item for key, item in value.items() if key != "logical_sha256"} - if claimed != canonical_json_sha256(unsigned): - raise ValueError("selection receipt logical SHA-256 mismatch") - return value - - -def _canonical_selection_path( - *, - external_root: Path, - study_id: str, - hypothesis_id: str, -) -> Path | None: - root = ( - external_root.expanduser().resolve() - / "studies" - / study_id - / "selections" - ) - if hypothesis_id == "RC-H00R": - return root / "operating_point.preliminary.json" - if hypothesis_id == "RC-H01": - return root / "operating_point.json" - return None - - -def _cost_estimate( - protocol: dict[str, Any], - arm: ArmSpec, - *, - prompt_tokens: int, - completion_tokens: int, -) -> dict[str, float]: - rates = protocol["pricing"]["models"][arm.model] - sample = completion_tokens * float(rates["sample"]) / 1_000_000 - return { - "cached_prefill": ( - prompt_tokens * float(rates["prefill_cached"]) / 1_000_000 + sample - ), - "uncached_prefill": ( - prompt_tokens * float(rates["prefill_uncached"]) / 1_000_000 + sample - ), - } - - -def _tracking_step( - job: JobSpec, - *, - tasks: list[EvaluationTask], -) -> int: - task_index = {task.task_id: index for index, task in enumerate(tasks)} - return (job.attempt_index - 1) * len(tasks) + task_index[job.task_id] - - -def _summary_path(store: AttemptStore, run_key: RunKey) -> Path: - relative = store.run_relative_path(run_key) / "summary.json" - return store.external_root.joinpath(*relative.parts) - - -def _selection_paths(store: AttemptStore, study_id: str) -> tuple[Path, Path]: - root = store.external_root / "studies" / study_id / "selections" - return ( - root / "operating_point.preliminary.json", - root / "operating_point.json", - ) - - -def _preflight_renderers( - *, - tasks: list[EvaluationTask], - arms: dict[str, ArmSpec], - samplers: dict[str, TinkerSampler], -) -> dict[tuple[str, str], list[Any]]: - messages: dict[tuple[str, str], list[Any]] = {} - for arm_id, arm in arms.items(): - sampler = samplers[arm_id] - for task in tasks: - rendered_messages = _messages(task, arm) - prompt = sampler.build_prompt(rendered_messages) - if int(prompt.length) + arm.max_output_tokens > arm.context_tokens: - raise ValueError( - f"{arm_id}/{task.task_id} exceeds its context window: " - f"{prompt.length}+{arm.max_output_tokens}>{arm.context_tokens}" - ) - messages[(arm_id, task.task_id)] = rendered_messages - return messages - - -def _preflight_references(tasks: list[EvaluationTask], evaluator: PixCellEvaluator) -> None: - for task in tasks: - evidence = evaluator.validate_reference(task.reference) - if evidence["target_image_sha256"] != task.reference.target_image_sha256: - raise ValueError(f"{task.task_id} reference validation changed its digest") - - -def _expected_request( - *, - arm: ArmSpec, - protocol: dict[str, Any], - job: JobSpec, -) -> dict[str, int | float]: - return { - "max_tokens": arm.max_output_tokens, - "temperature": float(protocol["sampling"]["temperature"]), - "top_p": float(protocol["sampling"]["top_p"]), - "seed": job.seed, - } - - -def _finite_number(value: Any, *, field: str, key: AttemptKey) -> float: - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not math.isfinite(float(value)) - ): - raise ValueError(f"{key}: {field} must be a finite number") - return float(value) - - -def _unit_interval(value: Any, *, field: str, key: AttemptKey) -> float: - result = _finite_number(value, field=field, key=key) - if not 0.0 <= result <= 1.0: - raise ValueError(f"{key}: {field} must be in [0, 1]") - return result - - -def _validate_existing_receipt( - *, - key: AttemptKey, - job: JobSpec, - arm: ArmSpec, - protocol: dict[str, Any], - receipt: dict[str, Any], - expected_prompt_tokens: int, - run_manifest_record_sha256: str, -) -> None: - expected_fields = { - "request", - "sample", - "sampling_seconds", - "raw_completion_sha256", - "completion_sha256", - "cost_estimate_usd", - "run_manifest_record_sha256", - } - if set(receipt) != expected_fields: - raise ValueError( - f"{key}: sampling receipt fields differ from the launch schema" - ) - if receipt["run_manifest_record_sha256"] != run_manifest_record_sha256: - raise ValueError( - f"{key}: sampling receipt belongs to another launch manifest" - ) - if receipt["request"] != _expected_request( - arm=arm, - protocol=protocol, - job=job, - ): - raise ValueError( - f"{key}: sampling request differs from the exact current job" - ) - - sample = receipt["sample"] - required_sample_fields = { - "prompt_tokens", - "completion_tokens", - "stop_reason", - "cap_hit", - "raw_text", - "completion_text", - "reasoning_text", - "answer_text", - "reasoning_tokens_exact", - "answer_tokens_exact", - "reasoning_tokens_estimate", - "answer_tokens_estimate", - "channel_token_count_basis", - "channel_parse_complete", - } - if not isinstance(sample, dict) or set(sample) != required_sample_fields: - raise ValueError( - f"{key}: sampled completion fields differ from the launch schema" - ) - prompt_tokens = sample["prompt_tokens"] - completion_tokens = sample["completion_tokens"] - if ( - isinstance(prompt_tokens, bool) - or not isinstance(prompt_tokens, int) - or prompt_tokens != expected_prompt_tokens - ): - raise ValueError( - f"{key}: stored prompt length differs from the current renderer" - ) - if ( - isinstance(completion_tokens, bool) - or not isinstance(completion_tokens, int) - or not 0 <= completion_tokens <= arm.max_output_tokens - or prompt_tokens + arm.max_output_tokens > arm.context_tokens - ): - raise ValueError(f"{key}: stored completion has invalid token accounting") - if ( - not isinstance(sample["stop_reason"], str) - or not isinstance(sample["cap_hit"], bool) - or sample["cap_hit"] != (sample["stop_reason"] == "length") - or (sample["cap_hit"] and completion_tokens != arm.max_output_tokens) - ): - raise ValueError( - f"{key}: stored stop reason and cap state are inconsistent" - ) - for field in ("raw_text", "completion_text", "answer_text"): - if not isinstance(sample[field], str): - raise ValueError(f"{key}: sample.{field} must be text") - if sample["reasoning_text"] is not None and not isinstance( - sample["reasoning_text"], - str, - ): - raise ValueError(f"{key}: sample.reasoning_text must be text or null") - if ( - not isinstance(sample["channel_token_count_basis"], str) - or not sample["channel_token_count_basis"] - ): - raise ValueError( - f"{key}: sample.channel_token_count_basis must be non-empty text" - ) - if not isinstance(sample["channel_parse_complete"], bool): - raise ValueError( - f"{key}: sample.channel_parse_complete must be boolean" - ) - - reasoning_exact = sample["reasoning_tokens_exact"] - answer_exact = sample["answer_tokens_exact"] - if (reasoning_exact is None) != (answer_exact is None): - raise ValueError(f"{key}: exact channel token accounting is partial") - if reasoning_exact is not None and ( - isinstance(reasoning_exact, bool) - or not isinstance(reasoning_exact, int) - or reasoning_exact < 0 - or isinstance(answer_exact, bool) - or not isinstance(answer_exact, int) - or answer_exact < 0 - or reasoning_exact + answer_exact != completion_tokens - ): - raise ValueError( - f"{key}: exact channel tokens do not partition completion" - ) - for field in ("reasoning_tokens_estimate", "answer_tokens_estimate"): - value = sample[field] - if value is not None and ( - isinstance(value, bool) or not isinstance(value, int) or value < 0 - ): - raise ValueError( - f"{key}: sample.{field} must be nonnegative or null" - ) - - raw_text = sample["raw_text"] - completion_text = sample["completion_text"] - if receipt["raw_completion_sha256"] != _sha256_text(raw_text): - raise ValueError(f"{key}: raw completion digest mismatch") - if receipt["completion_sha256"] != _sha256_text(completion_text): - raise ValueError(f"{key}: completion digest mismatch") - if _finite_number( - receipt["sampling_seconds"], - field="sampling_seconds", - key=key, - ) < 0.0: - raise ValueError(f"{key}: sampling_seconds must be nonnegative") - expected_cost = _cost_estimate( - protocol, - arm, - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - ) - if receipt["cost_estimate_usd"] != expected_cost: - raise ValueError(f"{key}: cost evidence does not reproduce") - - -def _validate_existing_evaluation( - *, - key: AttemptKey, - task: EvaluationTask, - receipt: dict[str, Any], - evaluation: dict[str, Any], -) -> None: - required_fields = { - "status", - "attribution", - "pure_executable", - "measurement_available", - "measured_iou", - "measured_dice", - "aggregation_iou", - "program", - "program_sha256", - "reference_sha256", - "violations", - "error", - "retryable", - "evaluation_seconds", - "diagnostics", - } - if not required_fields.issubset(evaluation) or not set(evaluation).issubset( - required_fields | {"render_sha256"} - ): - raise ValueError(f"{key}: evaluation fields differ from the launch schema") - try: - attribution = Attribution(str(evaluation["attribution"])) - status = EvaluationStatus(str(evaluation["status"])) - except ValueError as exc: - raise ValueError( - f"{key}: evaluation has an unknown status or attribution" - ) from exc - if attribution is not Attribution.MODEL: - raise RuntimeError( - f"{key}: existing evaluation is {attribution.value}/{status.value}; " - "refusing to buy more samples" - ) - - completion = str(receipt["sample"]["completion_text"]) - program = evaluation["program"] - if not isinstance(program, str) or program != extract_code(completion): - raise ValueError( - f"{key}: stored program differs from deterministic extraction" - ) - if evaluation["program_sha256"] != _sha256_text(program): - raise ValueError(f"{key}: program digest mismatch") - if evaluation["reference_sha256"] != task.reference.target_image_sha256: - raise ValueError(f"{key}: evaluation belongs to another reference") - if ( - not isinstance(evaluation["pure_executable"], bool) - or evaluation["pure_executable"] != (status is EvaluationStatus.OK) - ): - raise ValueError(f"{key}: pure-executable flag is inconsistent") - if not isinstance(evaluation["measurement_available"], bool): - raise ValueError( - f"{key}: measurement-availability flag must be boolean" - ) - measured_iou = evaluation["measured_iou"] - measured_dice = evaluation["measured_dice"] - if (measured_iou is None) != (measured_dice is None): - raise ValueError(f"{key}: geometry metrics are partial") - if evaluation["measurement_available"] != (measured_iou is not None): - raise ValueError( - f"{key}: measurement-availability flag is inconsistent" - ) - if evaluation["measurement_available"] != ( - status is EvaluationStatus.OK - ): - raise ValueError( - f"{key}: successful execution and geometry measurement disagree" - ) - if not isinstance(evaluation["diagnostics"], dict): - raise ValueError(f"{key}: evaluation diagnostics must be an object") - expected_aggregation = 0.0 - if measured_iou is not None: - expected_aggregation = _unit_interval( - measured_iou, - field="measured_iou", - key=key, - ) - _unit_interval(measured_dice, field="measured_dice", key=key) - render_sha = evaluation.get("render_sha256") - if ( - not isinstance(render_sha, str) - or len(render_sha) != 64 - or any( - character not in "0123456789abcdef" - for character in render_sha - ) - ): - raise ValueError( - f"{key}: measured geometry has no render digest" - ) - if evaluation["diagnostics"].get("render_sha256") != render_sha: - raise ValueError( - f"{key}: render digest differs from evaluator diagnostics" - ) - if ( - evaluation["diagnostics"].get("reference_sha256") - != task.reference.target_image_sha256 - ): - raise ValueError( - f"{key}: diagnostics belong to another reference" - ) - aggregation_iou = _unit_interval( - evaluation["aggregation_iou"], - field="aggregation_iou", - key=key, - ) - if aggregation_iou != expected_aggregation: - raise ValueError(f"{key}: aggregation IoU does not reproduce") - if ( - not isinstance(evaluation["violations"], list) - or any( - not isinstance(item, str) for item in evaluation["violations"] - ) - or not isinstance(evaluation["retryable"], bool) - or not isinstance(evaluation["diagnostics"], dict) - ): - raise ValueError( - f"{key}: evaluation diagnostics have an invalid shape" - ) - if evaluation["retryable"]: - raise ValueError( - f"{key}: model-attributed evaluation cannot be retryable" - ) - if evaluation["error"] is not None and not isinstance( - evaluation["error"], - str, - ): - raise ValueError(f"{key}: evaluation error must be text or null") - if _finite_number( - evaluation["evaluation_seconds"], - field="evaluation_seconds", - key=key, - ) < 0.0: - raise ValueError(f"{key}: evaluation_seconds must be nonnegative") - - -def _validate_existing_attempts_before_sampling( - *, - store: AttemptStore, - study_id: str, - jobs: list[JobSpec], - tasks: list[EvaluationTask], - arms: dict[str, ArmSpec], - protocol: dict[str, Any], - samplers: dict[str, TinkerSampler], - messages: dict[tuple[str, str], list[Any]], - run_manifest_record_sha256_by_arm: dict[str, str], - require_receipt_evaluations: bool = True, -) -> None: - """Bind every resumed record before any remote sampling is possible.""" - - if not jobs: - raise ValueError("baseline wave contains no jobs") - task_by_id = {task.task_id: task for task in tasks} - hypothesis_ids = {job.hypothesis_id for job in jobs} - if len(hypothesis_ids) != 1: - raise ValueError("baseline wave contains multiple hypotheses") - hypothesis_id = next(iter(hypothesis_ids)) - prompt_lengths: dict[tuple[str, str], int] = {} - for arm_id in arms: - run_key = RunKey(study_id, hypothesis_id, arm_id) - manifest = store.load_run_manifest(run_key) - expected_manifest_sha = run_manifest_record_sha256_by_arm[arm_id] - if manifest is None or manifest.record_sha256 != expected_manifest_sha: - raise ValueError(f"{run_key}: launch manifest is missing or stale") - for task in tasks: - prompt = samplers[arm_id].build_prompt( - messages[(arm_id, task.task_id)] - ) - prompt_lengths[(arm_id, task.task_id)] = int(prompt.length) - - seen: set[AttemptKey] = set() - for job in jobs: - key = _attempt_key(study_id, job) - if key in seen: - raise ValueError(f"duplicate launch job: {key}") - seen.add(key) - receipt = store.load_sampling_receipt(key) - evaluation = store.load_evaluation(key) - if receipt is None: - if evaluation is not None: - raise ValueError( - f"{key}: evaluation exists without a sampling receipt" - ) - continue - _validate_existing_receipt( - key=key, - job=job, - arm=arms[job.arm_id], - protocol=protocol, - receipt=receipt.payload, - expected_prompt_tokens=prompt_lengths[ - (job.arm_id, job.task_id) - ], - run_manifest_record_sha256=( - run_manifest_record_sha256_by_arm[job.arm_id] - ), - ) - if evaluation is None: - if require_receipt_evaluations: - raise RuntimeError( - f"{key}: existing receipt has not been evaluated; " - "refusing to buy more samples" - ) - continue - _validate_existing_evaluation( - key=key, - task=task_by_id[job.task_id], - receipt=receipt.payload, - evaluation=evaluation.payload, - ) - - -async def _sample_missing( - *, - store: AttemptStore, - study_id: str, - jobs: list[JobSpec], - tasks: list[EvaluationTask], - arms: dict[str, ArmSpec], - protocol: dict[str, Any], - samplers: dict[str, TinkerSampler], - messages: dict[tuple[str, str], list[Any]], - run_manifest_record_sha256_by_arm: dict[str, str], -) -> None: - semaphore = asyncio.Semaphore(int(protocol["sampling"]["sampling_concurrency"])) - - async def sample_one(job: JobSpec) -> None: - key = _attempt_key(study_id, job) - if store.load_sampling_receipt(key) is not None: - return - arm = arms[job.arm_id] - request = SamplingRequest( - max_tokens=arm.max_output_tokens, - temperature=float(protocol["sampling"]["temperature"]), - top_p=float(protocol["sampling"]["top_p"]), - seed=job.seed, - ) - started = time.monotonic() - async with semaphore: - sample = await samplers[job.arm_id].sample( - messages[(job.arm_id, job.task_id)], - request, - ) - elapsed = time.monotonic() - started - sample_payload = sample.to_dict() - payload = { - "request": { - "max_tokens": request.max_tokens, - "temperature": request.temperature, - "top_p": request.top_p, - "seed": request.seed, - }, - "sample": sample_payload, - "sampling_seconds": elapsed, - "raw_completion_sha256": _sha256_text(sample.raw_text), - "completion_sha256": _sha256_text(sample.completion_text), - "run_manifest_record_sha256": run_manifest_record_sha256_by_arm[ - job.arm_id - ], - "cost_estimate_usd": _cost_estimate( - protocol, - arm, - prompt_tokens=sample.prompt_tokens, - completion_tokens=sample.completion_tokens, - ), - } - store.write_sampling_receipt(key, payload) - - outcomes = await asyncio.gather( - *(sample_one(job) for job in jobs), - return_exceptions=True, - ) - failures = [ - f"{jobs[index].arm_id}/{jobs[index].task_id}/" - f"attempt-{jobs[index].attempt_index}: {type(outcome).__name__}" - for index, outcome in enumerate(outcomes) - if isinstance(outcome, BaseException) - ] - if failures: - raise RuntimeError( - "one or more model requests failed after all sibling requests " - "settled and successful receipts were persisted:\n" - + "\n".join(failures) - ) - - -def _evaluate_missing( - *, - store: AttemptStore, - study_id: str, - jobs: list[JobSpec], - tasks: list[EvaluationTask], - evaluator: PixCellEvaluator, - tracker_by_arm: dict[str, WandbRunSession] | None, -) -> None: - task_by_id = {task.task_id: task for task in tasks} - pending: list[tuple[JobSpec, AttemptKey, StoredRecord]] = [] - for job in jobs: - key = _attempt_key(study_id, job) - if store.load_evaluation(key) is not None: - continue - receipt = store.load_sampling_receipt(key) - if receipt is None: - raise RuntimeError(f"missing sampling receipt for {key}") - pending.append((job, key, receipt)) - - failures: list[str] = [] - for job in jobs: - key = _attempt_key(study_id, job) - existing = store.load_evaluation(key) - if ( - existing is not None - and existing.payload.get("attribution") != Attribution.MODEL.value - ): - failures.append( - f"{key}: {existing.payload.get('attribution')}/" - f"{existing.payload.get('status')}; " - f"record={existing.relative_path}" - ) - batch_size = evaluator.max_workers * 4 - for start in range(0, len(pending), batch_size): - batch = pending[start : start + batch_size] - requests = [ - ( - task_by_id[job.task_id].reference, - receipt.payload["sample"]["completion_text"], - ) - for job, _key, receipt in batch - ] - results = evaluator.evaluate_batch(requests) - for (job, key, receipt), result in zip(batch, results, strict=True): - receipt_payload = receipt.payload - completion = str(receipt_payload["sample"]["completion_text"]) - program = extract_code(completion) - measured_iou = float(result.iou) if result.iou is not None else None - measured_dice = float(result.dice) if result.dice is not None else None - aggregation_iou = ( - measured_iou - if result.attribution is Attribution.MODEL and measured_iou is not None - else 0.0 - ) - pure = result.status is EvaluationStatus.OK - payload: dict[str, Any] = { - "status": result.status.value, - "attribution": result.attribution.value, - "pure_executable": pure, - "measurement_available": result.iou is not None, - "measured_iou": measured_iou, - "measured_dice": measured_dice, - "aggregation_iou": aggregation_iou, - "program": program, - "program_sha256": _sha256_text(program), - "reference_sha256": task_by_id[ - job.task_id - ].reference.target_image_sha256, - "violations": list(result.violations), - "error": result.error, - "retryable": bool(result.retryable), - "evaluation_seconds": float(result.latency_seconds), - "diagnostics": _safe_json(result.metrics), - } - render_sha256 = result.metrics.get("render_sha256") - if render_sha256 is not None: - payload["render_sha256"] = render_sha256 - if result.attribution is not Attribution.MODEL: - diagnostic = store.write_evaluator_failure( - key, - { - "status": result.status.value, - "attribution": result.attribution.value, - "error": result.error, - "retryable": bool(result.retryable), - "violations": list(result.violations), - "program_sha256": _sha256_text(program), - "reference_sha256": task_by_id[ - job.task_id - ].reference.target_image_sha256, - "sampling_record_sha256": receipt.record_sha256, - }, - ) - failures.append( - f"{key}: {result.attribution.value}/" - f"{result.status.value}; " - f"diagnostic={diagnostic.relative_path}" - ) - continue - store.write_evaluation(key, payload) - tracking_metrics = { - "attempt/iou": aggregation_iou, - "attempt/pure_executable": int(pure), - "attempt/cap_hit": int( - receipt_payload["sample"]["cap_hit"] - ), - "attempt/prompt_tokens": int( - receipt_payload["sample"]["prompt_tokens"] - ), - "attempt/completion_tokens": int( - receipt_payload["sample"]["completion_tokens"] - ), - "attempt/sampling_seconds": float( - receipt_payload["sampling_seconds"] - ), - "attempt/evaluation_seconds": float(result.latency_seconds), - "attempt/estimated_uncached_cost_usd": float( - receipt_payload["cost_estimate_usd"][ - "uncached_prefill" - ] - ), - } - if tracker_by_arm is not None: - tracker_by_arm[job.arm_id].mirror_saved_metrics( - key=key, - source="evaluation", - metrics=tracking_metrics, - step=_tracking_step(job, tasks=tasks), - ) - if failures: - raise RuntimeError( - "baseline evaluation aborted on verifier/reference failures:\n" - + "\n".join(failures) - ) - - -def _validate_selection_provenance( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, - protocol: dict[str, Any], - hypothesis_id: str, - selection: dict[str, Any] | None, -) -> None: - """Recompute a canonical operating-point receipt from immutable attempts.""" - - if hypothesis_id not in {"RC-H00R", "RC-H01"}: - if selection is not None: - raise ValueError(f"{hypothesis_id} must not receive a selection receipt") - return - if selection is None: - raise ValueError(f"{hypothesis_id} requires a selection receipt") - if selection.get("source_git_sha") != expected_source_sha: - raise ValueError("operating-point selection source SHA differs from launch SHA") - - store = AttemptStore(repo_root=repo_root, external_root=external_root) - manifest = load_task_manifest(task_manifest_path(repo_root), repo_root=repo_root) - tasks = load_task_set( - repo_root=repo_root, - manifest=manifest, - task_set=protocol["hypotheses"]["RC-H00"]["task_set"], - ) - task_ids = [task.task_id for task in tasks] - h00_jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H00", - wave_name="complete", - task_ids=task_ids, - ) - h00_binding, _bound_tasks, _bound_jobs = build_launch_binding( - repo_root=repo_root, - hypothesis_id="RC-H00", - wave_name="complete", - expected_source_sha=expected_source_sha, - external_root=external_root, - ) - summaries: dict[str, dict[str, Any]] = {} - for arm_id in protocol["hypotheses"]["RC-H00"]["arm_ids"]: - run_key = RunKey(protocol["study_id"], "RC-H00", arm_id) - run_manifest = store.load_run_manifest(run_key) - if run_manifest is None or run_manifest.payload != _run_manifest( - h00_binding, - arm_id, - ): - raise ValueError(f"RC-H00/{arm_id} run manifest is missing or stale") - summaries[arm_id] = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id="RC-H00", - arm_id=arm_id, - jobs=h00_jobs, - tasks=tasks, - ) - - rescue_summary = None - if selection.get("rescue_run"): - rescue_arm = str(protocol["hypotheses"]["RC-H00R"]["arm_ids"][0]) - h00r_jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H00R", - wave_name="complete", - task_ids=task_ids, - selection=selection, - ) - h00r_binding, _bound_tasks, _bound_jobs = build_launch_binding( - repo_root=repo_root, - hypothesis_id="RC-H00R", - wave_name="complete", - expected_source_sha=expected_source_sha, - external_root=external_root, - selection=selection, - ) - run_key = RunKey(protocol["study_id"], "RC-H00R", rescue_arm) - run_manifest = store.load_run_manifest(run_key) - if run_manifest is None or run_manifest.payload != _run_manifest( - h00r_binding, - rescue_arm, - ): - raise ValueError("RC-H00R run manifest is missing or stale") - rescue_summary = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id="RC-H00R", - arm_id=rescue_arm, - jobs=h00r_jobs, - tasks=tasks, - ) - - expected = choose_operating_point( - protocol=protocol, - source_git_sha=expected_source_sha, - source_summaries=summaries, - rescue_summary=rescue_summary, - ) - if selection != expected: - raise ValueError( - "operating-point selection does not reproduce from immutable attempts" - ) - if hypothesis_id == "RC-H00R" and ( - not expected["rescue_required"] or expected["rescue_run"] - ): - raise ValueError("RC-H00R requires the incomplete triggered preliminary receipt") - if hypothesis_id == "RC-H01" and not expected["complete"]: - raise ValueError("RC-H01 requires a complete operating-point selection") - - -def _write_summaries_and_selection( - *, - store: AttemptStore, - protocol: dict[str, Any], - binding: dict[str, Any], - jobs: list[JobSpec], - tasks: list[EvaluationTask], -) -> dict[str, dict[str, Any]]: - hypothesis_id = str(binding["hypothesis_id"]) - summaries: dict[str, dict[str, Any]] = {} - for arm_id in binding["arms"]: - run_key = RunKey(protocol["study_id"], hypothesis_id, arm_id) - summary = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id=hypothesis_id, - arm_id=arm_id, - jobs=jobs, - tasks=tasks, - ) - atomic_write_json(_summary_path(store, run_key), summary) - summaries[arm_id] = summary - - if binding["wave"] != "complete": - return summaries - preliminary_path, final_path = _selection_paths(store, protocol["study_id"]) - if hypothesis_id == "RC-H00": - selection = choose_operating_point( - protocol=protocol, - source_git_sha=binding["source_git_sha"], - source_summaries=summaries, - ) - atomic_write_json(preliminary_path, selection) - if selection["complete"]: - atomic_write_json(final_path, selection) - elif hypothesis_id == "RC-H00R": - manifest = json.loads(preliminary_path.read_text(encoding="utf-8")) - original = { - arm_id: value - for arm_id, value in manifest["arm_summaries"].items() - if arm_id in protocol["hypotheses"]["RC-H00"]["arm_ids"] - } - if len(summaries) != 1: - raise ValueError("RC-H00R must contain exactly one rescue arm") - selection = choose_operating_point( - protocol=protocol, - source_git_sha=binding["source_git_sha"], - source_summaries=original, - rescue_summary=next(iter(summaries.values())), - ) - atomic_write_json(final_path, selection) - return summaries - - -async def run_baseline( - *, - repo_root: Path, - hypothesis_id: str, - wave_name: str, - expected_source_sha: str, - external_root: Path, - confirmation: str, - selection_path: Path | None = None, - protocol_file: Path | None = None, -) -> dict[str, Any]: - """Run one frozen wave. No training client is ever constructed.""" - - root = repo_root.expanduser().resolve() - protocol = ( - load_protocol(root) - if protocol_file is None - else load_protocol(root, protocol_file=protocol_file) - ) - canonical_selection = _canonical_selection_path( - external_root=external_root, - study_id=protocol["study_id"], - hypothesis_id=hypothesis_id, - ) - if canonical_selection is None: - if selection_path is not None: - raise ValueError(f"{hypothesis_id} does not accept a selection receipt") - else: - if selection_path is None: - raise ValueError( - f"{hypothesis_id} requires --selection {canonical_selection}" - ) - observed_selection = selection_path.expanduser().resolve(strict=True) - if observed_selection != canonical_selection: - raise ValueError( - "selection receipt must be the canonical file under the study root" - ) - expected_confirmation = protocol["launch"]["confirmation_token"] - if confirmation != expected_confirmation: - raise ValueError( - f"paid baseline launch requires --confirm-spend {expected_confirmation}" - ) - selection = _selection_document(selection_path) - binding, tasks, jobs = build_launch_binding( - repo_root=root, - hypothesis_id=hypothesis_id, - wave_name=wave_name, - expected_source_sha=expected_source_sha, - external_root=external_root, - selection=selection, - protocol_file=protocol_file, - ) - _validate_selection_provenance( - repo_root=root, - external_root=external_root, - expected_source_sha=expected_source_sha, - protocol=protocol, - hypothesis_id=hypothesis_id, - selection=selection, - ) - validate_credentials(protocol) - validate_wandb_access(expected_entity=protocol["tracking"].get("entity")) - arms = { - arm_id: arm_spec(protocol, arm_id) - for arm_id in resolved_arm_ids( - protocol, - hypothesis_id, - selection=selection, - ) - } - store = AttemptStore(repo_root=root, external_root=external_root) - shared_service: dict[str, Any] = {} - - def service_factory() -> Any: - if "client" not in shared_service: - import tinker - - shared_service["client"] = tinker.ServiceClient( - user_metadata={ - "study": protocol["study_id"], - "hypothesis": hypothesis_id, - "source_sha": expected_source_sha, - } - ) - return shared_service["client"] - - samplers = { - arm_id: TinkerSampler( - _sampler_target(arm), - service_client_factory=service_factory, - ) - for arm_id, arm in arms.items() - } - # Renderer/tokenizer construction, complete context accounting, reference - # validation, launch manifests, and locks all precede the first ServiceClient. - messages = _preflight_renderers( - tasks=tasks, - arms=arms, - samplers=samplers, - ) - with PixCellEvaluator( - max_workers=int(protocol["sampling"]["evaluator_workers"]), - evaluator_retries=1, - require_isolation=True, - ) as evaluator: - _preflight_references(tasks, evaluator) - with ExitStack() as stack: - run_manifest_record_sha256_by_arm: dict[str, str] = {} - for arm_id in arms: - run_key = RunKey(protocol["study_id"], hypothesis_id, arm_id) - stack.enter_context(store.acquire_run_lock(run_key)) - manifest = store.create_or_verify_run_manifest( - run_key, - _run_manifest(binding, arm_id), - ) - run_manifest_record_sha256_by_arm[arm_id] = ( - manifest.record_sha256 - ) - _validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=tasks, - arms=arms, - protocol=protocol, - samplers=samplers, - messages=messages, - run_manifest_record_sha256_by_arm=( - run_manifest_record_sha256_by_arm - ), - require_receipt_evaluations=False, - ) - resumed_jobs = [ - job - for job in jobs - if store.load_sampling_receipt( - _attempt_key(protocol["study_id"], job) - ) - is not None - ] - _evaluate_missing( - store=store, - study_id=protocol["study_id"], - jobs=resumed_jobs, - tasks=tasks, - evaluator=evaluator, - tracker_by_arm=None, - ) - _validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=tasks, - arms=arms, - protocol=protocol, - samplers=samplers, - messages=messages, - run_manifest_record_sha256_by_arm=( - run_manifest_record_sha256_by_arm - ), - require_receipt_evaluations=True, - ) - await _sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=tasks, - arms=arms, - protocol=protocol, - samplers=samplers, - messages=messages, - run_manifest_record_sha256_by_arm=( - run_manifest_record_sha256_by_arm - ), - ) - mirrors = { - arm_id: WandbMetricsMirror( - store=store, - project=protocol["tracking"]["project"], - source_git_sha=expected_source_sha, - entity=protocol["tracking"].get("entity"), - group=protocol["tracking"].get("group"), - ) - for arm_id in arms - } - trackers = { - arm_id: stack.enter_context( - mirror.session( - RunKey(protocol["study_id"], hypothesis_id, arm_id) - ) - ) - for arm_id, mirror in mirrors.items() - } - _evaluate_missing( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=tasks, - evaluator=evaluator, - tracker_by_arm=trackers, - ) - summaries = _write_summaries_and_selection( - store=store, - protocol=protocol, - binding=binding, - jobs=jobs, - tasks=tasks, - ) - return { - "binding": binding, - "summaries": summaries, - "mean_arm_iou": ( - statistics.fmean( - value["mean_iou_at_1"] for value in summaries.values() - ) - if summaries - else 0.0 - ), - } diff --git a/rl/evaluation/samplers.py b/rl/evaluation/samplers.py deleted file mode 100644 index 2c51cf9b..00000000 --- a/rl/evaluation/samplers.py +++ /dev/null @@ -1,531 +0,0 @@ -"""Lazy, one-completion Tinker samplers for frozen baseline evaluations. - -This module owns transport and renderer bookkeeping only. Callers supply the -already-built cookbook ``Message`` conversation, and the evaluation runner owns -task selection, persistence, and geometry scoring. -""" - -from __future__ import annotations - -import base64 -import io -import math -from collections.abc import Callable, Sequence -from dataclasses import asdict, dataclass -from importlib.metadata import PackageNotFoundError, version -from typing import Any, Protocol - - -QWEN_MODEL = "Qwen/Qwen3.6-35B-A3B" -INKLING_MODEL = "thinkingmachines/Inkling" -QWEN_RENDERERS = frozenset({"qwen3_5", "qwen3_5_disable_thinking"}) -INKLING_RENDERER = "tml_v0" -TML_RENDERERS_VERSION = "0.1.0" - - -class SamplerConfigurationError(ValueError): - """A target or sampling request is not fully and unambiguously bound.""" - - -class SamplingProtocolError(RuntimeError): - """Tinker returned a response outside the one-completion contract.""" - - -@dataclass(frozen=True) -class SamplerTarget: - """Exact model location plus the renderer needed to interpret its tokens. - - ``model_name`` identifies the base family for tokenizer/renderer loading. - Exactly one of ``base_model`` and ``model_path`` is the Tinker sampling - location. A checkpoint therefore remains explicitly tied to its base - family instead of trying to infer that family from an opaque URI. - """ - - model_name: str - renderer_name: str - base_model: str | None = None - model_path: str | None = None - effort: float | None = None - - def __post_init__(self) -> None: - locations = int(self.base_model is not None) + int(self.model_path is not None) - if locations != 1: - raise SamplerConfigurationError("exactly one of base_model or model_path must be set") - if self.base_model is not None and self.base_model != self.model_name: - raise SamplerConfigurationError( - "base_model must exactly match model_name; aliases are not accepted" - ) - if self.model_path is not None and not self.model_path.strip(): - raise SamplerConfigurationError("model_path must be non-empty") - - if self.model_name == QWEN_MODEL: - if self.renderer_name not in QWEN_RENDERERS: - raise SamplerConfigurationError( - f"{QWEN_MODEL} requires one of {sorted(QWEN_RENDERERS)}" - ) - if self.effort is not None: - raise SamplerConfigurationError( - "effort is a TML/Inkling setting and must be omitted for Qwen" - ) - return - - if self.model_name == INKLING_MODEL: - if self.renderer_name != INKLING_RENDERER: - raise SamplerConfigurationError( - f"{INKLING_MODEL} requires renderer_name={INKLING_RENDERER!r}" - ) - if ( - self.effort is None - or isinstance(self.effort, bool) - or not math.isfinite(self.effort) - or not 0.0 <= self.effort < 1.0 - ): - raise SamplerConfigurationError( - "Inkling effort must be an explicit finite number in [0, 1)" - ) - return - - raise SamplerConfigurationError(f"unsupported model_name: {self.model_name!r}") - - @property - def client_kwargs(self) -> dict[str, str]: - if self.base_model is not None: - return {"base_model": self.base_model} - assert self.model_path is not None - return {"model_path": self.model_path} - - -@dataclass(frozen=True) -class SamplingRequest: - """Every stochastic and length control sent to Tinker, with no defaults.""" - - max_tokens: int - temperature: float - top_p: float - seed: int - - def __post_init__(self) -> None: - if ( - isinstance(self.max_tokens, bool) - or not isinstance(self.max_tokens, int) - or self.max_tokens <= 0 - ): - raise SamplerConfigurationError("max_tokens must be a positive integer") - if ( - isinstance(self.temperature, bool) - or not math.isfinite(self.temperature) - or self.temperature < 0.0 - ): - raise SamplerConfigurationError("temperature must be a finite non-negative number") - if ( - isinstance(self.top_p, bool) - or not math.isfinite(self.top_p) - or not 0.0 < self.top_p <= 1.0 - ): - raise SamplerConfigurationError("top_p must be finite and in (0, 1]") - if isinstance(self.seed, bool) or not isinstance(self.seed, int) or self.seed < 0: - raise SamplerConfigurationError("seed must be a non-negative integer") - - -@dataclass(frozen=True) -class SampleResult: - """One sampled completion with transport and channel accounting. - - Qwen channel counts are exact sampled-token partitions. Inkling's TML - parser identifies the channels exactly, but its public parser does not - expose per-channel token spans. Its ``*_estimate`` fields therefore count - only re-encoded channel text and deliberately exclude TML framing tokens. - """ - - prompt_tokens: int - completion_tokens: int - stop_reason: str - cap_hit: bool - raw_text: str - completion_text: str - reasoning_text: str | None - answer_text: str - reasoning_tokens_exact: int | None - answer_tokens_exact: int | None - reasoning_tokens_estimate: int | None - answer_tokens_estimate: int | None - channel_token_count_basis: str - channel_parse_complete: bool - - def to_dict(self) -> dict[str, Any]: - return asdict(self) - - -@dataclass(frozen=True) -class _DecodedCompletion: - raw_text: str - completion_text: str - reasoning_text: str | None - answer_text: str - reasoning_tokens_exact: int | None - answer_tokens_exact: int | None - reasoning_tokens_estimate: int | None - answer_tokens_estimate: int | None - channel_token_count_basis: str - channel_parse_complete: bool - - -class _Codec(Protocol): - def build_prompt(self, messages: Sequence[Any]) -> Any: ... - - def stop_sequences(self) -> list[int]: ... - - def decode(self, tokens: Sequence[int]) -> _DecodedCompletion: ... - - -class _QwenCodec: - def __init__(self, renderer: Any, tokenizer: Any, renderer_name: str) -> None: - self._renderer = renderer - self._tokenizer = tokenizer - self._renderer_name = renderer_name - self._think_close = list(tokenizer.encode("", add_special_tokens=False)) - if not self._think_close: - raise SamplingProtocolError("Qwen tokenizer produced no tokens") - - @classmethod - def load(cls, target: SamplerTarget) -> _QwenCodec: - from tinker_cookbook.image_processing_utils import get_image_processor - from tinker_cookbook.renderers import get_renderer - from tinker_cookbook.tokenizer_utils import get_tokenizer - - tokenizer = get_tokenizer(target.model_name) - image_processor = get_image_processor(target.model_name) - renderer = get_renderer( - target.renderer_name, - tokenizer, - image_processor, - model_name=target.model_name, - ) - return cls(renderer, tokenizer, target.renderer_name) - - def build_prompt(self, messages: Sequence[Any]) -> Any: - return self._renderer.build_generation_prompt(list(messages)) - - def stop_sequences(self) -> list[int]: - return list(self._renderer.get_stop_sequences()) - - @staticmethod - def _first_subsequence(tokens: Sequence[int], needle: Sequence[int]) -> int | None: - limit = len(tokens) - len(needle) + 1 - for index in range(max(0, limit)): - if list(tokens[index : index + len(needle)]) == list(needle): - return index - return None - - def decode(self, tokens: Sequence[int]) -> _DecodedCompletion: - sampled = list(tokens) - raw_text = str(self._tokenizer.decode(sampled)) - if self._renderer_name == "qwen3_5_disable_thinking": - return _DecodedCompletion( - raw_text=raw_text, - completion_text=raw_text, - reasoning_text="", - answer_text=raw_text, - reasoning_tokens_exact=0, - answer_tokens_exact=len(sampled), - reasoning_tokens_estimate=None, - answer_tokens_estimate=None, - channel_token_count_basis=("qwen_disabled_thinking_all_sampled_tokens_are_answer"), - channel_parse_complete=True, - ) - - close_at = self._first_subsequence(sampled, self._think_close) - if close_at is None: - # The generation prompt has already opened the thinking channel. - # Until is sampled, every sampled token is in that channel. - return _DecodedCompletion( - raw_text=raw_text, - completion_text=f"\n{raw_text}", - reasoning_text=raw_text, - answer_text="", - reasoning_tokens_exact=len(sampled), - answer_tokens_exact=0, - reasoning_tokens_estimate=None, - answer_tokens_estimate=None, - channel_token_count_basis=("qwen_thinking_open_channel_without_closing_delimiter"), - channel_parse_complete=False, - ) - - answer_at = close_at + len(self._think_close) - reasoning_tokens = sampled[:close_at] - answer_tokens = sampled[answer_at:] - reasoning_text = str(self._tokenizer.decode(reasoning_tokens)) - answer_text = str(self._tokenizer.decode(answer_tokens)).lstrip() - return _DecodedCompletion( - raw_text=raw_text, - completion_text=f"\n{reasoning_text}\n{answer_text}", - reasoning_text=reasoning_text, - answer_text=answer_text, - reasoning_tokens_exact=answer_at, - answer_tokens_exact=len(sampled) - answer_at, - reasoning_tokens_estimate=None, - answer_tokens_estimate=None, - channel_token_count_basis=( - "qwen_sampled_tokens_partitioned_at_first_think_close_" - "closing_delimiter_assigned_to_reasoning" - ), - channel_parse_complete=True, - ) - - -def _pil_to_data_uri(image: Any) -> str: - buffer = io.BytesIO() - image.convert("RGB").save(buffer, format="PNG") - payload = base64.b64encode(buffer.getvalue()).decode("ascii") - return f"data:image/png;base64,{payload}" - - -def _normalize_tml_media(messages: Sequence[Any]) -> list[dict[str, Any]]: - """Convert cookbook ImagePart values to the OSS image_url shape TML reads.""" - - normalized: list[dict[str, Any]] = [] - for message in messages: - item = dict(message) - content = item.get("content") - if not isinstance(content, list): - normalized.append(item) - continue - parts: list[Any] = [] - for part in content: - if isinstance(part, dict) and part.get("type") == "image": - image = part.get("image") - if isinstance(image, str): - url = image - elif hasattr(image, "convert") and hasattr(image, "save"): - url = _pil_to_data_uri(image) - else: - raise TypeError( - "TML ImagePart.image must be a path/data URI string or PIL image" - ) - parts.append({"type": "image_url", "image_url": {"url": url}}) - else: - parts.append(part) - normalized.append({**item, "content": parts}) - return normalized - - -class _InklingCodec: - def __init__(self, native_renderer: Any, tokenizer: Any, effort: float) -> None: - self._renderer = native_renderer - self._tokenizer = tokenizer - self._effort = effort - - @classmethod - def load(cls, target: SamplerTarget) -> _InklingCodec: - try: - installed = version("tml-renderers") - except PackageNotFoundError as exc: - raise SamplerConfigurationError( - f"tml-renderers=={TML_RENDERERS_VERSION} is required for Inkling" - ) from exc - if installed != TML_RENDERERS_VERSION: - raise SamplerConfigurationError( - f"Inkling requires tml-renderers=={TML_RENDERERS_VERSION}; found {installed}" - ) - - from tml_renderers import tokenizers as tml_tokenizers - from tml_renderers import v0 as tml_v0 - - tokenizer = tml_tokenizers.o200k_base_chat() - return cls(tml_v0.Renderer(tokenizer), tokenizer, float(target.effort)) - - def _render_input(self, messages: Sequence[Any]) -> list[Any]: - from tml_renderers import chat as tml_chat - - return list(tml_chat.OpenAIMessage.from_oss_messages(_normalize_tml_media(messages))) - - def build_prompt(self, messages: Sequence[Any]) -> Any: - from tml_renderers import tinker as tml_tinker - - spans, _parser = self._renderer.render_for_completion_with_effort( - self._render_input(messages), - self._effort, - ) - return tml_tinker.token_spans_to_tinker_model_input(spans) - - def stop_sequences(self) -> list[int]: - return list(self._renderer.stop()) - - @staticmethod - def _raw_decode_fallback(raw_text: str, *, reason: str) -> _DecodedCompletion: - """Keep truncated TML output evaluable without inventing channel facts. - - A capped Inkling response can end inside TML framing. Depending on the - exact boundary, the native parser either raises or returns no textual - channel at all. The historical TML shim preserved the tokenizer's raw - decode in both cases, allowing downstream code extraction a best-effort - recovery path. The channel partition is unknowable in that state, so - channel-specific text/counts remain unset and the parse is incomplete. - """ - - return _DecodedCompletion( - raw_text=raw_text, - completion_text=raw_text, - reasoning_text=None, - answer_text="", - reasoning_tokens_exact=None, - answer_tokens_exact=None, - reasoning_tokens_estimate=None, - answer_tokens_estimate=None, - channel_token_count_basis=( - f"tml_{reason}_raw_decode_fallback_channel_partition_unknown" - ), - channel_parse_complete=False, - ) - - def decode(self, tokens: Sequence[int]) -> _DecodedCompletion: - from tml_renderers import chat as tml_chat - - sampled = list(tokens) - raw_text = str(self._tokenizer.decode(sampled)) - _spans, parser = self._renderer.render_for_completion([]) - try: - parsed = list(parser.parse_tokens(sampled)) - except Exception: - return self._raw_decode_fallback( - raw_text, - reason="parse_failed", - ) - - reasoning_parts: list[str] = [] - answer_parts: list[str] = [] - saw_end = False - for message in parsed: - content = message.content - if isinstance(content, tml_chat.ModelEndSampling): - saw_end = True - elif isinstance(content, tml_chat.Thinking): - reasoning_parts.append(getattr(content, "text", str(content))) - elif isinstance(content, tml_chat.Text): - answer_parts.append(getattr(content, "text", str(content))) - else: - # Tools are not part of PixCell's contract. Keeping an unknown - # model channel visible in the answer is safer than dropping it. - answer_parts.append(str(content)) - - reasoning_text = "\n".join(reasoning_parts) - answer_text = "\n".join(answer_parts) - reasoning_estimate = sum( - len(self._tokenizer.encode_ordinary(part)) for part in reasoning_parts - ) - answer_estimate = sum(len(self._tokenizer.encode_ordinary(part)) for part in answer_parts) - completion_text = ( - "".join(f"{part}\n" for part in reasoning_parts) + answer_text - ) - if not any(part.strip() for part in (*reasoning_parts, *answer_parts)): - return self._raw_decode_fallback( - raw_text, - reason="parse_had_no_usable_channel_text", - ) - return _DecodedCompletion( - raw_text=raw_text, - completion_text=completion_text, - reasoning_text=reasoning_text, - answer_text=answer_text, - reasoning_tokens_exact=None, - answer_tokens_exact=None, - reasoning_tokens_estimate=reasoning_estimate, - answer_tokens_estimate=answer_estimate, - channel_token_count_basis=( - "estimate_o200k_encode_ordinary_of_parsed_channel_text_excludes_tml_framing" - ), - channel_parse_complete=saw_end, - ) - - -def _build_codec(target: SamplerTarget) -> _Codec: - if target.model_name == QWEN_MODEL: - return _QwenCodec.load(target) - if target.model_name == INKLING_MODEL: - return _InklingCodec.load(target) - raise AssertionError(f"unvalidated sampler target: {target.model_name}") - - -def _default_service_client_factory() -> Any: - import tinker - - return tinker.ServiceClient() - - -class TinkerSampler: - """A lazy sampling client with a strict one-request/one-completion surface.""" - - def __init__( - self, - target: SamplerTarget, - *, - service_client_factory: Callable[[], Any] | None = None, - ) -> None: - self.target = target - self._service_client_factory = service_client_factory or _default_service_client_factory - self._codec: _Codec | None = None - self._sampling_client: Any | None = None - - def _get_codec(self) -> _Codec: - if self._codec is None: - self._codec = _build_codec(self.target) - return self._codec - - def build_prompt(self, messages: Sequence[Any]) -> Any: - if not messages: - raise SamplerConfigurationError("messages must not be empty") - return self._get_codec().build_prompt(messages) - - def _get_sampling_client(self) -> Any: - if self._sampling_client is None: - service = self._service_client_factory() - self._sampling_client = service.create_sampling_client(**self.target.client_kwargs) - return self._sampling_client - - async def sample( - self, - messages: Sequence[Any], - request: SamplingRequest, - ) -> SampleResult: - """Sample exactly one completion; this is the first method that creates a client.""" - - import tinker - - codec = self._get_codec() - prompt = self.build_prompt(messages) - response = await self._get_sampling_client().sample_async( - prompt=prompt, - num_samples=1, - sampling_params=tinker.SamplingParams( - max_tokens=request.max_tokens, - temperature=request.temperature, - top_p=request.top_p, - seed=request.seed, - stop=codec.stop_sequences(), - ), - ) - sequences = list(response.sequences) - if len(sequences) != 1: - raise SamplingProtocolError( - f"expected exactly one completion, received {len(sequences)}" - ) - sequence = sequences[0] - tokens = list(sequence.tokens) - stop_reason = str(sequence.stop_reason) - decoded = codec.decode(tokens) - return SampleResult( - prompt_tokens=int(prompt.length), - completion_tokens=len(tokens), - stop_reason=stop_reason, - cap_hit=stop_reason == "length", - raw_text=decoded.raw_text, - completion_text=decoded.completion_text, - reasoning_text=decoded.reasoning_text, - answer_text=decoded.answer_text, - reasoning_tokens_exact=decoded.reasoning_tokens_exact, - answer_tokens_exact=decoded.answer_tokens_exact, - reasoning_tokens_estimate=decoded.reasoning_tokens_estimate, - answer_tokens_estimate=decoded.answer_tokens_estimate, - channel_token_count_basis=decoded.channel_token_count_basis, - channel_parse_complete=decoded.channel_parse_complete, - ) diff --git a/rl/evaluation/summarize.py b/rl/evaluation/summarize.py deleted file mode 100644 index 450592c7..00000000 --- a/rl/evaluation/summarize.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Deterministic aggregation and Qwen operating-point selection.""" - -from __future__ import annotations - -import json -import math -import os -import statistics -import uuid -from collections import Counter, defaultdict -from pathlib import Path -from typing import Any - -from rl.evaluation.attempt_store import AttemptKey, AttemptStore -from rl.evaluation.protocol import JobSpec -from rl.evaluation.tasks import EvaluationTask, canonical_json_sha256 - - -SUMMARY_SCHEMA = "pixcell-baseline-summary-v1" -SELECTION_SCHEMA = "pixcell-operating-point-selection-v1" - - -def _percentile(values: list[int | float], fraction: float) -> float: - if not values: - return 0.0 - ordered = sorted(float(value) for value in values) - position = fraction * (len(ordered) - 1) - lower = math.floor(position) - upper = math.ceil(position) - if lower == upper: - return ordered[lower] - weight = position - lower - return ordered[lower] * (1 - weight) + ordered[upper] * weight - - -def _attempt_key(study_id: str, job: JobSpec) -> AttemptKey: - return AttemptKey( - study_id=study_id, - hypothesis_id=job.hypothesis_id, - arm_id=job.arm_id, - task_id=job.task_id, - attempt_index=job.attempt_index, - ) - - -def summarize_arm( - *, - store: AttemptStore, - study_id: str, - hypothesis_id: str, - arm_id: str, - jobs: list[JobSpec], - tasks: list[EvaluationTask], -) -> dict[str, Any]: - relevant = [ - job - for job in jobs - if job.hypothesis_id == hypothesis_id and job.arm_id == arm_id - ] - task_by_id = {task.task_id: task for task in tasks} - attempts: list[dict[str, Any]] = [] - missing: list[dict[str, Any]] = [] - for job in relevant: - key = _attempt_key(study_id, job) - receipt = store.load_sampling_receipt(key) - evaluation = store.load_evaluation(key) - if receipt is None or evaluation is None: - missing.append(key.as_dict()) - continue - if evaluation.payload.get("attribution") != "model": - raise ValueError( - f"{key} is not a model-attributed result and cannot be summarized" - ) - task = task_by_id[job.task_id] - attempts.append( - { - "task_id": job.task_id, - "level": task.level, - "representation_id": task.representation_id, - "attempt_index": job.attempt_index, - "receipt": receipt.payload, - "evaluation": evaluation.payload, - } - ) - - by_task: dict[str, list[dict[str, Any]]] = defaultdict(list) - by_representation: dict[str, list[dict[str, Any]]] = defaultdict(list) - by_level: dict[str, list[dict[str, Any]]] = defaultdict(list) - for attempt in attempts: - by_task[attempt["task_id"]].append(attempt) - by_representation[attempt["representation_id"]].append(attempt) - by_level[attempt["level"]].append(attempt) - - def score(value: dict[str, Any]) -> float: - return float(value["evaluation"]["aggregation_iou"]) - - task_means = [ - statistics.fmean(score(value) for value in values) - for values in by_task.values() - ] - task_bests = [ - max(score(value) for value in values) - for values in by_task.values() - ] - representation_means = [ - statistics.fmean(score(value) for value in values) - for values in by_representation.values() - ] - completion_tokens = [ - int(value["receipt"]["sample"]["completion_tokens"]) for value in attempts - ] - prompt_tokens = [ - int(value["receipt"]["sample"]["prompt_tokens"]) for value in attempts - ] - sampling_seconds = [ - float(value["receipt"]["sampling_seconds"]) for value in attempts - ] - evaluation_seconds = [ - float(value["evaluation"]["evaluation_seconds"]) for value in attempts - ] - pure_count = sum( - bool(value["evaluation"]["pure_executable"]) for value in attempts - ) - cap_count = sum(bool(value["receipt"]["sample"]["cap_hit"]) for value in attempts) - cached_cost = sum( - float(value["receipt"]["cost_estimate_usd"]["cached_prefill"]) - for value in attempts - ) - uncached_cost = sum( - float(value["receipt"]["cost_estimate_usd"]["uncached_prefill"]) - for value in attempts - ) - statuses = Counter( - str(value["evaluation"]["status"]) for value in attempts - ) - levels: dict[str, Any] = {} - for level, values in sorted(by_level.items()): - level_tasks: dict[str, list[dict[str, Any]]] = defaultdict(list) - for value in values: - level_tasks[value["task_id"]].append(value) - levels[level] = { - "attempts": len(values), - "tasks": len(level_tasks), - "mean_iou_at_1": statistics.fmean( - statistics.fmean(score(item) for item in task_values) - for task_values in level_tasks.values() - ), - "pure_executable_rate": ( - sum(bool(value["evaluation"]["pure_executable"]) for value in values) - / len(values) - ), - } - - count = len(attempts) - summary = { - "schema_version": SUMMARY_SCHEMA, - "study_id": study_id, - "hypothesis_id": hypothesis_id, - "arm_id": arm_id, - "complete": not missing and len(attempts) == len(relevant), - "expected_attempts": len(relevant), - "attempts": count, - "tasks": len(by_task), - "representations": len(by_representation), - "mean_iou_at_1": statistics.fmean(task_means) if task_means else 0.0, - "mean_best_at_k_iou": statistics.fmean(task_bests) if task_bests else 0.0, - "representation_macro_mean_iou": ( - statistics.fmean(representation_means) - if representation_means - else 0.0 - ), - "pure_executable_rate": pure_count / count if count else 0.0, - "cap_hit_rate": cap_count / count if count else 0.0, - "completion_tokens_total": sum(completion_tokens), - "completion_tokens_median": ( - statistics.median(completion_tokens) if completion_tokens else 0.0 - ), - "completion_tokens_p95": _percentile(completion_tokens, 0.95), - "prompt_tokens_total": sum(prompt_tokens), - "sampling_seconds_total": sum(sampling_seconds), - "evaluation_seconds_total": sum(evaluation_seconds), - "estimated_cached_cost_usd": cached_cost, - "estimated_uncached_cost_usd": uncached_cost, - "statuses": dict(sorted(statuses.items())), - "levels": levels, - "missing": missing, - } - summary["logical_sha256"] = canonical_json_sha256(summary) - return summary - - -def choose_operating_point( - *, - protocol: dict[str, Any], - source_git_sha: str, - source_summaries: dict[str, dict[str, Any]], - rescue_summary: dict[str, Any] | None = None, -) -> dict[str, Any]: - expected = set(protocol["hypotheses"]["RC-H00"]["arm_ids"]) - if set(source_summaries) != expected: - raise ValueError("operating-point selection requires every RC-H00 arm") - if not all(value["complete"] for value in source_summaries.values()): - raise ValueError("operating-point selection requires complete RC-H00 summaries") - trigger = protocol["hypotheses"]["RC-H00R"]["trigger"] - source_arm = str(trigger["source_arm"]) - rescue_required = ( - float(source_summaries[source_arm][trigger["metric"]]) - > float(trigger["threshold"]) - ) - candidates = dict(source_summaries) - rescue_run = rescue_summary is not None - if rescue_summary is not None: - if not rescue_summary["complete"]: - raise ValueError("rescue summary is incomplete") - candidates[str(rescue_summary["arm_id"])] = rescue_summary - complete = not rescue_required or rescue_run - selected: str | None = None - if complete: - primary = protocol["operating_point_selection"]["primary_metric"] - best = max(float(value[primary]) for value in candidates.values()) - tie_band = float(protocol["operating_point_selection"]["metric_tie_band"]) - eligible = [ - (arm_id, value) - for arm_id, value in candidates.items() - if float(value[primary]) >= best - tie_band - ] - eligible.sort( - key=lambda item: ( - -float(item[1]["pure_executable_rate"]), - float(item[1]["estimated_uncached_cost_usd"]), - float(item[1]["completion_tokens_median"]), - item[0], - ) - ) - selected = eligible[0][0] - document = { - "schema_version": SELECTION_SCHEMA, - "study_id": protocol["study_id"], - "protocol_sha256": protocol["logical_sha256"], - "source_git_sha": source_git_sha, - "source_hypothesis": "RC-H00", - "complete": complete, - "rescue_required": rescue_required, - "rescue_run": rescue_run, - "selected_arm": selected, - "selection_rule": protocol["operating_point_selection"], - "arm_summaries": candidates, - } - document["logical_sha256"] = canonical_json_sha256(document) - return document - - -def atomic_write_json(path: Path, document: dict[str, Any]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - raw = json.dumps(document, indent=2, sort_keys=True) + "\n" - temporary = path.parent / f".{path.name}.tmp-{os.getpid()}-{uuid.uuid4().hex}" - try: - with temporary.open("x", encoding="utf-8") as handle: - handle.write(raw) - handle.flush() - os.fsync(handle.fileno()) - os.replace(temporary, path) - directory_fd = os.open(path.parent, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) - finally: - temporary.unlink(missing_ok=True) diff --git a/rl/evaluation/tasks.py b/rl/evaluation/tasks.py deleted file mode 100644 index 94ba31c1..00000000 --- a/rl/evaluation/tasks.py +++ /dev/null @@ -1,346 +0,0 @@ -"""Frozen evaluation-task selection and model/reference-safe loading.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from PIL import Image - -from rl.common.contracts import ( - ModelObservation, - VerifierReference, - observation_from_row, - reference_from_row, - sampler_from_row, -) -from rl.common.dataset_io import parquet_paths - - -TASK_MANIFEST_SCHEMA = "pixcell-evaluation-task-manifest-v1" -OPERATING_POINT_TASK_SET = "qwen_operating_point" -BENCHMARK_TASK_SET = "f1_f8" -_SELECTION_SEED = "representation-curriculum-v1-operating-point-20260727" -_DEV_ROWS_PER_LEVEL = 8 -_LEVELS = ("L0", "L1", "L2", "L3", "L4") -_DEV_COLUMNS = ( - "id", - "level", - "image", - "target_image", - "footprint_um", - "representation_id", - "leakage_group_id", - "image_sha256", - "target_image_sha256", - "realization_slot", - "split_role", -) - - -@dataclass(frozen=True) -class EvaluationTask: - """A task with no supervised label in memory.""" - - task_id: str - level: str - representation_id: str - observation: ModelObservation - reference: VerifierReference - - -def canonical_json_sha256(value: Any) -> str: - payload = json.dumps( - value, - sort_keys=True, - separators=(",", ":"), - ensure_ascii=True, - ).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def _read_depth_validation_rows(dataset_root: Path) -> list[dict[str, Any]]: - import pyarrow.parquet as pq - - rows: list[dict[str, Any]] = [] - for path in parquet_paths( - dataset_root, - configuration="depth", - split="validation", - ): - schema = set(pq.read_schema(path).names) - missing = set(_DEV_COLUMNS) - schema - if missing: - raise ValueError( - f"{path} is missing evaluation columns: {sorted(missing)}" - ) - rows.extend(pq.read_table(path, columns=list(_DEV_COLUMNS)).to_pylist()) - return rows - - -def _row_rank(row: dict[str, Any], *, namespace: str) -> str: - payload = ( - f"{_SELECTION_SEED}\0{namespace}\0{row['representation_id']}\0{row['id']}" - ) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def _dev_manifest_entries(dataset_root: Path) -> list[dict[str, Any]]: - rows = _read_depth_validation_rows(dataset_root) - by_level_representation: dict[str, dict[str, list[dict[str, Any]]]] = { - level: {} for level in _LEVELS - } - for row in rows: - level = str(row["level"]).upper() - if level not in by_level_representation: - raise ValueError(f"unexpected validation level: {level!r}") - by_level_representation[level].setdefault( - str(row["representation_id"]), - [], - ).append(row) - - selected: list[dict[str, Any]] = [] - for level in _LEVELS: - representations = by_level_representation[level] - ranked_representations = sorted( - representations, - key=lambda value: hashlib.sha256( - f"{_SELECTION_SEED}\0{level}\0{value}".encode() - ).hexdigest(), - ) - if len(ranked_representations) < _DEV_ROWS_PER_LEVEL: - raise ValueError( - f"{level} has only {len(ranked_representations)} representations" - ) - for representation_id in ranked_representations[:_DEV_ROWS_PER_LEVEL]: - row = min( - representations[representation_id], - key=lambda value: _row_rank(value, namespace=level), - ) - sampler = sampler_from_row(row) - observation = observation_from_row(row) - reference = reference_from_row(row) - selected.append( - { - "task_id": sampler.opaque_id, - "source": "depth/validation", - "level": level, - "representation_id": representation_id, - "image_sha256": observation.image_sha256, - "target_image_sha256": reference.target_image_sha256, - "footprint_um": list(observation.footprint_um), - } - ) - return selected - - -def _benchmark_entry(repo_root: Path, index: int) -> dict[str, Any]: - relative = Path("data") / "benchmark" / f"final_{index}" - root = repo_root / relative - image_path = root / "device_bw.png" - footprint_path = root / "footprint.json" - meta_path = root / "meta.json" - image = image_path.read_bytes() - footprint_doc = json.loads(footprint_path.read_text(encoding="utf-8")) - meta = json.loads(meta_path.read_text(encoding="utf-8")) - footprint = [float(value) for value in footprint_doc["footprint_um"]] - digest = hashlib.sha256(image).hexdigest() - with Image.open(image_path) as raster: - image_size = [int(raster.width), int(raster.height)] - return { - "task_id": f"F{index}", - "source": str(relative), - "source_id": str(meta["id"]), - "level": "BENCHMARK", - "representation_id": f"benchmark-f{index}", - "image_sha256": digest, - "target_image_sha256": digest, - "footprint_um": footprint, - "image_size_px": image_size, - } - - -def build_task_manifest(repo_root: Path) -> dict[str, Any]: - root = repo_root.expanduser().resolve() - manifest = { - "schema_version": TASK_MANIFEST_SCHEMA, - "selection": { - "seed": _SELECTION_SEED, - "algorithm": ( - "sha256-rank representations per level, then sha256-rank held-out " - "realizations within each selected representation" - ), - "depth_validation_rows_per_level": _DEV_ROWS_PER_LEVEL, - }, - "task_sets": { - OPERATING_POINT_TASK_SET: _dev_manifest_entries(root / "dataset"), - BENCHMARK_TASK_SET: [ - _benchmark_entry(root, index) for index in range(1, 9) - ], - }, - } - manifest["logical_sha256"] = canonical_json_sha256(manifest) - return manifest - - -def build_benchmark_task_manifest(repo_root: Path) -> dict[str, Any]: - """Bind only the fixed repository benchmark without touching dataset rows.""" - - root = repo_root.expanduser().resolve() - manifest = { - "schema_version": TASK_MANIFEST_SCHEMA, - "selection": { - "algorithm": ( - "ordered fixed repository fixtures " - "data/benchmark/final_1 through final_8" - ), - "seed": None, - }, - "task_sets": { - BENCHMARK_TASK_SET: [ - _benchmark_entry(root, index) for index in range(1, 9) - ], - }, - } - manifest["logical_sha256"] = canonical_json_sha256(manifest) - return manifest - - -def load_task_manifest(path: Path, *, repo_root: Path) -> dict[str, Any]: - manifest = json.loads(path.read_text(encoding="utf-8")) - if manifest.get("schema_version") != TASK_MANIFEST_SCHEMA: - raise ValueError("unsupported evaluation task-manifest schema") - logical_sha = manifest.pop("logical_sha256", None) - observed_sha = canonical_json_sha256(manifest) - manifest["logical_sha256"] = logical_sha - if logical_sha != observed_sha: - raise ValueError("evaluation task-manifest logical SHA-256 mismatch") - task_sets = set(manifest.get("task_sets", {})) - if task_sets == {BENCHMARK_TASK_SET}: - expected = build_benchmark_task_manifest(repo_root) - elif task_sets == {OPERATING_POINT_TASK_SET, BENCHMARK_TASK_SET}: - expected = build_task_manifest(repo_root) - else: - raise ValueError( - f"unsupported evaluation task-set collection: {sorted(task_sets)}" - ) - if manifest != expected: - raise ValueError("evaluation task manifest does not match frozen sources") - return manifest - - -def _task_from_depth_row(row: dict[str, Any]) -> EvaluationTask: - sampler = sampler_from_row(row) - return EvaluationTask( - task_id=sampler.opaque_id, - level=sampler.level.upper(), - representation_id=sampler.representation_id, - observation=observation_from_row(row), - reference=reference_from_row(row), - ) - - -def _load_depth_tasks(dataset_root: Path) -> dict[str, EvaluationTask]: - return { - task.task_id: task - for task in ( - _task_from_depth_row(row) - for row in _read_depth_validation_rows(dataset_root) - ) - } - - -def _load_benchmark_task(repo_root: Path, entry: dict[str, Any]) -> EvaluationTask: - root = repo_root / entry["source"] - image = (root / "device_bw.png").read_bytes() - digest = hashlib.sha256(image).hexdigest() - footprint = tuple(float(value) for value in entry["footprint_um"]) - if digest != entry["image_sha256"]: - raise ValueError(f"{entry['task_id']} benchmark image digest changed") - observation = ModelObservation( - image_bytes=image, - footprint_um=footprint, - image_sha256=digest, - ) - reference = VerifierReference( - target_image_bytes=image, - footprint_um=footprint, - target_image_sha256=digest, - ) - return EvaluationTask( - task_id=str(entry["task_id"]), - level="BENCHMARK", - representation_id=str(entry["representation_id"]), - observation=observation, - reference=reference, - ) - - -def load_task_set( - *, - repo_root: Path, - manifest: dict[str, Any], - task_set: str, -) -> list[EvaluationTask]: - entries = list(manifest["task_sets"].get(task_set, ())) - if not entries: - raise ValueError(f"unknown or empty task set: {task_set!r}") - if task_set == OPERATING_POINT_TASK_SET: - available = _load_depth_tasks(repo_root / "dataset") - tasks = [] - for entry in entries: - task = available.get(str(entry["task_id"])) - if task is None: - raise ValueError(f"missing depth task {entry['task_id']}") - tasks.append(task) - elif task_set == BENCHMARK_TASK_SET: - tasks = [_load_benchmark_task(repo_root, entry) for entry in entries] - else: - raise ValueError(f"unsupported task set: {task_set!r}") - - for entry, task in zip(entries, tasks, strict=True): - observed = { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "image_sha256": task.observation.image_sha256, - "target_image_sha256": task.reference.target_image_sha256, - "footprint_um": list(task.observation.footprint_um), - } - for key, value in observed.items(): - if entry.get(key) != value: - raise ValueError( - f"{task.task_id} manifest mismatch for {key}: " - f"{entry.get(key)!r} != {value!r}" - ) - return tasks - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, required=True) - result.add_argument("--output", type=Path, required=True) - result.add_argument("--benchmark-only", action="store_true") - return result - - -def main() -> None: - args = parser().parse_args() - document = ( - build_benchmark_task_manifest(args.repo_root) - if args.benchmark_only - else build_task_manifest(args.repo_root) - ) - args.output.parent.mkdir(parents=True, exist_ok=True) - args.output.write_text( - json.dumps(document, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - - -if __name__ == "__main__": - main() diff --git a/rl/evaluation/tests/test_attempt_store.py b/rl/evaluation/tests/test_attempt_store.py deleted file mode 100644 index e085765e..00000000 --- a/rl/evaluation/tests/test_attempt_store.py +++ /dev/null @@ -1,239 +0,0 @@ -from __future__ import annotations - -import hashlib -import json -from pathlib import Path - -import pytest - -from rl.evaluation.attempt_store import ( - AttemptKey, - AttemptStore, - ImmutableRecordError, - InvalidRecordError, - ManifestDriftError, - RunKey, - RunLockedError, - RunLockRequiredError, - UnsafeStoreRootError, -) - - -def _digest(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def _manifest(**updates: object) -> dict: - value = { - "source_git_sha": "a" * 40, - "protocol_sha256": _digest("protocol"), - "task_set_sha256": _digest("tasks"), - "prompt_sha256": _digest("prompt"), - "sandbox_image_ref": "sha256:" + "b" * 64, - "sandbox_image_id": "sha256:" + "b" * 64, - "runtime": { - "python": "3.13.14", - "packages": {"tinker": "0.22.7"}, - "tinker_cookbook_commit": "c" * 40, - }, - "model_binding": { - "provider": "tinker", - "model": "Qwen/Qwen3.6-35B-A3B", - "renderer": "qwen3_5_disable_thinking", - "temperature": 1.0, - "top_p": 1.0, - "max_output_tokens": 4096, - }, - } - value.update(updates) - return value - - -@pytest.fixture -def roots(tmp_path: Path) -> tuple[Path, Path]: - repo = tmp_path / "repo" - repo.mkdir() - return repo, tmp_path / "external-record" - - -def test_store_rejects_repository_output_and_unsafe_keys( - roots: tuple[Path, Path], -) -> None: - repo, _ = roots - with pytest.raises(UnsafeStoreRootError): - AttemptStore(repo_root=repo, external_root=repo / "runs") - with pytest.raises(UnsafeStoreRootError): - AttemptStore(repo_root=repo, external_root=repo.parent) - with pytest.raises(ValueError): - AttemptKey("study", "hypothesis", "arm", "../escape", 1) - with pytest.raises(ValueError): - AttemptKey("study", "hypothesis", "arm", "task", 0) - - -def test_manifest_is_create_or_verify_and_paths_are_stable( - roots: tuple[Path, Path], -) -> None: - repo, external = roots - store = AttemptStore(repo_root=repo, external_root=external) - run_key = RunKey("study-v1", "H-QWEN-ZERO", "off-4k") - first = store.create_or_verify_run_manifest(run_key, _manifest()) - second = store.create_or_verify_run_manifest(run_key, _manifest()) - - assert first == second - assert str(first.relative_path) == ( - "studies/study-v1/hypotheses/H-QWEN-ZERO/" - "arms/off-4k/run_manifest.json" - ) - with pytest.raises(ManifestDriftError): - store.create_or_verify_run_manifest( - run_key, - _manifest(prompt_sha256=_digest("changed prompt")), - ) - assert store.load_run_manifest(run_key) == first - - -def test_resume_uses_receipt_without_calling_sampler_and_records_evaluation( - roots: tuple[Path, Path], -) -> None: - repo, external = roots - store = AttemptStore(repo_root=repo, external_root=external) - key = AttemptKey("study-v1", "H-QWEN-ZERO", "off-4k", "F1", 1) - store.create_or_verify_run_manifest(key.run_key, _manifest()) - calls = 0 - - def sample() -> dict: - nonlocal calls - calls += 1 - return { - "raw_completion": "print('first')", - "completion_sha256": _digest("print('first')"), - "finish_reason": "stop", - "output_tokens": 7, - "latency_seconds": 0.25, - } - - with pytest.raises(RunLockRequiredError): - store.resume_or_sample(key, sample) - with store.acquire_run_lock(key.run_key): - created = store.resume_or_sample(key, sample) - resumed = store.resume_or_sample( - key, - lambda: pytest.fail("a saved receipt must never be resampled"), - ) - - assert calls == 1 - assert created.resumed is False - assert resumed.resumed is True - assert resumed.record == created.record - assert str(created.record.relative_path).endswith( - "tasks/F1/attempts/000001/sampling_receipt.json" - ) - - evaluation = store.write_evaluation( - key, - { - "extracted_code": "print('first')", - "raw_iou": 0.75, - "measured_dice": None, - "render_sha256": None, - "pure_executable_rate": 1.0, - }, - ) - assert store.load_evaluation(key) == evaluation - with pytest.raises(ImmutableRecordError): - store.write_evaluation( - key, - { - "extracted_code": "print('different')", - "raw_iou": 0.8, - "pure_executable_rate": 1.0, - }, - ) - - -def test_evaluator_failures_are_diagnostic_and_do_not_block_final_evaluation( - roots: tuple[Path, Path], -) -> None: - repo, external = roots - store = AttemptStore(repo_root=repo, external_root=external) - key = AttemptKey("study-v1", "H-QWEN-ZERO", "off-4k", "F1", 1) - store.write_sampling_receipt(key, {"raw_completion": "print('first')"}) - diagnostic = { - "status": "worker_error", - "attribution": "evaluator", - "error": "container unavailable", - "retryable": True, - "program_sha256": _digest("print('first')"), - "reference_sha256": _digest("reference"), - "sampling_record_sha256": _digest("receipt"), - } - first = store.write_evaluator_failure(key, diagnostic) - repeated = store.write_evaluator_failure(key, diagnostic) - - assert first == repeated - assert store.load_evaluation(key) is None - assert store.list_evaluator_failures(key) == (first,) - - final = store.write_evaluation( - key, - { - "status": "syntax_error", - "attribution": "model", - "aggregation_iou": 0.0, - }, - ) - assert store.load_evaluation(key) == final - assert store.list_evaluator_failures(key) == (first,) - - -def test_run_lock_is_nonblocking_across_store_instances( - roots: tuple[Path, Path], -) -> None: - repo, external = roots - first = AttemptStore(repo_root=repo, external_root=external) - second = AttemptStore(repo_root=repo, external_root=external) - run_key = RunKey("study-v1", "H-QWEN-ZERO", "off-4k") - - with first.acquire_run_lock(run_key): - with pytest.raises(RunLockedError): - with second.acquire_run_lock(run_key): - pass - with second.acquire_run_lock(run_key): - pass - - -@pytest.mark.parametrize( - "payload", - [ - {"raw_completion": "x", "raw_iou": float("nan")}, - {"raw_completion": "x", "raw_iou": 1.01}, - {"raw_completion": "x", "completion_sha256": "not-a-hash"}, - {"raw_completion": "x", "output_tokens": -1}, - {"raw_completion": "x", "temperature": 2.01}, - ], -) -def test_receipts_reject_nonfinite_out_of_range_and_bad_hashes( - roots: tuple[Path, Path], - payload: dict, -) -> None: - repo, external = roots - store = AttemptStore(repo_root=repo, external_root=external) - key = AttemptKey("study-v1", "H-QWEN-ZERO", "off-4k", "F1", 1) - with pytest.raises(InvalidRecordError): - store.write_sampling_receipt(key, payload) - - -def test_read_detects_tampering_through_record_hash( - roots: tuple[Path, Path], -) -> None: - repo, external = roots - store = AttemptStore(repo_root=repo, external_root=external) - key = AttemptKey("study-v1", "H-QWEN-ZERO", "off-4k", "F1", 1) - stored = store.write_sampling_receipt(key, {"raw_completion": "original"}) - path = external / Path(*stored.relative_path.parts) - document = json.loads(path.read_text(encoding="utf-8")) - document["payload"]["raw_completion"] = "tampered" - path.write_text(json.dumps(document), encoding="utf-8") - - with pytest.raises(InvalidRecordError, match="payload hash mismatch"): - store.load_sampling_receipt(key) diff --git a/rl/evaluation/tests/test_protocol.py b/rl/evaluation/tests/test_protocol.py deleted file mode 100644 index 8700b464..00000000 --- a/rl/evaluation/tests/test_protocol.py +++ /dev/null @@ -1,215 +0,0 @@ -from __future__ import annotations - -import json -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.evaluation.protocol import ( - deterministic_seed, - jobs_for_wave, - load_archived_protocol, - load_protocol, - resolved_arm_ids, -) -from rl.evaluation.tasks import load_task_manifest -from rl.studies.representation_curriculum_v1 import ( - run_baseline as archived_runner, -) - - -REPO_ROOT = Path(__file__).resolve().parents[3] -TASK_MANIFEST = ( - REPO_ROOT / "rl/studies/representation_curriculum_v1/task_manifest.json" -) -BASE_PROTOCOL = TASK_MANIFEST.with_name("protocol.json") -QWEN32_PROTOCOL = ( - TASK_MANIFEST.parent / "extensions/qwen32/protocol.json" -) -QWEN60_PROTOCOL = ( - TASK_MANIFEST.parent / "extensions/qwen60/protocol.json" -) - - -def _protocol() -> dict: - return load_archived_protocol(REPO_ROOT, protocol_file=BASE_PROTOCOL) - - -def _selection( - selected: str, - *, - complete: bool = True, - protocol: dict | None = None, -) -> dict: - protocol = protocol or _protocol() - return { - "schema_version": "pixcell-operating-point-selection-v1", - "study_id": protocol["study_id"], - "protocol_sha256": protocol["logical_sha256"], - "source_hypothesis": "RC-H00", - "selected_arm": selected, - "rescue_required": False, - "rescue_run": False, - "complete": complete, - } - - -def _task_ids(task_set: str) -> list[str]: - manifest = load_task_manifest(TASK_MANIFEST, repo_root=REPO_ROOT) - return [value["task_id"] for value in manifest["task_sets"][task_set]] - - -def test_protocol_freezes_baseline_and_forbids_unfrozen_training(): - protocol = _protocol() - assert protocol["study_id"] == "representation-curriculum-v1" - assert protocol["hypotheses"]["RC-H00"]["status"] == "frozen" - assert protocol["hypotheses"]["RC-H01"]["status"] == "frozen" - assert protocol["hypotheses"]["RC-H02"]["status"] == "frozen" - assert all( - value["status"] == "launch_forbidden_until_protocol_frozen" - for value in protocol["future_hypothesis_registry"].values() - ) - - -def test_qwen_factorial_is_matched_and_seed_paired(): - protocol = _protocol() - arms = [ - protocol["arms"][value] - for value in protocol["hypotheses"]["RC-H00"]["arm_ids"] - ] - assert {(value["thinking"], value["max_output_tokens"]) for value in arms} == { - (False, 4096), - (False, 16384), - (True, 4096), - (True, 16384), - } - first = deterministic_seed(protocol, task_id="same", attempt_index=1) - assert first == deterministic_seed( - protocol, - task_id="same", - attempt_index=1, - ) - assert first != deterministic_seed( - protocol, - task_id="same", - attempt_index=2, - ) - - -def test_wave_job_counts_are_exact(): - protocol = _protocol() - dev = _task_ids("qwen_operating_point") - bench = _task_ids("f1_f8") - assert len( - jobs_for_wave( - protocol, - hypothesis_id="RC-H00", - wave_name="smoke", - task_ids=dev, - ) - ) == 4 - assert len( - jobs_for_wave( - protocol, - hypothesis_id="RC-H00", - wave_name="complete", - task_ids=dev, - ) - ) == 320 - assert len( - jobs_for_wave( - protocol, - hypothesis_id="RC-H02", - wave_name="smoke", - task_ids=bench, - ) - ) == 8 - assert len( - jobs_for_wave( - protocol, - hypothesis_id="RC-H02", - wave_name="complete", - task_ids=bench, - ) - ) == 32 - - -def test_qwen_baseline_keeps_incumbent_control_when_winner_differs(): - protocol = _protocol() - assert resolved_arm_ids( - protocol, - "RC-H01", - selection=_selection("qwen-off-4096", protocol=protocol), - ) == ["qwen-off-4096"] - assert resolved_arm_ids( - protocol, - "RC-H01", - selection=_selection("qwen-on-16384", protocol=protocol), - ) == ["qwen-off-4096", "qwen-on-16384"] - - -def test_conditional_rescue_is_fail_closed(): - protocol = _protocol() - with pytest.raises(ValueError, match="forbidden"): - resolved_arm_ids( - protocol, - "RC-H00R", - selection=_selection("qwen-on-16384", protocol=protocol), - ) - triggered = _selection( - "qwen-on-16384", - complete=False, - protocol=protocol, - ) - triggered["rescue_required"] = True - assert resolved_arm_ids( - protocol, - "RC-H00R", - selection=triggered, - ) == ["qwen-on-32768-confirmation"] - - -@pytest.mark.parametrize( - "protocol_file", - (BASE_PROTOCOL, QWEN32_PROTOCOL, QWEN60_PROTOCOL), -) -def test_superseded_v1_protocols_are_rejected_for_paid_launch( - protocol_file: Path, -): - with pytest.raises( - ValueError, - match="prompt contract does not match the runtime", - ): - load_protocol(REPO_ROOT, protocol_file=protocol_file) - - -def test_archived_runner_binds_its_own_protocol( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, - capsys: pytest.CaptureFixture[str], -): - captured: dict[str, object] = {} - - async def fake_run_baseline(**kwargs): - captured.update(kwargs) - return {"summaries": {}} - - class FakeParser: - @staticmethod - def parse_args(): - return SimpleNamespace( - hypothesis="RC-H02", - wave="smoke", - expected_source_sha="a" * 40, - confirm_spend="PIXCELL_PHASE1_BASELINE", - external_root=tmp_path, - selection=None, - ) - - monkeypatch.setattr(archived_runner, "run_baseline", fake_run_baseline) - monkeypatch.setattr(archived_runner, "parser", FakeParser) - archived_runner.main() - assert captured["protocol_file"] == BASE_PROTOCOL - assert captured["hypothesis_id"] == "RC-H02" - assert json.loads(capsys.readouterr().out) == {} diff --git a/rl/evaluation/tests/test_runner.py b/rl/evaluation/tests/test_runner.py deleted file mode 100644 index 495035aa..00000000 --- a/rl/evaluation/tests/test_runner.py +++ /dev/null @@ -1,1104 +0,0 @@ -from __future__ import annotations - -import asyncio -import hashlib -import io -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -import pytest -from PIL import Image - -from rl.common.contracts import ModelObservation, VerifierReference -from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus -from rl.evaluation import runner -from rl.evaluation.attempt_store import AttemptKey, AttemptStore, RunKey -from rl.evaluation.protocol import ArmSpec, JobSpec -from rl.evaluation.samplers import SampleResult -from rl.evaluation.tasks import EvaluationTask - - -def _image_bytes() -> bytes: - image = Image.new("L", (4, 4), color=255) - image.putpixel((1, 1), 0) - buffer = io.BytesIO() - image.save(buffer, format="PNG") - return buffer.getvalue() - - -def _task(task_id: str = "F1") -> EvaluationTask: - image = _image_bytes() - digest = hashlib.sha256(image).hexdigest() - return EvaluationTask( - task_id=task_id, - level="BENCHMARK", - representation_id=f"benchmark-{task_id.lower()}", - observation=ModelObservation( - image_bytes=image, - footprint_um=(4.0, 4.0), - image_sha256=digest, - ), - reference=VerifierReference( - target_image_bytes=image, - footprint_um=(4.0, 4.0), - target_image_sha256=digest, - ), - ) - - -def _arm(arm_id: str = "qwen-off-4096") -> ArmSpec: - return ArmSpec( - arm_id=arm_id, - provider="tinker", - model="Qwen/Qwen3.6-35B-A3B", - renderer="qwen3_5_disable_thinking", - thinking=False, - thinking_effort=None, - max_output_tokens=4096, - context_tokens=65536, - max_image_long_edge=1440, - ) - - -def _job( - *, - arm_id: str = "qwen-off-4096", - task_id: str = "F1", - attempt_index: int = 1, -) -> JobSpec: - return JobSpec( - hypothesis_id="RC-H01", - arm_id=arm_id, - task_id=task_id, - attempt_index=attempt_index, - seed=20260727, - ) - - -def _protocol() -> dict[str, Any]: - return { - "study_id": "representation-curriculum-v1", - "launch": {"confirmation_token": "RUN_PHASE1_BASELINES"}, - "sampling": { - "sampling_concurrency": 2, - "evaluator_workers": 1, - "temperature": 1.0, - "top_p": 1.0, - }, - "pricing": { - "models": { - "Qwen/Qwen3.6-35B-A3B": { - "prefill_cached": 0.108, - "prefill_uncached": 0.54, - "sample": 1.335, - } - } - }, - "tracking": { - "project": "pixcell-test", - "entity": None, - }, - } - - -def _store(tmp_path: Path) -> AttemptStore: - repo = tmp_path / "repo" - repo.mkdir() - return AttemptStore( - repo_root=repo, - external_root=tmp_path / "external", - ) - - -def _sample_result(program: str = "print('sample')") -> SampleResult: - return SampleResult( - prompt_tokens=100, - completion_tokens=10, - stop_reason="stop", - cap_hit=False, - raw_text=program, - completion_text=program, - reasoning_text="", - answer_text=program, - reasoning_tokens_exact=0, - answer_tokens_exact=10, - reasoning_tokens_estimate=None, - answer_tokens_estimate=None, - channel_token_count_basis="test", - channel_parse_complete=True, - ) - - -def _binding( - *, - protocol: dict[str, Any], - arm: ArmSpec, - hypothesis_id: str, - source_sha: str = "a" * 40, -) -> dict[str, Any]: - return { - "hypothesis_id": hypothesis_id, - "source_git_sha": source_sha, - "protocol_sha256": "b" * 64, - "task_set_sha256": "c" * 64, - "prompt_sha256": "d" * 64, - "arms": { - arm.arm_id: { - "model": arm.model, - "renderer": arm.renderer, - "thinking": arm.thinking, - "thinking_effort": arm.thinking_effort, - "max_output_tokens": arm.max_output_tokens, - "context_tokens": arm.context_tokens, - "max_image_long_edge": arm.max_image_long_edge, - } - }, - "sampling": protocol["sampling"], - "runtime": { - "python": "3.13.14", - "packages": {"tinker": "0.22.7"}, - "tinker_cookbook_commit": "f" * 40, - }, - "sandbox": { - "image_ref": "sha256:" + "e" * 64, - "image_id": "sha256:" + "e" * 64, - }, - } - - -def _manifest_record( - *, - store: AttemptStore, - protocol: dict[str, Any], - arm: ArmSpec, - hypothesis_id: str, - binding: dict[str, Any], -) -> Any: - run_key = RunKey(protocol["study_id"], hypothesis_id, arm.arm_id) - with store.acquire_run_lock(run_key): - return store.create_or_verify_run_manifest( - run_key, - runner._run_manifest(binding, arm.arm_id), - ) - - -def _receipt_payload( - *, - protocol: dict[str, Any], - arm: ArmSpec, - job: JobSpec, - manifest_sha: str, - prompt_tokens: int = 100, -) -> dict[str, Any]: - sample = _sample_result().to_dict() - sample["prompt_tokens"] = prompt_tokens - return { - "request": runner._expected_request( - arm=arm, - protocol=protocol, - job=job, - ), - "sample": sample, - "sampling_seconds": 0.1, - "raw_completion_sha256": runner._sha256_text(sample["raw_text"]), - "completion_sha256": runner._sha256_text(sample["completion_text"]), - "run_manifest_record_sha256": manifest_sha, - "cost_estimate_usd": runner._cost_estimate( - protocol, - arm, - prompt_tokens=prompt_tokens, - completion_tokens=sample["completion_tokens"], - ), - } - - -def _evaluation_payload( - *, - task: EvaluationTask, - attribution: Attribution = Attribution.MODEL, - status: EvaluationStatus = EvaluationStatus.SYNTAX_ERROR, -) -> dict[str, Any]: - program = runner.extract_code(_sample_result().completion_text) - return { - "status": status.value, - "attribution": attribution.value, - "pure_executable": status is EvaluationStatus.OK, - "measurement_available": False, - "measured_iou": None, - "measured_dice": None, - "aggregation_iou": 0.0, - "program": program, - "program_sha256": runner._sha256_text(program), - "reference_sha256": task.reference.target_image_sha256, - "violations": [], - "error": "expected test failure", - "retryable": attribution is not Attribution.MODEL, - "evaluation_seconds": 0.1, - "diagnostics": {}, - } - - -class _CountingSampler: - def __init__(self) -> None: - self.calls = 0 - - def build_prompt(self, _messages: list[Any]) -> SimpleNamespace: - return SimpleNamespace(length=100) - - async def sample( - self, - _messages: list[Any], - _request: Any, - ) -> SampleResult: - self.calls += 1 - return _sample_result() - - -class _SavedRecordTracker: - def __init__(self, store: AttemptStore) -> None: - self.store = store - self.calls = 0 - - def mirror_saved_metrics( - self, - *, - key: AttemptKey, - source: str, - metrics: dict[str, int | float], - step: int, - ) -> SimpleNamespace: - assert source == "evaluation" - assert self.store.load_evaluation(key) is not None - assert metrics["attempt/iou"] >= 0.0 - assert step >= 0 - self.calls += 1 - return SimpleNamespace(mirrored=True) - - -def test_reference_preflight_failure_happens_before_remote_client_creation( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, -) -> None: - """A local reference failure must remain a zero-spend failure.""" - - protocol = _protocol() - task = _task() - arm = _arm() - job = JobSpec( - hypothesis_id="RC-H02", - arm_id=arm.arm_id, - task_id=task.task_id, - attempt_index=1, - seed=20260727, - ) - source_sha = "a" * 40 - client_calls = 0 - repo_root = tmp_path / "repo" - repo_root.mkdir() - - binding = { - "hypothesis_id": job.hypothesis_id, - "source_git_sha": source_sha, - "protocol_sha256": "b" * 64, - "task_set_sha256": "c" * 64, - "prompt_sha256": "d" * 64, - "arms": {arm.arm_id: {"model": arm.model}}, - "sampling": protocol["sampling"], - "runtime": { - "python": "3.13.14", - "packages": {"tinker": "0.22.7"}, - "tinker_cookbook_commit": "f" * 40, - }, - "sandbox": { - "image_ref": "sha256:" + "e" * 64, - "image_id": "sha256:" + "e" * 64, - }, - } - - class FakeSampler: - def __init__( - self, - _target: Any, - *, - service_client_factory: Any, - ) -> None: - self.service_client_factory = service_client_factory - - def build_prompt(self, _messages: list[Any]) -> SimpleNamespace: - return SimpleNamespace(length=100) - - async def sample(self, _messages: list[Any], _request: Any) -> Any: - nonlocal client_calls - client_calls += 1 - self.service_client_factory() - raise AssertionError("sampling must not begin") - - class BadReferenceEvaluator: - max_workers = 1 - - def __init__(self, **kwargs: Any) -> None: - assert kwargs["require_isolation"] is True - - def __enter__(self) -> BadReferenceEvaluator: - return self - - def __exit__(self, *_args: object) -> None: - pass - - def validate_reference(self, _reference: VerifierReference) -> dict[str, Any]: - raise ValueError("broken local reference") - - monkeypatch.setattr(runner, "load_protocol", lambda _root, **_kwargs: protocol) - monkeypatch.setattr( - runner, - "build_launch_binding", - lambda **_kwargs: (binding, [task], [job]), - ) - monkeypatch.setattr(runner, "validate_credentials", lambda _protocol: None) - monkeypatch.setattr( - runner, - "validate_wandb_access", - lambda **_kwargs: {"username": "test", "entity": None}, - ) - monkeypatch.setattr( - runner, - "resolved_arm_ids", - lambda *_args, **_kwargs: [arm.arm_id], - ) - monkeypatch.setattr(runner, "arm_spec", lambda *_args, **_kwargs: arm) - monkeypatch.setattr(runner, "TinkerSampler", FakeSampler) - monkeypatch.setattr(runner, "PixCellEvaluator", BadReferenceEvaluator) - monkeypatch.setattr(runner, "_messages", lambda _task, _arm: [{"role": "user"}]) - - with pytest.raises(ValueError, match="broken local reference"): - asyncio.run( - runner.run_baseline( - repo_root=repo_root, - hypothesis_id=job.hypothesis_id, - wave_name="smoke", - expected_source_sha=source_sha, - external_root=tmp_path / "external", - confirmation=protocol["launch"]["confirmation_token"], - ) - ) - - assert client_calls == 0 - assert list((tmp_path / "external").rglob("sampling_receipt.json")) == [] - - -def test_receipt_precedes_evaluation_and_resume_never_resamples( - tmp_path: Path, -) -> None: - store = _store(tmp_path) - task = _task() - arm = _arm() - job = _job() - sampler = _CountingSampler() - protocol = _protocol() - key = runner._attempt_key(protocol["study_id"], job) - messages = {(arm.arm_id, task.task_id): [{"role": "user"}]} - - asyncio.run( - runner._sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=[job], - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm={arm.arm_id: "a" * 64}, - ) - ) - first = store.load_sampling_receipt(key) - assert first is not None - - asyncio.run( - runner._sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=[job], - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm={arm.arm_id: "a" * 64}, - ) - ) - assert sampler.calls == 1 - assert store.load_sampling_receipt(key) == first - - class ReceiptAwareEvaluator: - max_workers = 1 - - def evaluate_batch( - self, - requests: list[tuple[VerifierReference, str]], - ) -> list[EvaluationResult]: - assert store.load_sampling_receipt(key) == first - assert len(requests) == 1 - return [ - EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=0.75, - dice=0.8, - metrics={"render_sha256": "f" * 64}, - latency_seconds=0.1, - ) - ] - - tracker = _SavedRecordTracker(store) - runner._evaluate_missing( - store=store, - study_id=protocol["study_id"], - jobs=[job], - tasks=[task], - evaluator=ReceiptAwareEvaluator(), - tracker_by_arm={arm.arm_id: tracker}, - ) - assert store.load_evaluation(key) is not None - assert tracker.calls == 1 - - -def test_smoke_expands_to_complete_without_duplicate_sampling( - tmp_path: Path, -) -> None: - """The complete wave must reuse its smoke subset and add only missing jobs.""" - - store = _store(tmp_path) - protocol = _protocol() - tasks = [_task("F1"), _task("F2")] - arms = { - arm_id: _arm(arm_id) - for arm_id in ("qwen-off-4096", "qwen-off-16384") - } - samplers = {arm_id: _CountingSampler() for arm_id in arms} - messages = { - (arm_id, task.task_id): [{"role": "user"}] - for arm_id in arms - for task in tasks - } - smoke = [ - _job(arm_id=arm_id, task_id="F1", attempt_index=1) - for arm_id in arms - ] - complete = [ - _job( - arm_id=arm_id, - task_id=task.task_id, - attempt_index=attempt_index, - ) - for attempt_index in (1, 2) - for task in tasks - for arm_id in arms - ] - - for jobs in (smoke, complete, complete): - asyncio.run( - runner._sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=tasks, - arms=arms, - protocol=protocol, - samplers=samplers, - messages=messages, - run_manifest_record_sha256_by_arm={ - arm_id: "a" * 64 for arm_id in arms - }, - ) - ) - - assert {arm_id: sampler.calls for arm_id, sampler in samplers.items()} == { - "qwen-off-4096": 4, - "qwen-off-16384": 4, - } - assert len(list(store.external_root.rglob("sampling_receipt.json"))) == 8 - - -def test_sampling_wave_persists_siblings_before_raising_transport_failure( - tmp_path: Path, -) -> None: - """One failed request must not cancel paid sibling generations.""" - - store = _store(tmp_path) - protocol = _protocol() - protocol["sampling"]["sampling_concurrency"] = 4 - task = _task() - arm = _arm() - jobs = [ - JobSpec( - hypothesis_id="RC-H01", - arm_id=arm.arm_id, - task_id=task.task_id, - attempt_index=index, - seed=20260727 + index, - ) - for index in range(1, 5) - ] - started: list[int] = [] - - class OneFailureSampler(_CountingSampler): - async def sample( - self, - _messages: list[Any], - request: Any, - ) -> SampleResult: - self.calls += 1 - started.append(request.seed) - await asyncio.sleep(0) - if request.seed == jobs[0].seed: - raise ConnectionError("simulated transport failure") - return _sample_result() - - sampler = OneFailureSampler() - with pytest.raises(RuntimeError, match="sibling requests settled"): - asyncio.run( - runner._sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages={ - (arm.arm_id, task.task_id): [{"role": "user"}] - }, - run_manifest_record_sha256_by_arm={ - arm.arm_id: "a" * 64 - }, - ) - ) - - assert started == [job.seed for job in jobs] - assert store.load_sampling_receipt( - runner._attempt_key(protocol["study_id"], jobs[0]) - ) is None - assert all( - store.load_sampling_receipt( - runner._attempt_key(protocol["study_id"], job) - ) - is not None - for job in jobs[1:] - ) - - -def test_resume_preflight_rejects_stale_request_before_new_sampling( - tmp_path: Path, -) -> None: - """An immutable receipt cannot be silently adopted by another job.""" - - store = _store(tmp_path) - protocol = _protocol() - task = _task() - arm = _arm() - jobs = [_job(attempt_index=1), _job(attempt_index=2)] - binding = _binding( - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - ) - manifest = _manifest_record( - store=store, - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - binding=binding, - ) - stale = _receipt_payload( - protocol=protocol, - arm=arm, - job=jobs[0], - manifest_sha=manifest.record_sha256, - ) - stale["request"]["seed"] += 1 - store.write_sampling_receipt( - runner._attempt_key(protocol["study_id"], jobs[0]), - stale, - ) - sampler = _CountingSampler() - messages = {(arm.arm_id, task.task_id): [{"role": "user"}]} - - with pytest.raises(ValueError, match="exact current job"): - runner._validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm={ - arm.arm_id: manifest.record_sha256 - }, - ) - - assert sampler.calls == 0 - assert store.load_sampling_receipt( - runner._attempt_key(protocol["study_id"], jobs[1]) - ) is None - - -def test_complete_resume_rejects_saved_verifier_failure_before_sampling( - tmp_path: Path, -) -> None: - """A bad smoke evaluation must stop a complete wave before attempts 2-4.""" - - store = _store(tmp_path) - protocol = _protocol() - task = _task() - arm = _arm() - jobs = [_job(attempt_index=1), _job(attempt_index=2)] - binding = _binding( - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - ) - manifest = _manifest_record( - store=store, - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - binding=binding, - ) - key = runner._attempt_key(protocol["study_id"], jobs[0]) - store.write_sampling_receipt( - key, - _receipt_payload( - protocol=protocol, - arm=arm, - job=jobs[0], - manifest_sha=manifest.record_sha256, - ), - ) - store.write_evaluation( - key, - _evaluation_payload( - task=task, - attribution=Attribution.EVALUATOR, - status=EvaluationStatus.WORKER_ERROR, - ), - ) - sampler = _CountingSampler() - messages = {(arm.arm_id, task.task_id): [{"role": "user"}]} - - with pytest.raises(RuntimeError, match="refusing to buy more samples"): - runner._validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm={ - arm.arm_id: manifest.record_sha256 - }, - ) - - assert sampler.calls == 0 - assert store.load_sampling_receipt( - runner._attempt_key(protocol["study_id"], jobs[1]) - ) is None - - -def test_run_baseline_complete_fails_before_client_on_bad_smoke_record( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, -) -> None: - """The paid entrypoint applies the resume audit before sampling.""" - - protocol = _protocol() - task = _task() - arm = _arm() - jobs = [ - JobSpec( - hypothesis_id="RC-H05", - arm_id=arm.arm_id, - task_id=task.task_id, - attempt_index=index, - seed=20260727 + index, - ) - for index in (1, 2) - ] - source_sha = "a" * 40 - binding = _binding( - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - source_sha=source_sha, - ) - repo_root = tmp_path / "repo" - repo_root.mkdir() - external_root = tmp_path / "external" - store = AttemptStore(repo_root=repo_root, external_root=external_root) - manifest = _manifest_record( - store=store, - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - binding=binding, - ) - key = runner._attempt_key(protocol["study_id"], jobs[0]) - store.write_sampling_receipt( - key, - _receipt_payload( - protocol=protocol, - arm=arm, - job=jobs[0], - manifest_sha=manifest.record_sha256, - ), - ) - store.write_evaluation( - key, - _evaluation_payload( - task=task, - attribution=Attribution.REFERENCE, - status=EvaluationStatus.REFERENCE_ERROR, - ), - ) - client_calls = 0 - - class FakeSampler: - def __init__( - self, - _target: Any, - *, - service_client_factory: Any, - ) -> None: - self.service_client_factory = service_client_factory - - def build_prompt(self, _messages: list[Any]) -> SimpleNamespace: - return SimpleNamespace(length=100) - - async def sample(self, _messages: list[Any], _request: Any) -> Any: - nonlocal client_calls - client_calls += 1 - self.service_client_factory() - raise AssertionError("resume audit must stop before sampling") - - class ValidReferenceEvaluator: - max_workers = 1 - - def __init__(self, **kwargs: Any) -> None: - assert kwargs["require_isolation"] is True - - def __enter__(self) -> ValidReferenceEvaluator: - return self - - def __exit__(self, *_args: object) -> None: - pass - - def validate_reference( - self, - reference: VerifierReference, - ) -> dict[str, Any]: - return {"target_image_sha256": reference.target_image_sha256} - - def evaluate_batch(self, _requests: Any) -> Any: - raise AssertionError("resume audit must stop before evaluation") - - monkeypatch.setattr(runner, "load_protocol", lambda _root, **_kwargs: protocol) - monkeypatch.setattr( - runner, - "build_launch_binding", - lambda **_kwargs: (binding, [task], jobs), - ) - monkeypatch.setattr(runner, "validate_credentials", lambda _protocol: None) - monkeypatch.setattr( - runner, - "validate_wandb_access", - lambda **_kwargs: {"username": "test", "entity": None}, - ) - monkeypatch.setattr( - runner, - "resolved_arm_ids", - lambda *_args, **_kwargs: [arm.arm_id], - ) - monkeypatch.setattr(runner, "arm_spec", lambda *_args, **_kwargs: arm) - monkeypatch.setattr(runner, "TinkerSampler", FakeSampler) - monkeypatch.setattr(runner, "PixCellEvaluator", ValidReferenceEvaluator) - monkeypatch.setattr( - runner, - "_messages", - lambda _task, _arm: [{"role": "user"}], - ) - - with pytest.raises(RuntimeError, match="refusing to buy more samples"): - asyncio.run( - runner.run_baseline( - repo_root=repo_root, - hypothesis_id=jobs[0].hypothesis_id, - wave_name="complete", - expected_source_sha=source_sha, - external_root=external_root, - confirmation=protocol["launch"]["confirmation_token"], - ) - ) - - assert client_calls == 0 - assert store.load_sampling_receipt( - runner._attempt_key(protocol["study_id"], jobs[1]) - ) is None - - -def test_complete_resume_reuses_valid_model_failure_and_samples_only_missing( - tmp_path: Path, -) -> None: - """A legitimate model failure remains evidence and does not poison resume.""" - - store = _store(tmp_path) - protocol = _protocol() - task = _task() - arm = _arm() - jobs = [_job(attempt_index=1), _job(attempt_index=2)] - binding = _binding( - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - ) - manifest = _manifest_record( - store=store, - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - binding=binding, - ) - key = runner._attempt_key(protocol["study_id"], jobs[0]) - store.write_sampling_receipt( - key, - _receipt_payload( - protocol=protocol, - arm=arm, - job=jobs[0], - manifest_sha=manifest.record_sha256, - ), - ) - store.write_evaluation(key, _evaluation_payload(task=task)) - sampler = _CountingSampler() - messages = {(arm.arm_id, task.task_id): [{"role": "user"}]} - manifest_shas = {arm.arm_id: manifest.record_sha256} - - runner._validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm=manifest_shas, - ) - asyncio.run( - runner._sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm=manifest_shas, - ) - ) - - assert sampler.calls == 1 - assert len(list(store.external_root.rglob("sampling_receipt.json"))) == 2 - - -def test_partial_receipt_is_evaluated_before_complete_wave_expands( - tmp_path: Path, -) -> None: - """A crash between sampling and scoring resumes without deadlock or spend.""" - - store = _store(tmp_path) - protocol = _protocol() - task = _task() - arm = _arm() - jobs = [_job(attempt_index=1), _job(attempt_index=2)] - binding = _binding( - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - ) - manifest = _manifest_record( - store=store, - protocol=protocol, - arm=arm, - hypothesis_id=jobs[0].hypothesis_id, - binding=binding, - ) - first_key = runner._attempt_key(protocol["study_id"], jobs[0]) - store.write_sampling_receipt( - first_key, - _receipt_payload( - protocol=protocol, - arm=arm, - job=jobs[0], - manifest_sha=manifest.record_sha256, - ), - ) - sampler = _CountingSampler() - messages = {(arm.arm_id, task.task_id): [{"role": "user"}]} - manifest_shas = {arm.arm_id: manifest.record_sha256} - - runner._validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm=manifest_shas, - require_receipt_evaluations=False, - ) - - class ModelFailureEvaluator: - max_workers = 1 - - def evaluate_batch( - self, - requests: list[tuple[VerifierReference, str]], - ) -> list[EvaluationResult]: - assert len(requests) == 1 - return [ - EvaluationResult( - status=EvaluationStatus.SYNTAX_ERROR, - attribution=Attribution.MODEL, - error="invalid syntax", - latency_seconds=0.1, - ) - ] - - runner._evaluate_missing( - store=store, - study_id=protocol["study_id"], - jobs=[jobs[0]], - tasks=[task], - evaluator=ModelFailureEvaluator(), - tracker_by_arm=None, - ) - assert store.load_evaluation(first_key) is not None - - runner._validate_existing_attempts_before_sampling( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm=manifest_shas, - require_receipt_evaluations=True, - ) - asyncio.run( - runner._sample_missing( - store=store, - study_id=protocol["study_id"], - jobs=jobs, - tasks=[task], - arms={arm.arm_id: arm}, - protocol=protocol, - samplers={arm.arm_id: sampler}, - messages=messages, - run_manifest_record_sha256_by_arm=manifest_shas, - ) - ) - assert sampler.calls == 1 - assert len(list(store.external_root.rglob("sampling_receipt.json"))) == 2 - - -def test_saved_verifier_failure_is_diagnostic_and_receipt_can_be_re_evaluated( - tmp_path: Path, -) -> None: - """Infrastructure failure is preserved without poisoning the paid receipt.""" - - store = _store(tmp_path) - task = _task() - arm = _arm() - job = _job() - protocol = _protocol() - key = runner._attempt_key(protocol["study_id"], job) - store.write_sampling_receipt( - key, - { - "sample": _sample_result().to_dict(), - "sampling_seconds": 0.1, - "cost_estimate_usd": { - "cached_prefill": 0.0, - "uncached_prefill": 0.0, - }, - }, - ) - - class FailingEvaluator: - max_workers = 1 - - def evaluate_batch( - self, - _requests: list[tuple[VerifierReference, str]], - ) -> list[EvaluationResult]: - return [ - EvaluationResult( - status=EvaluationStatus.WORKER_ERROR, - attribution=Attribution.EVALUATOR, - error="worker crashed", - latency_seconds=0.1, - retryable=True, - ) - ] - - tracker = _SavedRecordTracker(store) - with pytest.raises(RuntimeError, match="verifier/reference failures"): - runner._evaluate_missing( - store=store, - study_id=protocol["study_id"], - jobs=[job], - tasks=[task], - evaluator=FailingEvaluator(), - tracker_by_arm={arm.arm_id: tracker}, - ) - assert store.load_evaluation(key) is None - assert len(store.list_evaluator_failures(key)) == 1 - assert tracker.calls == 0 - - class RecoveredEvaluator: - max_workers = 1 - - def evaluate_batch( - self, - _requests: list[tuple[VerifierReference, str]], - ) -> list[EvaluationResult]: - return [ - EvaluationResult( - status=EvaluationStatus.SYNTAX_ERROR, - attribution=Attribution.MODEL, - error="model syntax error", - latency_seconds=0.1, - ) - ] - - runner._evaluate_missing( - store=store, - study_id=protocol["study_id"], - jobs=[job], - tasks=[task], - evaluator=RecoveredEvaluator(), - tracker_by_arm={arm.arm_id: tracker}, - ) - assert store.load_evaluation(key) is not None - assert len(store.list_evaluator_failures(key)) == 1 - assert tracker.calls == 1 diff --git a/rl/evaluation/tests/test_samplers.py b/rl/evaluation/tests/test_samplers.py deleted file mode 100644 index e1d3d1b5..00000000 --- a/rl/evaluation/tests/test_samplers.py +++ /dev/null @@ -1,450 +0,0 @@ -from __future__ import annotations - -import asyncio -from types import SimpleNamespace - -import pytest - -from rl.evaluation import samplers -from rl.evaluation.samplers import ( - INKLING_MODEL, - QWEN_MODEL, - SampleResult, - SamplerConfigurationError, - SamplerTarget, - SamplingProtocolError, - SamplingRequest, - TinkerSampler, -) - - -def test_target_binds_exactly_one_location_and_renderer_contract() -> None: - qwen = SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5_disable_thinking", - base_model=QWEN_MODEL, - ) - assert qwen.client_kwargs == {"base_model": QWEN_MODEL} - - checkpoint = SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5", - model_path="tinker://run/sampler_weights/000001", - ) - assert checkpoint.client_kwargs == {"model_path": "tinker://run/sampler_weights/000001"} - - with pytest.raises(SamplerConfigurationError, match="exactly one"): - SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5", - ) - with pytest.raises(SamplerConfigurationError, match="exactly one"): - SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5", - base_model=QWEN_MODEL, - model_path="tinker://run/sampler_weights/1", - ) - with pytest.raises(SamplerConfigurationError, match="exactly match"): - SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5", - base_model="Qwen/Qwen3.6-35B-A3B-alias", - ) - with pytest.raises(SamplerConfigurationError, match="requires one of"): - SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qml_v0", - base_model=QWEN_MODEL, - ) - - -def test_inkling_requires_explicit_valid_effort() -> None: - target = SamplerTarget( - model_name=INKLING_MODEL, - renderer_name="tml_v0", - base_model=INKLING_MODEL, - effort=0.9, - ) - assert target.effort == 0.9 - - for effort in (None, -0.1, 1.0, float("nan"), True): - with pytest.raises(SamplerConfigurationError, match="explicit finite"): - SamplerTarget( - model_name=INKLING_MODEL, - renderer_name="tml_v0", - base_model=INKLING_MODEL, - effort=effort, - ) - - -@pytest.mark.parametrize( - ("kwargs", "message"), - [ - ({"max_tokens": 0, "temperature": 1.0, "top_p": 1.0, "seed": 1}, "max_tokens"), - ({"max_tokens": 1, "temperature": -0.1, "top_p": 1.0, "seed": 1}, "temperature"), - ({"max_tokens": 1, "temperature": 1.0, "top_p": 0.0, "seed": 1}, "top_p"), - ({"max_tokens": 1, "temperature": 1.0, "top_p": 1.0, "seed": -1}, "seed"), - ], -) -def test_sampling_request_has_no_implicit_invalid_controls(kwargs, message) -> None: - with pytest.raises(SamplerConfigurationError, match=message): - SamplingRequest(**kwargs) - - -class _FakeTokenizer: - _decode = { - 1: "plan ", - 2: "carefully", - 99: "", - 3: "\n\n```python\npass\n```", - } - - def encode(self, text, *, add_special_tokens): - assert add_special_tokens is False - assert text == "" - return [99] - - def decode(self, tokens): - return "".join(self._decode[token] for token in tokens) - - -class _FakeRenderer: - def build_generation_prompt(self, messages): - return SimpleNamespace(length=17, messages=messages) - - def get_stop_sequences(self): - return [777] - - -def test_qwen_channel_accounting_is_an_exact_sampled_token_partition() -> None: - thinking = samplers._QwenCodec(_FakeRenderer(), _FakeTokenizer(), "qwen3_5") - decoded = thinking.decode([1, 2, 99, 3]) - assert decoded.reasoning_text == "plan carefully" - assert decoded.answer_text == "```python\npass\n```" - assert decoded.reasoning_tokens_exact == 3 - assert decoded.answer_tokens_exact == 1 - assert decoded.reasoning_tokens_exact + decoded.answer_tokens_exact == 4 - assert decoded.channel_parse_complete is True - - truncated = thinking.decode([1, 2]) - assert truncated.reasoning_tokens_exact == 2 - assert truncated.answer_tokens_exact == 0 - assert truncated.answer_text == "" - assert truncated.channel_parse_complete is False - - direct = samplers._QwenCodec( - _FakeRenderer(), - _FakeTokenizer(), - "qwen3_5_disable_thinking", - ).decode([3]) - assert direct.reasoning_tokens_exact == 0 - assert direct.answer_tokens_exact == 1 - assert direct.channel_parse_complete is True - - -def test_sampler_is_lazy_and_sends_exactly_one_explicit_sample(monkeypatch) -> None: - tinker = pytest.importorskip( - "tinker", - reason="Tinker SDK env (rl/requirements-lock.txt) not installed", - ) - - calls: dict[str, object] = {"factory": 0} - - class FakeCodec: - def build_prompt(self, messages): - calls["messages"] = messages - return SimpleNamespace(length=123) - - def stop_sequences(self): - return [456] - - def decode(self, tokens): - calls["decoded"] = list(tokens) - return samplers._DecodedCompletion( - raw_text="raw", - completion_text="answer", - reasoning_text="", - answer_text="answer", - reasoning_tokens_exact=0, - answer_tokens_exact=len(tokens), - reasoning_tokens_estimate=None, - answer_tokens_estimate=None, - channel_token_count_basis="test", - channel_parse_complete=True, - ) - - class FakeSamplingClient: - async def sample_async(self, **kwargs): - calls["sample"] = kwargs - return SimpleNamespace( - sequences=[SimpleNamespace(tokens=[10, 11], stop_reason="length")] - ) - - class FakeService: - def create_sampling_client(self, **kwargs): - calls["client"] = kwargs - return FakeSamplingClient() - - def factory(): - calls["factory"] = int(calls["factory"]) + 1 - return FakeService() - - monkeypatch.setattr(samplers, "_build_codec", lambda _target: FakeCodec()) - target = SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5_disable_thinking", - base_model=QWEN_MODEL, - ) - sampler = TinkerSampler(target, service_client_factory=factory) - assert calls["factory"] == 0 - - # Prompt validation and tokenization remain zero-client operations. - assert sampler.build_prompt([{"role": "user", "content": "prompt"}]).length == 123 - assert calls["factory"] == 0 - - result = asyncio.run( - sampler.sample( - [{"role": "user", "content": "prompt"}], - SamplingRequest( - max_tokens=8192, - temperature=1.0, - top_p=0.95, - seed=20260727, - ), - ) - ) - assert isinstance(result, SampleResult) - assert calls["factory"] == 1 - assert calls["client"] == {"base_model": QWEN_MODEL} - sample_call = calls["sample"] - assert sample_call["num_samples"] == 1 - params = sample_call["sampling_params"] - assert isinstance(params, tinker.SamplingParams) - assert params.max_tokens == 8192 - assert params.temperature == 1.0 - assert params.top_p == 0.95 - assert params.seed == 20260727 - assert params.stop == [456] - assert result.prompt_tokens == 123 - assert result.completion_tokens == 2 - assert result.stop_reason == "length" - assert result.cap_hit is True - assert result.raw_text == "raw" - assert result.answer_text == "answer" - - -def test_sampler_rejects_backend_multi_completion(monkeypatch) -> None: - pytest.importorskip( - "tinker", - reason="Tinker SDK env (rl/requirements-lock.txt) not installed", - ) - - class FakeCodec: - def build_prompt(self, _messages): - return SimpleNamespace(length=1) - - def stop_sequences(self): - return [2] - - class FakeSamplingClient: - async def sample_async(self, **_kwargs): - return SimpleNamespace( - sequences=[ - SimpleNamespace(tokens=[1], stop_reason="stop"), - SimpleNamespace(tokens=[2], stop_reason="stop"), - ] - ) - - class FakeService: - def create_sampling_client(self, **_kwargs): - return FakeSamplingClient() - - monkeypatch.setattr(samplers, "_build_codec", lambda _target: FakeCodec()) - sampler = TinkerSampler( - SamplerTarget( - model_name=QWEN_MODEL, - renderer_name="qwen3_5", - base_model=QWEN_MODEL, - ), - service_client_factory=FakeService, - ) - with pytest.raises(SamplingProtocolError, match="exactly one"): - asyncio.run( - sampler.sample( - [{"role": "user", "content": "prompt"}], - SamplingRequest( - max_tokens=8, - temperature=1.0, - top_p=1.0, - seed=1, - ), - ) - ) - - -def test_tml_renderer_uses_instance_effort_and_channel_estimates() -> None: - pytest.importorskip("tml_renderers") - pytest.importorskip( - "tinker", - reason="Tinker SDK env (rl/requirements-lock.txt) not installed", - ) - from tml_renderers import chat as tml_chat - from tml_renderers import tokenizers as tml_tokenizers - from tml_renderers import v0 as tml_v0 - - tokenizer = tml_tokenizers.o200k_base_chat() - codec = samplers._InklingCodec(tml_v0.Renderer(tokenizer), tokenizer, 0.9) - prompt = codec.build_prompt([{"role": "user", "content": "draw it"}]) - assert prompt.length > 0 - - model = tml_chat.Author(tml_chat.AuthorKind.Model) - messages = [ - tml_chat.Message(tml_chat.Thinking("inspect geometry"), model), - tml_chat.Message(tml_chat.Text("```python\npass\n```"), model), - tml_chat.Message(tml_chat.ModelEndSampling(), model), - ] - example = codec._renderer.render_for_sft(messages)[0] - sampled: list[int] = [] - for wrapper in example.input_token_spans: - inner = wrapper.span - if hasattr(inner, "tokens"): - sampled.extend(inner.tokens) - - decoded = codec.decode(sampled) - assert decoded.reasoning_text == "inspect geometry" - assert decoded.answer_text == "```python\npass\n```" - assert decoded.reasoning_tokens_exact is None - assert decoded.answer_tokens_exact is None - assert decoded.reasoning_tokens_estimate == len(tokenizer.encode_ordinary("inspect geometry")) - assert decoded.answer_tokens_estimate == len(tokenizer.encode_ordinary("```python\npass\n```")) - assert decoded.channel_token_count_basis.startswith("estimate_") - assert "excludes_tml_framing" in decoded.channel_token_count_basis - assert decoded.channel_parse_complete is True - - -class _FakeTmlTokenizer: - def __init__(self, raw_text: str) -> None: - self.raw_text = raw_text - - def decode(self, tokens): - assert list(tokens) == [11, 12] - return self.raw_text - - def encode_ordinary(self, text): - return list(text.encode("utf-8")) - - -class _FakeTmlParser: - def __init__(self, parsed=None, error: Exception | None = None) -> None: - self.parsed = [] if parsed is None else parsed - self.error = error - - def parse_tokens(self, tokens): - assert list(tokens) == [11, 12] - if self.error is not None: - raise self.error - return self.parsed - - -class _FakeTmlRenderer: - def __init__(self, parser: _FakeTmlParser) -> None: - self.parser = parser - - def render_for_completion(self, messages): - assert messages == [] - return [], self.parser - - -@pytest.mark.parametrize( - ("parsed", "error", "expected_basis"), - [ - ( - None, - ValueError("truncated TML object"), - "tml_parse_failed_raw_decode_fallback_channel_partition_unknown", - ), - ( - [], - None, - ( - "tml_parse_had_no_usable_channel_text_raw_decode_fallback_" - "channel_partition_unknown" - ), - ), - ], -) -def test_inkling_raw_decode_fallback_preserves_unparseable_completion( - parsed, - error, - expected_basis, -) -> None: - pytest.importorskip("tml_renderers") - raw = "partial framing followed by ```python\nprint('recover me')\n```" - codec = samplers._InklingCodec( - _FakeTmlRenderer(_FakeTmlParser(parsed=parsed, error=error)), - _FakeTmlTokenizer(raw), - 0.9, - ) - - decoded = codec.decode([11, 12]) - - assert decoded.raw_text == raw - assert decoded.completion_text == raw - assert decoded.reasoning_text is None - assert decoded.answer_text == "" - assert decoded.reasoning_tokens_exact is None - assert decoded.answer_tokens_exact is None - assert decoded.reasoning_tokens_estimate is None - assert decoded.answer_tokens_estimate is None - assert decoded.channel_token_count_basis == expected_basis - assert decoded.channel_parse_complete is False - - -def test_inkling_raw_decode_fallback_rejects_terminal_or_whitespace_as_usable() -> None: - pytest.importorskip("tml_renderers") - from tml_renderers import chat as tml_chat - - model = tml_chat.Author(tml_chat.AuthorKind.Model) - parsed = [ - tml_chat.Message(tml_chat.Thinking(" \n\t"), model), - tml_chat.Message(tml_chat.Text(" "), model), - tml_chat.Message(tml_chat.ModelEndSampling(), model), - ] - raw = "```python\nprint('raw survives')\n```" - codec = samplers._InklingCodec( - _FakeTmlRenderer(_FakeTmlParser(parsed=parsed)), - _FakeTmlTokenizer(raw), - 0.9, - ) - - decoded = codec.decode([11, 12]) - - assert decoded.completion_text == raw - assert decoded.channel_parse_complete is False - assert "parse_had_no_usable_channel_text" in decoded.channel_token_count_basis - - -def test_inkling_preserves_usable_parsed_channels_without_terminal_marker() -> None: - pytest.importorskip("tml_renderers") - from tml_renderers import chat as tml_chat - - model = tml_chat.Author(tml_chat.AuthorKind.Model) - parsed = [ - tml_chat.Message(tml_chat.Thinking("inspect"), model), - tml_chat.Message(tml_chat.Text("```python\npass\n```"), model), - ] - codec = samplers._InklingCodec( - _FakeTmlRenderer(_FakeTmlParser(parsed=parsed)), - _FakeTmlTokenizer("raw TML serialization"), - 0.9, - ) - - decoded = codec.decode([11, 12]) - - assert decoded.completion_text == "inspect\n```python\npass\n```" - assert decoded.reasoning_text == "inspect" - assert decoded.answer_text == "```python\npass\n```" - assert decoded.reasoning_tokens_estimate == len("inspect".encode("utf-8")) - assert decoded.answer_tokens_estimate == len("```python\npass\n```".encode("utf-8")) - assert decoded.channel_parse_complete is False diff --git a/rl/evaluation/tests/test_tasks.py b/rl/evaluation/tests/test_tasks.py deleted file mode 100644 index 427e1559..00000000 --- a/rl/evaluation/tests/test_tasks.py +++ /dev/null @@ -1,72 +0,0 @@ -from __future__ import annotations - -from collections import Counter -from pathlib import Path - -from rl.evaluation.tasks import ( - BENCHMARK_TASK_SET, - OPERATING_POINT_TASK_SET, - build_task_manifest, - load_task_manifest, - load_task_set, -) - - -REPO_ROOT = Path(__file__).resolve().parents[3] -MANIFEST_PATH = ( - REPO_ROOT / "rl/studies/representation_curriculum_v1/task_manifest.json" -) - - -def test_frozen_task_manifest_rebuilds_exactly(): - frozen = load_task_manifest(MANIFEST_PATH, repo_root=REPO_ROOT) - assert frozen == build_task_manifest(REPO_ROOT) - dev = frozen["task_sets"][OPERATING_POINT_TASK_SET] - benchmark = frozen["task_sets"][BENCHMARK_TASK_SET] - assert len(dev) == 40 - assert Counter(value["level"] for value in dev) == { - "L0": 8, - "L1": 8, - "L2": 8, - "L3": 8, - "L4": 8, - } - assert len({value["representation_id"] for value in dev}) == 40 - assert [value["task_id"] for value in benchmark] == [ - f"F{index}" for index in range(1, 9) - ] - - -def test_evaluation_loader_has_no_supervised_label(): - manifest = load_task_manifest(MANIFEST_PATH, repo_root=REPO_ROOT) - for task_set, expected in ( - (OPERATING_POINT_TASK_SET, 40), - (BENCHMARK_TASK_SET, 8), - ): - tasks = load_task_set( - repo_root=REPO_ROOT, - manifest=manifest, - task_set=task_set, - ) - assert len(tasks) == expected - assert set(tasks[0].__dataclass_fields__) == { - "task_id", - "level", - "representation_id", - "observation", - "reference", - } - assert not hasattr(tasks[0], "label") - - -def test_benchmark_model_and_reference_bytes_are_same_but_separate(): - manifest = load_task_manifest(MANIFEST_PATH, repo_root=REPO_ROOT) - tasks = load_task_set( - repo_root=REPO_ROOT, - manifest=manifest, - task_set=BENCHMARK_TASK_SET, - ) - for task in tasks: - assert task.observation.image_bytes == task.reference.target_image_bytes - assert task.observation.image_sha256 == task.reference.target_image_sha256 - assert task.observation is not task.reference diff --git a/rl/evaluation/tests/test_tracking.py b/rl/evaluation/tests/test_tracking.py deleted file mode 100644 index 1b56594f..00000000 --- a/rl/evaluation/tests/test_tracking.py +++ /dev/null @@ -1,382 +0,0 @@ -from __future__ import annotations - -import hashlib -import os -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.evaluation.attempt_store import AttemptKey, AttemptStore -from rl.evaluation.tracking import ( - TrackingContractError, - WandbMetricsMirror, - deterministic_wandb_run_id, - validate_wandb_access, -) - -SOURCE_GIT_SHA = "a" * 40 - - -class FakeSettings: - def __init__(self, **kwargs: object) -> None: - self.kwargs = kwargs - - -class FakeRun: - def __init__(self, owner: FakeWandb) -> None: - self.owner = owner - - def log( - self, - metrics: dict[str, int | float], - *, - step: int, - commit: bool, - ) -> None: - if self.owner.fail_log: - raise RuntimeError("log failed") - self.owner.logs.append((metrics, step, commit)) - - def finish(self) -> None: - self.owner.finished += 1 - if self.owner.fail_finish: - raise RuntimeError("finish failed") - - -class FakeWandb: - Settings = FakeSettings - - def __init__( - self, - *, - fail: bool = False, - fail_log: bool = False, - fail_finish: bool = False, - ) -> None: - self.fail = fail - self.fail_log = fail_log - self.fail_finish = fail_finish - self.init_calls: list[dict] = [] - self.logs: list[tuple[dict[str, int | float], int, bool]] = [] - self.finished = 0 - self.environment: dict[str, str | None] = {} - - def init(self, **kwargs: object) -> FakeRun: - self.init_calls.append(kwargs) - self.environment = { - name: os.environ.get(name) - for name in ( - "WANDB_DIR", - "WANDB_DISABLE_GIT", - "WANDB_DISABLE_CODE", - "WANDB_SAVE_CODE", - ) - } - if self.fail: - raise ConnectionError("offline") - return FakeRun(self) - - -def _store(tmp_path: Path) -> AttemptStore: - repo = tmp_path / "repo" - repo.mkdir() - return AttemptStore( - repo_root=repo, - external_root=tmp_path / "external-record", - ) - - -def _key() -> AttemptKey: - return AttemptKey("study-v1", "H-QWEN-ZERO", "off-4k", "F1", 1) - - -def test_wandb_run_id_is_bound_to_the_exact_source_commit() -> None: - first = deterministic_wandb_run_id( - _key().run_key, - source_git_sha="a" * 40, - ) - second = deterministic_wandb_run_id( - _key().run_key, - source_git_sha="b" * 40, - ) - - assert first != second - with pytest.raises(TrackingContractError, match="Git object SHA"): - deterministic_wandb_run_id( - _key().run_key, - source_git_sha="not-a-sha", - ) - - -def test_wandb_access_preflight_binds_the_expected_entity() -> None: - class FakeAccess: - @staticmethod - def Api(*, timeout: int) -> SimpleNamespace: - assert timeout == 20 - return SimpleNamespace( - viewer=SimpleNamespace(username="aadarwal", entity="qpaig-mit"), - default_entity="qpaig-mit", - ) - - assert validate_wandb_access( - expected_entity="qpaig-mit", - wandb_module=FakeAccess, - ) == {"username": "aadarwal", "entity": "qpaig-mit"} - with pytest.raises(TrackingContractError, match="entity mismatch"): - validate_wandb_access( - expected_entity="another-team", - wandb_module=FakeAccess, - ) - - -def test_wandb_access_preflight_fails_closed() -> None: - class OfflineAccess: - @staticmethod - def Api(*, timeout: int) -> None: - assert timeout == 20 - raise ConnectionError("offline") - - with pytest.raises(TrackingContractError, match="credential preflight"): - validate_wandb_access( - expected_entity=None, - wandb_module=OfflineAccess, - ) - - -def test_tracking_refuses_to_run_before_local_record_exists( - tmp_path: Path, -) -> None: - store = _store(tmp_path) - fake = FakeWandb() - mirror = WandbMetricsMirror( - store=store, - project="pixcell", - source_git_sha=SOURCE_GIT_SHA, - wandb_module=fake, - ) - - with pytest.raises(TrackingContractError, match="before local state"): - mirror.mirror_saved_metrics( - key=_key(), - source="sampling_receipt", - metrics={"output_tokens": 10}, - step=0, - ) - assert fake.init_calls == [] - - -def test_metrics_only_mirror_uses_resumable_id_and_disables_source_upload( - tmp_path: Path, -) -> None: - store = _store(tmp_path) - key = _key() - store.write_sampling_receipt( - key, - { - "raw_completion": "secret program", - "output_tokens": 10, - }, - ) - fake = FakeWandb() - mirror = WandbMetricsMirror( - store=store, - project="pixcell-baselines", - source_git_sha=SOURCE_GIT_SHA, - entity="qpaig-mit", - group="phase1-zero-shot", - wandb_module=fake, - ) - - result = mirror.mirror_saved_metrics( - key=key, - source="sampling_receipt", - metrics={"output_tokens": 10, "latency_seconds": 1.25}, - step=3, - ) - - assert result.mirrored is True - assert result.wandb_run_id == deterministic_wandb_run_id( - key.run_key, - source_git_sha=SOURCE_GIT_SHA, - ) - assert fake.logs == [ - ({"output_tokens": 10, "latency_seconds": 1.25}, 3, True) - ] - assert fake.finished == 1 - call = fake.init_calls[0] - assert call["id"] == result.wandb_run_id - assert call["reinit"] == "create_new" - assert call["resume"] == "allow" - assert call["group"] == "phase1-zero-shot" - assert call["dir"] == str(store.external_root / ".wandb") - assert "config" not in call - assert "secret program" not in repr(call) - assert call["settings"].kwargs == { - "disable_code": True, - "disable_git": True, - "disable_job_creation": True, - "save_code": False, - "x_disable_machine_info": True, - "x_disable_stats": True, - "x_save_requirements": False, - } - assert fake.environment == { - "WANDB_DIR": str(store.external_root / ".wandb"), - "WANDB_DISABLE_GIT": "true", - "WANDB_DISABLE_CODE": "true", - "WANDB_SAVE_CODE": "false", - } - - -def test_persistent_arm_session_initializes_once_and_finishes_once( - tmp_path: Path, -) -> None: - store = _store(tmp_path) - first = _key() - second = AttemptKey( - first.study_id, - first.hypothesis_id, - first.arm_id, - "F2", - 1, - ) - store.write_sampling_receipt(first, {"raw_completion": "first"}) - store.write_sampling_receipt(second, {"raw_completion": "second"}) - fake = FakeWandb() - mirror = WandbMetricsMirror( - store=store, - project="pixcell-baselines", - source_git_sha=SOURCE_GIT_SHA, - wandb_module=fake, - ) - - with mirror.session(first.run_key) as session: - first_result = session.mirror_saved_metrics( - key=first, - source="sampling_receipt", - metrics={"output_tokens": 10}, - step=0, - ) - second_result = session.mirror_saved_metrics( - key=second, - source="sampling_receipt", - metrics={"output_tokens": 20}, - step=1, - ) - - assert first_result.mirrored and second_result.mirrored - assert len(fake.init_calls) == 1 - assert len(fake.logs) == 2 - assert fake.finished == 1 - - -def test_tracking_failure_is_a_local_error_and_keeps_saved_sample( - tmp_path: Path, -) -> None: - store = _store(tmp_path) - key = _key() - saved = store.write_sampling_receipt( - key, - { - "raw_completion": "durable sample", - "output_tokens": 10, - }, - ) - mirror = WandbMetricsMirror( - store=store, - project="pixcell-baselines", - source_git_sha=SOURCE_GIT_SHA, - wandb_module=FakeWandb(fail=True), - ) - - result = mirror.mirror_saved_metrics( - key=key, - source="sampling_receipt", - metrics={"output_tokens": 10}, - step=0, - ) - - assert result.mirrored is False - assert store.load_sampling_receipt(key) == saved - errors = store.list_tracking_errors(key) - assert len(errors) == 1 - assert result.tracking_error_relative_path == str(errors[0].relative_path) - payload = errors[0].payload - assert payload["error_type"] == "ConnectionError" - assert payload["operation"] == "init" - assert payload["source_record_sha256"] == saved.record_sha256 - assert payload["metrics_sha256"] == hashlib.sha256( - b'{"output_tokens":10}' - ).hexdigest() - assert "durable sample" not in repr(payload) - - -@pytest.mark.parametrize( - ("fake", "operation"), - [ - (FakeWandb(fail_log=True), "log"), - (FakeWandb(fail_finish=True), "finish"), - ], -) -def test_log_and_finish_failures_each_create_local_error_records( - tmp_path: Path, - fake: FakeWandb, - operation: str, -) -> None: - store = _store(tmp_path) - key = _key() - saved = store.write_sampling_receipt(key, {"raw_completion": "durable"}) - mirror = WandbMetricsMirror( - store=store, - project="pixcell-baselines", - source_git_sha=SOURCE_GIT_SHA, - wandb_module=fake, - ) - - with mirror.session(key.run_key) as session: - result = session.mirror_saved_metrics( - key=key, - source="sampling_receipt", - metrics={"output_tokens": 10}, - step=0, - ) - - errors = store.list_tracking_errors(key) - assert len(errors) == 1 - assert errors[0].payload["operation"] == operation - assert store.load_sampling_receipt(key) == saved - if operation == "log": - assert result.mirrored is False - else: - # The metric reached W&B; only finalization failed afterward. - assert result.mirrored is True - - -def test_tracking_contract_rejects_bad_metrics_and_repo_wandb_dir( - tmp_path: Path, -) -> None: - store = _store(tmp_path) - with pytest.raises(TrackingContractError, match="outside"): - WandbMetricsMirror( - store=store, - project="pixcell", - source_git_sha=SOURCE_GIT_SHA, - wandb_dir=store.repo_root / "wandb", - wandb_module=FakeWandb(), - ) - key = _key() - store.write_sampling_receipt(key, {"raw_completion": "saved"}) - mirror = WandbMetricsMirror( - store=store, - project="pixcell", - source_git_sha=SOURCE_GIT_SHA, - wandb_module=FakeWandb(), - ) - with pytest.raises(TrackingContractError): - mirror.mirror_saved_metrics( - key=key, - source="sampling_receipt", - metrics={"iou": float("inf")}, - step=0, - ) diff --git a/rl/evaluation/tracking.py b/rl/evaluation/tracking.py deleted file mode 100644 index 0456116a..00000000 --- a/rl/evaluation/tracking.py +++ /dev/null @@ -1,430 +0,0 @@ -"""Metrics-only W&B mirroring for immutable local attempt records.""" - -from __future__ import annotations - -import hashlib -import importlib -import json -import math -import os -import re -import threading -from collections.abc import Mapping -from contextlib import contextmanager -from dataclasses import dataclass -from pathlib import Path -from types import ModuleType -from typing import Any, Literal - -from .attempt_store import AttemptKey, AttemptStore, RunKey, StoredRecord - - -TRACKING_SCHEMA_VERSION = "pixcell-baseline-wandb-mirror-v2" -_METRIC_NAME = re.compile(r"^[A-Za-z][A-Za-z0-9_./-]{0,127}$") -_SOURCE_GIT_SHA = re.compile(r"^[0-9a-f]{40}(?:[0-9a-f]{24})?$") -_WANDB_ENVIRONMENT_LOCK = threading.RLock() - - -class TrackingContractError(RuntimeError): - """Tracking was requested without a valid saved record or metric set.""" - - -@dataclass(frozen=True) -class TrackingResult: - mirrored: bool - wandb_run_id: str - source_record_sha256: str - tracking_error_relative_path: str | None = None - - -def deterministic_wandb_run_id( - run_key: RunKey, - *, - source_git_sha: str, -) -> str: - """Return the stable W&B run ID used for create-or-resume.""" - - if not _SOURCE_GIT_SHA.fullmatch(source_git_sha): - raise TrackingContractError("source_git_sha must be a Git object SHA") - canonical = json.dumps( - { - "schema_version": TRACKING_SCHEMA_VERSION, - "source_git_sha": source_git_sha, - **run_key.as_dict(), - }, - ensure_ascii=True, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - return "pxc-" + hashlib.sha256(canonical).hexdigest()[:28] - - -def validate_wandb_access( - *, - expected_entity: str | None, - wandb_module: ModuleType | Any | None = None, -) -> dict[str, str | None]: - """Verify the configured W&B identity before any paid sampling begins.""" - - module = wandb_module or importlib.import_module("wandb") - try: - api = module.Api(timeout=20) - viewer = api.viewer - username = getattr(viewer, "username", None) - entity = getattr(viewer, "entity", None) or getattr( - api, - "default_entity", - None, - ) - except Exception as exc: - raise TrackingContractError("W&B credential preflight failed") from exc - if not isinstance(username, str) or not username.strip(): - raise TrackingContractError("W&B credential resolved no authenticated user") - if expected_entity is not None and entity != expected_entity: - raise TrackingContractError( - f"W&B entity mismatch: {entity!r} != {expected_entity!r}" - ) - return {"username": username, "entity": entity} - - -def _metrics_payload(metrics: Mapping[str, int | float]) -> dict[str, int | float]: - if not isinstance(metrics, Mapping) or not metrics: - raise TrackingContractError("tracking metrics must be a non-empty mapping") - result: dict[str, int | float] = {} - for key, value in metrics.items(): - if not isinstance(key, str) or not _METRIC_NAME.fullmatch(key): - raise TrackingContractError(f"invalid metric name: {key!r}") - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not math.isfinite(float(value)) - ): - raise TrackingContractError( - f"metric {key!r} must be a finite integer or float" - ) - result[key] = value - return result - - -def _metrics_sha256(metrics: Mapping[str, int | float]) -> str: - raw = json.dumps( - metrics, - allow_nan=False, - ensure_ascii=True, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - return hashlib.sha256(raw).hexdigest() - - -@contextmanager -def _wandb_environment(wandb_dir: Path): - names = ("WANDB_DIR", "WANDB_DISABLE_GIT", "WANDB_DISABLE_CODE", "WANDB_SAVE_CODE") - previous = {name: os.environ.get(name) for name in names} - os.environ.update( - { - "WANDB_DIR": str(wandb_dir), - "WANDB_DISABLE_GIT": "true", - "WANDB_DISABLE_CODE": "true", - "WANDB_SAVE_CODE": "false", - } - ) - try: - yield - finally: - for name, value in previous.items(): - if value is None: - os.environ.pop(name, None) - else: - os.environ[name] = value - - -class WandbMetricsMirror: - """Mirror metrics only after their source attempt record is durable. - - W&B is deliberately an observability mirror, never the experiment record. - Any W&B exception is converted into an immutable local tracking-error - record while the sampling receipt/evaluation remains valid. - """ - - def __init__( - self, - *, - store: AttemptStore, - project: str, - source_git_sha: str, - entity: str | None = None, - group: str | None = None, - wandb_dir: Path | None = None, - wandb_module: ModuleType | Any | None = None, - ) -> None: - if not project.strip(): - raise TrackingContractError("W&B project must be non-empty") - if not _SOURCE_GIT_SHA.fullmatch(source_git_sha): - raise TrackingContractError("source_git_sha must be a Git object SHA") - self.store = store - self.project = project - self.source_git_sha = source_git_sha - self.entity = entity - self.group = group - if self.group is not None and not self.group.strip(): - raise TrackingContractError("W&B group must be non-empty when provided") - requested = ( - Path(wandb_dir).expanduser() - if wandb_dir is not None - else store.external_root / ".wandb" - ).resolve(strict=False) - if requested == store.repo_root or requested.is_relative_to(store.repo_root): - raise TrackingContractError("WANDB_DIR must be outside the repository") - requested.mkdir(parents=True, exist_ok=True, mode=0o700) - self.wandb_dir = requested.resolve(strict=True) - if self.wandb_dir == store.repo_root or self.wandb_dir.is_relative_to( - store.repo_root - ): - raise TrackingContractError("WANDB_DIR resolves inside the repository") - self._wandb_module = wandb_module - - def mirror_saved_metrics( - self, - *, - key: AttemptKey, - source: Literal["sampling_receipt", "evaluation"], - metrics: Mapping[str, int | float], - step: int, - ) -> TrackingResult: - """Compatibility helper for one record and one short-lived session.""" - - with self.session(key.run_key) as session: - return session.mirror_saved_metrics( - key=key, - source=source, - metrics=metrics, - step=step, - ) - - def session(self, run_key: RunKey) -> WandbRunSession: - """Create a lazy, persistent one-init-per-arm tracking session.""" - - return WandbRunSession(mirror=self, run_key=run_key) - - def _load_saved_source( - self, - *, - key: AttemptKey, - source: Literal["sampling_receipt", "evaluation"], - ) -> StoredRecord | None: - if source == "sampling_receipt": - return self.store.load_sampling_receipt(key) - return self.store.load_evaluation(key) - - def _settings(self, module: Any) -> Any: - return module.Settings( - disable_code=True, - disable_git=True, - disable_job_creation=True, - save_code=False, - x_disable_machine_info=True, - x_disable_stats=True, - x_save_requirements=False, - ) - - def _init_kwargs(self, run_key: RunKey, run_id: str, module: Any) -> dict[str, Any]: - result: dict[str, Any] = { - "project": self.project, - "id": run_id, - "name": ( - f"{run_key.study_id}/{run_key.hypothesis_id}/{run_key.arm_id}" - f"@{self.source_git_sha[:12]}" - ), - # The baseline runner keeps one session per arm open while arms - # execute concurrently. W&B otherwise returns the process-global - # active run, silently collapsing every arm into the first run. - "reinit": "create_new", - "resume": "allow", - "dir": str(self.wandb_dir), - "settings": self._settings(module), - } - if self.entity is not None: - result["entity"] = self.entity - if self.group is not None: - result["group"] = self.group - return result - - def _module(self) -> Any: - if self._wandb_module is not None: - return self._wandb_module - return importlib.import_module("wandb") - - def _record_tracking_error( - self, - *, - key: AttemptKey, - saved: StoredRecord, - source: Literal["sampling_receipt", "evaluation"], - run_id: str, - step: int, - metrics_sha256: str, - operation: Literal["init", "log", "finish"], - error: Exception, - ) -> StoredRecord: - return self.store.write_tracking_error( - key, - { - "schema_version": TRACKING_SCHEMA_VERSION, - "source_record_type": source, - "source_record_relative_path": str(saved.relative_path), - "source_record_sha256": saved.record_sha256, - "wandb_run_id": run_id, - "wandb_step": step, - "metrics_sha256": metrics_sha256, - "operation": operation, - "error_type": type(error).__name__, - "error_message": str(error)[:4096], - }, - ) - - -class WandbRunSession: - """One lazily initialized W&B run for one immutable PixCell arm.""" - - def __init__(self, *, mirror: WandbMetricsMirror, run_key: RunKey) -> None: - self.mirror = mirror - self.run_key = run_key - self.run_id = deterministic_wandb_run_id( - run_key, - source_git_sha=mirror.source_git_sha, - ) - self._run: Any | None = None - self._closed = False - self._last_context: tuple[ - AttemptKey, - Literal["sampling_receipt", "evaluation"], - StoredRecord, - int, - str, - ] | None = None - - def __enter__(self) -> WandbRunSession: - if self._closed: - raise TrackingContractError("a closed W&B session cannot be reopened") - # Initialization remains lazy so the first external tracking action - # cannot precede its immutable local source record. - return self - - def __exit__(self, *_: object) -> None: - self.close() - - def mirror_saved_metrics( - self, - *, - key: AttemptKey, - source: Literal["sampling_receipt", "evaluation"], - metrics: Mapping[str, int | float], - step: int, - ) -> TrackingResult: - if self._closed: - raise TrackingContractError("W&B session is closed") - if key.run_key != self.run_key: - raise TrackingContractError( - "attempt key belongs to a different W&B arm session" - ) - if isinstance(step, bool) or not isinstance(step, int) or step < 0: - raise TrackingContractError("W&B step must be a nonnegative integer") - clean_metrics = _metrics_payload(metrics) - saved = self.mirror._load_saved_source(key=key, source=source) - if saved is None: - raise TrackingContractError( - f"cannot mirror {source} metrics before local state is saved" - ) - metrics_sha = _metrics_sha256(clean_metrics) - self._last_context = (key, source, saved, step, metrics_sha) - try: - if self._run is None: - self._initialize() - except Exception as exc: - error = self.mirror._record_tracking_error( - key=key, - saved=saved, - source=source, - run_id=self.run_id, - step=step, - metrics_sha256=metrics_sha, - operation="init", - error=exc, - ) - return TrackingResult( - mirrored=False, - wandb_run_id=self.run_id, - source_record_sha256=saved.record_sha256, - tracking_error_relative_path=str(error.relative_path), - ) - - try: - with _WANDB_ENVIRONMENT_LOCK, _wandb_environment( - self.mirror.wandb_dir - ): - # No task IDs, prompts, completions, programs, configs, - # artifacts, tables, or source files cross this boundary. - self._run.log(dict(clean_metrics), step=step, commit=True) - except Exception as exc: - error = self.mirror._record_tracking_error( - key=key, - saved=saved, - source=source, - run_id=self.run_id, - step=step, - metrics_sha256=metrics_sha, - operation="log", - error=exc, - ) - return TrackingResult( - mirrored=False, - wandb_run_id=self.run_id, - source_record_sha256=saved.record_sha256, - tracking_error_relative_path=str(error.relative_path), - ) - - return TrackingResult( - mirrored=True, - wandb_run_id=self.run_id, - source_record_sha256=saved.record_sha256, - ) - - def _initialize(self) -> None: - module = self.mirror._module() - init_kwargs = self.mirror._init_kwargs(self.run_key, self.run_id, module) - # os.environ is process-global. Serialize this short section so - # parallel samplers cannot restore WANDB_DIR beneath another init. - with _WANDB_ENVIRONMENT_LOCK, _wandb_environment(self.mirror.wandb_dir): - run = module.init(**init_kwargs) - if run is None: - raise RuntimeError("wandb.init returned no run") - self._run = run - - def close(self) -> None: - if self._closed: - return - self._closed = True - if self._run is None: - return - try: - with _WANDB_ENVIRONMENT_LOCK, _wandb_environment( - self.mirror.wandb_dir - ): - self._run.finish() - except Exception as exc: - if self._last_context is None: - raise - key, source, saved, step, metrics_sha = self._last_context - self.mirror._record_tracking_error( - key=key, - saved=saved, - source=source, - run_id=self.run_id, - step=step, - metrics_sha256=metrics_sha, - operation="finish", - error=exc, - ) - finally: - self._run = None diff --git a/rl/requirements-lock.txt b/rl/requirements-lock.txt index 1a90fa5b..94bd961f 100644 --- a/rl/requirements-lock.txt +++ b/rl/requirements-lock.txt @@ -1,7 +1,6 @@ -r ../dataset/requirements-lock.txt chz==0.4.0 tinker==0.22.7 -tml-renderers==0.1.0 torch==2.12.1 transformers==5.5.3 tinker-cookbook @ git+https://github.com/thinking-machines-lab/tinker-cookbook.git@ca232b084411029df565a36095642918a097fa7f diff --git a/rl/stack.json b/rl/stack.json index f86af299..ed78006e 100644 --- a/rl/stack.json +++ b/rl/stack.json @@ -8,7 +8,6 @@ "pyarrow": "24.0.0", "tinker": "0.22.7", "tinker-cookbook": "0.4.3.dev2+gca232b084", - "tml-renderers": "0.1.0", "torch": "2.12.1", "transformers": "5.5.3", "wandb": "0.28.0" diff --git a/rl/studies/__init__.py b/rl/studies/__init__.py deleted file mode 100644 index c4bba494..00000000 --- a/rl/studies/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Versioned training and evaluation studies.""" diff --git a/rl/studies/representation_curriculum_v1/README.md b/rl/studies/representation_curriculum_v1/README.md deleted file mode 100644 index 35896ee4..00000000 --- a/rl/studies/representation_curriculum_v1/README.md +++ /dev/null @@ -1,160 +0,0 @@ -# Representation curriculum v1 - -> **Superseded diagnostic record.** This study and its Qwen 32k/60k -> extensions used the `pixcell-blind-phase-a-direct-v1` prompt contract. That -> one-shot contract did not state the output-file requirement enforced by the -> evaluator, so its measurements are not canonical baselines or paper -> results. The protocol JSON and study files remain unchanged for provenance. -> Current paid launch code rejects this prompt contract. - -This archived study recorded preliminary zero-shot measurements before any -curriculum training began. It had two merge boundaries: - -1. Phase 0 freezes and validates the protocol, task manifest, prompt, sampling - adapters, evaluator, result schema, and source SHA without creating a - Tinker client. -2. Phase 1 samples and evaluates the frozen base models, then commits a - validated evidence export in a separate PR. - -The SFT and RL hypotheses reserved in [`protocol.json`](protocol.json) are not -launchable in either phase. - -## Fixed model contract - -Each model receives one maximum-visibility image, the physical footprint, the -PixCell primitive catalogue, and the existing blind Phase-A programming task. -It returns one Python program. Display calibration, the target raster, -representation labels, expected code, and verifier diagnostics are not -model-visible. - -Every completion passes through the same strict source gate, isolated -execution boundary, GDS render, and absolute-scale IoU evaluator. A model -failure contributes zero IoU to aggregation. A reference or evaluator failure -aborts the wave instead of becoming a zero-scored model attempt. - -## Frozen hypotheses - -| ID | Purpose | Tasks and attempts | -|---|---|---| -| `RC-H00` | Select the Qwen inference operating point | 40 fixed `depth/validation` tasks, eight per level L0–L4 and one representation per task; thinking on/off × 4,096/16,384 output tokens; two paired attempts per task | -| `RC-H00R` | Resolve truncation at the largest registered Qwen thinking budget | Conditional 32,768-token confirmation only when `qwen-on-16384` has a cap-hit rate above 5% | -| `RC-H01` | Qwen zero-shot F1–F8 baseline | The incumbent `qwen-off-4096` and the selected `RC-H00` arm, omitting a duplicate arm; four attempts per figure | -| `RC-H02` | Inkling zero-shot F1–F8 baseline | Thinking effort 0.9, 20,000 output tokens; four attempts per figure | - -The `RC-H00` arms use identical task-attempt seeds. Selection first maximizes -representation-macro mean raw IoU. Arms within 0.01 of the best are ordered by -pure executable rate, estimated uncached cost, median completion tokens, then -arm ID. The rule and all model, renderer, image, context, sampling, pricing, -task, and tracking settings are sealed in -[`protocol.json`](protocol.json). [`task_manifest.json`](task_manifest.json) -binds the exact tasks and source digests. - -## Historical launch order - -The commands below document how the superseded record was produced. They are -not runnable against the current prompt contract and must not be used to spend -or report a canonical baseline. - -The launch must come from a clean checkout at the exact Phase 0 merge SHA. -Mutable output must be outside the repository. Build the committed evaluator -image and export the immutable image ID it prints: - -```bash -python rl/common/sandbox/build.py -export PIXCELL_EVALUATOR_IMAGE=sha256: - -export PIXCELL_STUDY_ROOT=/absolute/path/outside/PixCell -export TINKER_API_KEY= -export WANDB_API_KEY= -SOURCE_SHA="$(git rev-parse HEAD)" - -PYTHONPATH=src:. python -m \ - rl.studies.representation_curriculum_v1.audit_phase0 \ - --expected-source-sha "$SOURCE_SHA" -``` - -The audit builds every frozen renderer/prompt combination without creating a -Tinker client, validates all 48 references, executes one accepted program in -the isolated evaluator, and round-trips the attempt-record schema. Only after -it passes, run the Qwen operating-point smoke and complete waves: - -```bash -PYTHONPATH=src:. python -m rl.studies.representation_curriculum_v1.run_baseline \ - --hypothesis RC-H00 --wave smoke \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE - -PYTHONPATH=src:. python -m rl.studies.representation_curriculum_v1.run_baseline \ - --hypothesis RC-H00 --wave complete \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE -``` - -The smoke wave records attempt 1 for the first fixed development task in each -of the four arms. The complete wave reuses those receipts and fills two -attempts for all 40 tasks. If its preliminary selection requires `RC-H00R`, run -the conditional confirmation before continuing: - -```bash -PRELIMINARY="$PIXCELL_STUDY_ROOT/studies/representation-curriculum-v1/selections/operating_point.preliminary.json" - -PYTHONPATH=src:. python -m rl.studies.representation_curriculum_v1.run_baseline \ - --hypothesis RC-H00R --wave complete --selection "$PRELIMINARY" \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE -``` - -Then start the Qwen and Inkling F1–F8 smoke waves concurrently. Each records -attempt 1 for all eight figures. Inspect and validate those 8-attempt results -per arm before starting the complete waves, which reuse attempt 1 and fill -attempts 2–4: - -```bash -SELECTION="$PIXCELL_STUDY_ROOT/studies/representation-curriculum-v1/selections/operating_point.json" - -PYTHONPATH=src:. python -m rl.studies.representation_curriculum_v1.run_baseline \ - --hypothesis RC-H01 --wave smoke --selection "$SELECTION" \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE - -PYTHONPATH=src:. python -m rl.studies.representation_curriculum_v1.run_baseline \ - --hypothesis RC-H02 --wave smoke \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE -``` - -Use the same commands with `--wave complete` only after both smoke records pass. -The immutable local receipt is the sampling boundary: restarting a wave -evaluates an existing completion or fills a missing attempt; it does not -resample a saved attempt. - -After every complete wave validates, freeze and revalidate the compact Phase 1 -record: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_curriculum_v1.freeze_phase1 \ - --external-root "$PIXCELL_STUDY_ROOT" \ - --expected-source-sha "$SOURCE_SHA" - -PYTHONPATH=src:. python -m \ - rl.studies.representation_curriculum_v1.freeze_phase1 \ - --external-root "$PIXCELL_STUDY_ROOT" \ - --expected-source-sha "$SOURCE_SHA" \ - --validate -``` - -The freezer accepts source changes only inside the fixed Phase 1 evidence -directory. The resulting files form the separate Phase 1 PR. - -## Record ownership - -The external attempt ledger is authoritative while the run is active. It -contains immutable sampling receipts, exact programs, evaluator evidence, -summaries, and the operating-point selection. W&B receives metrics only after -the corresponding local record is durable. A W&B outage is recorded locally -and cannot change an evaluation. - -The Phase 1 PR freezes the validated release subset under -[`data/training/representation-curriculum-v1/`](../../../data/training/representation-curriculum-v1/). -Secrets, mutable W&B files, model caches, and checkpoints are never committed. diff --git a/rl/studies/representation_curriculum_v1/__init__.py b/rl/studies/representation_curriculum_v1/__init__.py deleted file mode 100644 index 23b991f7..00000000 --- a/rl/studies/representation_curriculum_v1/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""The frozen representation-curriculum-v1 study.""" diff --git a/rl/studies/representation_curriculum_v1/audit_phase0.py b/rl/studies/representation_curriculum_v1/audit_phase0.py deleted file mode 100644 index 998a299d..00000000 --- a/rl/studies/representation_curriculum_v1/audit_phase0.py +++ /dev/null @@ -1,322 +0,0 @@ -#!/usr/bin/env python3 -"""Run the zero-spend audit required before a baseline launch is committed.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -import tempfile -from pathlib import Path -from typing import Any - -from rl.common.dataset_io import parquet_paths -from rl.common.evaluator import Attribution, EvaluationStatus, PixCellEvaluator -from rl.common.output import extract_code -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.attempt_store import AttemptKey, AttemptStore, RunKey -from rl.evaluation.protocol import arm_spec, load_protocol, task_manifest_path -from rl.evaluation.runner import ( - _FORBIDDEN_PROMPT_FRAGMENTS, - _preflight_references, - _preflight_renderers, -) -from rl.evaluation.samplers import TinkerSampler -from rl.evaluation.tasks import ( - BENCHMARK_TASK_SET, - OPERATING_POINT_TASK_SET, - load_task_manifest, - load_task_set, -) - - -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") - - -REPO_ROOT = Path(__file__).resolve().parents[3] -AUDIT_SCHEMA = "pixcell-representation-curriculum-phase0-audit-v1" - - -def _git_state(repo_root: Path) -> tuple[str, bool]: - head = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo_root, - text=True, - ).strip() - status = subprocess.check_output( - ["git", "status", "--porcelain", "--untracked-files=all"], - cwd=repo_root, - text=True, - ).strip() - return head, not bool(status) - - -def _exact_code(dataset_root: Path, opaque_id: str) -> str: - import pyarrow.parquet as pq - - matches: list[str] = [] - for path in parquet_paths( - dataset_root, - configuration="depth", - split="validation", - ): - for row in pq.read_table(path, columns=["id", "code"]).to_pylist(): - row_id = str(row["id"]) - observed = hashlib.sha256(row_id.encode("utf-8")).hexdigest()[:20] - if observed == opaque_id: - matches.append(str(row["code"])) - if len(matches) != 1 or not matches[0].strip(): - raise ValueError( - f"expected one supervised label for audit task {opaque_id}; " - f"found {len(matches)}" - ) - return matches[0] - - -def _samplers(protocol: dict[str, Any]) -> dict[str, TinkerSampler]: - def forbidden_client() -> Any: - raise AssertionError("Phase 0 audit attempted to create a Tinker client") - - from rl.evaluation.runner import _sampler_target - - return { - arm_id: TinkerSampler( - _sampler_target(arm_spec(protocol, arm_id)), - service_client_factory=forbidden_client, - ) - for arm_id in protocol["arms"] - } - - -def _schema_smoke( - *, - repo_root: Path, - protocol: dict[str, Any], - sandbox: Any, -) -> dict[str, str]: - with tempfile.TemporaryDirectory(prefix="pixcell-phase0-audit-") as temporary: - store = AttemptStore( - repo_root=repo_root, - external_root=Path(temporary) / "records", - ) - run_key = RunKey(protocol["study_id"], "RC-H02", "inkling-e09-20000") - manifest = { - "source_git_sha": "0" * 40, - "protocol_sha256": protocol["logical_sha256"], - "task_set_sha256": "1" * 64, - "prompt_sha256": "2" * 64, - "sandbox_image_ref": sandbox.image_ref, - "sandbox_image_id": sandbox.image_id, - "runtime": validate_runtime_stack(repo_root), - "model_binding": {"model": "thinkingmachines/Inkling"}, - } - with store.acquire_run_lock(run_key): - saved_manifest = store.create_or_verify_run_manifest(run_key, manifest) - key = AttemptKey( - protocol["study_id"], - "RC-H02", - "inkling-e09-20000", - "F1", - 1, - ) - completion = "```python\nprint('audit')\n```" - receipt = store.write_sampling_receipt( - key, - { - "request": { - "max_tokens": 20000, - "temperature": 1.0, - "top_p": 1.0, - "seed": 1, - }, - "sample": { - "prompt_tokens": 100, - "completion_tokens": 10, - "stop_reason": "stop", - "cap_hit": False, - "raw_text": completion, - "completion_text": completion, - "reasoning_text": "", - "answer_text": completion, - "reasoning_tokens_exact": 0, - "answer_tokens_exact": 10, - "reasoning_tokens_estimate": None, - "answer_tokens_estimate": None, - "channel_token_count_basis": "phase0_schema_audit", - "channel_parse_complete": True, - }, - "sampling_seconds": 0.1, - "raw_completion_sha256": hashlib.sha256( - completion.encode("utf-8") - ).hexdigest(), - "completion_sha256": hashlib.sha256( - completion.encode("utf-8") - ).hexdigest(), - "cost_estimate_usd": { - "cached_prefill": 0.0, - "uncached_prefill": 0.0, - }, - }, - ) - evaluation = store.write_evaluation( - key, - { - "status": EvaluationStatus.SYNTAX_ERROR.value, - "attribution": Attribution.MODEL.value, - "pure_executable": False, - "measurement_available": False, - "measured_iou": None, - "measured_dice": None, - "aggregation_iou": 0.0, - "program": "print('audit')", - "program_sha256": hashlib.sha256( - b"print('audit')" - ).hexdigest(), - "reference_sha256": "3" * 64, - "violations": [], - "error": "schema audit", - "retryable": False, - "evaluation_seconds": 0.1, - "diagnostics": {}, - }, - ) - if store.load_sampling_receipt(key) != receipt: - raise AssertionError("sampling receipt did not round-trip") - if store.load_evaluation(key) != evaluation: - raise AssertionError("evaluation record did not round-trip") - if store.load_run_manifest(run_key) != saved_manifest: - raise AssertionError("run manifest did not round-trip") - return { - "run_manifest_record_sha256": saved_manifest.record_sha256, - "sampling_record_sha256": receipt.record_sha256, - "evaluation_record_sha256": evaluation.record_sha256, - } - - -def run_audit( - *, - repo_root: Path, - expected_source_sha: str | None = None, -) -> dict[str, Any]: - root = repo_root.expanduser().resolve(strict=True) - head, clean = _git_state(root) - if expected_source_sha is not None: - if head != expected_source_sha: - raise ValueError(f"source SHA mismatch: {head} != {expected_source_sha}") - if not clean: - raise ValueError("source worktree must be clean for the sealed audit") - - protocol = load_protocol(root, protocol_file=PROTOCOL_FILE) - manifest = load_task_manifest(task_manifest_path(root), repo_root=root) - task_sets = { - name: load_task_set(repo_root=root, manifest=manifest, task_set=name) - for name in (OPERATING_POINT_TASK_SET, BENCHMARK_TASK_SET) - } - samplers = _samplers(protocol) - dev_arm_ids = [ - arm_id for arm_id in protocol["arms"] if arm_id.startswith("qwen-") - ] - benchmark_arm_ids = list(protocol["arms"]) - renderer_cases = 0 - for task_set, arm_ids in ( - (task_sets[OPERATING_POINT_TASK_SET], dev_arm_ids), - (task_sets[BENCHMARK_TASK_SET], benchmark_arm_ids), - ): - arms = {arm_id: arm_spec(protocol, arm_id) for arm_id in arm_ids} - rendered = _preflight_renderers( - tasks=task_set, - arms=arms, - samplers={arm_id: samplers[arm_id] for arm_id in arm_ids}, - ) - renderer_cases += len(rendered) - for (arm_id, task_id), messages in rendered.items(): - visible = repr(messages) - leaked = [ - fragment - for fragment in _FORBIDDEN_PROMPT_FRAGMENTS - if fragment in visible - ] - if leaked: - raise ValueError( - f"{arm_id}/{task_id} leaked verifier-only prompt text: {leaked}" - ) - - from rl.common.isolation import require_execution_boundary - - sandbox = require_execution_boundary() - all_tasks = [ - *task_sets[OPERATING_POINT_TASK_SET], - *task_sets[BENCHMARK_TASK_SET], - ] - with PixCellEvaluator( - max_workers=1, - evaluator_retries=1, - require_isolation=True, - ) as evaluator: - _preflight_references(all_tasks, evaluator) - audit_task = task_sets[OPERATING_POINT_TASK_SET][0] - exact_code = _exact_code(root / "dataset", audit_task.task_id) - wrapped = f"audit\n```python\n{exact_code}\n```" - if extract_code(wrapped) != exact_code.strip(): - raise ValueError("output extractor changed the known audit program") - result = evaluator.evaluate(audit_task.reference, wrapped) - if ( - result.status is not EvaluationStatus.OK - or result.attribution is not Attribution.MODEL - or result.iou is None - ): - raise ValueError( - "isolated evaluator failed the accepted-program audit: " - f"{result.status.value}/{result.attribution.value}: {result.error}" - ) - - runtime = validate_runtime_stack(root) - - report = { - "schema_version": AUDIT_SCHEMA, - "source_git_sha": head, - "source_worktree_clean": clean, - "protocol_sha256": protocol["logical_sha256"], - "task_manifest_sha256": manifest["logical_sha256"], - "task_counts": { - name: len(tasks) for name, tasks in sorted(task_sets.items()) - }, - "renderer_prompt_cases": renderer_cases, - "model_client_created": False, - "reference_count": len(all_tasks), - "extractor_exact": True, - "evaluator": { - "status": result.status.value, - "iou": result.iou, - "sandbox_image_ref": sandbox.image_ref, - "sandbox_image_id": sandbox.image_id, - }, - "record_schema": _schema_smoke( - repo_root=root, - protocol=protocol, - sandbox=sandbox, - ), - "runtime": runtime, - } - return report - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=REPO_ROOT) - result.add_argument("--expected-source-sha") - return result - - -def main() -> None: - args = parser().parse_args() - report = run_audit( - repo_root=args.repo_root, - expected_source_sha=args.expected_source_sha, - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/extensions/__init__.py b/rl/studies/representation_curriculum_v1/extensions/__init__.py deleted file mode 100644 index 06149dc8..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Registered extensions to the frozen representation-curriculum study.""" diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/README.md b/rl/studies/representation_curriculum_v1/extensions/qwen32/README.md deleted file mode 100644 index 3292bd2a..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Qwen 32k F1-F8 extension - -> **Superseded diagnostic record.** This extension used the retired v1 prompt -> contract. Its measurements are not canonical baselines or paper results, -> and current paid launch code rejects the protocol. The files are retained -> for provenance. - -This is the quality-first extension to the frozen Phase 1 baseline study. It -was registered after the `RC-H01` smoke wave showed that 7 of 8 Qwen -thinking-on/16k completions reached the output cap. - -`RC-H03` keeps the original F1-F8 tasks, blind prompt, renderer, image policy, -temperature, seeds, evaluator, and four attempts per figure. Its only changed -model setting is the 32,768-token output cap. The extension uses its own study -ID, source commit, and external ledger. It does not rewrite the original -operating-point selection or `RC-H01` evidence. - -The historical launch command was: - -```bash -python -m rl.studies.representation_curriculum_v1.extensions.qwen32.run_baseline \ - --wave smoke \ - --external-root "$PIXCELL_STUDY_ROOT" \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE -``` - -After verifying all eight sampling and evaluation records, repeat with -`--wave complete`. The complete wave is resumable and contains 32 samples. diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/__init__.py b/rl/studies/representation_curriculum_v1/extensions/qwen32/__init__.py deleted file mode 100644 index 20b83d83..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Quality-first Qwen 32k F1-F8 baseline extension.""" diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/freeze.py b/rl/studies/representation_curriculum_v1/extensions/qwen32/freeze.py deleted file mode 100644 index df2684ec..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/freeze.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -"""Freeze or validate the tracked Qwen 32k F1-F8 baseline addendum.""" - -from __future__ import annotations - -import argparse -import json -import re -from pathlib import Path, PurePosixPath -from typing import Any - -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.attempt_store import AttemptStore, RunKey -from rl.evaluation.protocol import jobs_for_wave, load_protocol, task_manifest_path -from rl.evaluation.tasks import canonical_json_sha256, load_task_manifest -from rl.evaluation.tracking import deterministic_wandb_run_id -from rl.studies.representation_curriculum_v1.freeze_phase1 import ( - Phase1FreezeError, - _assert_exact_output, - _collect_run_attempts, - _git_head, - _json_bytes, - _jsonl_bytes, - _load_run, - _prompt_binding, - _require_source_clean_except_output, - _sha256_bytes, - _task_set, - _validate_roots, - _write_create_or_verify, -) - - -REPO_ROOT = Path(__file__).resolve().parents[5] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") -OUTPUT_RELATIVE_ROOT = Path( - "data/training/representation-curriculum-v1/phase1-qwen32" -) -RELEASE_SCHEMA = "pixcell-representation-curriculum-qwen32-release-v1" -_SOURCE_SHA = re.compile(r"^[0-9a-f]{40}$") - - -def _output_root(repo_root: Path, requested: Path | None) -> Path: - root = repo_root.expanduser().resolve(strict=True) - expected = (root / OUTPUT_RELATIVE_ROOT).resolve(strict=False) - if expected == root or not expected.is_relative_to(root): - raise Phase1FreezeError( - "the tracked Qwen 32k path resolves outside the source repository" - ) - observed = ( - expected - if requested is None - else requested.expanduser().resolve(strict=False) - ) - if observed != expected: - raise Phase1FreezeError( - f"Qwen 32k evidence must be written exactly to {expected}" - ) - return expected - - -def _collect_bundle( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, -) -> tuple[dict[PurePosixPath, bytes], dict[str, Any]]: - if not _SOURCE_SHA.fullmatch(expected_source_sha): - raise Phase1FreezeError( - "expected_source_sha must be a full lowercase Git SHA" - ) - if _git_head(repo_root) != expected_source_sha: - raise Phase1FreezeError("source HEAD differs from the paid-launch commit") - - protocol = load_protocol(repo_root, protocol_file=PROTOCOL_FILE) - runtime = validate_runtime_stack(repo_root) - manifest = load_task_manifest( - task_manifest_path(repo_root), - repo_root=repo_root, - ) - prompt_binding, prompt_sha = _prompt_binding() - tasks, task_set_sha = _task_set( - repo_root=repo_root, - protocol=protocol, - manifest=manifest, - hypothesis_id="RC-H03", - ) - jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H03", - wave_name="complete", - task_ids=[task.task_id for task in tasks], - ) - arm_id = str(protocol["hypotheses"]["RC-H03"]["arm_ids"][0]) - store = AttemptStore(repo_root=repo_root, external_root=external_root) - evidence, sandbox = _load_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H03", - arm_id=arm_id, - source_git_sha=expected_source_sha, - tasks=tasks, - jobs=jobs, - task_set_sha256=task_set_sha, - prompt_sha256=prompt_sha, - sandbox=None, - runtime=runtime, - ) - attempts, programs = _collect_run_attempts( - protocol=protocol, - store=store, - evidence=evidence, - ) - attempts.sort(key=lambda row: (row["task_id"], row["attempt_index"])) - - summary_document = { - "schema_version": "pixcell-representation-curriculum-qwen32-summary-v1", - "study_id": protocol["study_id"], - "hypothesis_id": "RC-H03", - "arm_id": arm_id, - "summary": evidence.summary, - } - summary_document["logical_sha256"] = canonical_json_sha256(summary_document) - files: dict[PurePosixPath, bytes] = { - PurePosixPath("summary.json"): _json_bytes(summary_document), - PurePosixPath("attempts.jsonl"): _jsonl_bytes(attempts), - **programs, - } - file_inventory = [ - { - "path": path.as_posix(), - "sha256": _sha256_bytes(content), - "bytes": len(content), - } - for path, content in sorted(files.items(), key=lambda item: item[0]) - ] - release_manifest: dict[str, Any] = { - "schema_version": RELEASE_SCHEMA, - "study_id": protocol["study_id"], - "phase": "phase1-quality-first-qwen32-addendum", - "source_git_sha": expected_source_sha, - "protocol_path": str(PROTOCOL_FILE.relative_to(repo_root)), - "protocol_sha256": protocol["logical_sha256"], - "base_study": protocol["base_study"], - "decision": protocol["decision"], - "task_manifest_sha256": manifest["logical_sha256"], - "dataset": protocol["dataset"], - "prompt": prompt_binding, - "prompt_sha256": prompt_sha, - "sandbox": { - "image_ref": sandbox[0], - "image_id": sandbox[1], - }, - "runtime": runtime, - "run": { - "hypothesis_id": "RC-H03", - "arm_id": arm_id, - "attempts": len(attempts), - "run_manifest_record_sha256": evidence.manifest.record_sha256, - "wandb_run_id": deterministic_wandb_run_id( - RunKey(protocol["study_id"], "RC-H03", arm_id), - source_git_sha=expected_source_sha, - ), - }, - "files": file_inventory, - } - release_manifest["logical_sha256"] = canonical_json_sha256(release_manifest) - return files, release_manifest - - -def freeze( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, - output: Path | None = None, - validate_only: bool = False, -) -> dict[str, Any]: - output_root = _output_root(repo_root, output) - source, external = _validate_roots( - repo_root=repo_root, - external_root=external_root, - output_root=output_root, - ) - _require_source_clean_except_output( - repo_root=source, - output_root=output_root, - ) - files, manifest = _collect_bundle( - repo_root=source, - external_root=external, - expected_source_sha=expected_source_sha, - ) - expected = { - PurePosixPath("release_manifest.json"): _json_bytes(manifest), - **files, - } - if validate_only: - _assert_exact_output(output_root, expected) - else: - _write_create_or_verify(output_root, expected) - return manifest - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=REPO_ROOT) - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--output", type=Path) - result.add_argument("--validate", action="store_true") - return result - - -def main() -> None: - args = parser().parse_args() - report = freeze( - repo_root=args.repo_root, - external_root=args.external_root, - expected_source_sha=args.expected_source_sha, - output=args.output, - validate_only=args.validate, - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/protocol.json b/rl/studies/representation_curriculum_v1/extensions/qwen32/protocol.json deleted file mode 100644 index a2f7919b..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/protocol.json +++ /dev/null @@ -1,116 +0,0 @@ -{ - "arms": { - "qwen-on-32768": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 32768, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": true, - "thinking_effort": null - } - }, - "base_study": { - "hypothesis": "RC-H01", - "protocol_sha256": "1e2869e6c640463e4cf4233a2806dadb5368c4a22a24634c755bb1d3dcc8e349", - "source_git_sha": "b4b56ce2929298aa0d9334d7cdaf69f227329349", - "study_id": "representation-curriculum-v1" - }, - "contract_version": "pixcell-blind-phase-a-direct-v1", - "dataset": { - "configuration": "depth", - "development_split": "validation", - "logical_release_sha256": "676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b", - "repository": "qpaig-mit/pixcell", - "revision": "v2.0.0" - }, - "decision": { - "basis": "RC-H01 smoke observed a 0.875 cap-hit rate for qwen-on-16384", - "canonical_selection_logical_sha256": "14e1cd1b71cf345afece19fe110c15e9c734879e3149545489e67df801e6928b", - "policy": "quality_first_user_directed_extension", - "prior_32k_confirmation": { - "arm_id": "qwen-on-32768-confirmation", - "attempts": 80, - "cap_hit_rate": 0.0, - "hypothesis_id": "RC-H00R", - "summary_logical_sha256": "45c291e95c3b6f1f1c7145190c36f532b4e9acb713c0f11a66225af394b2e67f" - }, - "trigger_evidence": { - "arm_id": "qwen-on-16384", - "attempts": 8, - "cap_hit_rate": 0.875, - "hypothesis_id": "RC-H01", - "summary_logical_sha256": "241912e11d77ae5d96a9b5d7814300ff3a03dfe673670c591c5235c39e31d59c", - "wave": "smoke" - } - }, - "hypotheses": { - "RC-H03": { - "arm_ids": [ - "qwen-on-32768" - ], - "attempts_per_task": 4, - "name": "Qwen quality-first 32k zero-shot F1-F8 baseline", - "status": "frozen", - "task_set": "f1_f8", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2, - 3, - 4 - ], - "task_count": 8 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 8 - } - } - } - }, - "launch": { - "confirmation_token": "PIXCELL_PHASE1_BASELINE", - "mutable_output_environment": "PIXCELL_STUDY_ROOT" - }, - "logical_sha256": "bd51768349f512741080fe4f080f343d311e81c5946186a9541565737db9da33", - "pricing": { - "as_of": "2026-07-27", - "currency": "USD", - "models": { - "Qwen/Qwen3.6-35B-A3B": { - "prefill_cached": 0.108, - "prefill_uncached": 0.54, - "sample": 1.335 - } - }, - "source": "https://tinker-docs.thinkingmachines.ai/tinker/models/", - "unit": "per_million_tokens" - }, - "sampling": { - "evaluator_workers": 8, - "require_candidate_isolation": true, - "sampling_concurrency": 8, - "seed_namespace": "pixcell-representation-curriculum-v1", - "temperature": 1.0, - "top_p": 1.0 - }, - "schema_version": "pixcell-representation-curriculum-protocol-v1", - "study_id": "representation-curriculum-v1-qwen32", - "task_manifest": { - "logical_sha256": "e32a0a1522fba3c90163e5687c65ab5e92fdb77f4f875a73cbfcdaa432b44272", - "path": "task_manifest.json" - }, - "tracking": { - "entity": "aadarwal-massachusetts-institute-of-technology", - "group": "phase1-qwen32", - "local_record_is_authoritative": true, - "mode": "online", - "project": "pixcell-representation-curriculum-v1", - "provider": "wandb" - } -} diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/run_baseline.py b/rl/studies/representation_curriculum_v1/extensions/qwen32/run_baseline.py deleted file mode 100644 index a625111d..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/run_baseline.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 -"""Run the fixed Qwen thinking-on/32k F1-F8 baseline extension.""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import os -from pathlib import Path - -from rl.evaluation.runner import run_baseline - - -REPO_ROOT = Path(__file__).resolve().parents[5] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--wave", required=True, choices=("smoke", "complete")) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--confirm-spend", default="") - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_STUDY_ROOT"]) - if os.environ.get("PIXCELL_STUDY_ROOT") - else None - ), - ) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit( - "set PIXCELL_STUDY_ROOT or provide --external-root outside the repository" - ) - report = asyncio.run( - run_baseline( - repo_root=REPO_ROOT, - protocol_file=PROTOCOL_FILE, - hypothesis_id="RC-H03", - wave_name=args.wave, - expected_source_sha=args.expected_source_sha, - external_root=args.external_root, - confirmation=args.confirm_spend, - ) - ) - print(json.dumps(report["summaries"], indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/test_freeze.py b/rl/studies/representation_curriculum_v1/extensions/qwen32/test_freeze.py deleted file mode 100644 index 6fcbf334..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/test_freeze.py +++ /dev/null @@ -1,99 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.evaluation.attempt_store import RunKey -from rl.evaluation.tracking import deterministic_wandb_run_id -from rl.studies.representation_curriculum_v1.extensions.qwen32 import freeze -from rl.studies.representation_curriculum_v1.freeze_phase1 import Phase1FreezeError - - -REPO_ROOT = Path(__file__).resolve().parents[5] - - -def test_collect_bundle_uses_canonical_wandb_identity( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, -): - source_sha = "a" * 40 - protocol = { - "study_id": "representation-curriculum-v1-qwen32", - "logical_sha256": "b" * 64, - "hypotheses": { - "RC-H03": { - "arm_ids": ["qwen-on-32768"], - "task_set": "f1_f8", - } - }, - "base_study": {"study_id": "representation-curriculum-v1"}, - "decision": {"policy": "quality_first_user_directed_extension"}, - "dataset": {"revision": "v2.0.0"}, - } - evidence = SimpleNamespace( - summary={"complete": True}, - manifest=SimpleNamespace(record_sha256="c" * 64), - ) - task = SimpleNamespace(task_id="F1") - job = SimpleNamespace(task_id="F1", attempt_index=1) - - monkeypatch.setattr(freeze, "_git_head", lambda _root: source_sha) - monkeypatch.setattr( - freeze, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr(freeze, "validate_runtime_stack", lambda _root: {}) - monkeypatch.setattr( - freeze, - "load_task_manifest", - lambda *_args, **_kwargs: {"logical_sha256": "d" * 64}, - ) - monkeypatch.setattr(freeze, "_prompt_binding", lambda: ({}, "e" * 64)) - monkeypatch.setattr( - freeze, - "_task_set", - lambda **_kwargs: ([task], "f" * 64), - ) - monkeypatch.setattr(freeze, "jobs_for_wave", lambda *_args, **_kwargs: [job]) - monkeypatch.setattr( - freeze, - "_load_run", - lambda **_kwargs: (evidence, ("sha256:image", "sha256:image")), - ) - monkeypatch.setattr( - freeze, - "_collect_run_attempts", - lambda **_kwargs: ( - [{"task_id": "F1", "attempt_index": 1}], - {}, - ), - ) - - _files, manifest = freeze._collect_bundle( - repo_root=REPO_ROOT, - external_root=tmp_path, - expected_source_sha=source_sha, - ) - assert manifest["run"]["wandb_run_id"] == deterministic_wandb_run_id( - RunKey( - "representation-curriculum-v1-qwen32", - "RC-H03", - "qwen-on-32768", - ), - source_git_sha=source_sha, - ) - - -def test_output_root_rejects_symlink_escape(tmp_path: Path): - repo = tmp_path / "repo" - parent = repo / "data/training/representation-curriculum-v1" - outside = tmp_path / "outside" - parent.mkdir(parents=True) - outside.mkdir() - (parent / "phase1-qwen32").symlink_to(outside, target_is_directory=True) - - with pytest.raises(Phase1FreezeError, match="outside the source repository"): - freeze._output_root(repo, None) diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen32/test_protocol.py b/rl/studies/representation_curriculum_v1/extensions/qwen32/test_protocol.py deleted file mode 100644 index 62f0bdce..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen32/test_protocol.py +++ /dev/null @@ -1,109 +0,0 @@ -from __future__ import annotations - -from pathlib import Path - -import pytest - -from rl.evaluation.protocol import ( - _require_protocol_at_head, - deterministic_seed, - jobs_for_wave, - load_archived_protocol, - load_protocol, -) -from rl.evaluation.tasks import load_task_manifest - - -REPO_ROOT = Path(__file__).resolve().parents[5] -BASE_PROTOCOL = ( - REPO_ROOT / "rl/studies/representation_curriculum_v1/protocol.json" -) -EXTENSION_PROTOCOL = Path(__file__).with_name("protocol.json") -TASK_MANIFEST = ( - REPO_ROOT / "rl/studies/representation_curriculum_v1/task_manifest.json" -) - - -def _benchmark_task_ids() -> list[str]: - manifest = load_task_manifest(TASK_MANIFEST, repo_root=REPO_ROOT) - return [value["task_id"] for value in manifest["task_sets"]["f1_f8"]] - - -def test_extension_is_one_fixed_quality_first_arm(): - protocol = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - assert protocol["study_id"] == "representation-curriculum-v1-qwen32" - assert protocol["base_study"]["protocol_sha256"] == load_archived_protocol( - REPO_ROOT, - protocol_file=BASE_PROTOCOL, - )["logical_sha256"] - assert protocol["hypotheses"]["RC-H03"]["arm_ids"] == ["qwen-on-32768"] - assert protocol["arms"]["qwen-on-32768"] == { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 32768, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": True, - "thinking_effort": None, - } - - -def test_extension_smoke_and_complete_are_exact_f1_f8_g4_jobs(): - protocol = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - task_ids = _benchmark_task_ids() - smoke = jobs_for_wave( - protocol, - hypothesis_id="RC-H03", - wave_name="smoke", - task_ids=task_ids, - ) - complete = jobs_for_wave( - protocol, - hypothesis_id="RC-H03", - wave_name="complete", - task_ids=task_ids, - ) - assert len(smoke) == 8 - assert len(complete) == 32 - assert {job.arm_id for job in complete} == {"qwen-on-32768"} - assert {job.task_id for job in smoke} == set(task_ids) - assert {job.attempt_index for job in complete} == {1, 2, 3, 4} - - -def test_extension_keeps_base_seed_pairing(): - base = load_archived_protocol(REPO_ROOT, protocol_file=BASE_PROTOCOL) - extension = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - for task_id in _benchmark_task_ids(): - for attempt_index in range(1, 5): - assert deterministic_seed( - extension, - task_id=task_id, - attempt_index=attempt_index, - ) == deterministic_seed( - base, - task_id=task_id, - attempt_index=attempt_index, - ) - - -def test_protocol_override_must_be_source_controlled(tmp_path: Path): - outside = tmp_path / "protocol.json" - outside.write_text(EXTENSION_PROTOCOL.read_text(encoding="utf-8"), encoding="utf-8") - with pytest.raises(ValueError, match="inside the source repository"): - load_protocol(REPO_ROOT, protocol_file=outside) - - -def test_paid_protocol_must_be_tracked_by_head(): - _require_protocol_at_head(REPO_ROOT, BASE_PROTOCOL) - with pytest.raises(ValueError, match="tracked by the paid-launch commit"): - _require_protocol_at_head(REPO_ROOT, REPO_ROOT / ".git") diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/README.md b/rl/studies/representation_curriculum_v1/extensions/qwen60/README.md deleted file mode 100644 index 9da38e57..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/README.md +++ /dev/null @@ -1,35 +0,0 @@ -# Qwen 60k F1-F8 extension - -> **Superseded diagnostic record.** This extension used the retired v1 prompt -> contract. Its measurements are not canonical baselines or paper results, -> and current paid launch code rejects the protocol. The files are retained -> for provenance. - -This is the near-context-limit extension to the frozen Phase 1 baseline study. -It follows the accepted Qwen 32k extension in pull request 7 and records the -user's quality-first decision to test the strongest practical setting for a -self-hostable model. - -Qwen has a 65,536-token context window shared by the prompt and completion, so -a literal 65,536-token completion cannot be requested. Under the frozen -renderer, the largest F1-F8 prompt is 3,685 tokens. The 60,000-token cap -therefore fits every task and leaves at least 1,851 tokens of context reserve. - -`RC-H04` keeps the original F1-F8 tasks, blind prompt, renderer, image policy, -temperature, seed namespace, evaluator, and four attempts per figure. Its only -changed model setting relative to `RC-H03` is the output cap. It has its own -study ID, W&B run identity, external ledger, and tracked evidence directory, -so it cannot overwrite the 16k or 32k records. - -The historical launch command was: - -```bash -python -m rl.studies.representation_curriculum_v1.extensions.qwen60.run_baseline \ - --wave smoke \ - --external-root "$PIXCELL_STUDY_ROOT" \ - --expected-source-sha "$SOURCE_SHA" \ - --confirm-spend PIXCELL_PHASE1_BASELINE -``` - -After verifying all eight sampling and evaluation records, repeat with -`--wave complete`. The complete wave is resumable and contains 32 samples. diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/__init__.py b/rl/studies/representation_curriculum_v1/extensions/qwen60/__init__.py deleted file mode 100644 index ec4c4493..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Near-context-limit Qwen 60k F1-F8 baseline extension.""" diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/freeze.py b/rl/studies/representation_curriculum_v1/extensions/qwen60/freeze.py deleted file mode 100644 index 05326ca7..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/freeze.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -"""Freeze or validate the tracked Qwen 60k F1-F8 baseline addendum.""" - -from __future__ import annotations - -import argparse -import json -import re -from pathlib import Path, PurePosixPath -from typing import Any - -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.attempt_store import AttemptStore, RunKey -from rl.evaluation.protocol import jobs_for_wave, load_protocol, task_manifest_path -from rl.evaluation.tasks import canonical_json_sha256, load_task_manifest -from rl.evaluation.tracking import deterministic_wandb_run_id -from rl.studies.representation_curriculum_v1.freeze_phase1 import ( - Phase1FreezeError, - _assert_exact_output, - _collect_run_attempts, - _git_head, - _json_bytes, - _jsonl_bytes, - _load_run, - _prompt_binding, - _require_source_clean_except_output, - _sha256_bytes, - _task_set, - _validate_roots, - _write_create_or_verify, -) - - -REPO_ROOT = Path(__file__).resolve().parents[5] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") -OUTPUT_RELATIVE_ROOT = Path( - "data/training/representation-curriculum-v1/phase1-qwen60" -) -RELEASE_SCHEMA = "pixcell-representation-curriculum-qwen60-release-v1" -_SOURCE_SHA = re.compile(r"^[0-9a-f]{40}$") - - -def _output_root(repo_root: Path, requested: Path | None) -> Path: - root = repo_root.expanduser().resolve(strict=True) - expected = (root / OUTPUT_RELATIVE_ROOT).resolve(strict=False) - if expected == root or not expected.is_relative_to(root): - raise Phase1FreezeError( - "the tracked Qwen 60k path resolves outside the source repository" - ) - observed = ( - expected - if requested is None - else requested.expanduser().resolve(strict=False) - ) - if observed != expected: - raise Phase1FreezeError( - f"Qwen 60k evidence must be written exactly to {expected}" - ) - return expected - - -def _collect_bundle( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, -) -> tuple[dict[PurePosixPath, bytes], dict[str, Any]]: - if not _SOURCE_SHA.fullmatch(expected_source_sha): - raise Phase1FreezeError( - "expected_source_sha must be a full lowercase Git SHA" - ) - if _git_head(repo_root) != expected_source_sha: - raise Phase1FreezeError("source HEAD differs from the paid-launch commit") - - protocol = load_protocol(repo_root, protocol_file=PROTOCOL_FILE) - runtime = validate_runtime_stack(repo_root) - manifest = load_task_manifest( - task_manifest_path(repo_root), - repo_root=repo_root, - ) - prompt_binding, prompt_sha = _prompt_binding() - tasks, task_set_sha = _task_set( - repo_root=repo_root, - protocol=protocol, - manifest=manifest, - hypothesis_id="RC-H04", - ) - jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H04", - wave_name="complete", - task_ids=[task.task_id for task in tasks], - ) - arm_id = str(protocol["hypotheses"]["RC-H04"]["arm_ids"][0]) - store = AttemptStore(repo_root=repo_root, external_root=external_root) - evidence, sandbox = _load_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H04", - arm_id=arm_id, - source_git_sha=expected_source_sha, - tasks=tasks, - jobs=jobs, - task_set_sha256=task_set_sha, - prompt_sha256=prompt_sha, - sandbox=None, - runtime=runtime, - ) - attempts, programs = _collect_run_attempts( - protocol=protocol, - store=store, - evidence=evidence, - ) - attempts.sort(key=lambda row: (row["task_id"], row["attempt_index"])) - - summary_document = { - "schema_version": "pixcell-representation-curriculum-qwen60-summary-v1", - "study_id": protocol["study_id"], - "hypothesis_id": "RC-H04", - "arm_id": arm_id, - "summary": evidence.summary, - } - summary_document["logical_sha256"] = canonical_json_sha256(summary_document) - files: dict[PurePosixPath, bytes] = { - PurePosixPath("summary.json"): _json_bytes(summary_document), - PurePosixPath("attempts.jsonl"): _jsonl_bytes(attempts), - **programs, - } - file_inventory = [ - { - "path": path.as_posix(), - "sha256": _sha256_bytes(content), - "bytes": len(content), - } - for path, content in sorted(files.items(), key=lambda item: item[0]) - ] - release_manifest: dict[str, Any] = { - "schema_version": RELEASE_SCHEMA, - "study_id": protocol["study_id"], - "phase": "phase1-quality-first-qwen60-addendum", - "source_git_sha": expected_source_sha, - "protocol_path": str(PROTOCOL_FILE.relative_to(repo_root)), - "protocol_sha256": protocol["logical_sha256"], - "base_study": protocol["base_study"], - "decision": protocol["decision"], - "task_manifest_sha256": manifest["logical_sha256"], - "dataset": protocol["dataset"], - "prompt": prompt_binding, - "prompt_sha256": prompt_sha, - "sandbox": { - "image_ref": sandbox[0], - "image_id": sandbox[1], - }, - "runtime": runtime, - "run": { - "hypothesis_id": "RC-H04", - "arm_id": arm_id, - "attempts": len(attempts), - "run_manifest_record_sha256": evidence.manifest.record_sha256, - "wandb_run_id": deterministic_wandb_run_id( - RunKey(protocol["study_id"], "RC-H04", arm_id), - source_git_sha=expected_source_sha, - ), - }, - "files": file_inventory, - } - release_manifest["logical_sha256"] = canonical_json_sha256(release_manifest) - return files, release_manifest - - -def freeze( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, - output: Path | None = None, - validate_only: bool = False, -) -> dict[str, Any]: - output_root = _output_root(repo_root, output) - source, external = _validate_roots( - repo_root=repo_root, - external_root=external_root, - output_root=output_root, - ) - _require_source_clean_except_output( - repo_root=source, - output_root=output_root, - ) - files, manifest = _collect_bundle( - repo_root=source, - external_root=external, - expected_source_sha=expected_source_sha, - ) - expected = { - PurePosixPath("release_manifest.json"): _json_bytes(manifest), - **files, - } - if validate_only: - _assert_exact_output(output_root, expected) - else: - _write_create_or_verify(output_root, expected) - return manifest - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=REPO_ROOT) - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--output", type=Path) - result.add_argument("--validate", action="store_true") - return result - - -def main() -> None: - args = parser().parse_args() - report = freeze( - repo_root=args.repo_root, - external_root=args.external_root, - expected_source_sha=args.expected_source_sha, - output=args.output, - validate_only=args.validate, - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/protocol.json b/rl/studies/representation_curriculum_v1/extensions/qwen60/protocol.json deleted file mode 100644 index 43d5ccdb..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/protocol.json +++ /dev/null @@ -1,123 +0,0 @@ -{ - "arms": { - "qwen-on-60000": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 60000, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": true, - "thinking_effort": null - } - }, - "base_study": { - "arm_id": "qwen-on-32768", - "github_pull_request": 7, - "hypothesis": "RC-H03", - "merge_git_sha": "53de41ad43c0eeec36aa73bf4ae466f199711dfc", - "protocol_sha256": "bd51768349f512741080fe4f080f343d311e81c5946186a9541565737db9da33", - "source_git_sha": "53de41ad43c0eeec36aa73bf4ae466f199711dfc", - "study_id": "representation-curriculum-v1-qwen32" - }, - "contract_version": "pixcell-blind-phase-a-direct-v1", - "dataset": { - "configuration": "depth", - "development_split": "validation", - "logical_release_sha256": "676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b", - "repository": "qpaig-mit/pixcell", - "revision": "v2.0.0" - }, - "decision": { - "context_fit": { - "context_tokens": 65536, - "largest_frozen_f1_f8_prompt_tokens": 3685, - "minimum_context_reserve_tokens": 1851, - "minimum_f1_f8_completion_capacity_tokens": 61851, - "selected_max_output_tokens": 60000 - }, - "policy": "quality_first_user_directed_near_context_limit_extension", - "rationale": "The 65536-token context includes the rendered prompt, so a literal 64k completion cannot fit. A 60000-token cap is the largest round-number quality arm with at least 1851 tokens of reserve on every frozen F1-F8 task.", - "trigger_evidence": { - "arm_id": "qwen-on-32768", - "attempts": 8, - "cap_hit_rate": 0.125, - "hypothesis_id": "RC-H03", - "mean_iou": 0.02505331854632315, - "pure_executable_rate": 0.125, - "run_manifest_record_sha256": "d6ed8fc91e5a18bcc364bbd6fb167b12188a9ac3c08a8886de10774405aa42d5", - "source_git_sha": "53de41ad43c0eeec36aa73bf4ae466f199711dfc", - "summary_logical_sha256": "144769709b59150270487f72f5a9b84e95f5a4d8f6ec0cfcba4378190f79da95", - "wave": "smoke" - }, - "user_direction": "Test the strongest practical self-hostable Qwen setting even when it costs more." - }, - "hypotheses": { - "RC-H04": { - "arm_ids": [ - "qwen-on-60000" - ], - "attempts_per_task": 4, - "name": "Qwen quality-first 60k zero-shot F1-F8 baseline", - "status": "frozen", - "task_set": "f1_f8", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2, - 3, - 4 - ], - "task_count": 8 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 8 - } - } - } - }, - "launch": { - "confirmation_token": "PIXCELL_PHASE1_BASELINE", - "mutable_output_environment": "PIXCELL_STUDY_ROOT" - }, - "logical_sha256": "84d78f49ac16d3e1a13ccb73a8bddfb8837dad7abd61f6a93bb99ec027b490a1", - "pricing": { - "as_of": "2026-07-27", - "currency": "USD", - "models": { - "Qwen/Qwen3.6-35B-A3B": { - "prefill_cached": 0.108, - "prefill_uncached": 0.54, - "sample": 1.335 - } - }, - "source": "https://tinker-docs.thinkingmachines.ai/tinker/models/", - "unit": "per_million_tokens" - }, - "sampling": { - "evaluator_workers": 8, - "require_candidate_isolation": true, - "sampling_concurrency": 8, - "seed_namespace": "pixcell-representation-curriculum-v1", - "temperature": 1.0, - "top_p": 1.0 - }, - "schema_version": "pixcell-representation-curriculum-protocol-v1", - "study_id": "representation-curriculum-v1-qwen60", - "task_manifest": { - "logical_sha256": "e32a0a1522fba3c90163e5687c65ab5e92fdb77f4f875a73cbfcdaa432b44272", - "path": "task_manifest.json" - }, - "tracking": { - "entity": "aadarwal-massachusetts-institute-of-technology", - "group": "phase1-qwen60", - "local_record_is_authoritative": true, - "mode": "online", - "project": "pixcell-representation-curriculum-v1", - "provider": "wandb" - } -} diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/run_baseline.py b/rl/studies/representation_curriculum_v1/extensions/qwen60/run_baseline.py deleted file mode 100644 index b899f43b..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/run_baseline.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 -"""Run the fixed Qwen thinking-on/60k F1-F8 baseline extension.""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import os -from pathlib import Path - -from rl.evaluation.runner import run_baseline - - -REPO_ROOT = Path(__file__).resolve().parents[5] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--wave", required=True, choices=("smoke", "complete")) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--confirm-spend", default="") - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_STUDY_ROOT"]) - if os.environ.get("PIXCELL_STUDY_ROOT") - else None - ), - ) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit( - "set PIXCELL_STUDY_ROOT or provide --external-root outside the repository" - ) - report = asyncio.run( - run_baseline( - repo_root=REPO_ROOT, - protocol_file=PROTOCOL_FILE, - hypothesis_id="RC-H04", - wave_name=args.wave, - expected_source_sha=args.expected_source_sha, - external_root=args.external_root, - confirmation=args.confirm_spend, - ) - ) - print(json.dumps(report["summaries"], indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/test_freeze.py b/rl/studies/representation_curriculum_v1/extensions/qwen60/test_freeze.py deleted file mode 100644 index 55cd9416..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/test_freeze.py +++ /dev/null @@ -1,119 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.evaluation.attempt_store import RunKey -from rl.evaluation.tracking import deterministic_wandb_run_id -from rl.studies.representation_curriculum_v1.extensions.qwen60 import freeze -from rl.studies.representation_curriculum_v1.freeze_phase1 import Phase1FreezeError - - -REPO_ROOT = Path(__file__).resolve().parents[5] - - -def test_collect_bundle_uses_distinct_canonical_wandb_identity( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, -): - source_sha = "a" * 40 - protocol = { - "study_id": "representation-curriculum-v1-qwen60", - "logical_sha256": "b" * 64, - "hypotheses": { - "RC-H04": { - "arm_ids": ["qwen-on-60000"], - "task_set": "f1_f8", - } - }, - "base_study": {"study_id": "representation-curriculum-v1-qwen32"}, - "decision": { - "policy": "quality_first_user_directed_near_context_limit_extension" - }, - "dataset": {"revision": "v2.0.0"}, - } - evidence = SimpleNamespace( - summary={"complete": True}, - manifest=SimpleNamespace(record_sha256="c" * 64), - ) - task = SimpleNamespace(task_id="F1") - job = SimpleNamespace(task_id="F1", attempt_index=1) - - monkeypatch.setattr(freeze, "_git_head", lambda _root: source_sha) - monkeypatch.setattr( - freeze, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr(freeze, "validate_runtime_stack", lambda _root: {}) - monkeypatch.setattr( - freeze, - "load_task_manifest", - lambda *_args, **_kwargs: {"logical_sha256": "d" * 64}, - ) - monkeypatch.setattr(freeze, "_prompt_binding", lambda: ({}, "e" * 64)) - monkeypatch.setattr( - freeze, - "_task_set", - lambda **_kwargs: ([task], "f" * 64), - ) - monkeypatch.setattr(freeze, "jobs_for_wave", lambda *_args, **_kwargs: [job]) - monkeypatch.setattr( - freeze, - "_load_run", - lambda **_kwargs: (evidence, ("sha256:image", "sha256:image")), - ) - monkeypatch.setattr( - freeze, - "_collect_run_attempts", - lambda **_kwargs: ( - [{"task_id": "F1", "attempt_index": 1}], - {}, - ), - ) - - _files, manifest = freeze._collect_bundle( - repo_root=REPO_ROOT, - external_root=tmp_path, - expected_source_sha=source_sha, - ) - expected = deterministic_wandb_run_id( - RunKey( - "representation-curriculum-v1-qwen60", - "RC-H04", - "qwen-on-60000", - ), - source_git_sha=source_sha, - ) - qwen32 = deterministic_wandb_run_id( - RunKey( - "representation-curriculum-v1-qwen32", - "RC-H03", - "qwen-on-32768", - ), - source_git_sha=source_sha, - ) - assert manifest["run"]["wandb_run_id"] == expected - assert manifest["run"]["wandb_run_id"] != qwen32 - - -def test_output_root_rejects_symlink_escape(tmp_path: Path): - repo = tmp_path / "repo" - parent = repo / "data/training/representation-curriculum-v1" - outside = tmp_path / "outside" - parent.mkdir(parents=True) - outside.mkdir() - (parent / "phase1-qwen60").symlink_to(outside, target_is_directory=True) - - with pytest.raises(Phase1FreezeError, match="outside the source repository"): - freeze._output_root(repo, None) - - -def test_output_root_is_fixed_and_cannot_overwrite_qwen32(tmp_path: Path): - repo = tmp_path / "repo" - repo.mkdir() - qwen32 = repo / "data/training/representation-curriculum-v1/phase1-qwen32" - with pytest.raises(Phase1FreezeError, match="must be written exactly"): - freeze._output_root(repo, qwen32) diff --git a/rl/studies/representation_curriculum_v1/extensions/qwen60/test_protocol.py b/rl/studies/representation_curriculum_v1/extensions/qwen60/test_protocol.py deleted file mode 100644 index 6c9e4ecb..00000000 --- a/rl/studies/representation_curriculum_v1/extensions/qwen60/test_protocol.py +++ /dev/null @@ -1,225 +0,0 @@ -from __future__ import annotations - -import json -import subprocess -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.evaluation.protocol import ( - _require_protocol_at_head, - deterministic_seed, - jobs_for_wave, - load_archived_protocol, - load_protocol, - task_manifest_path, -) -from rl.evaluation.tasks import load_task_manifest, load_task_set -from rl.studies.representation_curriculum_v1.extensions.qwen60 import ( - run_baseline as fixed_runner, -) - - -REPO_ROOT = Path(__file__).resolve().parents[5] -BASE_PROTOCOL = ( - REPO_ROOT / "rl/studies/representation_curriculum_v1/protocol.json" -) -QWEN32_PROTOCOL = ( - REPO_ROOT - / "rl/studies/representation_curriculum_v1/extensions/qwen32/protocol.json" -) -EXTENSION_PROTOCOL = Path(__file__).with_name("protocol.json") -QWEN32_MERGE_SHA = "53de41ad43c0eeec36aa73bf4ae466f199711dfc" - - -def _benchmark_tasks(): - manifest = load_task_manifest(task_manifest_path(REPO_ROOT), repo_root=REPO_ROOT) - return load_task_set( - repo_root=REPO_ROOT, - manifest=manifest, - task_set="f1_f8", - ) - - -def test_extension_is_one_fixed_near_context_limit_arm(): - protocol = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - qwen32 = load_archived_protocol( - REPO_ROOT, - protocol_file=QWEN32_PROTOCOL, - ) - assert protocol["study_id"] == "representation-curriculum-v1-qwen60" - assert protocol["base_study"] == { - "arm_id": "qwen-on-32768", - "github_pull_request": 7, - "hypothesis": "RC-H03", - "merge_git_sha": QWEN32_MERGE_SHA, - "protocol_sha256": qwen32["logical_sha256"], - "source_git_sha": QWEN32_MERGE_SHA, - "study_id": "representation-curriculum-v1-qwen32", - } - assert protocol["hypotheses"]["RC-H04"]["arm_ids"] == ["qwen-on-60000"] - assert protocol["arms"]["qwen-on-60000"] == { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 60000, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": True, - "thinking_effort": None, - } - for field in ( - "contract_version", - "dataset", - "launch", - "pricing", - "sampling", - "task_manifest", - ): - assert protocol[field] == qwen32[field] - qwen32_arm = dict(qwen32["arms"]["qwen-on-32768"]) - qwen32_arm["max_output_tokens"] = 60000 - assert protocol["arms"]["qwen-on-60000"] == qwen32_arm - assert protocol["decision"]["trigger_evidence"] == { - "arm_id": "qwen-on-32768", - "attempts": 8, - "cap_hit_rate": 0.125, - "hypothesis_id": "RC-H03", - "mean_iou": 0.02505331854632315, - "pure_executable_rate": 0.125, - "run_manifest_record_sha256": ( - "d6ed8fc91e5a18bcc364bbd6fb167b12188a9ac3c08a8886de10774405aa42d5" - ), - "source_git_sha": QWEN32_MERGE_SHA, - "summary_logical_sha256": ( - "144769709b59150270487f72f5a9b84e95f5a4d8f6ec0cfcba4378190f79da95" - ), - "wave": "smoke", - } - - -def test_qwen32_base_commit_contains_the_bound_protocol_and_pr_merge(): - relative = QWEN32_PROTOCOL.relative_to(REPO_ROOT).as_posix() - committed = subprocess.check_output( - ["git", "show", f"{QWEN32_MERGE_SHA}:{relative}"], - cwd=REPO_ROOT, - ) - assert json.loads(committed) == json.loads(QWEN32_PROTOCOL.read_bytes()) - subject = subprocess.check_output( - ["git", "show", "-s", "--format=%s", QWEN32_MERGE_SHA], - cwd=REPO_ROOT, - text=True, - ).strip() - assert subject == "Merge pull request #7 from QPG-MIT/agent/qwen32-f1f8-baseline" - - -def test_extension_smoke_and_complete_are_exact_f1_f8_g4_jobs(): - protocol = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - task_ids = [task.task_id for task in _benchmark_tasks()] - smoke = jobs_for_wave( - protocol, - hypothesis_id="RC-H04", - wave_name="smoke", - task_ids=task_ids, - ) - complete = jobs_for_wave( - protocol, - hypothesis_id="RC-H04", - wave_name="complete", - task_ids=task_ids, - ) - assert len(smoke) == 8 - assert len(complete) == 32 - assert {job.arm_id for job in complete} == {"qwen-on-60000"} - assert {job.task_id for job in smoke} == set(task_ids) - assert {job.attempt_index for job in complete} == {1, 2, 3, 4} - - -def test_extension_keeps_base_seed_pairing(): - base = load_archived_protocol(REPO_ROOT, protocol_file=BASE_PROTOCOL) - extension = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - for task in _benchmark_tasks(): - for attempt_index in range(1, 5): - assert deterministic_seed( - extension, - task_id=task.task_id, - attempt_index=attempt_index, - ) == deterministic_seed( - base, - task_id=task.task_id, - attempt_index=attempt_index, - ) - - -def test_archived_60k_context_arithmetic_is_internally_consistent(): - protocol = load_archived_protocol( - REPO_ROOT, - protocol_file=EXTENSION_PROTOCOL, - ) - arm = protocol["arms"]["qwen-on-60000"] - context_fit = protocol["decision"]["context_fit"] - largest_prompt = context_fit["largest_frozen_f1_f8_prompt_tokens"] - context_tokens = arm["context_tokens"] - max_output_tokens = arm["max_output_tokens"] - minimum_reserve = context_tokens - largest_prompt - max_output_tokens - assert context_tokens - largest_prompt == context_fit[ - "minimum_f1_f8_completion_capacity_tokens" - ] - assert minimum_reserve == context_fit["minimum_context_reserve_tokens"] - assert minimum_reserve >= 0 - assert context_fit["selected_max_output_tokens"] == max_output_tokens - assert largest_prompt + 64_000 > context_tokens - assert largest_prompt + max_output_tokens <= context_tokens - - -def test_protocol_override_must_be_source_controlled(tmp_path: Path): - outside = tmp_path / "protocol.json" - outside.write_text(EXTENSION_PROTOCOL.read_text(encoding="utf-8"), encoding="utf-8") - with pytest.raises(ValueError, match="inside the source repository"): - load_protocol(REPO_ROOT, protocol_file=outside) - - -def test_fixed_runner_forwards_the_extension_protocol_to_the_head_guard( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, - capsys: pytest.CaptureFixture[str], -): - captured: dict[str, object] = {} - - async def fake_run_baseline(**kwargs): - captured.update(kwargs) - return {"summaries": {}} - - class FakeParser: - @staticmethod - def parse_args(): - return SimpleNamespace( - wave="smoke", - expected_source_sha="a" * 40, - confirm_spend="PIXCELL_PHASE1_BASELINE", - external_root=tmp_path, - ) - - monkeypatch.setattr(fixed_runner, "run_baseline", fake_run_baseline) - monkeypatch.setattr(fixed_runner, "parser", FakeParser) - fixed_runner.main() - assert captured["protocol_file"] == EXTENSION_PROTOCOL - assert captured["hypothesis_id"] == "RC-H04" - assert captured["wave_name"] == "smoke" - assert json.loads(capsys.readouterr().out) == {} - - -def test_paid_protocol_guard_accepts_head_and_rejects_non_protocol_path(): - _require_protocol_at_head(REPO_ROOT, BASE_PROTOCOL) - with pytest.raises(ValueError, match="tracked by the paid-launch commit"): - _require_protocol_at_head(REPO_ROOT, REPO_ROOT / ".git") diff --git a/rl/studies/representation_curriculum_v1/freeze_phase1.py b/rl/studies/representation_curriculum_v1/freeze_phase1.py deleted file mode 100644 index 47a83c88..00000000 --- a/rl/studies/representation_curriculum_v1/freeze_phase1.py +++ /dev/null @@ -1,1027 +0,0 @@ -#!/usr/bin/env python3 -"""Freeze or validate the compact, tracked Phase 1 baseline evidence.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import os -import re -import subprocess -import uuid -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any - -from rl.common.output import extract_code -from rl.common.preprocess import IMAGE_PREPROCESS_VERSION -from rl.common.prompt import CONTRACT_VERSION, prompt_asset_hashes -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.attempt_store import ( - AttemptKey, - AttemptStore, - RunKey, - StoredRecord, -) -from rl.evaluation.protocol import ( - JobSpec, - jobs_for_wave, - load_protocol, - resolved_arm_ids, - task_manifest_path, -) -from rl.evaluation.summarize import choose_operating_point, summarize_arm -from rl.evaluation.tasks import ( - EvaluationTask, - canonical_json_sha256, - load_task_manifest, - load_task_set, -) -from rl.evaluation.tracking import deterministic_wandb_run_id - - -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PHASE1_RELATIVE_ROOT = Path( - "data/training/representation-curriculum-v1/phase1" -) -RELEASE_SCHEMA = "pixcell-representation-curriculum-phase1-release-v1" -SUMMARIES_SCHEMA = "pixcell-representation-curriculum-phase1-summaries-v1" -ATTEMPT_INDEX_SCHEMA = "pixcell-representation-curriculum-attempt-v1" -_SOURCE_SHA = re.compile(r"^[0-9a-f]{40}$") -_HYPOTHESIS_ORDER = ("RC-H00", "RC-H00R", "RC-H01", "RC-H02") - - -class Phase1FreezeError(RuntimeError): - """The external ledger cannot certify the tracked Phase 1 release.""" - - -@dataclass(frozen=True) -class _RunEvidence: - hypothesis_id: str - arm_id: str - tasks: tuple[EvaluationTask, ...] - jobs: tuple[JobSpec, ...] - manifest: StoredRecord - summary: dict[str, Any] - - -@dataclass(frozen=True) -class _Bundle: - files: dict[PurePosixPath, bytes] - release_manifest: dict[str, Any] - - @property - def all_files(self) -> dict[PurePosixPath, bytes]: - return { - PurePosixPath("release_manifest.json"): _json_bytes( - self.release_manifest - ), - **self.files, - } - - -def _json_bytes(value: Any) -> bytes: - try: - return ( - json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - indent=2, - sort_keys=True, - ) - + "\n" - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise Phase1FreezeError("release evidence is not finite JSON") from exc - - -def _jsonl_bytes(values: list[dict[str, Any]]) -> bytes: - try: - return b"".join( - ( - json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ) - + "\n" - ).encode("utf-8") - for value in values - ) - except (TypeError, ValueError) as exc: - raise Phase1FreezeError("attempt index is not finite JSON") from exc - - -def _sha256_bytes(value: bytes) -> str: - return hashlib.sha256(value).hexdigest() - - -def _load_json(path: Path) -> dict[str, Any]: - def reject_constant(value: str) -> Any: - raise Phase1FreezeError(f"{path} contains non-finite number {value}") - - def reject_duplicates(pairs: list[tuple[str, Any]]) -> dict[str, Any]: - result: dict[str, Any] = {} - for key, value in pairs: - if key in result: - raise Phase1FreezeError( - f"{path} contains duplicate key {key!r}" - ) - result[key] = value - return result - - try: - value = json.loads( - path.read_bytes(), - parse_constant=reject_constant, - object_pairs_hook=reject_duplicates, - ) - except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: - raise Phase1FreezeError(f"cannot read canonical JSON from {path}") from exc - if not isinstance(value, dict): - raise Phase1FreezeError(f"{path} must contain a JSON object") - return value - - -def _git_head(repo_root: Path) -> str: - try: - return subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo_root, - text=True, - ).strip() - except (OSError, subprocess.CalledProcessError) as exc: - raise Phase1FreezeError("repository HEAD cannot be resolved") from exc - - -def _changed_paths(repo_root: Path) -> set[Path]: - commands = ( - ["git", "diff", "--name-only", "-z"], - ["git", "diff", "--cached", "--name-only", "-z"], - ["git", "ls-files", "--others", "--exclude-standard", "-z"], - ) - result: set[Path] = set() - for command in commands: - try: - output = subprocess.check_output(command, cwd=repo_root) - except (OSError, subprocess.CalledProcessError) as exc: - raise Phase1FreezeError("repository worktree cannot be inspected") from exc - for raw in output.split(b"\0"): - if raw: - result.add((repo_root / os.fsdecode(raw)).resolve(strict=False)) - return result - - -def _require_source_clean_except_output( - *, - repo_root: Path, - output_root: Path, -) -> None: - drift = sorted( - str(path.relative_to(repo_root)) - for path in _changed_paths(repo_root) - if path != output_root and not path.is_relative_to(output_root) - ) - if drift: - raise Phase1FreezeError( - "source worktree differs from the paid-launch commit outside the " - f"tracked Phase 1 output: {drift}" - ) - - -def _phase1_output_root(repo_root: Path, requested: Path | None) -> Path: - root = repo_root.expanduser().resolve(strict=True) - expected = (root / PHASE1_RELATIVE_ROOT).resolve(strict=False) - if expected == root or not expected.is_relative_to(root): - raise Phase1FreezeError( - "the tracked Phase 1 path resolves outside the source repository" - ) - observed = ( - expected - if requested is None - else requested.expanduser().resolve(strict=False) - ) - if observed != expected: - raise Phase1FreezeError( - f"Phase 1 evidence must be written exactly to {expected}" - ) - return expected - - -def _validate_roots( - *, - repo_root: Path, - external_root: Path, - output_root: Path, -) -> tuple[Path, Path]: - source = repo_root.expanduser().resolve(strict=True) - external = external_root.expanduser().resolve(strict=True) - if external == source or external.is_relative_to(source): - raise Phase1FreezeError( - "the mutable attempt ledger must remain outside the source repository" - ) - if ( - output_root == external - or output_root.is_relative_to(external) - or external.is_relative_to(output_root) - ): - raise Phase1FreezeError("source ledger and tracked output overlap") - return source, external - - -def _prompt_binding() -> tuple[dict[str, str], str]: - binding = { - "contract_version": CONTRACT_VERSION, - "preprocess_version": IMAGE_PREPROCESS_VERSION, - **prompt_asset_hashes(), - } - return binding, canonical_json_sha256(binding) - - -def _task_set( - *, - repo_root: Path, - protocol: dict[str, Any], - manifest: dict[str, Any], - hypothesis_id: str, -) -> tuple[list[EvaluationTask], str]: - task_set_name = str(protocol["hypotheses"][hypothesis_id]["task_set"]) - tasks = load_task_set( - repo_root=repo_root, - manifest=manifest, - task_set=task_set_name, - ) - task_set_sha = canonical_json_sha256(manifest["task_sets"][task_set_name]) - return tasks, task_set_sha - - -def _expected_run_manifest( - *, - protocol: dict[str, Any], - hypothesis_id: str, - arm_id: str, - source_git_sha: str, - task_set_sha256: str, - prompt_sha256: str, - sandbox_image_ref: str, - sandbox_image_id: str, - runtime: dict[str, Any], -) -> dict[str, Any]: - if hypothesis_id != "RC-H01": - allowed = resolved_arm_ids( - protocol, - hypothesis_id, - selection=( - {"rescue_required": True} - if hypothesis_id == "RC-H00R" - else None - ), - ) - if arm_id not in allowed: - raise Phase1FreezeError(f"{hypothesis_id} does not register {arm_id}") - return { - "source_git_sha": source_git_sha, - "protocol_sha256": protocol["logical_sha256"], - "task_set_sha256": task_set_sha256, - "prompt_sha256": prompt_sha256, - "sandbox_image_ref": sandbox_image_ref, - "sandbox_image_id": sandbox_image_id, - "runtime": runtime, - "model_binding": { - "arm_id": arm_id, - **protocol["arms"][arm_id], - "temperature": protocol["sampling"]["temperature"], - "top_p": protocol["sampling"]["top_p"], - }, - } - - -def _load_run( - *, - store: AttemptStore, - protocol: dict[str, Any], - hypothesis_id: str, - arm_id: str, - source_git_sha: str, - tasks: list[EvaluationTask], - jobs: list[JobSpec], - task_set_sha256: str, - prompt_sha256: str, - sandbox: tuple[str, str] | None, - runtime: dict[str, Any], -) -> tuple[_RunEvidence, tuple[str, str]]: - run_key = RunKey(protocol["study_id"], hypothesis_id, arm_id) - manifest = store.load_run_manifest(run_key) - if manifest is None: - raise Phase1FreezeError(f"missing run manifest for {hypothesis_id}/{arm_id}") - observed_sandbox = ( - str(manifest.payload.get("sandbox_image_ref", "")), - str(manifest.payload.get("sandbox_image_id", "")), - ) - if sandbox is not None and observed_sandbox != sandbox: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} used a different evaluator sandbox" - ) - expected = _expected_run_manifest( - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - source_git_sha=source_git_sha, - task_set_sha256=task_set_sha256, - prompt_sha256=prompt_sha256, - sandbox_image_ref=observed_sandbox[0], - sandbox_image_id=observed_sandbox[1], - runtime=runtime, - ) - if manifest.payload != expected: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} run manifest does not match the frozen launch" - ) - summary = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id=hypothesis_id, - arm_id=arm_id, - jobs=jobs, - tasks=tasks, - ) - if not summary["complete"]: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} is incomplete: {summary['missing']}" - ) - external_summary = ( - store.external_root.joinpath(*store.run_relative_path(run_key).parts) - / "summary.json" - ) - if not external_summary.is_file(): - raise Phase1FreezeError( - f"missing canonical summary for {hypothesis_id}/{arm_id}" - ) - if _load_json(external_summary) != summary: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} summary does not reproduce from attempts" - ) - return ( - _RunEvidence( - hypothesis_id=hypothesis_id, - arm_id=arm_id, - tasks=tuple(tasks), - jobs=tuple( - job - for job in jobs - if job.hypothesis_id == hypothesis_id and job.arm_id == arm_id - ), - manifest=manifest, - summary=summary, - ), - observed_sandbox, - ) - - -def _validate_receipt_and_evaluation( - *, - protocol: dict[str, Any], - store: AttemptStore, - task: EvaluationTask, - job: JobSpec, -) -> tuple[StoredRecord, StoredRecord, str]: - key = AttemptKey( - protocol["study_id"], - job.hypothesis_id, - job.arm_id, - job.task_id, - job.attempt_index, - ) - receipt = store.load_sampling_receipt(key) - evaluation = store.load_evaluation(key) - if receipt is None or evaluation is None: - raise Phase1FreezeError(f"missing immutable attempt record for {key}") - - request = receipt.payload.get("request") - sample = receipt.payload.get("sample") - if not isinstance(request, dict) or not isinstance(sample, dict): - raise Phase1FreezeError(f"{key} has an invalid sampling payload") - arm = protocol["arms"][job.arm_id] - expected_request = { - "max_tokens": int(arm["max_output_tokens"]), - "temperature": protocol["sampling"]["temperature"], - "top_p": protocol["sampling"]["top_p"], - "seed": job.seed, - } - if request != expected_request: - raise Phase1FreezeError(f"{key} sampling request differs from protocol") - required_sample = { - "prompt_tokens", - "completion_tokens", - "stop_reason", - "cap_hit", - "raw_text", - "completion_text", - "reasoning_text", - "answer_text", - "reasoning_tokens_exact", - "answer_tokens_exact", - "reasoning_tokens_estimate", - "answer_tokens_estimate", - "channel_token_count_basis", - "channel_parse_complete", - } - if set(sample) != required_sample: - raise Phase1FreezeError(f"{key} sample fields differ from the frozen schema") - prompt_tokens = sample["prompt_tokens"] - completion_tokens = sample["completion_tokens"] - if ( - isinstance(prompt_tokens, bool) - or not isinstance(prompt_tokens, int) - or prompt_tokens < 1 - or isinstance(completion_tokens, bool) - or not isinstance(completion_tokens, int) - or completion_tokens < 0 - or completion_tokens > int(request["max_tokens"]) - ): - raise Phase1FreezeError(f"{key} has invalid token accounting") - if bool(sample["cap_hit"]) != (sample["stop_reason"] == "length"): - raise Phase1FreezeError(f"{key} cap-hit flag differs from its stop reason") - if sample["cap_hit"] and completion_tokens != int(request["max_tokens"]): - raise Phase1FreezeError(f"{key} length stop did not reach the output cap") - reasoning_exact = sample["reasoning_tokens_exact"] - answer_exact = sample["answer_tokens_exact"] - if (reasoning_exact is None) != (answer_exact is None): - raise Phase1FreezeError(f"{key} has a partial exact channel partition") - if reasoning_exact is not None and ( - isinstance(reasoning_exact, bool) - or not isinstance(reasoning_exact, int) - or reasoning_exact < 0 - or isinstance(answer_exact, bool) - or not isinstance(answer_exact, int) - or answer_exact < 0 - or reasoning_exact + answer_exact != completion_tokens - ): - raise Phase1FreezeError(f"{key} exact channel tokens do not partition output") - raw_text = str(sample["raw_text"]) - completion = str(sample["completion_text"]) - if receipt.payload.get("raw_completion_sha256") != hashlib.sha256( - raw_text.encode("utf-8") - ).hexdigest(): - raise Phase1FreezeError(f"{key} raw completion digest mismatch") - if receipt.payload.get("completion_sha256") != hashlib.sha256( - completion.encode("utf-8") - ).hexdigest(): - raise Phase1FreezeError(f"{key} completion digest mismatch") - if not math.isfinite(float(receipt.payload.get("sampling_seconds", -1))): - raise Phase1FreezeError(f"{key} has non-finite sampling duration") - costs = receipt.payload.get("cost_estimate_usd") - if not isinstance(costs, dict) or set(costs) != { - "cached_prefill", - "uncached_prefill", - }: - raise Phase1FreezeError(f"{key} has invalid cost evidence") - rates = protocol["pricing"]["models"][arm["model"]] - sample_cost = completion_tokens * float(rates["sample"]) / 1_000_000 - expected_costs = { - "cached_prefill": ( - prompt_tokens * float(rates["prefill_cached"]) / 1_000_000 - + sample_cost - ), - "uncached_prefill": ( - prompt_tokens * float(rates["prefill_uncached"]) / 1_000_000 - + sample_cost - ), - } - if costs != expected_costs: - raise Phase1FreezeError(f"{key} cost evidence does not reproduce") - - measured = evaluation.payload - required_evaluation = { - "status", - "attribution", - "pure_executable", - "measurement_available", - "measured_iou", - "measured_dice", - "aggregation_iou", - "program", - "program_sha256", - "reference_sha256", - "violations", - "error", - "retryable", - "evaluation_seconds", - "diagnostics", - } - allowed_evaluation = required_evaluation | {"render_sha256"} - if not required_evaluation.issubset(measured) or not set(measured).issubset( - allowed_evaluation - ): - raise Phase1FreezeError( - f"{key} evaluation fields differ from the frozen schema" - ) - if measured["attribution"] != "model": - raise Phase1FreezeError( - f"{key} is an evaluator/reference failure, not a model result" - ) - program = str(measured["program"]) - if program != extract_code(completion): - raise Phase1FreezeError(f"{key} stored program does not match extraction") - if measured["program_sha256"] != hashlib.sha256( - program.encode("utf-8") - ).hexdigest(): - raise Phase1FreezeError(f"{key} program digest mismatch") - if measured["reference_sha256"] != task.reference.target_image_sha256: - raise Phase1FreezeError(f"{key} reference digest mismatch") - aggregation_iou = float(measured["aggregation_iou"]) - if not math.isfinite(aggregation_iou) or not 0 <= aggregation_iou <= 1: - raise Phase1FreezeError(f"{key} has invalid aggregation IoU") - measured_iou = measured["measured_iou"] - expected_iou = 0.0 if measured_iou is None else float(measured_iou) - if aggregation_iou != expected_iou: - raise Phase1FreezeError(f"{key} aggregation IoU is not the raw model IoU") - if bool(measured["measurement_available"]) != (measured_iou is not None): - raise Phase1FreezeError(f"{key} measurement-availability flag is inconsistent") - if measured_iou is not None and "render_sha256" not in measured: - raise Phase1FreezeError(f"{key} measured geometry has no render digest") - if bool(measured["pure_executable"]) != (measured["status"] == "ok"): - raise Phase1FreezeError(f"{key} executable flag is inconsistent with status") - return receipt, evaluation, program - - -def _collect_run_attempts( - *, - protocol: dict[str, Any], - store: AttemptStore, - evidence: _RunEvidence, -) -> tuple[list[dict[str, Any]], dict[PurePosixPath, bytes]]: - tasks = {task.task_id: task for task in evidence.tasks} - rows: list[dict[str, Any]] = [] - programs: dict[PurePosixPath, bytes] = {} - for job in evidence.jobs: - task = tasks[job.task_id] - receipt, evaluation, program = _validate_receipt_and_evaluation( - protocol=protocol, - store=store, - task=task, - job=job, - ) - program_path = PurePosixPath( - "programs", - job.hypothesis_id, - job.arm_id, - job.task_id, - f"attempt_{job.attempt_index:02d}.py", - ) - program_bytes = program.encode("utf-8") - if program_path in programs and programs[program_path] != program_bytes: - raise Phase1FreezeError(f"program-path collision at {program_path}") - programs[program_path] = program_bytes - sample = receipt.payload["sample"] - measured = evaluation.payload - rows.append( - { - "schema_version": ATTEMPT_INDEX_SCHEMA, - "study_id": protocol["study_id"], - "hypothesis_id": job.hypothesis_id, - "arm_id": job.arm_id, - "task_id": job.task_id, - "level": task.level, - "representation_id": task.representation_id, - "attempt_index": job.attempt_index, - "seed": job.seed, - "status": measured["status"], - "violations": measured["violations"], - "error": measured["error"], - "retryable": measured["retryable"], - "pure_executable": measured["pure_executable"], - "measurement_available": measured["measurement_available"], - "aggregation_iou": measured["aggregation_iou"], - "measured_iou": measured["measured_iou"], - "measured_dice": measured["measured_dice"], - "cap_hit": sample["cap_hit"], - "stop_reason": sample["stop_reason"], - "prompt_tokens": sample["prompt_tokens"], - "completion_tokens": sample["completion_tokens"], - "reasoning_tokens_exact": sample["reasoning_tokens_exact"], - "answer_tokens_exact": sample["answer_tokens_exact"], - "reasoning_tokens_estimate": sample[ - "reasoning_tokens_estimate" - ], - "answer_tokens_estimate": sample["answer_tokens_estimate"], - "channel_token_count_basis": sample[ - "channel_token_count_basis" - ], - "channel_parse_complete": sample["channel_parse_complete"], - "estimated_cost_usd": receipt.payload["cost_estimate_usd"], - "sampling_seconds": receipt.payload["sampling_seconds"], - "evaluation_seconds": measured["evaluation_seconds"], - "diagnostics": measured["diagnostics"], - "raw_completion_sha256": receipt.payload[ - "raw_completion_sha256" - ], - "completion_sha256": receipt.payload["completion_sha256"], - "sampling_record_sha256": receipt.record_sha256, - "evaluation_record_sha256": evaluation.record_sha256, - "program_path": program_path.as_posix(), - "program_sha256": measured["program_sha256"], - "reference_sha256": measured["reference_sha256"], - **( - {"render_sha256": measured["render_sha256"]} - if measured.get("render_sha256") is not None - else {} - ), - } - ) - return rows, programs - - -def _selection_path(store: AttemptStore, name: str) -> Path: - return ( - store.external_root - / "studies" - / "representation-curriculum-v1" - / "selections" - / name - ) - - -def _require_selection(path: Path, expected: dict[str, Any]) -> None: - if not path.is_file(): - raise Phase1FreezeError(f"missing canonical selection receipt: {path}") - if _load_json(path) != expected: - raise Phase1FreezeError( - f"{path.name} does not reproduce from immutable attempts" - ) - - -def _collect_bundle( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, -) -> _Bundle: - if not _SOURCE_SHA.fullmatch(expected_source_sha): - raise Phase1FreezeError( - "expected source SHA must be a full lowercase Git commit SHA" - ) - if _git_head(repo_root) != expected_source_sha: - raise Phase1FreezeError("current source HEAD differs from the launch SHA") - protocol = load_protocol(repo_root, protocol_file=PROTOCOL_FILE) - manifest = load_task_manifest(task_manifest_path(repo_root), repo_root=repo_root) - prompt_binding, prompt_sha = _prompt_binding() - runtime = validate_runtime_stack(repo_root) - store = AttemptStore(repo_root=repo_root, external_root=external_root) - source_git_sha = expected_source_sha - sandbox: tuple[str, str] | None = None - runs: list[_RunEvidence] = [] - - dev_tasks, dev_task_sha = _task_set( - repo_root=repo_root, - protocol=protocol, - manifest=manifest, - hypothesis_id="RC-H00", - ) - dev_ids = [task.task_id for task in dev_tasks] - h00_jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H00", - wave_name="complete", - task_ids=dev_ids, - ) - h00_summaries: dict[str, dict[str, Any]] = {} - for arm_id in protocol["hypotheses"]["RC-H00"]["arm_ids"]: - evidence, sandbox = _load_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H00", - arm_id=str(arm_id), - source_git_sha=source_git_sha, - tasks=dev_tasks, - jobs=h00_jobs, - task_set_sha256=dev_task_sha, - prompt_sha256=prompt_sha, - sandbox=sandbox, - runtime=runtime, - ) - runs.append(evidence) - h00_summaries[str(arm_id)] = evidence.summary - - preliminary = choose_operating_point( - protocol=protocol, - source_git_sha=source_git_sha, - source_summaries=h00_summaries, - ) - _require_selection( - _selection_path(store, "operating_point.preliminary.json"), - preliminary, - ) - final_selection = preliminary - if preliminary["rescue_required"]: - rescue_arm = str(protocol["hypotheses"]["RC-H00R"]["arm_ids"][0]) - h00r_jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H00R", - wave_name="complete", - task_ids=dev_ids, - selection=preliminary, - ) - rescue, sandbox = _load_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H00R", - arm_id=rescue_arm, - source_git_sha=source_git_sha, - tasks=dev_tasks, - jobs=h00r_jobs, - task_set_sha256=dev_task_sha, - prompt_sha256=prompt_sha, - sandbox=sandbox, - runtime=runtime, - ) - runs.append(rescue) - final_selection = choose_operating_point( - protocol=protocol, - source_git_sha=source_git_sha, - source_summaries=h00_summaries, - rescue_summary=rescue.summary, - ) - if not final_selection["complete"]: - raise Phase1FreezeError("operating-point selection is not complete") - _require_selection( - _selection_path(store, "operating_point.json"), - final_selection, - ) - - benchmark_tasks, benchmark_task_sha = _task_set( - repo_root=repo_root, - protocol=protocol, - manifest=manifest, - hypothesis_id="RC-H01", - ) - benchmark_ids = [task.task_id for task in benchmark_tasks] - for hypothesis_id in ("RC-H01", "RC-H02"): - jobs = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name="complete", - task_ids=benchmark_ids, - selection=final_selection if hypothesis_id == "RC-H01" else None, - ) - for arm_id in resolved_arm_ids( - protocol, - hypothesis_id, - selection=final_selection if hypothesis_id == "RC-H01" else None, - ): - evidence, sandbox = _load_run( - store=store, - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - source_git_sha=source_git_sha, - tasks=benchmark_tasks, - jobs=jobs, - task_set_sha256=benchmark_task_sha, - prompt_sha256=prompt_sha, - sandbox=sandbox, - runtime=runtime, - ) - runs.append(evidence) - - attempts: list[dict[str, Any]] = [] - programs: dict[PurePosixPath, bytes] = {} - for evidence in runs: - run_attempts, run_programs = _collect_run_attempts( - protocol=protocol, - store=store, - evidence=evidence, - ) - attempts.extend(run_attempts) - for path, content in run_programs.items(): - if path in programs and programs[path] != content: - raise Phase1FreezeError(f"duplicate program evidence at {path}") - programs[path] = content - attempts.sort( - key=lambda value: ( - _HYPOTHESIS_ORDER.index(value["hypothesis_id"]), - value["arm_id"], - value["task_id"], - value["attempt_index"], - ) - ) - - summaries_document = { - "schema_version": SUMMARIES_SCHEMA, - "study_id": protocol["study_id"], - "summaries": { - f"{run.hypothesis_id}/{run.arm_id}": run.summary for run in runs - }, - } - summaries_document["logical_sha256"] = canonical_json_sha256( - summaries_document - ) - files: dict[PurePosixPath, bytes] = { - PurePosixPath("operating_point_selection.json"): _json_bytes( - final_selection - ), - PurePosixPath("summaries.json"): _json_bytes(summaries_document), - PurePosixPath("attempts.jsonl"): _jsonl_bytes(attempts), - **programs, - } - file_inventory = [ - { - "path": path.as_posix(), - "sha256": _sha256_bytes(content), - "bytes": len(content), - } - for path, content in sorted(files.items(), key=lambda item: item[0]) - ] - release_manifest: dict[str, Any] = { - "schema_version": RELEASE_SCHEMA, - "study_id": protocol["study_id"], - "phase": "phase1-zero-shot-baselines", - "source_git_sha": source_git_sha, - "protocol_sha256": protocol["logical_sha256"], - "task_manifest_sha256": manifest["logical_sha256"], - "dataset": protocol["dataset"], - "prompt": prompt_binding, - "prompt_sha256": prompt_sha, - "sandbox": { - "image_ref": sandbox[0] if sandbox is not None else "", - "image_id": sandbox[1] if sandbox is not None else "", - }, - "selection_sha256": final_selection["logical_sha256"], - "selected_qwen_arm": final_selection["selected_arm"], - "attempt_count": len(attempts), - "program_count": len(programs), - "runs": [ - { - "hypothesis_id": run.hypothesis_id, - "arm_id": run.arm_id, - "expected_attempts": len(run.jobs), - "run_manifest_payload": run.manifest.payload, - "run_manifest_payload_sha256": run.manifest.payload_sha256, - "run_manifest_record_sha256": run.manifest.record_sha256, - "summary_sha256": run.summary["logical_sha256"], - "wandb_run_id": deterministic_wandb_run_id( - RunKey( - protocol["study_id"], - run.hypothesis_id, - run.arm_id, - ), - source_git_sha=source_git_sha, - ), - } - for run in runs - ], - "tracking": { - "provider": protocol["tracking"]["provider"], - "project": protocol["tracking"]["project"], - "entity": protocol["tracking"].get("entity"), - "group": protocol["tracking"].get("group"), - "local_record_is_authoritative": protocol["tracking"][ - "local_record_is_authoritative" - ], - }, - "files": file_inventory, - "excluded_external_state": [ - "raw_completion_text", - "reasoning_text", - "answer_text", - "wandb_state", - "tracking_errors", - ], - } - release_manifest["logical_sha256"] = canonical_json_sha256( - release_manifest - ) - return _Bundle(files=files, release_manifest=release_manifest) - - -def _assert_exact_output(output_root: Path, expected: dict[PurePosixPath, bytes]) -> None: - if not output_root.is_dir(): - raise Phase1FreezeError(f"tracked Phase 1 release is missing: {output_root}") - observed_paths = { - PurePosixPath(path.relative_to(output_root).as_posix()) - for path in output_root.rglob("*") - if path.is_file() - } - expected_paths = set(expected) - if observed_paths != expected_paths: - raise Phase1FreezeError( - "tracked Phase 1 file inventory differs: " - f"missing={sorted(str(value) for value in expected_paths - observed_paths)}, " - f"extra={sorted(str(value) for value in observed_paths - expected_paths)}" - ) - for relative, content in expected.items(): - path = output_root.joinpath(*relative.parts) - if path.is_symlink() or path.read_bytes() != content: - raise Phase1FreezeError(f"tracked evidence differs at {relative}") - - -def _write_create_or_verify( - output_root: Path, - expected: dict[PurePosixPath, bytes], -) -> None: - output_root.mkdir(parents=True, exist_ok=True) - observed_files = [ - path for path in output_root.rglob("*") if path.is_file() or path.is_symlink() - ] - expected_paths = { - output_root.joinpath(*relative.parts): content - for relative, content in expected.items() - } - extras = sorted( - str(path.relative_to(output_root)) - for path in observed_files - if path not in expected_paths - ) - if extras: - raise Phase1FreezeError( - f"refusing to overwrite a non-canonical Phase 1 directory: {extras}" - ) - for path, content in expected_paths.items(): - if path.exists() or path.is_symlink(): - if path.is_symlink() or path.read_bytes() != content: - raise Phase1FreezeError( - f"refusing to replace existing evidence at {path}" - ) - continue - path.parent.mkdir(parents=True, exist_ok=True) - temporary = path.parent / f".{path.name}.tmp-{os.getpid()}-{uuid.uuid4().hex}" - try: - with temporary.open("xb") as handle: - handle.write(content) - handle.flush() - os.fsync(handle.fileno()) - os.replace(temporary, path) - finally: - temporary.unlink(missing_ok=True) - _assert_exact_output(output_root, expected) - - -def freeze_phase1( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, - output: Path | None = None, - validate_only: bool = False, -) -> dict[str, Any]: - """Recompute the canonical record, then write or validate exact bytes.""" - - output_root = _phase1_output_root(repo_root, output) - source, external = _validate_roots( - repo_root=repo_root, - external_root=external_root, - output_root=output_root, - ) - _require_source_clean_except_output( - repo_root=source, - output_root=output_root, - ) - bundle = _collect_bundle( - repo_root=source, - external_root=external, - expected_source_sha=expected_source_sha, - ) - expected = bundle.all_files - if validate_only: - _assert_exact_output(output_root, expected) - else: - _write_create_or_verify(output_root, expected) - return bundle.release_manifest - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=REPO_ROOT) - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--output", type=Path) - result.add_argument( - "--validate", - action="store_true", - help="validate an existing tracked release without writing", - ) - return result - - -def main() -> None: - args = parser().parse_args() - report = freeze_phase1( - repo_root=args.repo_root, - external_root=args.external_root, - expected_source_sha=args.expected_source_sha, - output=args.output, - validate_only=args.validate, - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/protocol.json b/rl/studies/representation_curriculum_v1/protocol.json deleted file mode 100644 index addde724..00000000 --- a/rl/studies/representation_curriculum_v1/protocol.json +++ /dev/null @@ -1,260 +0,0 @@ -{ - "arms": { - "inkling-e09-20000": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 20000, - "model": "thinkingmachines/Inkling", - "provider": "tinker", - "renderer": "tml_v0", - "thinking": true, - "thinking_effort": 0.9 - }, - "qwen-off-16384": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 16384, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5_disable_thinking", - "thinking": false, - "thinking_effort": null - }, - "qwen-off-4096": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 4096, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5_disable_thinking", - "thinking": false, - "thinking_effort": null - }, - "qwen-on-16384": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 16384, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": true, - "thinking_effort": null - }, - "qwen-on-32768-confirmation": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 32768, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": true, - "thinking_effort": null - }, - "qwen-on-4096": { - "context_tokens": 65536, - "max_image_long_edge": 1440, - "max_output_tokens": 4096, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": true, - "thinking_effort": null - } - }, - "contract_version": "pixcell-blind-phase-a-direct-v1", - "dataset": { - "configuration": "depth", - "development_split": "validation", - "logical_release_sha256": "676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b", - "repository": "qpaig-mit/pixcell", - "revision": "v2.0.0" - }, - "future_hypothesis_registry": { - "RC-H10": { - "name": "mixed full-depth SFT", - "status": "launch_forbidden_until_protocol_frozen" - }, - "RC-H11": { - "name": "sequential L0-to-L4 SFT", - "status": "launch_forbidden_until_protocol_frozen" - }, - "RC-H12": { - "name": "L0 SFT followed by L1-to-L4 curriculum RL", - "status": "launch_forbidden_until_protocol_frozen" - }, - "RC-H13": { - "name": "full-depth SFT followed by curriculum RL", - "status": "launch_forbidden_until_protocol_frozen" - }, - "RC-H20": { - "name": "Inkling advanced-component on-policy RL", - "status": "launch_forbidden_until_protocol_frozen" - } - }, - "hypotheses": { - "RC-H00": { - "arm_ids": [ - "qwen-off-4096", - "qwen-off-16384", - "qwen-on-4096", - "qwen-on-16384" - ], - "attempts_per_task": 2, - "name": "Qwen zero-shot renderer by output-budget operating point", - "status": "frozen", - "task_set": "qwen_operating_point", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2 - ], - "task_count": 40 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 1 - } - } - }, - "RC-H00R": { - "arm_ids": [ - "qwen-on-32768-confirmation" - ], - "attempts_per_task": 2, - "name": "Qwen thinking-budget truncation rescue", - "status": "conditional", - "task_set": "qwen_operating_point", - "trigger": { - "metric": "cap_hit_rate", - "operator": ">", - "source_arm": "qwen-on-16384", - "source_hypothesis": "RC-H00", - "threshold": 0.05 - }, - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2 - ], - "task_count": 40 - } - } - }, - "RC-H01": { - "arm_roles": { - "incumbent": "qwen-off-4096", - "selected": "RC-H00" - }, - "attempts_per_task": 4, - "name": "Qwen zero-shot F1-F8 baseline", - "skip_duplicate_selected_arm": true, - "status": "frozen", - "task_set": "f1_f8", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2, - 3, - 4 - ], - "task_count": 8 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 8 - } - } - }, - "RC-H02": { - "arm_ids": [ - "inkling-e09-20000" - ], - "attempts_per_task": 4, - "name": "Inkling zero-shot F1-F8 baseline", - "status": "frozen", - "task_set": "f1_f8", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2, - 3, - 4 - ], - "task_count": 8 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 8 - } - } - } - }, - "launch": { - "confirmation_token": "PIXCELL_PHASE1_BASELINE", - "mutable_output_environment": "PIXCELL_STUDY_ROOT" - }, - "logical_sha256": "1e2869e6c640463e4cf4233a2806dadb5368c4a22a24634c755bb1d3dcc8e349", - "operating_point_selection": { - "conditional_rescue_hypothesis": "RC-H00R", - "metric_tie_band": 0.01, - "model_failures_score_as_iou": 0.0, - "primary_metric": "representation_macro_mean_iou", - "source_hypothesis": "RC-H00", - "tie_breakers": [ - "pure_executable_rate_desc", - "estimated_uncached_cost_usd_asc", - "completion_tokens_median_asc", - "arm_id_asc" - ] - }, - "pricing": { - "as_of": "2026-07-27", - "currency": "USD", - "models": { - "Qwen/Qwen3.6-35B-A3B": { - "prefill_cached": 0.108, - "prefill_uncached": 0.54, - "sample": 1.335 - }, - "thinkingmachines/Inkling": { - "prefill_cached": 0.374, - "prefill_uncached": 1.87, - "sample": 4.68 - } - }, - "source": "https://tinker-docs.thinkingmachines.ai/tinker/models/", - "unit": "per_million_tokens" - }, - "sampling": { - "evaluator_workers": 8, - "require_candidate_isolation": true, - "sampling_concurrency": 8, - "seed_namespace": "pixcell-representation-curriculum-v1", - "temperature": 1.0, - "top_p": 1.0 - }, - "schema_version": "pixcell-representation-curriculum-protocol-v1", - "study_id": "representation-curriculum-v1", - "task_manifest": { - "logical_sha256": "e32a0a1522fba3c90163e5687c65ab5e92fdb77f4f875a73cbfcdaa432b44272", - "path": "task_manifest.json" - }, - "tracking": { - "entity": "aadarwal-massachusetts-institute-of-technology", - "group": "phase1-zero-shot", - "local_record_is_authoritative": true, - "mode": "online", - "project": "pixcell-representation-curriculum-v1", - "provider": "wandb" - } -} diff --git a/rl/studies/representation_curriculum_v1/run_baseline.py b/rl/studies/representation_curriculum_v1/run_baseline.py deleted file mode 100644 index 8485dbb5..00000000 --- a/rl/studies/representation_curriculum_v1/run_baseline.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python3 -"""Run one frozen, resumable Phase 1 baseline wave.""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import os -from pathlib import Path - -from rl.evaluation.runner import run_baseline - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument( - "--hypothesis", - required=True, - choices=("RC-H00", "RC-H00R", "RC-H01", "RC-H02"), - ) - result.add_argument("--wave", required=True, choices=("smoke", "complete")) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--confirm-spend", default="") - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_STUDY_ROOT"]) - if os.environ.get("PIXCELL_STUDY_ROOT") - else None - ), - ) - result.add_argument("--selection", type=Path) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit( - "set PIXCELL_STUDY_ROOT or provide --external-root outside the repository" - ) - report = asyncio.run( - run_baseline( - repo_root=REPO_ROOT, - protocol_file=PROTOCOL_FILE, - hypothesis_id=args.hypothesis, - wave_name=args.wave, - expected_source_sha=args.expected_source_sha, - external_root=args.external_root, - confirmation=args.confirm_spend, - selection_path=args.selection, - ) - ) - print(json.dumps(report["summaries"], indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v1/task_manifest.json b/rl/studies/representation_curriculum_v1/task_manifest.json deleted file mode 100644 index 69ce1026..00000000 --- a/rl/studies/representation_curriculum_v1/task_manifest.json +++ /dev/null @@ -1,631 +0,0 @@ -{ - "logical_sha256": "e32a0a1522fba3c90163e5687c65ab5e92fdb77f4f875a73cbfcdaa432b44272", - "schema_version": "pixcell-evaluation-task-manifest-v1", - "selection": { - "algorithm": "sha256-rank representations per level, then sha256-rank held-out realizations within each selected representation", - "depth_validation_rows_per_level": 8, - "seed": "representation-curriculum-v1-operating-point-20260727" - }, - "task_sets": { - "f1_f8": [ - { - "footprint_um": [ - 40.0, - 1.7 - ], - "image_sha256": "0fbd3bb59c59f81b606be1b5d8d948b941b73ca240d4bb31ba9c9d72e6808d3c", - "image_size_px": [ - 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atomic_write_json, - choose_operating_point, - summarize_arm, -) -from rl.evaluation.tasks import EvaluationTask, canonical_json_sha256 -from rl.studies.representation_curriculum_v1 import freeze_phase1 as freezer - - -SOURCE_SHA = "a" * 40 -PROMPT_SHA = "b" * 64 -SANDBOX = ("sha256:" + "c" * 64, "sha256:" + "c" * 64) -RUNTIME = { - "python": "3.13.14", - "packages": {"tinker": "0.22.7"}, - "tinker_cookbook_commit": "f" * 40, -} - - -def _arm(model: str, max_tokens: int) -> dict[str, Any]: - return { - "provider": "tinker", - "model": model, - "renderer": f"renderer-{model}", - "thinking": False, - "thinking_effort": None, - "max_output_tokens": max_tokens, - "context_tokens": 65536, - "max_image_long_edge": 1440, - } - - -def _protocol() -> dict[str, Any]: - return { - "study_id": "representation-curriculum-v1", - "logical_sha256": "d" * 64, - "dataset": { - "repository": "example/pixcell", - "revision": "test", - "configuration": "depth", - }, - "arms": { - "q0": _arm("qwen", 4096), - "q1": _arm("qwen", 16384), - "qr": _arm("qwen", 32768), - "ink": _arm("inkling", 20000), - }, - "hypotheses": { - "RC-H00": { - "arm_ids": ["q0", "q1"], - "attempts_per_task": 1, - "task_set": "dev", - "waves": { - "complete": { - "attempt_indices": [1], - "task_count": 1, - } - }, - }, - "RC-H00R": { - "arm_ids": ["qr"], - "attempts_per_task": 1, - "task_set": "dev", - "trigger": { - "source_arm": "q1", - "metric": "cap_hit_rate", - "operator": ">", - "threshold": 0.05, - }, - "waves": { - "complete": { - "attempt_indices": [1], - "task_count": 1, - } - }, - }, - "RC-H01": { - "arm_roles": {"incumbent": "q0", "selected": "RC-H00"}, - "attempts_per_task": 1, - "task_set": "bench", - "waves": { - "complete": { - "attempt_indices": [1], - "task_count": 1, - } - }, - }, - "RC-H02": { - "arm_ids": ["ink"], - "attempts_per_task": 1, - "task_set": "bench", - "waves": { - "complete": { - "attempt_indices": [1], - "task_count": 1, - } - }, - }, - }, - "sampling": { - "temperature": 1.0, - "top_p": 1.0, - "seed_namespace": "phase1-freezer-test", - }, - "pricing": { - "models": { - "qwen": { - "prefill_cached": 0.1, - "prefill_uncached": 0.5, - "sample": 1.3, - }, - "inkling": { - "prefill_cached": 0.3, - "prefill_uncached": 1.8, - "sample": 4.6, - }, - } - }, - "tracking": { - "provider": "wandb", - "project": "pixcell-test", - "entity": "test", - "group": "phase1", - "local_record_is_authoritative": True, - }, - "operating_point_selection": { - "primary_metric": "representation_macro_mean_iou", - "metric_tie_band": 0.01, - "tie_breakers": [ - "pure_executable_rate_desc", - "estimated_uncached_cost_usd_asc", - "completion_tokens_median_asc", - "arm_id_asc", - ], - }, - } - - -def _task(task_id: str, level: str) -> EvaluationTask: - image = f"image-{task_id}".encode() - digest = hashlib.sha256(image).hexdigest() - return EvaluationTask( - task_id=task_id, - level=level, - representation_id=f"representation-{task_id}", - observation=ModelObservation( - image_bytes=image, - footprint_um=(10.0, 5.0), - image_sha256=digest, - ), - reference=VerifierReference( - target_image_bytes=image, - footprint_um=(10.0, 5.0), - target_image_sha256=digest, - ), - ) - - -def _manifest(dev: EvaluationTask, bench: EvaluationTask) -> dict[str, Any]: - value = { - "task_sets": { - "dev": [{"task_id": dev.task_id}], - "bench": [{"task_id": bench.task_id}], - } - } - value["logical_sha256"] = canonical_json_sha256(value) - return value - - -def _write_attempt( - *, - store: AttemptStore, - protocol: dict[str, Any], - task: EvaluationTask, - job: Any, - score: float, - cap_hit: bool, -) -> None: - key = AttemptKey( - protocol["study_id"], - job.hypothesis_id, - job.arm_id, - job.task_id, - job.attempt_index, - ) - completion = f"```python\nprint('{job.hypothesis_id}-{job.arm_id}')\n```" - program = extract_code(completion) - raw = f"private reasoning\n{completion}" - rates = protocol["pricing"]["models"][ - protocol["arms"][job.arm_id]["model"] - ] - prompt_tokens = 100 - completion_tokens = ( - protocol["arms"][job.arm_id]["max_output_tokens"] - if cap_hit - else 20 - ) - sample_cost = completion_tokens * rates["sample"] / 1_000_000 - store.write_sampling_receipt( - key, - { - "request": { - "max_tokens": protocol["arms"][job.arm_id][ - "max_output_tokens" - ], - "temperature": 1.0, - "top_p": 1.0, - "seed": job.seed, - }, - "sample": { - "prompt_tokens": prompt_tokens, - "completion_tokens": completion_tokens, - "stop_reason": "length" if cap_hit else "stop", - "cap_hit": cap_hit, - "raw_text": raw, - "completion_text": completion, - "reasoning_text": "private reasoning", - "answer_text": completion, - "reasoning_tokens_exact": 2, - "answer_tokens_exact": completion_tokens - 2, - "reasoning_tokens_estimate": None, - "answer_tokens_estimate": None, - "channel_token_count_basis": "test", - "channel_parse_complete": True, - }, - "sampling_seconds": 0.5, - "raw_completion_sha256": hashlib.sha256(raw.encode()).hexdigest(), - "completion_sha256": hashlib.sha256(completion.encode()).hexdigest(), - "cost_estimate_usd": { - "cached_prefill": ( - prompt_tokens * rates["prefill_cached"] / 1_000_000 - + sample_cost - ), - "uncached_prefill": ( - prompt_tokens * rates["prefill_uncached"] / 1_000_000 - + sample_cost - ), - }, - }, - ) - store.write_evaluation( - key, - { - "status": "ok", - "attribution": "model", - "pure_executable": True, - "measurement_available": True, - "measured_iou": score, - "measured_dice": min(1.0, score + 0.1), - "aggregation_iou": score, - "program": program, - "program_sha256": hashlib.sha256(program.encode()).hexdigest(), - "reference_sha256": task.reference.target_image_sha256, - "violations": [], - "error": None, - "retryable": False, - "evaluation_seconds": 0.25, - "diagnostics": {}, - "render_sha256": "e" * 64, - }, - ) - - -def _write_run( - *, - store: AttemptStore, - protocol: dict[str, Any], - hypothesis_id: str, - arm_id: str, - tasks: list[EvaluationTask], - jobs: list[Any], - task_set_sha: str, - score: float, - cap_hit: bool = False, -) -> dict[str, Any]: - run_key = RunKey(protocol["study_id"], hypothesis_id, arm_id) - store.create_or_verify_run_manifest( - run_key, - freezer._expected_run_manifest( - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - source_git_sha=SOURCE_SHA, - task_set_sha256=task_set_sha, - prompt_sha256=PROMPT_SHA, - sandbox_image_ref=SANDBOX[0], - sandbox_image_id=SANDBOX[1], - runtime=RUNTIME, - ), - ) - task_by_id = {task.task_id: task for task in tasks} - for job in jobs: - if job.arm_id == arm_id: - _write_attempt( - store=store, - protocol=protocol, - task=task_by_id[job.task_id], - job=job, - score=score, - cap_hit=cap_hit, - ) - summary = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id=hypothesis_id, - arm_id=arm_id, - jobs=jobs, - tasks=tasks, - ) - path = ( - store.external_root.joinpath(*store.run_relative_path(run_key).parts) - / "summary.json" - ) - atomic_write_json(path, summary) - return summary - - -def _prepare( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, - *, - trigger_rescue: bool = False, - include_rescue: bool = True, -) -> tuple[Path, Path]: - repo = tmp_path / "repo" - repo.mkdir() - external = tmp_path / "external" - protocol = _protocol() - dev = _task("D1", "L0") - bench = _task("F1", "BENCHMARK") - manifest = _manifest(dev, bench) - - monkeypatch.setattr(freezer, "_git_head", lambda _root: SOURCE_SHA) - monkeypatch.setattr(freezer, "_changed_paths", lambda _root: set()) - monkeypatch.setattr( - freezer, - "validate_runtime_stack", - lambda _root: RUNTIME, - ) - monkeypatch.setattr( - freezer, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - freezer, - "load_task_manifest", - lambda _path, *, repo_root: manifest, - ) - monkeypatch.setattr( - freezer, - "load_task_set", - lambda *, repo_root, manifest, task_set: ( - [dev] if task_set == "dev" else [bench] - ), - ) - monkeypatch.setattr( - freezer, - "_prompt_binding", - lambda: ({"contract_version": "test"}, PROMPT_SHA), - ) - - store = AttemptStore(repo_root=repo, external_root=external) - dev_sha = canonical_json_sha256(manifest["task_sets"]["dev"]) - bench_sha = canonical_json_sha256(manifest["task_sets"]["bench"]) - h00_jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H00", - wave_name="complete", - task_ids=[dev.task_id], - ) - h00 = { - "q0": _write_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H00", - arm_id="q0", - tasks=[dev], - jobs=h00_jobs, - task_set_sha=dev_sha, - score=0.2, - ), - "q1": _write_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H00", - arm_id="q1", - tasks=[dev], - jobs=h00_jobs, - task_set_sha=dev_sha, - score=0.4, - cap_hit=trigger_rescue, - ), - } - preliminary = choose_operating_point( - protocol=protocol, - source_git_sha=SOURCE_SHA, - source_summaries=h00, - ) - selection_root = ( - external - / "studies" - / protocol["study_id"] - / "selections" - ) - atomic_write_json( - selection_root / "operating_point.preliminary.json", - preliminary, - ) - final = preliminary - if trigger_rescue and include_rescue: - rescue_jobs = jobs_for_wave( - protocol, - hypothesis_id="RC-H00R", - wave_name="complete", - task_ids=[dev.task_id], - selection=preliminary, - ) - rescue_summary = _write_run( - store=store, - protocol=protocol, - hypothesis_id="RC-H00R", - arm_id="qr", - tasks=[dev], - jobs=rescue_jobs, - task_set_sha=dev_sha, - score=0.5, - ) - final = choose_operating_point( - protocol=protocol, - source_git_sha=SOURCE_SHA, - source_summaries=h00, - rescue_summary=rescue_summary, - ) - atomic_write_json(selection_root / "operating_point.json", final) - if trigger_rescue and not include_rescue: - return repo, external - - for hypothesis_id in ("RC-H01", "RC-H02"): - jobs = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name="complete", - task_ids=[bench.task_id], - selection=final if hypothesis_id == "RC-H01" else None, - ) - for arm_id in resolved_arm_ids( - protocol, - hypothesis_id, - selection=final if hypothesis_id == "RC-H01" else None, - ): - _write_run( - store=store, - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - tasks=[bench], - jobs=jobs, - task_set_sha=bench_sha, - score=0.3, - ) - return repo, external - - -def test_freeze_is_deterministic_idempotent_and_excludes_raw_text( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - report = freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - output = repo / freezer.PHASE1_RELATIVE_ROOT - first = { - path.relative_to(output): path.read_bytes() - for path in output.rglob("*") - if path.is_file() - } - assert report["attempt_count"] == 5 - assert report["selected_qwen_arm"] == "q1" - assert b"private reasoning" not in b"".join(first.values()) - assert not any(b"```python" in value for value in first.values()) - - repeated = freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - assert repeated == report - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - validate_only=True, - ) - assert first == { - path.relative_to(output): path.read_bytes() - for path in output.rglob("*") - if path.is_file() - } - - -def test_validate_rejects_tampered_tracked_evidence( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - attempts = repo / freezer.PHASE1_RELATIVE_ROOT / "attempts.jsonl" - attempts.write_bytes(attempts.read_bytes() + b"{}\n") - with pytest.raises(freezer.Phase1FreezeError, match="differs"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - validate_only=True, - ) - - -def test_triggered_rescue_is_mandatory( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare( - tmp_path, - monkeypatch, - trigger_rescue=True, - include_rescue=False, - ) - with pytest.raises(freezer.Phase1FreezeError, match="missing run manifest"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_triggered_rescue_is_included_in_the_canonical_release( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare( - tmp_path, - monkeypatch, - trigger_rescue=True, - include_rescue=True, - ) - report = freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - assert report["selected_qwen_arm"] == "qr" - assert report["attempt_count"] == 6 - assert any( - run["hypothesis_id"] == "RC-H00R" for run in report["runs"] - ) - - -def test_output_location_is_fixed( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - with pytest.raises(freezer.Phase1FreezeError, match="exactly"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - output=repo / "somewhere-else", - ) - - -def test_source_drift_outside_the_release_directory_is_rejected( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - monkeypatch.setattr( - freezer, - "_changed_paths", - lambda _root: {repo / "rl" / "unexpected.py"}, - ) - with pytest.raises(freezer.Phase1FreezeError, match="source worktree differs"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) diff --git a/rl/studies/representation_curriculum_v2/README.md b/rl/studies/representation_curriculum_v2/README.md deleted file mode 100644 index 302ad422..00000000 --- a/rl/studies/representation_curriculum_v2/README.md +++ /dev/null @@ -1,114 +0,0 @@ -# Direct-policy zero-shot baselines v2 - -This study measures the untrained Qwen and Inkling policies on the fixed F1–F8 -benchmark under the corrected `pixcell-direct-reconstruction-v2` contract. It -does not select an operating point and does not train a model. - -## What the policy receives - -Each attempt is one user turn, ordered as: - -1. the benchmark's `device_bw.png`, decoded as RGB and downscaled only when - needed with aspect ratio preserved and a 1920-pixel longest edge; -2. the image semantics: black is material on layer `(1, 0)`, white is - background; -3. the benchmark's physical `[x-length, y-width]` footprint; -4. the exact extended primitive catalogue from - `src/michaelangelo/reference/gds_factory_function_catalogue_extended.md`; -5. the direct programming requirements in - `rl/common/assets/phase_a_direct_v2.md`. - -There is no system message. For F1–F8, `device_bw.png` is both the visible -input and the silhouette against which the verifier later measures the -candidate; there is no separate hidden reference raster. The policy receives -the image once, in the user message. It never receives the verifier's -calibration, reference code, IoU, or answer-derived metadata. - -The image and footprint are the normal Phase-A inputs defined by -`src/homi/briefs/phase_a_recreation.md`. The primitive catalogue is the -repository's permitted programming vocabulary. The direct programming -requirements retain the normal Phase-A source, parameterization, and output -rules while making the one-shot transport boundary explicit: the answer must -be a complete program that writes `device.gds`. -`rl/common/preprocess.py` is the authority for the deterministic image -transformation. - -## Frozen arms - -| Hypothesis | Policy | Reasoning | Output ceiling | Attempts | -|---|---|---|---:|---:| -| `RC-H05` | `Qwen/Qwen3.6-35B-A3B` | `qwen3_5`, thinking on | 60,000 | 4 per figure | -| `RC-H06` | `thinkingmachines/Inkling` | `tml_v0`, effort 0.9 | 60,000 | 4 per figure | - -Both arms use a 65,536-token context, a 1920-pixel maximum image edge, -temperature 1.0, top-p 1.0, sampling concurrency 8, and the strict isolated -evaluator. A local renderer preflight measures every complete prompt and -rejects the launch if `prompt tokens + 60,000` exceeds the context. -The frozen 1920-pixel edge leaves positive context headroom for every Qwen and -Inkling F1–F8 input at the 60,000-token ceiling. - -`smoke` means attempt index 1 for each of F1–F8: eight independent model -requests per arm. Those eight receipts are already the first quarter of the -declared best-at-4 baseline. After they execute and evaluate correctly, -`complete` resumes the same run and adds attempt indices 2–4; it does not -discard or repeat attempt 1. - -## Before launch - -The launch path is deliberately fail-closed. It requires a clean, exact source -commit; sealed protocol and task-manifest digests; the pinned runtime; the -immutable candidate-execution boundary; exact renderer context accounting; -valid references; credentials; an external attempt ledger; and the explicit -`PIXCELL_BASELINE_V2` spend confirmation. No Tinker service client exists until -those checks pass. - -Inspect exact local renderer token accounting without making a remote request: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_curriculum_v2.audit_context -``` - -After committing the launch source, run the complete zero-spend audit with the -immutable evaluator image already selected: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_curriculum_v2.audit_phase0 \ - --expected-source-sha "$(git rev-parse HEAD)" -``` - -That audit needs no model or tracking credentials. It works offline, renders -all 16 arm/figure contexts, validates all eight references, executes one -fixed primitive-only probe against F1 inside the required sandbox, and -round-trips the run-manifest, sampling-receipt, and evaluation-record schemas. -A paid smoke launch is not ready unless this sealed audit passes. - -Paid launch commands are intentionally not presented as casual copy-paste -examples. Use `run_baseline.py --help`, review the sealed commit, run `smoke`, -inspect all eight records, and only then resume `complete`. - -## Freeze completed evidence - -After both 32-attempt arms are complete, freeze the external ledger into the -small tracked record: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_curriculum_v2.freeze_phase1 \ - --external-root "$PIXCELL_STUDY_ROOT" \ - --expected-source-sha "$(git rev-parse HEAD)" -``` - -The command reproduces both summaries from immutable attempt receipts, -re-renders every prompt offline, and re-executes all 64 extracted programs in -the exact launch-bound sandbox before accepting their scores. It writes -`release_manifest.json`, `checksums.sha256`, `summaries.json`, -`attempts.jsonl`, and `programs.jsonl` under -`data/training/representation-curriculum-v2/phase1`. `programs.jsonl` contains -only programs that passed the source, execution, and geometry measurement -path, making every nonzero reported geometry independently re-evaluable. -Failed-output fallback text is not published. Raw completions, separate -reasoning and answer channels, mutable W&B state, and credentials remain -outside Git. Add `--validate` to prove that an existing tracked record matches -the authoritative ledger byte for byte. diff --git a/rl/studies/representation_curriculum_v2/__init__.py b/rl/studies/representation_curriculum_v2/__init__.py deleted file mode 100644 index 0cdf1d6b..00000000 --- a/rl/studies/representation_curriculum_v2/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Canonical direct-policy zero-shot baselines under the v2 prompt contract.""" diff --git a/rl/studies/representation_curriculum_v2/audit_context.py b/rl/studies/representation_curriculum_v2/audit_context.py deleted file mode 100644 index 17690a28..00000000 --- a/rl/studies/representation_curriculum_v2/audit_context.py +++ /dev/null @@ -1,117 +0,0 @@ -#!/usr/bin/env python3 -"""Render every v2 baseline input and prove that it fits without remote access.""" - -from __future__ import annotations - -import json -import os -from contextlib import contextmanager -from pathlib import Path -from typing import Any, Iterator - -from rl.evaluation.protocol import arm_spec, load_protocol -from rl.evaluation.runner import _messages, _sampler_target -from rl.evaluation.samplers import TinkerSampler -from rl.evaluation.tasks import load_task_manifest, load_task_set - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") -TASK_MANIFEST_FILE = Path(__file__).with_name("task_manifest.json") - - -@contextmanager -def _offline_model_assets() -> Iterator[None]: - """Fail closed rather than fetching a missing renderer asset.""" - - names = ("HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE") - previous = {name: os.environ.get(name) for name in names} - os.environ.update({name: "1" for name in names}) - try: - yield - finally: - for name, value in previous.items(): - if value is None: - os.environ.pop(name, None) - else: - os.environ[name] = value - - -def audit_context() -> dict[str, Any]: - """Return exact renderer lengths; raise before any remote client can exist.""" - - protocol = load_protocol(REPO_ROOT, protocol_file=PROTOCOL_FILE) - manifest = load_task_manifest(TASK_MANIFEST_FILE, repo_root=REPO_ROOT) - tasks = load_task_set( - repo_root=REPO_ROOT, - manifest=manifest, - task_set="f1_f8", - ) - arm_ids = [ - arm_id - for hypothesis in protocol["hypotheses"].values() - for arm_id in hypothesis["arm_ids"] - ] - rows: list[dict[str, Any]] = [] - summaries: dict[str, dict[str, int]] = {} - - def forbidden_remote_client() -> Any: - raise AssertionError("context audit must not create a remote client") - - with _offline_model_assets(): - for arm_id in arm_ids: - arm = arm_spec(protocol, arm_id) - sampler = TinkerSampler( - _sampler_target(arm), - service_client_factory=forbidden_remote_client, - ) - arm_rows: list[dict[str, Any]] = [] - for task in tasks: - prompt = sampler.build_prompt(_messages(task, arm)) - prompt_tokens = int(prompt.length) - headroom = ( - arm.context_tokens - arm.max_output_tokens - prompt_tokens - ) - if headroom < 0: - raise ValueError( - f"{arm_id}/{task.task_id} exceeds its context window: " - f"{prompt_tokens}+{arm.max_output_tokens}>" - f"{arm.context_tokens}" - ) - arm_rows.append( - { - "arm_id": arm_id, - "task_id": task.task_id, - "prompt_tokens": prompt_tokens, - "max_output_tokens": arm.max_output_tokens, - "context_tokens": arm.context_tokens, - "headroom_tokens": headroom, - } - ) - rows.extend(arm_rows) - summaries[arm_id] = { - "task_count": len(arm_rows), - "max_prompt_tokens": max( - row["prompt_tokens"] for row in arm_rows - ), - "min_headroom_tokens": min( - row["headroom_tokens"] for row in arm_rows - ), - } - return { - "schema_version": "pixcell-direct-context-audit-v1", - "study_id": protocol["study_id"], - "protocol_sha256": protocol["logical_sha256"], - "task_manifest_sha256": manifest["logical_sha256"], - "remote_client_created": False, - "summaries": summaries, - "rows": rows, - } - - -def main() -> None: - print(json.dumps(audit_context(), indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v2/audit_phase0.py b/rl/studies/representation_curriculum_v2/audit_phase0.py deleted file mode 100644 index 1e868389..00000000 --- a/rl/studies/representation_curriculum_v2/audit_phase0.py +++ /dev/null @@ -1,492 +0,0 @@ -#!/usr/bin/env python3 -"""Prove the v2 baseline launch boundary locally without making a model request.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import os -import subprocess -import tempfile -from contextlib import contextmanager -from pathlib import Path -from typing import Any, Iterator - -from rl.common.evaluator import Attribution, EvaluationStatus, PixCellEvaluator -from rl.common.isolation import ExecutionBoundary, require_execution_boundary -from rl.common.output import extract_code -from rl.common.preprocess import IMAGE_PREPROCESS_VERSION -from rl.common.prompt import ( - CONTRACT_VERSION, - build_prompt_text, - prompt_asset_hashes, -) -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.attempt_store import AttemptKey, AttemptStore, RunKey -from rl.evaluation.protocol import arm_spec, load_protocol -from rl.evaluation.runner import _messages, _preflight_references, _sampler_target -from rl.evaluation.samplers import TinkerSampler -from rl.evaluation.tasks import ( - BENCHMARK_TASK_SET, - canonical_json_sha256, - load_task_manifest, - load_task_set, -) - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") -TASK_MANIFEST_FILE = Path(__file__).with_name("task_manifest.json") -AUDIT_SCHEMA = "pixcell-representation-curriculum-phase0-audit-v2" -EXPECTED_STUDY_ID = "representation-curriculum-v2" -EXPECTED_ARMS = ("inkling-e09-60000", "qwen-on-60000") -EXPECTED_IMAGE_LONG_EDGE = 1920 -_AUDIT_PROBE_PROGRAM = """\ -import gdsfactory as gf - -length = 40.0 -width = 1.7 -layer = (1, 0) - - -@gf.cell -def device(): - component = gf.Component() - component << gf.components.rectangle( - size=(length, width), - layer=layer, - centered=True, - ) - return component - - -if __name__ == "__main__": - device().write_gds("device.gds") -""" -_FORBIDDEN_MODEL_TEXT = ( - "calibration", - "display note", - "ground truth", - "iou", - "leakage_group_id", - "magnification", - "px/um", - "realization_slot", - "reference code", - "representation_id", - "scale_px_per_um", - "split_role", - "target raster", - "target_image", -) - - -def _git_state(repo_root: Path) -> tuple[str, bool]: - head = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo_root, - text=True, - ).strip() - status = subprocess.check_output( - ["git", "status", "--porcelain", "--untracked-files=all"], - cwd=repo_root, - text=True, - ).strip() - return head, not bool(status) - - -def _assert_prompt_is_model_safe(text: str, *, case_id: str) -> None: - normalized = text.casefold() - leaked = [ - fragment - for fragment in _FORBIDDEN_MODEL_TEXT - if fragment.casefold() in normalized - ] - if leaked: - raise ValueError( - f"{case_id} leaked verifier/answer-only prompt fields: {leaked}" - ) - - -@contextmanager -def _offline_model_assets() -> Iterator[None]: - """Forbid tokenizer/image-processor cache misses from reaching the network.""" - - names = ("HF_HUB_OFFLINE", "TRANSFORMERS_OFFLINE") - previous = {name: os.environ.get(name) for name in names} - os.environ.update({name: "1" for name in names}) - try: - yield - finally: - for name, value in previous.items(): - if value is None: - os.environ.pop(name, None) - else: - os.environ[name] = value - - -def _renderer_audit( - *, - protocol: dict[str, Any], - tasks: list[Any], -) -> dict[str, Any]: - remote_client_calls = 0 - - def forbidden_remote_client() -> Any: - nonlocal remote_client_calls - remote_client_calls += 1 - raise AssertionError("Phase 0 audit attempted to create a Tinker client") - - rows: list[dict[str, Any]] = [] - summaries: dict[str, dict[str, int]] = {} - with _offline_model_assets(): - for arm_id in EXPECTED_ARMS: - arm = arm_spec(protocol, arm_id) - if arm.max_image_long_edge != EXPECTED_IMAGE_LONG_EDGE: - raise ValueError( - f"{arm_id} must use the frozen " - f"{EXPECTED_IMAGE_LONG_EDGE}px image policy" - ) - sampler = TinkerSampler( - _sampler_target(arm), - service_client_factory=forbidden_remote_client, - ) - arm_rows: list[dict[str, Any]] = [] - for task in tasks: - text = build_prompt_text(task.observation) - _assert_prompt_is_model_safe( - text, - case_id=f"{arm_id}/{task.task_id}", - ) - prompt = sampler.build_prompt(_messages(task, arm)) - prompt_tokens = int(prompt.length) - headroom = ( - arm.context_tokens - - arm.max_output_tokens - - prompt_tokens - ) - if headroom < 0: - raise ValueError( - f"{arm_id}/{task.task_id} exceeds context: " - f"{prompt_tokens}+{arm.max_output_tokens}>" - f"{arm.context_tokens}" - ) - arm_rows.append( - { - "arm_id": arm_id, - "task_id": task.task_id, - "prompt_tokens": prompt_tokens, - "headroom_tokens": headroom, - } - ) - rows.extend(arm_rows) - summaries[arm_id] = { - "prompt_cases": len(arm_rows), - "prompt_tokens_min": min( - row["prompt_tokens"] for row in arm_rows - ), - "prompt_tokens_max": max( - row["prompt_tokens"] for row in arm_rows - ), - "minimum_headroom_tokens": min( - row["headroom_tokens"] for row in arm_rows - ), - } - - expected_cases = len(EXPECTED_ARMS) * len(tasks) - if len(rows) != expected_cases or expected_cases != 16: - raise ValueError( - f"expected exactly 16 arm/task prompt cases, observed {len(rows)}" - ) - if remote_client_calls: - raise AssertionError("renderer audit created a remote model client") - return { - "model_client_created": False, - "prompt_cases": len(rows), - "arms": summaries, - "rows": rows, - } - - -def _run_manifest( - *, - protocol: dict[str, Any], - arm_id: str, - source_git_sha: str, - task_set_sha256: str, - prompt_sha256: str, - runtime: dict[str, Any], - boundary: ExecutionBoundary, -) -> dict[str, Any]: - return { - "source_git_sha": source_git_sha, - "protocol_sha256": protocol["logical_sha256"], - "task_set_sha256": task_set_sha256, - "prompt_sha256": prompt_sha256, - "sandbox_image_ref": boundary.image_ref, - "sandbox_image_id": boundary.image_id, - "runtime": runtime, - "model_binding": { - "arm_id": arm_id, - **protocol["arms"][arm_id], - "temperature": protocol["sampling"]["temperature"], - "top_p": protocol["sampling"]["top_p"], - }, - } - - -def _schema_smoke( - *, - repo_root: Path, - protocol: dict[str, Any], - source_git_sha: str, - task_set_sha256: str, - prompt_sha256: str, - runtime: dict[str, Any], - boundary: ExecutionBoundary, -) -> dict[str, dict[str, str]]: - hypothesis_by_arm = { - hypothesis["arm_ids"][0]: hypothesis_id - for hypothesis_id, hypothesis in protocol["hypotheses"].items() - } - records: dict[str, dict[str, str]] = {} - with tempfile.TemporaryDirectory(prefix="pixcell-phase0-v2-") as temporary: - store = AttemptStore( - repo_root=repo_root, - external_root=Path(temporary) / "records", - ) - for arm_id in EXPECTED_ARMS: - hypothesis_id = hypothesis_by_arm[arm_id] - run_key = RunKey(protocol["study_id"], hypothesis_id, arm_id) - key = AttemptKey( - protocol["study_id"], - hypothesis_id, - arm_id, - "F1", - 1, - ) - completion = "```python\nprint('phase0 schema audit')\n```" - with store.acquire_run_lock(run_key): - run = store.create_or_verify_run_manifest( - run_key, - _run_manifest( - protocol=protocol, - arm_id=arm_id, - source_git_sha=source_git_sha, - task_set_sha256=task_set_sha256, - prompt_sha256=prompt_sha256, - runtime=runtime, - boundary=boundary, - ), - ) - receipt = store.write_sampling_receipt( - key, - { - "request": { - "max_tokens": protocol["arms"][arm_id][ - "max_output_tokens" - ], - "temperature": protocol["sampling"]["temperature"], - "top_p": protocol["sampling"]["top_p"], - "seed": 1, - }, - "sample": { - "prompt_tokens": 100, - "completion_tokens": 10, - "stop_reason": "stop", - "cap_hit": False, - "raw_text": completion, - "completion_text": completion, - "reasoning_text": "", - "answer_text": completion, - "reasoning_tokens_exact": 0, - "answer_tokens_exact": 10, - "reasoning_tokens_estimate": None, - "answer_tokens_estimate": None, - "channel_token_count_basis": "phase0_schema_audit", - "channel_parse_complete": True, - }, - "sampling_seconds": 0.0, - "raw_completion_sha256": hashlib.sha256( - completion.encode("utf-8") - ).hexdigest(), - "completion_sha256": hashlib.sha256( - completion.encode("utf-8") - ).hexdigest(), - "run_manifest_record_sha256": run.record_sha256, - "cost_estimate_usd": { - "cached_prefill": 0.0, - "uncached_prefill": 0.0, - }, - }, - ) - evaluation = store.write_evaluation( - key, - { - "status": EvaluationStatus.SYNTAX_ERROR.value, - "attribution": Attribution.MODEL.value, - "pure_executable": False, - "measurement_available": False, - "measured_iou": None, - "measured_dice": None, - "aggregation_iou": 0.0, - "program": "print('phase0 schema audit')", - "program_sha256": hashlib.sha256( - b"print('phase0 schema audit')" - ).hexdigest(), - "reference_sha256": "3" * 64, - "violations": [], - "error": "schema audit", - "retryable": False, - "evaluation_seconds": 0.0, - "diagnostics": {}, - }, - ) - if store.load_run_manifest(run_key) != run: - raise AssertionError("run manifest did not round-trip") - if store.load_sampling_receipt(key) != receipt: - raise AssertionError("sampling receipt did not round-trip") - if store.load_evaluation(key) != evaluation: - raise AssertionError("evaluation record did not round-trip") - records[arm_id] = { - "run_manifest_record_sha256": run.record_sha256, - "sampling_record_sha256": receipt.record_sha256, - "evaluation_record_sha256": evaluation.record_sha256, - } - return records - - -def run_audit( - *, - repo_root: Path, - expected_source_sha: str | None = None, -) -> dict[str, Any]: - root = repo_root.expanduser().resolve(strict=True) - head, clean = _git_state(root) - if expected_source_sha is not None: - if head != expected_source_sha: - raise ValueError( - f"source SHA mismatch: {head} != {expected_source_sha}" - ) - if not clean: - raise ValueError("source worktree must be clean for the sealed audit") - - protocol_file = root / PROTOCOL_FILE.relative_to(REPO_ROOT) - manifest_file = root / TASK_MANIFEST_FILE.relative_to(REPO_ROOT) - protocol = load_protocol(root, protocol_file=protocol_file) - if protocol["study_id"] != EXPECTED_STUDY_ID: - raise ValueError("Phase 0 loaded the wrong study") - if tuple(sorted(protocol["arms"])) != EXPECTED_ARMS: - raise ValueError("Phase 0 loaded an unexpected baseline arm set") - manifest = load_task_manifest(manifest_file, repo_root=root) - tasks = load_task_set( - repo_root=root, - manifest=manifest, - task_set=BENCHMARK_TASK_SET, - ) - if [task.task_id for task in tasks] != [ - f"F{index}" for index in range(1, 9) - ]: - raise ValueError("Phase 0 did not resolve exact F1-F8 order") - - runtime = validate_runtime_stack(root) - renderer = _renderer_audit(protocol=protocol, tasks=tasks) - boundary = require_execution_boundary() - with PixCellEvaluator( - max_workers=1, - evaluator_retries=1, - require_isolation=True, - ) as evaluator: - if evaluator.execution_boundary is None: - raise AssertionError("accepted-program audit is not isolated") - if evaluator.execution_boundary.image_id != boundary.image_id: - raise ValueError("sandbox identity changed during Phase 0") - _preflight_references(tasks, evaluator) - probe = _AUDIT_PROBE_PROGRAM.strip() - wrapped = f"```python\n{probe}\n```" - if extract_code(wrapped) != probe: - raise ValueError("output extractor changed the fixed audit probe") - result = evaluator.evaluate(tasks[0].reference, wrapped) - if ( - result.status is not EvaluationStatus.OK - or result.attribution is not Attribution.MODEL - or result.iou is None - ): - raise ValueError( - "isolated evaluator rejected the fixed primitive-only probe: " - f"{result.status.value}/{result.attribution.value}: {result.error}" - ) - - prompt_binding = { - "contract_version": CONTRACT_VERSION, - "preprocess_version": IMAGE_PREPROCESS_VERSION, - **prompt_asset_hashes(), - } - prompt_sha256 = canonical_json_sha256(prompt_binding) - task_set_sha256 = canonical_json_sha256( - manifest["task_sets"][BENCHMARK_TASK_SET] - ) - return { - "schema_version": AUDIT_SCHEMA, - "study_id": protocol["study_id"], - "source_git_sha": head, - "source_worktree_clean": clean, - "protocol_sha256": protocol["logical_sha256"], - "task_manifest_sha256": manifest["logical_sha256"], - "task_set_sha256": task_set_sha256, - "prompt": prompt_binding, - "prompt_sha256": prompt_sha256, - "renderer": renderer, - "reference_count": len(tasks), - "runtime": runtime, - "sandbox": { - "image_ref": boundary.image_ref, - "image_id": boundary.image_id, - }, - "evaluator_probe": { - "task_id": tasks[0].task_id, - "program_sha256": hashlib.sha256( - probe.encode("utf-8") - ).hexdigest(), - "status": result.status.value, - "iou": result.iou, - }, - "record_schema": _schema_smoke( - repo_root=root, - protocol=protocol, - source_git_sha=head, - task_set_sha256=task_set_sha256, - prompt_sha256=prompt_sha256, - runtime=runtime, - boundary=boundary, - ), - "model_client_created": False, - "network_required": False, - "credentials_required": False, - } - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=REPO_ROOT) - result.add_argument("--expected-source-sha") - return result - - -def main() -> None: - args = parser().parse_args() - print( - json.dumps( - run_audit( - repo_root=args.repo_root, - expected_source_sha=args.expected_source_sha, - ), - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v2/freeze_phase1.py b/rl/studies/representation_curriculum_v2/freeze_phase1.py deleted file mode 100644 index f7f98fe4..00000000 --- a/rl/studies/representation_curriculum_v2/freeze_phase1.py +++ /dev/null @@ -1,1414 +0,0 @@ -#!/usr/bin/env python3 -"""Freeze or validate the compact Phase 1 evidence for the v2 baselines.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import os -import re -import subprocess -import uuid -from contextlib import contextmanager -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any, Iterator - -from rl.common.evaluator import Attribution, EvaluationResult, PixCellEvaluator -from rl.common.output import extract_code -from rl.common.preprocess import IMAGE_PREPROCESS_VERSION -from rl.common.prompt import CONTRACT_VERSION, prompt_asset_hashes -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.attempt_store import ( - AttemptKey, - AttemptStore, - RunKey, - StoredRecord, -) -from rl.evaluation.protocol import ( - JobSpec, - arm_spec, - jobs_for_wave, - load_protocol, -) -from rl.evaluation.runner import _messages, _sampler_target -from rl.evaluation.samplers import TinkerSampler -from rl.evaluation.summarize import summarize_arm -from rl.evaluation.tasks import ( - EvaluationTask, - canonical_json_sha256, - load_task_manifest, - load_task_set, -) -from rl.evaluation.tracking import deterministic_wandb_run_id - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") -TASK_MANIFEST_FILE = Path(__file__).with_name("task_manifest.json") -PHASE1_RELATIVE_ROOT = Path( - "data/training/representation-curriculum-v2/phase1" -) -RELEASE_SCHEMA = "pixcell-representation-curriculum-phase1-release-v2" -SUMMARIES_SCHEMA = "pixcell-representation-curriculum-phase1-summaries-v2" -ATTEMPT_INDEX_SCHEMA = "pixcell-representation-curriculum-attempt-v2" -PROGRAM_INDEX_SCHEMA = "pixcell-representation-curriculum-program-v2" -EXPECTED_CONTRACT = "pixcell-direct-reconstruction-v2" -EXPECTED_STUDY = "representation-curriculum-v2" -EXPECTED_HYPOTHESES = { - "RC-H05": "qwen-on-60000", - "RC-H06": "inkling-e09-60000", -} -EXPECTED_TASK_IDS = tuple(f"F{index}" for index in range(1, 9)) -EXPECTED_ATTEMPTS = tuple(range(1, 5)) -_SOURCE_SHA = re.compile(r"^[0-9a-f]{40}$") -_SHA256 = re.compile(r"^[0-9a-f]{64}$") - - -class Phase1FreezeError(RuntimeError): - """The external ledger cannot certify the tracked v2 Phase 1 release.""" - - -@dataclass(frozen=True) -class _RunEvidence: - hypothesis_id: str - arm_id: str - tasks: tuple[EvaluationTask, ...] - jobs: tuple[JobSpec, ...] - manifest: StoredRecord - summary: dict[str, Any] - - -@dataclass(frozen=True) -class _Bundle: - files: dict[PurePosixPath, bytes] - release_manifest: dict[str, Any] - - @property - def all_files(self) -> dict[PurePosixPath, bytes]: - release_path = PurePosixPath("release_manifest.json") - release_bytes = _json_bytes(self.release_manifest) - checksummed = {release_path: release_bytes, **self.files} - checksums = b"".join( - ( - f"{_sha256_bytes(content)} {path.as_posix()}\n" - ).encode("ascii") - for path, content in sorted( - checksummed.items(), - key=lambda item: item[0], - ) - ) - return { - **checksummed, - PurePosixPath("checksums.sha256"): checksums, - } - - -@dataclass(frozen=True) -class _AttemptEvidence: - row: dict[str, Any] - program_row: dict[str, Any] - task: EvaluationTask - stored_evaluation: dict[str, Any] - - -def _json_bytes(value: Any) -> bytes: - try: - return ( - json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - indent=2, - sort_keys=True, - ) - + "\n" - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise Phase1FreezeError("release evidence is not finite JSON") from exc - - -def _jsonl_bytes(values: list[dict[str, Any]]) -> bytes: - try: - return b"".join( - ( - json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ) - + "\n" - ).encode("utf-8") - for value in values - ) - except (TypeError, ValueError) as exc: - raise Phase1FreezeError("attempt index is not finite JSON") from exc - - -def _sha256_bytes(value: bytes) -> str: - return hashlib.sha256(value).hexdigest() - - -def _load_json(path: Path) -> dict[str, Any]: - def reject_constant(value: str) -> Any: - raise Phase1FreezeError(f"{path} contains non-finite number {value}") - - def reject_duplicates(pairs: list[tuple[str, Any]]) -> dict[str, Any]: - result: dict[str, Any] = {} - for key, value in pairs: - if key in result: - raise Phase1FreezeError( - f"{path} contains duplicate key {key!r}" - ) - result[key] = value - return result - - try: - value = json.loads( - path.read_bytes(), - parse_constant=reject_constant, - object_pairs_hook=reject_duplicates, - ) - except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc: - raise Phase1FreezeError( - f"cannot read canonical JSON from {path}" - ) from exc - if not isinstance(value, dict): - raise Phase1FreezeError(f"{path} must contain a JSON object") - return value - - -def _git_head(repo_root: Path) -> str: - try: - return subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo_root, - text=True, - ).strip() - except (OSError, subprocess.CalledProcessError) as exc: - raise Phase1FreezeError("repository HEAD cannot be resolved") from exc - - -def _require_launch_source_state( - *, - repo_root: Path, - output_root: Path, - expected_source_sha: str, - validate_only: bool, -) -> None: - """Bind source to launch SHA while allowing a later data-only commit.""" - - head = _git_head(repo_root) - if head == expected_source_sha: - return - if not validate_only: - raise Phase1FreezeError("current source HEAD differs from the launch SHA") - try: - subprocess.run( - [ - "git", - "merge-base", - "--is-ancestor", - expected_source_sha, - head, - ], - cwd=repo_root, - check=True, - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - changed = subprocess.check_output( - [ - "git", - "diff", - "--name-only", - "-z", - f"{expected_source_sha}..{head}", - ], - cwd=repo_root, - ) - except (OSError, subprocess.CalledProcessError) as exc: - raise Phase1FreezeError( - "the launch SHA is not an ancestor of the evidence commit" - ) from exc - drift = [] - for raw in changed.split(b"\0"): - if not raw: - continue - path = (repo_root / os.fsdecode(raw)).resolve(strict=False) - if path != output_root and not path.is_relative_to(output_root): - drift.append(str(path.relative_to(repo_root))) - if drift: - raise Phase1FreezeError( - "the evidence commit changes source beyond the frozen Phase 1 " - f"record: {sorted(drift)}" - ) - - -def _changed_paths(repo_root: Path) -> set[Path]: - commands = ( - ["git", "diff", "--name-only", "-z"], - ["git", "diff", "--cached", "--name-only", "-z"], - ["git", "ls-files", "--others", "--exclude-standard", "-z"], - ) - result: set[Path] = set() - for command in commands: - try: - output = subprocess.check_output(command, cwd=repo_root) - except (OSError, subprocess.CalledProcessError) as exc: - raise Phase1FreezeError( - "repository worktree cannot be inspected" - ) from exc - for raw in output.split(b"\0"): - if raw: - result.add( - (repo_root / os.fsdecode(raw)).resolve(strict=False) - ) - return result - - -def _require_source_clean_except_output( - *, - repo_root: Path, - output_root: Path, -) -> None: - drift = sorted( - str(path.relative_to(repo_root)) - for path in _changed_paths(repo_root) - if path != output_root and not path.is_relative_to(output_root) - ) - if drift: - raise Phase1FreezeError( - "source worktree differs from the paid-launch commit outside the " - f"tracked v2 Phase 1 output: {drift}" - ) - - -def _phase1_output_root(repo_root: Path, requested: Path | None) -> Path: - root = repo_root.expanduser().resolve(strict=True) - expected = (root / PHASE1_RELATIVE_ROOT).resolve(strict=False) - if expected == root or not expected.is_relative_to(root): - raise Phase1FreezeError( - "the tracked v2 Phase 1 path resolves outside the repository" - ) - observed = ( - expected - if requested is None - else requested.expanduser().resolve(strict=False) - ) - if observed != expected: - raise Phase1FreezeError( - f"v2 Phase 1 evidence must be written exactly to {expected}" - ) - return expected - - -def _validate_roots( - *, - repo_root: Path, - external_root: Path, - output_root: Path, -) -> tuple[Path, Path]: - source = repo_root.expanduser().resolve(strict=True) - external = external_root.expanduser().resolve(strict=True) - if external == source or external.is_relative_to(source): - raise Phase1FreezeError( - "the mutable attempt ledger must remain outside the repository" - ) - if ( - output_root == external - or output_root.is_relative_to(external) - or external.is_relative_to(output_root) - ): - raise Phase1FreezeError("source ledger and tracked output overlap") - return source, external - - -def _prompt_binding() -> tuple[dict[str, str], str]: - binding = { - "contract_version": CONTRACT_VERSION, - "preprocess_version": IMAGE_PREPROCESS_VERSION, - **prompt_asset_hashes(), - } - if binding["contract_version"] != EXPECTED_CONTRACT: - raise Phase1FreezeError( - "the active prompt is not the direct reconstruction v2 contract" - ) - return binding, canonical_json_sha256(binding) - - -@contextmanager -def _offline_model_assets() -> Iterator[None]: - """Prevent prompt certification from consulting mutable remote assets.""" - - names = ( - "HF_DATASETS_OFFLINE", - "HF_HUB_OFFLINE", - "TRANSFORMERS_OFFLINE", - ) - previous = {name: os.environ.get(name) for name in names} - os.environ.update({name: "1" for name in names}) - try: - yield - finally: - for name, value in previous.items(): - if value is None: - os.environ.pop(name, None) - else: - os.environ[name] = value - - -def _expected_prompt_lengths( - protocol: dict[str, Any], - tasks: list[EvaluationTask], -) -> dict[tuple[str, str], int]: - """Re-render every bound input without allowing a remote model client.""" - - def forbidden_remote_client() -> Any: - raise Phase1FreezeError( - "prompt certification must not create a remote model client" - ) - - result: dict[tuple[str, str], int] = {} - with _offline_model_assets(): - for arm_id in EXPECTED_HYPOTHESES.values(): - arm = protocol["arms"][arm_id] - resolved_arm = arm_spec(protocol, arm_id) - sampler = TinkerSampler( - _sampler_target(resolved_arm), - service_client_factory=forbidden_remote_client, - ) - for task in tasks: - prompt = sampler.build_prompt(_messages(task, resolved_arm)) - prompt_tokens = int(prompt.length) - if ( - prompt_tokens + int(arm["max_output_tokens"]) - > int(arm["context_tokens"]) - ): - raise Phase1FreezeError( - f"{arm_id}/{task.task_id} no longer fits its context" - ) - result[(arm_id, task.task_id)] = prompt_tokens - expected_count = len(EXPECTED_HYPOTHESES) * len(tasks) - if len(result) != expected_count: - raise Phase1FreezeError( - "prompt certification did not cover every arm/task input" - ) - return result - - -def _validate_protocol_shape(protocol: dict[str, Any]) -> None: - if protocol.get("study_id") != EXPECTED_STUDY: - raise Phase1FreezeError("the protocol is not representation-curriculum-v2") - if protocol.get("contract_version") != EXPECTED_CONTRACT: - raise Phase1FreezeError("the protocol does not bind the v2 prompt") - if set(protocol.get("hypotheses", {})) != set(EXPECTED_HYPOTHESES): - raise Phase1FreezeError("the v2 Phase 1 hypothesis registry changed") - if set(protocol.get("arms", {})) != set(EXPECTED_HYPOTHESES.values()): - raise Phase1FreezeError("the v2 Phase 1 arm registry changed") - if protocol.get("sampling", {}).get("temperature") != 1.0: - raise Phase1FreezeError("v2 Phase 1 requires temperature 1.0") - if protocol.get("sampling", {}).get("top_p") != 1.0: - raise Phase1FreezeError("v2 Phase 1 requires top-p 1.0") - - qwen = protocol["arms"]["qwen-on-60000"] - if { - "model": qwen.get("model"), - "renderer": qwen.get("renderer"), - "thinking": qwen.get("thinking"), - "thinking_effort": qwen.get("thinking_effort"), - "max_output_tokens": qwen.get("max_output_tokens"), - "context_tokens": qwen.get("context_tokens"), - "max_image_long_edge": qwen.get("max_image_long_edge"), - } != { - "model": "Qwen/Qwen3.6-35B-A3B", - "renderer": "qwen3_5", - "thinking": True, - "thinking_effort": None, - "max_output_tokens": 60000, - "context_tokens": 65536, - "max_image_long_edge": 1920, - }: - raise Phase1FreezeError("the Qwen 60k arm changed") - - inkling = protocol["arms"]["inkling-e09-60000"] - if { - "model": inkling.get("model"), - "renderer": inkling.get("renderer"), - "thinking": inkling.get("thinking"), - "thinking_effort": inkling.get("thinking_effort"), - "max_output_tokens": inkling.get("max_output_tokens"), - "context_tokens": inkling.get("context_tokens"), - "max_image_long_edge": inkling.get("max_image_long_edge"), - } != { - "model": "thinkingmachines/Inkling", - "renderer": "tml_v0", - "thinking": True, - "thinking_effort": 0.9, - "max_output_tokens": 60000, - "context_tokens": 65536, - "max_image_long_edge": 1920, - }: - raise Phase1FreezeError("the Inkling 60k arm changed") - - for hypothesis_id, arm_id in EXPECTED_HYPOTHESES.items(): - hypothesis = protocol["hypotheses"][hypothesis_id] - expected_waves = { - "smoke": {"attempt_indices": [1], "task_count": 8}, - "complete": { - "attempt_indices": list(EXPECTED_ATTEMPTS), - "task_count": 8, - }, - } - if ( - hypothesis.get("arm_ids") != [arm_id] - or hypothesis.get("attempts_per_task") != 4 - or hypothesis.get("task_set") != "f1_f8" - or hypothesis.get("status") != "frozen" - or hypothesis.get("waves") != expected_waves - ): - raise Phase1FreezeError(f"{hypothesis_id} changed") - - -def _expected_run_manifest( - *, - protocol: dict[str, Any], - hypothesis_id: str, - arm_id: str, - source_git_sha: str, - task_set_sha256: str, - prompt_sha256: str, - sandbox_image_ref: str, - sandbox_image_id: str, - runtime: dict[str, Any], -) -> dict[str, Any]: - if EXPECTED_HYPOTHESES.get(hypothesis_id) != arm_id: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} is not a registered v2 Phase 1 run" - ) - return { - "source_git_sha": source_git_sha, - "protocol_sha256": protocol["logical_sha256"], - "task_set_sha256": task_set_sha256, - "prompt_sha256": prompt_sha256, - "sandbox_image_ref": sandbox_image_ref, - "sandbox_image_id": sandbox_image_id, - "runtime": runtime, - "model_binding": { - "arm_id": arm_id, - **protocol["arms"][arm_id], - "temperature": protocol["sampling"]["temperature"], - "top_p": protocol["sampling"]["top_p"], - }, - } - - -def _load_run( - *, - store: AttemptStore, - protocol: dict[str, Any], - hypothesis_id: str, - arm_id: str, - source_git_sha: str, - tasks: list[EvaluationTask], - jobs: list[JobSpec], - task_set_sha256: str, - prompt_sha256: str, - sandbox: tuple[str, str] | None, - runtime: dict[str, Any], -) -> tuple[_RunEvidence, tuple[str, str]]: - run_key = RunKey(protocol["study_id"], hypothesis_id, arm_id) - manifest = store.load_run_manifest(run_key) - if manifest is None: - raise Phase1FreezeError( - f"missing run manifest for {hypothesis_id}/{arm_id}" - ) - observed_sandbox = ( - str(manifest.payload.get("sandbox_image_ref", "")), - str(manifest.payload.get("sandbox_image_id", "")), - ) - if sandbox is not None and observed_sandbox != sandbox: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} used a different evaluator sandbox" - ) - expected = _expected_run_manifest( - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - source_git_sha=source_git_sha, - task_set_sha256=task_set_sha256, - prompt_sha256=prompt_sha256, - sandbox_image_ref=observed_sandbox[0], - sandbox_image_id=observed_sandbox[1], - runtime=runtime, - ) - if manifest.payload != expected: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} run manifest does not match its launch" - ) - - run_jobs = tuple( - job - for job in jobs - if job.hypothesis_id == hypothesis_id and job.arm_id == arm_id - ) - if len(run_jobs) != 32: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} does not resolve to exactly 32 attempts" - ) - summary = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id=hypothesis_id, - arm_id=arm_id, - jobs=jobs, - tasks=tasks, - ) - if ( - not summary["complete"] - or summary["expected_attempts"] != 32 - or summary["attempts"] != 32 - or summary["tasks"] != 8 - or summary["missing"] - ): - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} is not a complete F1-F8 G4 run" - ) - external_summary = ( - store.external_root.joinpath(*store.run_relative_path(run_key).parts) - / "summary.json" - ) - if not external_summary.is_file(): - raise Phase1FreezeError( - f"missing canonical summary for {hypothesis_id}/{arm_id}" - ) - if _load_json(external_summary) != summary: - raise Phase1FreezeError( - f"{hypothesis_id}/{arm_id} summary does not reproduce from attempts" - ) - return ( - _RunEvidence( - hypothesis_id=hypothesis_id, - arm_id=arm_id, - tasks=tuple(tasks), - jobs=run_jobs, - manifest=manifest, - summary=summary, - ), - observed_sandbox, - ) - - -def _finite_unit_interval( - value: Any, - *, - key: AttemptKey, - field: str, -) -> float: - if isinstance(value, bool) or not isinstance(value, (int, float)): - raise Phase1FreezeError(f"{key} has invalid {field}") - result = float(value) - if not math.isfinite(result) or not 0.0 <= result <= 1.0: - raise Phase1FreezeError(f"{key} has invalid {field}") - return result - - -def _validate_receipt_and_evaluation( - *, - protocol: dict[str, Any], - store: AttemptStore, - task: EvaluationTask, - job: JobSpec, - expected_prompt_tokens: int, - run_manifest_record_sha256: str, -) -> tuple[StoredRecord, StoredRecord]: - key = AttemptKey( - protocol["study_id"], - job.hypothesis_id, - job.arm_id, - job.task_id, - job.attempt_index, - ) - receipt = store.load_sampling_receipt(key) - evaluation = store.load_evaluation(key) - if receipt is None or evaluation is None: - raise Phase1FreezeError(f"missing immutable attempt record for {key}") - - required_receipt = { - "request", - "sample", - "sampling_seconds", - "raw_completion_sha256", - "completion_sha256", - "cost_estimate_usd", - "run_manifest_record_sha256", - } - if set(receipt.payload) != required_receipt: - raise Phase1FreezeError( - f"{key} sampling receipt fields differ from the frozen schema" - ) - if ( - receipt.payload["run_manifest_record_sha256"] - != run_manifest_record_sha256 - ): - raise Phase1FreezeError( - f"{key} sampling receipt belongs to another run manifest" - ) - request = receipt.payload.get("request") - sample = receipt.payload.get("sample") - if not isinstance(request, dict) or not isinstance(sample, dict): - raise Phase1FreezeError(f"{key} has an invalid sampling payload") - arm = protocol["arms"][job.arm_id] - expected_request = { - "max_tokens": int(arm["max_output_tokens"]), - "temperature": protocol["sampling"]["temperature"], - "top_p": protocol["sampling"]["top_p"], - "seed": job.seed, - } - if request != expected_request: - raise Phase1FreezeError(f"{key} sampling request differs from protocol") - required_sample = { - "prompt_tokens", - "completion_tokens", - "stop_reason", - "cap_hit", - "raw_text", - "completion_text", - "reasoning_text", - "answer_text", - "reasoning_tokens_exact", - "answer_tokens_exact", - "reasoning_tokens_estimate", - "answer_tokens_estimate", - "channel_token_count_basis", - "channel_parse_complete", - } - if set(sample) != required_sample: - raise Phase1FreezeError( - f"{key} sample fields differ from the frozen schema" - ) - prompt_tokens = sample["prompt_tokens"] - completion_tokens = sample["completion_tokens"] - if ( - isinstance(prompt_tokens, bool) - or not isinstance(prompt_tokens, int) - or prompt_tokens != expected_prompt_tokens - or isinstance(completion_tokens, bool) - or not isinstance(completion_tokens, int) - or completion_tokens < 0 - or completion_tokens > int(request["max_tokens"]) - or prompt_tokens + int(request["max_tokens"]) - > int(arm["context_tokens"]) - ): - raise Phase1FreezeError( - f"{key} has invalid or non-reproducible token accounting" - ) - if not isinstance(sample["cap_hit"], bool): - raise Phase1FreezeError(f"{key} has a non-boolean cap-hit flag") - if sample["cap_hit"] != (sample["stop_reason"] == "length"): - raise Phase1FreezeError( - f"{key} cap-hit flag differs from its stop reason" - ) - if sample["cap_hit"] and completion_tokens != int(request["max_tokens"]): - raise Phase1FreezeError( - f"{key} length stop did not reach the output cap" - ) - - reasoning_exact = sample["reasoning_tokens_exact"] - answer_exact = sample["answer_tokens_exact"] - if (reasoning_exact is None) != (answer_exact is None): - raise Phase1FreezeError(f"{key} has a partial exact channel partition") - if reasoning_exact is not None and ( - isinstance(reasoning_exact, bool) - or not isinstance(reasoning_exact, int) - or reasoning_exact < 0 - or isinstance(answer_exact, bool) - or not isinstance(answer_exact, int) - or answer_exact < 0 - or reasoning_exact + answer_exact != completion_tokens - ): - raise Phase1FreezeError( - f"{key} exact channel tokens do not partition output" - ) - for field in ("reasoning_tokens_estimate", "answer_tokens_estimate"): - value = sample[field] - if value is not None and ( - isinstance(value, bool) - or not isinstance(value, int) - or value < 0 - ): - raise Phase1FreezeError(f"{key} has invalid {field}") - if not isinstance(sample["channel_parse_complete"], bool): - raise Phase1FreezeError(f"{key} has invalid channel parse state") - - raw_text = str(sample["raw_text"]) - completion = str(sample["completion_text"]) - if receipt.payload.get("raw_completion_sha256") != _sha256_bytes( - raw_text.encode("utf-8") - ): - raise Phase1FreezeError(f"{key} raw completion digest mismatch") - if receipt.payload.get("completion_sha256") != _sha256_bytes( - completion.encode("utf-8") - ): - raise Phase1FreezeError(f"{key} completion digest mismatch") - sampling_seconds = receipt.payload.get("sampling_seconds") - if ( - isinstance(sampling_seconds, bool) - or not isinstance(sampling_seconds, (int, float)) - or not math.isfinite(float(sampling_seconds)) - or float(sampling_seconds) < 0 - ): - raise Phase1FreezeError(f"{key} has invalid sampling duration") - costs = receipt.payload.get("cost_estimate_usd") - if not isinstance(costs, dict) or set(costs) != { - "cached_prefill", - "uncached_prefill", - }: - raise Phase1FreezeError(f"{key} has invalid cost evidence") - rates = protocol["pricing"]["models"][arm["model"]] - sample_cost = completion_tokens * float(rates["sample"]) / 1_000_000 - expected_costs = { - "cached_prefill": ( - prompt_tokens * float(rates["prefill_cached"]) / 1_000_000 - + sample_cost - ), - "uncached_prefill": ( - prompt_tokens * float(rates["prefill_uncached"]) / 1_000_000 - + sample_cost - ), - } - if costs != expected_costs: - raise Phase1FreezeError(f"{key} cost evidence does not reproduce") - - measured = evaluation.payload - required_evaluation = { - "status", - "attribution", - "pure_executable", - "measurement_available", - "measured_iou", - "measured_dice", - "aggregation_iou", - "program", - "program_sha256", - "reference_sha256", - "violations", - "error", - "retryable", - "evaluation_seconds", - "diagnostics", - } - allowed_evaluation = required_evaluation | {"render_sha256"} - if not required_evaluation.issubset(measured) or not set(measured).issubset( - allowed_evaluation - ): - raise Phase1FreezeError( - f"{key} evaluation fields differ from the frozen schema" - ) - if measured["attribution"] != "model": - raise Phase1FreezeError( - f"{key} is an evaluator/reference failure, not a model result" - ) - program = str(measured["program"]) - if program != extract_code(completion): - raise Phase1FreezeError( - f"{key} stored program does not match deterministic extraction" - ) - if measured["program_sha256"] != _sha256_bytes( - program.encode("utf-8") - ): - raise Phase1FreezeError(f"{key} program digest mismatch") - if measured["reference_sha256"] != task.reference.target_image_sha256: - raise Phase1FreezeError(f"{key} reference digest mismatch") - if not _SHA256.fullmatch(str(measured["program_sha256"])): - raise Phase1FreezeError(f"{key} has invalid program digest") - - aggregation_iou = _finite_unit_interval( - measured["aggregation_iou"], - key=key, - field="aggregation_iou", - ) - measured_iou = measured["measured_iou"] - measured_dice = measured["measured_dice"] - if (measured_iou is None) != (measured_dice is None): - raise Phase1FreezeError(f"{key} has partial geometry metrics") - expected_iou = 0.0 - if measured_iou is not None: - expected_iou = _finite_unit_interval( - measured_iou, - key=key, - field="measured_iou", - ) - _finite_unit_interval( - measured_dice, - key=key, - field="measured_dice", - ) - if aggregation_iou != expected_iou: - raise Phase1FreezeError( - f"{key} aggregation IoU is not the raw model IoU" - ) - if ( - not isinstance(measured["measurement_available"], bool) - or measured["measurement_available"] != (measured_iou is not None) - ): - raise Phase1FreezeError( - f"{key} measurement-availability flag is inconsistent" - ) - if measured["measurement_available"] != (measured["status"] == "ok"): - raise Phase1FreezeError( - f"{key} successful execution and geometry measurement disagree" - ) - if not isinstance(measured["diagnostics"], dict): - raise Phase1FreezeError(f"{key} has invalid evaluator diagnostics") - if measured_iou is not None: - render_sha = measured.get("render_sha256") - if not isinstance(render_sha, str) or not _SHA256.fullmatch(render_sha): - raise Phase1FreezeError( - f"{key} measured geometry has no valid render digest" - ) - if measured["diagnostics"].get("render_sha256") != render_sha: - raise Phase1FreezeError( - f"{key} render digest differs from evaluator diagnostics" - ) - if ( - measured["diagnostics"].get("reference_sha256") - != task.reference.target_image_sha256 - ): - raise Phase1FreezeError( - f"{key} diagnostics belong to another reference" - ) - if ( - not isinstance(measured["pure_executable"], bool) - or measured["pure_executable"] != (measured["status"] == "ok") - ): - raise Phase1FreezeError( - f"{key} executable flag is inconsistent with status" - ) - if measured["retryable"] is not False: - raise Phase1FreezeError( - f"{key} model-attributed evaluation cannot be retryable" - ) - evaluation_seconds = measured["evaluation_seconds"] - if ( - isinstance(evaluation_seconds, bool) - or not isinstance(evaluation_seconds, (int, float)) - or not math.isfinite(float(evaluation_seconds)) - or float(evaluation_seconds) < 0 - ): - raise Phase1FreezeError(f"{key} has invalid evaluation duration") - return receipt, evaluation - - -def _attempt_row( - *, - protocol: dict[str, Any], - store: AttemptStore, - evidence: _RunEvidence, - job: JobSpec, - expected_prompt_tokens: int, -) -> _AttemptEvidence: - tasks = {task.task_id: task for task in evidence.tasks} - task = tasks[job.task_id] - receipt, evaluation = _validate_receipt_and_evaluation( - protocol=protocol, - store=store, - task=task, - job=job, - expected_prompt_tokens=expected_prompt_tokens, - run_manifest_record_sha256=evidence.manifest.record_sha256, - ) - sample = receipt.payload["sample"] - measured = evaluation.payload - row = { - "schema_version": ATTEMPT_INDEX_SCHEMA, - "study_id": protocol["study_id"], - "hypothesis_id": job.hypothesis_id, - "arm_id": job.arm_id, - "task_id": job.task_id, - "attempt_index": job.attempt_index, - "seed": job.seed, - "status": measured["status"], - "pure_executable": measured["pure_executable"], - "measurement_available": measured["measurement_available"], - "aggregation_iou": measured["aggregation_iou"], - "measured_iou": measured["measured_iou"], - "measured_dice": measured["measured_dice"], - "violations": measured["violations"], - "cap_hit": sample["cap_hit"], - "stop_reason": sample["stop_reason"], - "prompt_tokens": sample["prompt_tokens"], - "completion_tokens": sample["completion_tokens"], - "reasoning_tokens_exact": sample["reasoning_tokens_exact"], - "answer_tokens_exact": sample["answer_tokens_exact"], - "reasoning_tokens_estimate": sample[ - "reasoning_tokens_estimate" - ], - "answer_tokens_estimate": sample["answer_tokens_estimate"], - "channel_token_count_basis": sample["channel_token_count_basis"], - "channel_parse_complete": sample["channel_parse_complete"], - "estimated_cost_usd": receipt.payload["cost_estimate_usd"], - "sampling_seconds": receipt.payload["sampling_seconds"], - "evaluation_seconds": measured["evaluation_seconds"], - "raw_completion_sha256": receipt.payload[ - "raw_completion_sha256" - ], - "completion_sha256": receipt.payload["completion_sha256"], - "program_sha256": measured["program_sha256"], - "reference_sha256": measured["reference_sha256"], - "render_sha256": measured.get("render_sha256"), - "sampling_record_sha256": receipt.record_sha256, - "evaluation_record_sha256": evaluation.record_sha256, - } - program = str(measured["program"]) - return _AttemptEvidence( - row=row, - program_row={ - "schema_version": PROGRAM_INDEX_SCHEMA, - "study_id": protocol["study_id"], - "hypothesis_id": job.hypothesis_id, - "arm_id": job.arm_id, - "task_id": job.task_id, - "attempt_index": job.attempt_index, - "program": program, - "program_sha256": measured["program_sha256"], - "reference_sha256": measured["reference_sha256"], - }, - task=task, - stored_evaluation=measured, - ) - - -def _same_optional_metric(observed: float | None, expected: Any) -> bool: - if observed is None or expected is None: - return observed is None and expected is None - return math.isclose( - float(observed), - float(expected), - rel_tol=0.0, - abs_tol=1e-12, - ) - - -def _compare_replayed_evaluation( - evidence: _AttemptEvidence, - replayed: EvaluationResult, -) -> None: - """Require deterministic recomputation to reproduce the stored verdict.""" - - row = evidence.row - stored = evidence.stored_evaluation - identity = ( - f"{row['hypothesis_id']}/{row['arm_id']}/" - f"{row['task_id']}/attempt-{row['attempt_index']}" - ) - if replayed.attribution is not Attribution.MODEL: - raise Phase1FreezeError( - f"{identity} replay produced an evaluator/reference failure: " - f"{replayed.attribution.value}/{replayed.status.value}" - ) - if replayed.status.value != stored["status"]: - raise Phase1FreezeError( - f"{identity} replay status differs: " - f"{replayed.status.value} != {stored['status']}" - ) - if tuple(replayed.violations) != tuple(stored["violations"]): - raise Phase1FreezeError(f"{identity} replay purity verdict differs") - if not _same_optional_metric(replayed.iou, stored["measured_iou"]): - raise Phase1FreezeError(f"{identity} replay IoU differs") - if not _same_optional_metric(replayed.dice, stored["measured_dice"]): - raise Phase1FreezeError(f"{identity} replay Dice differs") - - replay_measurement = replayed.iou is not None - replay_iou = float(replayed.iou) if replayed.iou is not None else 0.0 - if replay_measurement != stored["measurement_available"]: - raise Phase1FreezeError( - f"{identity} replay measurement availability differs" - ) - if not math.isclose( - replay_iou, - float(stored["aggregation_iou"]), - rel_tol=0.0, - abs_tol=1e-12, - ): - raise Phase1FreezeError(f"{identity} replay aggregation IoU differs") - if (replayed.status.value == "ok") != stored["pure_executable"]: - raise Phase1FreezeError(f"{identity} replay executable verdict differs") - - replay_render = replayed.metrics.get("render_sha256") - if replay_render != stored.get("render_sha256"): - raise Phase1FreezeError(f"{identity} replay render digest differs") - replay_reference = replayed.metrics.get("reference_sha256") - if replay_measurement and ( - replay_reference != evidence.task.reference.target_image_sha256 - ): - raise Phase1FreezeError(f"{identity} replay reference digest differs") - - -def _replay_attempts( - attempts: list[_AttemptEvidence], - *, - expected_sandbox: tuple[str, str], -) -> None: - """Re-execute all recorded programs inside the immutable sandbox.""" - - requests = [ - (evidence.task.reference, evidence.program_row["program"]) - for evidence in attempts - ] - with PixCellEvaluator( - max_workers=8, - evaluator_retries=1, - require_isolation=True, - ) as evaluator: - boundary = evaluator.execution_boundary - if boundary is None or ( - boundary.image_ref, - boundary.image_id, - ) != expected_sandbox: - raise Phase1FreezeError( - "certification sandbox differs from the paid launch sandbox" - ) - replayed = evaluator.evaluate_batch(requests) - if len(replayed) != len(attempts): - raise Phase1FreezeError("evaluator replay returned the wrong row count") - for evidence, result in zip(attempts, replayed, strict=True): - _compare_replayed_evaluation(evidence, result) - - -def _published_program_rows( - attempts: list[_AttemptEvidence], -) -> list[dict[str, Any]]: - """Publish executable code, never raw fallback text from failed outputs.""" - - return [ - evidence.program_row - for evidence in attempts - if evidence.row["status"] == "ok" - ] - - -def _collect_bundle( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, -) -> _Bundle: - if not _SOURCE_SHA.fullmatch(expected_source_sha): - raise Phase1FreezeError( - "expected source SHA must be a full lowercase Git commit SHA" - ) - protocol = load_protocol(repo_root, protocol_file=PROTOCOL_FILE) - _validate_protocol_shape(protocol) - manifest = load_task_manifest(TASK_MANIFEST_FILE, repo_root=repo_root) - prompt_binding, prompt_sha = _prompt_binding() - runtime = validate_runtime_stack(repo_root) - store = AttemptStore(repo_root=repo_root, external_root=external_root) - - tasks = load_task_set( - repo_root=repo_root, - manifest=manifest, - task_set="f1_f8", - ) - task_ids = tuple(task.task_id for task in tasks) - if task_ids != EXPECTED_TASK_IDS: - raise Phase1FreezeError( - f"the benchmark is not ordered exactly F1-F8: {task_ids}" - ) - task_set_sha = canonical_json_sha256(manifest["task_sets"]["f1_f8"]) - prompt_lengths = _expected_prompt_lengths(protocol, tasks) - - sandbox: tuple[str, str] | None = None - runs: list[_RunEvidence] = [] - for hypothesis_id, arm_id in EXPECTED_HYPOTHESES.items(): - jobs = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name="complete", - task_ids=list(task_ids), - ) - evidence, sandbox = _load_run( - store=store, - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - source_git_sha=expected_source_sha, - tasks=tasks, - jobs=jobs, - task_set_sha256=task_set_sha, - prompt_sha256=prompt_sha, - sandbox=sandbox, - runtime=runtime, - ) - runs.append(evidence) - - attempt_evidence = [ - _attempt_row( - protocol=protocol, - store=store, - evidence=run, - job=job, - expected_prompt_tokens=prompt_lengths[ - (job.arm_id, job.task_id) - ], - ) - for run in runs - for job in run.jobs - ] - attempt_evidence.sort( - key=lambda value: ( - value.row["hypothesis_id"], - value.row["task_id"], - value.row["attempt_index"], - ) - ) - if len(attempt_evidence) != 64: - raise Phase1FreezeError("v2 Phase 1 must contain exactly 64 attempts") - if sandbox is None: - raise Phase1FreezeError("v2 Phase 1 has no bound evaluator sandbox") - _replay_attempts( - attempt_evidence, - expected_sandbox=sandbox, - ) - attempts = [evidence.row for evidence in attempt_evidence] - programs = _published_program_rows(attempt_evidence) - - summaries_document = { - "schema_version": SUMMARIES_SCHEMA, - "study_id": protocol["study_id"], - "summaries": { - f"{run.hypothesis_id}/{run.arm_id}": run.summary for run in runs - }, - } - summaries_document["logical_sha256"] = canonical_json_sha256( - summaries_document - ) - files: dict[PurePosixPath, bytes] = { - PurePosixPath("attempts.jsonl"): _jsonl_bytes(attempts), - PurePosixPath("programs.jsonl"): _jsonl_bytes(programs), - PurePosixPath("summaries.json"): _json_bytes(summaries_document), - } - file_inventory = [ - { - "path": path.as_posix(), - "sha256": _sha256_bytes(content), - "bytes": len(content), - } - for path, content in sorted(files.items(), key=lambda item: item[0]) - ] - release_manifest: dict[str, Any] = { - "schema_version": RELEASE_SCHEMA, - "study_id": protocol["study_id"], - "phase": "phase1-zero-shot-baselines", - "source_git_sha": expected_source_sha, - "protocol_sha256": protocol["logical_sha256"], - "task_manifest_sha256": manifest["logical_sha256"], - "task_set_sha256": task_set_sha, - "task_ids": list(task_ids), - "prompt": prompt_binding, - "prompt_sha256": prompt_sha, - "runtime": runtime, - "sandbox": { - "image_ref": sandbox[0] if sandbox is not None else "", - "image_id": sandbox[1] if sandbox is not None else "", - }, - "attempt_count": len(attempts), - "program_count": len(programs), - "certification": { - "prompt_token_counts_recomputed_offline": True, - "programs_reexecuted_in_bound_sandbox": len(attempts), - "stored_and_replayed_metrics_must_match": True, - "published_programs_are_successful_executables_only": True, - }, - "runs": [ - { - "hypothesis_id": run.hypothesis_id, - "arm_id": run.arm_id, - "expected_attempts": len(run.jobs), - "run_manifest_payload": run.manifest.payload, - "run_manifest_payload_sha256": run.manifest.payload_sha256, - "run_manifest_record_sha256": run.manifest.record_sha256, - "summary_sha256": run.summary["logical_sha256"], - "wandb_run_id": deterministic_wandb_run_id( - RunKey( - protocol["study_id"], - run.hypothesis_id, - run.arm_id, - ), - source_git_sha=expected_source_sha, - ), - } - for run in runs - ], - "tracking": { - "provider": protocol["tracking"]["provider"], - "project": protocol["tracking"]["project"], - "entity": protocol["tracking"].get("entity"), - "group": protocol["tracking"].get("group"), - "local_record_is_authoritative": protocol["tracking"][ - "local_record_is_authoritative" - ], - }, - "files": file_inventory, - "excluded_external_state": [ - "raw_completion_text", - "completion_text", - "reasoning_text", - "answer_text", - "wandb_state", - "tracking_errors", - ], - } - release_manifest["logical_sha256"] = canonical_json_sha256( - release_manifest - ) - return _Bundle(files=files, release_manifest=release_manifest) - - -def _assert_exact_output( - output_root: Path, - expected: dict[PurePosixPath, bytes], -) -> None: - if not output_root.is_dir(): - raise Phase1FreezeError( - f"tracked v2 Phase 1 release is missing: {output_root}" - ) - observed_paths = { - PurePosixPath(path.relative_to(output_root).as_posix()) - for path in output_root.rglob("*") - if path.is_file() - } - expected_paths = set(expected) - if observed_paths != expected_paths: - raise Phase1FreezeError( - "tracked v2 Phase 1 file inventory differs: " - f"missing={sorted(str(value) for value in expected_paths - observed_paths)}, " - f"extra={sorted(str(value) for value in observed_paths - expected_paths)}" - ) - for relative, content in expected.items(): - path = output_root.joinpath(*relative.parts) - if path.is_symlink() or path.read_bytes() != content: - raise Phase1FreezeError(f"tracked evidence differs at {relative}") - - -def _write_create_or_verify( - output_root: Path, - expected: dict[PurePosixPath, bytes], -) -> None: - output_root.mkdir(parents=True, exist_ok=True) - observed_files = [ - path - for path in output_root.rglob("*") - if path.is_file() or path.is_symlink() - ] - expected_paths = { - output_root.joinpath(*relative.parts): content - for relative, content in expected.items() - } - extras = sorted( - str(path.relative_to(output_root)) - for path in observed_files - if path not in expected_paths - ) - if extras: - raise Phase1FreezeError( - "refusing to overwrite a non-canonical v2 Phase 1 directory: " - f"{extras}" - ) - for path, content in expected_paths.items(): - if path.exists() or path.is_symlink(): - if path.is_symlink() or path.read_bytes() != content: - raise Phase1FreezeError( - f"refusing to replace existing evidence at {path}" - ) - continue - path.parent.mkdir(parents=True, exist_ok=True) - temporary = ( - path.parent / f".{path.name}.tmp-{os.getpid()}-{uuid.uuid4().hex}" - ) - try: - with temporary.open("xb") as handle: - handle.write(content) - handle.flush() - os.fsync(handle.fileno()) - os.replace(temporary, path) - finally: - temporary.unlink(missing_ok=True) - _assert_exact_output(output_root, expected) - - -def freeze_phase1( - *, - repo_root: Path, - external_root: Path, - expected_source_sha: str, - output: Path | None = None, - validate_only: bool = False, -) -> dict[str, Any]: - """Recompute the canonical record, then write or validate exact bytes.""" - - output_root = _phase1_output_root(repo_root, output) - source, external = _validate_roots( - repo_root=repo_root, - external_root=external_root, - output_root=output_root, - ) - _require_source_clean_except_output( - repo_root=source, - output_root=output_root, - ) - if not _SOURCE_SHA.fullmatch(expected_source_sha): - raise Phase1FreezeError( - "expected source SHA must be a full lowercase Git commit SHA" - ) - _require_launch_source_state( - repo_root=source, - output_root=output_root, - expected_source_sha=expected_source_sha, - validate_only=validate_only, - ) - bundle = _collect_bundle( - repo_root=source, - external_root=external, - expected_source_sha=expected_source_sha, - ) - expected = bundle.all_files - if validate_only: - _assert_exact_output(output_root, expected) - else: - _write_create_or_verify(output_root, expected) - return bundle.release_manifest - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=REPO_ROOT) - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--output", type=Path) - result.add_argument( - "--validate", - action="store_true", - help="validate an existing tracked release without writing", - ) - return result - - -def main() -> None: - args = parser().parse_args() - report = freeze_phase1( - repo_root=args.repo_root, - external_root=args.external_root, - expected_source_sha=args.expected_source_sha, - output=args.output, - validate_only=args.validate, - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v2/protocol.json b/rl/studies/representation_curriculum_v2/protocol.json deleted file mode 100644 index a1db4eed..00000000 --- a/rl/studies/representation_curriculum_v2/protocol.json +++ /dev/null @@ -1,129 +0,0 @@ -{ - "arms": { - "inkling-e09-60000": { - "context_tokens": 65536, - "max_image_long_edge": 1920, - "max_output_tokens": 60000, - "model": "thinkingmachines/Inkling", - "provider": "tinker", - "renderer": "tml_v0", - "thinking": true, - "thinking_effort": 0.9 - }, - "qwen-on-60000": { - "context_tokens": 65536, - "max_image_long_edge": 1920, - "max_output_tokens": 60000, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": true, - "thinking_effort": null - } - }, - "contract_version": "pixcell-direct-reconstruction-v2", - "hypotheses": { - "RC-H05": { - "arm_ids": [ - "qwen-on-60000" - ], - "attempts_per_task": 4, - "name": "Qwen thinking-on 60k zero-shot F1-F8 baseline", - "status": "frozen", - "task_set": "f1_f8", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2, - 3, - 4 - ], - "task_count": 8 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 8 - } - } - }, - "RC-H06": { - "arm_ids": [ - "inkling-e09-60000" - ], - "attempts_per_task": 4, - "name": "Inkling effort-0.9 60k zero-shot F1-F8 baseline", - "status": "frozen", - "task_set": "f1_f8", - "waves": { - "complete": { - "attempt_indices": [ - 1, - 2, - 3, - 4 - ], - "task_count": 8 - }, - "smoke": { - "attempt_indices": [ - 1 - ], - "task_count": 8 - } - } - } - }, - "launch": { - "confirmation_token": "PIXCELL_BASELINE_V2", - "mutable_output_environment": "PIXCELL_STUDY_ROOT" - }, - "logical_sha256": "ee1493e7932ae8f549ba419d7859315617b492e6c4b6c3404f6853090c374dca", - "pricing": { - "as_of": "2026-07-27", - "currency": "USD", - "models": { - "Qwen/Qwen3.6-35B-A3B": { - "prefill_cached": 0.108, - "prefill_uncached": 0.54, - "sample": 1.335 - }, - "thinkingmachines/Inkling": { - "prefill_cached": 0.374, - "prefill_uncached": 1.87, - "sample": 4.68 - } - }, - "source": "https://tinker-docs.thinkingmachines.ai/tinker/models/", - "unit": "per_million_tokens" - }, - "purpose": { - "kind": "direct-policy-zero-shot-baseline", - "operating_point_selection": false, - "training": false - }, - "sampling": { - "evaluator_workers": 8, - "require_candidate_isolation": true, - "sampling_concurrency": 8, - "seed_namespace": "pixcell-representation-curriculum-v2-direct-baselines", - "temperature": 1.0, - "top_p": 1.0 - }, - "schema_version": "pixcell-representation-curriculum-protocol-v1", - "study_id": "representation-curriculum-v2", - "task_manifest": { - "logical_sha256": "05ca50526df05a3a79aa785504e36c7538106e5abdb72b64e86b20cc6f94a890", - "path": "task_manifest.json" - }, - "tracking": { - "entity": "aadarwal-massachusetts-institute-of-technology", - "group": "direct-zero-shot-v2", - "local_record_is_authoritative": true, - "mode": "online", - "project": "pixcell-representation-curriculum-v2", - "provider": "wandb" - } -} diff --git a/rl/studies/representation_curriculum_v2/run_baseline.py b/rl/studies/representation_curriculum_v2/run_baseline.py deleted file mode 100644 index 24216ad4..00000000 --- a/rl/studies/representation_curriculum_v2/run_baseline.py +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env python3 -"""Run one frozen, resumable v2 direct-policy baseline wave.""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import os -from pathlib import Path - -from rl.evaluation.runner import run_baseline - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PROTOCOL_FILE = Path(__file__).with_name("protocol.json") - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument( - "--hypothesis", - required=True, - choices=("RC-H05", "RC-H06"), - ) - result.add_argument("--wave", required=True, choices=("smoke", "complete")) - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--confirm-spend", default="") - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_STUDY_ROOT"]) - if os.environ.get("PIXCELL_STUDY_ROOT") - else None - ), - ) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit( - "set PIXCELL_STUDY_ROOT or provide --external-root outside the repository" - ) - report = asyncio.run( - run_baseline( - repo_root=REPO_ROOT, - protocol_file=PROTOCOL_FILE, - hypothesis_id=args.hypothesis, - wave_name=args.wave, - expected_source_sha=args.expected_source_sha, - external_root=args.external_root, - confirmation=args.confirm_spend, - ) - ) - print(json.dumps(report["summaries"], indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_curriculum_v2/task_manifest.json b/rl/studies/representation_curriculum_v2/task_manifest.json deleted file mode 100644 index d7e64b81..00000000 --- a/rl/studies/representation_curriculum_v2/task_manifest.json +++ /dev/null @@ -1,148 +0,0 @@ -{ - "logical_sha256": "05ca50526df05a3a79aa785504e36c7538106e5abdb72b64e86b20cc6f94a890", - "schema_version": "pixcell-evaluation-task-manifest-v1", - "selection": { - "algorithm": "ordered fixed repository fixtures data/benchmark/final_1 through final_8", - "seed": null - }, - "task_sets": { - "f1_f8": [ - { - "footprint_um": [ - 40.0, - 1.7 - ], - "image_sha256": "0fbd3bb59c59f81b606be1b5d8d948b941b73ca240d4bb31ba9c9d72e6808d3c", - "image_size_px": [ - 1616, - 656 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f1", - "source": "data/benchmark/final_1", - "source_id": "final_1", - "target_image_sha256": "0fbd3bb59c59f81b606be1b5d8d948b941b73ca240d4bb31ba9c9d72e6808d3c", - "task_id": "F1" - }, - { - "footprint_um": [ - 25.0, - 10.0 - ], - "image_sha256": "f9aac3ccc82c4528647f741d619e246e003e42aab13a0b5687d6a9811068576b", - "image_size_px": [ - 1888, - 2272 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f2", - "source": "data/benchmark/final_2", - "source_id": "final_2", - "target_image_sha256": "f9aac3ccc82c4528647f741d619e246e003e42aab13a0b5687d6a9811068576b", - "task_id": "F2" - }, - { - "footprint_um": [ - 120.0, - 4.0 - ], - "image_sha256": "4962daf2f6b430a3124ba83f0702742b019c24121272f111363c991d847e3c3e", - "image_size_px": [ - 2688, - 1568 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f3", - "source": "data/benchmark/final_3", - "source_id": "final_3", - "target_image_sha256": "4962daf2f6b430a3124ba83f0702742b019c24121272f111363c991d847e3c3e", - "task_id": "F3" - }, - { - "footprint_um": [ - 18.0, - 3.0 - ], - "image_sha256": "a57c3d7acbffa63f691d580bba31ada077ad552c94f360271160912b7e30d701", - "image_size_px": [ - 1584, - 672 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f4", - "source": "data/benchmark/final_4", - "source_id": "final_4", - "target_image_sha256": "a57c3d7acbffa63f691d580bba31ada077ad552c94f360271160912b7e30d701", - "task_id": "F4" - }, - { - "footprint_um": [ - 18.24, - 0.7 - ], - "image_sha256": "6e850e58b0768aa18e828b7b0a4d1b38db4460cef7f3fef0782a58909bf457a3", - "image_size_px": [ - 3712, - 1152 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f5", - "source": "data/benchmark/final_5", - "source_id": "final_5", - "target_image_sha256": "6e850e58b0768aa18e828b7b0a4d1b38db4460cef7f3fef0782a58909bf457a3", - "task_id": "F5" - }, - { - "footprint_um": [ - 4.7, - 4.7 - ], - "image_sha256": "bacae5d13895fd2462fb77fda14d06c56080dd408f362323bc5a7ef2618596a5", - "image_size_px": [ - 1424, - 752 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f6", - "source": "data/benchmark/final_6", - "source_id": "final_6", - "target_image_sha256": "bacae5d13895fd2462fb77fda14d06c56080dd408f362323bc5a7ef2618596a5", - "task_id": "F6" - }, - { - "footprint_um": [ - 3.0, - 5.5 - ], - "image_sha256": "91847bf5797234e985065e7828fbbc306c92c26e281c685175e0efaa4749ce18", - "image_size_px": [ - 1612, - 809 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f7", - "source": "data/benchmark/final_7", - "source_id": "final_7", - "target_image_sha256": "91847bf5797234e985065e7828fbbc306c92c26e281c685175e0efaa4749ce18", - "task_id": "F7" - }, - { - "footprint_um": [ - 120.0, - 4.0 - ], - "image_sha256": "6654508aff5e3fb52f0cecb66a0caa055049e82f6c6ad296386c9e44c341758b", - "image_size_px": [ - 4800, - 3584 - ], - "level": "BENCHMARK", - "representation_id": "benchmark-f8", - "source": "data/benchmark/final_8", - "source_id": "final_8", - "target_image_sha256": "6654508aff5e3fb52f0cecb66a0caa055049e82f6c6ad296386c9e44c341758b", - "task_id": "F8" - } - ] - } -} diff --git a/rl/studies/representation_curriculum_v2/tests/__init__.py b/rl/studies/representation_curriculum_v2/tests/__init__.py deleted file mode 100644 index c7eb3129..00000000 --- a/rl/studies/representation_curriculum_v2/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for the active representation-curriculum-v2 study.""" diff --git a/rl/studies/representation_curriculum_v2/tests/test_audit_phase0.py b/rl/studies/representation_curriculum_v2/tests/test_audit_phase0.py deleted file mode 100644 index 612a029c..00000000 --- a/rl/studies/representation_curriculum_v2/tests/test_audit_phase0.py +++ /dev/null @@ -1,221 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from homi.conductor.source_policy import strict_purity_violations -from rl.common.isolation import ExecutionBoundary -from rl.studies.representation_curriculum_v2 import audit_phase0 - - -REPO_ROOT = Path(__file__).resolve().parents[4] - - -def _protocol() -> dict: - return { - "study_id": "representation-curriculum-v2", - "logical_sha256": "1" * 64, - "arms": { - "inkling-e09-60000": { - "model": "thinkingmachines/Inkling", - "renderer": "tml_v0", - "thinking": True, - "thinking_effort": 0.9, - "context_tokens": 65_536, - "max_output_tokens": 60_000, - "max_image_long_edge": 1920, - "provider": "tinker", - }, - "qwen-on-60000": { - "model": "Qwen/Qwen3.6-35B-A3B", - "renderer": "qwen3_5", - "thinking": True, - "thinking_effort": None, - "context_tokens": 65_536, - "max_output_tokens": 60_000, - "max_image_long_edge": 1920, - "provider": "tinker", - }, - }, - "hypotheses": { - "RC-H05": {"arm_ids": ["qwen-on-60000"]}, - "RC-H06": {"arm_ids": ["inkling-e09-60000"]}, - }, - "sampling": {"temperature": 1.0, "top_p": 1.0}, - } - - -def _boundary(tmp_path: Path) -> ExecutionBoundary: - return ExecutionBoundary( - runtime_path=Path("/usr/bin/docker"), - daemon_endpoint="unix:///var/run/docker.sock", - image_ref="sha256:" + "a" * 64, - image_id="sha256:" + "a" * 64, - workspace_root=tmp_path, - ) - - -def test_forbidden_prompt_fields_fail_closed() -> None: - audit_phase0._assert_prompt_is_model_safe( - "image, footprint, catalogue, and program", - case_id="clean", - ) - with pytest.raises(ValueError, match="verifier/answer-only"): - audit_phase0._assert_prompt_is_model_safe( - "Here is calibration.json and the target raster.", - case_id="leak", - ) - - -def test_evaluator_probe_is_fixed_and_strictly_primitive_only() -> None: - probe = audit_phase0._AUDIT_PROBE_PROGRAM - assert strict_purity_violations(probe) == [] - assert 'write_gds("device.gds")' in probe - assert "gf.components.rectangle" in probe - assert "dataset" not in probe.casefold() - - -def test_renderer_audit_covers_exactly_sixteen_contexts( - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = _protocol() - tasks = [ - SimpleNamespace( - task_id=f"F{index}", - observation=SimpleNamespace(footprint_um=(1.0, 1.0)), - ) - for index in range(1, 9) - ] - - class FakeSampler: - def __init__(self, _target, *, service_client_factory): - self.service_client_factory = service_client_factory - - @staticmethod - def build_prompt(messages): - return SimpleNamespace(length=messages) - - def fake_arm_spec(document, arm_id): - return SimpleNamespace( - **document["arms"][arm_id], - arm_id=arm_id, - ) - - monkeypatch.setattr(audit_phase0, "TinkerSampler", FakeSampler) - monkeypatch.setattr(audit_phase0, "arm_spec", fake_arm_spec) - monkeypatch.setattr(audit_phase0, "_sampler_target", lambda arm: arm) - monkeypatch.setattr( - audit_phase0, - "_messages", - lambda task, _arm: 4000 + int(task.task_id[1:]), - ) - monkeypatch.setattr( - audit_phase0, - "build_prompt_text", - lambda _observation: "image, footprint, catalogue, and program", - ) - - report = audit_phase0._renderer_audit( - protocol=protocol, - tasks=tasks, - ) - assert report["model_client_created"] is False - assert report["prompt_cases"] == 16 - assert set(report["arms"]) == set(audit_phase0.EXPECTED_ARMS) - for summary in report["arms"].values(): - assert summary == { - "prompt_cases": 8, - "prompt_tokens_min": 4001, - "prompt_tokens_max": 4008, - "minimum_headroom_tokens": 1528, - } - - -def test_renderer_audit_rejects_negative_headroom( - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = _protocol() - tasks = [ - SimpleNamespace( - task_id=f"F{index}", - observation=SimpleNamespace(footprint_um=(1.0, 1.0)), - ) - for index in range(1, 9) - ] - - class OversizeSampler: - def __init__(self, _target, *, service_client_factory): - self.service_client_factory = service_client_factory - - @staticmethod - def build_prompt(_messages): - return SimpleNamespace(length=5537) - - monkeypatch.setattr(audit_phase0, "TinkerSampler", OversizeSampler) - monkeypatch.setattr( - audit_phase0, - "arm_spec", - lambda document, arm_id: SimpleNamespace( - **document["arms"][arm_id], - arm_id=arm_id, - ), - ) - monkeypatch.setattr(audit_phase0, "_sampler_target", lambda arm: arm) - monkeypatch.setattr(audit_phase0, "_messages", lambda _task, _arm: []) - monkeypatch.setattr( - audit_phase0, - "build_prompt_text", - lambda _observation: "image, footprint, catalogue, and program", - ) - with pytest.raises(ValueError, match="exceeds context"): - audit_phase0._renderer_audit(protocol=protocol, tasks=tasks) - - -def test_schema_smoke_round_trips_both_arms(tmp_path: Path) -> None: - protocol = _protocol() - runtime = { - "python": "3.13.5", - "packages": {"gdsfactory": "9.20.7"}, - "tinker_cookbook_commit": "b" * 40, - } - report = audit_phase0._schema_smoke( - repo_root=REPO_ROOT, - protocol=protocol, - source_git_sha="c" * 40, - task_set_sha256="d" * 64, - prompt_sha256="e" * 64, - runtime=runtime, - boundary=_boundary(tmp_path), - ) - assert set(report) == set(audit_phase0.EXPECTED_ARMS) - assert all( - set(records) - == { - "run_manifest_record_sha256", - "sampling_record_sha256", - "evaluation_record_sha256", - } - for records in report.values() - ) - - -def test_sealed_audit_rejects_dirty_or_wrong_source( - monkeypatch: pytest.MonkeyPatch, -) -> None: - monkeypatch.setattr( - audit_phase0, - "_git_state", - lambda _root: ("a" * 40, False), - ) - with pytest.raises(ValueError, match="source worktree must be clean"): - audit_phase0.run_audit( - repo_root=REPO_ROOT, - expected_source_sha="a" * 40, - ) - with pytest.raises(ValueError, match="source SHA mismatch"): - audit_phase0.run_audit( - repo_root=REPO_ROOT, - expected_source_sha="b" * 40, - ) diff --git a/rl/studies/representation_curriculum_v2/tests/test_freeze_phase1.py b/rl/studies/representation_curriculum_v2/tests/test_freeze_phase1.py deleted file mode 100644 index e11762db..00000000 --- a/rl/studies/representation_curriculum_v2/tests/test_freeze_phase1.py +++ /dev/null @@ -1,842 +0,0 @@ -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -import pytest - -from rl.common.contracts import ModelObservation, VerifierReference -from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus -from rl.common.output import extract_code -from rl.evaluation.attempt_store import AttemptKey, AttemptStore, RunKey -from rl.evaluation.protocol import jobs_for_wave -from rl.evaluation.summarize import atomic_write_json, summarize_arm -from rl.evaluation.tasks import EvaluationTask, canonical_json_sha256 -from rl.studies.representation_curriculum_v2 import freeze_phase1 as freezer - - -SOURCE_SHA = "a" * 40 -PROMPT_SHA = "b" * 64 -SANDBOX = ("sha256:" + "c" * 64, "sha256:" + "c" * 64) -RUNTIME = { - "python": "3.13.14", - "packages": {"tinker": "0.22.7"}, - "tinker_cookbook_commit": "f" * 40, -} - - -def _arm( - *, - model: str, - renderer: str, - effort: float | None, -) -> dict[str, Any]: - return { - "provider": "tinker", - "model": model, - "renderer": renderer, - "thinking": True, - "thinking_effort": effort, - "max_output_tokens": 60000, - "context_tokens": 65536, - "max_image_long_edge": 1920, - } - - -def _protocol() -> dict[str, Any]: - hypotheses = { - "RC-H05": { - "name": "Qwen thinking-on 60k zero-shot F1-F8 baseline", - "arm_ids": ["qwen-on-60000"], - "attempts_per_task": 4, - "task_set": "f1_f8", - "status": "frozen", - "waves": { - "smoke": {"attempt_indices": [1], "task_count": 8}, - "complete": { - "attempt_indices": [1, 2, 3, 4], - "task_count": 8, - }, - }, - }, - "RC-H06": { - "name": "Inkling effort-0.9 60k zero-shot F1-F8 baseline", - "arm_ids": ["inkling-e09-60000"], - "attempts_per_task": 4, - "task_set": "f1_f8", - "status": "frozen", - "waves": { - "smoke": {"attempt_indices": [1], "task_count": 8}, - "complete": { - "attempt_indices": [1, 2, 3, 4], - "task_count": 8, - }, - }, - }, - } - return { - "study_id": freezer.EXPECTED_STUDY, - "contract_version": freezer.EXPECTED_CONTRACT, - "logical_sha256": "d" * 64, - "dataset": { - "repository": "example/pixcell", - "revision": "test", - "configuration": "depth", - }, - "arms": { - "qwen-on-60000": _arm( - model="Qwen/Qwen3.6-35B-A3B", - renderer="qwen3_5", - effort=None, - ), - "inkling-e09-60000": _arm( - model="thinkingmachines/Inkling", - renderer="tml_v0", - effort=0.9, - ), - }, - "hypotheses": hypotheses, - "sampling": { - "temperature": 1.0, - "top_p": 1.0, - "seed_namespace": "phase1-v2-freezer-test", - }, - "pricing": { - "models": { - "Qwen/Qwen3.6-35B-A3B": { - "prefill_cached": 0.1, - "prefill_uncached": 0.5, - "sample": 1.3, - }, - "thinkingmachines/Inkling": { - "prefill_cached": 0.3, - "prefill_uncached": 1.8, - "sample": 4.6, - }, - } - }, - "tracking": { - "provider": "wandb", - "project": "pixcell-test", - "entity": "test", - "group": "phase1", - "local_record_is_authoritative": True, - }, - } - - -def _task(index: int) -> EvaluationTask: - task_id = f"F{index}" - image = f"image-{task_id}".encode() - digest = hashlib.sha256(image).hexdigest() - return EvaluationTask( - task_id=task_id, - level="BENCHMARK", - representation_id=f"benchmark-f{index}", - observation=ModelObservation( - image_bytes=image, - footprint_um=(10.0, 5.0), - image_sha256=digest, - ), - reference=VerifierReference( - target_image_bytes=image, - footprint_um=(10.0, 5.0), - target_image_sha256=digest, - ), - ) - - -def _manifest(tasks: list[EvaluationTask]) -> dict[str, Any]: - value = { - "task_sets": { - "f1_f8": [{"task_id": task.task_id} for task in tasks], - } - } - value["logical_sha256"] = canonical_json_sha256(value) - return value - - -def _write_attempt( - *, - store: AttemptStore, - protocol: dict[str, Any], - task: EvaluationTask, - job: Any, - manifest_sha: str, - bad_program_hash: bool = False, -) -> None: - key = AttemptKey( - protocol["study_id"], - job.hypothesis_id, - job.arm_id, - job.task_id, - job.attempt_index, - ) - completion = ( - "```python\n" - f"print('{job.hypothesis_id}-{job.arm_id}-{job.task_id}')\n" - "```" - ) - program = extract_code(completion) - raw = f"secret raw reasoning\n{completion}" - rates = protocol["pricing"]["models"][ - protocol["arms"][job.arm_id]["model"] - ] - prompt_tokens = 100 - completion_tokens = 20 - sample_cost = completion_tokens * rates["sample"] / 1_000_000 - store.write_sampling_receipt( - key, - { - "request": { - "max_tokens": 60000, - "temperature": 1.0, - "top_p": 1.0, - "seed": job.seed, - }, - "sample": { - "prompt_tokens": prompt_tokens, - "completion_tokens": completion_tokens, - "stop_reason": "stop", - "cap_hit": False, - "raw_text": raw, - "completion_text": completion, - "reasoning_text": "secret raw reasoning", - "answer_text": completion, - "reasoning_tokens_exact": 2, - "answer_tokens_exact": 18, - "reasoning_tokens_estimate": None, - "answer_tokens_estimate": None, - "channel_token_count_basis": "test", - "channel_parse_complete": True, - }, - "sampling_seconds": 0.5, - "raw_completion_sha256": hashlib.sha256(raw.encode()).hexdigest(), - "completion_sha256": hashlib.sha256( - completion.encode() - ).hexdigest(), - "run_manifest_record_sha256": manifest_sha, - "cost_estimate_usd": { - "cached_prefill": ( - prompt_tokens * rates["prefill_cached"] / 1_000_000 - + sample_cost - ), - "uncached_prefill": ( - prompt_tokens * rates["prefill_uncached"] / 1_000_000 - + sample_cost - ), - }, - }, - ) - store.write_evaluation( - key, - { - "status": "ok", - "attribution": "model", - "pure_executable": True, - "measurement_available": True, - "measured_iou": 0.25, - "measured_dice": 0.4, - "aggregation_iou": 0.25, - "program": program, - "program_sha256": ( - "0" * 64 - if bad_program_hash - else hashlib.sha256(program.encode()).hexdigest() - ), - "reference_sha256": task.reference.target_image_sha256, - "violations": [], - "error": None, - "retryable": False, - "evaluation_seconds": 0.25, - "diagnostics": { - "render_sha256": "e" * 64, - "reference_sha256": task.reference.target_image_sha256, - }, - "render_sha256": "e" * 64, - }, - ) - - -def _write_run( - *, - store: AttemptStore, - protocol: dict[str, Any], - hypothesis_id: str, - arm_id: str, - tasks: list[EvaluationTask], - task_set_sha: str, - omit_last: bool = False, - bad_program_hash: bool = False, -) -> None: - jobs = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name="complete", - task_ids=[task.task_id for task in tasks], - ) - manifest = store.create_or_verify_run_manifest( - RunKey(protocol["study_id"], hypothesis_id, arm_id), - freezer._expected_run_manifest( - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - source_git_sha=SOURCE_SHA, - task_set_sha256=task_set_sha, - prompt_sha256=PROMPT_SHA, - sandbox_image_ref=SANDBOX[0], - sandbox_image_id=SANDBOX[1], - runtime=RUNTIME, - ), - ) - tasks_by_id = {task.task_id: task for task in tasks} - selected = jobs[:-1] if omit_last else jobs - for index, job in enumerate(selected): - _write_attempt( - store=store, - protocol=protocol, - task=tasks_by_id[job.task_id], - job=job, - manifest_sha=manifest.record_sha256, - bad_program_hash=bad_program_hash and index == 0, - ) - summary = summarize_arm( - store=store, - study_id=protocol["study_id"], - hypothesis_id=hypothesis_id, - arm_id=arm_id, - jobs=jobs, - tasks=tasks, - ) - path = ( - store.external_root.joinpath( - *store.run_relative_path( - RunKey(protocol["study_id"], hypothesis_id, arm_id) - ).parts - ) - / "summary.json" - ) - atomic_write_json(path, summary) - - -def _prepare( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, - *, - omit_last: bool = False, - bad_program_hash: bool = False, -) -> tuple[Path, Path]: - repo = tmp_path / "repo" - repo.mkdir() - external = tmp_path / "external" - protocol = _protocol() - tasks = [_task(index) for index in range(1, 9)] - manifest = _manifest(tasks) - - monkeypatch.setattr(freezer, "_git_head", lambda _root: SOURCE_SHA) - monkeypatch.setattr(freezer, "_changed_paths", lambda _root: set()) - monkeypatch.setattr( - freezer, - "validate_runtime_stack", - lambda _root: RUNTIME, - ) - monkeypatch.setattr( - freezer, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - freezer, - "load_task_manifest", - lambda _path, *, repo_root: manifest, - ) - monkeypatch.setattr( - freezer, - "load_task_set", - lambda *, repo_root, manifest, task_set: tasks, - ) - monkeypatch.setattr( - freezer, - "_prompt_binding", - lambda: ( - { - "contract_version": freezer.EXPECTED_CONTRACT, - "preprocess_version": "test", - }, - PROMPT_SHA, - ), - ) - monkeypatch.setattr( - freezer, - "_expected_prompt_lengths", - lambda _protocol, tasks: { - (arm_id, task.task_id): 100 - for arm_id in freezer.EXPECTED_HYPOTHESES.values() - for task in tasks - }, - ) - monkeypatch.setattr( - freezer, - "_replay_attempts", - lambda _attempts, *, expected_sandbox: None, - ) - - store = AttemptStore(repo_root=repo, external_root=external) - task_set_sha = canonical_json_sha256(manifest["task_sets"]["f1_f8"]) - for index, (hypothesis_id, arm_id) in enumerate( - freezer.EXPECTED_HYPOTHESES.items() - ): - _write_run( - store=store, - protocol=protocol, - hypothesis_id=hypothesis_id, - arm_id=arm_id, - tasks=tasks, - task_set_sha=task_set_sha, - omit_last=omit_last and index == 0, - bad_program_hash=bad_program_hash and index == 0, - ) - return repo, external - - -def test_freeze_is_exact_idempotent_and_excludes_private_text( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - report = freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - output = repo / freezer.PHASE1_RELATIVE_ROOT - first = { - path.relative_to(output): path.read_bytes() - for path in output.rglob("*") - if path.is_file() - } - assert report["attempt_count"] == 64 - assert report["program_count"] == 64 - assert set(first) == { - Path("attempts.jsonl"), - Path("programs.jsonl"), - Path("summaries.json"), - Path("release_manifest.json"), - Path("checksums.sha256"), - } - combined = b"".join(first.values()) - assert b"secret raw reasoning" not in combined - assert b"```python" not in combined - programs = [ - json.loads(line) - for line in first[Path("programs.jsonl")].decode().splitlines() - ] - assert len(programs) == 64 - assert all("program" in row for row in programs) - assert all( - hashlib.sha256(row["program"].encode()).hexdigest() - == row["program_sha256"] - for row in programs - ) - - checksums = first[Path("checksums.sha256")].decode().splitlines() - assert len(checksums) == 4 - for line in checksums: - digest, relative = line.split(" ", 1) - assert digest == hashlib.sha256( - first[Path(relative)] - ).hexdigest() - - repeated = freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - assert repeated == report - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - validate_only=True, - ) - assert first == { - path.relative_to(output): path.read_bytes() - for path in output.rglob("*") - if path.is_file() - } - - -def test_freeze_rejects_an_incomplete_32_attempt_arm( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare( - tmp_path, - monkeypatch, - omit_last=True, - ) - with pytest.raises( - freezer.Phase1FreezeError, - match="not a complete F1-F8 G4 run", - ): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_freeze_rejects_a_program_digest_that_does_not_match_code( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare( - tmp_path, - monkeypatch, - bad_program_hash=True, - ) - with pytest.raises(freezer.Phase1FreezeError, match="program digest mismatch"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_validate_rejects_tampered_tracked_evidence( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - attempts = repo / freezer.PHASE1_RELATIVE_ROOT / "attempts.jsonl" - attempts.write_bytes(attempts.read_bytes() + b"{}\n") - with pytest.raises(freezer.Phase1FreezeError, match="differs"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - validate_only=True, - ) - - -def test_freeze_rejects_prompt_binding_drift( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - monkeypatch.setattr( - freezer, - "_prompt_binding", - lambda: ( - {"contract_version": freezer.EXPECTED_CONTRACT}, - "9" * 64, - ), - ) - with pytest.raises( - freezer.Phase1FreezeError, - match="run manifest does not match its launch", - ): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_freeze_rejects_runtime_binding_drift( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - monkeypatch.setattr( - freezer, - "validate_runtime_stack", - lambda _root: { - **RUNTIME, - "python": "3.13.15", - }, - ) - with pytest.raises( - freezer.Phase1FreezeError, - match="run manifest does not match its launch", - ): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_freeze_rejects_a_summary_not_reproduced_from_attempts( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - path = ( - external - / "studies" - / freezer.EXPECTED_STUDY - / "hypotheses" - / "RC-H05" - / "arms" - / "qwen-on-60000" - / "summary.json" - ) - summary = json.loads(path.read_text()) - summary["attempts"] = 31 - path.write_text(json.dumps(summary)) - with pytest.raises( - freezer.Phase1FreezeError, - match="summary does not reproduce", - ): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_source_drift_outside_release_is_rejected( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - monkeypatch.setattr( - freezer, - "_changed_paths", - lambda _root: {repo / "rl" / "unexpected.py"}, - ) - with pytest.raises(freezer.Phase1FreezeError, match="source worktree differs"): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_protocol_shape_rejects_a_non_60k_arm() -> None: - protocol = _protocol() - protocol["arms"]["qwen-on-60000"]["max_output_tokens"] = 59999 - with pytest.raises(freezer.Phase1FreezeError, match="Qwen 60k arm changed"): - freezer._validate_protocol_shape(protocol) - - -def test_release_manifest_is_finite_json( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - release = json.loads( - ( - repo - / freezer.PHASE1_RELATIVE_ROOT - / "release_manifest.json" - ).read_text() - ) - assert release["runtime"] == RUNTIME - assert release["task_ids"] == list(freezer.EXPECTED_TASK_IDS) - assert [run["expected_attempts"] for run in release["runs"]] == [32, 32] - assert release["certification"] == { - "programs_reexecuted_in_bound_sandbox": 64, - "prompt_token_counts_recomputed_offline": True, - "published_programs_are_successful_executables_only": True, - "stored_and_replayed_metrics_must_match": True, - } - - -def test_freeze_rejects_prompt_token_count_that_does_not_reproduce( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, external = _prepare(tmp_path, monkeypatch) - monkeypatch.setattr( - freezer, - "_expected_prompt_lengths", - lambda _protocol, tasks: { - (arm_id, task.task_id): 101 - for arm_id in freezer.EXPECTED_HYPOTHESES.values() - for task in tasks - }, - ) - with pytest.raises( - freezer.Phase1FreezeError, - match="non-reproducible token accounting", - ): - freezer.freeze_phase1( - repo_root=repo, - external_root=external, - expected_source_sha=SOURCE_SHA, - ) - - -def test_replay_comparison_rejects_metric_drift() -> None: - task = _task(1) - evidence = freezer._AttemptEvidence( - row={ - "hypothesis_id": "RC-H05", - "arm_id": "qwen-on-60000", - "task_id": "F1", - "attempt_index": 1, - }, - program_row={ - "program": "print('test')", - "program_sha256": hashlib.sha256( - b"print('test')" - ).hexdigest(), - "reference_sha256": task.reference.target_image_sha256, - }, - task=task, - stored_evaluation={ - "status": "ok", - "attribution": "model", - "pure_executable": True, - "measurement_available": True, - "measured_iou": 0.25, - "measured_dice": 0.4, - "aggregation_iou": 0.25, - "violations": [], - "render_sha256": "e" * 64, - }, - ) - replayed = EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=0.26, - dice=0.4, - metrics={ - "render_sha256": "e" * 64, - "reference_sha256": task.reference.target_image_sha256, - }, - ) - with pytest.raises(freezer.Phase1FreezeError, match="replay IoU differs"): - freezer._compare_replayed_evaluation(evidence, replayed) - - -def test_failed_fallback_text_is_not_published_as_program_source() -> None: - task = _task(1) - private_fallback = "private reasoning without an answer" - evidence = freezer._AttemptEvidence( - row={ - "hypothesis_id": "RC-H05", - "arm_id": "qwen-on-60000", - "task_id": "F1", - "attempt_index": 1, - "status": "syntax_error", - }, - program_row={ - "program": private_fallback, - "program_sha256": hashlib.sha256( - private_fallback.encode() - ).hexdigest(), - "reference_sha256": task.reference.target_image_sha256, - }, - task=task, - stored_evaluation={}, - ) - assert freezer._published_program_rows([evidence]) == [] - - -def test_validation_allows_a_descendant_commit_containing_only_evidence( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo = tmp_path / "repo" - output = repo / freezer.PHASE1_RELATIVE_ROOT - output.mkdir(parents=True) - monkeypatch.setattr(freezer, "_git_head", lambda _root: "b" * 40) - monkeypatch.setattr( - freezer.subprocess, - "run", - lambda *_args, **_kwargs: SimpleNamespace(returncode=0), - ) - monkeypatch.setattr( - freezer.subprocess, - "check_output", - lambda *_args, **_kwargs: ( - b"data/training/representation-curriculum-v2/" - b"phase1/attempts.jsonl\0" - ), - ) - freezer._require_launch_source_state( - repo_root=repo, - output_root=output, - expected_source_sha=SOURCE_SHA, - validate_only=True, - ) - - -def test_validation_rejects_source_drift_after_launch( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo = tmp_path / "repo" - output = repo / freezer.PHASE1_RELATIVE_ROOT - output.mkdir(parents=True) - monkeypatch.setattr(freezer, "_git_head", lambda _root: "b" * 40) - monkeypatch.setattr( - freezer.subprocess, - "run", - lambda *_args, **_kwargs: SimpleNamespace(returncode=0), - ) - monkeypatch.setattr( - freezer.subprocess, - "check_output", - lambda *_args, **_kwargs: b"rl/common/prompt.py\0", - ) - with pytest.raises( - freezer.Phase1FreezeError, - match="changes source beyond", - ): - freezer._require_launch_source_state( - repo_root=repo, - output_root=output, - expected_source_sha=SOURCE_SHA, - validate_only=True, - ) - - -def test_replay_rejects_a_different_immutable_sandbox( - monkeypatch: pytest.MonkeyPatch, -) -> None: - class WrongSandboxEvaluator: - def __init__(self, **_kwargs: Any) -> None: - self.execution_boundary = SimpleNamespace( - image_ref="sha256:" + "1" * 64, - image_id="sha256:" + "1" * 64, - ) - - def __enter__(self) -> WrongSandboxEvaluator: - return self - - def __exit__(self, *_args: object) -> None: - pass - - def evaluate_batch(self, _requests: Any) -> Any: - raise AssertionError("sandbox mismatch must precede evaluation") - - monkeypatch.setattr( - freezer, - "PixCellEvaluator", - WrongSandboxEvaluator, - ) - with pytest.raises( - freezer.Phase1FreezeError, - match="certification sandbox differs", - ): - freezer._replay_attempts( - [], - expected_sandbox=SANDBOX, - ) diff --git a/rl/studies/representation_curriculum_v2/tests/test_v2_protocol.py b/rl/studies/representation_curriculum_v2/tests/test_v2_protocol.py deleted file mode 100644 index b0e038d5..00000000 --- a/rl/studies/representation_curriculum_v2/tests/test_v2_protocol.py +++ /dev/null @@ -1,201 +0,0 @@ -from __future__ import annotations - -import json -import os -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.evaluation import tasks as task_loader -from rl.evaluation.protocol import jobs_for_wave, load_protocol -from rl.evaluation.tasks import ( - build_benchmark_task_manifest, - load_task_manifest, -) -from rl.studies.representation_curriculum_v2 import ( - audit_context as context_auditor, -) -from rl.studies.representation_curriculum_v2 import ( - run_baseline as active_runner, -) - - -REPO_ROOT = Path(__file__).resolve().parents[4] -STUDY_ROOT = REPO_ROOT / "rl/studies/representation_curriculum_v2" -PROTOCOL_FILE = STUDY_ROOT / "protocol.json" -TASK_MANIFEST_FILE = STUDY_ROOT / "task_manifest.json" - - -def _protocol() -> dict: - return load_protocol(REPO_ROOT, protocol_file=PROTOCOL_FILE) - - -def _task_ids() -> list[str]: - manifest = load_task_manifest(TASK_MANIFEST_FILE, repo_root=REPO_ROOT) - return [entry["task_id"] for entry in manifest["task_sets"]["f1_f8"]] - - -def test_v2_is_only_two_direct_policy_zero_shot_baselines(): - protocol = _protocol() - assert protocol["study_id"] == "representation-curriculum-v2" - assert protocol["contract_version"] == "pixcell-direct-reconstruction-v2" - assert protocol["purpose"] == { - "kind": "direct-policy-zero-shot-baseline", - "operating_point_selection": False, - "training": False, - } - assert set(protocol["hypotheses"]) == {"RC-H05", "RC-H06"} - assert "dataset" not in protocol - assert protocol["hypotheses"]["RC-H05"]["arm_ids"] == ["qwen-on-60000"] - assert protocol["hypotheses"]["RC-H06"]["arm_ids"] == [ - "inkling-e09-60000" - ] - assert "operating_point_selection" not in protocol - assert "future_hypothesis_registry" not in protocol - - -def test_v2_model_and_sampling_bindings_are_exact(): - protocol = _protocol() - assert protocol["arms"] == { - "inkling-e09-60000": { - "context_tokens": 65536, - "max_image_long_edge": 1920, - "max_output_tokens": 60000, - "model": "thinkingmachines/Inkling", - "provider": "tinker", - "renderer": "tml_v0", - "thinking": True, - "thinking_effort": 0.9, - }, - "qwen-on-60000": { - "context_tokens": 65536, - "max_image_long_edge": 1920, - "max_output_tokens": 60000, - "model": "Qwen/Qwen3.6-35B-A3B", - "provider": "tinker", - "renderer": "qwen3_5", - "thinking": True, - "thinking_effort": None, - }, - } - assert protocol["sampling"] == { - "evaluator_workers": 8, - "require_candidate_isolation": True, - "sampling_concurrency": 8, - "seed_namespace": ( - "pixcell-representation-curriculum-v2-direct-baselines" - ), - "temperature": 1.0, - "top_p": 1.0, - } - assert protocol["launch"]["confirmation_token"] == "PIXCELL_BASELINE_V2" - - -def test_v2_uses_the_exact_frozen_f1_f8_tasks( - monkeypatch: pytest.MonkeyPatch, -): - active = json.loads(TASK_MANIFEST_FILE.read_text(encoding="utf-8")) - canonical = build_benchmark_task_manifest(REPO_ROOT) - assert active == canonical - assert ( - active["logical_sha256"] - == "05ca50526df05a3a79aa785504e36c7538106e5abdb72b64e86b20cc6f94a890" - ) - - def reject_depth_read(_root: Path) -> list[dict]: - raise AssertionError("v2 benchmark manifest must not read depth rows") - - monkeypatch.setattr( - task_loader, - "_read_depth_validation_rows", - reject_depth_read, - ) - assert _task_ids() == [f"F{index}" for index in range(1, 9)] - - -@pytest.mark.parametrize("hypothesis_id", ("RC-H05", "RC-H06")) -def test_v2_smoke_is_attempt_one_and_complete_is_best_at_four( - hypothesis_id: str, -): - protocol = _protocol() - task_ids = _task_ids() - smoke = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name="smoke", - task_ids=task_ids, - ) - complete = jobs_for_wave( - protocol, - hypothesis_id=hypothesis_id, - wave_name="complete", - task_ids=task_ids, - ) - assert len(smoke) == 8 - assert {job.attempt_index for job in smoke} == {1} - assert len(complete) == 32 - assert {job.attempt_index for job in complete} == {1, 2, 3, 4} - assert set(smoke).issubset(set(complete)) - - -def test_exact_renderer_contexts_fit_without_a_remote_client(): - pytest.importorskip("tinker") - pytest.importorskip("tml_renderers") - report = context_auditor.audit_context() - assert report["remote_client_created"] is False - assert report["summaries"] == { - "inkling-e09-60000": { - "task_count": 8, - "max_prompt_tokens": 4159, - "min_headroom_tokens": 1377, - }, - "qwen-on-60000": { - "task_count": 8, - "max_prompt_tokens": 5357, - "min_headroom_tokens": 179, - }, - } - - -def test_context_audit_forces_offline_assets_and_restores_environment( - monkeypatch: pytest.MonkeyPatch, -): - monkeypatch.delenv("HF_HUB_OFFLINE", raising=False) - monkeypatch.setenv("TRANSFORMERS_OFFLINE", "previous") - with context_auditor._offline_model_assets(): - assert os.environ["HF_HUB_OFFLINE"] == "1" - assert os.environ["TRANSFORMERS_OFFLINE"] == "1" - assert "HF_HUB_OFFLINE" not in os.environ - assert os.environ["TRANSFORMERS_OFFLINE"] == "previous" - - -def test_active_runner_binds_v2_and_accepts_no_selection( - monkeypatch: pytest.MonkeyPatch, - tmp_path: Path, - capsys: pytest.CaptureFixture[str], -): - captured: dict[str, object] = {} - - async def fake_run_baseline(**kwargs): - captured.update(kwargs) - return {"summaries": {}} - - class FakeParser: - @staticmethod - def parse_args(): - return SimpleNamespace( - hypothesis="RC-H05", - wave="smoke", - expected_source_sha="a" * 40, - confirm_spend="PIXCELL_BASELINE_V2", - external_root=tmp_path, - ) - - monkeypatch.setattr(active_runner, "run_baseline", fake_run_baseline) - monkeypatch.setattr(active_runner, "parser", FakeParser) - active_runner.main() - assert captured["protocol_file"] == PROTOCOL_FILE - assert captured["hypothesis_id"] == "RC-H05" - assert "selection_path" not in captured - assert json.loads(capsys.readouterr().out) == {} diff --git a/rl/studies/representation_training_v1/README.md b/rl/studies/representation_training_v1/README.md deleted file mode 100644 index 70db5ea6..00000000 --- a/rl/studies/representation_training_v1/README.md +++ /dev/null @@ -1,384 +0,0 @@ -# Representation training v1 - -This study owns the first training campaign over the released PixCell -curriculum. It compares five Qwen learning paths and one bounded Inkling -policy-improvement path while keeping the model input, evaluator, dataset, and -F1–F8 benchmark fixed. - -No command in this directory runs at import time. A paid launch requires the -committed protocol, an exact clean source commit, the pinned runtime, the -immutable candidate sandbox, valid Tinker and W&B identities, an external -ledger, and the explicit `PIXCELL_TRAINING_V1` confirmation. - -## Branches - -```text -Base Qwen -├── qwen-base-rl-l0 → l1 → l2 → l3 → l4 -├── qwen-mixed-sft -│ └── qwen-mixed-rl-l{first unmastered ... L4} -└── qwen-l0-sft - ├── qwen-sequential-sft-l1 → l2 → l3 → l4 - └── qwen-l0-rl-l1 → l2 → l3 → l4 - -Base Inkling -└── inkling-l4-rl -``` - -The pure-RL branch tests whether verifier-only learning can acquire the full -primitive-to-component curriculum directly from base Qwen. The L0 SFT -checkpoint is shared unchanged by the sequential-SFT and -composition-by-RL hypotheses. Mixed SFT supplies the checkpoint for the -strongest-open-model hypothesis. Each new SFT level loads its parent weights -with a fresh optimizer and restarts the linear schedule; that reset is part of -the sequential-SFT hypothesis. The fixed L0→RL path always enters at L1 after -its reviewed L0 checkpoint; it is not blocked by an invented mastery threshold. -The mixed-SFT path evaluates L0–L4 and enters RL at the first level below the -protocol-sealed thresholds of 0.80 mean raw IoU or 0.95 pure-executable rate. - -Each Qwen RL level is one optimizer run with sealed ceilings at steps 1, 5, 10, -15, 20, 25, and 30. In the pure-RL branch, L0 begins directly from base Qwen. -Its step-1 smoke establishes rollout health, and an immutable rollout-health -receipt is required before step 5. Starting at step 5, progress is measured on -the same fixed held-out panel: realization slot 6, with exactly one row for -every representation at the current level. A report from that panel gates each -later ceiling. Entry into each pure-RL L1–L4 stage requires two reports from -the exact preceding-level terminal checkpoint: its prior-level slot-6 report -and its pre-update slot-6 baseline on the new level. Both reports are bound -into one approval and canonically recomputed at launch. F1–F8 remains a -periodic transfer report and never gates this curriculum. - -The L0-SFT→RL branch uses the same rollout-health and fixed slot-6 cadence from -L1 onward. Its L1 entry is intentionally different: the L0-SFT checkpoint was -produced by an earlier frozen source, so smoke requires its exact parent -receipt plus a reviewed source transition. It does not require the obsolete -1,092-row depth report, F1–F8, or a fabricated same-source level-panel record. -L2–L4 entry requires the preceding completed level's slot-6 report. - -In the pure-RL branch, each level first evaluates its actual parent policy on -that level's slot-6 panel, then reuses the identical tasks after steps 5, 10, -15, 20, 25, and 30. For L0, that pre-update parent is base Qwen and the panel -has 93 tasks. For L1 through L4, it is the preceding level's completed -checkpoint evaluated on the new level before its first update. This separates -learning from changes in sampled training-task difficulty. Final checkpoint -selection uses the separate 546-row slot-7 panel across L0–L4. Inkling uses -sealed ceilings at steps 1, 6, and 14. These are continuation waves, not -independently initialized runs. - -The 21 exact stage definitions, parent edges, wave ceilings, models, recipes, -dataset release, and evaluation identities are in -[`protocol.json`](protocol.json). - -## Policy input and objective - -Every policy receives the established direct Phase-A interface: - -1. the maximum-visibility component image; -2. the physical footprint; -3. black/white layer semantics; -4. the permitted extended primitive catalogue; -5. the direct one-shot programming contract. - -The prompt is assembled by [`rl/common/prompt.py`](../../common/prompt.py). -The image transform is owned by -[`rl/common/preprocess.py`](../../common/preprocess.py). Calibration, -`target_image`, reference code, representation labels, split metadata, and IoU -remain verifier-only. - -Qwen SFT places loss on exactly the Python program and the assistant end token. -Prompt tokens, image tokens, and the empty thinking header have zero weight. -RL receives no answer program and optimizes raw absolute-scale IoU. Model -failures receive zero; reference or evaluator failures abort rather than -becoming negative examples. Ports, counts, and structural witnesses remain -diagnostics. - -## Frozen recipes - -| Policy | Training | Important settings | -|---|---|---| -| Qwen | mixed or level SFT | Qwen3.6 35B A3B, thinking renderer, LoRA 32, batch 64, one physical pass, Adam `1e-4`, linear decay | -| Qwen | curriculum RL | G4, 8 groups per step, `1e-5`, importance sampling, KL 0, 20% earlier-level replay, 30 steps per level | -| Inkling | advanced L4 RL | native TMLv0, effort 0.9, G4, 8 groups per step, `1e-5`, importance sampling, KL 0 | - -Qwen and Inkling both use a 1920-pixel longest edge, temperature 1, top-p 1, -and a 60,000-token output ceiling inside the 65,536-token context. The pinned -Cookbook exposes temperature but not top-p in its RL config; the pinned Tinker -SDK default of top-p 1 is asserted by a contract test. Every prompt is rendered -locally before launch and must retain positive context headroom. - -SFT stages run continuously to their complete one-pass checkpoint. They are -not stopped after a one-step smoke because the pinned Cookbook finalizes the -SFT epoch state when `max_steps` is reached. Initial health is confirmed from -the first local/W&B telemetry while the same run continues. - -RL has no built-in sampling validation. This prevents a one-step 60k smoke -from silently creating dozens of extra completions. F1–F8 and held-out depth -evaluation are explicit checkpoint jobs. - -Tinker reuses the mutable alias `final` when a run continues. The study never -uses that alias as an RL promotion identity. Every wave receipt instead binds -the unique numeric checkpoint at that exact ceiling. SFT receipts inventory -the numeric quarter checkpoints and terminal `final`; all mixed-SFT inventory -checkpoints are evaluated on the full 1,092-row depth validation split. The -selected parent is the finite report with the highest representation-macro -mean IoU, then pure-executable rate, then later progress. - -## Records and W&B - -Set `PIXCELL_TRAINING_ROOT` to storage outside the repository. The external -ledger is authoritative: - -```text -$PIXCELL_TRAINING_ROOT/ -└── studies/representation-training-v1/ - └── stages//runs// - ├── run_manifest.json - ├── invocations/ - ├── candidates/ - ├── waves/ - ├── tinker/ - └── wandb/ -``` - -Each manifest binds the source commit, protocol, prompt assets, all frozen -dataset artifacts, runtime and Cookbook checkout, sandbox image, exact finite -task schedule, model, recipe, parent checkpoint, and tracking identity. -Candidate records retain the exact sampled token IDs, raw response, separated -reasoning/final channels, extracted program, stop state, and deterministic -measurement. A crash retry gets a new invocation ID, so it cannot overwrite -earlier paid samples. - -`r0-retry1` is reserved for the L0-SFT→L1–L4 RL path after its original `r0` -smoke stopped before Tinker client creation. The original run has no samples, -checkpoints, metrics, or wave receipt and remains excluded from results. The -retry consumes the exact historical `qwen-l0-sft/r0` checkpoint; it is an -operational identity, not a second statistical replicate. - -W&B is the live observability mirror: - -- entity: `aadarwal-massachusetts-institute-of-technology` -- project: `pixcell-representation-training-v1` -- name: `representation-training-v1//@` -- group: `representation-training-v1/qwen-open` or - `representation-training-v1/inkling` -- tags: stage, model, kind, replicate, and hypothesis IDs - -The run ID is deterministic in source commit, protocol digest, stage, and -replicate. RL continuation uses `WANDB_RESUME=must`; a fresh stage uses -`WANDB_RESUME=never`. Git and source-code upload are disabled. -Checkpoint and fixed level-panel evaluations are authoritative external-ledger -jobs, not separate W&B training runs. W&B mirrors live optimizer runs; the -immutable evaluation records supply checkpoint-selection and promotion -evidence. - -## Zero-spend gate - -After this source is committed, run: - -```bash -PIXCELL_EVALUATOR_IMAGE='' \ -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.audit_phase0 \ - --expected-source-sha "$(git rev-parse HEAD)" -``` - -It checks all four initial roots: - -- `qwen-mixed-sft` over all 3,468 depth training rows; -- `qwen-l0-sft` over all 642 L0 training rows; -- `qwen-base-rl-l0/smoke` over the 642 L0 training rows; -- `inkling-l4-rl/smoke` over the 648 L4 training rows. - -The audit makes no model request. It hashes every frozen release artifact, -tokenizes every selected policy input, verifies every source and reference, -seals the exact task batches, checks the installed Cookbook source commit, and -executes a known-good program inside the immutable sandbox. - -## Initial start order - -The first paid starts may run in parallel: - -1. start `qwen-base-rl-l0/smoke` directly from base Qwen. Its first 8×G4 - rollout batch is the untouched policy's on-policy starting measurement, - immediately followed by one optimizer update; -2. start `qwen-mixed-sft/complete`, confirm its first metric and local - checkpoint activity without stopping it; -3. start `qwen-l0-sft/complete`, confirm the same; -4. start `inkling-l4-rl/smoke`, which ends after one update; -5. evaluate selected checkpoints only when the result will govern a - continuation or final comparison. - -The optional `level-progress-l0` base panel remains available for reporting, -but it does not gate the direct RL branch. - -The guarded launcher exposes no model, data, optimizer, token, schedule, or -W&B override: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.run_stage \ - --stage \ - --wave \ - --replicate r0 \ - --expected-source-sha "$(git rev-parse HEAD)" \ - --confirm-spend PIXCELL_TRAINING_V1 -``` - -Dependent stages fail closed until their parent checkpoint and required -evaluation or promotion receipt exist. Repeating a completed command verifies -its immutable receipt and exits without touching Tinker or W&B. - -## Evaluation and promotion - -The progress panel is one attempt on each F1–F8 figure. The depth panel is the -full 1,092-row held-out-parameter split. The Inkling promotion panel contains -36 L4 anchors, six anchors from each earlier level, and F1–F8. Reference -rasters, calibration, labels, and target code never enter model messages. - -Evaluate one checkpoint named in its wave receipt: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.evaluate_checkpoint \ - --repo-root "$(pwd)" \ - --stage \ - --wave \ - --checkpoint-name \ - --replicate r0 \ - --panel \ - --source-git-sha "$(git rev-parse HEAD)" \ - --wave-receipt-record-sha256 \ - --external-root "$PIXCELL_TRAINING_ROOT" \ - --confirm-spend PIXCELL_TRAINING_V1 -``` - -The final F1–F8 benchmark is a separate, reporting-only v2 panel; it does not -change progress, depth, or promotion records. It makes four independently -seeded requests per figure (32 total), resumes only missing attempts, and -reports mean@1 plus per-figure and aggregate best@4. Its records live under -`evaluations///final-benchmark-best4-v2/`: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.evaluate_final_benchmark \ - --repo-root "$(pwd)" \ - --stage \ - --wave \ - --checkpoint-name \ - --replicate r0 \ - --source-git-sha "$(git rev-parse HEAD)" \ - --wave-receipt-record-sha256 \ - --external-root "$PIXCELL_TRAINING_ROOT" \ - --confirm-spend PIXCELL_TRAINING_V1 -``` - -The mixed-SFT renderer postmortem is a fixed, non-canonical diagnostic. It -samples only the final checkpoint with `qwen3_5_disable_thinking` and steps -14, 28, and 42 with `qwen3_5`, once each on F1–F8. Its 32 rows live under -`diagnostics/qwen-sft-renderer-diagnostic-v1/`, use a diagnostic-only schema, -and are explicitly ineligible for checkpoint promotion. Sampling the three -periodic checkpoints is a deliberate postmortem-only exception to the study's -terminal-checkpoint evaluation rule: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.evaluate_renderer_diagnostic \ - --repo-root "$(pwd)" \ - --evaluator-source-git-sha "$(git rev-parse HEAD)" \ - --producer-wave-receipt-record-sha256 \ - --external-root "$PIXCELL_TRAINING_ROOT" \ - --confirm-spend PIXCELL_TRAINING_V1 -``` - -After reviewing that immutable report, bind it to exactly one dependent wave: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.record_approval \ - --stage \ - --wave \ - --replicate r0 \ - --evaluation-stage \ - --evaluation-wave \ - --evaluation-checkpoint \ - --evaluation-report-record-sha256 \ - --expected-source-sha "$(git rev-parse HEAD)" \ - --confirm-approval PIXCELL_APPROVE_TRAINING_V1 -``` - -Approvals bind the report, source commit, protocol digest, task panel, -checkpoint inventory, training receipt, and selection decision. They cannot be -reused for a different wave or checkpoint. The launcher treats the stored -approval as an index and canonically revalidates its bound report and metrics -before every paid continuation. - -Pure-RL L1–L4 entry evaluates the same parent terminal checkpoint twice: once -on the preceding level's slot-6 panel and once on the new level's slot-6 panel. -Pass the preceding-level report as the primary evaluation above and the -new-level report as: - -```bash - --entry-baseline-report-record-sha256 -``` - -The combined approval fails unless both reports resolve to the same parent -stage, wave, checkpoint, checkpoint inventory, run manifest, and wave receipt. - -The L0-SFT→L1-RL smoke is the one receipt-only exception. It does not invent or -wait for an evaluation report. At the clean consumer commit, explicitly review -and enumerate every path changed since the source commit in the immutable -L0-SFT manifest: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.record_parent_transition \ - --replicate r0 \ - --expected-source-sha "$(git rev-parse HEAD)" \ - --approve-changed-path \ - --approve-changed-path \ - --confirm-approval PIXCELL_APPROVE_TRAINING_V1 -``` - -This approval recomputes the parent manifest, terminal receipt, invocation, -checkpoint inventory, local artifact hashes, unchanged prompt, dataset, model, -SFT recipe and programming contract, plus the full ancestor-to-consumer Git -transition. The L1 smoke launcher revalidates all of it before W&B or Tinker. - -An evaluation must always run from the same clean commit as its training -manifest. To adopt that already-reviewed evidence from a later descendant, -record a v2 approval at the clean consumer commit and explicitly enumerate the -complete reviewed Git path scope: - -```bash -PYTHONPATH=src:. python -m \ - rl.studies.representation_training_v1.record_approval \ - --stage \ - --wave \ - --replicate r0 \ - --evaluation-stage \ - --evaluation-wave \ - --evaluation-checkpoint \ - --evaluation-report-record-sha256 \ - --evidence-source-sha \ - --expected-source-sha "$(git rev-parse HEAD)" \ - --approve-changed-path \ - --approve-changed-path \ - --confirm-approval PIXCELL_APPROVE_TRAINING_V1 -``` - -Every path from `git diff --name-status --no-renames HEAD` must -appear exactly once. The approval records both commit trees, ancestry, and a -plumbing-level inventory of every path, status, mode, and old/new object ID. -It also binds the producer report and evaluation manifest, training receipt -and checkpoint inventory, and all protocol, prompt, dataset, model/renderer, -sampling, and sandbox evidence. The dependent child manifest embeds that one -stage/wave/replicate transition. A same-source approval remains byte-for-byte -v1 and must omit both transition flags. - -## Baseline completion - -Attempts two through four of the zero-shot Qwen and Inkling F1–F8 baselines -remain the separate `representation-curriculum-v2` study. They are evaluation -only and do not block training. They may resume in the background with that -study's `complete` wave, but local Docker evaluation should not contend with a -live RL rollout unless a host-wide worker budget is in place. diff --git a/rl/studies/representation_training_v1/__init__.py b/rl/studies/representation_training_v1/__init__.py deleted file mode 100644 index 9a7298b9..00000000 --- a/rl/studies/representation_training_v1/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Sealed post-training study over the PixCell representation curriculum.""" diff --git a/rl/studies/representation_training_v1/audit_phase0.py b/rl/studies/representation_training_v1/audit_phase0.py deleted file mode 100644 index ae36d890..00000000 --- a/rl/studies/representation_training_v1/audit_phase0.py +++ /dev/null @@ -1,82 +0,0 @@ -#!/usr/bin/env python3 -"""Run every initial training preflight without credentials or model spend.""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - -from .preflight import stage_preflight -from .protocol import ( - INITIAL_STAGE_IDS, - load_protocol, - repository_root, - stage_spec, - validate_source_sha, -) - - -def run_audit( - *, - repo_root: Path, - expected_source_sha: str | None = None, -) -> dict[str, Any]: - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol( - root, - require_committed=expected_source_sha is not None, - ) - source_git_sha = ( - validate_source_sha(root, expected_source_sha) - if expected_source_sha is not None - else None - ) - reports: dict[str, Any] = {} - for stage_id in INITIAL_STAGE_IDS: - stage = stage_spec(protocol, stage_id) - wave = min( - stage.waves, - key=lambda name: int(stage.waves[name]["max_steps"]), - ) - reports[stage_id] = stage_preflight( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name=wave, - require_sandbox=stage.kind == "rl", - ) - return { - "schema_version": "pixcell-representation-training-phase0-audit-v1", - "study_id": protocol["study_id"], - "source_git_sha": source_git_sha, - "model_client_created": False, - "paid_request_made": False, - "initial_stages": reports, - } - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, default=repository_root()) - result.add_argument("--expected-source-sha") - return result - - -def main() -> None: - args = parser().parse_args() - print( - json.dumps( - run_audit( - repo_root=args.repo_root, - expected_source_sha=args.expected_source_sha, - ), - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/dataset_binding.py b/rl/studies/representation_training_v1/dataset_binding.py deleted file mode 100644 index f9ee50d8..00000000 --- a/rl/studies/representation_training_v1/dataset_binding.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Frozen dataset provenance shared by launch and promotion gates.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - -from rl.common.dataset_io import load_rows, parquet_paths - -from .protocol import file_sha256 - - -def _dataset_binding(dataset_root: Path, protocol: dict[str, Any]) -> dict[str, Any]: - freeze_path = dataset_root / "depth-v1" / "factory" / "freeze.json" - freeze = json.loads(freeze_path.read_text(encoding="utf-8")) - expected = protocol["dataset"]["logical_release_sha256"] - if freeze.get("logical_release_sha256") != expected: - raise RuntimeError("local depth release differs from the training protocol") - train = load_rows(dataset_root, configuration="depth", split="train") - validation = load_rows(dataset_root, configuration="depth", split="validation") - if ( - len(train) != protocol["dataset"]["train_rows"] - or len(validation) != protocol["dataset"]["validation_rows"] - ): - raise RuntimeError("local depth row counts differ from the training protocol") - reference_artifacts = freeze.get("reference_artifacts") - if not isinstance(reference_artifacts, dict) or not reference_artifacts: - raise RuntimeError("depth freeze has no reference artifact inventory") - observed_artifacts: dict[str, str] = {} - release_root = freeze_path.parents[1] - for relative, expected_sha in sorted(reference_artifacts.items()): - path = (release_root / relative).resolve(strict=True) - if not path.is_relative_to(release_root.resolve()): - raise RuntimeError("depth reference artifact escaped its release") - observed = file_sha256(path) - if observed != expected_sha: - raise RuntimeError(f"depth reference artifact changed: {relative}") - observed_artifacts[str(relative)] = observed - return { - "logical_release_sha256": expected, - "freeze_file_sha256": file_sha256(freeze_path), - "train_rows": len(train), - "validation_rows": len(validation), - "parquet_shards": { - str(path.relative_to(dataset_root)): file_sha256(path) - for split in ("train", "validation") - for path in parquet_paths( - dataset_root, - configuration="depth", - split=split, - ) - }, - "reference_artifacts_sha256": hashlib.sha256( - json.dumps( - observed_artifacts, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - ).hexdigest(), - "reference_artifact_count": len(observed_artifacts), - } diff --git a/rl/studies/representation_training_v1/evaluate_checkpoint.py b/rl/studies/representation_training_v1/evaluate_checkpoint.py deleted file mode 100644 index 789e3d72..00000000 --- a/rl/studies/representation_training_v1/evaluate_checkpoint.py +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env python3 -"""CLI entry point for the sealed representation-training checkpoint evaluator.""" - -from rl.studies.representation_training_v1.evaluation import main - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/evaluate_final_benchmark.py b/rl/studies/representation_training_v1/evaluate_final_benchmark.py deleted file mode 100644 index b44a65f4..00000000 --- a/rl/studies/representation_training_v1/evaluate_final_benchmark.py +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env python3 -"""CLI entry point for the versioned final-checkpoint best@4 evaluator.""" - -from rl.studies.representation_training_v1.final_benchmark import main - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/evaluate_renderer_diagnostic.py b/rl/studies/representation_training_v1/evaluate_renderer_diagnostic.py deleted file mode 100644 index 52027426..00000000 --- a/rl/studies/representation_training_v1/evaluate_renderer_diagnostic.py +++ /dev/null @@ -1,8 +0,0 @@ -#!/usr/bin/env python3 -"""CLI for the sealed, non-canonical Qwen SFT renderer diagnostic.""" - -from rl.studies.representation_training_v1.renderer_diagnostic import main - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/evaluation.py b/rl/studies/representation_training_v1/evaluation.py deleted file mode 100644 index 01feb81a..00000000 --- a/rl/studies/representation_training_v1/evaluation.py +++ /dev/null @@ -1,2219 +0,0 @@ -"""Receipt-bound checkpoint evaluation for representation-training-v1. - -The evaluator is deliberately narrower than the general baseline runner: - -* the sampler checkpoint can only come from an immutable training-wave receipt; -* the model, renderer, effort, image limit, context, and sampling policy come - from the committed study protocol; -* every prompt and verifier reference is checked before a Tinker client exists; -* sampled output is persisted before execution, so an infrastructure fault can - be retried without paying for another completion; and -* candidate execution always uses the pinned isolation boundary. -""" - -from __future__ import annotations - -import argparse -import asyncio -import errno -import fcntl -import hashlib -import json -import math -import os -import re -import stat -import subprocess -import uuid -from collections import Counter, defaultdict -from collections.abc import Callable, Mapping, Sequence -from contextlib import AbstractContextManager -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any - -from rl.common.contracts import ( - ModelObservation, - observation_from_row, - reference_from_row, - sampler_from_row, -) -from rl.common.dataset_io import parquet_paths -from rl.common.evaluator import ( - Attribution, - EvaluationResult, - EvaluationStatus, - PixCellEvaluator, -) -from rl.common.prompt import build_prompt_text, prompt_asset_hashes -from rl.evaluation.tasks import ( - BENCHMARK_TASK_SET, - EvaluationTask, - canonical_json_sha256, - load_task_manifest, - load_task_set, -) -from rl.track_a.tinker_data import _message, _renderer - -from .dataset_binding import _dataset_binding -from .protocol import ( - STUDY_ID, - StageSpec, - file_sha256, - load_protocol, - protocol_path, - stage_spec, - validate_stage_replicate, - validate_source_sha, -) -from .store import StageKey, TrainingRecord, TrainingStore - - -EVALUATION_SCHEMA_VERSION = "pixcell-training-checkpoint-evaluation-v1" -EVALUATION_RECORD_SCHEMA_VERSION = "pixcell-training-evaluation-record-v1" -PANEL_PROGRESS = "progress" -PANEL_DEPTH_VALIDATION = "depth-validation" -PANEL_INKLING_PROMOTION = "inkling-promotion" -LEVEL_PROGRESS_PANELS = { - "L0": "level-progress-l0", - "L1": "level-progress-l1", - "L2": "level-progress-l2", - "L3": "level-progress-l3", - "L4": "level-progress-l4", -} -PANEL_DEPTH_FINAL_SELECTION = "depth-final-selection" -LEVEL_PROGRESS_ROWS = { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108, -} -_MODEL_EVALUATION_STATUSES = frozenset( - { - EvaluationStatus.OK.value, - EvaluationStatus.SYNTAX_ERROR.value, - EvaluationStatus.SOURCE_REJECTED.value, - EvaluationStatus.RUNTIME_TIMEOUT.value, - EvaluationStatus.RUNTIME_ERROR.value, - EvaluationStatus.NO_GDS.value, - EvaluationStatus.BAD_GDS.value, - } -) -PANELS = ( - PANEL_PROGRESS, - PANEL_DEPTH_VALIDATION, - PANEL_INKLING_PROMOTION, -) -SUPPORTED_PANELS = ( - *PANELS, - *LEVEL_PROGRESS_PANELS.values(), - PANEL_DEPTH_FINAL_SELECTION, -) -MAX_IMAGE_LONG_EDGE = 1920 -MAX_OUTPUT_TOKENS = 60_000 -TEMPERATURE = 1.0 -TOP_P = 1.0 -SAMPLE_CONCURRENCY = 8 -EVALUATION_BATCH_SIZE = 32 -EVALUATOR_WORKERS = 8 -_PANEL_SELECTION_SEED = ( - "pixcell-representation-training-v1-inkling-promotion-20260728" -) -_LEVEL_PROGRESS_SELECTION = "validation-realization-slot-6" -_DEPTH_FINAL_SELECTION = "validation-realization-slot-7" -_DEPTH_COLUMNS = ( - "id", - "level", - "image", - "target_image", - "footprint_um", - "representation_id", - "leakage_group_id", - "image_sha256", - "target_image_sha256", - "realization_slot", - "split_role", -) -_IDENTIFIER = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$") -_TINKER_PATH = re.compile(r"^tinker://[^\s]+$") -_SHA256 = re.compile(r"^[0-9a-f]{64}$") -_MAX_RECORD_BYTES = 32 * 1024 * 1024 -_FORBIDDEN_PROMPT_FRAGMENTS = ( - "DISPLAY NOTE", - "magnification x:y", - "px/um", - "target_image", - "calibration.json", -) - - -class CheckpointEvaluationError(RuntimeError): - """The requested checkpoint evaluation is not safe or reproducible.""" - - -class CheckpointReceiptError(CheckpointEvaluationError): - """The requested checkpoint is not bound to an exact training receipt.""" - - -class SamplingInfrastructureFault(CheckpointEvaluationError): - """Tinker sampling failed outside the model's generated program.""" - - -class ReferencePanelFault(CheckpointEvaluationError): - """A private verifier reference is invalid or changed.""" - - -class EvaluationInfrastructureFault(CheckpointEvaluationError): - """The isolated evaluator failed independently of model output.""" - - -class ImmutableEvaluationRecordError(CheckpointEvaluationError): - """A create-only evaluation record already contains different bytes.""" - - -@dataclass(frozen=True) -class CheckpointBinding: - key: StageKey - stage: StageSpec - wave: str - checkpoint_name: str - checkpoint: dict[str, Any] - checkpoint_inventory_sha256: str - sampler_path: str - run_manifest: TrainingRecord - wave_receipt: TrainingRecord - - -@dataclass(frozen=True) -class BaseModelBinding: - """An explicit base-model sampler identity with no fictitious receipt.""" - - key: StageKey - stage: StageSpec - wave: str - checkpoint_name: str - checkpoint: dict[str, Any] - checkpoint_inventory_sha256: str - sampler_path: str - binding_sha256: str - - -EvaluationBinding = CheckpointBinding | BaseModelBinding - - -@dataclass(frozen=True) -class PreparedTask: - task: EvaluationTask - prompt_tokens: int - prompt_text_sha256: str - reference_evidence: dict[str, Any] - - -def _canonical_bytes(value: Any) -> bytes: - try: - return json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise CheckpointEvaluationError( - "evaluation record is not finite JSON" - ) from exc - - -def _canonical_sha256(value: Any) -> str: - return hashlib.sha256(_canonical_bytes(value)).hexdigest() - - -def _safe_json(value: Any) -> Any: - if value is None or isinstance(value, (str, int, bool)): - return value - if isinstance(value, float): - if not math.isfinite(value): - raise EvaluationInfrastructureFault( - "evaluator returned a non-finite number" - ) - return value - if isinstance(value, Mapping): - return {str(key): _safe_json(item) for key, item in value.items()} - if isinstance(value, (list, tuple)): - return [_safe_json(item) for item in value] - if hasattr(value, "item"): - return _safe_json(value.item()) - return str(value) - - -def _record_document( - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], -) -> dict[str, Any]: - normalized = json.loads(_canonical_bytes(payload)) - document = { - "schema_version": EVALUATION_RECORD_SCHEMA_VERSION, - "record_type": record_type, - "key": dict(key), - "payload": normalized, - "payload_sha256": _canonical_sha256(normalized), - } - document["record_sha256"] = _canonical_sha256(document) - return document - - -def _validate_record( - value: Mapping[str, Any], - *, - record_type: str, - key: Mapping[str, Any], -) -> dict[str, Any]: - required = { - "schema_version", - "record_type", - "key", - "payload", - "payload_sha256", - "record_sha256", - } - if set(value) != required: - raise CheckpointEvaluationError("evaluation record schema changed") - if value["schema_version"] != EVALUATION_RECORD_SCHEMA_VERSION: - raise CheckpointEvaluationError("evaluation record has a foreign schema") - if value["record_type"] != record_type or value["key"] != dict(key): - raise CheckpointEvaluationError( - "evaluation record key differs from its canonical path" - ) - payload = value["payload"] - if not isinstance(payload, dict): - raise CheckpointEvaluationError("evaluation record payload is not an object") - if value["payload_sha256"] != _canonical_sha256(payload): - raise CheckpointEvaluationError("evaluation payload digest mismatch") - unsigned = dict(value) - observed = str(unsigned.pop("record_sha256")) - if observed != _canonical_sha256(unsigned) or not _SHA256.fullmatch(observed): - raise CheckpointEvaluationError("evaluation envelope digest mismatch") - return dict(value) - - -class _EvaluationLock(AbstractContextManager["_EvaluationLock"]): - def __init__(self, path: Path) -> None: - self.path = path - self.fd: int | None = None - - def __enter__(self) -> "_EvaluationLock": - self.path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - flags = os.O_RDWR | os.O_CREAT - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(self.path, flags, 0o600) - fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) - except BlockingIOError as exc: - if "fd" in locals(): - os.close(fd) - raise CheckpointEvaluationError( - "this exact checkpoint panel is already active" - ) from exc - self.fd = fd - return self - - def __exit__(self, *_args: object) -> None: - if self.fd is not None: - fcntl.flock(self.fd, fcntl.LOCK_UN) - os.close(self.fd) - self.fd = None - - -class CheckpointEvaluationStore: - """Create-only records under one receipt-bound training stage.""" - - def __init__( - self, - *, - stage_path: Path, - wave: str, - checkpoint_name: str, - panel: str, - ) -> None: - if ( - not _IDENTIFIER.fullmatch(wave) - or not _IDENTIFIER.fullmatch(checkpoint_name) - or panel not in SUPPORTED_PANELS - ): - raise ValueError("invalid checkpoint-evaluation identity") - self.stage_path = stage_path.expanduser().resolve(strict=True) - requested = ( - self.stage_path - / "evaluations" - / wave - / checkpoint_name - / panel - ) - requested.mkdir(parents=True, exist_ok=True, mode=0o700) - resolved = requested.resolve(strict=True) - if resolved != requested or not resolved.is_relative_to(self.stage_path): - raise CheckpointEvaluationError( - "evaluation root must be a real directory inside its stage" - ) - self.root = resolved - - def acquire_lock(self) -> _EvaluationLock: - return _EvaluationLock(self.root / ".lock") - - @staticmethod - def _task_component(task_id: str) -> str: - if not _IDENTIFIER.fullmatch(task_id): - raise ValueError(f"unsafe task ID: {task_id!r}") - return task_id - - def load_manifest(self) -> dict[str, Any] | None: - return self._load( - PurePosixPath("manifest.json"), - record_type="evaluation_manifest", - key={}, - ) - - def create_or_verify_manifest( - self, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify( - PurePosixPath("manifest.json"), - record_type="evaluation_manifest", - key={}, - payload=payload, - ) - - def load_sample(self, task_id: str) -> dict[str, Any] | None: - component = self._task_component(task_id) - return self._load( - PurePosixPath("candidates", component, "sample.json"), - record_type="checkpoint_sample", - key={"task_id": task_id}, - ) - - def create_or_verify_sample( - self, - task_id: str, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - component = self._task_component(task_id) - return self._create_or_verify( - PurePosixPath("candidates", component, "sample.json"), - record_type="checkpoint_sample", - key={"task_id": task_id}, - payload=payload, - ) - - def load_evaluation(self, task_id: str) -> dict[str, Any] | None: - component = self._task_component(task_id) - return self._load( - PurePosixPath("candidates", component, "evaluation.json"), - record_type="checkpoint_evaluation", - key={"task_id": task_id}, - ) - - def create_or_verify_evaluation( - self, - task_id: str, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - component = self._task_component(task_id) - return self._create_or_verify( - PurePosixPath("candidates", component, "evaluation.json"), - record_type="checkpoint_evaluation", - key={"task_id": task_id}, - payload=payload, - ) - - def load_report(self) -> dict[str, Any] | None: - return self._load( - PurePosixPath("report.json"), - record_type="checkpoint_report", - key={}, - ) - - def create_or_verify_report( - self, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify( - PurePosixPath("report.json"), - record_type="checkpoint_report", - key={}, - payload=payload, - ) - - def _path(self, relative: PurePosixPath) -> Path: - path = self.root.joinpath(*relative.parts) - parent = path.parent.resolve(strict=False) - if parent != self.root and not parent.is_relative_to(self.root): - raise CheckpointEvaluationError("evaluation record escaped its root") - return path - - def _create_or_verify( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], - ) -> dict[str, Any]: - expected = _record_document( - record_type=record_type, - key=key, - payload=payload, - ) - path = self._path(relative) - path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - raw = _canonical_bytes(expected) + b"\n" - temporary = path.parent / f".{path.name}.{os.getpid()}.{uuid.uuid4().hex}" - flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(temporary, flags, 0o600) - try: - with os.fdopen(fd, "wb", closefd=False) as stream: - stream.write(raw) - stream.flush() - os.fsync(stream.fileno()) - finally: - os.close(fd) - try: - os.link(temporary, path) - directory_fd = os.open(path.parent, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) - except FileExistsError: - observed = self._load( - relative, - record_type=record_type, - key=key, - ) - if observed is None or _canonical_bytes(observed) != _canonical_bytes( - expected - ): - raise ImmutableEvaluationRecordError( - f"{relative} already contains different data" - ) - return observed - finally: - try: - temporary.unlink() - except FileNotFoundError: - pass - return expected - - def _load( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - ) -> dict[str, Any] | None: - path = self._path(relative) - if not path.exists(): - return None - return _validate_record( - self._read(path), - record_type=record_type, - key=key, - ) - - @staticmethod - def _read(path: Path) -> dict[str, Any]: - flags = os.O_RDONLY - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(path, flags) - except OSError as exc: - if exc.errno == errno.ELOOP: - raise CheckpointEvaluationError( - f"{path} must not be a symlink" - ) from exc - raise - try: - metadata = os.fstat(fd) - if ( - not stat.S_ISREG(metadata.st_mode) - or metadata.st_size > _MAX_RECORD_BYTES - ): - raise CheckpointEvaluationError( - f"{path} is not a bounded regular file" - ) - chunks: list[bytes] = [] - remaining = _MAX_RECORD_BYTES + 1 - while remaining: - chunk = os.read(fd, min(1024 * 1024, remaining)) - if not chunk: - break - chunks.append(chunk) - remaining -= len(chunk) - raw = b"".join(chunks) - if len(raw) > _MAX_RECORD_BYTES: - raise CheckpointEvaluationError( - f"{path} exceeds the record bound" - ) - finally: - os.close(fd) - try: - value = json.loads(raw) - except (UnicodeDecodeError, json.JSONDecodeError) as exc: - raise CheckpointEvaluationError( - f"{path} is not valid JSON" - ) from exc - if not isinstance(value, dict): - raise CheckpointEvaluationError(f"{path} must contain an object") - return value - - -def _depth_rows(dataset_root: Path) -> list[dict[str, Any]]: - import pyarrow.parquet as pq - - rows: list[dict[str, Any]] = [] - for path in parquet_paths( - dataset_root, - configuration="depth", - split="validation", - ): - schema = set(pq.read_schema(path).names) - missing = set(_DEPTH_COLUMNS) - schema - if missing: - raise ReferencePanelFault( - f"{path} is missing evaluation fields: {sorted(missing)}" - ) - rows.extend( - pq.read_table(path, columns=list(_DEPTH_COLUMNS)).to_pylist() - ) - return rows - - -def _depth_task(row: Mapping[str, Any]) -> EvaluationTask: - sampler = sampler_from_row(row) - return EvaluationTask( - task_id=sampler.opaque_id, - level=sampler.level.upper(), - representation_id=sampler.representation_id, - observation=observation_from_row(row), - reference=reference_from_row(row), - ) - - -def _benchmark_tasks( - *, - repo_root: Path, - protocol: Mapping[str, Any], -) -> list[EvaluationTask]: - manifest = ( - protocol_path(repo_root).parent - / str(protocol["evaluation"]["task_manifest"]) - ).resolve(strict=True) - if not manifest.is_relative_to(repo_root): - raise ReferencePanelFault("benchmark manifest escaped the repository") - document = load_task_manifest(manifest, repo_root=repo_root) - if ( - document["logical_sha256"] - != protocol["evaluation"]["task_manifest_logical_sha256"] - ): - raise ReferencePanelFault( - "benchmark manifest differs from the training protocol" - ) - tasks = load_task_set( - repo_root=repo_root, - manifest=document, - task_set=BENCHMARK_TASK_SET, - ) - if [task.task_id for task in tasks] != [f"F{index}" for index in range(1, 9)]: - raise ReferencePanelFault("progress panel is not exact ordered F1-F8") - return tasks - - -def _selection_rank(*values: str) -> str: - payload = "\0".join((_PANEL_SELECTION_SEED, *values)) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def _inkling_depth_tasks(rows: Sequence[dict[str, Any]]) -> list[EvaluationTask]: - quotas = {"L0": 6, "L1": 6, "L2": 6, "L3": 6, "L4": 36} - by_level_representation: dict[str, dict[str, list[dict[str, Any]]]] = { - level: {} for level in quotas - } - for row in rows: - level = str(row["level"]).upper() - if level not in by_level_representation: - raise ReferencePanelFault(f"unexpected depth level: {level!r}") - by_level_representation[level].setdefault( - str(row["representation_id"]), - [], - ).append(row) - - selected: list[EvaluationTask] = [] - for level, count in quotas.items(): - represented = by_level_representation[level] - representation_ids = sorted( - represented, - key=lambda value: _selection_rank("representation", level, value), - ) - if len(representation_ids) < count: - raise ReferencePanelFault( - f"{level} has too few representations for Inkling promotion" - ) - for representation_id in representation_ids[:count]: - row = min( - represented[representation_id], - key=lambda value: _selection_rank( - "realization", - level, - representation_id, - str(value["id"]), - ), - ) - selected.append(_depth_task(row)) - return selected - - -def _one_realization_per_representation( - rows: Sequence[dict[str, Any]], - *, - realization_slot: int, - level: str | None = None, -) -> list[EvaluationTask]: - """Select one exact held-out realization for every requested representation.""" - - requested_level = level.upper() if level is not None else None - by_representation: dict[tuple[str, str], list[dict[str, Any]]] = defaultdict(list) - for row in rows: - row_level = str(row["level"]).upper() - if requested_level is not None and row_level != requested_level: - continue - if row.get("realization_slot") == realization_slot: - by_representation[(row_level, str(row["representation_id"]))].append(row) - if not by_representation: - raise ReferencePanelFault( - f"no validation rows use realization slot {realization_slot}" - ) - - selected: list[EvaluationTask] = [] - for identity, candidates in sorted(by_representation.items()): - if len(candidates) != 1: - raise ReferencePanelFault( - f"{identity[0]}/{identity[1]} has {len(candidates)} rows " - f"for validation realization slot {realization_slot}" - ) - selected.append(_depth_task(candidates[0])) - return selected - - -def _level_for_progress_panel(panel: str) -> str | None: - return next( - (level for level, panel_id in LEVEL_PROGRESS_PANELS.items() if panel == panel_id), - None, - ) - - -def build_panel_tasks( - *, - repo_root: Path, - protocol: Mapping[str, Any], - panel: str, -) -> list[EvaluationTask]: - """Build one exact label-free evaluation panel.""" - - if panel not in SUPPORTED_PANELS: - raise ValueError(f"unsupported panel {panel!r}") - if panel == PANEL_PROGRESS: - tasks = _benchmark_tasks(repo_root=repo_root, protocol=protocol) - else: - rows = _depth_rows(repo_root / "dataset") - if len(rows) != int(protocol["dataset"]["validation_rows"]): - raise ReferencePanelFault( - "depth validation row count differs from the protocol" - ) - if panel == PANEL_DEPTH_VALIDATION: - tasks = sorted( - (_depth_task(row) for row in rows), - key=lambda task: ( - task.level, - task.representation_id, - task.task_id, - ), - ) - elif panel == PANEL_INKLING_PROMOTION: - tasks = _inkling_depth_tasks(rows) - tasks.extend(_benchmark_tasks(repo_root=repo_root, protocol=protocol)) - elif panel == PANEL_DEPTH_FINAL_SELECTION: - tasks = _one_realization_per_representation( - rows, - realization_slot=7, - ) - else: - level = _level_for_progress_panel(panel) - if level is None: - raise ValueError(f"unsupported panel {panel!r}") - tasks = _one_realization_per_representation( - rows, - realization_slot=6, - level=level, - ) - - expected = { - PANEL_PROGRESS: 8, - PANEL_DEPTH_VALIDATION: 1092, - PANEL_INKLING_PROMOTION: 68, - PANEL_DEPTH_FINAL_SELECTION: 546, - **{ - panel_id: LEVEL_PROGRESS_ROWS[level] - for level, panel_id in LEVEL_PROGRESS_PANELS.items() - }, - }[panel] - if len(tasks) != expected: - raise ReferencePanelFault( - f"{panel} selected {len(tasks)} tasks; expected {expected}" - ) - task_ids = [task.task_id for task in tasks] - if len(task_ids) != len(set(task_ids)): - raise ReferencePanelFault(f"{panel} selected duplicate task IDs") - if panel == PANEL_DEPTH_VALIDATION: - representations = {task.representation_id for task in tasks} - if len(representations) != 546: - raise ReferencePanelFault( - "depth validation no longer spans 546 representations" - ) - if panel == PANEL_DEPTH_FINAL_SELECTION: - representations = { - (task.level, task.representation_id) - for task in tasks - } - if len(representations) != 546: - raise ReferencePanelFault( - "depth final-selection no longer spans 546 representations" - ) - level = _level_for_progress_panel(panel) - if level is not None: - if ( - {task.level for task in tasks} != {level} - or len({task.representation_id for task in tasks}) - != LEVEL_PROGRESS_ROWS[level] - ): - raise ReferencePanelFault( - f"{panel} no longer contains one {level} row per representation" - ) - if panel == PANEL_INKLING_PROMOTION: - counts = Counter(task.level for task in tasks) - expected_counts = { - "BENCHMARK": 8, - "L0": 6, - "L1": 6, - "L2": 6, - "L3": 6, - "L4": 36, - } - if counts != expected_counts: - raise ReferencePanelFault( - f"Inkling promotion panel changed: {dict(counts)}" - ) - return tasks - - -def _task_manifest(tasks: Sequence[EvaluationTask], *, panel: str) -> dict[str, Any]: - entries = [ - { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "image_sha256": task.observation.image_sha256, - "target_image_sha256": task.reference.target_image_sha256, - "footprint_um": list(task.observation.footprint_um), - } - for task in tasks - ] - return { - "schema_version": "pixcell-training-evaluation-task-panel-v1", - "panel": panel, - "selection_seed": ( - _PANEL_SELECTION_SEED - if panel == PANEL_INKLING_PROMOTION - else ( - _LEVEL_PROGRESS_SELECTION - if panel in LEVEL_PROGRESS_PANELS.values() - else ( - _DEPTH_FINAL_SELECTION - if panel == PANEL_DEPTH_FINAL_SELECTION - else None - ) - ) - ), - "task_count": len(entries), - "task_ids": [item["task_id"] for item in entries], - "logical_sha256": canonical_json_sha256(entries), - } - - -def _load_checkpoint_binding( - *, - store: TrainingStore, - protocol: dict[str, Any], - stage_id: str, - wave: str, - checkpoint_name: str, - replicate_id: str, - expected_receipt_sha256: str, - source_git_sha: str, -) -> CheckpointBinding: - if not _SHA256.fullmatch(expected_receipt_sha256): - raise CheckpointReceiptError( - "wave receipt SHA-256 must be explicit and complete" - ) - stage = stage_spec(protocol, stage_id) - stage.wave(wave) - key = StageKey(STUDY_ID, stage.stage_id, replicate_id) - manifest = store.load_manifest(key) - receipt = store.load_wave_receipt(key, wave=wave) - if manifest is None or receipt is None: - raise CheckpointReceiptError( - f"{stage_id}/{wave}/{replicate_id} has no complete training receipt" - ) - if receipt.record_sha256 != expected_receipt_sha256: - raise CheckpointReceiptError( - "the external wave receipt differs from the requested digest" - ) - expected_manifest = receipt.payload.get("run_manifest_record_sha256") - if expected_manifest != manifest.record_sha256: - raise CheckpointReceiptError( - "wave receipt and training run manifest differ" - ) - expected_fields = { - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "contract_version": protocol["contract_version"], - } - for field, expected in expected_fields.items(): - if manifest.payload.get(field) != expected: - raise CheckpointReceiptError( - f"training manifest {field} differs from this evaluation" - ) - manifest_stage = manifest.payload.get("stage") - if ( - not isinstance(manifest_stage, Mapping) - or manifest_stage.get("stage_id") != stage.stage_id - or manifest_stage.get("model_key") != stage.model_key - ): - raise CheckpointReceiptError("training manifest has the wrong stage") - manifest_dataset = manifest.payload.get("dataset") - if ( - not isinstance(manifest_dataset, Mapping) - or manifest_dataset.get("logical_release_sha256") - != protocol["dataset"]["logical_release_sha256"] - ): - raise CheckpointReceiptError("training manifest has the wrong dataset") - if not _IDENTIFIER.fullmatch(checkpoint_name): - raise CheckpointReceiptError("checkpoint name is not a safe identifier") - terminal = receipt.payload.get("checkpoint") - inventory = receipt.payload.get("checkpoint_inventory") - if not isinstance(terminal, Mapping) or not isinstance(inventory, Mapping): - raise CheckpointReceiptError( - "wave receipt has no immutable checkpoint inventory" - ) - if set(inventory) != {"count", "entries", "logical_sha256"}: - raise CheckpointReceiptError("checkpoint inventory schema changed") - entries = inventory.get("entries") - count = inventory.get("count") - inventory_sha256 = str(inventory.get("logical_sha256", "")) - if ( - not isinstance(entries, list) - or not entries - or isinstance(count, bool) - or not isinstance(count, int) - or count != len(entries) - or inventory_sha256 != _canonical_sha256(entries) - or not _SHA256.fullmatch(inventory_sha256) - ): - raise CheckpointReceiptError("checkpoint inventory digest or count changed") - entry_fields = { - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - "role", - "training_progress_fraction", - } - normalized_entries: list[dict[str, Any]] = [] - names: set[str] = set() - previous_progress = -1.0 - terminal_name = ( - "final" - if stage.kind == "sft" - else f"{int(stage.wave(wave)['max_steps']):06d}" - ) - if stage.kind == "rl" and len(entries) != 1: - raise CheckpointReceiptError( - "an RL wave must expose only its immutable ceiling checkpoint" - ) - for index, value in enumerate(entries): - if not isinstance(value, Mapping) or set(value) != entry_fields: - raise CheckpointReceiptError("checkpoint inventory entry schema changed") - entry = dict(value) - name = str(entry["name"]) - role = str(entry["role"]) - progress = entry["training_progress_fraction"] - if ( - not _IDENTIFIER.fullmatch(name) - or name in names - or role not in {"periodic", "terminal"} - or isinstance(progress, bool) - or not isinstance(progress, (int, float)) - or not math.isfinite(float(progress)) - or not 0.0 < float(progress) <= 1.0 - or float(progress) <= previous_progress - or not _TINKER_PATH.fullmatch(str(entry["state_path"])) - or not _TINKER_PATH.fullmatch(str(entry["sampler_path"])) - ): - raise CheckpointReceiptError("checkpoint inventory entry is invalid") - if role == "periodic": - if ( - stage.kind != "sft" - or not re.fullmatch(r"[0-9]{6}", name) - or entry["final"] is not False - or float(progress) >= 1.0 - or index == len(entries) - 1 - ): - raise CheckpointReceiptError( - "periodic checkpoint inventory entry is invalid" - ) - elif ( - name != terminal_name - or entry["final"] is not True - or float(progress) != 1.0 - or index != len(entries) - 1 - or ( - stage.kind == "rl" - and entry["batch"] != int(stage.wave(wave)["max_steps"]) - ) - ): - raise CheckpointReceiptError( - "terminal checkpoint inventory entry is invalid" - ) - names.add(name) - previous_progress = float(progress) - normalized_entries.append(entry) - terminal_fields = { - key: normalized_entries[-1][key] - for key in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - } - if dict(terminal) != terminal_fields: - raise CheckpointReceiptError( - "terminal checkpoint alias differs from the inventory" - ) - selected = next( - (entry for entry in normalized_entries if entry["name"] == checkpoint_name), - None, - ) - if selected is None: - raise CheckpointReceiptError( - f"checkpoint {checkpoint_name!r} is not in the wave receipt" - ) - sampler_path = str(selected["sampler_path"]) - if ( - receipt.payload.get("stage_id") != stage.stage_id - or receipt.payload.get("wave") != wave - ): - raise CheckpointReceiptError("wave receipt identity differs from its path") - return CheckpointBinding( - key=key, - stage=stage, - wave=wave, - checkpoint_name=checkpoint_name, - checkpoint=selected, - checkpoint_inventory_sha256=inventory_sha256, - sampler_path=sampler_path, - run_manifest=manifest, - wave_receipt=receipt, - ) - - -def _base_model_binding( - *, - protocol: dict[str, Any], - stage_id: str, - replicate_id: str, - source_git_sha: str, -) -> BaseModelBinding: - stage = stage_spec(protocol, stage_id) - if ( - stage.stage_id != "qwen-base-rl-l0" - or stage.model_key != "qwen" - or stage.parent != "base:qwen" - ): - raise CheckpointReceiptError( - "base-model evaluation is restricted to qwen-base-rl-l0" - ) - key = StageKey(STUDY_ID, stage.stage_id, replicate_id) - model = dict(protocol["models"]["qwen"]) - checkpoint = { - "name": "base", - "model": model["model"], - "renderer": model["renderer"], - "thinking": model["thinking"], - } - inventory_sha256 = _canonical_sha256([checkpoint]) - identity = { - "schema_version": "pixcell-training-base-model-binding-v1", - "study_id": STUDY_ID, - "stage_id": stage.stage_id, - "replicate_id": replicate_id, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "model": checkpoint, - "checkpoint_inventory_sha256": inventory_sha256, - } - return BaseModelBinding( - key=key, - stage=stage, - wave="base", - checkpoint_name="base", - checkpoint=checkpoint, - checkpoint_inventory_sha256=inventory_sha256, - sampler_path=str(model["model"]), - binding_sha256=_canonical_sha256(identity), - ) - - -def _sample_binding_identity(binding: EvaluationBinding) -> dict[str, Any]: - common = { - "checkpoint_name": binding.checkpoint_name, - "checkpoint_inventory_sha256": binding.checkpoint_inventory_sha256, - "checkpoint": binding.checkpoint, - } - if isinstance(binding, CheckpointBinding): - return { - "training_wave_receipt_record_sha256": ( - binding.wave_receipt.record_sha256 - ), - **common, - } - return { - "base_model_binding_sha256": binding.binding_sha256, - **common, - } - - -def _sampler_provenance(binding: EvaluationBinding) -> dict[str, Any]: - common = { - "sampler_path": binding.sampler_path, - "checkpoint_name": binding.checkpoint_name, - "checkpoint": binding.checkpoint, - "checkpoint_inventory_sha256": binding.checkpoint_inventory_sha256, - } - if isinstance(binding, CheckpointBinding): - return { - **common, - "training_run_manifest_record_sha256": ( - binding.run_manifest.record_sha256 - ), - "training_wave_receipt_record_sha256": ( - binding.wave_receipt.record_sha256 - ), - "training_wave_receipt_relative_path": str( - binding.wave_receipt.relative_path - ), - } - return { - **common, - "binding_kind": "base-model", - "base_model_binding_sha256": binding.binding_sha256, - } - - -def _sampling_client_kwargs(binding: EvaluationBinding) -> dict[str, str]: - if isinstance(binding, BaseModelBinding): - return {"base_model": binding.sampler_path} - return {"model_path": binding.sampler_path} - - -def _model_binding( - protocol: Mapping[str, Any], - stage: StageSpec, -) -> dict[str, Any]: - model = dict(protocol["models"][stage.model_key]) - exact = { - "max_image_long_edge": MAX_IMAGE_LONG_EDGE, - "max_output_tokens": MAX_OUTPUT_TOKENS, - "context_tokens": 65_536, - } - for field, expected in exact.items(): - if model.get(field) != expected: - raise CheckpointEvaluationError( - f"{stage.model_key} {field} differs from the evaluation contract" - ) - if model.get("thinking") is not True: - raise CheckpointEvaluationError("checkpoint evaluation requires thinking on") - return model - - -def _direct_prompt_text(observation: ModelObservation) -> str: - text = build_prompt_text(observation) - leaked = [fragment for fragment in _FORBIDDEN_PROMPT_FRAGMENTS if fragment in text] - if leaked: - raise CheckpointEvaluationError( - f"direct Phase-A prompt leaked verifier fields: {leaked}" - ) - return text - - -def _preflight_tasks( - *, - tasks: Sequence[EvaluationTask], - renderer: Any, - evaluator: PixCellEvaluator, - context_tokens: int, -) -> tuple[list[PreparedTask], dict[str, Any]]: - """Check every prompt and reference before any sampling client is created.""" - - prepared: list[PreparedTask] = [] - prompt_digest = hashlib.sha256() - reference_digest = hashlib.sha256() - for task in tasks: - prompt_text = _direct_prompt_text(task.observation) - prompt_text_sha256 = hashlib.sha256( - prompt_text.encode("utf-8") - ).hexdigest() - try: - evidence = evaluator.validate_reference(task.reference) - except Exception as exc: - raise ReferencePanelFault( - f"{task.task_id} reference validation failed: {exc}" - ) from exc - if ( - evidence.get("target_image_sha256") - != task.reference.target_image_sha256 - ): - raise ReferencePanelFault( - f"{task.task_id} reference digest changed during validation" - ) - prompt = renderer.build_generation_prompt( - [_message(task, max_image=MAX_IMAGE_LONG_EDGE)] - ) - prompt_tokens = int(prompt.length) - if prompt_tokens + MAX_OUTPUT_TOKENS > context_tokens: - raise CheckpointEvaluationError( - f"{task.task_id} exceeds context: " - f"{prompt_tokens}+{MAX_OUTPUT_TOKENS}>{context_tokens}" - ) - prompt_digest.update(task.task_id.encode("utf-8")) - prompt_digest.update(str(prompt_tokens).encode("ascii")) - prompt_digest.update(bytes.fromhex(prompt_text_sha256)) - prompt_digest.update(bytes.fromhex(task.observation.image_sha256)) - reference_digest.update(task.task_id.encode("utf-8")) - reference_digest.update( - _canonical_bytes( - { - "target_image_sha256": task.reference.target_image_sha256, - "evidence": evidence, - } - ) - ) - prepared.append( - PreparedTask( - task=task, - prompt_tokens=prompt_tokens, - prompt_text_sha256=prompt_text_sha256, - reference_evidence=dict(evidence), - ) - ) - prompt_lengths = [item.prompt_tokens for item in prepared] - return prepared, { - "task_count": len(prepared), - "prompt_tokens": { - "min": min(prompt_lengths), - "max": max(prompt_lengths), - }, - "minimum_context_headroom": ( - context_tokens - MAX_OUTPUT_TOKENS - max(prompt_lengths) - ), - "prompt_set_sha256": prompt_digest.hexdigest(), - "reference_set_sha256": reference_digest.hexdigest(), - } - - -def _sample_seed( - *, - protocol_sha256: str, - binding: EvaluationBinding, - panel: str, - task_id: str, -) -> int: - digest = hashlib.sha256( - "\0".join( - ( - "pixcell-checkpoint-evaluation-seed-v1", - protocol_sha256, - binding.stage.stage_id, - binding.wave, - binding.checkpoint_name, - binding.checkpoint_inventory_sha256, - binding.key.replicate_id, - panel, - task_id, - ) - ).encode("utf-8") - ).digest() - return int.from_bytes(digest[:4], "big") & 0x7FFFFFFF - - -def _message_reasoning(message: Any) -> str: - content = message.get("content") - if not isinstance(content, list): - return "" - return "\n".join( - str(part.get("thinking", "")) - for part in content - if isinstance(part, Mapping) and part.get("type") == "thinking" - ) - - -def _decode_sample(renderer: Any, sequence: Any) -> dict[str, Any]: - from tinker_cookbook.renderers import get_text_content - - tokens = [int(token) for token in sequence.tokens] - raw_text = str(renderer.tokenizer.decode(tokens)) - try: - message, termination = renderer.parse_response(tokens) - except Exception as exc: - raise SamplingInfrastructureFault( - f"renderer could not parse a sampled response: {exc}" - ) from exc - answer_text = str(get_text_content(message)) - reasoning_text = _message_reasoning(message) - stop_reason = str(sequence.stop_reason) - cap_hit = ( - len(tokens) >= MAX_OUTPUT_TOKENS - or "length" in stop_reason.lower() - or "max_token" in stop_reason.lower() - ) - return { - "completion_tokens": len(tokens), - "stop_reason": stop_reason, - "cap_hit": cap_hit, - "raw_text": raw_text, - "raw_text_sha256": hashlib.sha256(raw_text.encode("utf-8")).hexdigest(), - "answer_text": answer_text, - "answer_text_sha256": hashlib.sha256( - answer_text.encode("utf-8") - ).hexdigest(), - "reasoning_text": reasoning_text, - "reasoning_text_sha256": hashlib.sha256( - reasoning_text.encode("utf-8") - ).hexdigest(), - "channel_parse_complete": bool(termination.is_clean), - } - - -def _sample_payload( - *, - prepared: PreparedTask, - response: dict[str, Any], - seed: int, - manifest_record_sha256: str, - binding: EvaluationBinding, -) -> dict[str, Any]: - return { - "evaluation_manifest_record_sha256": manifest_record_sha256, - **_sample_binding_identity(binding), - "task": { - "task_id": prepared.task.task_id, - "level": prepared.task.level, - "representation_id": prepared.task.representation_id, - "image_sha256": prepared.task.observation.image_sha256, - "target_image_sha256": prepared.task.reference.target_image_sha256, - }, - "request": { - "num_samples": 1, - "seed": seed, - "max_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "prompt_tokens": prepared.prompt_tokens, - "prompt_text_sha256": prepared.prompt_text_sha256, - }, - "response": response, - } - - -def _evaluation_payload( - *, - task: EvaluationTask, - result: EvaluationResult, - sample_record_sha256: str, - manifest_record_sha256: str, -) -> dict[str, Any]: - if result.attribution is Attribution.REFERENCE: - raise ReferencePanelFault( - f"{task.task_id}: {result.status.value}: {result.error}" - ) - if result.attribution is Attribution.EVALUATOR: - raise EvaluationInfrastructureFault( - f"{task.task_id}: {result.status.value}: {result.error}" - ) - if result.attribution is not Attribution.MODEL: - raise EvaluationInfrastructureFault( - f"{task.task_id}: evaluator returned an unknown attribution" - ) - iou = 0.0 if result.iou is None else float(result.iou) - if not math.isfinite(iou) or not 0.0 <= iou <= 1.0: - raise EvaluationInfrastructureFault( - f"{task.task_id}: evaluator returned invalid IoU {result.iou!r}" - ) - if result.status is EvaluationStatus.OK and result.iou is None: - raise EvaluationInfrastructureFault( - f"{task.task_id}: successful evaluation has no IoU" - ) - return { - "evaluation_manifest_record_sha256": manifest_record_sha256, - "sample_record_sha256": sample_record_sha256, - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "status": result.status.value, - "attribution": result.attribution.value, - "pure_executable": result.status is EvaluationStatus.OK, - "raw_absolute_scale_iou": iou, - "dice": float(result.dice) if result.dice is not None else None, - "render_sha256": result.metrics.get("render_sha256"), - "reference_sha256": result.metrics.get("reference_sha256"), - "violations": list(result.violations), - "error": result.error, - "latency_seconds": float(result.latency_seconds), - "diagnostics": _safe_json(result.metrics), - } - - -def _validate_sample_record( - *, - record: Mapping[str, Any], - prepared: PreparedTask, - seed: int, - manifest_record_sha256: str, - binding: EvaluationBinding, -) -> None: - payload = record["payload"] - expected = { - "evaluation_manifest_record_sha256": manifest_record_sha256, - **_sample_binding_identity(binding), - } - for field, value in expected.items(): - if payload.get(field) != value: - raise ImmutableEvaluationRecordError( - f"{prepared.task.task_id} sample belongs to another evaluation" - ) - task = payload.get("task") - request = payload.get("request") - if not isinstance(task, Mapping) or not isinstance(request, Mapping): - raise ImmutableEvaluationRecordError("sample record is incomplete") - expected_task = { - "task_id": prepared.task.task_id, - "level": prepared.task.level, - "representation_id": prepared.task.representation_id, - "image_sha256": prepared.task.observation.image_sha256, - "target_image_sha256": prepared.task.reference.target_image_sha256, - } - expected_request = { - "num_samples": 1, - "seed": seed, - "max_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "prompt_tokens": prepared.prompt_tokens, - "prompt_text_sha256": prepared.prompt_text_sha256, - } - if task != expected_task or request != expected_request: - raise ImmutableEvaluationRecordError( - f"{prepared.task.task_id} sample request changed" - ) - - -def _validate_evaluation_record( - *, - record: Mapping[str, Any], - task: EvaluationTask, - sample_record_sha256: str, - manifest_record_sha256: str, -) -> None: - payload = record["payload"] - expected = { - "evaluation_manifest_record_sha256": manifest_record_sha256, - "sample_record_sha256": sample_record_sha256, - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "attribution": Attribution.MODEL.value, - } - for field, value in expected.items(): - if payload.get(field) != value: - raise ImmutableEvaluationRecordError( - f"{task.task_id} evaluation record changed" - ) - status = str(payload.get("status", "")) - if status not in _MODEL_EVALUATION_STATUSES: - raise ImmutableEvaluationRecordError( - f"{task.task_id} has a non-model evaluation status" - ) - if payload.get("pure_executable") != (status == EvaluationStatus.OK.value): - raise ImmutableEvaluationRecordError( - f"{task.task_id} status and pure-executable flag differ" - ) - iou = payload.get("raw_absolute_scale_iou") - if ( - isinstance(iou, bool) - or not isinstance(iou, (int, float)) - or not math.isfinite(float(iou)) - or not 0.0 <= float(iou) <= 1.0 - ): - raise ImmutableEvaluationRecordError( - f"{task.task_id} has an invalid stored IoU" - ) - if status != EvaluationStatus.OK.value and float(iou) != 0.0: - raise ImmutableEvaluationRecordError( - f"{task.task_id} failed model output has nonzero IoU" - ) - - -async def _sample_missing( - *, - prepared: Sequence[PreparedTask], - renderer: Any, - sampling_client: Any, - store: CheckpointEvaluationStore, - manifest_record_sha256: str, - binding: EvaluationBinding, - protocol_sha256: str, - panel: str, -) -> None: - import tinker - - semaphore = asyncio.Semaphore(SAMPLE_CONCURRENCY) - - async def sample_one(item: PreparedTask) -> None: - seed = _sample_seed( - protocol_sha256=protocol_sha256, - binding=binding, - panel=panel, - task_id=item.task.task_id, - ) - existing = store.load_sample(item.task.task_id) - if existing is not None: - _validate_sample_record( - record=existing, - prepared=item, - seed=seed, - manifest_record_sha256=manifest_record_sha256, - binding=binding, - ) - return - messages = [_message(item.task, max_image=MAX_IMAGE_LONG_EDGE)] - prompt_text = str(messages[0]["content"][1]["text"]) - if ( - hashlib.sha256(prompt_text.encode("utf-8")).hexdigest() - != item.prompt_text_sha256 - ): - raise SamplingInfrastructureFault( - f"{item.task.task_id} prompt text changed after preflight" - ) - prompt = renderer.build_generation_prompt(messages) - if int(prompt.length) != item.prompt_tokens: - raise SamplingInfrastructureFault( - f"{item.task.task_id} prompt changed after preflight" - ) - async with semaphore: - try: - result = await sampling_client.sample_async( - prompt=prompt, - num_samples=1, - sampling_params=tinker.SamplingParams( - stop=renderer.get_stop_sequences(), - max_tokens=MAX_OUTPUT_TOKENS, - temperature=TEMPERATURE, - top_p=TOP_P, - seed=seed, - ), - ) - except Exception as exc: - raise SamplingInfrastructureFault( - f"{item.task.task_id} sampling failed: {exc}" - ) from exc - sequences = list(result.sequences) - if len(sequences) != 1: - raise SamplingInfrastructureFault( - f"{item.task.task_id} returned {len(sequences)} samples, expected 1" - ) - response = _decode_sample(renderer, sequences[0]) - store.create_or_verify_sample( - item.task.task_id, - _sample_payload( - prepared=item, - response=response, - seed=seed, - manifest_record_sha256=manifest_record_sha256, - binding=binding, - ), - ) - - await asyncio.gather(*(sample_one(item) for item in prepared)) - - -def _score_missing( - *, - prepared: Sequence[PreparedTask], - evaluator: PixCellEvaluator, - store: CheckpointEvaluationStore, - manifest_record_sha256: str, -) -> None: - pending: list[tuple[PreparedTask, dict[str, Any]]] = [] - for item in prepared: - sample = store.load_sample(item.task.task_id) - if sample is None: - raise CheckpointEvaluationError( - f"{item.task.task_id} has no resumable sample" - ) - existing = store.load_evaluation(item.task.task_id) - if existing is not None: - _validate_evaluation_record( - record=existing, - task=item.task, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - ) - continue - pending.append((item, sample)) - - for offset in range(0, len(pending), EVALUATION_BATCH_SIZE): - batch = pending[offset : offset + EVALUATION_BATCH_SIZE] - requests = [ - ( - item.task.reference, - str(sample["payload"]["response"]["answer_text"]), - ) - for item, sample in batch - ] - try: - results = evaluator.evaluate_batch(requests) - except Exception as exc: - raise EvaluationInfrastructureFault( - f"isolated evaluation batch failed: {exc}" - ) from exc - if len(results) != len(batch): - raise EvaluationInfrastructureFault( - "isolated evaluator returned a different result count" - ) - faults: list[CheckpointEvaluationError] = [] - for (item, sample), result in zip(batch, results, strict=True): - try: - payload = _evaluation_payload( - task=item.task, - result=result, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - ) - except (ReferencePanelFault, EvaluationInfrastructureFault) as exc: - faults.append(exc) - continue - store.create_or_verify_evaluation(item.task.task_id, payload) - if faults: - raise faults[0] - - -def _aggregate(values: Sequence[Mapping[str, Any]]) -> dict[str, Any]: - if not values: - raise CheckpointEvaluationError("cannot aggregate an empty panel") - ious = [float(value["raw_absolute_scale_iou"]) for value in values] - pure = [bool(value["pure_executable"]) for value in values] - return { - "tasks": len(values), - "mean_raw_absolute_scale_iou": sum(ious) / len(ious), - "pure_executable": sum(pure), - "pure_executable_rate": sum(pure) / len(pure), - "statuses": dict( - sorted(Counter(str(value["status"]) for value in values).items()) - ), - } - - -def validate_checkpoint_report( - record: Mapping[str, Any], - *, - manifest_record_sha256: str, - binding: EvaluationBinding, - panel: str, - expected_task_ids: Sequence[str], -) -> dict[str, Any]: - """Validate the immutable report identity used by promotion gates.""" - - validated = _validate_record( - record, - record_type="checkpoint_report", - key={}, - ) - payload = validated["payload"] - expected_identity = { - "schema_version": EVALUATION_SCHEMA_VERSION, - "study_id": STUDY_ID, - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "replicate_id": binding.key.replicate_id, - "panel": panel, - } - for field, expected in expected_identity.items(): - if payload.get(field) != expected: - raise CheckpointEvaluationError( - f"checkpoint report {field} differs from its request" - ) - provenance = payload.get("provenance") - summary = payload.get("summary") - records = payload.get("records") - if ( - not isinstance(provenance, Mapping) - or not isinstance(summary, Mapping) - or not isinstance(records, list) - ): - raise CheckpointEvaluationError("checkpoint report is incomplete") - sampler = provenance.get("sampler") - task_panel = provenance.get("task_panel") - if not isinstance(sampler, Mapping) or not isinstance(task_panel, Mapping): - raise CheckpointEvaluationError( - "checkpoint report has incomplete provenance" - ) - expected_provenance = { - "evaluation_manifest_record_sha256": manifest_record_sha256, - } - for field, expected in expected_provenance.items(): - if provenance.get(field) != expected: - raise CheckpointEvaluationError( - f"checkpoint report {field} differs from its manifest" - ) - expected_sampler = _sampler_provenance(binding) - if any( - sampler.get(field) != expected - for field, expected in expected_sampler.items() - ): - raise CheckpointEvaluationError( - "checkpoint report sampler provenance differs from training" - ) - if isinstance(binding, BaseModelBinding) and any( - field in sampler - for field in ( - "training_run_manifest_record_sha256", - "training_wave_receipt_record_sha256", - "training_wave_receipt_relative_path", - ) - ): - raise CheckpointEvaluationError( - "base-model report fabricates training provenance" - ) - task_ids = [str(value) for value in expected_task_ids] - if ( - task_panel.get("task_ids") != task_ids - or task_panel.get("task_count") != len(task_ids) - or summary.get("tasks") != len(task_ids) - or [item.get("task_id") for item in records] != task_ids - ): - raise CheckpointEvaluationError( - "checkpoint report task inventory differs from the sealed panel" - ) - iou = summary.get("mean_raw_absolute_scale_iou") - pure_rate = summary.get("pure_executable_rate") - representation_macro = payload.get("representation_macro_mean_iou") - if any( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not math.isfinite(float(value)) - or not 0.0 <= float(value) <= 1.0 - for value in (iou, pure_rate, representation_macro) - ): - raise CheckpointEvaluationError( - "checkpoint report summary contains invalid metrics" - ) - return validated - - -def _report_payload( - *, - repo_root: Path, - prepared: Sequence[PreparedTask], - store: CheckpointEvaluationStore, - manifest_record: Mapping[str, Any], - binding: EvaluationBinding, - protocol: Mapping[str, Any], - source_git_sha: str, - panel: str, - dataset: Mapping[str, Any], - sandbox: Mapping[str, Any], -) -> dict[str, Any]: - records: list[dict[str, Any]] = [] - by_level: dict[str, list[dict[str, Any]]] = defaultdict(list) - by_representation: dict[str, list[dict[str, Any]]] = defaultdict(list) - for item in prepared: - sample = store.load_sample(item.task.task_id) - evaluation = store.load_evaluation(item.task.task_id) - if sample is None or evaluation is None: - raise CheckpointEvaluationError( - f"{item.task.task_id} is incomplete" - ) - _validate_evaluation_record( - record=evaluation, - task=item.task, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=str(manifest_record["record_sha256"]), - ) - payload = evaluation["payload"] - response = sample["payload"]["response"] - record = { - "task_id": item.task.task_id, - "level": item.task.level, - "representation_id": item.task.representation_id, - "status": payload["status"], - "pure_executable": payload["pure_executable"], - "raw_absolute_scale_iou": payload["raw_absolute_scale_iou"], - "completion_tokens": response["completion_tokens"], - "stop_reason": response["stop_reason"], - "cap_hit": response["cap_hit"], - "channel_parse_complete": response["channel_parse_complete"], - "answer_text_sha256": response["answer_text_sha256"], - "render_sha256": payload["render_sha256"], - "sample_record_sha256": sample["record_sha256"], - "evaluation_record_sha256": evaluation["record_sha256"], - } - records.append(record) - by_level[item.task.level].append(record) - by_representation[item.task.representation_id].append(record) - representation_summaries = { - key: _aggregate(values) - for key, values in sorted(by_representation.items()) - } - return { - "schema_version": EVALUATION_SCHEMA_VERSION, - "study_id": STUDY_ID, - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "replicate_id": binding.key.replicate_id, - "panel": panel, - "summary": _aggregate(records), - "representation_macro_mean_iou": ( - sum( - value["mean_raw_absolute_scale_iou"] - for value in representation_summaries.values() - ) - / len(representation_summaries) - ), - "levels": { - key: _aggregate(values) for key, values in sorted(by_level.items()) - }, - "representations": representation_summaries, - "records": records, - "provenance": { - "source_git_sha": source_git_sha, - "protocol": { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - }, - "dataset": { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": ( - "fixed-f1-f8" - if panel == PANEL_PROGRESS - else ( - "depth/validation+fixed-f1-f8" - if panel == PANEL_INKLING_PROMOTION - else ( - "depth/validation/slot-7" - if panel == PANEL_DEPTH_FINAL_SELECTION - else ( - f"depth/validation/{_level_for_progress_panel(panel)}/slot-6" - if _level_for_progress_panel(panel) is not None - else "depth/validation" - ) - ) - ) - ), - "logical_release_sha256": dataset["logical_release_sha256"], - "freeze_file_sha256": dataset["freeze_file_sha256"], - "parquet_shards": dataset["parquet_shards"], - }, - "task_panel": _task_manifest( - [item.task for item in prepared], - panel=panel, - ), - "sampler": { - "model": protocol["models"][binding.stage.model_key]["model"], - "renderer": protocol["models"][binding.stage.model_key]["renderer"], - "effort": protocol["models"][binding.stage.model_key].get("effort"), - **_sampler_provenance(binding), - }, - "sampling": { - "attempts_per_task": 1, - "max_output_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "max_image_long_edge": MAX_IMAGE_LONG_EDGE, - "seed_algorithm": "sha256-bound deterministic 31-bit seed", - }, - "prompt_assets": prompt_asset_hashes(), - "sandbox": dict(sandbox), - "evaluation_manifest_record_sha256": manifest_record["record_sha256"], - }, - } - - -def _sandbox_provenance(evaluator: PixCellEvaluator) -> dict[str, Any]: - boundary = evaluator.execution_boundary - if boundary is None: - raise EvaluationInfrastructureFault( - "checkpoint evaluation has no execution isolation boundary" - ) - return { - "runtime_path": str(boundary.runtime_path), - "daemon_endpoint": boundary.daemon_endpoint, - "image_ref": boundary.image_ref, - "image_id": boundary.image_id, - "workspace_root": str(boundary.workspace_root), - } - - -def _default_evaluator_factory() -> PixCellEvaluator: - return PixCellEvaluator( - max_workers=EVALUATOR_WORKERS, - evaluator_retries=1, - require_isolation=True, - ) - - -def _default_service_client_factory() -> Any: - import tinker - - return tinker.ServiceClient() - - -async def run_checkpoint_evaluation( - *, - repo_root: Path, - stage_id: str, - wave: str, - checkpoint_name: str, - replicate_id: str, - panel: str, - expected_source_sha: str, - expected_wave_receipt_sha256: str, - external_root: Path, - confirmation: str, - service_client_factory: Callable[[], Any] = _default_service_client_factory, - evaluator_factory: Callable[[], PixCellEvaluator] = _default_evaluator_factory, - base_model: bool = False, -) -> dict[str, Any]: - """Run or resume one exact checkpoint panel. - - ``service_client_factory`` is intentionally injectable so tests can prove - the entire zero-spend preflight precedes the first paid client. - """ - - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol(root, require_committed=True) - source_git_sha = validate_source_sha(root, expected_source_sha) - if confirmation != protocol["launch"]["confirmation_token"]: - raise CheckpointEvaluationError( - "explicit paid-evaluation confirmation is missing" - ) - try: - validate_stage_replicate( - protocol, - stage_id=stage_id, - replicate_id=replicate_id, - ) - except ValueError as exc: - raise CheckpointEvaluationError(str(exc)) from exc - training_store = TrainingStore( - repo_root=root, - external_root=external_root, - ) - if base_model: - if ( - wave != "base" - or checkpoint_name != "base" - or expected_wave_receipt_sha256 - ): - raise CheckpointReceiptError( - "base-model evaluation must use wave/checkpoint 'base' and no receipt" - ) - binding: EvaluationBinding = _base_model_binding( - protocol=protocol, - stage_id=stage_id, - replicate_id=replicate_id, - source_git_sha=source_git_sha, - ) - if panel != LEVEL_PROGRESS_PANELS["L0"]: - raise CheckpointEvaluationError( - "base Qwen evaluation is restricted to level-progress-l0" - ) - else: - binding = _load_checkpoint_binding( - store=training_store, - protocol=protocol, - stage_id=stage_id, - wave=wave, - checkpoint_name=checkpoint_name, - replicate_id=replicate_id, - expected_receipt_sha256=expected_wave_receipt_sha256, - source_git_sha=source_git_sha, - ) - if panel == PANEL_INKLING_PROMOTION and binding.stage.model_key != "inkling": - raise CheckpointEvaluationError( - "inkling-promotion is restricted to an Inkling checkpoint" - ) - model = _model_binding(protocol, binding.stage) - dataset = _dataset_binding(root / "dataset", protocol) - tasks = build_panel_tasks(repo_root=root, protocol=protocol, panel=panel) - renderer = _renderer( - model["model"], - model["renderer"], - effort=model.get("effort"), - ) - base_stage_lock = ( - training_store.acquire_stage_lock(binding.key) - if isinstance(binding, BaseModelBinding) - else None - ) - if base_stage_lock is not None: - base_stage_lock.__enter__() - try: - evaluator = evaluator_factory() - except Exception: - if base_stage_lock is not None: - base_stage_lock.__exit__(None, None, None) - raise - try: - prepared, preflight = _preflight_tasks( - tasks=tasks, - renderer=renderer, - evaluator=evaluator, - context_tokens=int(model["context_tokens"]), - ) - sandbox = _sandbox_provenance(evaluator) - panel_manifest = _task_manifest(tasks, panel=panel) - evaluation_stage_path = training_store.stage_path(binding.key) - if isinstance(binding, BaseModelBinding): - evaluation_stage_path.mkdir(parents=True, exist_ok=True, mode=0o700) - evaluation_store = CheckpointEvaluationStore( - stage_path=evaluation_stage_path, - wave=wave, - checkpoint_name=binding.checkpoint_name, - panel=panel, - ) - with evaluation_store.acquire_lock(): - manifest_payload = { - "study_id": STUDY_ID, - "stage_id": binding.stage.stage_id, - "wave": wave, - "checkpoint_name": binding.checkpoint_name, - "replicate_id": replicate_id, - "panel": panel, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": file_sha256(protocol_path(root)), - "contract_version": protocol["contract_version"], - "dataset": dataset, - "task_panel": panel_manifest, - "model": model, - "sampling": { - "attempts_per_task": 1, - "max_output_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "max_image_long_edge": MAX_IMAGE_LONG_EDGE, - "sample_concurrency": SAMPLE_CONCURRENCY, - "evaluation_batch_size": EVALUATION_BATCH_SIZE, - "evaluator_workers": EVALUATOR_WORKERS, - }, - "prompt_assets": prompt_asset_hashes(), - "preflight": preflight, - "sandbox": sandbox, - "sampler": _sampler_provenance(binding), - } - manifest = evaluation_store.create_or_verify_manifest( - manifest_payload - ) - existing_report = evaluation_store.load_report() - if existing_report is not None: - validate_checkpoint_report( - existing_report, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - panel=panel, - expected_task_ids=[item.task.task_id for item in prepared], - ) - return { - "status": "already_complete", - "stage_id": binding.stage.stage_id, - "wave": wave, - "checkpoint_name": binding.checkpoint_name, - "panel": panel, - "report": str( - evaluation_store.root / "report.json" - ), - "report_record_sha256": existing_report["record_sha256"], - "summary": existing_report["payload"]["summary"], - } - - missing_samples = [ - item - for item in prepared - if evaluation_store.load_sample(item.task.task_id) is None - ] - if missing_samples: - if not os.environ.get("TINKER_API_KEY"): - raise CheckpointEvaluationError("TINKER_API_KEY is not present") - try: - validate_source_sha(root, source_git_sha) - except (OSError, ValueError, subprocess.SubprocessError) as exc: - raise CheckpointEvaluationError( - "source changed after evaluation preflight; refusing " - "paid sampling access" - ) from exc - # The first paid-capable object is created only after all - # prompts, references, records, and sandbox provenance pass. - service = service_client_factory() - sampling_client = service.create_sampling_client( - **_sampling_client_kwargs(binding) - ) - await _sample_missing( - prepared=missing_samples, - renderer=renderer, - sampling_client=sampling_client, - store=evaluation_store, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - protocol_sha256=str(protocol["logical_sha256"]), - panel=panel, - ) - - # Validate every existing sample against this exact manifest before - # execution. A resumed run therefore cannot silently mix requests. - for item in prepared: - seed = _sample_seed( - protocol_sha256=str(protocol["logical_sha256"]), - binding=binding, - panel=panel, - task_id=item.task.task_id, - ) - sample = evaluation_store.load_sample(item.task.task_id) - if sample is None: - raise CheckpointEvaluationError( - f"{item.task.task_id} sampling did not persist a record" - ) - _validate_sample_record( - record=sample, - prepared=item, - seed=seed, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - ) - _score_missing( - prepared=prepared, - evaluator=evaluator, - store=evaluation_store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - report_payload = _report_payload( - repo_root=root, - prepared=prepared, - store=evaluation_store, - manifest_record=manifest, - binding=binding, - protocol=protocol, - source_git_sha=source_git_sha, - panel=panel, - dataset=dataset, - sandbox=sandbox, - ) - report = evaluation_store.create_or_verify_report(report_payload) - validate_checkpoint_report( - report, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - panel=panel, - expected_task_ids=[item.task.task_id for item in prepared], - ) - return { - "status": "complete", - "stage_id": binding.stage.stage_id, - "wave": wave, - "checkpoint_name": binding.checkpoint_name, - "panel": panel, - "report": str(evaluation_store.root / "report.json"), - "report_record_sha256": report["record_sha256"], - "summary": report_payload["summary"], - } - finally: - try: - evaluator.close() - finally: - if base_stage_lock is not None: - base_stage_lock.__exit__(None, None, None) - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, required=True) - result.add_argument("--stage", required=True) - result.add_argument("--wave", required=True) - result.add_argument("--checkpoint-name", required=True) - result.add_argument("--replicate", default="r0") - result.add_argument("--panel", choices=SUPPORTED_PANELS, required=True) - result.add_argument("--source-git-sha", required=True) - result.add_argument("--wave-receipt-record-sha256", default="") - result.add_argument("--base-model", action="store_true") - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--confirm-spend", default="") - return result - - -def main() -> None: - args = parser().parse_args() - report = asyncio.run( - run_checkpoint_evaluation( - repo_root=args.repo_root, - stage_id=args.stage, - wave=args.wave, - checkpoint_name=args.checkpoint_name, - replicate_id=args.replicate, - panel=args.panel, - expected_source_sha=args.source_git_sha, - expected_wave_receipt_sha256=args.wave_receipt_record_sha256, - external_root=args.external_root, - confirmation=args.confirm_spend, - base_model=args.base_model, - ) - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/final_benchmark.py b/rl/studies/representation_training_v1/final_benchmark.py deleted file mode 100644 index 0b98149a..00000000 --- a/rl/studies/representation_training_v1/final_benchmark.py +++ /dev/null @@ -1,1724 +0,0 @@ -"""Receipt-bound final-checkpoint F1--F8 best@4 evaluation. - -This is deliberately a new, versioned path rather than an extension of the -v1 checkpoint panels. The progress, depth, and promotion records therefore -keep their original one-attempt semantics and byte contracts. -""" - -from __future__ import annotations - -import argparse -import asyncio -import hashlib -import json -import math -import os -import uuid -from collections import Counter -from collections.abc import Callable, Mapping, Sequence -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any - -from rl.common.evaluator import ( - Attribution, - EvaluationResult, - EvaluationStatus, - PixCellEvaluator, -) -from rl.common.prompt import prompt_asset_hashes -from rl.evaluation.tasks import EvaluationTask, canonical_json_sha256 -from rl.track_a.tinker_data import _message, _renderer - -from . import evaluation as checkpoint_v1 -from .dataset_binding import _dataset_binding -from .protocol import ( - STUDY_ID, - file_sha256, - load_protocol, - protocol_path, - validate_stage_replicate, - validate_source_sha, -) -from .store import TrainingStore - - -PANEL_FINAL_BENCHMARK_V2 = "final-benchmark-best4-v2" -FINAL_BENCHMARK_SCHEMA_VERSION = "pixcell-training-final-benchmark-v2" -FINAL_BENCHMARK_RECORD_SCHEMA_VERSION = "pixcell-training-final-benchmark-record-v2" -FINAL_BENCHMARK_TASK_PANEL_SCHEMA_VERSION = "pixcell-training-final-benchmark-task-panel-v2" -ATTEMPTS_PER_TASK = 4 -EXPECTED_TASK_IDS = tuple(f"F{index}" for index in range(1, 9)) -EXPECTED_ATTEMPTS = len(EXPECTED_TASK_IDS) * ATTEMPTS_PER_TASK -SAMPLE_CONCURRENCY = 8 -EVALUATION_BATCH_SIZE = 32 -EVALUATOR_WORKERS = 8 -MAX_IMAGE_LONG_EDGE = checkpoint_v1.MAX_IMAGE_LONG_EDGE -MAX_OUTPUT_TOKENS = checkpoint_v1.MAX_OUTPUT_TOKENS -TEMPERATURE = checkpoint_v1.TEMPERATURE -TOP_P = checkpoint_v1.TOP_P -_SEED_DOMAIN = "pixcell-representation-training-final-benchmark-v2" -_RESPONSE_FIELDS = { - "token_ids", - "completion_tokens", - "stop_reason", - "cap_hit", - "raw_text", - "raw_text_sha256", - "answer_text", - "answer_text_sha256", - "reasoning_text", - "reasoning_text_sha256", - "token_decode_complete", - "token_decode_error", - "channel_parse_complete", - "channel_parse_error", -} -_SAMPLE_PAYLOAD_FIELDS = { - "final_benchmark_manifest_record_sha256", - "training_wave_receipt_record_sha256", - "panel", - "checkpoint_name", - "checkpoint_inventory_sha256", - "checkpoint", - "task", - "attempt_index", - "request", - "response", -} -_EVALUATION_PAYLOAD_FIELDS = { - "final_benchmark_manifest_record_sha256", - "sample_record_sha256", - "panel", - "task_id", - "level", - "representation_id", - "attempt_index", - "seed", - "status", - "attribution", - "pure_executable", - "raw_absolute_scale_iou", - "dice", - "render_sha256", - "reference_sha256", - "violations", - "error", - "latency_seconds", - "diagnostics", -} -_REPORT_RECORD_FIELDS = { - "task_id", - "attempt_index", - "seed", - "status", - "attribution", - "pure_executable", - "raw_absolute_scale_iou", - "completion_tokens", - "stop_reason", - "cap_hit", - "channel_parse_complete", - "answer_text_sha256", - "render_sha256", - "reference_sha256", - "violations", - "error", - "sample_record_sha256", - "evaluation_record_sha256", -} -_REPORT_PAYLOAD_FIELDS = { - "schema_version", - "study_id", - "stage_id", - "wave", - "checkpoint_name", - "replicate_id", - "panel", - "summary", - "figures", - "records", - "attempt_records_sha256", - "provenance", -} - - -class FinalBenchmarkError(checkpoint_v1.CheckpointEvaluationError): - """The requested final benchmark is unsafe, incomplete, or inconsistent.""" - - -class ImmutableFinalBenchmarkRecordError(checkpoint_v1.ImmutableEvaluationRecordError): - """A v2 final-benchmark record cannot be replaced or mixed.""" - - -@dataclass(frozen=True) -class PreparedAttempt: - prepared: checkpoint_v1.PreparedTask - attempt_index: int - seed: int - - @property - def task(self) -> EvaluationTask: - return self.prepared.task - - @property - def key(self) -> tuple[str, int]: - return (self.task.task_id, self.attempt_index) - - -def _canonical_bytes(value: Any) -> bytes: - try: - return json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise FinalBenchmarkError("final-benchmark record is not finite JSON") from exc - - -def _canonical_sha256(value: Any) -> str: - return hashlib.sha256(_canonical_bytes(value)).hexdigest() - - -def _record_document( - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], -) -> dict[str, Any]: - normalized = json.loads(_canonical_bytes(payload)) - document = { - "schema_version": FINAL_BENCHMARK_RECORD_SCHEMA_VERSION, - "record_type": record_type, - "key": dict(key), - "payload": normalized, - "payload_sha256": _canonical_sha256(normalized), - } - document["record_sha256"] = _canonical_sha256(document) - return document - - -def _validate_record( - value: Mapping[str, Any], - *, - record_type: str, - key: Mapping[str, Any], -) -> dict[str, Any]: - required = { - "schema_version", - "record_type", - "key", - "payload", - "payload_sha256", - "record_sha256", - } - if set(value) != required: - raise FinalBenchmarkError("final-benchmark record schema changed") - if value["schema_version"] != FINAL_BENCHMARK_RECORD_SCHEMA_VERSION: - raise FinalBenchmarkError("final-benchmark record has a foreign schema") - if value["record_type"] != record_type or value["key"] != dict(key): - raise FinalBenchmarkError("final-benchmark record key differs from its canonical path") - payload = value["payload"] - if not isinstance(payload, dict): - raise FinalBenchmarkError("final-benchmark payload is not an object") - if value["payload_sha256"] != _canonical_sha256(payload): - raise FinalBenchmarkError("final-benchmark payload digest mismatch") - unsigned = dict(value) - observed = unsigned.pop("record_sha256") - if ( - not isinstance(observed, str) - or not checkpoint_v1._SHA256.fullmatch(observed) - or observed != _canonical_sha256(unsigned) - ): - raise FinalBenchmarkError("final-benchmark envelope digest mismatch") - return dict(value) - - -class FinalBenchmarkStore(checkpoint_v1.CheckpointEvaluationStore): - """Create-only v2 attempt records in a panel-isolated namespace.""" - - def __init__( - self, - *, - stage_path: Path, - wave: str, - checkpoint_name: str, - ) -> None: - if not checkpoint_v1._IDENTIFIER.fullmatch(wave) or not checkpoint_v1._IDENTIFIER.fullmatch( - checkpoint_name - ): - raise ValueError("invalid final-benchmark identity") - self.stage_path = stage_path.expanduser().resolve(strict=True) - requested = ( - self.stage_path / "evaluations" / wave / checkpoint_name / PANEL_FINAL_BENCHMARK_V2 - ) - requested.mkdir(parents=True, exist_ok=True, mode=0o700) - resolved = requested.resolve(strict=True) - if resolved != requested or not resolved.is_relative_to(self.stage_path): - raise FinalBenchmarkError( - "final-benchmark root must be a real directory inside its stage" - ) - self.root = resolved - - @staticmethod - def _attempt_component(attempt_index: int) -> str: - if ( - isinstance(attempt_index, bool) - or not isinstance(attempt_index, int) - or not 1 <= attempt_index <= ATTEMPTS_PER_TASK - ): - raise ValueError(f"attempt index must be in [1, {ATTEMPTS_PER_TASK}]") - return f"{attempt_index:06d}" - - @staticmethod - def _attempt_key(task_id: str, attempt_index: int) -> dict[str, Any]: - return { - "task_id": task_id, - "attempt_index": attempt_index, - } - - def _attempt_path( - self, - task_id: str, - attempt_index: int, - filename: str, - ) -> PurePosixPath: - task = self._task_component(task_id) - attempt = self._attempt_component(attempt_index) - return PurePosixPath("attempts", task, attempt, filename) - - def load_manifest(self) -> dict[str, Any] | None: - return self._load_v2( - PurePosixPath("manifest.json"), - record_type="final_benchmark_manifest", - key={}, - ) - - def create_or_verify_manifest( - self, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_v2( - PurePosixPath("manifest.json"), - record_type="final_benchmark_manifest", - key={}, - payload=payload, - ) - - def load_sample( - self, - task_id: str, - attempt_index: int, - ) -> dict[str, Any] | None: - return self._load_v2( - self._attempt_path(task_id, attempt_index, "sample.json"), - record_type="final_benchmark_sample", - key=self._attempt_key(task_id, attempt_index), - ) - - def create_or_verify_sample( - self, - task_id: str, - attempt_index: int, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_v2( - self._attempt_path(task_id, attempt_index, "sample.json"), - record_type="final_benchmark_sample", - key=self._attempt_key(task_id, attempt_index), - payload=payload, - ) - - def load_evaluation( - self, - task_id: str, - attempt_index: int, - ) -> dict[str, Any] | None: - return self._load_v2( - self._attempt_path(task_id, attempt_index, "evaluation.json"), - record_type="final_benchmark_evaluation", - key=self._attempt_key(task_id, attempt_index), - ) - - def create_or_verify_evaluation( - self, - task_id: str, - attempt_index: int, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_v2( - self._attempt_path(task_id, attempt_index, "evaluation.json"), - record_type="final_benchmark_evaluation", - key=self._attempt_key(task_id, attempt_index), - payload=payload, - ) - - def load_report(self) -> dict[str, Any] | None: - return self._load_v2( - PurePosixPath("report.json"), - record_type="final_benchmark_report", - key={}, - ) - - def create_or_verify_report( - self, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_v2( - PurePosixPath("report.json"), - record_type="final_benchmark_report", - key={}, - payload=payload, - ) - - def assert_attempt_inventory( - self, - attempts: Sequence[PreparedAttempt], - ) -> None: - """Reject foreign rows, alternate indices, and symlinks before resume.""" - - attempts_root = self.root / "attempts" - if not attempts_root.exists(): - return - expected_files = { - self._path( - self._attempt_path( - attempt.task.task_id, - attempt.attempt_index, - filename, - ) - ) - for attempt in attempts - for filename in ("sample.json", "evaluation.json") - } - expected_directories = {attempts_root} - for path in expected_files: - expected_directories.update(path.parents) - for path in attempts_root.rglob("*"): - if path.is_symlink(): - raise FinalBenchmarkError( - f"final-benchmark attempt inventory contains a symlink: {path}" - ) - if path.is_file() and path not in expected_files: - raise FinalBenchmarkError( - f"final-benchmark attempt inventory contains a foreign row: {path}" - ) - if path.is_dir() and path not in expected_directories: - raise FinalBenchmarkError( - "final-benchmark attempt inventory contains an unexpected " - f"task or attempt: {path}" - ) - - def _create_or_verify_v2( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], - ) -> dict[str, Any]: - expected = _record_document( - record_type=record_type, - key=key, - payload=payload, - ) - path = self._path(relative) - path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - raw = _canonical_bytes(expected) + b"\n" - temporary = path.parent / f".{path.name}.{os.getpid()}.{uuid.uuid4().hex}" - flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(temporary, flags, 0o600) - try: - with os.fdopen(fd, "wb", closefd=False) as stream: - stream.write(raw) - stream.flush() - os.fsync(stream.fileno()) - finally: - os.close(fd) - try: - os.link(temporary, path) - directory_fd = os.open(path.parent, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) - except FileExistsError: - observed = self._load_v2( - relative, - record_type=record_type, - key=key, - ) - if observed is None or _canonical_bytes(observed) != _canonical_bytes(expected): - raise ImmutableFinalBenchmarkRecordError( - f"{relative} already contains different data" - ) - return observed - finally: - try: - temporary.unlink() - except FileNotFoundError: - pass - return expected - - def _load_v2( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - ) -> dict[str, Any] | None: - path = self._path(relative) - if not path.exists(): - return None - return _validate_record( - self._read(path), - record_type=record_type, - key=key, - ) - - -def _final_checkpoint( - binding: checkpoint_v1.CheckpointBinding, -) -> None: - checkpoint = binding.checkpoint - if ( - checkpoint.get("role") != "terminal" - or checkpoint.get("final") is not True - or checkpoint.get("training_progress_fraction") != 1.0 - ): - raise checkpoint_v1.CheckpointReceiptError( - "final benchmark requires the terminal checkpoint in its wave receipt" - ) - - -def _task_panel(tasks: Sequence[EvaluationTask]) -> dict[str, Any]: - entries = [ - { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "image_sha256": task.observation.image_sha256, - "target_image_sha256": task.reference.target_image_sha256, - "footprint_um": list(task.observation.footprint_um), - } - for task in tasks - ] - if tuple(item["task_id"] for item in entries) != EXPECTED_TASK_IDS: - raise checkpoint_v1.ReferencePanelFault("final benchmark must be exact ordered F1-F8") - return { - "schema_version": FINAL_BENCHMARK_TASK_PANEL_SCHEMA_VERSION, - "panel": PANEL_FINAL_BENCHMARK_V2, - "task_count": len(entries), - "task_ids": list(EXPECTED_TASK_IDS), - "logical_sha256": canonical_json_sha256(entries), - } - - -def _sample_seed( - *, - source_git_sha: str, - protocol_sha256: str, - dataset_sha256: str, - task_panel_sha256: str, - binding: checkpoint_v1.CheckpointBinding, - task_id: str, - attempt_index: int, -) -> int: - digest = hashlib.sha256( - "\0".join( - ( - _SEED_DOMAIN, - source_git_sha, - protocol_sha256, - dataset_sha256, - task_panel_sha256, - binding.stage.stage_id, - binding.wave, - binding.checkpoint_name, - binding.checkpoint_inventory_sha256, - binding.wave_receipt.record_sha256, - binding.key.replicate_id, - PANEL_FINAL_BENCHMARK_V2, - task_id, - str(attempt_index), - ) - ).encode("utf-8") - ).digest() - return int.from_bytes(digest[:4], "big") & 0x7FFFFFFF - - -def _attempts( - *, - prepared: Sequence[checkpoint_v1.PreparedTask], - source_git_sha: str, - protocol_sha256: str, - dataset_sha256: str, - task_panel_sha256: str, - binding: checkpoint_v1.CheckpointBinding, -) -> list[PreparedAttempt]: - attempts = [ - PreparedAttempt( - prepared=item, - attempt_index=attempt_index, - seed=_sample_seed( - source_git_sha=source_git_sha, - protocol_sha256=protocol_sha256, - dataset_sha256=dataset_sha256, - task_panel_sha256=task_panel_sha256, - binding=binding, - task_id=item.task.task_id, - attempt_index=attempt_index, - ), - ) - for item in prepared - for attempt_index in range(1, ATTEMPTS_PER_TASK + 1) - ] - if len(attempts) != EXPECTED_ATTEMPTS: - raise FinalBenchmarkError( - f"final benchmark resolved {len(attempts)} attempts, expected {EXPECTED_ATTEMPTS}" - ) - keys = [attempt.key for attempt in attempts] - seeds = [attempt.seed for attempt in attempts] - if len(set(keys)) != EXPECTED_ATTEMPTS: - raise FinalBenchmarkError("final benchmark resolved duplicate attempt keys") - if len(set(seeds)) != EXPECTED_ATTEMPTS: - raise FinalBenchmarkError("final benchmark deterministic seeds collided") - return attempts - - -def _seed_inventory(attempts: Sequence[PreparedAttempt]) -> list[dict[str, Any]]: - return [ - { - "task_id": attempt.task.task_id, - "attempt_index": attempt.attempt_index, - "seed": attempt.seed, - } - for attempt in attempts - ] - - -def _decode_response(renderer: Any, sequence: Any) -> dict[str, Any]: - from tinker_cookbook.renderers import get_text_content - - tokens = [int(token) for token in sequence.tokens] - token_decode_error: str | None = None - try: - raw_text = str(renderer.tokenizer.decode(tokens)) - except Exception as exc: - raw_text = "" - token_decode_error = str(exc).replace("\x00", "")[-1200:] - channel_parse_error: str | None = None - if token_decode_error is not None: - answer_text = "" - reasoning_text = "" - channel_parse_complete = False - else: - try: - message, termination = renderer.parse_response(tokens) - answer_text = str(get_text_content(message)) - reasoning_text = checkpoint_v1._message_reasoning(message) - channel_parse_complete = bool(termination.is_clean) - except Exception as exc: - # Malformed channel structure is model output. Keep the exact raw - # response and let the deterministic evaluator score extracted code. - answer_text = raw_text - reasoning_text = "" - channel_parse_complete = False - channel_parse_error = str(exc).replace("\x00", "")[-1200:] - stop_reason = str(sequence.stop_reason) - cap_hit = ( - len(tokens) >= MAX_OUTPUT_TOKENS - or "length" in stop_reason.lower() - or "max_token" in stop_reason.lower() - ) - return { - "token_ids": tokens, - "completion_tokens": len(tokens), - "stop_reason": stop_reason, - "cap_hit": cap_hit, - "raw_text": raw_text, - "raw_text_sha256": hashlib.sha256(raw_text.encode("utf-8")).hexdigest(), - "answer_text": answer_text, - "answer_text_sha256": hashlib.sha256(answer_text.encode("utf-8")).hexdigest(), - "reasoning_text": reasoning_text, - "reasoning_text_sha256": hashlib.sha256(reasoning_text.encode("utf-8")).hexdigest(), - "token_decode_complete": token_decode_error is None, - "token_decode_error": token_decode_error, - "channel_parse_complete": channel_parse_complete, - "channel_parse_error": channel_parse_error, - } - - -def _sample_payload( - *, - attempt: PreparedAttempt, - response: Mapping[str, Any], - manifest_record_sha256: str, - binding: checkpoint_v1.CheckpointBinding, -) -> dict[str, Any]: - prepared = attempt.prepared - return { - "final_benchmark_manifest_record_sha256": manifest_record_sha256, - "training_wave_receipt_record_sha256": (binding.wave_receipt.record_sha256), - "panel": PANEL_FINAL_BENCHMARK_V2, - "checkpoint_name": binding.checkpoint_name, - "checkpoint_inventory_sha256": binding.checkpoint_inventory_sha256, - "checkpoint": binding.checkpoint, - "task": { - "task_id": prepared.task.task_id, - "level": prepared.task.level, - "representation_id": prepared.task.representation_id, - "image_sha256": prepared.task.observation.image_sha256, - "target_image_sha256": prepared.task.reference.target_image_sha256, - }, - "attempt_index": attempt.attempt_index, - "request": { - "num_samples": 1, - "independent_request": True, - "seed": attempt.seed, - "max_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "prompt_tokens": prepared.prompt_tokens, - "prompt_text_sha256": prepared.prompt_text_sha256, - }, - "response": dict(response), - } - - -def _validate_response(response: Any, *, attempt: PreparedAttempt) -> None: - if not isinstance(response, Mapping) or set(response) != _RESPONSE_FIELDS: - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} raw response is incomplete") - tokens = response["token_ids"] - if ( - not isinstance(tokens, list) - or any( - isinstance(token, bool) or not isinstance(token, int) or token < 0 for token in tokens - ) - or response["completion_tokens"] != len(tokens) - ): - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} token inventory changed") - for field in ("raw_text", "answer_text", "reasoning_text"): - text = response[field] - if ( - not isinstance(text, str) - or response[f"{field}_sha256"] != hashlib.sha256(text.encode("utf-8")).hexdigest() - ): - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} {field} digest changed") - stop_reason = response["stop_reason"] - expected_cap = len(tokens) >= MAX_OUTPUT_TOKENS or ( - isinstance(stop_reason, str) - and ("length" in stop_reason.lower() or "max_token" in stop_reason.lower()) - ) - if ( - not isinstance(stop_reason, str) - or not isinstance(response["cap_hit"], bool) - or response["cap_hit"] is not expected_cap - or not isinstance(response["token_decode_complete"], bool) - or response["token_decode_complete"] is not (response["token_decode_error"] is None) - or ( - response["token_decode_error"] is not None - and not isinstance(response["token_decode_error"], str) - ) - or not isinstance(response["channel_parse_complete"], bool) - or ( - response["channel_parse_error"] is not None - and not isinstance(response["channel_parse_error"], str) - ) - or (response["channel_parse_complete"] and response["channel_parse_error"] is not None) - or ( - not response["token_decode_complete"] - and ( - response["channel_parse_complete"] - or any(response[field] for field in ("raw_text", "answer_text", "reasoning_text")) - ) - ) - ): - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} response diagnostics changed") - - -def _validate_sample_record( - *, - record: Mapping[str, Any], - attempt: PreparedAttempt, - manifest_record_sha256: str, - binding: checkpoint_v1.CheckpointBinding, -) -> None: - payload = record["payload"] - if set(payload) != _SAMPLE_PAYLOAD_FIELDS: - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} sample payload schema changed") - expected_top = { - "final_benchmark_manifest_record_sha256": manifest_record_sha256, - "training_wave_receipt_record_sha256": (binding.wave_receipt.record_sha256), - "panel": PANEL_FINAL_BENCHMARK_V2, - "checkpoint_name": binding.checkpoint_name, - "checkpoint_inventory_sha256": binding.checkpoint_inventory_sha256, - "checkpoint": binding.checkpoint, - "attempt_index": attempt.attempt_index, - } - for field, expected in expected_top.items(): - if payload.get(field) != expected: - raise ImmutableFinalBenchmarkRecordError( - f"{attempt.key} sample belongs to another final benchmark" - ) - expected_task = { - "task_id": attempt.task.task_id, - "level": attempt.task.level, - "representation_id": attempt.task.representation_id, - "image_sha256": attempt.task.observation.image_sha256, - "target_image_sha256": attempt.task.reference.target_image_sha256, - } - expected_request = { - "num_samples": 1, - "independent_request": True, - "seed": attempt.seed, - "max_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "prompt_tokens": attempt.prepared.prompt_tokens, - "prompt_text_sha256": attempt.prepared.prompt_text_sha256, - } - if payload.get("task") != expected_task or payload.get("request") != expected_request: - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} sample request changed") - _validate_response(payload.get("response"), attempt=attempt) - - -def _evaluation_payload( - *, - attempt: PreparedAttempt, - result: EvaluationResult, - sample_record_sha256: str, - manifest_record_sha256: str, -) -> dict[str, Any]: - payload = checkpoint_v1._evaluation_payload( - task=attempt.task, - result=result, - sample_record_sha256=sample_record_sha256, - manifest_record_sha256=manifest_record_sha256, - ) - if result.status is not EvaluationStatus.OK: - payload["raw_absolute_scale_iou"] = 0.0 - payload["dice"] = None - payload["pure_executable"] = False - try: - normalized = json.loads(_canonical_bytes(payload)) - except FinalBenchmarkError as exc: - raise checkpoint_v1.EvaluationInfrastructureFault( - f"{attempt.key}: evaluator returned non-finite diagnostics" - ) from exc - latency = normalized.get("latency_seconds") - dice = normalized.get("dice") - if ( - isinstance(latency, bool) - or not isinstance(latency, (int, float)) - or not math.isfinite(float(latency)) - or float(latency) < 0.0 - or ( - dice is not None - and ( - isinstance(dice, bool) - or not isinstance(dice, (int, float)) - or not math.isfinite(float(dice)) - or not 0.0 <= float(dice) <= 1.0 - ) - ) - ): - raise checkpoint_v1.EvaluationInfrastructureFault( - f"{attempt.key}: evaluator returned invalid finite metrics" - ) - normalized["final_benchmark_manifest_record_sha256"] = normalized.pop( - "evaluation_manifest_record_sha256" - ) - normalized["panel"] = PANEL_FINAL_BENCHMARK_V2 - normalized["attempt_index"] = attempt.attempt_index - normalized["seed"] = attempt.seed - return normalized - - -def _validate_evaluation_record( - *, - record: Mapping[str, Any], - attempt: PreparedAttempt, - sample_record_sha256: str, - manifest_record_sha256: str, -) -> None: - payload = record["payload"] - if set(payload) != _EVALUATION_PAYLOAD_FIELDS: - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} evaluation payload schema changed") - expected = { - "final_benchmark_manifest_record_sha256": manifest_record_sha256, - "sample_record_sha256": sample_record_sha256, - "panel": PANEL_FINAL_BENCHMARK_V2, - "task_id": attempt.task.task_id, - "level": attempt.task.level, - "representation_id": attempt.task.representation_id, - "attempt_index": attempt.attempt_index, - "seed": attempt.seed, - "attribution": Attribution.MODEL.value, - } - for field, value in expected.items(): - if payload.get(field) != value: - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} evaluation record changed") - iou = payload.get("raw_absolute_scale_iou") - latency = payload.get("latency_seconds") - if ( - isinstance(iou, bool) - or not isinstance(iou, (int, float)) - or not math.isfinite(float(iou)) - or not 0.0 <= float(iou) <= 1.0 - or isinstance(latency, bool) - or not isinstance(latency, (int, float)) - or not math.isfinite(float(latency)) - or float(latency) < 0.0 - ): - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} has invalid stored metrics") - status = payload.get("status") - pure = payload.get("pure_executable") - if ( - status not in {item.value for item in EvaluationStatus} - or not isinstance(pure, bool) - or pure is not (status == EvaluationStatus.OK.value) - or (not pure and float(iou) != 0.0) - ): - raise ImmutableFinalBenchmarkRecordError( - f"{attempt.key} has inconsistent executable status" - ) - dice = payload.get("dice") - violations = payload.get("violations") - error = payload.get("error") - diagnostics = payload.get("diagnostics") - if ( - ( - dice is not None - and ( - isinstance(dice, bool) - or not isinstance(dice, (int, float)) - or not math.isfinite(float(dice)) - or not 0.0 <= float(dice) <= 1.0 - ) - ) - or not isinstance(violations, list) - or any(not isinstance(value, str) for value in violations) - or (error is not None and not isinstance(error, str)) - or not isinstance(diagnostics, Mapping) - ): - raise ImmutableFinalBenchmarkRecordError( - f"{attempt.key} has malformed evaluator diagnostics" - ) - for field in ("render_sha256", "reference_sha256"): - value = payload.get(field) - if value is not None and not checkpoint_v1._SHA256.fullmatch(str(value)): - raise ImmutableFinalBenchmarkRecordError(f"{attempt.key} has an invalid {field}") - # Re-run strict finite normalization over every nested diagnostic. - try: - checkpoint_v1._safe_json(payload) - _canonical_bytes(payload) - except ( - checkpoint_v1.EvaluationInfrastructureFault, - FinalBenchmarkError, - ) as exc: - raise ImmutableFinalBenchmarkRecordError( - f"{attempt.key} has non-finite diagnostics" - ) from exc - - -async def _sample_missing( - *, - attempts: Sequence[PreparedAttempt], - renderer: Any, - sampling_client: Any, - store: FinalBenchmarkStore, - manifest_record_sha256: str, - binding: checkpoint_v1.CheckpointBinding, -) -> None: - import tinker - - semaphore = asyncio.Semaphore(SAMPLE_CONCURRENCY) - - async def sample_one(attempt: PreparedAttempt) -> None: - messages = [_message(attempt.task, max_image=MAX_IMAGE_LONG_EDGE)] - prompt_text = str(messages[0]["content"][1]["text"]) - if ( - hashlib.sha256(prompt_text.encode("utf-8")).hexdigest() - != attempt.prepared.prompt_text_sha256 - ): - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} prompt text changed after preflight" - ) - prompt = renderer.build_generation_prompt(messages) - if int(prompt.length) != attempt.prepared.prompt_tokens: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} prompt changed after preflight" - ) - async with semaphore: - try: - result = await sampling_client.sample_async( - prompt=prompt, - num_samples=1, - sampling_params=tinker.SamplingParams( - stop=renderer.get_stop_sequences(), - max_tokens=MAX_OUTPUT_TOKENS, - temperature=TEMPERATURE, - top_p=TOP_P, - seed=attempt.seed, - ), - ) - except Exception as exc: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} sampling failed: {exc}" - ) from exc - sequences = list(result.sequences) - if len(sequences) != 1: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} returned {len(sequences)} samples, expected 1" - ) - response = _decode_response(renderer, sequences[0]) - # This durable record is the boundary before any candidate execution. - store.create_or_verify_sample( - attempt.task.task_id, - attempt.attempt_index, - _sample_payload( - attempt=attempt, - response=response, - manifest_record_sha256=manifest_record_sha256, - binding=binding, - ), - ) - if not response["token_decode_complete"]: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} raw token IDs were persisted but could not be " - f"decoded: {response['token_decode_error']}" - ) - - tasks = [asyncio.create_task(sample_one(attempt)) for attempt in attempts] - try: - outcomes = await asyncio.gather(*tasks, return_exceptions=True) - except BaseException: - for task in tasks: - if not task.done(): - task.cancel() - await asyncio.gather(*tasks, return_exceptions=True) - raise - faults = [outcome for outcome in outcomes if isinstance(outcome, BaseException)] - if faults: - # All sibling requests have settled and every successful raw response - # is durable before the run releases its lock or reports the fault. - raise faults[0] - - -def _score_missing( - *, - attempts: Sequence[PreparedAttempt], - evaluator: PixCellEvaluator, - store: FinalBenchmarkStore, - manifest_record_sha256: str, -) -> None: - pending: list[tuple[PreparedAttempt, dict[str, Any]]] = [] - for attempt in attempts: - sample = store.load_sample(attempt.task.task_id, attempt.attempt_index) - if sample is None: - raise FinalBenchmarkError(f"{attempt.key} has no resumable sample") - response = sample["payload"]["response"] - if not response["token_decode_complete"]: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} has persisted raw token IDs that the renderer " - f"could not decode: {response['token_decode_error']}" - ) - existing = store.load_evaluation( - attempt.task.task_id, - attempt.attempt_index, - ) - if existing is not None: - _validate_evaluation_record( - record=existing, - attempt=attempt, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - ) - continue - pending.append((attempt, sample)) - - for offset in range(0, len(pending), EVALUATION_BATCH_SIZE): - batch = pending[offset : offset + EVALUATION_BATCH_SIZE] - requests = [ - ( - attempt.task.reference, - str(sample["payload"]["response"]["answer_text"]), - ) - for attempt, sample in batch - ] - try: - results = evaluator.evaluate_batch(requests) - except Exception as exc: - raise checkpoint_v1.EvaluationInfrastructureFault( - f"isolated final-benchmark batch failed: {exc}" - ) from exc - if len(results) != len(batch): - raise checkpoint_v1.EvaluationInfrastructureFault( - "isolated evaluator returned a different result count" - ) - faults: list[checkpoint_v1.CheckpointEvaluationError] = [] - for (attempt, sample), result in zip(batch, results, strict=True): - try: - payload = _evaluation_payload( - attempt=attempt, - result=result, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - ) - except ( - checkpoint_v1.ReferencePanelFault, - checkpoint_v1.EvaluationInfrastructureFault, - ) as exc: - faults.append(exc) - continue - store.create_or_verify_evaluation( - attempt.task.task_id, - attempt.attempt_index, - payload, - ) - if faults: - raise faults[0] - - -def _validate_attempt_state( - *, - attempts: Sequence[PreparedAttempt], - store: FinalBenchmarkStore, - manifest_record_sha256: str, - binding: checkpoint_v1.CheckpointBinding, -) -> tuple[list[PreparedAttempt], list[PreparedAttempt]]: - store.assert_attempt_inventory(attempts) - missing_samples: list[PreparedAttempt] = [] - missing_evaluations: list[PreparedAttempt] = [] - for attempt in attempts: - sample = store.load_sample(attempt.task.task_id, attempt.attempt_index) - evaluation = store.load_evaluation( - attempt.task.task_id, - attempt.attempt_index, - ) - if sample is None: - if evaluation is not None: - raise ImmutableFinalBenchmarkRecordError( - f"{attempt.key} has an evaluation without its raw sample" - ) - missing_samples.append(attempt) - missing_evaluations.append(attempt) - continue - _validate_sample_record( - record=sample, - attempt=attempt, - manifest_record_sha256=manifest_record_sha256, - binding=binding, - ) - response = sample["payload"]["response"] - if not response["token_decode_complete"] and evaluation is not None: - raise ImmutableFinalBenchmarkRecordError( - f"{attempt.key} has an evaluation for an undecodable response" - ) - if evaluation is None: - missing_evaluations.append(attempt) - continue - _validate_evaluation_record( - record=evaluation, - attempt=attempt, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - ) - return missing_samples, missing_evaluations - - -def _diagnostics(records: Sequence[Mapping[str, Any]]) -> dict[str, Any]: - if not records: - raise FinalBenchmarkError("cannot summarize an empty attempt set") - count = len(records) - completions = [int(record["completion_tokens"]) for record in records] - pure = sum(bool(record["pure_executable"]) for record in records) - cap_hits = sum(bool(record["cap_hit"]) for record in records) - parse_complete = sum(bool(record["channel_parse_complete"]) for record in records) - return { - "attempt_count": count, - "pure_executable_count": pure, - "pure_executable_rate": pure / count, - "cap_hit_count": cap_hits, - "cap_hit_rate": cap_hits / count, - "channel_parse_complete_count": parse_complete, - "channel_parse_complete_rate": parse_complete / count, - "statuses": dict(sorted(Counter(str(record["status"]) for record in records).items())), - "stop_reasons": dict( - sorted(Counter(str(record["stop_reason"]) for record in records).items()) - ), - "completion_tokens": { - "min": min(completions), - "max": max(completions), - "mean": sum(completions) / count, - "total": sum(completions), - }, - } - - -def _summaries( - records: Sequence[Mapping[str, Any]], -) -> tuple[dict[str, Any], dict[str, Any]]: - if len(records) != EXPECTED_ATTEMPTS: - raise FinalBenchmarkError( - f"final report has {len(records)} attempts, expected {EXPECTED_ATTEMPTS}" - ) - expected_keys = [ - (task_id, attempt_index) - for task_id in EXPECTED_TASK_IDS - for attempt_index in range(1, ATTEMPTS_PER_TASK + 1) - ] - observed_keys = [ - (str(record.get("task_id")), record.get("attempt_index")) for record in records - ] - if observed_keys != expected_keys or len(set(observed_keys)) != len(observed_keys): - raise FinalBenchmarkError( - "final report attempt inventory is incomplete, duplicated, or reordered" - ) - figures: dict[str, Any] = {} - best_scores: list[float] = [] - all_scores: list[float] = [] - for task_id in EXPECTED_TASK_IDS: - figure_records = [record for record in records if record["task_id"] == task_id] - scores = [ - _unit_metric( - record["raw_absolute_scale_iou"], - field=f"{task_id} raw_absolute_scale_iou", - ) - for record in figure_records - ] - all_scores.extend(scores) - best_scores.append(max(scores)) - figures[task_id] = { - "mean_at_1_raw_absolute_scale_iou": sum(scores) / len(scores), - "best_at_4_raw_absolute_scale_iou": max(scores), - **_diagnostics(figure_records), - } - summary = { - "figure_count": len(EXPECTED_TASK_IDS), - "mean_at_1_raw_absolute_scale_iou": sum(all_scores) / len(all_scores), - "best_at_4_raw_absolute_scale_iou": (sum(best_scores) / len(best_scores)), - **_diagnostics(records), - } - return summary, figures - - -def _unit_metric(value: Any, *, field: str) -> float: - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not math.isfinite(float(value)) - or not 0.0 <= float(value) <= 1.0 - ): - raise FinalBenchmarkError(f"{field} must be finite and in [0, 1]") - return float(value) - - -def _report_payload( - *, - attempts: Sequence[PreparedAttempt], - store: FinalBenchmarkStore, - manifest_record: Mapping[str, Any], - binding: checkpoint_v1.CheckpointBinding, -) -> dict[str, Any]: - records: list[dict[str, Any]] = [] - manifest_sha = str(manifest_record["record_sha256"]) - for attempt in attempts: - sample = store.load_sample(attempt.task.task_id, attempt.attempt_index) - evaluation = store.load_evaluation( - attempt.task.task_id, - attempt.attempt_index, - ) - if sample is None or evaluation is None: - raise FinalBenchmarkError(f"{attempt.key} is incomplete") - _validate_sample_record( - record=sample, - attempt=attempt, - manifest_record_sha256=manifest_sha, - binding=binding, - ) - _validate_evaluation_record( - record=evaluation, - attempt=attempt, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_sha, - ) - response = sample["payload"]["response"] - measured = evaluation["payload"] - records.append( - { - "task_id": attempt.task.task_id, - "attempt_index": attempt.attempt_index, - "seed": attempt.seed, - "status": measured["status"], - "attribution": measured["attribution"], - "pure_executable": measured["pure_executable"], - "raw_absolute_scale_iou": measured["raw_absolute_scale_iou"], - "completion_tokens": response["completion_tokens"], - "stop_reason": response["stop_reason"], - "cap_hit": response["cap_hit"], - "channel_parse_complete": response["channel_parse_complete"], - "answer_text_sha256": response["answer_text_sha256"], - "render_sha256": measured["render_sha256"], - "reference_sha256": measured["reference_sha256"], - "violations": measured["violations"], - "error": measured["error"], - "sample_record_sha256": sample["record_sha256"], - "evaluation_record_sha256": evaluation["record_sha256"], - } - ) - summary, figures = _summaries(records) - return { - "schema_version": FINAL_BENCHMARK_SCHEMA_VERSION, - "study_id": STUDY_ID, - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "replicate_id": binding.key.replicate_id, - "panel": PANEL_FINAL_BENCHMARK_V2, - "summary": summary, - "figures": figures, - "records": records, - "attempt_records_sha256": canonical_json_sha256(records), - "provenance": { - "final_benchmark_manifest_record_sha256": manifest_sha, - "manifest": manifest_record["payload"], - }, - } - - -def validate_final_benchmark_report( - record: Mapping[str, Any], - *, - manifest_record: Mapping[str, Any], - binding: checkpoint_v1.CheckpointBinding, - attempts: Sequence[PreparedAttempt], -) -> dict[str, Any]: - """Validate a complete best@4 report without trusting claimed aggregates.""" - - validated = _validate_record( - record, - record_type="final_benchmark_report", - key={}, - ) - payload = validated["payload"] - if set(payload) != _REPORT_PAYLOAD_FIELDS: - raise FinalBenchmarkError("final-benchmark report payload schema changed") - expected_identity = { - "schema_version": FINAL_BENCHMARK_SCHEMA_VERSION, - "study_id": STUDY_ID, - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "replicate_id": binding.key.replicate_id, - "panel": PANEL_FINAL_BENCHMARK_V2, - } - for field, expected in expected_identity.items(): - if payload.get(field) != expected: - raise FinalBenchmarkError(f"final-benchmark report {field} differs from its request") - records = payload.get("records") - provenance = payload.get("provenance") - if not isinstance(records, list) or not isinstance(provenance, Mapping): - raise FinalBenchmarkError("final-benchmark report is incomplete") - if ( - provenance.get("final_benchmark_manifest_record_sha256") != manifest_record["record_sha256"] - or provenance.get("manifest") != manifest_record["payload"] - ): - raise FinalBenchmarkError("final-benchmark report provenance differs from its manifest") - if len(attempts) != EXPECTED_ATTEMPTS: - raise FinalBenchmarkError("validator did not receive the sealed 32 attempts") - expected_keys = [attempt.key for attempt in attempts] - observed_keys: list[tuple[str, int]] = [] - for row, attempt in zip(records, attempts, strict=False): - if not isinstance(row, Mapping) or set(row) != _REPORT_RECORD_FIELDS: - raise FinalBenchmarkError("final-benchmark attempt row is incomplete") - key = (str(row.get("task_id")), row.get("attempt_index")) - observed_keys.append(key) - if key != attempt.key or row.get("seed") != attempt.seed: - raise FinalBenchmarkError( - "final-benchmark attempt row has the wrong task, index, or seed" - ) - _unit_metric( - row.get("raw_absolute_scale_iou"), - field=f"{attempt.key} raw_absolute_scale_iou", - ) - if ( - row.get("attribution") != Attribution.MODEL.value - or row.get("status") not in {item.value for item in EvaluationStatus} - or not isinstance(row.get("pure_executable"), bool) - or not isinstance(row.get("cap_hit"), bool) - or not isinstance(row.get("channel_parse_complete"), bool) - or isinstance(row.get("completion_tokens"), bool) - or not isinstance(row.get("completion_tokens"), int) - or row["completion_tokens"] < 0 - ): - raise FinalBenchmarkError(f"{attempt.key} attempt diagnostics are invalid") - expected_cap = ( - row["completion_tokens"] >= MAX_OUTPUT_TOKENS - or "length" in str(row["stop_reason"]).lower() - or "max_token" in str(row["stop_reason"]).lower() - ) - if ( - row["pure_executable"] is not (row["status"] == EvaluationStatus.OK.value) - or (not row["pure_executable"] and float(row["raw_absolute_scale_iou"]) != 0.0) - or not isinstance(row.get("stop_reason"), str) - or row["cap_hit"] is not expected_cap - or not isinstance(row.get("violations"), list) - or any(not isinstance(value, str) for value in row["violations"]) - or (row.get("error") is not None and not isinstance(row["error"], str)) - ): - raise FinalBenchmarkError(f"{attempt.key} attempt status is internally inconsistent") - for field in ( - "answer_text_sha256", - "sample_record_sha256", - "evaluation_record_sha256", - ): - if not checkpoint_v1._SHA256.fullmatch(str(row.get(field, ""))): - raise FinalBenchmarkError(f"{attempt.key} {field} is not a SHA-256") - for field in ("render_sha256", "reference_sha256"): - value = row.get(field) - if value is not None and not checkpoint_v1._SHA256.fullmatch(str(value)): - raise FinalBenchmarkError(f"{attempt.key} {field} is not a SHA-256") - if ( - len(records) != EXPECTED_ATTEMPTS - or observed_keys != expected_keys - or len(set(observed_keys)) != EXPECTED_ATTEMPTS - ): - raise FinalBenchmarkError("final-benchmark report has incomplete or duplicate attempt rows") - summary, figures = _summaries(records) - if payload.get("summary") != summary or payload.get("figures") != figures: - raise FinalBenchmarkError("final-benchmark claimed metrics differ from its 32 attempts") - if payload.get("attempt_records_sha256") != canonical_json_sha256(records): - raise FinalBenchmarkError("final-benchmark attempt-set digest changed") - return validated - - -def _manifest_payload( - *, - repo_root: Path, - protocol: Mapping[str, Any], - source_git_sha: str, - dataset: Mapping[str, Any], - task_panel: Mapping[str, Any], - model: Mapping[str, Any], - preflight: Mapping[str, Any], - sandbox: Mapping[str, Any], - binding: checkpoint_v1.CheckpointBinding, - attempts: Sequence[PreparedAttempt], -) -> dict[str, Any]: - seeds = _seed_inventory(attempts) - return { - "schema_version": FINAL_BENCHMARK_SCHEMA_VERSION, - "study_id": STUDY_ID, - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "replicate_id": binding.key.replicate_id, - "panel": PANEL_FINAL_BENCHMARK_V2, - "source_git_sha": source_git_sha, - "protocol": { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - }, - "dataset": { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": "fixed-f1-f8", - **dict(dataset), - }, - "task_panel": dict(task_panel), - "prompt": { - "contract_version": protocol["contract_version"], - "assets": prompt_asset_hashes(), - "prompt_set_sha256": preflight["prompt_set_sha256"], - "prompt_tokens": preflight["prompt_tokens"], - "minimum_context_headroom": preflight["minimum_context_headroom"], - }, - "model": { - "model_key": binding.stage.model_key, - **dict(model), - }, - "sampler": { - "sampler_path": binding.sampler_path, - "checkpoint_name": binding.checkpoint_name, - "checkpoint": binding.checkpoint, - "checkpoint_inventory_sha256": (binding.checkpoint_inventory_sha256), - "training_run_manifest_record_sha256": (binding.run_manifest.record_sha256), - "training_wave_receipt_record_sha256": (binding.wave_receipt.record_sha256), - "training_wave_receipt_relative_path": str(binding.wave_receipt.relative_path), - }, - "sandbox": dict(sandbox), - "reference_set_sha256": preflight["reference_set_sha256"], - "sampling": { - "attempts_per_task": ATTEMPTS_PER_TASK, - "task_count": len(EXPECTED_TASK_IDS), - "total_attempts": EXPECTED_ATTEMPTS, - "independent_requests": True, - "num_samples_per_request": 1, - "max_output_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "max_image_long_edge": MAX_IMAGE_LONG_EDGE, - "sample_concurrency": SAMPLE_CONCURRENCY, - "evaluation_batch_size": EVALUATION_BATCH_SIZE, - "evaluator_workers": EVALUATOR_WORKERS, - "seed_algorithm": ("sha256-bound deterministic 31-bit seed with attempt index"), - "seed_domain": _SEED_DOMAIN, - "seeds": seeds, - "seeds_sha256": canonical_json_sha256(seeds), - }, - } - - -def _default_evaluator_factory() -> PixCellEvaluator: - return PixCellEvaluator( - max_workers=EVALUATOR_WORKERS, - evaluator_retries=1, - require_isolation=True, - ) - - -def _default_service_client_factory() -> Any: - import tinker - - return tinker.ServiceClient() - - -async def run_final_benchmark( - *, - repo_root: Path, - stage_id: str, - wave: str, - checkpoint_name: str, - replicate_id: str, - expected_source_sha: str, - expected_wave_receipt_sha256: str, - external_root: Path, - confirmation: str, - service_client_factory: Callable[[], Any] = _default_service_client_factory, - evaluator_factory: Callable[[], PixCellEvaluator] = _default_evaluator_factory, -) -> dict[str, Any]: - """Run or resume the exact 32-attempt final-checkpoint best@4 panel.""" - - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol(root, require_committed=True) - source_git_sha = validate_source_sha(root, expected_source_sha) - if confirmation != protocol["launch"]["confirmation_token"]: - raise FinalBenchmarkError("explicit paid-evaluation confirmation is missing") - try: - validate_stage_replicate( - protocol, - stage_id=stage_id, - replicate_id=replicate_id, - ) - except ValueError as exc: - raise FinalBenchmarkError(str(exc)) from exc - if protocol["evaluation"].get("final_benchmark_attempts") != ATTEMPTS_PER_TASK: - raise FinalBenchmarkError("protocol no longer seals four final-benchmark attempts") - - training_store = TrainingStore( - repo_root=root, - external_root=external_root, - ) - binding = checkpoint_v1._load_checkpoint_binding( - store=training_store, - protocol=protocol, - stage_id=stage_id, - wave=wave, - checkpoint_name=checkpoint_name, - replicate_id=replicate_id, - expected_receipt_sha256=expected_wave_receipt_sha256, - source_git_sha=source_git_sha, - ) - _final_checkpoint(binding) - model = checkpoint_v1._model_binding(protocol, binding.stage) - dataset = _dataset_binding(root / "dataset", protocol) - tasks = checkpoint_v1._benchmark_tasks(repo_root=root, protocol=protocol) - task_panel = _task_panel(tasks) - renderer = _renderer( - model["model"], - model["renderer"], - effort=model.get("effort"), - ) - evaluator = evaluator_factory() - try: - prepared, preflight = checkpoint_v1._preflight_tasks( - tasks=tasks, - renderer=renderer, - evaluator=evaluator, - context_tokens=int(model["context_tokens"]), - ) - sandbox = checkpoint_v1._sandbox_provenance(evaluator) - attempts = _attempts( - prepared=prepared, - source_git_sha=source_git_sha, - protocol_sha256=str(protocol["logical_sha256"]), - dataset_sha256=str(dataset["logical_release_sha256"]), - task_panel_sha256=str(task_panel["logical_sha256"]), - binding=binding, - ) - store = FinalBenchmarkStore( - stage_path=training_store.stage_path(binding.key), - wave=wave, - checkpoint_name=binding.checkpoint_name, - ) - with store.acquire_lock(): - manifest_payload = _manifest_payload( - repo_root=root, - protocol=protocol, - source_git_sha=source_git_sha, - dataset=dataset, - task_panel=task_panel, - model=model, - preflight=preflight, - sandbox=sandbox, - binding=binding, - attempts=attempts, - ) - manifest = store.create_or_verify_manifest(manifest_payload) - missing_samples, missing_evaluations = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - ) - existing_report = store.load_report() - if existing_report is not None: - if missing_samples or missing_evaluations: - raise FinalBenchmarkError( - "final-benchmark report exists with incomplete attempt rows" - ) - expected_payload = _report_payload( - attempts=attempts, - store=store, - manifest_record=manifest, - binding=binding, - ) - observed = store.create_or_verify_report(expected_payload) - validate_final_benchmark_report( - observed, - manifest_record=manifest, - binding=binding, - attempts=attempts, - ) - return { - "status": "already_complete", - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "panel": PANEL_FINAL_BENCHMARK_V2, - "report": str(store.root / "report.json"), - "report_record_sha256": observed["record_sha256"], - "summary": observed["payload"]["summary"], - } - - if missing_samples: - if not os.environ.get("TINKER_API_KEY"): - raise FinalBenchmarkError("TINKER_API_KEY is not present") - # Every prompt, reference, existing record, receipt, sandbox, - # seed, and manifest has passed before this paid-capable object. - service = service_client_factory() - sampling_client = service.create_sampling_client(model_path=binding.sampler_path) - await _sample_missing( - attempts=missing_samples, - renderer=renderer, - sampling_client=sampling_client, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - ) - - missing_samples, _ = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - ) - if missing_samples: - raise FinalBenchmarkError("sampling did not persist all 32 raw responses") - _score_missing( - attempts=attempts, - evaluator=evaluator, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - missing_samples, missing_evaluations = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - ) - if missing_samples or missing_evaluations: - raise FinalBenchmarkError( - "final benchmark is incomplete after deterministic evaluation" - ) - report_payload = _report_payload( - attempts=attempts, - store=store, - manifest_record=manifest, - binding=binding, - ) - report = store.create_or_verify_report(report_payload) - validate_final_benchmark_report( - report, - manifest_record=manifest, - binding=binding, - attempts=attempts, - ) - return { - "status": "complete", - "stage_id": binding.stage.stage_id, - "wave": binding.wave, - "checkpoint_name": binding.checkpoint_name, - "panel": PANEL_FINAL_BENCHMARK_V2, - "report": str(store.root / "report.json"), - "report_record_sha256": report["record_sha256"], - "summary": report_payload["summary"], - } - finally: - evaluator.close() - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, required=True) - result.add_argument("--stage", required=True) - result.add_argument("--wave", required=True) - result.add_argument("--checkpoint-name", required=True) - result.add_argument("--replicate", default="r0") - result.add_argument("--source-git-sha", required=True) - result.add_argument("--wave-receipt-record-sha256", required=True) - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--confirm-spend", default="") - return result - - -def main() -> None: - args = parser().parse_args() - report = asyncio.run( - run_final_benchmark( - repo_root=args.repo_root, - stage_id=args.stage, - wave=args.wave, - checkpoint_name=args.checkpoint_name, - replicate_id=args.replicate, - expected_source_sha=args.source_git_sha, - expected_wave_receipt_sha256=args.wave_receipt_record_sha256, - external_root=args.external_root, - confirmation=args.confirm_spend, - ) - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/launcher.py b/rl/studies/representation_training_v1/launcher.py deleted file mode 100644 index 38463b36..00000000 --- a/rl/studies/representation_training_v1/launcher.py +++ /dev/null @@ -1,2746 +0,0 @@ -"""Sealed, resumable launch controller for representation-training-v1.""" - -from __future__ import annotations - -import hashlib -import importlib -import json -import math -import os -import re -import subprocess -import uuid -from collections.abc import Awaitable, Callable, Iterator, Mapping -from contextlib import contextmanager -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from tinker_cookbook import checkpoint_utils -from tinker_cookbook.rl.train import Config as RLConfig -from tinker_cookbook.supervised.train import Config as SFTConfig - -from rl.evaluation.tracking import validate_wandb_access -from rl.track_a.tinker_data import ( - TrackARLDatasetBuilder, - TrackASupervisedDatasetBuilder, -) - -from .preflight import stage_preflight -from .promotion import ( - APPROVAL_SCHEMA_VERSION, - LEVEL_PROGRESS_PANELS, - PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION, - SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION, - PromotionError, - record_base_baseline_approval, - record_rollout_health_approval, - validate_parent_receipt_transition_approval, - validate_same_source_wave_approval, - validate_source_transition_approval, -) -from .protocol import ( - LEVELS, - STUDY_ID, - StageSpec, - file_sha256, - load_protocol, - parent_replicate_id, - stage_spec, - validate_stage_replicate, - validate_source_sha, -) -from .schedule import SCHEDULE_SEED -from .store import StageKey, TrainingRecord, TrainingStore, canonical_sha256 - - -WANDB_RUN_ID_PREFIX = "pxct-" -_TINKER_PATH = re.compile(r"^tinker://[^\s]+$") -_GIT_SHA = re.compile(r"^[0-9a-f]{40}$") -_SHA256 = re.compile(r"^[0-9a-f]{64}$") -_CHILD_SOURCE_TRANSITION_SCHEMA_VERSION = "pixcell-training-child-source-transition-v1" -_WAVE_ARTIFACT_SNAPSHOT_SCHEMA_VERSION = "pixcell-training-wave-artifact-snapshot-v1" -_COOKBOOK_FRESH_RESUME_BOOTSTRAP_FILES = frozenset( - {"code.diff", "config.json", "logs.log"} -) -_ENVIRONMENT_FIELDS = ( - "WANDB_DIR", - "WANDB_DISABLE_CODE", - "WANDB_DISABLE_GIT", - "WANDB_ENTITY", - "WANDB_JOB_TYPE", - "WANDB_MODE", - "WANDB_NAME", - "WANDB_PROJECT", - "WANDB_RESUME", - "WANDB_RUN_GROUP", - "WANDB_RUN_ID", - "WANDB_SAVE_CODE", - "WANDB_TAGS", -) - - -class TrainingLaunchError(RuntimeError): - """A paid stage is ambiguous, inconsistent, or not approved.""" - - -@dataclass(frozen=True) -class ParentBinding: - stage_id: str - wave: str - replicate_id: str - receipt: TrainingRecord - checkpoint: dict[str, Any] - source_git_sha: str - run_manifest: TrainingRecord - entry_approval: TrainingRecord | None = None - source_transition: dict[str, Any] | None = None - - -@dataclass(frozen=True) -class ResumeBinding: - """Exact optimizer state the pinned Cookbook is allowed to resume.""" - - batch: int - checkpoint: dict[str, Any] - - -def _child_source_transition_binding( - approval: TrainingRecord, -) -> dict[str, Any]: - payload = approval.payload - if payload.get("schema_version") not in { - SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION, - PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION, - }: - raise TrainingLaunchError( - "child source transition is not backed by an approved transition" - ) - transition = payload.get("source_transition") - if not isinstance(transition, Mapping): - raise TrainingLaunchError("transition approval has no source transition") - logical_sha256 = str(transition.get("logical_sha256", "")) - if not re.fullmatch(r"[0-9a-f]{64}", logical_sha256): - raise TrainingLaunchError( - "transition approval source transition has no logical digest" - ) - return { - "schema_version": _CHILD_SOURCE_TRANSITION_SCHEMA_VERSION, - "authorized_child_wave": str(payload.get("wave", "")), - "producer_source_git_sha": str(payload.get("evidence_source_git_sha", "")), - "consumer_source_git_sha": str(payload.get("source_git_sha", "")), - "approval_schema_version": str(payload.get("schema_version", "")), - "approval": { - "relative_path": str(approval.relative_path), - "payload_sha256": approval.payload_sha256, - "record_sha256": approval.record_sha256, - }, - "source_transition_logical_sha256": logical_sha256, - "source_transition": dict(transition), - } - - -def deterministic_wandb_run_id( - *, - source_git_sha: str, - protocol_logical_sha256: str, - key: StageKey, -) -> str: - digest = canonical_sha256( - { - "schema_version": "pixcell-training-wandb-identity-v1", - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol_logical_sha256, - **key.as_dict(), - } - ) - return WANDB_RUN_ID_PREFIX + digest[:27] - - -def _tracking_binding( - *, - protocol: Mapping[str, Any], - source_git_sha: str, - key: StageKey, - stage: StageSpec, - store: TrainingStore, -) -> dict[str, Any]: - model_family = "inkling" if stage.model_key == "inkling" else "qwen-open" - run_id = deterministic_wandb_run_id( - source_git_sha=source_git_sha, - protocol_logical_sha256=str(protocol["logical_sha256"]), - key=key, - ) - return { - "provider": "wandb", - "entity": protocol["tracking"]["entity"], - "project": protocol["tracking"]["project"], - "run_id": run_id, - "name": (f"{STUDY_ID}/{stage.stage_id}/{key.replicate_id}@{source_git_sha[:12]}"), - "group": f"{STUDY_ID}/{model_family}", - "job_type": stage.kind, - "tags": [ - STUDY_ID, - stage.stage_id, - stage.model_key, - stage.kind, - key.replicate_id, - *stage.hypotheses, - ], - "directory": str(store.stage_path(key) / "wandb"), - } - - -@contextmanager -def _wandb_environment( - binding: Mapping[str, Any], - *, - resume: bool, -) -> Iterator[None]: - previous = {name: os.environ.get(name) for name in _ENVIRONMENT_FIELDS} - directory = Path(str(binding["directory"])) - directory.mkdir(parents=True, exist_ok=True, mode=0o700) - os.environ.update( - { - "WANDB_DIR": str(directory), - "WANDB_DISABLE_CODE": "true", - "WANDB_DISABLE_GIT": "true", - "WANDB_ENTITY": str(binding["entity"]), - "WANDB_JOB_TYPE": str(binding["job_type"]), - "WANDB_MODE": "online", - "WANDB_NAME": str(binding["name"]), - "WANDB_PROJECT": str(binding["project"]), - "WANDB_RESUME": "must" if resume else "never", - "WANDB_RUN_GROUP": str(binding["group"]), - "WANDB_RUN_ID": str(binding["run_id"]), - "WANDB_SAVE_CODE": "false", - "WANDB_TAGS": ",".join(str(item) for item in binding["tags"]), - } - ) - try: - yield - finally: - for name, value in previous.items(): - if value is None: - os.environ.pop(name, None) - else: - os.environ[name] = value - - -def _finish_wandb_run(wandb_module: Any, run: Any) -> None: - """Best-effort cleanup after rejecting a live W&B binding.""" - - finish = getattr(run, "finish", None) - if callable(finish): - try: - finish() - return - except BaseException: - pass - module_finish = getattr(wandb_module, "finish", None) - if callable(module_finish): - try: - module_finish() - except BaseException: - pass - - -@contextmanager -def _sealed_wandb_initialization( - binding: Mapping[str, Any], - *, - resume: bool, - wandb_module: Any | None = None, - on_initialized: Callable[[Mapping[str, Any]], None] | None = None, -) -> Iterator[None]: - """Force Cookbook logging onto the exact run sealed in the manifest. - - The Cookbook intentionally owns the call to ``wandb.init``. This narrow - wrapper preserves that integration while replacing identity-bearing - arguments with the immutable launch binding. The observed live run is - checked and recorded before the Cookbook can construct a Tinker client. - """ - - module = wandb_module if wandb_module is not None else importlib.import_module("wandb") - if getattr(module, "run", None) is not None: - raise TrainingLaunchError("a W&B run is already active before sealed initialization") - original_init = getattr(module, "init", None) - if not callable(original_init): - raise TrainingLaunchError("the W&B module has no callable init") - init_calls = 0 - initialized_run: Any | None = None - - def sealed_init(*args: Any, **kwargs: Any) -> Any: - nonlocal init_calls, initialized_run - init_calls += 1 - if init_calls != 1: - raise TrainingLaunchError("the training process attempted multiple W&B initializations") - if args: - raise TrainingLaunchError("the training process used positional W&B identity arguments") - for field in ("project", "name"): - supplied = kwargs.get(field) - if supplied is not None and str(supplied) != str(binding[field]): - raise TrainingLaunchError(f"the training process supplied a different W&B {field}") - cookbook_config = kwargs.get("config") - sealed = dict(kwargs) - sealed.update( - { - # Cookbook logs the same config again immediately after init. - # RL callback fields are sanitized differently across the two - # W&B calls, so initialize identity first and write hparams - # through the same canonical post-init path both times. - "config": None, - "entity": str(binding["entity"]), - "project": str(binding["project"]), - "dir": str(binding["directory"]), - "id": str(binding["run_id"]), - "name": str(binding["name"]), - "group": str(binding["group"]), - "job_type": str(binding["job_type"]), - "tags": tuple(str(item) for item in binding["tags"]), - "mode": "online", - "resume": "must" if resume else "never", - "save_code": False, - } - ) - run = original_init(**sealed) - initialized_run = run - settings = getattr(run, "settings", None) - observed = { - "run_id": str(getattr(run, "id", "")), - "entity": str(getattr(run, "entity", "")), - "project": str(getattr(run, "project", "")), - "name": str(getattr(run, "name", "")), - "group": str(getattr(run, "group", "")), - "job_type": str(getattr(run, "job_type", "")), - "tags": [str(item) for item in getattr(run, "tags", ())], - "resumed": bool(getattr(run, "resumed", False)), - "url": str(getattr(run, "url", "")), - "requested_directory": str(binding["directory"]), - "resolved_root_directory": str(getattr(settings, "root_dir", "")), - "files_directory": str(getattr(run, "dir", "")), - "requested_resume": "must" if resume else "never", - "resolved_resume": str(getattr(settings, "resume", "")), - } - expected = { - "run_id": str(binding["run_id"]), - "entity": str(binding["entity"]), - "project": str(binding["project"]), - "name": str(binding["name"]), - "group": str(binding["group"]), - "job_type": str(binding["job_type"]), - "tags": [str(item) for item in binding["tags"]], - "resumed": resume, - "resolved_resume": "must" if resume else "never", - } - differences = [field for field, value in expected.items() if observed[field] != value] - requested_directory = Path(str(binding["directory"])).resolve(strict=False) - try: - resolved_directory = Path(observed["resolved_root_directory"]).resolve(strict=True) - except (OSError, RuntimeError): - differences.append("resolved_root_directory") - else: - if resolved_directory != requested_directory: - differences.append("resolved_root_directory") - try: - files_directory = Path(observed["files_directory"]).resolve(strict=True) - except (OSError, RuntimeError): - differences.append("files_directory") - else: - if not files_directory.is_relative_to(requested_directory): - differences.append("files_directory") - if differences: - _finish_wandb_run(module, run) - raise TrainingLaunchError( - "the live W&B run differs from its sealed " + ", ".join(differences) - ) - if cookbook_config is not None: - config = getattr(module, "config", None) - update = getattr(config, "update", None) - if not callable(update): - _finish_wandb_run(module, run) - raise TrainingLaunchError("the live W&B run has no configurable hparam store") - try: - # Use Config.update for both writes. W&B sanitizes callable - # fields differently in wandb.init, while the Cookbook writes - # the same hparams again immediately after init. Continuation - # waves intentionally change their ceiling and invocation - # metadata, so only the preseed update may replace prior-wave - # values; the Cookbook's following update is then identical. - update(cookbook_config, allow_val_change=resume) - except BaseException: - _finish_wandb_run(module, run) - raise - observed["hparams_preseeded"] = True - observed["hparams_allow_val_change"] = resume - else: - observed["hparams_preseeded"] = False - observed["hparams_allow_val_change"] = False - try: - if on_initialized is not None: - on_initialized(observed) - except BaseException: - _finish_wandb_run(module, run) - raise - return run - - module.init = sealed_init - try: - yield - except BaseException: - if initialized_run is not None: - _finish_wandb_run(module, initialized_run) - raise - else: - if init_calls != 1: - if initialized_run is not None: - _finish_wandb_run(module, initialized_run) - raise TrainingLaunchError("training returned without initializing its sealed W&B run") - finally: - module.init = original_init - - -def _checkpoint_from_inventory( - receipt: TrainingRecord, - selected: Mapping[str, Any], - *, - inventory_sha256: Any, -) -> dict[str, Any]: - inventory = receipt.payload.get("checkpoint_inventory") - if not isinstance(inventory, Mapping): - raise TrainingLaunchError("parent receipt has no checkpoint inventory") - if ( - inventory.get("logical_sha256") != inventory_sha256 - or canonical_sha256(inventory.get("entries")) != inventory_sha256 - ): - raise TrainingLaunchError("parent checkpoint inventory digest differs") - entries = inventory.get("entries") - if not isinstance(entries, list) or dict(selected) not in entries: - raise TrainingLaunchError("selected parent checkpoint is not in the parent receipt") - checkpoint = dict(selected) - if not _TINKER_PATH.fullmatch( - str(checkpoint.get("state_path", "")) - ) or not _TINKER_PATH.fullmatch(str(checkpoint.get("sampler_path", ""))): - raise TrainingLaunchError("selected parent checkpoint has invalid URIs") - return checkpoint - - -def _manifest_transition_approval( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - key: StageKey, - stage: StageSpec, - source_git_sha: str, - existing_manifest: TrainingRecord, -) -> TrainingRecord: - observed = existing_manifest.payload.get("source_transition") - if not isinstance(observed, Mapping): - raise TrainingLaunchError("cross-source parent requires a transition in the child manifest") - authorized_wave = str(observed.get("authorized_child_wave", "")) - if authorized_wave not in stage.waves: - raise TrainingLaunchError("child manifest transition names an unknown wave") - approval = store.load_wave_approval(key, wave=authorized_wave) - if approval is None: - raise TrainingLaunchError("child manifest transition approval is missing") - try: - if ( - approval.payload.get("schema_version") - == PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ): - validate_parent_receipt_transition_approval( - repo_root=repo_root, - protocol=protocol, - store=store, - stage=stage, - wave_name=authorized_wave, - replicate_id=key.replicate_id, - consumer_source_git_sha=source_git_sha, - approval=approval, - ) - else: - validate_source_transition_approval( - repo_root=repo_root, - protocol=protocol, - store=store, - stage=stage, - wave_name=authorized_wave, - replicate_id=key.replicate_id, - consumer_source_git_sha=source_git_sha, - approval=approval, - ) - except PromotionError as exc: - raise TrainingLaunchError( - f"child manifest source transition failed validation: {exc}" - ) from exc - expected = _child_source_transition_binding(approval) - if dict(observed) != expected: - raise TrainingLaunchError("child manifest source transition differs from its v2 approval") - return approval - - -def _parent_binding( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - key: StageKey, - stage: StageSpec, - source_git_sha: str, - approval: TrainingRecord | None, - existing_manifest: TrainingRecord | None, -) -> ParentBinding | None: - if stage.parent.startswith("base:"): - expected_model = stage.parent.split(":", 1)[1] - if expected_model != stage.model_key: - raise TrainingLaunchError("base parent and stage model differ") - if existing_manifest is not None and "source_transition" in existing_manifest.payload: - raise TrainingLaunchError("base-parent child manifest contains a source transition") - return None - - parent_stage_id, parent_wave = stage.parent.split(":", 1) - if parent_wave == "complete-or-skip": - frozen_parent = ( - existing_manifest.payload.get("parent_receipt") - if existing_manifest is not None - else None - ) - if isinstance(frozen_parent, Mapping): - selected_stage = str(frozen_parent.get("stage_id", "")) - selected_wave = str(frozen_parent.get("wave", "")) - elif approval is not None: - selected_stage = str(approval.payload.get("parent_stage_id", "")) - selected_wave = str(approval.payload.get("parent_wave", "")) - else: - raise TrainingLaunchError(f"{stage.stage_id} requires an approved dynamic parent") - allowed = {"qwen-mixed-sft"} - target_index = int(str(stage.current_level)[1:]) - allowed.update(f"qwen-mixed-rl-l{index}" for index in range(target_index)) - if selected_stage not in allowed or selected_wave != "complete": - raise TrainingLaunchError(f"{stage.stage_id} approval selected an invalid parent") - parent_stage_id, parent_wave = selected_stage, selected_wave - - parent_replicate = parent_replicate_id( - protocol, - stage_id=stage.stage_id, - replicate_id=key.replicate_id, - ) - parent_key = StageKey(key.study_id, parent_stage_id, parent_replicate) - parent_manifest = store.load_manifest(parent_key) - receipt = store.load_wave_receipt(parent_key, wave=parent_wave) - if parent_manifest is None or receipt is None: - raise TrainingLaunchError(f"{stage.stage_id} requires {parent_stage_id}/{parent_wave}") - parent_source_git_sha = str(parent_manifest.payload.get("source_git_sha", "")) - if not _GIT_SHA.fullmatch(parent_source_git_sha): - raise TrainingLaunchError("parent run manifest has an invalid source commit") - if ( - receipt.payload.get("stage_id") != parent_stage_id - or receipt.payload.get("wave") != parent_wave - or receipt.payload.get("run_manifest_record_sha256") != parent_manifest.record_sha256 - ): - raise TrainingLaunchError("parent receipt and run manifest identities differ") - if ( - parent_manifest.payload.get("contract_version") != protocol["contract_version"] - ): - raise TrainingLaunchError("parent run manifest differs from the consumer protocol") - historical_parent_transition = ( - ( - ( - approval is not None - and approval.payload.get("schema_version") - == PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ) - or ( - existing_manifest is not None - and isinstance( - existing_manifest.payload.get("source_transition"), - Mapping, - ) - and existing_manifest.payload["source_transition"].get( - "approval_schema_version" - ) - == PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ) - ) - and stage.stage_id == "qwen-l0-rl-l1" - and parent_stage_id == "qwen-l0-sft" - and parent_wave == "complete" - ) - if ( - parent_manifest.payload.get("protocol_logical_sha256") - != protocol["logical_sha256"] - and not historical_parent_transition - ): - raise TrainingLaunchError("parent run manifest differs from the consumer protocol") - parent_stage = parent_manifest.payload.get("stage") - if ( - not isinstance(parent_stage, Mapping) - or parent_stage.get("stage_id") != parent_stage_id - or parent_stage.get("model_key") != stage.model_key - ): - raise TrainingLaunchError("parent run manifest has the wrong stage or model") - checkpoint: dict[str, Any] - frozen_parent = ( - existing_manifest.payload.get("parent_receipt") if existing_manifest is not None else None - ) - if isinstance(frozen_parent, Mapping): - if ( - frozen_parent.get("stage_id") != parent_stage_id - or frozen_parent.get("wave") != parent_wave - or frozen_parent.get("replicate_id", parent_replicate) - != parent_replicate - or frozen_parent.get("record_sha256") != receipt.record_sha256 - ): - raise TrainingLaunchError("frozen stage manifest and current parent receipt differ") - frozen_checkpoint = frozen_parent.get("checkpoint") - if not isinstance(frozen_checkpoint, Mapping): - raise TrainingLaunchError("frozen stage parent has no checkpoint") - checkpoint = _checkpoint_from_inventory( - receipt, - frozen_checkpoint, - inventory_sha256=frozen_parent.get("checkpoint_inventory_logical_sha256"), - ) - else: - if approval is None: - raise TrainingLaunchError(f"{stage.stage_id} requires a selected parent checkpoint") - selected = approval.payload.get("selected_parent_checkpoint") - if not isinstance(selected, Mapping): - raise TrainingLaunchError( - f"{stage.stage_id} approval has no selected parent checkpoint" - ) - checkpoint = _checkpoint_from_inventory( - receipt, - selected, - inventory_sha256=approval.payload.get("selected_parent_checkpoint_inventory_sha256"), - ) - inventory = receipt.payload.get("checkpoint_inventory") - if not isinstance(inventory, Mapping): - raise TrainingLaunchError("parent receipt has no checkpoint inventory") - inventory_sha256 = str(inventory.get("logical_sha256", "")) - if approval is not None: - claimed = approval.payload.get("parent_receipt_record_sha256") - if claimed is not None and claimed != receipt.record_sha256: - raise TrainingLaunchError("approval and parent receipt differ") - claimed_parent_replicate = approval.payload.get("parent_replicate_id") - if ( - claimed_parent_replicate is not None - and claimed_parent_replicate != parent_replicate - ): - raise TrainingLaunchError("approval and parent execution identity differ") - selected = approval.payload.get("selected_parent_checkpoint") - if selected is not None and selected != checkpoint: - raise TrainingLaunchError("approval and selected parent checkpoint differ") - selected_inventory = approval.payload.get("selected_parent_checkpoint_inventory_sha256") - if selected_inventory is not None and selected_inventory != inventory_sha256: - raise TrainingLaunchError("approval and parent checkpoint inventory differ") - - entry_approval: TrainingRecord | None = None - source_transition: dict[str, Any] | None = None - if parent_source_git_sha != source_git_sha: - if existing_manifest is None: - if ( - approval is None - or approval.payload.get("schema_version") - not in { - SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION, - PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION, - } - ): - raise TrainingLaunchError( - "cross-source parent requires a v2 transition approval" - ) - entry_approval = approval - else: - entry_approval = _manifest_transition_approval( - repo_root=repo_root, - protocol=protocol, - store=store, - key=key, - stage=stage, - source_git_sha=source_git_sha, - existing_manifest=existing_manifest, - ) - if ( - entry_approval.payload.get("evidence_source_git_sha") != parent_source_git_sha - or entry_approval.payload.get("parent_stage_id") != parent_stage_id - or entry_approval.payload.get("parent_wave") != parent_wave - or entry_approval.payload.get("parent_receipt_record_sha256") != receipt.record_sha256 - or entry_approval.payload.get("selected_parent_checkpoint") != checkpoint - or entry_approval.payload.get("selected_parent_checkpoint_inventory_sha256") - != inventory_sha256 - ): - raise TrainingLaunchError("v2 transition approval differs from the selected parent") - raw_transition = entry_approval.payload.get("source_transition") - if not isinstance(raw_transition, Mapping): - raise TrainingLaunchError("v2 transition approval has no source transition") - source_transition = dict(raw_transition) - elif existing_manifest is not None and "source_transition" in existing_manifest.payload: - raise TrainingLaunchError("same-source parent cannot reuse a child source transition") - return ParentBinding( - stage_id=parent_stage_id, - wave=parent_wave, - replicate_id=parent_replicate, - receipt=receipt, - checkpoint=checkpoint, - source_git_sha=parent_source_git_sha, - run_manifest=parent_manifest, - entry_approval=entry_approval, - source_transition=source_transition, - ) - - -def _required_prior_wave(stage: StageSpec, wave_name: str) -> str | None: - target = int(stage.wave(wave_name)["max_steps"]) - earlier = [ - (int(wave["max_steps"]), name) - for name, wave in stage.waves.items() - if int(wave["max_steps"]) < target - ] - return max(earlier)[1] if earlier else None - - -def _approval_record( - *, - repo_root: Path, - store: TrainingStore, - key: StageKey, - stage: StageSpec, - wave_name: str, - source_git_sha: str, - protocol: Mapping[str, Any], -) -> TrainingRecord | None: - gate = str(stage.wave(wave_name)["approval"]) - prior_wave = _required_prior_wave(stage, wave_name) - prior: TrainingRecord | None = None - if prior_wave is not None: - prior = store.load_wave_receipt(key, wave=prior_wave) - if prior is None: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} requires its {prior_wave} receipt" - ) - if gate == "initial": - return None - if gate == "base-level-held-out-baseline-receipt": - if prior_wave is not None: - raise TrainingLaunchError("base baseline gate cannot follow a training wave") - try: - return record_base_baseline_approval( - repo_root=repo_root, - store=store, - key=key, - stage=stage, - wave_name=wave_name, - source_git_sha=source_git_sha, - protocol=protocol, - ) - except (PromotionError, ValueError) as exc: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} base baseline gate failed: {exc}" - ) from exc - if gate == "rollout-health-receipt": - if prior_wave != "smoke" or prior is None: - raise TrainingLaunchError( - "rollout-health gate requires the exact smoke receipt" - ) - try: - return record_rollout_health_approval( - store=store, - key=key, - stage=stage, - wave_name=wave_name, - prior_wave=prior_wave, - prior_receipt=prior, - source_git_sha=source_git_sha, - protocol=protocol, - ) - except (PromotionError, ValueError) as exc: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} rollout-health gate failed: {exc}" - ) from exc - if gate == "parent-receipt-and-source-transition": - if prior_wave is not None: - raise TrainingLaunchError( - "historical-parent transition cannot follow a child training wave" - ) - approval = store.load_wave_approval(key, wave=wave_name) - if approval is None: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} requires an immutable {gate} approval" - ) - try: - validate_parent_receipt_transition_approval( - repo_root=repo_root, - protocol=protocol, - store=store, - stage=stage, - wave_name=wave_name, - replicate_id=key.replicate_id, - consumer_source_git_sha=source_git_sha, - approval=approval, - ) - except PromotionError as exc: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} parent transition failed: {exc}" - ) from exc - return approval - approval = store.load_wave_approval(key, wave=wave_name) - if approval is None: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} requires an immutable {gate} approval" - ) - schema_version = approval.payload.get("schema_version") - if schema_version == APPROVAL_SCHEMA_VERSION: - if any( - field in approval.payload for field in ("evidence_source_git_sha", "source_transition") - ): - raise TrainingLaunchError("same-source v1 approval contains source-transition fields") - try: - validate_same_source_wave_approval( - repo_root=repo_root, - protocol=protocol, - store=store, - stage=stage, - wave_name=wave_name, - replicate_id=key.replicate_id, - source_git_sha=source_git_sha, - approval=approval, - ) - except PromotionError as exc: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} same-source approval " - f"failed validation: {exc}" - ) from exc - elif schema_version == SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION: - try: - validate_source_transition_approval( - repo_root=repo_root, - protocol=protocol, - store=store, - stage=stage, - wave_name=wave_name, - replicate_id=key.replicate_id, - consumer_source_git_sha=source_git_sha, - approval=approval, - ) - except PromotionError as exc: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} source-transition approval failed validation: {exc}" - ) from exc - else: - raise TrainingLaunchError(f"{stage.stage_id}/{wave_name} approval has a foreign schema") - expected = { - "decision": "approve", - "approval_gate": gate, - "stage_id": stage.stage_id, - "wave": wave_name, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - } - for field, value in expected.items(): - if approval.payload.get(field) != value: - raise TrainingLaunchError(f"{stage.stage_id}/{wave_name} approval has wrong {field}") - if gate == "clean-smoke-receipt": - if prior_wave is None or prior is None: - raise TrainingLaunchError("clean-smoke gate has no prior wave receipt") - if ( - approval.payload.get("prior_wave") != prior_wave - or approval.payload.get("prior_receipt_record_sha256") != prior.record_sha256 - ): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} approval is not bound to its {prior_wave} receipt" - ) - elif gate == "promotion-receipt-and-explicit-human-approval": - if prior_wave is None or prior is None: - raise TrainingLaunchError("promotion gate has no prior wave receipt") - evidence = approval.payload.get("evaluation") - if not isinstance(evidence, Mapping): - raise TrainingLaunchError("promotion approval has no evaluation evidence") - expected_evidence = { - "stage_id": stage.stage_id, - "wave": prior_wave, - "panel": "inkling-promotion", - "training_wave_receipt_record_sha256": prior.record_sha256, - } - if any(evidence.get(field) != value for field, value in expected_evidence.items()): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} promotion is not bound " - f"to its {prior_wave} evaluation" - ) - evaluated_checkpoint = evidence.get("checkpoint") - receipt_checkpoint = prior.payload.get("checkpoint") - checkpoint_fields = ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - if ( - not isinstance(evaluated_checkpoint, Mapping) - or not isinstance(receipt_checkpoint, Mapping) - or any( - evaluated_checkpoint.get(field) != receipt_checkpoint.get(field) - for field in checkpoint_fields - ) - ): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} promotion checkpoint differs " - f"from its {prior_wave} receipt" - ) - elif gate == "current-level-held-out-promotion-receipt": - if ( - prior_wave is None - or prior is None - or stage.current_level not in LEVEL_PROGRESS_PANELS - ): - raise TrainingLaunchError("current-level promotion has no prior receipt") - evidence = approval.payload.get("evaluation") - expected_evidence = { - "stage_id": stage.stage_id, - "wave": prior_wave, - "panel": LEVEL_PROGRESS_PANELS[stage.current_level], - "training_wave_receipt_record_sha256": prior.record_sha256, - } - if not isinstance(evidence, Mapping) or any( - evidence.get(field) != value - for field, value in expected_evidence.items() - ): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} is not bound to its " - f"{stage.current_level} held-out report" - ) - checkpoint_fields = ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - evaluated_checkpoint = evidence.get("checkpoint") - receipt_checkpoint = prior.payload.get("checkpoint") - if ( - approval.payload.get("prior_wave") != prior_wave - or approval.payload.get("prior_receipt_record_sha256") - != prior.record_sha256 - or not isinstance(evaluated_checkpoint, Mapping) - or not isinstance(receipt_checkpoint, Mapping) - or any( - evaluated_checkpoint.get(field) != receipt_checkpoint.get(field) - for field in checkpoint_fields - ) - or not _SHA256.fullmatch( - str(evidence.get("report_record_sha256", "")) - ) - ): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} held-out evidence differs " - f"from its {prior_wave} checkpoint" - ) - elif gate == "prior-level-held-out-promotion-receipt": - if stage.current_level not in LEVELS[1:]: - raise TrainingLaunchError("prior-level promotion has no prior level") - parent_stage_id, parent_wave = stage.parent.split(":", 1) - parent_key = StageKey(key.study_id, parent_stage_id, key.replicate_id) - parent_receipt = store.load_wave_receipt(parent_key, wave=parent_wave) - prior_level = LEVELS[LEVELS.index(stage.current_level) - 1] - evidence = approval.payload.get("evaluation") - expected_evidence = { - "stage_id": parent_stage_id, - "wave": parent_wave, - "panel": LEVEL_PROGRESS_PANELS[prior_level], - "training_wave_receipt_record_sha256": ( - parent_receipt.record_sha256 if parent_receipt is not None else None - ), - } - if parent_receipt is None or not isinstance(evidence, Mapping) or any( - evidence.get(field) != value - for field, value in expected_evidence.items() - ): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} is not bound to its " - f"{prior_level} parent held-out report" - ) - checkpoint_fields = ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - evaluated_checkpoint = evidence.get("checkpoint") - receipt_checkpoint = parent_receipt.payload.get("checkpoint") - if ( - approval.payload.get("parent_stage_id") != parent_stage_id - or approval.payload.get("parent_wave") != parent_wave - or approval.payload.get("parent_receipt_record_sha256") - != parent_receipt.record_sha256 - or approval.payload.get("selected_parent_checkpoint") - != evaluated_checkpoint - or not isinstance(evaluated_checkpoint, Mapping) - or not isinstance(receipt_checkpoint, Mapping) - or any( - evaluated_checkpoint.get(field) != receipt_checkpoint.get(field) - for field in checkpoint_fields - ) - or not _SHA256.fullmatch( - str(evidence.get("report_record_sha256", "")) - ) - ): - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} parent held-out evidence differs " - f"from its {parent_stage_id}/{parent_wave} checkpoint" - ) - return approval - - -def _manifest_payload( - *, - protocol: Mapping[str, Any], - source_git_sha: str, - stage: StageSpec, - preflight: Mapping[str, Any], - tracking: Mapping[str, Any], - parent: ParentBinding | None, -) -> dict[str, Any]: - payload = { - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": preflight["protocol_file_sha256"], - "contract_version": protocol["contract_version"], - "prompt_assets": preflight["prompt_assets"], - "dataset": preflight["dataset"], - "runtime": preflight["runtime"], - "sandbox": preflight["sandbox"], - "stage": { - "stage_id": stage.stage_id, - "kind": stage.kind, - "model_key": stage.model_key, - "recipe_key": stage.recipe_key, - "hypotheses": list(stage.hypotheses), - "parent_policy": stage.parent, - "levels": list(stage.levels), - "current_level": stage.current_level, - "replay_levels": list(stage.replay_levels), - "waves": stage.waves, - }, - "model": preflight["model"], - "recipe": preflight["recipe"], - "schedule": preflight["schedule"], - "tracking": dict(tracking), - "parent_receipt": ( - { - "relative_path": str(parent.receipt.relative_path), - "stage_id": parent.stage_id, - "wave": parent.wave, - "replicate_id": parent.replicate_id, - "record_sha256": parent.receipt.record_sha256, - "checkpoint": parent.checkpoint, - "checkpoint_inventory_logical_sha256": parent.receipt.payload[ - "checkpoint_inventory" - ]["logical_sha256"], - } - if parent is not None - else None - ), - } - if parent is not None and parent.source_transition is not None: - if parent.entry_approval is None: - raise TrainingLaunchError("cross-source parent has no v2 entry approval") - payload["source_transition"] = _child_source_transition_binding(parent.entry_approval) - return payload - - -def _candidate_recorder( - store: TrainingStore, - key: StageKey, - *, - invocation_id: str, -) -> Callable[[Mapping[str, object]], None]: - def record(value: Mapping[str, object]) -> None: - payload = dict(value) - try: - step = int(payload["step"]) - attempt = int(payload["attempt"]) - task = payload["task"] - if not isinstance(task, Mapping): - raise TypeError("task is not an object") - task_id = str(task["opaque_id"]) - except (KeyError, TypeError, ValueError) as exc: - raise TrainingLaunchError("candidate record has no immutable key") from exc - sample_payload = dict(payload) - evaluation = sample_payload.pop("evaluation", None) - if not isinstance(evaluation, Mapping): - raise TrainingLaunchError("candidate record has no evaluation") - sample = store.load_candidate_sample( - key, - invocation_id=invocation_id, - step=step, - task_id=task_id, - attempt=attempt, - ) - if sample is None or canonical_sha256(sample_payload) != sample.payload_sha256: - raise TrainingLaunchError( - "candidate evaluation does not match its persisted paid sample" - ) - store.write_candidate( - key, - invocation_id=invocation_id, - step=step, - task_id=task_id, - attempt=attempt, - payload={ - "sample_record": { - "relative_path": str(sample.relative_path), - "payload_sha256": sample.payload_sha256, - "record_sha256": sample.record_sha256, - }, - "evaluation": dict(evaluation), - }, - ) - - return record - - -def _candidate_sample_recorder( - store: TrainingStore, - key: StageKey, - *, - invocation_id: str, -) -> Callable[[Mapping[str, object]], None]: - def record(value: Mapping[str, object]) -> None: - payload = dict(value) - try: - step = int(payload["step"]) - attempt = int(payload["attempt"]) - task = payload["task"] - if not isinstance(task, Mapping): - raise TypeError("task is not an object") - task_id = str(task["opaque_id"]) - except (KeyError, TypeError, ValueError) as exc: - raise TrainingLaunchError("candidate sample has no immutable key") from exc - store.write_candidate_sample( - key, - invocation_id=invocation_id, - step=step, - task_id=task_id, - attempt=attempt, - payload=payload, - ) - - return record - - -def _resolved_launch_binding( - *, - repo_root: Path, - protocol: Mapping[str, Any], - stage: StageSpec, - wave_name: str, - store: TrainingStore, - key: StageKey, - parent: ParentBinding | None, - resume_batch: int | None, - resume_checkpoint: Mapping[str, Any] | None = None, -) -> dict[str, Any]: - """Return the complete scientific policy consumed by the Cookbook config.""" - - model = protocol["models"][stage.model_key] - recipe = protocol["recipes"][stage.recipe_key] - max_steps = int(stage.wave(wave_name)["max_steps"]) - checkpoint = parent.checkpoint if parent is not None else None - manifest = store.load_manifest(key) - if manifest is None: - raise TrainingLaunchError("resolved launch cannot precede its run manifest") - common: dict[str, Any] = { - "schema_version": "pixcell-resolved-training-launch-v1", - "stage_id": stage.stage_id, - "wave": wave_name, - "kind": stage.kind, - "model_name": model["model"], - "renderer_name": model["renderer"], - "renderer_effort": model.get("effort"), - "thinking": bool(model["thinking"]), - "context_tokens": int(model["context_tokens"]), - "max_image_long_edge": int(model["max_image_long_edge"]), - "lora_rank": int(model["lora_rank"]), - "learning_rate": float(recipe["learning_rate"]), - "max_steps": max_steps, - "dataset_root": str((repo_root / "dataset").relative_to(repo_root)), - "dataset_configuration": protocol["dataset"]["configuration"], - "dataset_revision": protocol["dataset"]["revision"], - "dataset_logical_release_sha256": protocol["dataset"]["logical_release_sha256"], - "schedule_seed": SCHEDULE_SEED, - "tinker_log_relative_path": str( - store.tinker_log_path(key).relative_to(store.external_root) - ), - "initial_checkpoint": checkpoint, - "resume_batch": resume_batch, - "resume_checkpoint": ( - dict(resume_checkpoint) if resume_checkpoint is not None else None - ), - "wandb_run_id": deterministic_wandb_run_id( - source_git_sha=str(manifest.payload["source_git_sha"]), - protocol_logical_sha256=str(protocol["logical_sha256"]), - key=key, - ), - } - if stage.kind == "sft": - common["dataset"] = { - "split": "train", - "levels": list(stage.levels), - "batch_size": int(recipe["batch_size"]), - "max_sequence_tokens": int(recipe["max_sequence_tokens"]), - "include_validation": bool(recipe["automatic_nll_validation"]), - "loss_mass": recipe["loss_mass"], - "target": recipe["target"], - } - common["optimizer"] = { - "name": "adam", - "beta1": float(recipe["adam_beta1"]), - "beta2": float(recipe["adam_beta2"]), - "epsilon": float(recipe["adam_epsilon"]), - "lr_schedule": recipe["lr_schedule"], - "num_epochs": int(recipe["effective_passes"]), - "optimizer_reset_each_stage": bool(recipe["optimizer_reset_each_stage"]), - } - common["checkpointing"] = { - "save_every": max( - 1, - round(max_steps * float(recipe["checkpoint_fraction"])), - ), - "rolling_save_every": int(recipe["rolling_save_every_steps"]), - "periodic_ttl_seconds": recipe["checkpoint_ttl_seconds"], - } - common["automatic_evaluation"] = { - "eval_every": 0, - "automatic_nll_validation": False, - } - else: - common["dataset"] = { - "split": "train", - "current_level": stage.current_level, - "replay_levels": list(stage.replay_levels), - "groups_per_batch": int(recipe["groups_per_batch"]), - "group_size": int(recipe["group_size"]), - "replay_fraction": float(recipe["replay_fraction"]), - "require_prefix_replay": stage.model_key == "qwen", - "validation_canaries": int(recipe["automatic_validation_rows"]), - } - common["sampling"] = { - "max_output_tokens": int(model["max_output_tokens"]), - "temperature": float(recipe["temperature"]), - # The pinned Cookbook constructs SamplingParams without top_p; - # Tinker's pinned default is 1.0 and preflight verifies that fact. - "top_p": float(recipe["top_p"]), - } - common["optimizer"] = { - "loss_fn": recipe["loss_fn"], - "kl_coefficient": float(recipe["kl_coefficient"]), - "remove_constant_reward_groups": bool(recipe["remove_constant_reward_groups"]), - "reward": recipe["reward"], - } - common["checkpointing"] = { - "policy": recipe["checkpoint_policy"], - "save_every": max_steps, - "rolling_save_every": int(recipe["rolling_save_every_steps"]), - "periodic_ttl_seconds": recipe["checkpoint_ttl_seconds"], - } - common["automatic_evaluation"] = { - "eval_every": 0, - "validation_rows": 0, - } - common["rollout"] = { - "error_policy": recipe["rollout_error_policy"], - "json_export": bool(recipe["rollout_json_export"]), - "groups_to_log": 0, - } - if stage.stage_id.startswith(("qwen-base-rl-", "qwen-l0-rl-")): - gate = str(stage.wave(wave_name)["approval"]) - gate_record = ( - store.load_base_baseline_approval(key, wave=wave_name) - if gate == "base-level-held-out-baseline-receipt" - else ( - store.load_rollout_health_approval(key, wave=wave_name) - if gate == "rollout-health-receipt" - else ( - store.load_wave_approval(key, wave=wave_name) - if gate - in { - "current-level-held-out-promotion-receipt", - "parent-receipt-and-source-transition", - "prior-level-held-out-promotion-receipt", - "prior-and-current-level-held-out-transition-receipt", - } - else None - ) - ) - ) - if gate != "initial": - if gate_record is None: - raise TrainingLaunchError( - f"{stage.stage_id}/{wave_name} has no immutable gate evidence" - ) - common["wave_gate_evidence"] = { - "approval_gate": gate, - "relative_path": str(gate_record.relative_path), - "payload_sha256": gate_record.payload_sha256, - "record_sha256": gate_record.record_sha256, - } - common["logical_sha256"] = canonical_sha256(common) - return common - - -def _invocation_payload( - *, - protocol: Mapping[str, Any], - stage: StageSpec, - wave_name: str, - source_git_sha: str, - invocation_id: str, - manifest: TrainingRecord, - preflight: Mapping[str, Any], - tracking: Mapping[str, Any], - resume_batch: int | None, - resolved_launch: Mapping[str, Any], - parent: ParentBinding | None, - resume_checkpoint: Mapping[str, Any] | None = None, -) -> dict[str, Any]: - """Build the immutable invocation record shared by launch and replay.""" - - return { - "invocation_id": invocation_id, - "stage_id": stage.stage_id, - "wave": wave_name, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "run_manifest_record_sha256": manifest.record_sha256, - "preflight_sha256": preflight["preflight_sha256"], - "wandb_run_id": tracking["run_id"], - "resume_batch": resume_batch, - "resume_checkpoint": ( - dict(resume_checkpoint) if resume_checkpoint is not None else None - ), - "resolved_launch": dict(resolved_launch), - "resolved_launch_sha256": resolved_launch["logical_sha256"], - "policy": { - "model": protocol["models"][stage.model_key]["model"], - "renderer": protocol["models"][stage.model_key]["renderer"], - "effort": protocol["models"][stage.model_key].get("effort"), - "max_output_tokens": protocol["models"][stage.model_key]["max_output_tokens"], - "temperature": protocol["recipes"][stage.recipe_key].get("temperature"), - "top_p": protocol["recipes"][stage.recipe_key].get("top_p"), - "reward": protocol["recipes"][stage.recipe_key].get("reward"), - "group_size": protocol["recipes"][stage.recipe_key].get("group_size"), - "groups_per_batch": protocol["recipes"][stage.recipe_key].get("groups_per_batch"), - }, - "initial_checkpoint": parent.checkpoint if parent is not None else None, - } - - -def build_tinker_config( - *, - repo_root: Path, - protocol: Mapping[str, Any], - stage: StageSpec, - wave_name: str, - store: TrainingStore, - key: StageKey, - parent: ParentBinding | None, - invocation: TrainingRecord | None = None, -) -> SFTConfig | RLConfig: - """Resolve one sealed Cookbook config without creating a service client.""" - - model = protocol["models"][stage.model_key] - recipe = protocol["recipes"][stage.recipe_key] - max_steps = int(stage.wave(wave_name)["max_steps"]) - checkpoint = parent.checkpoint if parent is not None else None - load_checkpoint_path = str(checkpoint["state_path"]) if checkpoint is not None else None - log_path = str(store.tinker_log_path(key)) - manifest = store.load_manifest(key) - if manifest is None: - raise TrainingLaunchError("Tinker config cannot precede its run manifest") - resolved_launch = _resolved_launch_binding( - repo_root=repo_root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - store=store, - key=key, - parent=parent, - resume_batch=( - int(invocation.payload["resume_batch"]) - if invocation is not None and invocation.payload.get("resume_batch") is not None - else None - ), - resume_checkpoint=( - invocation.payload.get("resume_checkpoint") - if invocation is not None - else None - ), - ) - if invocation is not None and invocation.payload.get("resolved_launch") != resolved_launch: - raise TrainingLaunchError("immutable invocation differs from the resolved Tinker launch") - tracking = _tracking_binding( - protocol=protocol, - source_git_sha=str(manifest.payload["source_git_sha"]), - key=key, - stage=stage, - store=store, - ) - common = { - "log_path": log_path, - "model_name": model["model"], - "recipe_name": f"pixcell_{STUDY_ID}_{stage.stage_id}", - "renderer_name": model["renderer"], - "load_checkpoint_path": load_checkpoint_path, - "learning_rate": float(recipe["learning_rate"]), - "lora_rank": int(model["lora_rank"]), - "wandb_project": tracking["project"], - "wandb_name": tracking["name"], - "max_steps": max_steps, - } - if stage.kind == "sft": - save_every = max( - 1, - round(max_steps * float(recipe["checkpoint_fraction"])), - ) - return SFTConfig( - **common, - dataset_builder=TrackASupervisedDatasetBuilder( - dataset_root=str(repo_root / "dataset"), - model_name=model["model"], - renderer_name=model["renderer"], - batch_size=int(recipe["batch_size"]), - max_length=int(recipe["max_sequence_tokens"]), - max_image=int(model["max_image_long_edge"]), - levels=",".join(stage.levels), - include_validation=bool(recipe["automatic_nll_validation"]), - schedule_seed=SCHEDULE_SEED, - ), - lr_schedule=str(recipe["lr_schedule"]), - num_epochs=int(recipe["effective_passes"]), - adam_beta1=float(recipe["adam_beta1"]), - adam_beta2=float(recipe["adam_beta2"]), - adam_eps=float(recipe["adam_epsilon"]), - save_every=save_every, - eval_every=0, - rolling_save_every=int(recipe["rolling_save_every_steps"]), - ttl_seconds=recipe["checkpoint_ttl_seconds"], - ) - return RLConfig( - **common, - dataset_builder=TrackARLDatasetBuilder( - dataset_root=str(repo_root / "dataset"), - batch_size=int(recipe["groups_per_batch"]), - group_size=int(recipe["group_size"]), - model_name=model["model"], - renderer_name=model["renderer"], - model_effort=model.get("effort"), - max_image=int(model["max_image_long_edge"]), - current_level=str(stage.current_level), - replay_levels=",".join(stage.replay_levels), - validation_canaries=int(recipe["automatic_validation_rows"]), - attempt_recorder=( - _candidate_recorder( - store, - key, - invocation_id=str(invocation.payload["invocation_id"]), - ) - if invocation is not None - else None - ), - sample_recorder=( - _candidate_sample_recorder( - store, - key, - invocation_id=str(invocation.payload["invocation_id"]), - ) - if invocation is not None - else None - ), - reward_policy="raw_iou", - require_prefix_replay=stage.model_key == "qwen", - schedule_seed=SCHEDULE_SEED, - attempt_metadata=( - { - "invocation_id": invocation.payload["invocation_id"], - "run_manifest_record_sha256": invocation.payload["run_manifest_record_sha256"], - "source_git_sha": invocation.payload["source_git_sha"], - "protocol_logical_sha256": invocation.payload["protocol_logical_sha256"], - "policy": invocation.payload["policy"], - "initial_checkpoint": invocation.payload["initial_checkpoint"], - } - if invocation is not None - else {} - ), - ), - max_tokens=int(model["max_output_tokens"]), - temperature=float(recipe["temperature"]), - eval_every=0, - save_every=max_steps, - rolling_save_every=int(recipe["rolling_save_every_steps"]), - loss_fn=str(recipe["loss_fn"]), - remove_constant_reward_groups=bool(recipe["remove_constant_reward_groups"]), - rollout_error_tolerance=( - False - if recipe["rollout_error_policy"] == "fail_fast" - else recipe["rollout_error_policy"] - ), - kl_penalty_coef=float(recipe["kl_coefficient"]), - num_groups_to_log=0, - rollout_json_export=bool(recipe["rollout_json_export"]), - ttl_seconds=recipe["checkpoint_ttl_seconds"], - ) - - -def _sha256_bytes(value: bytes) -> str: - return hashlib.sha256(value).hexdigest() - - -def _wave_artifact_snapshot( - *, - log_path: Path, - wave_name: str, - recorded: Mapping[str, Any] | None = None, -) -> tuple[dict[str, Any], dict[str, bytes]]: - """Freeze or verify the append-only log prefix belonging to one wave. - - Cookbook deliberately reuses ``log_path`` across resumable waves. A - receipt therefore cannot hash the live files directly: later waves append - to them. Each receipt instead binds create-only copies under its own wave - directory. During replay, the copies must still match their receipt and - the append-only live logs must retain the exact frozen prefix. - """ - - relative_root = Path("waves") / wave_name / "artifacts" - snapshot_root = log_path.parent / relative_root - expected_names = ("checkpoints.jsonl", "metrics.jsonl", "hparams.json") - stage_root = log_path.parent.resolve(strict=True) - resolved_snapshot_root = snapshot_root.resolve(strict=False) - if ( - resolved_snapshot_root == stage_root - or not resolved_snapshot_root.is_relative_to(stage_root) - ): - raise TrainingLaunchError("completed wave artifact snapshot escapes its stage") - - if recorded is None: - source_bytes: dict[str, bytes] = {} - for name in expected_names: - source = log_path / name - if source.is_symlink(): - raise TrainingLaunchError("live training artifact cannot be a symlink") - if source.is_file(): - source_bytes[name] = source.read_bytes() - if not {"checkpoints.jsonl", "metrics.jsonl"}.issubset(source_bytes): - raise TrainingLaunchError( - "training returned without checkpoint and metric artifacts" - ) - snapshot_root.mkdir(parents=True, exist_ok=True, mode=0o700) - for name, value in source_bytes.items(): - destination = snapshot_root / name - flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - descriptor = os.open(destination, flags, 0o600) - except FileExistsError: - if destination.is_symlink() or destination.read_bytes() != value: - raise TrainingLaunchError( - f"{wave_name} immutable artifact snapshot already differs" - ) - else: - try: - with os.fdopen(descriptor, "wb") as stream: - stream.write(value) - stream.flush() - os.fsync(stream.fileno()) - except BaseException: - destination.unlink(missing_ok=True) - raise - snapshot = { - "schema_version": _WAVE_ARTIFACT_SNAPSHOT_SCHEMA_VERSION, - "relative_root": relative_root.as_posix(), - "files": { - name: { - "byte_count": len(value), - "sha256": _sha256_bytes(value), - } - for name, value in sorted(source_bytes.items()) - }, - } - else: - snapshot = dict(recorded) - - if ( - set(snapshot) != {"schema_version", "relative_root", "files"} - or snapshot.get("schema_version") != _WAVE_ARTIFACT_SNAPSHOT_SCHEMA_VERSION - or snapshot.get("relative_root") != relative_root.as_posix() - or not isinstance(snapshot.get("files"), Mapping) - ): - raise TrainingLaunchError("completed wave artifact snapshot is malformed") - files = snapshot["files"] - if ( - not {"checkpoints.jsonl", "metrics.jsonl"}.issubset(files) - or not set(files).issubset(expected_names) - ): - raise TrainingLaunchError("completed wave artifact snapshot files differ") - - verified: dict[str, bytes] = {} - for name, metadata in files.items(): - if ( - not isinstance(name, str) - or not isinstance(metadata, Mapping) - or set(metadata) != {"byte_count", "sha256"} - or isinstance(metadata.get("byte_count"), bool) - or not isinstance(metadata.get("byte_count"), int) - or int(metadata["byte_count"]) < 0 - or not _SHA256.fullmatch(str(metadata.get("sha256", ""))) - ): - raise TrainingLaunchError("completed wave artifact snapshot entry is malformed") - snapshot_path = snapshot_root / name - if not snapshot_path.is_file() or snapshot_path.is_symlink(): - raise TrainingLaunchError("completed wave artifact snapshot is missing") - value = snapshot_path.read_bytes() - if ( - len(value) != int(metadata["byte_count"]) - or _sha256_bytes(value) != metadata["sha256"] - ): - raise TrainingLaunchError("completed wave artifact snapshot digest differs") - if name in {"checkpoints.jsonl", "metrics.jsonl"}: - live_path = log_path / name - if not live_path.is_file(): - if name == "checkpoints.jsonl": - raise TrainingLaunchError( - "completed wave is without a resumable checkpoint artifact" - ) - raise TrainingLaunchError("completed wave live artifact is missing") - with live_path.open("rb") as stream: - live_prefix = stream.read(len(value)) - if live_prefix != value: - raise TrainingLaunchError( - "completed wave canonical training artifacts no longer have " - "their frozen prefix" - ) - verified[name] = value - return snapshot, verified - - -def _checkpoint_records_from_bytes(value: bytes) -> list[Any]: - records: list[Any] = [] - try: - for raw_line in value.decode("utf-8").splitlines(): - if raw_line.strip(): - item = json.loads(raw_line) - if not isinstance(item, dict): - raise ValueError("checkpoint row is not an object") - records.append(checkpoint_utils.CheckpointRecord.from_dict(item)) - except (UnicodeDecodeError, ValueError, TypeError, json.JSONDecodeError) as exc: - raise TrainingLaunchError("checkpoint snapshot is not valid JSONL") from exc - return records - - -def _metrics_from_bytes(value: bytes) -> list[dict[str, Any]]: - records: list[dict[str, Any]] = [] - try: - for raw_line in value.decode("utf-8").splitlines(): - if raw_line.strip(): - item = json.loads(raw_line) - if not isinstance(item, dict): - raise ValueError("metric row is not an object") - records.append(item) - except (UnicodeDecodeError, ValueError, TypeError, json.JSONDecodeError) as exc: - raise TrainingLaunchError("metric snapshot is not valid JSONL") from exc - return records - - -def _checkpoint_receipt( - *, - stage: StageSpec, - wave_name: str, - log_path: Path, - manifest: TrainingRecord, - preflight: Mapping[str, Any], - invocation: TrainingRecord, - tracking: TrainingRecord | None, - artifact_snapshot: Mapping[str, Any] | None = None, -) -> dict[str, Any]: - snapshot, snapshot_files = _wave_artifact_snapshot( - log_path=log_path, - wave_name=wave_name, - recorded=artifact_snapshot, - ) - records = _checkpoint_records_from_bytes(snapshot_files["checkpoints.jsonl"]) - complete = [ - item for item in records if item.state_path is not None and item.sampler_path is not None - ] - if not complete: - raise TrainingLaunchError("training returned without a resumable checkpoint") - checkpoint = complete[-1] - target_steps = int(stage.wave(wave_name)["max_steps"]) - if stage.kind == "rl": - unique_name = f"{target_steps:06d}" - matching = [ - item for item in complete if item.name == unique_name and item.batch == target_steps - ] - if len(matching) != 1: - raise TrainingLaunchError( - f"RL wave has {len(matching)} unique {unique_name} checkpoints" - ) - checkpoint = matching[0] - if stage.kind == "sft": - if checkpoint.name != "final" or checkpoint.epoch != 1: - raise TrainingLaunchError("SFT terminal checkpoint is not the completed epoch") - if not _TINKER_PATH.fullmatch(str(checkpoint.state_path)) or not _TINKER_PATH.fullmatch( - str(checkpoint.sampler_path) - ): - raise TrainingLaunchError("checkpoint paths are not Tinker URIs") - recipe = preflight["recipe"] - save_every = ( - max( - 1, - round(target_steps * float(recipe["checkpoint_fraction"])), - ) - if stage.kind == "sft" - else target_steps - ) - expected_periodic_names = ( - [f"{step:06d}" for step in range(save_every, target_steps, save_every)] - if stage.kind == "sft" - else [] - ) - periodic_by_name: dict[str, Any] = {} - for item in complete: - if item.name not in expected_periodic_names: - continue - if item.name in periodic_by_name: - raise TrainingLaunchError(f"checkpoint inventory repeats {item.name}") - periodic_by_name[item.name] = item - if set(periodic_by_name) != set(expected_periodic_names): - raise TrainingLaunchError("periodic checkpoint inventory differs from the sealed cadence") - - def checkpoint_entry(item: Any, *, role: str) -> dict[str, Any]: - if not _TINKER_PATH.fullmatch(str(item.state_path)) or not _TINKER_PATH.fullmatch( - str(item.sampler_path) - ): - raise TrainingLaunchError("checkpoint inventory contains invalid URIs") - progress = 1.0 if role == "terminal" else int(item.name) / target_steps - return { - "name": str(item.name), - "batch": item.batch, - "epoch": item.epoch, - "final": role == "terminal", - "state_path": str(item.state_path), - "sampler_path": str(item.sampler_path), - "role": role, - "training_progress_fraction": progress, - } - - checkpoint_entries = [ - checkpoint_entry(periodic_by_name[name], role="periodic") - for name in expected_periodic_names - ] - terminal_entry = checkpoint_entry(checkpoint, role="terminal") - checkpoint_entries.append(terminal_entry) - local_artifacts = { - name: str(metadata["sha256"]) - for name, metadata in snapshot["files"].items() - } - candidate_root = log_path.parent / "candidates" / str(invocation.payload["invocation_id"]) - candidate_files = ( - sorted(candidate_root.rglob("evaluation.json")) if candidate_root.is_dir() else [] - ) - sample_files = sorted(candidate_root.rglob("sample.json")) if candidate_root.is_dir() else [] - candidate_inventory = [ - { - "path": str(path.relative_to(log_path.parent)), - "sha256": file_sha256(path), - } - for path in candidate_files - ] - if stage.kind == "rl": - start_batch = invocation.payload.get("resume_batch") - start = int(start_batch) if start_batch is not None else 0 - policy = invocation.payload["policy"] - expected_candidates = ( - (target_steps - start) * int(policy["groups_per_batch"]) * int(policy["group_size"]) - ) - if len(candidate_inventory) != expected_candidates: - raise TrainingLaunchError( - "candidate inventory does not cover every sampled rollout: " - f"{len(candidate_inventory)} != {expected_candidates}" - ) - if len(sample_files) != expected_candidates: - raise TrainingLaunchError( - "sample inventory does not cover every sampled rollout: " - f"{len(sample_files)} != {expected_candidates}" - ) - else: - expected_candidates = 0 - metrics = _metrics_from_bytes(snapshot_files["metrics.jsonl"]) - last_metrics = metrics[-1] if metrics else None - if last_metrics is None or last_metrics.get("step") != target_steps - 1: - raise TrainingLaunchError("metrics do not reach the exact wave ceiling") - required_metric = "optim/entropy" if stage.kind == "rl" else "train_mean_nll" - metric = last_metrics.get(required_metric) - if ( - isinstance(metric, bool) - or not isinstance(metric, (int, float)) - or not math.isfinite(float(metric)) - ): - raise TrainingLaunchError( - f"final metrics do not prove an optimizer update ({required_metric})" - ) - return { - "stage_id": stage.stage_id, - "wave": wave_name, - "max_steps": target_steps, - "run_manifest_record_sha256": manifest.record_sha256, - "invocation_record_sha256": invocation.record_sha256, - "invocation_id": invocation.payload["invocation_id"], - "invocation_tracking_record_sha256": ( - tracking.record_sha256 if tracking is not None else None - ), - "preflight_sha256": preflight["preflight_sha256"], - "checkpoint": { - key: terminal_entry[key] - for key in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - }, - "checkpoint_inventory": { - "count": len(checkpoint_entries), - "entries": checkpoint_entries, - "logical_sha256": canonical_sha256(checkpoint_entries), - }, - "artifact_snapshot": snapshot, - "local_artifact_sha256": local_artifacts, - "candidate_inventory": { - "count": len(candidate_inventory), - "expected_count": expected_candidates, - "logical_sha256": canonical_sha256(candidate_inventory), - }, - "sample_inventory": { - "count": len(sample_files), - "expected_count": expected_candidates, - "logical_sha256": canonical_sha256( - [ - { - "path": str(path.relative_to(log_path.parent)), - "sha256": file_sha256(path), - } - for path in sample_files - ] - ), - }, - "final_training_metric": { - "step": last_metrics["step"], - required_metric: float(metric), - }, - } - - -def _validate_completed_wave_receipt( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - key: StageKey, - stage: StageSpec, - wave_name: str, - manifest: TrainingRecord, - preflight: Mapping[str, Any], - parent: ParentBinding | None, - receipt: TrainingRecord, -) -> None: - """Fully validate an immutable completed-wave claim before replaying it.""" - - payload = receipt.payload - required_fields = { - "stage_id", - "wave", - "max_steps", - "run_manifest_record_sha256", - "invocation_record_sha256", - "invocation_id", - "invocation_tracking_record_sha256", - "preflight_sha256", - "checkpoint", - "checkpoint_inventory", - "artifact_snapshot", - "local_artifact_sha256", - "candidate_inventory", - "sample_inventory", - "final_training_metric", - } - if set(payload) != required_fields: - raise TrainingLaunchError("completed wave receipt fields differ from the canonical schema") - target_steps = int(stage.wave(wave_name)["max_steps"]) - if ( - payload.get("stage_id") != stage.stage_id - or payload.get("wave") != wave_name - or payload.get("max_steps") != target_steps - or payload.get("run_manifest_record_sha256") != manifest.record_sha256 - or payload.get("preflight_sha256") != preflight.get("preflight_sha256") - ): - raise TrainingLaunchError("completed wave receipt identity or preflight differs") - - invocation_id = payload.get("invocation_id") - if not isinstance(invocation_id, str): - raise TrainingLaunchError("completed wave receipt has no invocation ID") - try: - invocation = store.load_invocation(key, invocation_id=invocation_id) - except ValueError as exc: - raise TrainingLaunchError("completed wave receipt has an invalid invocation ID") from exc - if invocation is None or payload.get("invocation_record_sha256") != invocation.record_sha256: - raise TrainingLaunchError("completed wave receipt invocation is missing or differs") - resume_batch = invocation.payload.get("resume_batch") - if resume_batch is not None and ( - isinstance(resume_batch, bool) - or not isinstance(resume_batch, int) - or not 0 <= resume_batch <= target_steps - ): - raise TrainingLaunchError("completed wave invocation has an invalid resume batch") - resume_checkpoint = invocation.payload.get("resume_checkpoint") - if (resume_batch is None) != (resume_checkpoint is None): - raise TrainingLaunchError( - "completed wave invocation has an incomplete optimizer-resume binding" - ) - if resume_checkpoint is not None and not isinstance(resume_checkpoint, Mapping): - raise TrainingLaunchError( - "completed wave invocation optimizer-resume checkpoint is malformed" - ) - resolved_launch = _resolved_launch_binding( - repo_root=repo_root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - store=store, - key=key, - parent=parent, - resume_batch=resume_batch, - resume_checkpoint=resume_checkpoint, - ) - tracking_binding = manifest.payload.get("tracking") - if not isinstance(tracking_binding, Mapping): - raise TrainingLaunchError("completed wave manifest has no tracking binding") - expected_invocation = _invocation_payload( - protocol=protocol, - stage=stage, - wave_name=wave_name, - source_git_sha=str(manifest.payload.get("source_git_sha", "")), - invocation_id=invocation_id, - manifest=manifest, - preflight=preflight, - tracking=tracking_binding, - resume_batch=resume_batch, - resolved_launch=resolved_launch, - parent=parent, - resume_checkpoint=resume_checkpoint, - ) - if invocation.payload != expected_invocation: - raise TrainingLaunchError("completed wave invocation differs from the resolved launch") - - tracking_sha256 = payload.get("invocation_tracking_record_sha256") - tracking = store.load_invocation_tracking(key, invocation_id=invocation_id) - if tracking_sha256 is None: - if tracking is not None: - raise TrainingLaunchError("completed wave receipt omits existing tracking evidence") - else: - if ( - not isinstance(tracking_sha256, str) - or not _SHA256.fullmatch(tracking_sha256) - or tracking is None - or tracking.record_sha256 != tracking_sha256 - ): - raise TrainingLaunchError("completed wave tracking evidence is missing or differs") - expected_tracking = { - "invocation_record_sha256": invocation.record_sha256, - "run_manifest_record_sha256": manifest.record_sha256, - "source_git_sha": manifest.payload.get("source_git_sha"), - "protocol_logical_sha256": protocol["logical_sha256"], - "expected": dict(tracking_binding), - } - for field, expected in expected_tracking.items(): - if tracking.payload.get(field) != expected: - raise TrainingLaunchError(f"completed wave tracking evidence has wrong {field}") - observed = tracking.payload.get("observed") - if not isinstance(observed, Mapping): - raise TrainingLaunchError("completed wave tracking evidence has no live observation") - observed_expected = { - field: tracking_binding[field] - for field in ("run_id", "entity", "project", "name", "group", "job_type", "tags") - } - for field, expected in observed_expected.items(): - if observed.get(field) != expected: - raise TrainingLaunchError(f"completed wave live tracking has wrong {field}") - expected_resume = resume_batch is not None - if ( - observed.get("resumed") is not expected_resume - or observed.get("requested_resume") != ("must" if expected_resume else "never") - or observed.get("resolved_resume") != ("must" if expected_resume else "never") - or observed.get("requested_directory") != tracking_binding["directory"] - ): - raise TrainingLaunchError("completed wave live tracking resume binding differs") - - inventory = payload.get("checkpoint_inventory") - if not isinstance(inventory, Mapping) or set(inventory) != { - "count", - "entries", - "logical_sha256", - }: - raise TrainingLaunchError("completed wave checkpoint inventory is malformed") - entries = inventory.get("entries") - if ( - isinstance(inventory.get("count"), bool) - or not isinstance(inventory.get("count"), int) - or not isinstance(entries, list) - or inventory.get("count") != len(entries) - or inventory.get("logical_sha256") != canonical_sha256(entries) - ): - raise TrainingLaunchError("completed wave checkpoint inventory digest differs") - entry_fields = { - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - "role", - "training_progress_fraction", - } - names: set[str] = set() - terminal_entries: list[Mapping[str, Any]] = [] - periodic_entries: list[Mapping[str, Any]] = [] - for index, entry in enumerate(entries): - if not isinstance(entry, Mapping) or set(entry) != entry_fields: - raise TrainingLaunchError(f"completed wave checkpoint {index} is malformed") - name = entry.get("name") - role = entry.get("role") - progress = entry.get("training_progress_fraction") - if ( - not isinstance(name, str) - or name in names - or role not in {"periodic", "terminal"} - or entry.get("final") is not (role == "terminal") - or isinstance(entry.get("batch"), bool) - or ( - entry.get("batch") is not None - and (not isinstance(entry.get("batch"), int) or int(entry["batch"]) < 0) - ) - or isinstance(entry.get("epoch"), bool) - or ( - entry.get("epoch") is not None - and (not isinstance(entry.get("epoch"), int) or int(entry["epoch"]) < 0) - ) - or not _TINKER_PATH.fullmatch(str(entry.get("state_path", ""))) - or not _TINKER_PATH.fullmatch(str(entry.get("sampler_path", ""))) - or isinstance(progress, bool) - or not isinstance(progress, (int, float)) - or not math.isfinite(float(progress)) - or not 0.0 <= float(progress) <= 1.0 - ): - raise TrainingLaunchError(f"completed wave checkpoint {index} is invalid") - names.add(name) - if role == "terminal": - terminal_entries.append(entry) - if float(progress) != 1.0: - raise TrainingLaunchError("terminal checkpoint has wrong progress") - else: - periodic_entries.append(entry) - if len(terminal_entries) != 1: - raise TrainingLaunchError("completed wave must have one terminal checkpoint") - terminal = terminal_entries[0] - if stage.kind == "rl": - if ( - len(entries) != 1 - or terminal.get("name") != f"{target_steps:06d}" - or terminal.get("batch") != target_steps - ): - raise TrainingLaunchError("completed RL wave checkpoint differs from its ceiling") - else: - save_every = max( - 1, - round( - target_steps * float(protocol["recipes"][stage.recipe_key]["checkpoint_fraction"]) - ), - ) - expected_periodic = [f"{step:06d}" for step in range(save_every, target_steps, save_every)] - if ( - [str(entry["name"]) for entry in entries] != [*expected_periodic, "final"] - or terminal.get("name") != "final" - or terminal.get("epoch") != 1 - ): - raise TrainingLaunchError("completed SFT checkpoint cadence differs") - for entry in periodic_entries: - expected_progress = int(str(entry["name"])) / target_steps - if not math.isclose( - float(entry["training_progress_fraction"]), - expected_progress, - rel_tol=0.0, - abs_tol=1e-12, - ): - raise TrainingLaunchError("periodic checkpoint progress differs") - selected = payload.get("checkpoint") - checkpoint_fields = { - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - } - if not isinstance(selected, Mapping) or set(selected) != checkpoint_fields: - raise TrainingLaunchError("completed wave selected checkpoint is malformed") - if any(selected[field] != terminal[field] for field in checkpoint_fields): - raise TrainingLaunchError("completed wave selected checkpoint is not terminal") - - local_artifacts = payload.get("local_artifact_sha256") - if ( - not isinstance(local_artifacts, Mapping) - or not {"checkpoints.jsonl", "metrics.jsonl"}.issubset(local_artifacts) - or not set(local_artifacts).issubset({"checkpoints.jsonl", "metrics.jsonl", "hparams.json"}) - or any( - not isinstance(value, str) or not _SHA256.fullmatch(value) - for value in local_artifacts.values() - ) - ): - raise TrainingLaunchError("completed wave local artifact inventory is malformed") - - start = resume_batch if resume_batch is not None else 0 - recipe = protocol["recipes"][stage.recipe_key] - expected_candidates = ( - (target_steps - start) * int(recipe["groups_per_batch"]) * int(recipe["group_size"]) - if stage.kind == "rl" - else 0 - ) - candidate_root = store.tinker_log_path(key).parent / "candidates" / invocation_id - candidate_files = ( - sorted(candidate_root.rglob("evaluation.json")) if candidate_root.is_dir() else [] - ) - sample_files = sorted(candidate_root.rglob("sample.json")) if candidate_root.is_dir() else [] - for field, files in ( - ("candidate_inventory", candidate_files), - ("sample_inventory", sample_files), - ): - observed_inventory = [ - { - "path": str(path.relative_to(store.tinker_log_path(key).parent)), - "sha256": file_sha256(path), - } - for path in files - ] - recorded = payload.get(field) - if ( - not isinstance(recorded, Mapping) - or set(recorded) != {"count", "expected_count", "logical_sha256"} - or isinstance(recorded.get("count"), bool) - or not isinstance(recorded.get("count"), int) - or isinstance(recorded.get("expected_count"), bool) - or not isinstance(recorded.get("expected_count"), int) - or recorded.get("count") != len(observed_inventory) - or recorded.get("expected_count") != expected_candidates - or len(observed_inventory) != expected_candidates - or recorded.get("logical_sha256") != canonical_sha256(observed_inventory) - ): - raise TrainingLaunchError(f"completed wave {field} does not prove full coverage") - - final_metric = payload.get("final_training_metric") - required_metric = "optim/entropy" if stage.kind == "rl" else "train_mean_nll" - if ( - not isinstance(final_metric, Mapping) - or set(final_metric) != {"step", required_metric} - or final_metric.get("step") != target_steps - 1 - ): - raise TrainingLaunchError("completed wave final optimizer metric is malformed") - metric = final_metric.get(required_metric) - if ( - isinstance(metric, bool) - or not isinstance(metric, (int, float)) - or not math.isfinite(float(metric)) - ): - raise TrainingLaunchError("completed wave final optimizer metric is not finite") - - # A receipt is only a cache of facts reconstructed from the immutable - # invocation and the local training artifacts. Rebuild the canonical - # payload so a well-formed but forged hash, metric, checkpoint inventory, - # or rollout count cannot turn an incomplete run into ``already_complete``. - try: - reconstructed = _checkpoint_receipt( - stage=stage, - wave_name=wave_name, - log_path=store.tinker_log_path(key), - manifest=manifest, - preflight=preflight, - invocation=invocation, - tracking=tracking, - artifact_snapshot=payload.get("artifact_snapshot"), - ) - except TrainingLaunchError: - raise - except Exception as exc: - raise TrainingLaunchError( - "completed wave artifacts could not reconstruct the canonical receipt" - ) from exc - if payload != reconstructed: - raise TrainingLaunchError( - "completed wave receipt differs from the current canonical training artifacts" - ) - - -def _checkpoint_identity(item: Any) -> dict[str, Any]: - if hasattr(item, "to_dict"): - value = item.to_dict() - elif isinstance(item, Mapping): - value = dict(item) - else: - raise TrainingLaunchError("optimizer checkpoint has an unsupported record type") - try: - normalized = json.loads( - json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ) - ) - except (TypeError, ValueError) as exc: - raise TrainingLaunchError("optimizer checkpoint is not finite JSON") from exc - if not isinstance(normalized, dict): - raise TrainingLaunchError("optimizer checkpoint is not an object") - return normalized - - -def _matches_receipt_checkpoint(item: Any, expected: Mapping[str, Any]) -> bool: - return all( - getattr(item, field, None) == expected.get(field) - for field in ( - "name", - "batch", - "epoch", - "state_path", - "sampler_path", - ) - ) - - -def _validate_resume_state( - *, - stage: StageSpec, - wave_name: str, - log_path: Path, - prior_wave_receipt: TrainingRecord | None, -) -> ResumeBinding | None: - if not log_path.exists(): - return None - records = checkpoint_utils.load_checkpoints_file(str(log_path)) - target = int(stage.wave(wave_name)["max_steps"]) - resumable = [ - (index, item) - for index, item in enumerate(records) - if item.state_path is not None - ] - progress: list[int] = [] - for _index, item in resumable: - if isinstance(item.batch, bool) or not isinstance(item.batch, int): - raise TrainingLaunchError("resumable optimizer checkpoint has no integer batch") - progress.append(item.batch) - if progress and max(progress) > target: - raise TrainingLaunchError("local optimizer state is ahead of this wave") - - selected: Any | None = None - if prior_wave_receipt is not None: - expected = prior_wave_receipt.payload["checkpoint"] - matches = [ - (index, item) - for index, item in resumable - if _matches_receipt_checkpoint(item, expected) - ] - if len(matches) != 1: - raise TrainingLaunchError("optimizer log does not contain the prior-wave checkpoint") - expected_index, expected_item = matches[0] - expected_batch = expected.get("batch") - if ( - isinstance(expected_batch, bool) - or not isinstance(expected_batch, int) - or expected_batch < 0 - or expected_batch >= target - ): - raise TrainingLaunchError("prior-wave checkpoint cannot precede this wave") - maximum = max(progress) if progress else expected_batch - if maximum == expected_batch: - later = [ - item - for index, item in resumable - if index > expected_index - ] - if later: - # The pinned Cookbook always appends a permanent ``final`` - # checkpoint after the numbered wave-ceiling checkpoint. - # It represents the same completed batch, but it is a - # different URI. We recognize exactly that one safe shape - # and still pin the next launch to the receipt's numbered URI. - if ( - len(later) != 1 - or later[0].name != "final" - or later[0].batch != expected_batch - or later[0].state_path is None - or later[0].sampler_path is None - ): - raise TrainingLaunchError( - "optimizer log has ambiguous state after the prior-wave checkpoint" - ) - selected = expected_item - else: - selected_index, candidate = resumable[-1] - if ( - selected_index <= expected_index - or candidate.batch != maximum - or candidate.name == "final" - or candidate.batch >= target - ): - raise TrainingLaunchError( - "optimizer log does not have an exact in-wave resume checkpoint" - ) - selected = candidate - elif resumable: - maximum = max(progress) - _selected_index, candidate = resumable[-1] - if ( - candidate.batch != maximum - or candidate.name == "final" - or candidate.batch >= target - ): - raise TrainingLaunchError( - "training reached its wave ceiling without an immutable receipt" - ) - selected = candidate - - if not records and any(log_path.iterdir()): - raise TrainingLaunchError( - "training log has no resumable checkpoint; reconcile it before retrying" - ) - if selected is None: - return None - return ResumeBinding( - batch=int(selected.batch), - checkpoint=_checkpoint_identity(selected), - ) - - -@contextmanager -def _sealed_cookbook_resume( - *, - stage: StageSpec, - wave_name: str, - log_path: Path, - prior_wave_receipt: TrainingRecord | None, - expected: ResumeBinding | None, -) -> Iterator[None]: - """Make Cookbook consume exactly the optimizer state in the invocation.""" - - original = checkpoint_utils.get_last_checkpoint - expected_log = log_path.expanduser().resolve(strict=False) - fresh_at_entry = False - if expected is None: - observed_at_entry = _validate_resume_state( - stage=stage, - wave_name=wave_name, - log_path=log_path, - prior_wave_receipt=prior_wave_receipt, - ) - if observed_at_entry is not None: - raise TrainingLaunchError( - "optimizer state changed after the immutable invocation was recorded" - ) - fresh_at_entry = not log_path.exists() or not any(log_path.iterdir()) - - def sealed(log_dir: str, required_key: str = "state_path") -> Any: - requested = Path(log_dir).expanduser().resolve(strict=False) - if requested != expected_log: - return original(log_dir, required_key=required_key) - if fresh_at_entry: - if stage.kind == "sft": - if log_path.exists() and ( - not log_path.is_dir() or any(log_path.iterdir()) - ): - raise TrainingLaunchError( - "fresh SFT resume boundary contains unexpected training artifacts" - ) - return None - if not log_path.is_dir(): - raise TrainingLaunchError( - "fresh Cookbook bootstrap did not create its training log directory" - ) - entries = tuple(log_path.iterdir()) - names = {entry.name for entry in entries} - if ( - names != _COOKBOOK_FRESH_RESUME_BOOTSTRAP_FILES - or any(entry.is_symlink() or not entry.is_file() for entry in entries) - ): - raise TrainingLaunchError( - "fresh Cookbook bootstrap created unexpected training artifacts" - ) - try: - config = json.loads( - (log_path / "config.json").read_text(encoding="utf-8") - ) - except (OSError, UnicodeError, json.JSONDecodeError) as exc: - raise TrainingLaunchError( - "fresh Cookbook bootstrap config is not valid JSON" - ) from exc - if not isinstance(config, dict): - raise TrainingLaunchError( - "fresh Cookbook bootstrap config is not a JSON object" - ) - return None - observed = _validate_resume_state( - stage=stage, - wave_name=wave_name, - log_path=log_path, - prior_wave_receipt=prior_wave_receipt, - ) - if observed != expected: - raise TrainingLaunchError( - "optimizer state changed after the immutable invocation was recorded" - ) - if observed is None: - return None - records = checkpoint_utils.load_checkpoints_file(str(log_path)) - matches = [ - item - for item in records - if item.state_path is not None - and _checkpoint_identity(item) == observed.checkpoint - ] - if len(matches) != 1 or not matches[0].has(required_key): - raise TrainingLaunchError( - "sealed optimizer checkpoint is missing at the Cookbook boundary" - ) - return matches[0] - - checkpoint_utils.get_last_checkpoint = sealed - try: - yield - finally: - checkpoint_utils.get_last_checkpoint = original - - -async def run_training_stage( - *, - repo_root: Path, - stage_id: str, - wave_name: str, - replicate_id: str, - expected_source_sha: str, - external_root: Path, - confirmation: str, - sft_main: Callable[[SFTConfig], Awaitable[None]] | None = None, - rl_main: Callable[[RLConfig], Awaitable[None]] | None = None, - wandb_access_validator: Callable[..., Mapping[str, Any]] = validate_wandb_access, -) -> dict[str, Any]: - """Run or resume one exact paid wave after every local gate succeeds.""" - - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol(root, require_committed=True) - source_git_sha = validate_source_sha(root, expected_source_sha) - if confirmation != protocol["launch"]["confirmation_token"]: - raise TrainingLaunchError("explicit paid-training confirmation is missing") - stage = stage_spec(protocol, stage_id) - try: - validate_stage_replicate( - protocol, - stage_id=stage.stage_id, - replicate_id=replicate_id, - ) - except ValueError as exc: - raise TrainingLaunchError(str(exc)) from exc - if wave_name not in stage.waves: - stage.wave(wave_name) - if not os.environ.get("TINKER_API_KEY"): - raise TrainingLaunchError("TINKER_API_KEY is not present") - if not os.environ.get("WANDB_API_KEY"): - raise TrainingLaunchError("WANDB_API_KEY is not present") - - preflight = stage_preflight( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - require_sandbox=stage.kind == "rl", - ) - store = TrainingStore(repo_root=root, external_root=external_root) - key = StageKey(STUDY_ID, stage.stage_id, replicate_id) - with store.acquire_stage_lock(key): - existing_manifest = store.load_manifest(key) - if ( - existing_manifest is not None - and existing_manifest.payload.get("source_git_sha") != source_git_sha - ): - raise TrainingLaunchError("existing child manifest belongs to another source commit") - if str(stage.wave(wave_name)["approval"]) == "rollout-health-receipt": - if existing_manifest is None: - raise TrainingLaunchError( - "rollout-health continuation has no completed smoke manifest" - ) - smoke_receipt = store.load_wave_receipt(key, wave="smoke") - if smoke_receipt is None: - raise TrainingLaunchError( - "rollout-health continuation has no smoke receipt" - ) - smoke_preflight = stage_preflight( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name="smoke", - require_sandbox=True, - ) - smoke_parent = _parent_binding( - repo_root=root, - protocol=protocol, - store=store, - key=key, - stage=stage, - source_git_sha=source_git_sha, - approval=None, - existing_manifest=existing_manifest, - ) - _validate_completed_wave_receipt( - repo_root=root, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name="smoke", - manifest=existing_manifest, - preflight=smoke_preflight, - parent=smoke_parent, - receipt=smoke_receipt, - ) - approval = _approval_record( - repo_root=root, - store=store, - key=key, - stage=stage, - wave_name=wave_name, - source_git_sha=source_git_sha, - protocol=protocol, - ) - parent = _parent_binding( - repo_root=root, - protocol=protocol, - store=store, - key=key, - stage=stage, - source_git_sha=source_git_sha, - approval=approval, - existing_manifest=existing_manifest, - ) - tracking = _tracking_binding( - protocol=protocol, - source_git_sha=source_git_sha, - key=key, - stage=stage, - store=store, - ) - manifest = store.create_or_verify_manifest( - key, - _manifest_payload( - protocol=protocol, - source_git_sha=source_git_sha, - stage=stage, - preflight=preflight, - tracking=tracking, - parent=parent, - ), - ) - existing = store.load_wave_receipt(key, wave=wave_name) - if existing is not None: - _validate_completed_wave_receipt( - repo_root=root, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name=wave_name, - manifest=manifest, - preflight=preflight, - parent=parent, - receipt=existing, - ) - return { - "status": "already_complete", - "stage_id": stage.stage_id, - "wave": wave_name, - "receipt": str(existing.relative_path), - "receipt_sha256": existing.record_sha256, - } - log_path = store.tinker_log_path(key) - prior_wave_name = _required_prior_wave(stage, wave_name) - prior_wave_receipt = ( - store.load_wave_receipt(key, wave=prior_wave_name) - if prior_wave_name is not None - else None - ) - resume_binding = _validate_resume_state( - stage=stage, - wave_name=wave_name, - log_path=log_path, - prior_wave_receipt=prior_wave_receipt, - ) - resume_batch = resume_binding.batch if resume_binding is not None else None - resume_checkpoint = ( - resume_binding.checkpoint if resume_binding is not None else None - ) - resume = resume_batch is not None - # Install one continuous environment before the first W&B SDK call. - # This avoids the SDK caching an unbound session during access - # validation. Programmatic init arguments below are a second, - # independently checked identity boundary. - with _wandb_environment(tracking, resume=resume): - try: - # Preflight and transition validation can be lengthy. Bind - # the actual live boundary to the clean commit once more - # after every local record has been resolved. - validate_source_sha(root, source_git_sha) - except (OSError, ValueError, subprocess.SubprocessError) as exc: - raise TrainingLaunchError( - "source changed after preflight; refusing live W&B or Tinker access" - ) from exc - wandb_access_validator(expected_entity=tracking["entity"]) - resolved_launch = _resolved_launch_binding( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - store=store, - key=key, - parent=parent, - resume_batch=resume_batch, - resume_checkpoint=resume_checkpoint, - ) - invocation_id = f"{wave_name}-{uuid.uuid4().hex}" - invocation = store.begin_invocation( - key, - invocation_id=invocation_id, - payload=_invocation_payload( - protocol=protocol, - stage=stage, - wave_name=wave_name, - source_git_sha=source_git_sha, - invocation_id=invocation_id, - manifest=manifest, - preflight=preflight, - tracking=tracking, - resume_batch=resume_batch, - resolved_launch=resolved_launch, - parent=parent, - resume_checkpoint=resume_checkpoint, - ), - ) - config = build_tinker_config( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - store=store, - key=key, - parent=parent, - invocation=invocation, - ) - - def record_live_tracking(observed: Mapping[str, Any]) -> None: - store.write_invocation_tracking( - key, - invocation_id=invocation_id, - payload={ - "invocation_record_sha256": invocation.record_sha256, - "run_manifest_record_sha256": manifest.record_sha256, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "expected": dict(tracking), - "observed": dict(observed), - }, - ) - - with _sealed_cookbook_resume( - stage=stage, - wave_name=wave_name, - log_path=log_path, - prior_wave_receipt=prior_wave_receipt, - expected=resume_binding, - ): - if stage.kind == "sft": - if sft_main is None: - from tinker_cookbook.supervised.train import main as resolved_main - else: - resolved_main = sft_main - if sft_main is None: - with _sealed_wandb_initialization( - tracking, - resume=resume, - on_initialized=record_live_tracking, - ): - await resolved_main(config) - else: - await resolved_main(config) - else: - if stage.model_key == "inkling": - from rl.track_b.tml_shim import install_inkling_runtime - - install_inkling_runtime( - effort=float(protocol["models"]["inkling"]["effort"]) - ) - if rl_main is None: - from tinker_cookbook.rl.train import main as resolved_main - else: - resolved_main = rl_main - if rl_main is None: - with _sealed_wandb_initialization( - tracking, - resume=resume, - on_initialized=record_live_tracking, - ): - await resolved_main(config) - else: - await resolved_main(config) - live_tracking = store.load_invocation_tracking( - key, - invocation_id=invocation_id, - ) - built_in_main = sft_main is None if stage.kind == "sft" else rl_main is None - if built_in_main and live_tracking is None: - raise TrainingLaunchError("training returned without an observed W&B tracking record") - receipt = store.write_wave_receipt( - key, - wave=wave_name, - payload=_checkpoint_receipt( - stage=stage, - wave_name=wave_name, - log_path=log_path, - manifest=manifest, - preflight=preflight, - invocation=invocation, - tracking=live_tracking, - ), - ) - return { - "status": "complete", - "stage_id": stage.stage_id, - "wave": wave_name, - "manifest": str(manifest.relative_path), - "manifest_sha256": manifest.record_sha256, - "receipt": str(receipt.relative_path), - "receipt_sha256": receipt.record_sha256, - "wandb_run_id": tracking["run_id"], - "checkpoint": receipt.payload["checkpoint"], - } diff --git a/rl/studies/representation_training_v1/preflight.py b/rl/studies/representation_training_v1/preflight.py deleted file mode 100644 index c8b83947..00000000 --- a/rl/studies/representation_training_v1/preflight.py +++ /dev/null @@ -1,381 +0,0 @@ -"""Zero-spend validation for one sealed training stage.""" - -from __future__ import annotations - -import hashlib -import importlib.metadata -import json -import subprocess -from pathlib import Path -from typing import Any -from urllib.parse import unquote, urlparse - -from tinker import SamplingParams -from tinker_cookbook.renderers import Message, TextPart, TrainOnWhat - -from homi.conductor.source_policy import strict_purity_violations - -from rl.common.evaluator import Attribution, EvaluationStatus, PixCellEvaluator -from rl.common.isolation import require_execution_boundary -from rl.common.prompt import prompt_asset_hashes -from rl.common.runtime import validate_runtime_stack -from rl.track_a.tinker_data import _message, _renderer - -from .dataset_binding import _dataset_binding -from .protocol import StageSpec, file_sha256, protocol_path -from .schedule import ( - stage_replay_tasks, - stage_schedule_document, - stage_tasks, - verify_sft_step_count, -) - - -def _positive_target_text(model_input: Any, weights: Any, tokenizer: Any) -> str: - offset = 0 - tokens: list[int] = [] - for chunk in model_input.chunks: - length = int(chunk.length) - chunk_weights = weights[offset : offset + length] - chunk_tokens = getattr(chunk, "tokens", None) - if chunk_tokens is not None: - tokens.extend( - int(token) - for token, weight in zip(chunk_tokens, chunk_weights, strict=True) - if float(weight) > 0 - ) - elif int((chunk_weights > 0).sum().item()) != 0: - raise RuntimeError("an image token unexpectedly carries SFT loss") - offset += length - if offset != int(model_input.length) or not tokens: - raise RuntimeError("SFT target mask is empty or misaligned") - return str(tokenizer.decode(tokens)) - - -def _audit_sft( - dataset_root: Path, - stage: StageSpec, - model: dict[str, Any], - recipe: dict[str, Any], - *, - max_steps: int, -) -> dict[str, Any]: - verify_sft_step_count(dataset_root, stage, max_steps=max_steps) - renderer = _renderer( - model["model"], - model["renderer"], - effort=model.get("effort"), - ) - lengths: list[int] = [] - trained: list[int] = [] - for task in stage_tasks(dataset_root, stage): - assistant = Message( - role="assistant", - content=[TextPart(type="text", text=task.label)], - ) - model_input, weights = renderer.build_supervised_example( - [_message(task, max_image=int(model["max_image_long_edge"])), assistant], - train_on_what=TrainOnWhat.LAST_ASSISTANT_MESSAGE, - ) - length = int(model_input.length) - positive = int((weights > 0).sum().item()) - if length > int(recipe["max_sequence_tokens"]): - raise RuntimeError( - f"{task.sampler.opaque_id} has {length} tokens, exceeding " - f"{recipe['max_sequence_tokens']}" - ) - target = _positive_target_text(model_input, weights, renderer.tokenizer) - expected_target = task.label + "<|im_end|>" - if target != expected_target: - raise RuntimeError( - f"{task.sampler.opaque_id} does not have an exact code-only SFT target" - ) - lengths.append(length) - trained.append(positive) - return { - "rows": len(lengths), - "sequence_tokens": { - "min": min(lengths), - "max": max(lengths), - }, - "trained_tokens": { - "min": min(trained), - "max": max(trained), - }, - "target_policy": recipe["target"], - } - - -def _audit_rl_context( - dataset_root: Path, - stage: StageSpec, - model: dict[str, Any], -) -> dict[str, Any]: - sampling_defaults = SamplingParams( - max_tokens=int(model["max_output_tokens"]), - temperature=1.0, - ) - if float(sampling_defaults.top_p) != 1.0: - raise RuntimeError("pinned Tinker top_p default is no longer 1.0") - renderer = _renderer( - model["model"], - model["renderer"], - effort=model.get("effort"), - ) - lengths: list[int] = [] - tasks = stage_tasks(dataset_root, stage) + stage_replay_tasks( - dataset_root, - stage, - ) - for task in tasks: - prompt = renderer.build_generation_prompt( - [_message(task, max_image=int(model["max_image_long_edge"]))] - ) - length = int(prompt.length) - if length + int(model["max_output_tokens"]) > int(model["context_tokens"]): - raise RuntimeError( - f"{task.sampler.opaque_id} leaves no room for the sealed output cap" - ) - lengths.append(length) - return { - "rows": len(lengths), - "prompt_tokens": { - "min": min(lengths), - "max": max(lengths), - }, - "minimum_context_headroom": ( - int(model["context_tokens"]) - - int(model["max_output_tokens"]) - - max(lengths) - ), - "sampling_defaults": { - "top_p": float(sampling_defaults.top_p), - }, - } - - -def _source_and_reference_audit( - dataset_root: Path, - stage: StageSpec, - *, - execute_probe: bool, -) -> dict[str, Any]: - tasks = stage_tasks(dataset_root, stage) - if stage.kind == "rl": - tasks.extend(stage_replay_tasks(dataset_root, stage)) - unique = {task.sampler.opaque_id: task for task in tasks} - source_sha = hashlib.sha256() - reference_sha = hashlib.sha256() - with PixCellEvaluator( - max_workers=1, - evaluator_retries=1, - require_isolation=execute_probe, - ) as evaluator: - for task_id, task in sorted(unique.items()): - violations = strict_purity_violations(task.label) - if violations: - raise RuntimeError( - f"{task_id} violates the strict source policy: {violations}" - ) - evidence = evaluator.validate_reference(task.reference) - if ( - evidence["target_image_sha256"] - != task.reference.target_image_sha256 - ): - raise RuntimeError(f"{task_id} reference digest changed") - source_sha.update(task_id.encode("utf-8")) - source_sha.update(hashlib.sha256(task.label.encode("utf-8")).digest()) - reference_sha.update(task_id.encode("utf-8")) - reference_sha.update( - bytes.fromhex(task.reference.target_image_sha256) - ) - probe: dict[str, Any] | None = None - if execute_probe: - first = unique[sorted(unique)[0]] - result = evaluator.evaluate(first.reference, first.label) - if ( - result.status is not EvaluationStatus.OK - or result.attribution is not Attribution.MODEL - or result.iou is None - or result.iou < 0.99 - ): - raise RuntimeError( - "isolated ground-truth probe failed: " - f"{result.status.value}/{result.attribution.value}/" - f"{result.iou}: {result.error}" - ) - probe = { - "task_id": first.sampler.opaque_id, - "status": result.status.value, - "iou": result.iou, - } - return { - "rows": len(unique), - "source_set_sha256": source_sha.hexdigest(), - "reference_set_sha256": reference_sha.hexdigest(), - "sandbox_probe": probe, - } - - -def _cookbook_source_binding(runtime: dict[str, Any]) -> dict[str, Any]: - expected = str(runtime["tinker_cookbook_commit"]) - distribution = importlib.metadata.distribution("tinker-cookbook") - version = importlib.metadata.version("tinker-cookbook") - direct = distribution.read_text("direct_url.json") - if not direct: - if f"g{expected[:10]}" not in version: - raise RuntimeError( - "installed Tinker Cookbook does not identify the pinned commit" - ) - return { - "kind": "installed-distribution", - "version": version, - "expected_commit": expected, - } - document = json.loads(direct) - if document.get("dir_info", {}).get("editable") is not True: - if f"g{expected[:10]}" not in version: - raise RuntimeError( - "direct Tinker Cookbook install does not identify the pinned commit" - ) - return { - "kind": "direct-install", - "version": version, - "expected_commit": expected, - "direct_url_sha256": hashlib.sha256(direct.encode()).hexdigest(), - } - parsed = urlparse(str(document.get("url", ""))) - if parsed.scheme != "file": - raise RuntimeError("editable Tinker Cookbook is not a local file checkout") - checkout = Path(unquote(parsed.path)).resolve(strict=True) - head = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=checkout, - text=True, - ).strip() - status = subprocess.check_output( - ["git", "status", "--porcelain", "--untracked-files=all"], - cwd=checkout, - text=True, - ).strip() - if head != expected or status: - raise RuntimeError( - "editable Tinker Cookbook differs from the pinned clean commit" - ) - return { - "kind": "clean-editable-checkout", - "path": str(checkout), - "git_sha": head, - } - - -def _sandbox_binding() -> dict[str, Any]: - boundary = require_execution_boundary() - return { - "runtime_path": str(boundary.runtime_path), - "daemon_endpoint": boundary.daemon_endpoint, - "image_ref": boundary.image_ref, - "image_id": boundary.image_id, - "workspace_root": str(boundary.workspace_root), - } - - -def _benchmark_binding(repo_root: Path, protocol: dict[str, Any]) -> dict[str, Any]: - manifest_path = ( - protocol_path(repo_root).parent / protocol["evaluation"]["task_manifest"] - ).resolve(strict=True) - if not manifest_path.is_relative_to(repo_root.resolve()): - raise RuntimeError("benchmark manifest escaped the repository") - document = json.loads(manifest_path.read_text(encoding="utf-8")) - expected = protocol["evaluation"]["task_manifest_logical_sha256"] - if document.get("logical_sha256") != expected: - raise RuntimeError("benchmark task manifest differs from the protocol") - unsigned = {key: value for key, value in document.items() if key != "logical_sha256"} - logical = hashlib.sha256( - json.dumps( - unsigned, - ensure_ascii=True, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - ).hexdigest() - if logical != expected: - raise RuntimeError("benchmark task manifest has an invalid logical digest") - task_set = document.get("task_sets", {}).get("f1_f8") - if not isinstance(task_set, list) or len(task_set) != 8: - raise RuntimeError("benchmark manifest does not contain exact F1-F8") - return { - "relative_path": str(manifest_path.relative_to(repo_root)), - "file_sha256": file_sha256(manifest_path), - "logical_sha256": logical, - "task_ids": [item["task_id"] for item in task_set], - } - - -def stage_preflight( - *, - repo_root: Path, - protocol: dict[str, Any], - stage: StageSpec, - wave_name: str, - require_sandbox: bool, -) -> dict[str, Any]: - """Validate all deterministic launch inputs without creating a client.""" - - dataset_root = repo_root / "dataset" - wave = stage.wave(wave_name) - model = dict(protocol["models"][stage.model_key]) - recipe = dict(protocol["recipes"][stage.recipe_key]) - schedule = stage_schedule_document( - dataset_root, - stage, - groups_per_batch=( - int(recipe["groups_per_batch"]) if stage.kind == "rl" else None - ), - ) - policy_audit = ( - _audit_sft( - dataset_root, - stage, - model, - recipe, - max_steps=int(wave["max_steps"]), - ) - if stage.kind == "sft" - else _audit_rl_context(dataset_root, stage, model) - ) - protocol_file = protocol_path(repo_root) - runtime = validate_runtime_stack(repo_root) - report = { - "ok": True, - "spend": False, - "stage_id": stage.stage_id, - "wave": wave_name, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": file_sha256(protocol_file), - "prompt_assets": prompt_asset_hashes(), - "benchmark": _benchmark_binding(repo_root, protocol), - "dataset": _dataset_binding(dataset_root, protocol), - "runtime": { - **runtime, - "cookbook_source": _cookbook_source_binding(runtime), - }, - "schedule": schedule, - "model": model, - "recipe": recipe, - "policy_audit": policy_audit, - "source_and_reference_audit": _source_and_reference_audit( - dataset_root, - stage, - execute_probe=require_sandbox, - ), - "sandbox": _sandbox_binding() if require_sandbox else None, - } - report["preflight_sha256"] = hashlib.sha256( - json.dumps( - report, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - ).hexdigest() - return report diff --git a/rl/studies/representation_training_v1/promotion.py b/rl/studies/representation_training_v1/promotion.py deleted file mode 100644 index 85be6cb9..00000000 --- a/rl/studies/representation_training_v1/promotion.py +++ /dev/null @@ -1,2773 +0,0 @@ -"""Fail-closed, human-confirmed promotion gates for the training study. - -This module never samples a model and never creates a Tinker client. It turns -one already-sealed checkpoint-evaluation report into the immutable approval -record consumed by :mod:`launcher`. -""" - -from __future__ import annotations - -import math -import re -import subprocess -from collections import Counter, defaultdict -from collections.abc import Mapping, Sequence -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from rl.common.prompt import prompt_asset_hashes -from rl.evaluation.tasks import canonical_json_sha256 - -from .dataset_binding import _dataset_binding -from .protocol import ( - LEVELS, - STUDY_ID, - StageSpec, - file_sha256, - load_protocol, - parent_replicate_id, - protocol_path, - stage_spec, - validate_stage_replicate, - validate_source_sha, -) -from .store import ( - StageKey, - TrainingRecord, - TrainingStore, - canonical_sha256, -) -from .source_transition import ( - SourceTransitionError, - approved_change_scope, - build_source_transition, - source_transition_git_facts, -) - - -APPROVAL_SCHEMA_VERSION = "pixcell-training-wave-approval-v1" -SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION = "pixcell-training-wave-approval-v2" -PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION = ( - "pixcell-training-parent-receipt-transition-approval-v1" -) -ROLLOUT_HEALTH_APPROVAL_SCHEMA_VERSION = ( - "pixcell-training-rollout-health-approval-v1" -) -BASE_BASELINE_APPROVAL_SCHEMA_VERSION = ( - "pixcell-training-base-baseline-approval-v1" -) -APPROVAL_CONFIRMATION_TOKEN = "PIXCELL_APPROVE_TRAINING_V1" -MASTERED_MINIMUM_IOU = 0.80 -MASTERED_MINIMUM_PURE_EXECUTABLE_RATE = 0.95 - -PANEL_PROGRESS = "progress" -PANEL_DEPTH_VALIDATION = "depth-validation" -PANEL_INKLING_PROMOTION = "inkling-promotion" -LEVEL_PROGRESS_PANELS = { - "L0": "level-progress-l0", - "L1": "level-progress-l1", - "L2": "level-progress-l2", - "L3": "level-progress-l3", - "L4": "level-progress-l4", -} -PANEL_DEPTH_FINAL_SELECTION = "depth-final-selection" -_INKLING_PANEL_SELECTION_SEED = ( - "pixcell-representation-training-v1-inkling-promotion-20260728" -) -_LEVEL_PROGRESS_SELECTION = "validation-realization-slot-6" -_DEPTH_FINAL_SELECTION = "validation-realization-slot-7" -_MODEL_EVALUATION_STATUSES = frozenset( - { - "ok", - "syntax_error", - "source_rejected", - "runtime_timeout", - "runtime_error", - "no_gds", - "bad_gds", - } -) -EXPECTED_PANEL_ROWS = { - PANEL_PROGRESS: 8, - PANEL_DEPTH_VALIDATION: 1_092, - PANEL_INKLING_PROMOTION: 68, - "level-progress-l0": 93, - "level-progress-l1": 127, - "level-progress-l2": 98, - "level-progress-l3": 120, - "level-progress-l4": 108, - PANEL_DEPTH_FINAL_SELECTION: 546, -} - -_REPORT_SCHEMA = "pixcell-training-checkpoint-evaluation-v1" -_SHA256 = re.compile(r"^[0-9a-f]{64}$") -_TINKER_PATH = re.compile(r"^tinker://[^\s]+$") -_IMMUTABLE_IMAGE = re.compile(r"^(?:sha256:[0-9a-f]{64}|[^\s@]+@sha256:[0-9a-f]{64})$") - - -class PromotionError(RuntimeError): - """An evaluation cannot authorize the requested training transition.""" - - -@dataclass(frozen=True) -class PanelTaskIdentity: - task_id: str - level: str - representation_id: str - image_sha256: str - target_image_sha256: str - footprint_um: tuple[float, float] - - -@dataclass(frozen=True) -class EvaluationEvidence: - """A fully checked immutable checkpoint report.""" - - key: StageKey - wave: str - checkpoint_name: str - checkpoint: dict[str, Any] - checkpoint_inventory_sha256: str - panel: str - report: dict[str, Any] - report_payload: dict[str, Any] - report_record_sha256: str - report_payload_sha256: str - report_relative_path: str - evaluation_manifest_payload_sha256: str - evaluation_manifest_record_sha256: str - evaluation_manifest_relative_path: str - training_manifest: TrainingRecord - training_receipt: TrainingRecord - - -@dataclass(frozen=True) -class ApprovalPlan: - gate: str - storage_wave: str - expected_panel: str - allowed_evaluation_checkpoints: tuple[tuple[str, str], ...] - entry_baseline_panel: str | None = None - - -def _mapping(value: Any, path: str) -> Mapping[str, Any]: - if not isinstance(value, Mapping): - raise PromotionError(f"{path} must be an object") - return value - - -def _integer(value: Any, path: str, *, minimum: int = 0) -> int: - if isinstance(value, bool) or not isinstance(value, int) or value < minimum: - raise PromotionError(f"{path} must be an integer >= {minimum}") - return value - - -def _unit(value: Any, path: str) -> float: - if isinstance(value, bool): - raise PromotionError(f"{path} must be numeric") - try: - result = float(value) - except (TypeError, ValueError) as exc: - raise PromotionError(f"{path} must be numeric") from exc - if not math.isfinite(result) or not 0.0 <= result <= 1.0: - raise PromotionError(f"{path} must be finite and in [0, 1]") - return result - - -def _close(actual: Any, expected: float, path: str) -> None: - value = _unit(actual, path) - if not math.isclose(value, expected, rel_tol=0.0, abs_tol=1e-12): - raise PromotionError(f"{path}={value!r}; expected {expected!r}") - - -def _sha(value: Any, path: str) -> str: - result = str(value) - if not _SHA256.fullmatch(result): - raise PromotionError(f"{path} must be a lowercase SHA-256") - return result - - -def _finite_json(value: Any, path: str) -> None: - if value is None or isinstance(value, (str, bool, int)): - return - if isinstance(value, float): - if not math.isfinite(value): - raise PromotionError(f"{path} contains a non-finite number") - return - if isinstance(value, Mapping): - for key, item in value.items(): - _finite_json(item, f"{path}.{key}") - return - if isinstance(value, (list, tuple)): - for index, item in enumerate(value): - _finite_json(item, f"{path}[{index}]") - return - raise PromotionError(f"{path} contains unsupported metric data") - - -def _parent_receipt_transition_evidence( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - replicate_id: str, -) -> tuple[TrainingRecord, TrainingRecord, TrainingRecord, dict[str, Any], dict[str, Any]]: - """Revalidate the completed historical L0-SFT parent without an evaluation.""" - - parent_key = StageKey(STUDY_ID, "qwen-l0-sft", replicate_id) - manifest = store.load_manifest(parent_key) - receipt = store.load_wave_receipt(parent_key, wave="complete") - if manifest is None or receipt is None: - raise PromotionError("the exact qwen-l0-sft/complete parent receipt is missing") - - parent_source = str(manifest.payload.get("source_git_sha", "")) - if not re.fullmatch(r"[0-9a-f]{40}", parent_source): - raise PromotionError("parent manifest has an invalid source commit") - if ( - receipt.payload.get("stage_id") != "qwen-l0-sft" - or receipt.payload.get("wave") != "complete" - or receipt.payload.get("run_manifest_record_sha256") != manifest.record_sha256 - ): - raise PromotionError("parent receipt and manifest identities differ") - - parent_stage = _mapping(manifest.payload.get("stage"), "parent_manifest.stage") - current_parent = stage_spec(dict(protocol), "qwen-l0-sft") - expected_stage = { - "stage_id": current_parent.stage_id, - "kind": current_parent.kind, - "model_key": current_parent.model_key, - "recipe_key": current_parent.recipe_key, - "hypotheses": list(current_parent.hypotheses), - "parent_policy": current_parent.parent, - "levels": list(current_parent.levels), - "current_level": current_parent.current_level, - "replay_levels": list(current_parent.replay_levels), - "waves": current_parent.waves, - } - local_dataset = _dataset_binding(repo_root / "dataset", protocol) - stable_equivalence = { - "contract_version": protocol["contract_version"], - "prompt_assets": prompt_asset_hashes(), - "dataset": local_dataset, - "model": dict(protocol["models"]["qwen"]), - "recipe": dict(protocol["recipes"]["qwen_sft"]), - "stage": expected_stage, - } - observed_equivalence = { - "contract_version": manifest.payload.get("contract_version"), - "prompt_assets": manifest.payload.get("prompt_assets"), - "dataset": manifest.payload.get("dataset"), - "model": manifest.payload.get("model"), - "recipe": manifest.payload.get("recipe"), - "stage": dict(parent_stage), - } - if observed_equivalence != stable_equivalence: - raise PromotionError( - "historical L0-SFT parent differs from the current stable " - "contract, prompt, dataset, model, recipe, or stage" - ) - - inventory = _mapping( - receipt.payload.get("checkpoint_inventory"), - "parent_receipt.checkpoint_inventory", - ) - entries = inventory.get("entries") - logical_sha256 = _sha( - inventory.get("logical_sha256"), - "parent_receipt.checkpoint_inventory.logical_sha256", - ) - if ( - not isinstance(entries, list) - or inventory.get("count") != len(entries) - or canonical_sha256(entries) != logical_sha256 - ): - raise PromotionError("parent checkpoint inventory digest differs") - terminals = [ - item - for item in entries - if isinstance(item, Mapping) - and item.get("role") == "terminal" - and item.get("final") is True - ] - if len(terminals) != 1: - raise PromotionError("parent checkpoint inventory has no unique terminal") - checkpoint = dict(terminals[0]) - selected = _mapping(receipt.payload.get("checkpoint"), "parent_receipt.checkpoint") - selected_fields = ("name", "batch", "epoch", "final", "state_path", "sampler_path") - if ( - any(selected.get(field) != checkpoint.get(field) for field in selected_fields) - or checkpoint.get("name") != "final" - or checkpoint.get("epoch") != 1 - or not _TINKER_PATH.fullmatch(str(checkpoint.get("state_path", ""))) - or not _TINKER_PATH.fullmatch(str(checkpoint.get("sampler_path", ""))) - ): - raise PromotionError("parent selected checkpoint is not the completed SFT terminal") - - invocation_id = str(receipt.payload.get("invocation_id", "")) - try: - invocation = store.load_invocation(parent_key, invocation_id=invocation_id) - except ValueError as exc: - raise PromotionError("parent receipt has an invalid invocation ID") from exc - if ( - invocation is None - or receipt.payload.get("invocation_record_sha256") != invocation.record_sha256 - or invocation.payload.get("run_manifest_record_sha256") != manifest.record_sha256 - or invocation.payload.get("source_git_sha") != parent_source - ): - raise PromotionError("parent invocation is missing or differs") - - artifacts = _mapping( - receipt.payload.get("local_artifact_sha256"), - "parent_receipt.local_artifact_sha256", - ) - if not {"checkpoints.jsonl", "metrics.jsonl"}.issubset(artifacts): - raise PromotionError("parent receipt lacks its local checkpoint evidence") - for name, expected_sha in artifacts.items(): - _sha(expected_sha, f"parent_receipt.local_artifact_sha256.{name}") - path = store.tinker_log_path(parent_key) / str(name) - if not path.is_file() or file_sha256(path) != expected_sha: - raise PromotionError(f"parent local artifact {name} differs from its receipt") - - evidence = { - "parent_run_manifest": { - "relative_path": str(manifest.relative_path), - "payload_sha256": manifest.payload_sha256, - "record_sha256": manifest.record_sha256, - }, - "parent_wave_receipt": { - "relative_path": str(receipt.relative_path), - "payload_sha256": receipt.payload_sha256, - "record_sha256": receipt.record_sha256, - }, - "parent_invocation": { - "relative_path": str(invocation.relative_path), - "payload_sha256": invocation.payload_sha256, - "record_sha256": invocation.record_sha256, - }, - "local_artifact_sha256": dict(artifacts), - "checkpoint_inventory": dict(inventory), - "selected_checkpoint": checkpoint, - } - return manifest, receipt, invocation, checkpoint, { - "stable_equivalence": stable_equivalence, - "evidence": evidence, - } - - -def _parent_receipt_transition_payload( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - stage: StageSpec, - wave_name: str, - replicate_id: str, - consumer_source_git_sha: str, - approved_change_paths: Sequence[str] | None, -) -> dict[str, Any]: - if ( - stage.stage_id != "qwen-l0-rl-l1" - or stage.parent != "qwen-l0-sft:complete" - or wave_name != "smoke" - or str(stage.wave(wave_name)["approval"]) - != "parent-receipt-and-source-transition" - ): - raise PromotionError("parent-receipt transition is not the L0-SFT→L1-RL entry") - producer_replicate_id = parent_replicate_id( - protocol, - stage_id=stage.stage_id, - replicate_id=replicate_id, - ) - manifest, receipt, invocation, checkpoint, documents = ( - _parent_receipt_transition_evidence( - repo_root=repo_root, - protocol=protocol, - store=store, - replicate_id=producer_replicate_id, - ) - ) - producer_source_git_sha = str(manifest.payload["source_git_sha"]) - if producer_source_git_sha == consumer_source_git_sha: - raise PromotionError( - "historical-parent transition requires different producer and consumer commits" - ) - scope = { - "study_id": STUDY_ID, - "child": { - "stage_id": stage.stage_id, - "wave": wave_name, - "replicate_id": replicate_id, - "approval_gate": "parent-receipt-and-source-transition", - }, - "producer_training": { - "stage_id": "qwen-l0-sft", - "wave": "complete", - "replicate_id": producer_replicate_id, - }, - } - equivalence = { - "producer_protocol": { - "logical_sha256": manifest.payload.get("protocol_logical_sha256"), - "file_sha256": manifest.payload.get("protocol_file_sha256"), - }, - "consumer_protocol": { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - }, - **documents["stable_equivalence"], - } - try: - transition = build_source_transition( - repo_root=repo_root, - producer_source_git_sha=producer_source_git_sha, - consumer_source_git_sha=consumer_source_git_sha, - scope=scope, - approved_change_paths=approved_change_paths, - equivalence=equivalence, - evidence=documents["evidence"], - ) - except SourceTransitionError as exc: - raise PromotionError(f"parent source transition is invalid: {exc}") from exc - inventory = _mapping( - receipt.payload.get("checkpoint_inventory"), - "parent_receipt.checkpoint_inventory", - ) - return { - "schema_version": PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "parent-receipt-and-source-transition", - "stage_id": stage.stage_id, - "wave": wave_name, - "source_git_sha": consumer_source_git_sha, - "evidence_source_git_sha": producer_source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "explicit_human_confirmation": True, - "parent_stage_id": "qwen-l0-sft", - "parent_wave": "complete", - "parent_replicate_id": producer_replicate_id, - "parent_manifest_record_sha256": manifest.record_sha256, - "parent_receipt_record_sha256": receipt.record_sha256, - "parent_invocation_record_sha256": invocation.record_sha256, - "selected_parent_checkpoint": checkpoint, - "selected_parent_checkpoint_inventory_sha256": inventory["logical_sha256"], - "source_transition": transition, - } - - -def validate_parent_receipt_transition_approval( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - stage: StageSpec, - wave_name: str, - replicate_id: str, - consumer_source_git_sha: str, - approval: TrainingRecord, -) -> None: - """Recompute the receipt-only historical-parent bridge at launch time.""" - - if ( - approval.payload.get("schema_version") - != PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ): - raise PromotionError("parent-receipt transition approval has a foreign schema") - if approval.payload.get("source_git_sha") != consumer_source_git_sha: - raise PromotionError("parent-receipt transition names another consumer") - try: - validate_source_sha(repo_root, consumer_source_git_sha) - except (OSError, ValueError, subprocess.SubprocessError) as exc: - raise PromotionError( - f"parent-receipt transition consumer is not the clean HEAD: {exc}" - ) from exc - approved_paths = _approved_change_paths_from_transition( - approval.payload.get("source_transition") - ) - expected = _parent_receipt_transition_payload( - repo_root=repo_root, - protocol=protocol, - store=store, - stage=stage, - wave_name=wave_name, - replicate_id=replicate_id, - consumer_source_git_sha=consumer_source_git_sha, - approved_change_paths=approved_paths, - ) - if approval.payload != expected: - raise PromotionError( - "parent-receipt transition approval differs from recomputed evidence" - ) - - -def record_parent_receipt_transition_approval( - *, - repo_root: Path, - external_root: Path, - stage_id: str, - wave_name: str, - replicate_id: str, - expected_source_sha: str, - approved_change_paths: Sequence[str] | None, - confirmation: str, -) -> dict[str, Any]: - """Record the one explicit bridge from historical L0 SFT to current L1 RL.""" - - if confirmation != APPROVAL_CONFIRMATION_TOKEN: - raise PromotionError("explicit human approval confirmation is missing") - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol(root, require_committed=True) - source_git_sha = validate_source_sha(root, expected_source_sha) - stage = stage_spec(protocol, stage_id) - try: - validate_stage_replicate( - protocol, - stage_id=stage.stage_id, - replicate_id=replicate_id, - ) - except ValueError as exc: - raise PromotionError(str(exc)) from exc - store = TrainingStore(repo_root=root, external_root=external_root) - payload = _parent_receipt_transition_payload( - repo_root=root, - protocol=protocol, - store=store, - stage=stage, - wave_name=wave_name, - replicate_id=replicate_id, - consumer_source_git_sha=source_git_sha, - approved_change_paths=approved_change_paths, - ) - key = StageKey(STUDY_ID, stage.stage_id, replicate_id) - with store.acquire_stage_lock(key): - validate_source_sha(root, source_git_sha) - rebuilt = _parent_receipt_transition_payload( - repo_root=root, - protocol=protocol, - store=store, - stage=stage, - wave_name=wave_name, - replicate_id=replicate_id, - consumer_source_git_sha=source_git_sha, - approved_change_paths=approved_change_paths, - ) - if rebuilt != payload: - raise PromotionError("parent-receipt transition changed before recording") - approval = store.write_wave_approval( - key, - wave=wave_name, - payload=payload, - ) - return { - "status": "approved", - "stage_id": stage.stage_id, - "wave": wave_name, - "approval": str(approval.relative_path), - "approval_record_sha256": approval.record_sha256, - "parent_receipt_record_sha256": payload["parent_receipt_record_sha256"], - "source_transition_logical_sha256": payload["source_transition"][ - "logical_sha256" - ], - } - - -def record_rollout_health_approval( - *, - store: TrainingStore, - key: StageKey, - stage: StageSpec, - wave_name: str, - prior_wave: str, - prior_receipt: TrainingRecord, - source_git_sha: str, - protocol: Mapping[str, Any], -) -> TrainingRecord: - """Derive the smoke→step-5 decision from exactly one sealed G4 receipt.""" - - if ( - stage.kind != "rl" - or stage.model_key != "qwen" - or wave_name != "step-5" - or prior_wave != "smoke" - or str(stage.wave(wave_name)["approval"]) != "rollout-health-receipt" - or int(stage.wave(prior_wave)["max_steps"]) != 1 - ): - raise PromotionError("rollout-health gate is not the Qwen smoke→step-5 transition") - recipe = _mapping(protocol["recipes"].get(stage.recipe_key), "recipe") - group_size = _integer(recipe.get("group_size"), "recipe.group_size", minimum=1) - groups_per_batch = _integer( - recipe.get("groups_per_batch"), - "recipe.groups_per_batch", - minimum=1, - ) - if group_size != 4 or groups_per_batch != 8: - raise PromotionError("rollout-health gate requires the sealed 8×G4 recipe") - expected_candidates = group_size * groups_per_batch - - if ( - prior_receipt.payload.get("stage_id") != stage.stage_id - or prior_receipt.payload.get("wave") != prior_wave - or prior_receipt.payload.get("max_steps") != 1 - ): - raise PromotionError("rollout-health receipt identity differs") - invocation_id = str(prior_receipt.payload.get("invocation_id", "")) - invocation = store.load_invocation(key, invocation_id=invocation_id) - if ( - invocation is None - or prior_receipt.payload.get("invocation_record_sha256") - != invocation.record_sha256 - ): - raise PromotionError("rollout-health receipt invocation is missing or differs") - manifest = store.load_manifest(key) - if ( - manifest is None - or prior_receipt.payload.get("run_manifest_record_sha256") - != manifest.record_sha256 - or manifest.payload.get("protocol_logical_sha256") - != protocol["logical_sha256"] - ): - raise PromotionError("rollout-health receipt manifest differs") - - recorded_inventory = _mapping( - prior_receipt.payload.get("candidate_inventory"), - "receipt.candidate_inventory", - ) - recorded_samples = _mapping( - prior_receipt.payload.get("sample_inventory"), - "receipt.sample_inventory", - ) - for name, inventory in ( - ("candidate", recorded_inventory), - ("sample", recorded_samples), - ): - if ( - _integer(inventory.get("count"), f"{name}_inventory.count") - != expected_candidates - or _integer( - inventory.get("expected_count"), - f"{name}_inventory.expected_count", - ) - != expected_candidates - ): - raise PromotionError( - f"rollout-health {name} inventory does not contain exactly " - f"{expected_candidates} records" - ) - _sha(inventory.get("logical_sha256"), f"{name}_inventory.logical_sha256") - - candidate_root = store.candidate_root(key) / invocation_id - candidate_files = ( - sorted(candidate_root.rglob("evaluation.json")) - if candidate_root.is_dir() - else [] - ) - sample_files = ( - sorted(candidate_root.rglob("sample.json")) - if candidate_root.is_dir() - else [] - ) - observed_inventory = [ - { - "path": str(path.relative_to(store.stage_path(key))), - "sha256": file_sha256(path), - } - for path in candidate_files - ] - if ( - len(candidate_files) != expected_candidates - or canonical_sha256(observed_inventory) - != recorded_inventory["logical_sha256"] - ): - raise PromotionError( - "rollout-health candidate files differ from the sealed receipt" - ) - observed_sample_inventory = [ - { - "path": str(path.relative_to(store.stage_path(key))), - "sha256": file_sha256(path), - } - for path in sample_files - ] - if ( - len(sample_files) != expected_candidates - or canonical_sha256(observed_sample_inventory) - != recorded_samples["logical_sha256"] - ): - raise PromotionError( - "rollout-health sample files differ from the sealed receipt" - ) - - rewards_by_group: dict[tuple[int, str], dict[int, float]] = defaultdict(dict) - executable = 0 - rewards: list[float] = [] - record_bindings: list[dict[str, Any]] = [] - sample_record_bindings: list[dict[str, Any]] = [] - from rl.common.evaluator import EvaluationStatus - - allowed_statuses = {item.value for item in EvaluationStatus} - for path in candidate_files: - relative = path.relative_to(candidate_root) - parts = relative.parts - if ( - len(parts) != 4 - or not re.fullmatch(r"step-[0-9]{6}", parts[0]) - or not re.fullmatch(r"attempt-[0-9]{2}", parts[2]) - or parts[3] != "evaluation.json" - ): - raise PromotionError("rollout-health candidate path is not canonical") - step = int(parts[0].removeprefix("step-")) - task_id = parts[1] - attempt = int(parts[2].removeprefix("attempt-")) - if step != 0: - raise PromotionError("rollout-health smoke contains a nonzero step") - record = store.load_candidate( - key, - invocation_id=invocation_id, - step=step, - task_id=task_id, - attempt=attempt, - ) - if record is None: - raise PromotionError("rollout-health candidate record disappeared") - payload = _mapping(record.payload, "candidate.payload") - sample_binding = _mapping(payload.get("sample_record"), "candidate.sample_record") - sample = store.load_candidate_sample( - key, - invocation_id=invocation_id, - step=step, - task_id=task_id, - attempt=attempt, - ) - if ( - sample is None - or sample_binding.get("relative_path") != str(sample.relative_path) - or sample_binding.get("payload_sha256") != sample.payload_sha256 - or sample_binding.get("record_sha256") != sample.record_sha256 - ): - raise PromotionError( - "rollout-health candidate is not bound to its paid sample" - ) - evaluation = _mapping(payload.get("evaluation"), "candidate.evaluation") - _finite_json(evaluation, "candidate.evaluation") - attribution = str(evaluation.get("attribution", "")) - if attribution != "model": - raise PromotionError( - "rollout-health contains an evaluator or reference fault" - ) - reward = _unit(evaluation.get("reward"), "candidate.evaluation.reward") - status = str(evaluation.get("status", "")) - if status not in allowed_statuses: - raise PromotionError("rollout-health contains an unknown evaluator status") - if status not in _MODEL_EVALUATION_STATUSES: - raise PromotionError( - "rollout-health contains an evaluator or reference status " - "mislabeled as model-attributed" - ) - iou = evaluation.get("iou") - dice = evaluation.get("dice") - if status == "ok": - if iou is None or dice is None: - raise PromotionError( - "rollout-health successful evaluation lacks IoU or Dice" - ) - measured_iou = _unit(iou, "candidate.evaluation.iou") - _unit(dice, "candidate.evaluation.dice") - if not math.isclose( - reward, - measured_iou, - rel_tol=0.0, - abs_tol=1e-12, - ): - raise PromotionError( - "rollout-health reward differs from raw IoU" - ) - executable += 1 - else: - if reward != 0.0: - raise PromotionError( - "rollout-health model failure has nonzero reward" - ) - if iou is not None or dice is not None: - raise PromotionError( - "rollout-health model failure contains geometry metrics" - ) - rewards.append(reward) - group = (step, task_id) - if attempt in rewards_by_group[group]: - raise PromotionError("rollout-health repeats a group attempt") - rewards_by_group[group][attempt] = reward - record_bindings.append( - { - "relative_path": str(record.relative_path), - "payload_sha256": record.payload_sha256, - "record_sha256": record.record_sha256, - } - ) - sample_record_bindings.append( - { - "relative_path": str(sample.relative_path), - "payload_sha256": sample.payload_sha256, - "record_sha256": sample.record_sha256, - } - ) - - if len(rewards_by_group) != groups_per_batch or any( - set(attempts) != set(range(1, group_size + 1)) - for attempts in rewards_by_group.values() - ): - raise PromotionError("rollout-health candidates are not exactly eight G4 groups") - nonconstant = sum( - max(attempts.values()) - min(attempts.values()) > 1e-12 - for attempts in rewards_by_group.values() - ) - if executable < 1: - raise PromotionError("rollout-health smoke has no pure executable candidate") - if nonconstant < 1: - raise PromotionError("rollout-health smoke has no nonconstant G4 group") - - payload = { - "schema_version": ROLLOUT_HEALTH_APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "rollout-health-receipt", - "stage_id": stage.stage_id, - "wave": wave_name, - "source_git_sha": source_git_sha, - "evidence_source_git_sha": manifest.payload.get("source_git_sha"), - "protocol_logical_sha256": protocol["logical_sha256"], - "prior_wave": prior_wave, - "prior_receipt_record_sha256": prior_receipt.record_sha256, - "invocation_id": invocation_id, - "candidate_inventory_logical_sha256": recorded_inventory[ - "logical_sha256" - ], - "sample_inventory_logical_sha256": recorded_samples[ - "logical_sha256" - ], - "candidate_record_bindings_sha256": canonical_sha256(record_bindings), - "sample_record_bindings_sha256": canonical_sha256( - sample_record_bindings - ), - "metrics": { - "candidates": expected_candidates, - "groups": groups_per_batch, - "group_size": group_size, - "mean_raw_absolute_scale_iou_including_zeros": ( - sum(rewards) / len(rewards) - ), - "pure_executable": executable, - "pure_executable_rate": executable / expected_candidates, - "nonconstant_reward_groups": nonconstant, - "nonconstant_reward_group_fraction": ( - nonconstant / groups_per_batch - ), - "evaluator_or_reference_faults": 0, - }, - } - return store.write_rollout_health_approval( - key, - wave=wave_name, - payload=payload, - ) - - -def record_base_baseline_approval( - *, - repo_root: Path, - store: TrainingStore, - key: StageKey, - stage: StageSpec, - wave_name: str, - source_git_sha: str, - protocol: Mapping[str, Any], -) -> TrainingRecord: - """Validate and bind the pre-update base-Qwen L0 held-out report.""" - - if ( - stage.stage_id != "qwen-base-rl-l0" - or stage.parent != "base:qwen" - or stage.current_level != "L0" - or wave_name != "smoke" - or str(stage.wave(wave_name)["approval"]) - != "base-level-held-out-baseline-receipt" - ): - raise PromotionError("base baseline gate is not qwen-base-rl-l0/smoke") - - from .evaluation import ( - CheckpointEvaluationError, - CheckpointEvaluationStore, - _base_model_binding, - _sampler_provenance, - validate_checkpoint_report as validate_native_report, - ) - - panel = LEVEL_PROGRESS_PANELS["L0"] - binding = _base_model_binding( - protocol=dict(protocol), - stage_id=stage.stage_id, - replicate_id=key.replicate_id, - source_git_sha=source_git_sha, - ) - report_store = CheckpointEvaluationStore( - stage_path=store.stage_path(key), - wave="base", - checkpoint_name="base", - panel=panel, - ) - manifest = report_store.load_manifest() - report = report_store.load_report() - if manifest is None or report is None: - raise PromotionError( - "qwen-base-rl-l0/smoke requires the completed base L0 panel" - ) - expected_tasks = _expected_panel_tasks( - repo_root=repo_root, - protocol=protocol, - panel=panel, - ) - try: - validate_native_report( - report, - manifest_record_sha256=str(manifest["record_sha256"]), - binding=binding, - panel=panel, - expected_task_ids=[task.task_id for task in expected_tasks], - ) - except CheckpointEvaluationError as exc: - raise PromotionError(f"base L0 report failed its native validator: {exc}") from exc - - report_payload = _mapping(report.get("payload"), "base_report.payload") - provenance = _mapping(report_payload.get("provenance"), "base_report.provenance") - manifest_payload = _mapping(manifest.get("payload"), "base_manifest.payload") - expected_identity = { - "study_id": STUDY_ID, - "stage_id": stage.stage_id, - "wave": "base", - "checkpoint_name": "base", - "replicate_id": key.replicate_id, - "panel": panel, - } - for document_name, document in ( - ("report", report_payload), - ("manifest", manifest_payload), - ): - for field, expected in expected_identity.items(): - if document.get(field) != expected: - raise PromotionError( - f"base {document_name} {field} differs from its request" - ) - expected_protocol = { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - } - if ( - provenance.get("source_git_sha") != source_git_sha - or manifest_payload.get("source_git_sha") != source_git_sha - or dict(_mapping(provenance.get("protocol"), "base_report.protocol")) - != expected_protocol - or any( - manifest_payload.get(field) != expected - for field, expected in { - "protocol_logical_sha256": expected_protocol["logical_sha256"], - "protocol_file_sha256": expected_protocol["file_sha256"], - "contract_version": expected_protocol["contract_version"], - }.items() - ) - ): - raise PromotionError("base L0 report source or protocol differs") - panel_document = _panel_manifest(expected_tasks, panel=panel) - observed_panel = _mapping( - provenance.get("task_panel"), - "base_report.task_panel", - ) - if ( - dict(observed_panel) != panel_document - or manifest_payload.get("task_panel") != panel_document - ): - raise PromotionError("base L0 report task panel differs") - - local_dataset = _dataset_binding(repo_root / "dataset", protocol) - if manifest_payload.get("dataset") != local_dataset: - raise PromotionError("base L0 manifest dataset differs") - expected_report_dataset = { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": "depth/validation/L0/slot-6", - "logical_release_sha256": local_dataset["logical_release_sha256"], - "freeze_file_sha256": local_dataset["freeze_file_sha256"], - "parquet_shards": local_dataset["parquet_shards"], - } - if provenance.get("dataset") != expected_report_dataset: - raise PromotionError("base L0 report dataset projection differs") - - model = dict(protocol["models"]["qwen"]) - expected_sampler = { - "model": model["model"], - "renderer": model["renderer"], - "effort": model.get("effort"), - **_sampler_provenance(binding), - } - if ( - manifest_payload.get("model") != model - or manifest_payload.get("sampler") != _sampler_provenance(binding) - or provenance.get("sampler") != expected_sampler - ): - raise PromotionError("base L0 report sampler differs") - expected_report_sampling = { - "attempts_per_task": 1, - "max_output_tokens": 60_000, - "temperature": 1.0, - "top_p": 1.0, - "max_image_long_edge": 1_920, - "seed_algorithm": "sha256-bound deterministic 31-bit seed", - } - expected_manifest_sampling = { - "attempts_per_task": 1, - "max_output_tokens": 60_000, - "temperature": 1.0, - "top_p": 1.0, - "max_image_long_edge": 1_920, - "sample_concurrency": 8, - "evaluation_batch_size": 32, - "evaluator_workers": 8, - } - if ( - provenance.get("sampling") != expected_report_sampling - or manifest_payload.get("sampling") != expected_manifest_sampling - ): - raise PromotionError("base L0 report sampling policy differs") - sandbox = _mapping(provenance.get("sandbox"), "base_report.sandbox") - if ( - manifest_payload.get("prompt_assets") != prompt_asset_hashes() - or provenance.get("prompt_assets") != prompt_asset_hashes() - or dict(sandbox) != manifest_payload.get("sandbox") - or provenance.get("evaluation_manifest_record_sha256") - != manifest.get("record_sha256") - ): - raise PromotionError("base L0 report runtime provenance differs") - if ( - not _IMMUTABLE_IMAGE.fullmatch(str(sandbox.get("image_ref", ""))) - or not re.fullmatch( - r"sha256:[0-9a-f]{64}", - str(sandbox.get("image_id", "")), - ) - ): - raise PromotionError("base L0 report sandbox is not immutable") - _validate_report_metrics(report_payload, expected_tasks=expected_tasks) - - payload = { - "schema_version": BASE_BASELINE_APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "base-level-held-out-baseline-receipt", - "stage_id": stage.stage_id, - "wave": wave_name, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "evaluation": { - "wave": "base", - "checkpoint_name": "base", - "panel": panel, - "base_model_binding_sha256": binding.binding_sha256, - "manifest_relative_path": str( - report_store.root.relative_to(store.external_root) - / "manifest.json" - ), - "manifest_payload_sha256": manifest["payload_sha256"], - "manifest_record_sha256": manifest["record_sha256"], - "report_relative_path": str( - report_store.root.relative_to(store.external_root) - / "report.json" - ), - "report_payload_sha256": report["payload_sha256"], - "report_record_sha256": report["record_sha256"], - }, - } - return store.write_base_baseline_approval( - key, - wave=wave_name, - payload=payload, - ) - - -def _required_prior_wave(stage: StageSpec, wave_name: str) -> str | None: - target = int(stage.wave(wave_name)["max_steps"]) - earlier = [ - (int(wave["max_steps"]), name) - for name, wave in stage.waves.items() - if int(wave["max_steps"]) < target - ] - return max(earlier)[1] if earlier else None - - -def _mixed_allowed_checkpoints(current_level: str) -> tuple[tuple[str, str], ...]: - index = LEVELS.index(current_level) - return ( - ("qwen-mixed-sft", "complete"), - *tuple((f"qwen-mixed-rl-l{prior}", "complete") for prior in range(index)), - ) - - -def approval_plan(stage: StageSpec, wave_name: str) -> ApprovalPlan: - """Resolve the exact report and approval-record location for one wave.""" - - gate = str(stage.wave(wave_name)["approval"]) - if gate == "initial": - raise PromotionError(f"{stage.stage_id}/{wave_name} needs no approval") - - if gate == "clean-smoke-receipt": - prior = _required_prior_wave(stage, wave_name) - if prior is None: - raise PromotionError("clean-smoke gate has no preceding wave") - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=PANEL_PROGRESS, - allowed_evaluation_checkpoints=((stage.stage_id, prior),), - ) - - if gate == "rollout-health-receipt": - raise PromotionError( - "rollout-health-receipt is derived automatically from the prior " - "wave receipt; it does not accept a human checkpoint approval" - ) - - if gate == "current-level-held-out-promotion-receipt": - prior = _required_prior_wave(stage, wave_name) - if prior is None or stage.current_level not in LEVEL_PROGRESS_PANELS: - raise PromotionError("current-level gate has no prior wave or level") - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=LEVEL_PROGRESS_PANELS[stage.current_level], - allowed_evaluation_checkpoints=((stage.stage_id, prior),), - ) - - if gate == "prior-level-held-out-promotion-receipt": - parent_stage, parent_wave = stage.parent.split(":", 1) - if ( - parent_stage == "base" - or parent_wave != "complete" - or stage.current_level not in LEVELS[1:] - ): - raise PromotionError("prior-level gate has no exact curriculum parent") - prior_level = LEVELS[LEVELS.index(stage.current_level) - 1] - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=LEVEL_PROGRESS_PANELS[prior_level], - allowed_evaluation_checkpoints=((parent_stage, parent_wave),), - ) - - if gate == "prior-and-current-level-held-out-transition-receipt": - parent_stage, parent_wave = stage.parent.split(":", 1) - if ( - stage.stage_id not in { - "qwen-base-rl-l1", - "qwen-base-rl-l2", - "qwen-base-rl-l3", - "qwen-base-rl-l4", - } - or parent_wave != "complete" - or stage.current_level not in LEVELS[1:] - ): - raise PromotionError( - "dual level-transition gate has no exact pure-RL parent" - ) - prior_level = LEVELS[LEVELS.index(stage.current_level) - 1] - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=LEVEL_PROGRESS_PANELS[prior_level], - allowed_evaluation_checkpoints=((parent_stage, parent_wave),), - entry_baseline_panel=LEVEL_PROGRESS_PANELS[stage.current_level], - ) - - if gate == "promotion-receipt-and-explicit-human-approval": - prior = _required_prior_wave(stage, wave_name) - if ( - stage.stage_id != "inkling-l4-rl" - or wave_name != "coverage-extension" - or prior != "pilot" - ): - raise PromotionError("unknown advanced-policy promotion gate") - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=PANEL_INKLING_PROMOTION, - allowed_evaluation_checkpoints=((stage.stage_id, prior),), - ) - - if gate in { - "entry-evaluation-and-promotion-receipt", - "parent-evaluation-receipt", - }: - parent_stage, parent_wave = stage.parent.split(":", 1) - if parent_stage == "base" or parent_wave == "complete-or-skip": - raise PromotionError(f"{gate} requires one exact trained parent") - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=PANEL_DEPTH_VALIDATION, - allowed_evaluation_checkpoints=((parent_stage, parent_wave),), - ) - - if gate == "first-unmastered-entry-receipt": - if stage.current_level not in LEVELS or not stage.stage_id.startswith("qwen-mixed-rl-"): - raise PromotionError("first-unmastered gate is not a mixed-Qwen stage") - return ApprovalPlan( - gate=gate, - storage_wave=wave_name, - expected_panel=PANEL_DEPTH_VALIDATION, - allowed_evaluation_checkpoints=_mixed_allowed_checkpoints(stage.current_level), - ) - - raise PromotionError(f"unsupported approval gate: {gate!r}") - - -def _expected_panel_tasks( - *, - repo_root: Path, - protocol: Mapping[str, Any], - panel: str, -) -> list[PanelTaskIdentity]: - # Import lazily: loading/recording approvals must remain importable when - # the optional Tinker training stack is not installed. - from .evaluation import build_panel_tasks - - tasks = build_panel_tasks( - repo_root=repo_root, - protocol=protocol, - panel=panel, - ) - return [ - PanelTaskIdentity( - task_id=task.task_id, - level=task.level, - representation_id=task.representation_id, - image_sha256=task.observation.image_sha256, - target_image_sha256=task.reference.target_image_sha256, - footprint_um=tuple(float(value) for value in task.observation.footprint_um), - ) - for task in tasks - ] - - -def _panel_manifest( - tasks: Sequence[PanelTaskIdentity], - *, - panel: str, -) -> dict[str, Any]: - entries = [ - { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "image_sha256": task.image_sha256, - "target_image_sha256": task.target_image_sha256, - "footprint_um": list(task.footprint_um), - } - for task in tasks - ] - return { - "schema_version": "pixcell-training-evaluation-task-panel-v1", - "panel": panel, - "selection_seed": ( - _INKLING_PANEL_SELECTION_SEED - if panel == PANEL_INKLING_PROMOTION - else ( - _LEVEL_PROGRESS_SELECTION - if panel in LEVEL_PROGRESS_PANELS.values() - else ( - _DEPTH_FINAL_SELECTION - if panel == PANEL_DEPTH_FINAL_SELECTION - else None - ) - ) - ), - "task_count": len(tasks), - "task_ids": [task.task_id for task in tasks], - "logical_sha256": canonical_json_sha256(entries), - } - - -def _aggregate(records: Sequence[Mapping[str, Any]]) -> dict[str, Any]: - if not records: - raise PromotionError("evaluation aggregate is empty") - ious = [ - _unit(record.get("raw_absolute_scale_iou"), "record.raw_absolute_scale_iou") - for record in records - ] - pure: list[bool] = [] - statuses: Counter[str] = Counter() - for index, record in enumerate(records): - observed = record.get("pure_executable") - if not isinstance(observed, bool): - raise PromotionError("record.pure_executable must be boolean") - status = str(record.get("status", "")) - if status not in _MODEL_EVALUATION_STATUSES: - raise PromotionError("record contains a non-model evaluation status") - if observed != (status == "ok"): - raise PromotionError("record status and pure-executable flag differ") - if status != "ok" and float(ious[index]) != 0.0: - raise PromotionError("failed model output has nonzero IoU") - pure.append(observed) - statuses[status] += 1 - return { - "tasks": len(records), - "mean_raw_absolute_scale_iou": sum(ious) / len(ious), - "pure_executable": sum(pure), - "pure_executable_rate": sum(pure) / len(pure), - "statuses": dict(sorted(statuses.items())), - } - - -def _validate_aggregate( - observed: Any, - expected: Mapping[str, Any], - *, - path: str, -) -> None: - value = _mapping(observed, path) - for field in ("tasks", "pure_executable"): - actual = _integer(value.get(field), f"{path}.{field}") - if actual != expected[field]: - raise PromotionError(f"{path}.{field}={actual}; expected {expected[field]}") - _close( - value.get("mean_raw_absolute_scale_iou"), - float(expected["mean_raw_absolute_scale_iou"]), - f"{path}.mean_raw_absolute_scale_iou", - ) - _close( - value.get("pure_executable_rate"), - float(expected["pure_executable_rate"]), - f"{path}.pure_executable_rate", - ) - statuses = _mapping(value.get("statuses"), f"{path}.statuses") - if dict(statuses) != expected["statuses"]: - raise PromotionError(f"{path}.statuses differs from its records") - - -def _validate_report_metrics( - report: Mapping[str, Any], - *, - expected_tasks: Sequence[PanelTaskIdentity], -) -> None: - raw_records = report.get("records") - if not isinstance(raw_records, list) or len(raw_records) != len(expected_tasks): - raise PromotionError(f"report.records must contain all {len(expected_tasks)} tasks") - records: list[Mapping[str, Any]] = [] - for index, (raw, task) in enumerate(zip(raw_records, expected_tasks, strict=True)): - path = f"records[{index}]" - record = _mapping(raw, path) - expected_identity = { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - } - for field, expected in expected_identity.items(): - if record.get(field) != expected: - raise PromotionError(f"{path}.{field}={record.get(field)!r}; expected {expected!r}") - _unit( - record.get("raw_absolute_scale_iou"), - f"{path}.raw_absolute_scale_iou", - ) - tokens = _integer(record.get("completion_tokens"), f"{path}.completion_tokens") - if tokens > 60_000: - raise PromotionError(f"{path}.completion_tokens exceeds the sealed cap") - for field in ("cap_hit", "channel_parse_complete"): - if not isinstance(record.get(field), bool): - raise PromotionError(f"{path}.{field} must be boolean") - for field in ( - "answer_text_sha256", - "sample_record_sha256", - "evaluation_record_sha256", - ): - _sha(record.get(field), f"{path}.{field}") - render = record.get("render_sha256") - if render is not None: - _sha(render, f"{path}.render_sha256") - # This also enforces the status/purity relationship. - _aggregate([record]) - records.append(record) - - expected_summary = _aggregate(records) - _validate_aggregate(report.get("summary"), expected_summary, path="summary") - - by_level: dict[str, list[Mapping[str, Any]]] = defaultdict(list) - by_representation: dict[str, list[Mapping[str, Any]]] = defaultdict(list) - for record in records: - by_level[str(record["level"])].append(record) - by_representation[str(record["representation_id"])].append(record) - observed_levels = _mapping(report.get("levels"), "levels") - expected_levels = {key: _aggregate(values) for key, values in sorted(by_level.items())} - if set(observed_levels) != set(expected_levels): - raise PromotionError("report.levels differs from the panel") - for key, expected in expected_levels.items(): - _validate_aggregate(observed_levels[key], expected, path=f"levels.{key}") - - observed_representations = _mapping( - report.get("representations"), - "representations", - ) - expected_representations = { - key: _aggregate(values) for key, values in sorted(by_representation.items()) - } - if set(observed_representations) != set(expected_representations): - raise PromotionError("report.representations differs from the panel") - for key, expected in expected_representations.items(): - _validate_aggregate( - observed_representations[key], - expected, - path=f"representations.{key}", - ) - macro = sum( - value["mean_raw_absolute_scale_iou"] for value in expected_representations.values() - ) / len(expected_representations) - _close( - report.get("representation_macro_mean_iou"), - macro, - "representation_macro_mean_iou", - ) - - -def _validate_report_payload( - *, - repo_root: Path, - protocol: Mapping[str, Any], - expected_source_sha: str, - key: StageKey, - wave: str, - checkpoint_name: str, - selected_checkpoint: Mapping[str, Any], - checkpoint_inventory_sha256: str, - panel: str, - report: Mapping[str, Any], - evaluation_manifest: Mapping[str, Any], - training_manifest: TrainingRecord, - training_receipt: TrainingRecord, - expected_tasks: Sequence[PanelTaskIdentity], -) -> None: - if report.get("schema_version") != _REPORT_SCHEMA: - raise PromotionError("checkpoint report has a foreign schema") - expected_identity = { - "study_id": STUDY_ID, - "stage_id": key.stage_id, - "wave": wave, - "checkpoint_name": checkpoint_name, - "replicate_id": key.replicate_id, - "panel": panel, - } - for field, expected in expected_identity.items(): - if report.get(field) != expected: - raise PromotionError(f"report {field} differs from its path") - - expected_rows = EXPECTED_PANEL_ROWS[panel] - if len(expected_tasks) != expected_rows: - raise PromotionError( - f"{panel} local task set has {len(expected_tasks)} rows, expected {expected_rows}" - ) - panel_document = _panel_manifest(expected_tasks, panel=panel) - provenance = _mapping(report.get("provenance"), "provenance") - observed_panel = _mapping(provenance.get("task_panel"), "provenance.task_panel") - for field, expected in panel_document.items(): - if observed_panel.get(field) != expected: - raise PromotionError(f"provenance.task_panel.{field} changed") - - if provenance.get("source_git_sha") != expected_source_sha: - raise PromotionError("report source commit differs") - protocol_binding = _mapping(provenance.get("protocol"), "provenance.protocol") - expected_protocol = { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - } - for field, expected in expected_protocol.items(): - if protocol_binding.get(field) != expected: - raise PromotionError(f"report protocol {field} differs") - - manifest_payload = _mapping(evaluation_manifest.get("payload"), "eval_manifest") - for field, expected in { - **expected_identity, - "source_git_sha": expected_source_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": expected_protocol["file_sha256"], - "contract_version": protocol["contract_version"], - }.items(): - if manifest_payload.get(field) != expected: - raise PromotionError(f"evaluation manifest {field} differs") - if manifest_payload.get("task_panel") != dict(observed_panel): - raise PromotionError("report and evaluation-manifest task panels differ") - - expected_assets = prompt_asset_hashes() - if ( - provenance.get("prompt_assets") != expected_assets - or manifest_payload.get("prompt_assets") != expected_assets - ): - raise PromotionError("evaluation prompt assets differ from this source") - - dataset = _mapping(provenance.get("dataset"), "provenance.dataset") - manifest_dataset = _mapping( - manifest_payload.get("dataset"), - "evaluation_manifest.dataset", - ) - try: - expected_local_dataset = _dataset_binding(repo_root / "dataset", protocol) - except Exception as exc: - raise PromotionError("local evaluation dataset could not be verified") from exc - if dict(manifest_dataset) != expected_local_dataset: - raise PromotionError("evaluation-manifest dataset differs from the frozen release") - - expected_report_dataset = { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": { - PANEL_PROGRESS: "fixed-f1-f8", - PANEL_DEPTH_VALIDATION: "depth/validation", - PANEL_INKLING_PROMOTION: "depth/validation+fixed-f1-f8", - PANEL_DEPTH_FINAL_SELECTION: "depth/validation/slot-7", - **{ - panel_id: f"depth/validation/{level}/slot-6" - for level, panel_id in LEVEL_PROGRESS_PANELS.items() - }, - }[panel], - "logical_release_sha256": expected_local_dataset[ - "logical_release_sha256" - ], - "freeze_file_sha256": expected_local_dataset["freeze_file_sha256"], - "parquet_shards": expected_local_dataset["parquet_shards"], - } - if dict(dataset) != expected_report_dataset: - raise PromotionError( - "report dataset projection differs from the evaluation manifest" - ) - - if ( - training_manifest.payload.get("source_git_sha") != expected_source_sha - or training_manifest.payload.get("protocol_logical_sha256") != protocol["logical_sha256"] - ): - raise PromotionError("training manifest provenance differs") - manifest_training_dataset = _mapping( - training_manifest.payload.get("dataset"), - "training_manifest.dataset", - ) - if dict(manifest_training_dataset) != expected_local_dataset: - raise PromotionError("training manifest dataset differs from the frozen release") - - sampler = _mapping(provenance.get("sampler"), "provenance.sampler") - evaluation_sampler = _mapping( - manifest_payload.get("sampler"), - "evaluation_manifest.sampler", - ) - sampler_path = str(selected_checkpoint.get("sampler_path", "")) - if not _TINKER_PATH.fullmatch(sampler_path): - raise PromotionError("selected inventory entry has no sampler checkpoint") - expected_sampler = { - "sampler_path": sampler_path, - "checkpoint_name": checkpoint_name, - "checkpoint": dict(selected_checkpoint), - "checkpoint_inventory_sha256": checkpoint_inventory_sha256, - "training_run_manifest_record_sha256": training_manifest.record_sha256, - "training_wave_receipt_record_sha256": training_receipt.record_sha256, - } - for field, expected in expected_sampler.items(): - if sampler.get(field) != expected or evaluation_sampler.get(field) != expected: - raise PromotionError(f"evaluation sampler {field} differs") - if sampler.get("training_wave_receipt_relative_path") != str(training_receipt.relative_path): - raise PromotionError("evaluation sampler receipt path differs") - - evaluated_stage = stage_spec(protocol, key.stage_id) - model = protocol["models"][evaluated_stage.model_key] - for field in ("model", "renderer", "effort"): - if sampler.get(field) != model.get(field): - raise PromotionError(f"evaluation sampler {field} differs") - if manifest_payload.get("model") != model: - raise PromotionError("evaluation manifest model differs") - - sampling = _mapping(provenance.get("sampling"), "provenance.sampling") - expected_sampling = { - "attempts_per_task": 1, - "max_output_tokens": 60_000, - "temperature": 1.0, - "top_p": 1.0, - "max_image_long_edge": 1_920, - } - for field, expected in expected_sampling.items(): - if sampling.get(field) != expected: - raise PromotionError(f"evaluation sampling {field} differs") - manifest_sampling = _mapping( - manifest_payload.get("sampling"), - "evaluation_manifest.sampling", - ) - for field, expected in expected_sampling.items(): - if manifest_sampling.get(field) != expected: - raise PromotionError(f"evaluation manifest sampling {field} differs") - - sandbox = _mapping(provenance.get("sandbox"), "provenance.sandbox") - if dict(sandbox) != manifest_payload.get("sandbox"): - raise PromotionError("report and evaluation-manifest sandboxes differ") - if not _IMMUTABLE_IMAGE.fullmatch(str(sandbox.get("image_ref", ""))): - raise PromotionError("evaluation sandbox image_ref is not immutable") - if not re.fullmatch(r"sha256:[0-9a-f]{64}", str(sandbox.get("image_id", ""))): - raise PromotionError("evaluation sandbox image_id is not immutable") - if provenance.get("evaluation_manifest_record_sha256") != evaluation_manifest.get( - "record_sha256" - ): - raise PromotionError("report is not bound to its evaluation manifest") - - if ( - training_receipt.payload.get("stage_id") != key.stage_id - or training_receipt.payload.get("wave") != wave - or training_receipt.payload.get("run_manifest_record_sha256") - != training_manifest.record_sha256 - ): - raise PromotionError("training receipt identity differs") - _validate_report_metrics(report, expected_tasks=expected_tasks) - - -def validate_checkpoint_report( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - stage_id: str, - wave: str, - checkpoint_name: str, - replicate_id: str, - panel: str, - expected_source_sha: str, - expected_report_record_sha256: str, -) -> EvaluationEvidence: - """Load and fully validate one sealed report from its canonical path.""" - - if panel not in EXPECTED_PANEL_ROWS: - raise PromotionError(f"unsupported evaluation panel: {panel!r}") - _sha(expected_report_record_sha256, "expected_report_record_sha256") - stage = stage_spec(dict(protocol), stage_id) - stage.wave(wave) - key = StageKey(STUDY_ID, stage.stage_id, replicate_id) - training_manifest = store.load_manifest(key) - training_receipt = store.load_wave_receipt(key, wave=wave) - if training_manifest is None or training_receipt is None: - raise PromotionError(f"{stage_id}/{wave}/{replicate_id} has no training receipt") - - from .evaluation import ( - CheckpointEvaluationError, - CheckpointEvaluationStore, - _load_checkpoint_binding, - validate_checkpoint_report as validate_evaluation_report, - ) - - evaluation_store = CheckpointEvaluationStore( - stage_path=store.stage_path(key), - wave=wave, - checkpoint_name=checkpoint_name, - panel=panel, - ) - evaluation_manifest = evaluation_store.load_manifest() - report = evaluation_store.load_report() - if evaluation_manifest is None or report is None: - raise PromotionError(f"{stage_id}/{wave}/{panel} has no complete evaluation report") - if report.get("record_sha256") != expected_report_record_sha256: - raise PromotionError("evaluation report digest differs from the reviewed one") - expected_tasks = _expected_panel_tasks( - repo_root=repo_root, - protocol=protocol, - panel=panel, - ) - try: - binding = _load_checkpoint_binding( - store=store, - protocol=dict(protocol), - stage_id=stage_id, - wave=wave, - checkpoint_name=checkpoint_name, - replicate_id=replicate_id, - expected_receipt_sha256=training_receipt.record_sha256, - source_git_sha=expected_source_sha, - ) - validate_evaluation_report( - report, - manifest_record_sha256=str(evaluation_manifest["record_sha256"]), - binding=binding, - panel=panel, - expected_task_ids=[task.task_id for task in expected_tasks], - ) - except CheckpointEvaluationError as exc: - raise PromotionError(f"checkpoint report failed its native validator: {exc}") from exc - payload = _mapping(report.get("payload"), "report.payload") - _validate_report_payload( - repo_root=repo_root, - protocol=protocol, - expected_source_sha=expected_source_sha, - key=key, - wave=wave, - checkpoint_name=checkpoint_name, - selected_checkpoint=binding.checkpoint, - checkpoint_inventory_sha256=binding.checkpoint_inventory_sha256, - panel=panel, - report=payload, - evaluation_manifest=evaluation_manifest, - training_manifest=training_manifest, - training_receipt=training_receipt, - expected_tasks=expected_tasks, - ) - return EvaluationEvidence( - key=key, - wave=wave, - checkpoint_name=checkpoint_name, - checkpoint=dict(binding.checkpoint), - checkpoint_inventory_sha256=binding.checkpoint_inventory_sha256, - panel=panel, - report=dict(report), - report_payload=dict(payload), - report_record_sha256=str(report["record_sha256"]), - report_payload_sha256=str(report["payload_sha256"]), - report_relative_path=str( - ( - TrainingStore.stage_relative_path(key) - / "evaluations" - / wave - / checkpoint_name - / panel - / "report.json" - ) - ), - evaluation_manifest_payload_sha256=_sha( - evaluation_manifest.get("payload_sha256"), - "evaluation_manifest.payload_sha256", - ), - evaluation_manifest_record_sha256=_sha( - evaluation_manifest.get("record_sha256"), - "evaluation_manifest.record_sha256", - ), - evaluation_manifest_relative_path=str( - ( - TrainingStore.stage_relative_path(key) - / "evaluations" - / wave - / checkpoint_name - / panel - / "manifest.json" - ) - ), - training_manifest=training_manifest, - training_receipt=training_receipt, - ) - - -def first_unmastered_level( - report: Mapping[str, Any], - *, - minimum_iou: float = MASTERED_MINIMUM_IOU, - minimum_pure_executable_rate: float = (MASTERED_MINIMUM_PURE_EXECUTABLE_RATE), -) -> str | None: - """Return the first L0→L4 level missing either established threshold.""" - - minimum_iou = _unit(minimum_iou, "minimum_iou") - minimum_pure_executable_rate = _unit( - minimum_pure_executable_rate, - "minimum_pure_executable_rate", - ) - summaries = _mapping(report.get("levels"), "report.levels") - for level in LEVELS: - summary = _mapping(summaries.get(level), f"report.levels.{level}") - iou = _unit( - summary.get("mean_raw_absolute_scale_iou"), - f"report.levels.{level}.mean_raw_absolute_scale_iou", - ) - pure = _unit( - summary.get("pure_executable_rate"), - f"report.levels.{level}.pure_executable_rate", - ) - if iou < minimum_iou or pure < minimum_pure_executable_rate: - return level - return None - - -def _terminal_checkpoint(evidence: EvaluationEvidence) -> None: - terminal = _mapping( - evidence.training_receipt.payload.get("checkpoint"), - "training_receipt.checkpoint", - ) - fields = ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - selected_alias = {field: evidence.checkpoint.get(field) for field in fields} - if selected_alias != dict(terminal): - raise PromotionError( - f"{evidence.key.stage_id}/{evidence.wave} must use its terminal checkpoint" - ) - - -def _best_mixed_checkpoint( - candidates: Sequence[Mapping[str, Any]], -) -> Mapping[str, Any]: - if not candidates: - raise PromotionError("mixed-SFT checkpoint ranking is empty") - - def rank(candidate: Mapping[str, Any]) -> tuple[float, float, float]: - return ( - _unit( - candidate.get("representation_macro_mean_iou"), - "checkpoint.representation_macro_mean_iou", - ), - _unit( - candidate.get("pure_executable_rate"), - "checkpoint.pure_executable_rate", - ), - _unit( - candidate.get("training_progress_fraction"), - "checkpoint.training_progress_fraction", - ), - ) - - return max(candidates, key=rank) - - -def _mixed_checkpoint_selection( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - selected: EvaluationEvidence, - expected_source_sha: str, -) -> dict[str, Any]: - if selected.key.stage_id != "qwen-mixed-sft": - raise PromotionError("mixed checkpoint ranking received another stage") - inventory = _mapping( - selected.training_receipt.payload.get("checkpoint_inventory"), - "training_receipt.checkpoint_inventory", - ) - entries = inventory.get("entries") - if not isinstance(entries, list) or not entries: - raise PromotionError("mixed-SFT receipt has no checkpoint inventory") - inventory_sha = _sha( - inventory.get("logical_sha256"), - "training_receipt.checkpoint_inventory.logical_sha256", - ) - if ( - inventory_sha != canonical_sha256(entries) - or inventory_sha != selected.checkpoint_inventory_sha256 - ): - raise PromotionError("mixed-SFT checkpoint inventory digest differs") - - reports: list[EvaluationEvidence] = [] - for raw_entry in entries: - entry = _mapping(raw_entry, "checkpoint_inventory.entries[]") - checkpoint_name = str(entry.get("name", "")) - if checkpoint_name == selected.checkpoint_name: - evidence = selected - else: - from .evaluation import CheckpointEvaluationStore - - report_store = CheckpointEvaluationStore( - stage_path=store.stage_path(selected.key), - wave=selected.wave, - checkpoint_name=checkpoint_name, - panel=PANEL_DEPTH_VALIDATION, - ) - report = report_store.load_report() - if report is None: - raise PromotionError( - "mixed-SFT promotion requires a complete depth-validation " - f"report for checkpoint {checkpoint_name!r}" - ) - evidence = validate_checkpoint_report( - repo_root=repo_root, - protocol=protocol, - store=store, - stage_id=selected.key.stage_id, - wave=selected.wave, - checkpoint_name=checkpoint_name, - replicate_id=selected.key.replicate_id, - panel=PANEL_DEPTH_VALIDATION, - expected_source_sha=expected_source_sha, - expected_report_record_sha256=str(report["record_sha256"]), - ) - if evidence.checkpoint != dict(entry): - raise PromotionError(f"checkpoint {checkpoint_name!r} report and inventory differ") - reports.append(evidence) - - candidates = [ - { - "checkpoint_name": evidence.checkpoint_name, - "checkpoint": evidence.checkpoint, - "report_record_sha256": evidence.report_record_sha256, - "representation_macro_mean_iou": _unit( - evidence.report_payload.get("representation_macro_mean_iou"), - "report.representation_macro_mean_iou", - ), - "pure_executable_rate": _unit( - _mapping( - evidence.report_payload.get("summary"), - "report.summary", - ).get("pure_executable_rate"), - "report.summary.pure_executable_rate", - ), - "training_progress_fraction": _unit( - evidence.checkpoint.get("training_progress_fraction"), - "checkpoint.training_progress_fraction", - ), - } - for evidence in reports - ] - winner = _best_mixed_checkpoint(candidates) - if winner["checkpoint_name"] != selected.checkpoint_name: - raise PromotionError( - f"mixed-SFT checkpoint {selected.checkpoint_name!r} is not the " - f"deterministic best; expected {winner['checkpoint_name']!r}" - ) - selection = { - "schema_version": "pixcell-mixed-sft-checkpoint-selection-v1", - "selection_rule": ( - "maximize full-depth-validation representation-macro raw IoU; " - "break ties by pure-executable rate, then later training progress" - ), - "all_inventory_checkpoints_required": True, - "inventory_logical_sha256": inventory_sha, - "candidates": candidates, - "selected_checkpoint_name": selected.checkpoint_name, - } - selection["logical_sha256"] = canonical_sha256(selection) - return selection - - -def _source_transition_document( - *, - repo_root: Path, - protocol: Mapping[str, Any], - stage: StageSpec, - wave_name: str, - plan: ApprovalPlan, - evidence: EvaluationEvidence, - producer_source_git_sha: str, - consumer_source_git_sha: str, - approved_change_paths: Sequence[str] | None, -) -> dict[str, Any]: - """Bind one ancestor-produced report to one exact consumer wave.""" - - provenance = _mapping( - evidence.report_payload.get("provenance"), - "report.provenance", - ) - report_protocol = _mapping( - provenance.get("protocol"), - "report.provenance.protocol", - ) - report_dataset = _mapping( - provenance.get("dataset"), - "report.provenance.dataset", - ) - report_sampler = _mapping( - provenance.get("sampler"), - "report.provenance.sampler", - ) - report_sampling = _mapping( - provenance.get("sampling"), - "report.provenance.sampling", - ) - report_prompt_assets = _mapping( - provenance.get("prompt_assets"), - "report.provenance.prompt_assets", - ) - report_sandbox = _mapping( - provenance.get("sandbox"), - "report.provenance.sandbox", - ) - training_dataset = _mapping( - evidence.training_manifest.payload.get("dataset"), - "training_manifest.dataset", - ) - training_model = _mapping( - evidence.training_manifest.payload.get("model"), - "training_manifest.model", - ) - checkpoint_inventory = _mapping( - evidence.training_receipt.payload.get("checkpoint_inventory"), - "training_receipt.checkpoint_inventory", - ) - evaluation_manifest_record_sha256 = _sha( - provenance.get("evaluation_manifest_record_sha256"), - "report.provenance.evaluation_manifest_record_sha256", - ) - if evaluation_manifest_record_sha256 != evidence.evaluation_manifest_record_sha256: - raise PromotionError("report and evaluation manifest record digests differ") - evaluated_stage = stage_spec(dict(protocol), evidence.key.stage_id) - consumer_protocol = { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - } - consumer_prompt_assets = prompt_asset_hashes() - parent_model = dict(protocol["models"][evaluated_stage.model_key]) - if ( - provenance.get("source_git_sha") != producer_source_git_sha - or evidence.training_manifest.payload.get("source_git_sha") != producer_source_git_sha - ): - raise PromotionError("source transition evidence is not producer-source-bound") - if dict(report_protocol) != consumer_protocol: - raise PromotionError("producer evaluation protocol differs from the consumer") - training_protocol = { - "logical_sha256": evidence.training_manifest.payload.get("protocol_logical_sha256"), - "file_sha256": evidence.training_manifest.payload.get("protocol_file_sha256"), - "contract_version": evidence.training_manifest.payload.get("contract_version"), - } - if training_protocol != consumer_protocol: - raise PromotionError("producer training protocol differs from the consumer") - if ( - dict(report_prompt_assets) != consumer_prompt_assets - or evidence.training_manifest.payload.get("prompt_assets") != consumer_prompt_assets - ): - raise PromotionError("producer prompt assets differ from the consumer") - if dict(training_model) != parent_model: - raise PromotionError("producer training model differs from the consumer parent model") - renderer_contract = { - field: parent_model.get(field) for field in ("model", "renderer", "effort") - } - if any(report_sampler.get(field) != expected for field, expected in renderer_contract.items()): - raise PromotionError("producer evaluation renderer differs from the consumer") - expected_dataset = { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "logical_release_sha256": protocol["dataset"]["logical_release_sha256"], - } - if any( - report_dataset.get(field) != expected for field, expected in expected_dataset.items() - ) or ( - training_dataset.get("logical_release_sha256") != expected_dataset["logical_release_sha256"] - ): - raise PromotionError("producer dataset differs from the consumer") - scope = { - "study_id": STUDY_ID, - "child": { - "stage_id": stage.stage_id, - "wave": wave_name, - "replicate_id": evidence.key.replicate_id, - "approval_gate": plan.gate, - }, - "producer_evaluation": { - "stage_id": evidence.key.stage_id, - "wave": evidence.wave, - "checkpoint_name": evidence.checkpoint_name, - "replicate_id": evidence.key.replicate_id, - "panel": evidence.panel, - }, - } - equivalence = { - "protocol": { - "producer_training": { - **training_protocol, - }, - "producer_evaluation": dict(report_protocol), - "consumer": consumer_protocol, - }, - "prompt_assets": { - "producer_training": dict( - _mapping( - evidence.training_manifest.payload.get("prompt_assets"), - "training_manifest.prompt_assets", - ) - ), - "producer_evaluation": dict(report_prompt_assets), - "consumer": consumer_prompt_assets, - }, - "dataset": { - "producer_training": dict(training_dataset), - "producer_evaluation": dict(report_dataset), - "consumer": { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "logical_release_sha256": protocol["dataset"]["logical_release_sha256"], - }, - }, - "task_panel": dict( - _mapping( - provenance.get("task_panel"), - "report.provenance.task_panel", - ) - ), - "model": { - "producer_training": dict(training_model), - "producer_evaluation": parent_model, - "consumer_parent": parent_model, - "consumer_child": dict(protocol["models"][stage.model_key]), - }, - "renderer": { - "producer_evaluation": { - field: report_sampler.get(field) for field in ("model", "renderer", "effort") - }, - "consumer_parent": { - field: protocol["models"][evaluated_stage.model_key].get(field) - for field in ("model", "renderer", "effort") - }, - "consumer_child": { - field: protocol["models"][stage.model_key].get(field) - for field in ("model", "renderer", "effort") - }, - }, - "sampling": dict(report_sampling), - "sandbox": { - "producer_training": evidence.training_manifest.payload.get("sandbox"), - "producer_evaluation": dict(report_sandbox), - }, - } - evidence_binding = { - "training_run_manifest": { - "relative_path": str(evidence.training_manifest.relative_path), - "payload_sha256": evidence.training_manifest.payload_sha256, - "record_sha256": evidence.training_manifest.record_sha256, - }, - "training_wave_receipt": { - "relative_path": str(evidence.training_receipt.relative_path), - "payload_sha256": evidence.training_receipt.payload_sha256, - "record_sha256": evidence.training_receipt.record_sha256, - }, - "checkpoint_inventory": dict(checkpoint_inventory), - "evaluation_manifest": { - "relative_path": evidence.evaluation_manifest_relative_path, - "payload_sha256": (evidence.evaluation_manifest_payload_sha256), - "record_sha256": evaluation_manifest_record_sha256, - }, - "evaluation_report": { - "relative_path": evidence.report_relative_path, - "payload_sha256": evidence.report_payload_sha256, - "record_sha256": evidence.report_record_sha256, - }, - "checkpoint": dict(evidence.checkpoint), - "checkpoint_inventory_logical_sha256": (evidence.checkpoint_inventory_sha256), - } - try: - return build_source_transition( - repo_root=repo_root, - producer_source_git_sha=producer_source_git_sha, - consumer_source_git_sha=consumer_source_git_sha, - scope=scope, - approved_change_paths=approved_change_paths, - equivalence=equivalence, - evidence=evidence_binding, - ) - except SourceTransitionError as exc: - raise PromotionError(f"source transition is invalid: {exc}") from exc - - -def _approved_change_paths_from_transition( - value: Any, -) -> tuple[str, ...]: - transition = _mapping(value, "approval.source_transition") - scope = _mapping( - transition.get("scope"), - "approval.source_transition.scope", - ) - approved = _mapping( - scope.get("approved_changes"), - "approval.source_transition.scope.approved_changes", - ) - paths = approved.get("paths") - if not isinstance(paths, list) or any(not isinstance(path, str) for path in paths): - raise PromotionError("approval source transition has no approved path inventory") - return tuple(paths) - - -def _require_same_evaluated_checkpoint( - primary: EvaluationEvidence, - baseline: EvaluationEvidence, -) -> None: - """Require two panel reports to share one exact parent artifact chain.""" - - fields = ( - ("stage key", primary.key, baseline.key), - ("wave", primary.wave, baseline.wave), - ("checkpoint name", primary.checkpoint_name, baseline.checkpoint_name), - ("checkpoint", primary.checkpoint, baseline.checkpoint), - ( - "checkpoint inventory", - primary.checkpoint_inventory_sha256, - baseline.checkpoint_inventory_sha256, - ), - ( - "training manifest", - primary.training_manifest.record_sha256, - baseline.training_manifest.record_sha256, - ), - ( - "training receipt", - primary.training_receipt.record_sha256, - baseline.training_receipt.record_sha256, - ), - ) - for field, primary_value, baseline_value in fields: - if baseline_value != primary_value: - raise PromotionError( - "prior-level promotion and current-level entry baseline " - f"must evaluate the exact same parent checkpoint ({field} differs)" - ) - - -def _approval_payload( - *, - stage: StageSpec, - wave_name: str, - plan: ApprovalPlan, - evidence: EvaluationEvidence, - source_git_sha: str, - protocol: Mapping[str, Any], - checkpoint_selection: Mapping[str, Any] | None = None, - entry_baseline_evidence: EvaluationEvidence | None = None, - evidence_source_git_sha: str | None = None, - source_transition: Mapping[str, Any] | None = None, -) -> dict[str, Any]: - if ( - evidence.key.stage_id, - evidence.wave, - ) not in plan.allowed_evaluation_checkpoints: - raise PromotionError( - f"{evidence.key.stage_id}/{evidence.wave} cannot approve {stage.stage_id}/{wave_name}" - ) - if evidence.panel != plan.expected_panel: - raise PromotionError( - f"{stage.stage_id}/{wave_name} requires {plan.expected_panel}, not {evidence.panel}" - ) - if plan.entry_baseline_panel is None: - if entry_baseline_evidence is not None: - raise PromotionError("this approval gate does not accept an entry baseline") - else: - if entry_baseline_evidence is None: - raise PromotionError( - f"{stage.stage_id}/{wave_name} requires the " - f"{plan.entry_baseline_panel} pre-update baseline" - ) - if entry_baseline_evidence.panel != plan.entry_baseline_panel: - raise PromotionError( - f"{stage.stage_id}/{wave_name} entry baseline requires " - f"{plan.entry_baseline_panel}, not {entry_baseline_evidence.panel}" - ) - _require_same_evaluated_checkpoint( - evidence, - entry_baseline_evidence, - ) - - mastery: dict[str, Any] | None = None - if plan.gate == "first-unmastered-entry-receipt": - thresholds = _mapping( - protocol["evaluation"].get("mastery_thresholds"), - "protocol.evaluation.mastery_thresholds", - ) - minimum_iou = _unit( - thresholds.get("minimum_mean_raw_absolute_scale_iou"), - "mastery_thresholds.minimum_mean_raw_absolute_scale_iou", - ) - minimum_pure = _unit( - thresholds.get("minimum_pure_executable_rate"), - "mastery_thresholds.minimum_pure_executable_rate", - ) - selected = first_unmastered_level( - evidence.report_payload, - minimum_iou=minimum_iou, - minimum_pure_executable_rate=minimum_pure, - ) - if selected is None: - raise PromotionError( - "all L0–L4 levels are mastered; mixed curriculum RL must not start" - ) - if selected != stage.current_level: - raise PromotionError(f"first unmastered level is {selected}, not {stage.current_level}") - mastery = { - "selection": "first-unmastered-l0-to-l4", - "selected_level": selected, - "minimum_mean_raw_absolute_scale_iou": minimum_iou, - "minimum_pure_executable_rate": minimum_pure, - } - elif plan.gate == "entry-evaluation-and-promotion-receipt": - if stage.current_level not in LEVELS: - raise PromotionError("curriculum entry stage has no current level") - mastery = { - "selection": "fixed-curriculum-entry-after-reviewed-parent-evaluation", - "entry_level": stage.current_level, - "hard_mastery_threshold_applied": False, - } - - cross_source = source_transition is not None - if cross_source != (evidence_source_git_sha is not None): - raise PromotionError("source transition and evidence source must be supplied together") - if evidence_source_git_sha is not None and evidence_source_git_sha == source_git_sha: - raise PromotionError("same-source approvals must retain the v1 schema") - - payload: dict[str, Any] = { - "schema_version": ( - SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION if cross_source else APPROVAL_SCHEMA_VERSION - ), - "decision": "approve", - "approval_gate": plan.gate, - "stage_id": stage.stage_id, - "wave": wave_name, - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "explicit_human_confirmation": True, - "evaluation": { - "stage_id": evidence.key.stage_id, - "wave": evidence.wave, - "checkpoint_name": evidence.checkpoint_name, - "checkpoint": evidence.checkpoint, - "checkpoint_inventory_sha256": (evidence.checkpoint_inventory_sha256), - "replicate_id": evidence.key.replicate_id, - "panel": evidence.panel, - "report_relative_path": evidence.report_relative_path, - "report_payload_sha256": evidence.report_payload_sha256, - "report_record_sha256": evidence.report_record_sha256, - "training_wave_receipt_record_sha256": (evidence.training_receipt.record_sha256), - }, - "mastery": mastery, - "checkpoint_selection": ( - dict(checkpoint_selection) if checkpoint_selection is not None else None - ), - } - if entry_baseline_evidence is not None: - payload["entry_baseline"] = { - "stage_id": entry_baseline_evidence.key.stage_id, - "wave": entry_baseline_evidence.wave, - "checkpoint_name": entry_baseline_evidence.checkpoint_name, - "checkpoint": entry_baseline_evidence.checkpoint, - "checkpoint_inventory_sha256": ( - entry_baseline_evidence.checkpoint_inventory_sha256 - ), - "replicate_id": entry_baseline_evidence.key.replicate_id, - "panel": entry_baseline_evidence.panel, - "report_relative_path": entry_baseline_evidence.report_relative_path, - "report_payload_sha256": ( - entry_baseline_evidence.report_payload_sha256 - ), - "report_record_sha256": ( - entry_baseline_evidence.report_record_sha256 - ), - "training_wave_receipt_record_sha256": ( - entry_baseline_evidence.training_receipt.record_sha256 - ), - } - if cross_source: - payload["evidence_source_git_sha"] = evidence_source_git_sha - payload["source_transition"] = dict(source_transition) - payload["evaluation"].update( - { - "training_run_manifest_record_sha256": (evidence.training_manifest.record_sha256), - "evaluation_manifest_relative_path": (evidence.evaluation_manifest_relative_path), - "evaluation_manifest_payload_sha256": (evidence.evaluation_manifest_payload_sha256), - "evaluation_manifest_record_sha256": (evidence.evaluation_manifest_record_sha256), - } - ) - # The launcher uses these top-level fields to resolve dynamic parents and - # to bind exact-parent gates. A same-stage smoke continuation must not - # claim its smoke receipt as the stage's external parent. - if plan.gate in { - "first-unmastered-entry-receipt", - "entry-evaluation-and-promotion-receipt", - "parent-evaluation-receipt", - "prior-level-held-out-promotion-receipt", - "prior-and-current-level-held-out-transition-receipt", - }: - payload.update( - { - "parent_stage_id": evidence.key.stage_id, - "parent_wave": evidence.wave, - "parent_receipt_record_sha256": (evidence.training_receipt.record_sha256), - "selected_parent_checkpoint": evidence.checkpoint, - "selected_parent_checkpoint_inventory_sha256": ( - evidence.checkpoint_inventory_sha256 - ), - } - ) - elif plan.gate in { - "clean-smoke-receipt", - "current-level-held-out-promotion-receipt", - }: - payload.update( - { - "prior_wave": evidence.wave, - "prior_receipt_record_sha256": (evidence.training_receipt.record_sha256), - } - ) - return payload - - -def validate_source_transition_approval( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - stage: StageSpec, - wave_name: str, - replicate_id: str, - consumer_source_git_sha: str, - approval: TrainingRecord, -) -> EvaluationEvidence: - """Revalidate every fact in one cross-source approval at launch time.""" - - payload = approval.payload - if payload.get("schema_version") != SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION: - raise PromotionError("cross-source approval has a foreign schema") - if payload.get("source_git_sha") != consumer_source_git_sha: - raise PromotionError("cross-source approval names another consumer") - try: - validate_source_sha(repo_root, consumer_source_git_sha) - except (OSError, ValueError, subprocess.SubprocessError) as exc: - raise PromotionError( - f"cross-source approval consumer is not the clean HEAD: {exc}" - ) from exc - producer_source_git_sha = str(payload.get("evidence_source_git_sha", "")) - if producer_source_git_sha == consumer_source_git_sha: - raise PromotionError("same-source approval cannot use the v2 schema") - approved_change_paths = _approved_change_paths_from_transition(payload.get("source_transition")) - try: - git_facts = source_transition_git_facts( - repo_root=repo_root, - producer_source_git_sha=producer_source_git_sha, - consumer_source_git_sha=consumer_source_git_sha, - ) - approved_change_scope(git_facts, approved_change_paths) - except SourceTransitionError as exc: - raise PromotionError(f"source transition is invalid: {exc}") from exc - - plan = approval_plan(stage, wave_name) - evaluation = _mapping(payload.get("evaluation"), "approval.evaluation") - if evaluation.get("replicate_id") != replicate_id: - raise PromotionError("cross-source approval names another replicate") - evidence = validate_checkpoint_report( - repo_root=repo_root, - protocol=protocol, - store=store, - stage_id=str(evaluation.get("stage_id", "")), - wave=str(evaluation.get("wave", "")), - checkpoint_name=str(evaluation.get("checkpoint_name", "")), - replicate_id=replicate_id, - panel=str(evaluation.get("panel", "")), - expected_source_sha=producer_source_git_sha, - expected_report_record_sha256=str(evaluation.get("report_record_sha256", "")), - ) - checkpoint_selection: dict[str, Any] | None = None - if plan.gate == "first-unmastered-entry-receipt" and evidence.key.stage_id == "qwen-mixed-sft": - checkpoint_selection = _mixed_checkpoint_selection( - repo_root=repo_root, - protocol=protocol, - store=store, - selected=evidence, - expected_source_sha=producer_source_git_sha, - ) - else: - _terminal_checkpoint(evidence) - transition = _source_transition_document( - repo_root=repo_root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - plan=plan, - evidence=evidence, - producer_source_git_sha=producer_source_git_sha, - consumer_source_git_sha=consumer_source_git_sha, - approved_change_paths=approved_change_paths, - ) - expected = _approval_payload( - stage=stage, - wave_name=wave_name, - plan=plan, - evidence=evidence, - source_git_sha=consumer_source_git_sha, - protocol=protocol, - checkpoint_selection=checkpoint_selection, - evidence_source_git_sha=producer_source_git_sha, - source_transition=transition, - ) - if payload != expected: - raise PromotionError("cross-source approval differs from its recomputed evidence") - return evidence - - -def validate_same_source_wave_approval( - *, - repo_root: Path, - protocol: Mapping[str, Any], - store: TrainingStore, - stage: StageSpec, - wave_name: str, - replicate_id: str, - source_git_sha: str, - approval: TrainingRecord, -) -> EvaluationEvidence: - """Recompute a same-source approval from its sealed reports at launch. - - The approval JSON is an index, not authority. Every report, manifest, - checkpoint inventory, receipt, aggregate, and task-panel fact is loaded - from its canonical external-ledger path and validated again before the - approval may authorize a paid launch. - """ - - payload = approval.payload - if payload.get("schema_version") != APPROVAL_SCHEMA_VERSION: - raise PromotionError("same-source approval has a foreign schema") - if payload.get("source_git_sha") != source_git_sha: - raise PromotionError("same-source approval names another source") - try: - validate_source_sha(repo_root, source_git_sha) - except (OSError, ValueError, subprocess.SubprocessError) as exc: - raise PromotionError( - f"same-source approval source is not the clean HEAD: {exc}" - ) from exc - - plan = approval_plan(stage, wave_name) - evaluation = _mapping(payload.get("evaluation"), "approval.evaluation") - if evaluation.get("replicate_id") != replicate_id: - raise PromotionError("same-source approval names another replicate") - if evaluation.get("panel") != plan.expected_panel: - raise PromotionError("same-source approval names the wrong promotion panel") - evidence = validate_checkpoint_report( - repo_root=repo_root, - protocol=protocol, - store=store, - stage_id=str(evaluation.get("stage_id", "")), - wave=str(evaluation.get("wave", "")), - checkpoint_name=str(evaluation.get("checkpoint_name", "")), - replicate_id=replicate_id, - panel=plan.expected_panel, - expected_source_sha=source_git_sha, - expected_report_record_sha256=str( - evaluation.get("report_record_sha256", "") - ), - ) - - entry_baseline_evidence: EvaluationEvidence | None = None - if plan.entry_baseline_panel is not None: - entry = _mapping( - payload.get("entry_baseline"), - "approval.entry_baseline", - ) - if entry.get("replicate_id") != replicate_id: - raise PromotionError("entry baseline names another replicate") - if entry.get("panel") != plan.entry_baseline_panel: - raise PromotionError("entry baseline names the wrong current-level panel") - entry_baseline_evidence = validate_checkpoint_report( - repo_root=repo_root, - protocol=protocol, - store=store, - stage_id=str(entry.get("stage_id", "")), - wave=str(entry.get("wave", "")), - checkpoint_name=str(entry.get("checkpoint_name", "")), - replicate_id=replicate_id, - panel=plan.entry_baseline_panel, - expected_source_sha=source_git_sha, - expected_report_record_sha256=str( - entry.get("report_record_sha256", "") - ), - ) - elif "entry_baseline" in payload: - raise PromotionError("approval unexpectedly contains an entry baseline") - - checkpoint_selection: dict[str, Any] | None = None - if ( - plan.gate == "first-unmastered-entry-receipt" - and evidence.key.stage_id == "qwen-mixed-sft" - ): - checkpoint_selection = _mixed_checkpoint_selection( - repo_root=repo_root, - protocol=protocol, - store=store, - selected=evidence, - expected_source_sha=source_git_sha, - ) - else: - _terminal_checkpoint(evidence) - - expected = _approval_payload( - stage=stage, - wave_name=wave_name, - plan=plan, - evidence=evidence, - source_git_sha=source_git_sha, - protocol=protocol, - checkpoint_selection=checkpoint_selection, - entry_baseline_evidence=entry_baseline_evidence, - ) - if payload != expected: - raise PromotionError( - "same-source approval differs from its canonically recomputed evidence" - ) - return evidence - - -def record_wave_approval( - *, - repo_root: Path, - external_root: Path, - stage_id: str, - wave_name: str, - replicate_id: str, - evaluation_stage_id: str, - evaluation_wave: str, - evaluation_checkpoint_name: str, - evaluation_report_record_sha256: str, - entry_baseline_report_record_sha256: str | None = None, - expected_source_sha: str, - evidence_source_sha: str | None = None, - approved_change_paths: Sequence[str] | None = None, - confirmation: str, -) -> dict[str, Any]: - """Validate reviewed evidence and atomically record one approval.""" - - if confirmation != APPROVAL_CONFIRMATION_TOKEN: - raise PromotionError("explicit human approval confirmation is missing") - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol(root, require_committed=True) - source_git_sha = validate_source_sha(root, expected_source_sha) - evidence_git_sha = source_git_sha if evidence_source_sha is None else evidence_source_sha - if evidence_git_sha != source_git_sha: - try: - git_facts = source_transition_git_facts( - repo_root=root, - producer_source_git_sha=evidence_git_sha, - consumer_source_git_sha=source_git_sha, - ) - approved_change_scope(git_facts, approved_change_paths) - except SourceTransitionError as exc: - raise PromotionError(f"source transition is invalid: {exc}") from exc - elif approved_change_paths is not None: - raise PromotionError("same-source v1 approval cannot include an approved change scope") - stage = stage_spec(protocol, stage_id) - try: - validate_stage_replicate( - protocol, - stage_id=stage.stage_id, - replicate_id=replicate_id, - ) - except ValueError as exc: - raise PromotionError(str(exc)) from exc - plan = approval_plan(stage, wave_name) - store = TrainingStore(repo_root=root, external_root=external_root) - evidence = validate_checkpoint_report( - repo_root=root, - protocol=protocol, - store=store, - stage_id=evaluation_stage_id, - wave=evaluation_wave, - checkpoint_name=evaluation_checkpoint_name, - replicate_id=replicate_id, - panel=plan.expected_panel, - expected_source_sha=evidence_git_sha, - expected_report_record_sha256=evaluation_report_record_sha256, - ) - entry_baseline_evidence: EvaluationEvidence | None = None - if plan.entry_baseline_panel is not None: - if entry_baseline_report_record_sha256 is None: - raise PromotionError( - f"{stage.stage_id}/{wave_name} requires a reviewed " - "current-level entry-baseline report" - ) - if evidence_git_sha != source_git_sha: - raise PromotionError( - "dual level-transition approval must be produced and consumed " - "at the same source commit" - ) - entry_baseline_evidence = validate_checkpoint_report( - repo_root=root, - protocol=protocol, - store=store, - stage_id=evaluation_stage_id, - wave=evaluation_wave, - checkpoint_name=evaluation_checkpoint_name, - replicate_id=replicate_id, - panel=plan.entry_baseline_panel, - expected_source_sha=evidence_git_sha, - expected_report_record_sha256=( - entry_baseline_report_record_sha256 - ), - ) - elif entry_baseline_report_record_sha256 is not None: - raise PromotionError( - "entry-baseline report was supplied for a single-panel gate" - ) - checkpoint_selection: dict[str, Any] | None = None - if plan.gate == "first-unmastered-entry-receipt" and evidence.key.stage_id == "qwen-mixed-sft": - checkpoint_selection = _mixed_checkpoint_selection( - repo_root=root, - protocol=protocol, - store=store, - selected=evidence, - expected_source_sha=evidence_git_sha, - ) - else: - _terminal_checkpoint(evidence) - transition = ( - _source_transition_document( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - plan=plan, - evidence=evidence, - producer_source_git_sha=evidence_git_sha, - consumer_source_git_sha=source_git_sha, - approved_change_paths=approved_change_paths, - ) - if evidence_git_sha != source_git_sha - else None - ) - payload = _approval_payload( - stage=stage, - wave_name=wave_name, - plan=plan, - evidence=evidence, - source_git_sha=source_git_sha, - protocol=protocol, - checkpoint_selection=checkpoint_selection, - entry_baseline_evidence=entry_baseline_evidence, - evidence_source_git_sha=(evidence_git_sha if transition is not None else None), - source_transition=transition, - ) - key = StageKey(STUDY_ID, stage.stage_id, replicate_id) - with store.acquire_stage_lock(key): - # Close the approval-time HEAD/dirty-tree gap immediately before the - # immutable external record is created. - validate_source_sha(root, source_git_sha) - if transition is not None: - rebuilt_transition = _source_transition_document( - repo_root=root, - protocol=protocol, - stage=stage, - wave_name=wave_name, - plan=plan, - evidence=evidence, - producer_source_git_sha=evidence_git_sha, - consumer_source_git_sha=source_git_sha, - approved_change_paths=approved_change_paths, - ) - if rebuilt_transition != transition: - raise PromotionError("source transition changed before approval recording") - approval = store.write_wave_approval( - key, - wave=plan.storage_wave, - payload=payload, - ) - result = { - "status": "approved", - "stage_id": stage.stage_id, - "wave": wave_name, - "approval_storage_wave": plan.storage_wave, - "approval": str(approval.relative_path), - "approval_record_sha256": approval.record_sha256, - "evaluation_report_record_sha256": evidence.report_record_sha256, - } - if transition is not None: - result.update( - { - "approval_schema_version": payload["schema_version"], - "evidence_source_git_sha": evidence_git_sha, - "consumer_source_git_sha": source_git_sha, - "source_transition_logical_sha256": transition["logical_sha256"], - } - ) - return result diff --git a/rl/studies/representation_training_v1/protocol.json b/rl/studies/representation_training_v1/protocol.json deleted file mode 100644 index 07402f1f..00000000 --- a/rl/studies/representation_training_v1/protocol.json +++ /dev/null @@ -1,969 +0,0 @@ -{ - "contract_version": "pixcell-direct-reconstruction-v2", - "dataset": { - "configuration": "depth", - "logical_release_sha256": "676c49134d4d044c7d84426cf8eeecf09302e74ca4aa548956f14b7631a4d80b", - "repo_id": "qpaig-mit/pixcell", - "revision": "v2.0.0", - "train_rows": 3468, - "validation_rows": 1092 - }, - "evaluation": { - "final_benchmark_attempts": 4, - "geometry_signal": "raw_absolute_scale_iou", - "mastery_thresholds": { - "minimum_mean_raw_absolute_scale_iou": 0.8, - "minimum_pure_executable_rate": 0.95 - }, - "parameter_holdout": { - "configuration": "depth", - "representations": 546, - "rows": 1092, - "split": "validation" - }, - "curriculum_progress_holdout": { - "baseline_checkpoints": { - "L0": "base:qwen", - "L1": "qwen-base-rl-l0:complete", - "L2": "qwen-base-rl-l1:complete", - "L3": "qwen-base-rl-l2:complete", - "L4": "qwen-base-rl-l3:complete" - }, - "configuration": "depth", - "evaluation_waves": [ - "step-5", - "step-10", - "step-15", - "step-20", - "step-25", - "complete" - ], - "panel_prefix": "level-progress", - "realization_slot": 6, - "representations_by_level": { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108 - }, - "rows_by_level": { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108 - }, - "split": "validation" - }, - "final_selection_holdout": { - "configuration": "depth", - "panel": "depth-final-selection", - "realization_slot": 7, - "representations": 546, - "rows": 546, - "split": "validation" - }, - "progress_benchmark_attempts": 1, - "task_manifest": "../representation_curriculum_v2/task_manifest.json", - "task_manifest_logical_sha256": "05ca50526df05a3a79aa785504e36c7538106e5abdb72b64e86b20cc6f94a890" - }, - "hypotheses": { - "RT-H01": { - "name": "mixed-depth-sft", - "question": "Does one balanced mixed pass teach the representation catalogue and parameter recovery?" - }, - "RT-H02": { - "name": "sequential-sft", - "question": "Does explicit L0 to L4 answer supervision outperform one mixed pass?" - }, - "RT-H03": { - "name": "composition-by-rl", - "question": "After primitive supervision at L0, can geometry-only RL discover L1 to L4 composition?" - }, - "RT-H04": { - "name": "full-sft-then-rl", - "question": "Does curriculum RL improve the strongest mixed-SFT open checkpoint?" - }, - "RT-H05": { - "name": "inkling-advanced-rl", - "question": "Can a strong base policy improve on depth-L4 without losing retained capability?" - }, - "RT-H06": { - "name": "composition-by-pure-curriculum-rl", - "question": "Can verifier-only curriculum RL teach primitives through complete components directly from base Qwen?" - } - }, - "launch": { - "confirmation_token": "PIXCELL_TRAINING_V1", - "parent_replicate_overrides": { - "qwen-l0-rl-l1": { - "r0-retry1": "r0" - } - }, - "paid_launch_enabled": true, - "replicate_stage_scopes": { - "r0": [ - "*" - ], - "r0-retry1": [ - "qwen-l0-rl-l1", - "qwen-l0-rl-l2", - "qwen-l0-rl-l3", - "qwen-l0-rl-l4" - ] - }, - "replicates": [ - "r0", - "r0-retry1" - ] - }, - "logical_sha256": "c9bc59bd81aa0e6235aa307149e6fde02a1baf733756f31fe200ed911ffbee86", - "models": { - "inkling": { - "context_tokens": 65536, - "effort": 0.9, - "lora_rank": 32, - "max_image_long_edge": 1920, - "max_output_tokens": 60000, - "model": "thinkingmachines/Inkling", - "renderer": "tml_v0", - "thinking": true - }, - "qwen": { - "context_tokens": 65536, - "lora_rank": 32, - "max_image_long_edge": 1920, - "max_output_tokens": 60000, - "model": "Qwen/Qwen3.6-35B-A3B", - "renderer": "qwen3_5", - "thinking": true - } - }, - "recipes": { - "inkling_l4_rl": { - "evaluation_panel": { - "benchmark_rows": 8, - "l4_rows": 36, - "retention_rows": 24 - }, - "group_size": 4, - "groups_per_batch": 8, - "kl_coefficient": 0.0, - "learning_rate": 1e-05, - "loss_fn": "importance_sampling", - "automatic_validation_rows": 0, - "remove_constant_reward_groups": true, - "checkpoint_ttl_seconds": null, - "rolling_save_every_steps": 1, - "rollout_error_policy": "fail_fast", - "rollout_json_export": false, - "checkpoint_policy": "unique_wave_ceiling", - "replay_fraction": 0.0, - "reward": "raw_absolute_scale_iou", - "temperature": 1.0, - "top_p": 1.0 - }, - "qwen_rl": { - "group_size": 4, - "groups_per_batch": 8, - "kl_coefficient": 0.0, - "learning_rate": 1e-05, - "loss_fn": "importance_sampling", - "automatic_validation_rows": 0, - "remove_constant_reward_groups": true, - "checkpoint_ttl_seconds": null, - "rolling_save_every_steps": 1, - "rollout_error_policy": "fail_fast", - "rollout_json_export": false, - "checkpoint_policy": "unique_wave_ceiling", - "replay_fraction": 0.2, - "reward": "raw_absolute_scale_iou", - "stage_steps": 30, - "temperature": 1.0, - "top_p": 1.0 - }, - "qwen_sft": { - "adam_beta1": 0.9, - "adam_beta2": 0.95, - "adam_epsilon": 1e-08, - "batch_size": 64, - "effective_passes": 1, - "automatic_nll_validation": false, - "checkpoint_fraction": 0.25, - "checkpoint_ttl_seconds": null, - "learning_rate": 0.0001, - "loss_mass": "equal_by_level_then_representation", - "lr_schedule": "linear", - "max_sequence_tokens": 6144, - "optimizer_reset_each_stage": true, - "rolling_save_every_steps": 1, - "target": "program_and_end_token_only" - } - }, - "schema_version": "pixcell-representation-training-protocol-v1", - "stages": { - "inkling-l4-rl": { - "current_level": "L4", - "hypotheses": [ - "RT-H05" - ], - "kind": "rl", - "model": "inkling", - "parent": "base:inkling", - "recipe": "inkling_l4_rl", - "replay_levels": [], - "waves": { - "coverage-extension": { - "approval": "promotion-receipt-and-explicit-human-approval", - "max_steps": 14 - }, - "pilot": { - "approval": "clean-smoke-receipt", - "max_steps": 6 - }, - "smoke": { - "approval": "initial", - "max_steps": 1 - } - } - }, - "qwen-base-rl-l0": { - "current_level": "L0", - "hypotheses": [ - "RT-H06" - ], - "kind": "rl", - "model": "qwen", - "parent": "base:qwen", - "recipe": "qwen_rl", - "replay_levels": [], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "initial", - "max_steps": 1 - } - } - }, - "qwen-base-rl-l1": { - "current_level": "L1", - "hypotheses": [ - "RT-H06" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-base-rl-l0:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-and-current-level-held-out-transition-receipt", - "max_steps": 1 - } - } - }, - "qwen-base-rl-l2": { - "current_level": "L2", - "hypotheses": [ - "RT-H06" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-base-rl-l1:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-and-current-level-held-out-transition-receipt", - "max_steps": 1 - } - } - }, - "qwen-base-rl-l3": { - "current_level": "L3", - "hypotheses": [ - "RT-H06" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-base-rl-l2:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1", - "L2" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-and-current-level-held-out-transition-receipt", - "max_steps": 1 - } - } - }, - "qwen-base-rl-l4": { - "current_level": "L4", - "hypotheses": [ - "RT-H06" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-base-rl-l3:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1", - "L2", - "L3" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-and-current-level-held-out-transition-receipt", - "max_steps": 1 - } - } - }, - "qwen-l0-rl-l1": { - "current_level": "L1", - "hypotheses": [ - "RT-H03" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-l0-sft:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "parent-receipt-and-source-transition", - "max_steps": 1 - } - } - }, - "qwen-l0-rl-l2": { - "current_level": "L2", - "hypotheses": [ - "RT-H03" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-l0-rl-l1:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-level-held-out-promotion-receipt", - "max_steps": 1 - } - } - }, - "qwen-l0-rl-l3": { - "current_level": "L3", - "hypotheses": [ - "RT-H03" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-l0-rl-l2:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1", - "L2" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-level-held-out-promotion-receipt", - "max_steps": 1 - } - } - }, - "qwen-l0-rl-l4": { - "current_level": "L4", - "hypotheses": [ - "RT-H03" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-l0-rl-l3:complete", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1", - "L2", - "L3" - ], - "waves": { - "complete": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "current-level-held-out-promotion-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "rollout-health-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "prior-level-held-out-promotion-receipt", - "max_steps": 1 - } - } - }, - "qwen-l0-sft": { - "hypotheses": [ - "RT-H02", - "RT-H03" - ], - "kind": "sft", - "levels": [ - "L0" - ], - "model": "qwen", - "parent": "base:qwen", - "recipe": "qwen_sft", - "waves": { - "complete": { - "approval": "initial", - "max_steps": 11 - } - } - }, - "qwen-mixed-rl-l0": { - "current_level": "L0", - "hypotheses": [ - "RT-H04" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-mixed-sft:complete", - "recipe": "qwen_rl", - "replay_levels": [], - "waves": { - "complete": { - "approval": "clean-smoke-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "clean-smoke-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "clean-smoke-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "clean-smoke-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "clean-smoke-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "clean-smoke-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "first-unmastered-entry-receipt", - "max_steps": 1 - } - } - }, - "qwen-mixed-rl-l1": { - "current_level": "L1", - "hypotheses": [ - "RT-H04" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-mixed-rl-l0:complete-or-skip", - "recipe": "qwen_rl", - "replay_levels": [ - "L0" - ], - "waves": { - "complete": { - "approval": "clean-smoke-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "clean-smoke-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "clean-smoke-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "clean-smoke-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "clean-smoke-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "clean-smoke-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "first-unmastered-entry-receipt", - "max_steps": 1 - } - } - }, - "qwen-mixed-rl-l2": { - "current_level": "L2", - "hypotheses": [ - "RT-H04" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-mixed-rl-l1:complete-or-skip", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1" - ], - "waves": { - "complete": { - "approval": "clean-smoke-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "clean-smoke-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "clean-smoke-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "clean-smoke-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "clean-smoke-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "clean-smoke-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "first-unmastered-entry-receipt", - "max_steps": 1 - } - } - }, - "qwen-mixed-rl-l3": { - "current_level": "L3", - "hypotheses": [ - "RT-H04" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-mixed-rl-l2:complete-or-skip", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1", - "L2" - ], - "waves": { - "complete": { - "approval": "clean-smoke-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "clean-smoke-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "clean-smoke-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "clean-smoke-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "clean-smoke-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "clean-smoke-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "first-unmastered-entry-receipt", - "max_steps": 1 - } - } - }, - "qwen-mixed-rl-l4": { - "current_level": "L4", - "hypotheses": [ - "RT-H04" - ], - "kind": "rl", - "model": "qwen", - "parent": "qwen-mixed-rl-l3:complete-or-skip", - "recipe": "qwen_rl", - "replay_levels": [ - "L0", - "L1", - "L2", - "L3" - ], - "waves": { - "complete": { - "approval": "clean-smoke-receipt", - "max_steps": 30 - }, - "step-10": { - "approval": "clean-smoke-receipt", - "max_steps": 10 - }, - "step-15": { - "approval": "clean-smoke-receipt", - "max_steps": 15 - }, - "step-20": { - "approval": "clean-smoke-receipt", - "max_steps": 20 - }, - "step-25": { - "approval": "clean-smoke-receipt", - "max_steps": 25 - }, - "step-5": { - "approval": "clean-smoke-receipt", - "max_steps": 5 - }, - "smoke": { - "approval": "first-unmastered-entry-receipt", - "max_steps": 1 - } - } - }, - "qwen-mixed-sft": { - "hypotheses": [ - "RT-H01", - "RT-H04" - ], - "kind": "sft", - "levels": [ - "L0", - "L1", - "L2", - "L3", - "L4" - ], - "model": "qwen", - "parent": "base:qwen", - "recipe": "qwen_sft", - "waves": { - "complete": { - "approval": "initial", - "max_steps": 55 - } - } - }, - "qwen-sequential-sft-l1": { - "hypotheses": [ - "RT-H02" - ], - "kind": "sft", - "levels": [ - "L1" - ], - "model": "qwen", - "parent": "qwen-l0-sft:complete", - "recipe": "qwen_sft", - "waves": { - "complete": { - "approval": "parent-evaluation-receipt", - "max_steps": 14 - } - } - }, - "qwen-sequential-sft-l2": { - "hypotheses": [ - "RT-H02" - ], - "kind": "sft", - "levels": [ - "L2" - ], - "model": "qwen", - "parent": "qwen-sequential-sft-l1:complete", - "recipe": "qwen_sft", - "waves": { - "complete": { - "approval": "parent-evaluation-receipt", - "max_steps": 10 - } - } - }, - "qwen-sequential-sft-l3": { - "hypotheses": [ - "RT-H02" - ], - "kind": "sft", - "levels": [ - "L3" - ], - "model": "qwen", - "parent": "qwen-sequential-sft-l2:complete", - "recipe": "qwen_sft", - "waves": { - "complete": { - "approval": "parent-evaluation-receipt", - "max_steps": 12 - } - } - }, - "qwen-sequential-sft-l4": { - "hypotheses": [ - "RT-H02" - ], - "kind": "sft", - "levels": [ - "L4" - ], - "model": "qwen", - "parent": "qwen-sequential-sft-l3:complete", - "recipe": "qwen_sft", - "waves": { - "complete": { - "approval": "parent-evaluation-receipt", - "max_steps": 11 - } - } - } - }, - "study_id": "representation-training-v1", - "tracking": { - "entity": "aadarwal-massachusetts-institute-of-technology", - "local_record_is_authoritative": true, - "project": "pixcell-representation-training-v1", - "provider": "wandb" - } -} diff --git a/rl/studies/representation_training_v1/protocol.py b/rl/studies/representation_training_v1/protocol.py deleted file mode 100644 index 6951e366..00000000 --- a/rl/studies/representation_training_v1/protocol.py +++ /dev/null @@ -1,552 +0,0 @@ -"""Load and validate the sealed representation-training study.""" - -from __future__ import annotations - -import hashlib -import json -import math -import re -import subprocess -from collections.abc import Mapping -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from rl.common.prompt import CONTRACT_VERSION -from rl.evaluation.tasks import canonical_json_sha256 - - -SCHEMA_VERSION = "pixcell-representation-training-protocol-v1" -STUDY_ID = "representation-training-v1" -STUDY_RELATIVE_PATH = Path("rl/studies/representation_training_v1") -PROTOCOL_FILENAME = "protocol.json" -LEVELS = ("L0", "L1", "L2", "L3", "L4") -INITIAL_STAGE_IDS = ( - "qwen-mixed-sft", - "qwen-l0-sft", - "qwen-base-rl-l0", - "inkling-l4-rl", -) -BASE_QWEN_RL_STAGE_IDS = tuple(f"qwen-base-rl-l{level}" for level in range(5)) -L0_SFT_QWEN_RL_STAGE_IDS = tuple( - f"qwen-l0-rl-l{level}" for level in range(1, 5) -) -_STAGE_ID = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$") -_GIT_SHA = re.compile(r"^[0-9a-f]{40}$") -_SHA256 = re.compile(r"^[0-9a-f]{64}$") - - -@dataclass(frozen=True) -class StageSpec: - stage_id: str - kind: str - model_key: str - recipe_key: str - hypotheses: tuple[str, ...] - parent: str - levels: tuple[str, ...] - current_level: str | None - replay_levels: tuple[str, ...] - waves: dict[str, dict[str, Any]] - - def wave(self, wave_name: str) -> dict[str, Any]: - try: - return dict(self.waves[wave_name]) - except KeyError as exc: - raise ValueError( - f"{self.stage_id} has no wave {wave_name!r}; " - f"expected one of {sorted(self.waves)}" - ) from exc - - -def repository_root() -> Path: - return Path(__file__).resolve().parents[3] - - -def protocol_path(repo_root: Path) -> Path: - return repo_root.expanduser().resolve() / STUDY_RELATIVE_PATH / PROTOCOL_FILENAME - - -def file_sha256(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def _logical_document(document: dict[str, Any]) -> dict[str, Any]: - return {key: value for key, value in document.items() if key != "logical_sha256"} - - -def _require_at_head(repo_root: Path, path: Path) -> None: - relative = path.resolve(strict=True).relative_to(repo_root.resolve()).as_posix() - try: - subprocess.check_output( - ["git", "ls-files", "--error-unmatch", "--", relative], - cwd=repo_root, - stderr=subprocess.DEVNULL, - ) - committed = subprocess.check_output( - ["git", "show", f"HEAD:{relative}"], - cwd=repo_root, - stderr=subprocess.DEVNULL, - ) - except (OSError, subprocess.CalledProcessError) as exc: - raise ValueError("training protocol is not committed at HEAD") from exc - if committed != path.read_bytes(): - raise ValueError("training protocol bytes differ from HEAD") - - -def _validate_model(key: str, value: dict[str, Any]) -> None: - required = { - "model", - "renderer", - "thinking", - "lora_rank", - "context_tokens", - "max_output_tokens", - "max_image_long_edge", - } - if not required.issubset(value): - raise ValueError(f"model {key} is missing fields") - if value["max_output_tokens"] >= value["context_tokens"]: - raise ValueError(f"model {key} output exhausts its context") - if key == "qwen": - if ( - value["model"] != "Qwen/Qwen3.6-35B-A3B" - or value["renderer"] != "qwen3_5" - or value.get("effort") is not None - ): - raise ValueError("qwen must use the thinking-on Qwen3.5 contract") - elif key == "inkling": - effort = value.get("effort") - if ( - value["model"] != "thinkingmachines/Inkling" - or value["renderer"] != "tml_v0" - or isinstance(effort, bool) - or not isinstance(effort, (int, float)) - or not math.isfinite(float(effort)) - or not 0 <= float(effort) < 1 - ): - raise ValueError("inkling must use native tml_v0 with explicit effort") - else: - raise ValueError(f"unknown model key: {key}") - - -def _stage_spec( - stage_id: str, - value: dict[str, Any], - *, - protocol: dict[str, Any], -) -> StageSpec: - if not _STAGE_ID.fullmatch(stage_id): - raise ValueError(f"invalid stage ID: {stage_id!r}") - kind = str(value.get("kind")) - if kind not in {"sft", "rl"}: - raise ValueError(f"{stage_id} has unsupported kind {kind!r}") - model_key = str(value.get("model")) - recipe_key = str(value.get("recipe")) - if model_key not in protocol["models"]: - raise ValueError(f"{stage_id} names an unknown model") - if recipe_key not in protocol["recipes"]: - raise ValueError(f"{stage_id} names an unknown recipe") - hypotheses = tuple(str(item) for item in value.get("hypotheses", ())) - if not hypotheses or any(item not in protocol["hypotheses"] for item in hypotheses): - raise ValueError(f"{stage_id} has invalid hypothesis consumers") - parent = str(value.get("parent", "")) - if not parent: - raise ValueError(f"{stage_id} has no checkpoint parent") - levels = tuple(str(item).upper() for item in value.get("levels", ())) - current = value.get("current_level") - current_level = str(current).upper() if current is not None else None - replay = tuple(str(item).upper() for item in value.get("replay_levels", ())) - if kind == "sft": - if not levels or any(item not in LEVELS for item in levels): - raise ValueError(f"{stage_id} has invalid SFT levels") - if current_level is not None or replay: - raise ValueError(f"{stage_id} mixes SFT and RL selectors") - else: - if levels or current_level not in LEVELS: - raise ValueError(f"{stage_id} has invalid RL level") - expected_replay = LEVELS[: LEVELS.index(current_level)] - if model_key == "qwen" and replay != expected_replay: - raise ValueError( - f"{stage_id} replay must be the ordered prefix before {current_level}" - ) - if model_key == "inkling" and replay: - raise ValueError("Inkling L4 pilot must not replay training rows") - waves = value.get("waves") - if not isinstance(waves, dict) or not waves: - raise ValueError(f"{stage_id} has no waves") - observed_steps: list[int] = [] - for wave_name, wave in waves.items(): - if not _STAGE_ID.fullmatch(str(wave_name)): - raise ValueError(f"{stage_id} has invalid wave name") - if not isinstance(wave, dict) or not str(wave.get("approval", "")): - raise ValueError(f"{stage_id}/{wave_name} has no approval gate") - steps = wave.get("max_steps") - if isinstance(steps, bool) or not isinstance(steps, int) or steps < 1: - raise ValueError(f"{stage_id}/{wave_name} has invalid max_steps") - observed_steps.append(steps) - if len(observed_steps) != len(set(observed_steps)): - raise ValueError(f"{stage_id} waves must have distinct step ceilings") - if kind == "sft" and tuple(waves) != ("complete",): - raise ValueError("SFT stages run continuously; they do not stop after a smoke batch") - return StageSpec( - stage_id=stage_id, - kind=kind, - model_key=model_key, - recipe_key=recipe_key, - hypotheses=hypotheses, - parent=parent, - levels=levels, - current_level=current_level, - replay_levels=replay, - waves={str(key): dict(item) for key, item in waves.items()}, - ) - - -def load_protocol( - repo_root: Path | None = None, - *, - require_committed: bool = False, -) -> dict[str, Any]: - root = (repo_root or repository_root()).expanduser().resolve() - path = protocol_path(root) - document = json.loads(path.read_text(encoding="utf-8")) - if document.get("schema_version") != SCHEMA_VERSION: - raise ValueError("unsupported training protocol schema") - if document.get("study_id") != STUDY_ID: - raise ValueError("training protocol has the wrong study ID") - if document.get("contract_version") != CONTRACT_VERSION: - raise ValueError("training protocol and prompt contract differ") - observed = canonical_json_sha256(_logical_document(document)) - if document.get("logical_sha256") != observed: - raise ValueError("training protocol logical SHA-256 mismatch") - if not _SHA256.fullmatch(str(document["dataset"]["logical_release_sha256"])): - raise ValueError("training protocol has an invalid dataset digest") - if document["evaluation"].get("mastery_thresholds") != { - "minimum_mean_raw_absolute_scale_iou": 0.8, - "minimum_pure_executable_rate": 0.95, - }: - raise ValueError("training protocol has the wrong mastery thresholds") - if document["evaluation"].get("curriculum_progress_holdout") != { - "baseline_checkpoints": { - "L0": "base:qwen", - "L1": "qwen-base-rl-l0:complete", - "L2": "qwen-base-rl-l1:complete", - "L3": "qwen-base-rl-l2:complete", - "L4": "qwen-base-rl-l3:complete", - }, - "configuration": "depth", - "evaluation_waves": [ - "step-5", - "step-10", - "step-15", - "step-20", - "step-25", - "complete", - ], - "panel_prefix": "level-progress", - "realization_slot": 6, - "representations_by_level": { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108, - }, - "rows_by_level": { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108, - }, - "split": "validation", - }: - raise ValueError("training protocol has the wrong curriculum progress holdout") - if document["evaluation"].get("final_selection_holdout") != { - "configuration": "depth", - "panel": "depth-final-selection", - "realization_slot": 7, - "representations": 546, - "rows": 546, - "split": "validation", - }: - raise ValueError("training protocol has the wrong final selection holdout") - for key, value in document["models"].items(): - _validate_model(str(key), value) - stages = { - stage_id: _stage_spec(stage_id, value, protocol=document) - for stage_id, value in document["stages"].items() - } - if set(INITIAL_STAGE_IDS) - set(stages): - raise ValueError("training protocol omits an initial branch") - if set(BASE_QWEN_RL_STAGE_IDS) - set(stages): - raise ValueError("training protocol omits the pure Qwen RL curriculum") - if set(document["recipes"]) != {"qwen_sft", "qwen_rl", "inkling_l4_rl"}: - raise ValueError("training protocol has an unexpected recipe set") - for recipe_name, recipe in document["recipes"].items(): - if recipe.get("checkpoint_ttl_seconds", "missing") is not None: - raise ValueError( - f"{recipe_name} periodic checkpoints must remain available " - "for receipt-bound evaluation" - ) - for stage in stages.values(): - if ":" not in stage.parent: - raise ValueError(f"{stage.stage_id} has a malformed parent") - parent_stage, parent_wave = stage.parent.split(":", 1) - if stage.parent.startswith("base:"): - if parent_wave != stage.model_key: - raise ValueError(f"{stage.stage_id} has the wrong base parent") - else: - if parent_stage not in stages: - raise ValueError( - f"{stage.stage_id} names unknown parent {stage.parent}" - ) - parent_spec = stages[parent_stage] - if parent_spec.model_key != stage.model_key: - raise ValueError(f"{stage.stage_id} crosses model families") - if ( - parent_wave != "complete-or-skip" - and parent_wave not in parent_spec.waves - ): - raise ValueError(f"{stage.stage_id} names an unknown parent wave") - if stage.kind == "sft" and stage.recipe_key != "qwen_sft": - raise ValueError(f"{stage.stage_id} has a non-SFT recipe") - if stage.kind == "rl" and stage.recipe_key not in { - "qwen_rl", - "inkling_l4_rl", - }: - raise ValueError(f"{stage.stage_id} has a non-RL recipe") - if stage.model_key == "inkling" and stage.kind != "rl": - raise ValueError("Inkling is sealed as an RL-only policy") - if stage.kind == "rl": - reward = document["recipes"][stage.recipe_key].get("reward") - if reward != document["evaluation"]["geometry_signal"]: - raise ValueError(f"{stage.stage_id} does not optimize the recorded signal") - if stage.wave("smoke")["max_steps"] != 1: - raise ValueError(f"{stage.stage_id} smoke must be exactly one step") - if stage.model_key == "qwen": - expected_waves = { - "smoke": 1, - "step-5": 5, - "step-10": 10, - "step-15": 15, - "step-20": 20, - "step-25": 25, - "complete": 30, - } - if { - name: int(wave["max_steps"]) - for name, wave in stage.waves.items() - } != expected_waves: - raise ValueError(f"{stage.stage_id} has wrong Qwen RL waves") - if stage.stage_id in BASE_QWEN_RL_STAGE_IDS: - expected_gates = { - "smoke": ( - "initial" - if stage.current_level == "L0" - else ( - "prior-and-current-level-held-out-" - "transition-receipt" - ) - ), - "step-5": "rollout-health-receipt", - "step-10": "current-level-held-out-promotion-receipt", - "step-15": "current-level-held-out-promotion-receipt", - "step-20": "current-level-held-out-promotion-receipt", - "step-25": "current-level-held-out-promotion-receipt", - "complete": "current-level-held-out-promotion-receipt", - } - observed_gates = { - name: str(stage.wave(name)["approval"]) - for name in expected_waves - } - if observed_gates != expected_gates: - raise ValueError( - f"{stage.stage_id} has wrong pure-RL gates" - ) - elif stage.stage_id in L0_SFT_QWEN_RL_STAGE_IDS: - expected_gates = { - "smoke": ( - "parent-receipt-and-source-transition" - if stage.current_level == "L1" - else "prior-level-held-out-promotion-receipt" - ), - "step-5": "rollout-health-receipt", - "step-10": "current-level-held-out-promotion-receipt", - "step-15": "current-level-held-out-promotion-receipt", - "step-20": "current-level-held-out-promotion-receipt", - "step-25": "current-level-held-out-promotion-receipt", - "complete": "current-level-held-out-promotion-receipt", - } - observed_gates = { - name: str(stage.wave(name)["approval"]) - for name in expected_waves - } - if observed_gates != expected_gates: - raise ValueError( - f"{stage.stage_id} has wrong L0-SFT-to-RL gates" - ) - elif any( - stage.wave(name)["approval"] != "clean-smoke-receipt" - for name in expected_waves - if name != "smoke" - ): - raise ValueError(f"{stage.stage_id} continuation gates changed") - else: - if set(stage.waves) != { - "smoke", - "pilot", - "coverage-extension", - }: - raise ValueError("Inkling pilot has the wrong wave set") - if ( - stage.wave("pilot")["max_steps"] != 6 - or stage.wave("coverage-extension")["max_steps"] != 14 - ): - raise ValueError("Inkling pilot ceilings changed") - if ( - document["recipes"][stage.recipe_key].get("checkpoint_policy") - != "unique_wave_ceiling" - ): - raise ValueError( - f"{stage.stage_id} does not bind unique wave checkpoints" - ) - for level, stage_id in enumerate(BASE_QWEN_RL_STAGE_IDS): - stage = stages[stage_id] - expected_parent = ( - "base:qwen" - if level == 0 - else f"qwen-base-rl-l{level - 1}:complete" - ) - if ( - stage.kind != "rl" - or stage.model_key != "qwen" - or stage.recipe_key != "qwen_rl" - or stage.hypotheses != ("RT-H06",) - or stage.current_level != f"L{level}" - or stage.replay_levels != LEVELS[:level] - or stage.parent != expected_parent - ): - raise ValueError(f"{stage_id} changed the pure Qwen RL curriculum") - for level, stage_id in enumerate(L0_SFT_QWEN_RL_STAGE_IDS, start=1): - stage = stages[stage_id] - expected_parent = ( - "qwen-l0-sft:complete" - if level == 1 - else f"qwen-l0-rl-l{level - 1}:complete" - ) - if ( - stage.kind != "rl" - or stage.model_key != "qwen" - or stage.recipe_key != "qwen_rl" - or stage.hypotheses != ("RT-H03",) - or stage.current_level != f"L{level}" - or stage.replay_levels != LEVELS[:level] - or stage.parent != expected_parent - ): - raise ValueError(f"{stage_id} changed the L0-SFT-to-RL curriculum") - exact_edges = { - stage.stage_id: stage.parent.split(":", 1)[0] - for stage in stages.values() - if not stage.parent.startswith("base:") - } - for origin in exact_edges: - seen: set[str] = set() - cursor = origin - while cursor in exact_edges: - if cursor in seen: - raise ValueError("training stage graph contains a cycle") - seen.add(cursor) - cursor = exact_edges[cursor] - if document["launch"].get("paid_launch_enabled") is not True: - raise ValueError("training protocol is not launch-enabled") - expected_retry_stages = list(L0_SFT_QWEN_RL_STAGE_IDS) - if document["launch"].get("replicates") != ["r0", "r0-retry1"]: - raise ValueError("training protocol has the wrong launch identities") - if document["launch"].get("replicate_stage_scopes") != { - "r0": ["*"], - "r0-retry1": expected_retry_stages, - }: - raise ValueError("training protocol has the wrong launch identity scopes") - if document["launch"].get("parent_replicate_overrides") != { - "qwen-l0-rl-l1": {"r0-retry1": "r0"} - }: - raise ValueError("training protocol has the wrong parent identity override") - if require_committed: - _require_at_head(root, path) - return document - - -def stage_spec(protocol: dict[str, Any], stage_id: str) -> StageSpec: - try: - value = protocol["stages"][stage_id] - except KeyError as exc: - raise ValueError(f"unknown training stage: {stage_id!r}") from exc - return _stage_spec(stage_id, value, protocol=protocol) - - -def validate_stage_replicate( - protocol: Mapping[str, Any], - *, - stage_id: str, - replicate_id: str, -) -> None: - """Validate one declared execution identity for one exact stage.""" - - if replicate_id not in protocol["launch"]["replicates"]: - raise ValueError("replicate is not declared in the protocol") - scopes = protocol["launch"]["replicate_stage_scopes"] - allowed = scopes.get(replicate_id) - if not isinstance(allowed, list) or ( - "*" not in allowed and stage_id not in allowed - ): - raise ValueError("replicate is not declared for this training stage") - - -def parent_replicate_id( - protocol: Mapping[str, Any], - *, - stage_id: str, - replicate_id: str, -) -> str: - """Resolve a protocol-bound parent execution identity.""" - - validate_stage_replicate( - protocol, - stage_id=stage_id, - replicate_id=replicate_id, - ) - overrides = protocol["launch"]["parent_replicate_overrides"] - stage_overrides = overrides.get(stage_id, {}) - if not isinstance(stage_overrides, Mapping): - raise ValueError("parent replicate override is malformed") - result = str(stage_overrides.get(replicate_id, replicate_id)) - if result not in protocol["launch"]["replicates"]: - raise ValueError("parent replicate override names an unknown identity") - return result - - -def validate_source_sha(repo_root: Path, expected_source_sha: str) -> str: - if not _GIT_SHA.fullmatch(expected_source_sha): - raise ValueError("expected source SHA must be a 40-character Git SHA") - root = repo_root.expanduser().resolve() - head = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=root, - text=True, - ).strip() - if head != expected_source_sha: - raise ValueError(f"HEAD {head} differs from expected {expected_source_sha}") - status = subprocess.check_output( - ["git", "status", "--porcelain", "--untracked-files=all"], - cwd=root, - text=True, - ).strip() - if status: - raise ValueError("paid training requires a completely clean worktree") - return head diff --git a/rl/studies/representation_training_v1/record_approval.py b/rl/studies/representation_training_v1/record_approval.py deleted file mode 100644 index 63eaee1c..00000000 --- a/rl/studies/representation_training_v1/record_approval.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -"""Record one reviewed checkpoint report as an immutable wave approval.""" - -from __future__ import annotations - -import argparse -import json -import os -from pathlib import Path - -from .promotion import ( - APPROVAL_CONFIRMATION_TOKEN, - record_wave_approval, -) -from .protocol import repository_root - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--stage", required=True) - result.add_argument("--wave", required=True) - result.add_argument("--replicate", default="r0") - result.add_argument("--evaluation-stage", required=True) - result.add_argument("--evaluation-wave", required=True) - result.add_argument("--evaluation-checkpoint", required=True) - result.add_argument("--evaluation-report-record-sha256", required=True) - result.add_argument( - "--entry-baseline-report-record-sha256", - default=None, - help=( - "second report required by the pure-base L1-L4 transition gate: " - "the exact parent checkpoint evaluated on the incoming level" - ), - ) - result.add_argument("--expected-source-sha", required=True) - result.add_argument( - "--evidence-source-sha", - default=None, - help=( - "exact ancestor commit that produced the evaluation report; " - "omit for the unchanged same-source v1 approval path" - ), - ) - result.add_argument( - "--approve-changed-path", - action="append", - default=None, - dest="approved_change_paths", - help=( - "one exact producer-to-consumer changed path reviewed for a v2 " - "transition; repeat for every changed path and omit for v1" - ), - ) - result.add_argument( - "--confirm-approval", - default="", - help=f"must be exactly {APPROVAL_CONFIRMATION_TOKEN}", - ) - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_TRAINING_ROOT"]) - if os.environ.get("PIXCELL_TRAINING_ROOT") - else None - ), - ) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit("set PIXCELL_TRAINING_ROOT or pass --external-root outside Git") - result = record_wave_approval( - repo_root=repository_root(), - external_root=args.external_root, - stage_id=args.stage, - wave_name=args.wave, - replicate_id=args.replicate, - evaluation_stage_id=args.evaluation_stage, - evaluation_wave=args.evaluation_wave, - evaluation_checkpoint_name=args.evaluation_checkpoint, - evaluation_report_record_sha256=(args.evaluation_report_record_sha256), - entry_baseline_report_record_sha256=( - args.entry_baseline_report_record_sha256 - ), - expected_source_sha=args.expected_source_sha, - evidence_source_sha=args.evidence_source_sha, - approved_change_paths=args.approved_change_paths, - confirmation=args.confirm_approval, - ) - print(json.dumps(result, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/record_parent_transition.py b/rl/studies/representation_training_v1/record_parent_transition.py deleted file mode 100644 index 871af6cb..00000000 --- a/rl/studies/representation_training_v1/record_parent_transition.py +++ /dev/null @@ -1,67 +0,0 @@ -#!/usr/bin/env python3 -"""Approve the exact historical L0-SFT receipt for the current L1-RL smoke.""" - -from __future__ import annotations - -import argparse -import json -import os -from pathlib import Path - -from .promotion import ( - APPROVAL_CONFIRMATION_TOKEN, - record_parent_receipt_transition_approval, -) -from .protocol import repository_root - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--replicate", default="r0") - result.add_argument("--expected-source-sha", required=True) - result.add_argument( - "--approve-changed-path", - action="append", - required=True, - dest="approved_change_paths", - help=( - "one exact path in the historical-parent-to-current committed diff; " - "repeat once for every changed path" - ), - ) - result.add_argument( - "--confirm-approval", - default="", - help=f"must be exactly {APPROVAL_CONFIRMATION_TOKEN}", - ) - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_TRAINING_ROOT"]) - if os.environ.get("PIXCELL_TRAINING_ROOT") - else None - ), - ) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit("set PIXCELL_TRAINING_ROOT or pass --external-root outside Git") - result = record_parent_receipt_transition_approval( - repo_root=repository_root(), - external_root=args.external_root, - stage_id="qwen-l0-rl-l1", - wave_name="smoke", - replicate_id=args.replicate, - expected_source_sha=args.expected_source_sha, - approved_change_paths=args.approved_change_paths, - confirmation=args.confirm_approval, - ) - print(json.dumps(result, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/renderer_diagnostic.py b/rl/studies/representation_training_v1/renderer_diagnostic.py deleted file mode 100644 index 914b5c32..00000000 --- a/rl/studies/representation_training_v1/renderer_diagnostic.py +++ /dev/null @@ -1,2078 +0,0 @@ -"""Sealed, non-canonical diagnostic for the mixed-SFT Qwen renderer mismatch. - -This module answers one narrow postmortem question: did the mixed-SFT adapter -learn a usable direct-code policy behind the closed-thinking prefix it saw -during teacher forcing, and when did canonical open-thinking generation -degrade? - -It is intentionally not an evaluation panel. Its records live below -``diagnostics/``, use a foreign record schema, carry an explicit -``not_canonical_evidence`` marker, and cannot satisfy any promotion gate. -""" - -from __future__ import annotations - -import argparse -import asyncio -import hashlib -import json -import math -import os -import uuid -from collections import Counter -from collections.abc import Callable, Mapping, Sequence -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any - -from tinker_cookbook.renderers import Message, TextPart, TrainOnWhat - -from rl.common.evaluator import ( - Attribution, - EvaluationResult, - EvaluationStatus, - PixCellEvaluator, -) -from rl.common.output import extract_code -from rl.common.prompt import prompt_asset_hashes -from rl.common.runtime import validate_runtime_stack -from rl.evaluation.tasks import EvaluationTask, canonical_json_sha256 -from rl.track_a.tinker_data import _message, _renderer - -from . import evaluation as checkpoint_v1 -from . import source_transition as source_transition_v2 -from .dataset_binding import _dataset_binding -from .preflight import ( - _cookbook_source_binding, - _positive_target_text, -) -from .protocol import ( - STUDY_ID, - file_sha256, - load_protocol, - protocol_path, - stage_spec, - validate_source_sha, -) -from .schedule import stage_tasks -from .store import TrainingStore - - -DIAGNOSTIC_ID = "qwen-sft-renderer-diagnostic-v1" -DIAGNOSTIC_SCHEMA_VERSION = "pixcell-qwen-sft-renderer-diagnostic-v1" -DIAGNOSTIC_RECORD_SCHEMA_VERSION = "pixcell-qwen-sft-renderer-diagnostic-record-v1" -TASK_PANEL_SCHEMA_VERSION = "pixcell-qwen-renderer-diagnostic-task-panel-v1" -PRODUCER_SOURCE_GIT_SHA = "bb80dbe666c7313f9a9f3bdb53765c35dfdeceab" -PRODUCER_STAGE_ID = "qwen-mixed-sft" -PRODUCER_WAVE = "complete" -PRODUCER_REPLICATE_ID = "r0" -EXPECTED_TASK_IDS = tuple(f"F{index}" for index in range(1, 9)) -MAX_IMAGE_LONG_EDGE = 1920 -MAX_OUTPUT_TOKENS = 60_000 -TEMPERATURE = 1.0 -TOP_P = 1.0 -SAMPLE_CONCURRENCY = 8 -EVALUATION_BATCH_SIZE = 32 -EVALUATOR_WORKERS = 8 -_SEED_DOMAIN = "pixcell-qwen-sft-renderer-diagnostic-seed-v1" -_PROMOTION_POLICY = "diagnostic-only-never-promotion-evidence" -_PROMPT_SOURCE_PATHS = ( - "rl/common/assets/phase_a_direct_v2.md", - "src/michaelangelo/reference/gds_factory_function_catalogue_extended.md", -) - - -@dataclass(frozen=True) -class ArmSpec: - arm_id: str - checkpoint_name: str - renderer: str - expected_sft_prefix_compatible: bool - - def as_dict(self) -> dict[str, Any]: - return { - "arm_id": self.arm_id, - "checkpoint_name": self.checkpoint_name, - "renderer": self.renderer, - "expected_sft_prefix_compatible": (self.expected_sft_prefix_compatible), - } - - -ARM_SPECS = ( - ArmSpec( - arm_id="final-no-thinking", - checkpoint_name="final", - renderer="qwen3_5_disable_thinking", - expected_sft_prefix_compatible=True, - ), - ArmSpec( - arm_id="step-14-thinking", - checkpoint_name="000014", - renderer="qwen3_5", - expected_sft_prefix_compatible=False, - ), - ArmSpec( - arm_id="step-28-thinking", - checkpoint_name="000028", - renderer="qwen3_5", - expected_sft_prefix_compatible=False, - ), - ArmSpec( - arm_id="step-42-thinking", - checkpoint_name="000042", - renderer="qwen3_5", - expected_sft_prefix_compatible=False, - ), -) -EXPECTED_CHECKPOINT_NAMES = ("000014", "000028", "000042", "final") -EXPECTED_SAMPLE_COUNT = len(ARM_SPECS) * len(EXPECTED_TASK_IDS) - - -class RendererDiagnosticError(checkpoint_v1.CheckpointEvaluationError): - """The renderer diagnostic is unsafe, inconsistent, or incomplete.""" - - -class ImmutableRendererDiagnosticError(checkpoint_v1.ImmutableEvaluationRecordError): - """A create-only diagnostic record already differs.""" - - -@dataclass(frozen=True) -class PreparedArm: - spec: ArmSpec - binding: checkpoint_v1.CheckpointBinding - renderer: Any - prepared: tuple[checkpoint_v1.PreparedTask, ...] - preflight: dict[str, Any] - - -@dataclass(frozen=True) -class PreparedAttempt: - arm: PreparedArm - prepared: checkpoint_v1.PreparedTask - seed: int - - @property - def task(self) -> EvaluationTask: - return self.prepared.task - - @property - def key(self) -> tuple[str, str]: - return (self.arm.spec.arm_id, self.task.task_id) - - -def _canonical_bytes(value: Any) -> bytes: - try: - return json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise RendererDiagnosticError("renderer diagnostic is not finite JSON") from exc - - -def _canonical_sha256(value: Any) -> str: - return hashlib.sha256(_canonical_bytes(value)).hexdigest() - - -def _record_document( - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], -) -> dict[str, Any]: - normalized = json.loads(_canonical_bytes(payload)) - document = { - "schema_version": DIAGNOSTIC_RECORD_SCHEMA_VERSION, - "record_type": record_type, - "key": dict(key), - "payload": normalized, - "payload_sha256": _canonical_sha256(normalized), - } - document["record_sha256"] = _canonical_sha256(document) - return document - - -def _validate_record( - value: Mapping[str, Any], - *, - record_type: str, - key: Mapping[str, Any], -) -> dict[str, Any]: - required = { - "schema_version", - "record_type", - "key", - "payload", - "payload_sha256", - "record_sha256", - } - if set(value) != required: - raise RendererDiagnosticError("diagnostic record schema changed") - if value["schema_version"] != DIAGNOSTIC_RECORD_SCHEMA_VERSION: - raise RendererDiagnosticError("diagnostic record has a foreign schema") - if value["record_type"] != record_type or value["key"] != dict(key): - raise RendererDiagnosticError("diagnostic record key differs from its canonical path") - payload = value["payload"] - if not isinstance(payload, dict): - raise RendererDiagnosticError("diagnostic payload is not an object") - if payload.get("not_canonical_evidence") is not True: - raise RendererDiagnosticError("diagnostic record lost its non-canonical marker") - if value["payload_sha256"] != _canonical_sha256(payload): - raise RendererDiagnosticError("diagnostic payload digest mismatch") - unsigned = dict(value) - observed = unsigned.pop("record_sha256") - if ( - not isinstance(observed, str) - or not checkpoint_v1._SHA256.fullmatch(observed) - or observed != _canonical_sha256(unsigned) - ): - raise RendererDiagnosticError("diagnostic envelope digest mismatch") - return dict(value) - - -class RendererDiagnosticStore(checkpoint_v1.CheckpointEvaluationStore): - """Create-only records in a namespace promotion code never reads.""" - - def __init__(self, *, stage_path: Path) -> None: - self.stage_path = stage_path.expanduser().resolve(strict=True) - requested = self.stage_path / "diagnostics" / DIAGNOSTIC_ID - requested.mkdir(parents=True, exist_ok=True, mode=0o700) - resolved = requested.resolve(strict=True) - if resolved != requested or not resolved.is_relative_to(self.stage_path): - raise RendererDiagnosticError( - "diagnostic root must be a real directory inside its stage" - ) - if "evaluations" in resolved.relative_to(self.stage_path).parts: - raise RendererDiagnosticError( - "diagnostic records must not enter the evaluation namespace" - ) - self.root = resolved - - @staticmethod - def _arm_component(arm_id: str) -> str: - expected = {spec.arm_id for spec in ARM_SPECS} - if arm_id not in expected: - raise ValueError(f"foreign diagnostic arm: {arm_id!r}") - return arm_id - - @classmethod - def _attempt_key(cls, arm_id: str, task_id: str) -> dict[str, str]: - cls._arm_component(arm_id) - cls._task_component(task_id) - return {"arm_id": arm_id, "task_id": task_id} - - @classmethod - def _attempt_path( - cls, - arm_id: str, - task_id: str, - filename: str, - ) -> PurePosixPath: - return PurePosixPath( - "arms", - cls._arm_component(arm_id), - "tasks", - cls._task_component(task_id), - filename, - ) - - def load_manifest(self) -> dict[str, Any] | None: - return self._load_diagnostic( - PurePosixPath("manifest.json"), - record_type="renderer_diagnostic_manifest", - key={}, - ) - - def create_or_verify_manifest( - self, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_diagnostic( - PurePosixPath("manifest.json"), - record_type="renderer_diagnostic_manifest", - key={}, - payload=payload, - ) - - def load_sample( - self, - arm_id: str, - task_id: str, - ) -> dict[str, Any] | None: - return self._load_diagnostic( - self._attempt_path(arm_id, task_id, "sample.json"), - record_type="renderer_diagnostic_sample", - key=self._attempt_key(arm_id, task_id), - ) - - def create_or_verify_sample( - self, - arm_id: str, - task_id: str, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_diagnostic( - self._attempt_path(arm_id, task_id, "sample.json"), - record_type="renderer_diagnostic_sample", - key=self._attempt_key(arm_id, task_id), - payload=payload, - ) - - def load_evaluation( - self, - arm_id: str, - task_id: str, - ) -> dict[str, Any] | None: - return self._load_diagnostic( - self._attempt_path(arm_id, task_id, "evaluation.json"), - record_type="renderer_diagnostic_evaluation", - key=self._attempt_key(arm_id, task_id), - ) - - def create_or_verify_evaluation( - self, - arm_id: str, - task_id: str, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_diagnostic( - self._attempt_path(arm_id, task_id, "evaluation.json"), - record_type="renderer_diagnostic_evaluation", - key=self._attempt_key(arm_id, task_id), - payload=payload, - ) - - def load_report(self) -> dict[str, Any] | None: - return self._load_diagnostic( - PurePosixPath("report.json"), - record_type="renderer_diagnostic_report", - key={}, - ) - - def create_or_verify_report( - self, - payload: Mapping[str, Any], - ) -> dict[str, Any]: - return self._create_or_verify_diagnostic( - PurePosixPath("report.json"), - record_type="renderer_diagnostic_report", - key={}, - payload=payload, - ) - - def assert_attempt_inventory( - self, - attempts: Sequence[PreparedAttempt], - ) -> None: - arms_root = self.root / "arms" - expected_files = { - self._path( - self._attempt_path( - attempt.arm.spec.arm_id, - attempt.task.task_id, - filename, - ) - ) - for attempt in attempts - for filename in ("sample.json", "evaluation.json") - } - expected_files.update( - { - self.root / ".lock", - self.root / "manifest.json", - self.root / "report.json", - } - ) - expected_directories = {self.root, arms_root} - for path in expected_files: - expected_directories.update(path.parents) - for path in self.root.rglob("*"): - if path.is_symlink(): - raise RendererDiagnosticError( - f"diagnostic record inventory contains a symlink: {path}" - ) - if path.is_file() and path not in expected_files: - raise RendererDiagnosticError( - f"diagnostic record inventory contains a foreign row: {path}" - ) - if path.is_dir() and path not in expected_directories: - raise RendererDiagnosticError( - f"diagnostic record inventory contains a foreign path: {path}" - ) - - def _create_or_verify_diagnostic( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], - ) -> dict[str, Any]: - expected = _record_document( - record_type=record_type, - key=key, - payload=payload, - ) - path = self._path(relative) - path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - raw = _canonical_bytes(expected) + b"\n" - temporary = path.parent / f".{path.name}.{os.getpid()}.{uuid.uuid4().hex}" - flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(temporary, flags, 0o600) - try: - with os.fdopen(fd, "wb", closefd=False) as stream: - stream.write(raw) - stream.flush() - os.fsync(stream.fileno()) - finally: - os.close(fd) - try: - os.link(temporary, path) - directory_fd = os.open(path.parent, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) - except FileExistsError: - observed = self._load_diagnostic( - relative, - record_type=record_type, - key=key, - ) - if observed is None or _canonical_bytes(observed) != _canonical_bytes(expected): - raise ImmutableRendererDiagnosticError( - f"{relative} already contains different data" - ) - return observed - finally: - try: - temporary.unlink() - except FileNotFoundError: - pass - return expected - - def _load_diagnostic( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - ) -> dict[str, Any] | None: - path = self._path(relative) - if not path.exists(): - return None - return _validate_record( - self._read(path), - record_type=record_type, - key=key, - ) - - -def _not_canonical() -> dict[str, Any]: - return { - "not_canonical_evidence": True, - "promotion_eligibility": { - "eligible": False, - "policy": _PROMOTION_POLICY, - }, - } - - -def _validate_fixed_protocol(protocol: Mapping[str, Any]) -> dict[str, Any]: - stage = stage_spec(dict(protocol), PRODUCER_STAGE_ID) - model = dict(protocol["models"][stage.model_key]) - recipe = dict(protocol["recipes"][stage.recipe_key]) - if ( - stage.kind != "sft" - or stage.model_key != "qwen" - or stage.recipe_key != "qwen_sft" - or stage.wave(PRODUCER_WAVE)["max_steps"] != 55 - ): - raise RendererDiagnosticError("producer SFT stage contract changed") - expected_model = { - "model": "Qwen/Qwen3.6-35B-A3B", - "renderer": "qwen3_5", - "thinking": True, - "max_image_long_edge": MAX_IMAGE_LONG_EDGE, - "max_output_tokens": MAX_OUTPUT_TOKENS, - "context_tokens": 65_536, - "lora_rank": 32, - } - if model != expected_model: - raise RendererDiagnosticError("Qwen model contract changed") - if ( - recipe.get("target") != "program_and_end_token_only" - or recipe.get("max_sequence_tokens") != 6144 - ): - raise RendererDiagnosticError("SFT target contract changed") - return { - "stage": stage, - "model": model, - "recipe": recipe, - } - - -def _source_provenance( - *, - repo_root: Path, - evaluator_source_git_sha: str, -) -> dict[str, Any]: - facts = source_transition_v2.source_transition_git_facts( - repo_root=repo_root, - producer_source_git_sha=PRODUCER_SOURCE_GIT_SHA, - consumer_source_git_sha=evaluator_source_git_sha, - ) - paths = (str(protocol_path(repo_root).relative_to(repo_root)), *_PROMPT_SOURCE_PATHS) - bindings: dict[str, Any] = {} - for relative in paths: - producer_bytes = source_transition_v2._git( - repo_root, - "show", - f"{PRODUCER_SOURCE_GIT_SHA}:{relative}", - ) - current_path = (repo_root / relative).resolve(strict=True) - if not current_path.is_relative_to(repo_root): - raise RendererDiagnosticError("source binding escaped the repository") - current_bytes = current_path.read_bytes() - producer_sha = hashlib.sha256(producer_bytes).hexdigest() - current_sha = hashlib.sha256(current_bytes).hexdigest() - if producer_sha != current_sha: - raise RendererDiagnosticError( - f"producer and evaluator task contract differ: {relative}" - ) - bindings[relative] = { - "producer_sha256": producer_sha, - "evaluator_sha256": current_sha, - "byte_identical": True, - } - return { - "producer_source_git_sha": PRODUCER_SOURCE_GIT_SHA, - "evaluator_source_git_sha": evaluator_source_git_sha, - "git_relationship": facts, - "task_contract_files": bindings, - "task_contract_files_sha256": _canonical_sha256(bindings), - } - - -def _load_matrix_bindings( - *, - store: TrainingStore, - protocol: dict[str, Any], - expected_wave_receipt_sha256: str, -) -> tuple[checkpoint_v1.CheckpointBinding, ...]: - bindings = tuple( - checkpoint_v1._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id=PRODUCER_STAGE_ID, - wave=PRODUCER_WAVE, - checkpoint_name=spec.checkpoint_name, - replicate_id=PRODUCER_REPLICATE_ID, - expected_receipt_sha256=expected_wave_receipt_sha256, - source_git_sha=PRODUCER_SOURCE_GIT_SHA, - ) - for spec in ARM_SPECS - ) - receipts = {binding.wave_receipt.record_sha256 for binding in bindings} - inventories = {binding.checkpoint_inventory_sha256 for binding in bindings} - manifests = {binding.run_manifest.record_sha256 for binding in bindings} - if len(receipts) != 1 or len(inventories) != 1 or len(manifests) != 1: - raise RendererDiagnosticError("matrix checkpoints do not share one exact producer receipt") - inventory = bindings[0].wave_receipt.payload["checkpoint_inventory"] - entries = inventory["entries"] - if tuple(str(entry["name"]) for entry in entries) != EXPECTED_CHECKPOINT_NAMES: - raise RendererDiagnosticError( - "producer checkpoint inventory differs from the sealed matrix" - ) - expected_paths = { - spec.checkpoint_name: binding.sampler_path - for spec, binding in zip(ARM_SPECS, bindings, strict=True) - } - if len(set(expected_paths.values())) != len(ARM_SPECS): - raise RendererDiagnosticError("producer checkpoint sampler paths are not unique") - return bindings - - -def _producer_training_provenance( - *, - repo_root: Path, - protocol: Mapping[str, Any], - bindings: Sequence[checkpoint_v1.CheckpointBinding], -) -> dict[str, Any]: - first = bindings[0] - manifest = first.run_manifest - receipt = first.wave_receipt - payload = manifest.payload - expected_manifest_fields = { - "source_git_sha": PRODUCER_SOURCE_GIT_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - "prompt_assets": prompt_asset_hashes(), - "model": protocol["models"]["qwen"], - "recipe": protocol["recipes"]["qwen_sft"], - } - for field, expected in expected_manifest_fields.items(): - if payload.get(field) != expected: - raise RendererDiagnosticError(f"producer training manifest {field} changed") - stage = payload.get("stage") - dataset = payload.get("dataset") - if ( - not isinstance(stage, Mapping) - or stage.get("stage_id") != PRODUCER_STAGE_ID - or stage.get("kind") != "sft" - or stage.get("model_key") != "qwen" - or stage.get("recipe_key") != "qwen_sft" - or not isinstance(dataset, Mapping) - or dataset.get("logical_release_sha256") != protocol["dataset"]["logical_release_sha256"] - ): - raise RendererDiagnosticError("producer training manifest stage or dataset changed") - inventory = receipt.payload["checkpoint_inventory"] - return { - "stage_id": PRODUCER_STAGE_ID, - "wave": PRODUCER_WAVE, - "replicate_id": PRODUCER_REPLICATE_ID, - "run_manifest": { - "relative_path": str(manifest.relative_path), - "payload_sha256": manifest.payload_sha256, - "record_sha256": manifest.record_sha256, - }, - "wave_receipt": { - "relative_path": str(receipt.relative_path), - "payload_sha256": receipt.payload_sha256, - "record_sha256": receipt.record_sha256, - }, - "checkpoint_inventory": inventory, - "checkpoint_inventory_sha256": first.checkpoint_inventory_sha256, - "samplers": { - binding.checkpoint_name: { - "checkpoint": binding.checkpoint, - "sampler_path": binding.sampler_path, - } - for binding in bindings - }, - } - - -def _normalized_segments( - model_input: Any, - *, - stop_positions: int | None = None, -) -> list[dict[str, Any]]: - """Normalize text chunk boundaries while preserving exact image identity.""" - - total = int(model_input.length) - stop = total if stop_positions is None else stop_positions - if isinstance(stop, bool) or not isinstance(stop, int) or not 0 <= stop <= total: - raise RendererDiagnosticError("invalid renderer-prefix slice") - segments: list[dict[str, Any]] = [] - consumed = 0 - for chunk in model_input.chunks: - if consumed >= stop: - break - length = int(chunk.length) - take = min(length, stop - consumed) - tokens = getattr(chunk, "tokens", None) - if tokens is not None: - selected = [int(token) for token in list(tokens)[:take]] - if segments and segments[-1]["type"] == "tokens": - segments[-1]["values"].extend(selected) - else: - segments.append({"type": "tokens", "values": selected}) - else: - if take != length: - raise RendererDiagnosticError("renderer prefix cuts through an image chunk") - data = getattr(chunk, "data", None) - if not isinstance(data, bytes): - raise RendererDiagnosticError("renderer emitted an unhashable image chunk") - segments.append( - { - "type": "image", - "length": length, - "format": str(getattr(chunk, "format", "")), - "data_sha256": hashlib.sha256(data).hexdigest(), - } - ) - consumed += take - if consumed != stop: - raise RendererDiagnosticError("renderer prefix length is inconsistent") - return segments - - -def _append_token( - segments: Sequence[Mapping[str, Any]], - token: int, -) -> list[dict[str, Any]]: - result = json.loads(_canonical_bytes(segments)) - if result and result[-1]["type"] == "tokens": - result[-1]["values"].append(token) - else: - result.append({"type": "tokens", "values": [token]}) - return result - - -def _first_positive_offset(weights: Any, *, length: int) -> int: - found = [index for index in range(length) if float(weights[index]) > 0.0] - if not found: - raise RendererDiagnosticError("SFT supervised target has no positive loss") - first = found[0] - if any(float(weights[index]) != 0.0 for index in range(first)): - raise RendererDiagnosticError("SFT loss mask is not prefix-zero") - return first - - -def _sft_prefix_audit( - *, - dataset_root: Path, - protocol: Mapping[str, Any], - tasks: Sequence[Any] | None = None, -) -> dict[str, Any]: - """Prove the actual teacher-forced prefix against both inference renderers.""" - - stage = stage_spec(dict(protocol), PRODUCER_STAGE_ID) - model = dict(protocol["models"]["qwen"]) - source_tasks = list(tasks) if tasks is not None else stage_tasks(dataset_root, stage) - if len(source_tasks) != (len(source_tasks) if tasks is not None else 3468): - raise RendererDiagnosticError("SFT prefix audit has the wrong row count") - training_renderer = _renderer(model["model"], model["renderer"]) - inference_renderers = { - name: _renderer(model["model"], name) for name in ("qwen3_5_disable_thinking", "qwen3_5") - } - per_renderer: dict[str, dict[str, Any]] = { - name: { - "compatible_rows": 0, - "incompatible_rows": 0, - "evidence": [], - } - for name in inference_renderers - } - source_digest = hashlib.sha256() - for task in source_tasks: - messages = [_message(task, max_image=MAX_IMAGE_LONG_EDGE)] - assistant = Message( - role="assistant", - content=[TextPart(type="text", text=task.label)], - ) - supervised, weights = training_renderer.build_supervised_example( - [*messages, assistant], - train_on_what=TrainOnWhat.LAST_ASSISTANT_MESSAGE, - ) - first = _first_positive_offset(weights, length=int(supervised.length)) - target_text = _positive_target_text( - supervised, - weights, - training_renderer.tokenizer, - ) - if target_text != task.label + "<|im_end|>": - raise RendererDiagnosticError( - f"{task.sampler.opaque_id} SFT target is not exact code plus end token" - ) - before = _normalized_segments(supervised, stop_positions=first) - through_first = _normalized_segments( - supervised, - stop_positions=first + 1, - ) - first_token_segment = through_first[-1] - if first_token_segment["type"] != "tokens": - raise RendererDiagnosticError("first positive loss token is not text") - first_token = int(first_token_segment["values"][-1]) - source_digest.update(task.sampler.opaque_id.encode("utf-8")) - source_digest.update(bytes.fromhex(task.observation.image_sha256)) - source_digest.update(hashlib.sha256(task.label.encode("utf-8")).digest()) - for name, renderer in inference_renderers.items(): - generation = renderer.build_generation_prompt(messages) - generation_segments = _normalized_segments(generation) - prefix_equal = generation_segments == before - through_equal = _append_token(generation_segments, first_token) == through_first - compatible = prefix_equal and through_equal - bucket = per_renderer[name] - bucket["compatible_rows" if compatible else "incompatible_rows"] += 1 - evidence = { - "task_id": task.sampler.opaque_id, - "first_positive_loss_offset": first, - "first_positive_token": first_token, - "supervised_prefix_sha256": _canonical_sha256(before), - "supervised_through_first_positive_sha256": (_canonical_sha256(through_first)), - "generation_prefix_sha256": _canonical_sha256(generation_segments), - "generation_plus_first_positive_sha256": _canonical_sha256( - _append_token(generation_segments, first_token) - ), - "prefix_equal": prefix_equal, - "through_first_positive_equal": through_equal, - } - bucket["evidence"].append(evidence) - results: dict[str, Any] = {} - for name, bucket in per_renderer.items(): - expected = name == "qwen3_5_disable_thinking" - compatible_rows = int(bucket["compatible_rows"]) - compatible = compatible_rows == len(source_tasks) - if compatible is not expected: - claim = "compatible" if expected else "incompatible" - raise RendererDiagnosticError(f"{name} is not uniformly {claim} with the SFT prefix") - evidence = bucket.pop("evidence") - results[name] = { - **bucket, - "rows": len(source_tasks), - "compatible_with_sft_supervised_prefix": compatible, - "expected_compatible_claim": expected, - "claim_verified": True, - "comparison_scope": ( - "generation prefix plus first target token through the first positive-loss token" - ), - "diagnosis": ( - "The no-thinking generation prefix exactly matches the " - "teacher-forced closed-thinking prefix." - if expected - else ( - "The canonical renderer leaves open, while code-only " - "SFT closes before the first positive-loss code token; " - "a compatibility claim is rejected." - ) - ), - "evidence_sha256": canonical_json_sha256(evidence), - "example": evidence[0], - } - return { - "schema_version": "pixcell-qwen-sft-prefix-audit-v1", - "rows": len(source_tasks), - "training_renderer": model["renderer"], - "train_on_what": "LAST_ASSISTANT_MESSAGE", - "target_policy": "program_and_end_token_only", - "source_set_sha256": source_digest.hexdigest(), - "renderers": results, - } - - -def _task_panel(tasks: Sequence[EvaluationTask]) -> dict[str, Any]: - entries = [ - { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "image_sha256": task.observation.image_sha256, - "target_image_sha256": task.reference.target_image_sha256, - "footprint_um": list(task.observation.footprint_um), - } - for task in tasks - ] - if tuple(item["task_id"] for item in entries) != EXPECTED_TASK_IDS: - raise checkpoint_v1.ReferencePanelFault("renderer diagnostic must be exact ordered F1-F8") - return { - "schema_version": TASK_PANEL_SCHEMA_VERSION, - "diagnostic_id": DIAGNOSTIC_ID, - "task_count": len(entries), - "task_ids": list(EXPECTED_TASK_IDS), - "logical_sha256": canonical_json_sha256(entries), - } - - -def _sample_seed( - *, - evaluator_source_git_sha: str, - protocol_sha256: str, - dataset_sha256: str, - task_panel_sha256: str, - arm: ArmSpec, - binding: checkpoint_v1.CheckpointBinding, - task_id: str, -) -> int: - digest = hashlib.sha256( - "\0".join( - ( - _SEED_DOMAIN, - PRODUCER_SOURCE_GIT_SHA, - evaluator_source_git_sha, - protocol_sha256, - dataset_sha256, - task_panel_sha256, - binding.wave_receipt.record_sha256, - binding.checkpoint_inventory_sha256, - arm.arm_id, - arm.checkpoint_name, - arm.renderer, - task_id, - "1", - ) - ).encode("utf-8") - ).digest() - return int.from_bytes(digest[:4], "big") & 0x7FFFFFFF - - -def _prepare_arms( - *, - protocol: Mapping[str, Any], - model: Mapping[str, Any], - bindings: Sequence[checkpoint_v1.CheckpointBinding], - tasks: Sequence[EvaluationTask], - evaluator: PixCellEvaluator, - evaluator_source_git_sha: str, - dataset_sha256: str, - task_panel_sha256: str, -) -> tuple[tuple[PreparedArm, ...], tuple[PreparedAttempt, ...]]: - arms: list[PreparedArm] = [] - attempts: list[PreparedAttempt] = [] - for spec, binding in zip(ARM_SPECS, bindings, strict=True): - renderer = _renderer(model["model"], spec.renderer) - prepared, preflight = checkpoint_v1._preflight_tasks( - tasks=tasks, - renderer=renderer, - evaluator=evaluator, - context_tokens=int(model["context_tokens"]), - ) - arm = PreparedArm( - spec=spec, - binding=binding, - renderer=renderer, - prepared=tuple(prepared), - preflight=preflight, - ) - arms.append(arm) - attempts.extend( - PreparedAttempt( - arm=arm, - prepared=item, - seed=_sample_seed( - evaluator_source_git_sha=evaluator_source_git_sha, - protocol_sha256=str(protocol["logical_sha256"]), - dataset_sha256=dataset_sha256, - task_panel_sha256=task_panel_sha256, - arm=spec, - binding=binding, - task_id=item.task.task_id, - ), - ) - for item in prepared - ) - if len(attempts) != EXPECTED_SAMPLE_COUNT: - raise RendererDiagnosticError(f"diagnostic resolved {len(attempts)} samples, expected 32") - keys = [attempt.key for attempt in attempts] - seeds = [attempt.seed for attempt in attempts] - if len(set(keys)) != EXPECTED_SAMPLE_COUNT: - raise RendererDiagnosticError("diagnostic attempt keys are not unique") - if len(set(seeds)) != EXPECTED_SAMPLE_COUNT: - raise RendererDiagnosticError("diagnostic deterministic seeds collided") - return tuple(arms), tuple(attempts) - - -def _decode_response(renderer: Any, sequence: Any) -> dict[str, Any]: - from tinker_cookbook.renderers import get_text_content - - tokens = [int(token) for token in sequence.tokens] - token_decode_error: str | None = None - try: - raw_text = str(renderer.tokenizer.decode(tokens)) - except Exception as exc: - raw_text = "" - token_decode_error = str(exc).replace("\x00", "")[-1200:] - channel_parse_error: str | None = None - channel_termination: str | None = None - if token_decode_error is not None: - answer_text = "" - reasoning_text = "" - channel_parse_complete = False - channel_parse_status = "token_decode_error" - else: - try: - message, termination = renderer.parse_response(tokens) - answer_text = str(get_text_content(message)) - reasoning_text = checkpoint_v1._message_reasoning(message) - channel_parse_complete = bool(termination.is_clean) - channel_termination = str(termination) - channel_parse_status = "clean" if channel_parse_complete else "unclean" - except Exception as exc: - answer_text = raw_text - reasoning_text = "" - channel_parse_complete = False - channel_parse_status = "error" - channel_parse_error = str(exc).replace("\x00", "")[-1200:] - extracted_program = extract_code(answer_text) - stop_reason = str(sequence.stop_reason) - cap_hit = ( - len(tokens) >= MAX_OUTPUT_TOKENS - or "length" in stop_reason.lower() - or "max_token" in stop_reason.lower() - ) - return { - "token_ids": tokens, - "token_ids_sha256": _canonical_sha256(tokens), - "completion_tokens": len(tokens), - "stop_reason": stop_reason, - "cap_hit": cap_hit, - "raw_text": raw_text, - "raw_text_sha256": hashlib.sha256(raw_text.encode("utf-8")).hexdigest(), - "answer_text": answer_text, - "answer_text_sha256": hashlib.sha256(answer_text.encode("utf-8")).hexdigest(), - "reasoning_text": reasoning_text, - "reasoning_text_sha256": hashlib.sha256(reasoning_text.encode("utf-8")).hexdigest(), - "extracted_program": extracted_program, - "extracted_program_sha256": hashlib.sha256(extracted_program.encode("utf-8")).hexdigest(), - "token_decode_complete": token_decode_error is None, - "token_decode_error": token_decode_error, - "channel_parse_complete": channel_parse_complete, - "channel_parse_status": channel_parse_status, - "channel_parse_error": channel_parse_error, - "channel_termination": channel_termination, - } - - -_RESPONSE_FIELDS = { - "token_ids", - "token_ids_sha256", - "completion_tokens", - "stop_reason", - "cap_hit", - "raw_text", - "raw_text_sha256", - "answer_text", - "answer_text_sha256", - "reasoning_text", - "reasoning_text_sha256", - "extracted_program", - "extracted_program_sha256", - "token_decode_complete", - "token_decode_error", - "channel_parse_complete", - "channel_parse_status", - "channel_parse_error", - "channel_termination", -} - - -def _validate_response( - response: Any, - *, - attempt: PreparedAttempt, -) -> None: - if not isinstance(response, Mapping) or set(response) != _RESPONSE_FIELDS: - raise ImmutableRendererDiagnosticError(f"{attempt.key} raw response schema changed") - tokens = response["token_ids"] - if ( - not isinstance(tokens, list) - or any( - isinstance(token, bool) or not isinstance(token, int) or token < 0 for token in tokens - ) - or response["completion_tokens"] != len(tokens) - or response["token_ids_sha256"] != _canonical_sha256(tokens) - ): - raise ImmutableRendererDiagnosticError(f"{attempt.key} token inventory changed") - for field in ( - "raw_text", - "answer_text", - "reasoning_text", - "extracted_program", - ): - text = response[field] - if ( - not isinstance(text, str) - or response[f"{field}_sha256"] != hashlib.sha256(text.encode("utf-8")).hexdigest() - ): - raise ImmutableRendererDiagnosticError(f"{attempt.key} {field} digest changed") - if response["extracted_program"] != extract_code(response["answer_text"]): - raise ImmutableRendererDiagnosticError( - f"{attempt.key} extracted program differs from the answer" - ) - stop_reason = response["stop_reason"] - expected_cap = len(tokens) >= MAX_OUTPUT_TOKENS or ( - isinstance(stop_reason, str) - and ("length" in stop_reason.lower() or "max_token" in stop_reason.lower()) - ) - parse_status = response["channel_parse_status"] - if ( - not isinstance(stop_reason, str) - or not isinstance(response["cap_hit"], bool) - or response["cap_hit"] is not expected_cap - or not isinstance(response["token_decode_complete"], bool) - or response["token_decode_complete"] is not (response["token_decode_error"] is None) - or ( - response["token_decode_error"] is not None - and not isinstance(response["token_decode_error"], str) - ) - or not isinstance(response["channel_parse_complete"], bool) - or parse_status not in {"clean", "unclean", "error", "token_decode_error"} - or response["channel_parse_complete"] is not (parse_status == "clean") - or ( - response["channel_parse_error"] is not None - and not isinstance(response["channel_parse_error"], str) - ) - or ( - response["channel_termination"] is not None - and not isinstance(response["channel_termination"], str) - ) - or (parse_status in {"clean", "unclean"} and response["channel_termination"] is None) - or ( - parse_status not in {"clean", "unclean"} and response["channel_termination"] is not None - ) - or ( - not response["token_decode_complete"] - and ( - parse_status != "token_decode_error" - or any( - response[field] - for field in ( - "raw_text", - "answer_text", - "reasoning_text", - "extracted_program", - ) - ) - ) - ) - ): - raise ImmutableRendererDiagnosticError(f"{attempt.key} response diagnostics changed") - - -def _sample_payload( - *, - attempt: PreparedAttempt, - response: Mapping[str, Any], - manifest_record_sha256: str, -) -> dict[str, Any]: - binding = attempt.arm.binding - return { - **_not_canonical(), - "diagnostic_manifest_record_sha256": manifest_record_sha256, - "producer_wave_receipt_record_sha256": (binding.wave_receipt.record_sha256), - "diagnostic_id": DIAGNOSTIC_ID, - "arm": attempt.arm.spec.as_dict(), - "checkpoint_inventory_sha256": binding.checkpoint_inventory_sha256, - "checkpoint": binding.checkpoint, - "task": { - "task_id": attempt.task.task_id, - "level": attempt.task.level, - "representation_id": attempt.task.representation_id, - "image_sha256": attempt.task.observation.image_sha256, - "target_image_sha256": attempt.task.reference.target_image_sha256, - }, - "request": { - "num_samples": 1, - "attempt_index": 1, - "seed": attempt.seed, - "max_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "prompt_tokens": attempt.prepared.prompt_tokens, - "prompt_text_sha256": attempt.prepared.prompt_text_sha256, - }, - "response": dict(response), - } - - -def _validate_sample_record( - *, - record: Mapping[str, Any], - attempt: PreparedAttempt, - manifest_record_sha256: str, -) -> None: - payload = record["payload"] - binding = attempt.arm.binding - expected = { - "diagnostic_manifest_record_sha256": manifest_record_sha256, - "producer_wave_receipt_record_sha256": (binding.wave_receipt.record_sha256), - "diagnostic_id": DIAGNOSTIC_ID, - "arm": attempt.arm.spec.as_dict(), - "checkpoint_inventory_sha256": binding.checkpoint_inventory_sha256, - "checkpoint": binding.checkpoint, - } - for field, value in expected.items(): - if payload.get(field) != value: - raise ImmutableRendererDiagnosticError( - f"{attempt.key} sample belongs to another diagnostic" - ) - if payload.get("not_canonical_evidence") is not True: - raise ImmutableRendererDiagnosticError(f"{attempt.key} sample lost non-canonical marker") - expected_task = { - "task_id": attempt.task.task_id, - "level": attempt.task.level, - "representation_id": attempt.task.representation_id, - "image_sha256": attempt.task.observation.image_sha256, - "target_image_sha256": attempt.task.reference.target_image_sha256, - } - expected_request = { - "num_samples": 1, - "attempt_index": 1, - "seed": attempt.seed, - "max_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "prompt_tokens": attempt.prepared.prompt_tokens, - "prompt_text_sha256": attempt.prepared.prompt_text_sha256, - } - if payload.get("task") != expected_task or payload.get("request") != expected_request: - raise ImmutableRendererDiagnosticError(f"{attempt.key} sample request changed") - _validate_response(payload.get("response"), attempt=attempt) - - -def _evaluation_payload( - *, - attempt: PreparedAttempt, - result: EvaluationResult, - sample_record_sha256: str, - manifest_record_sha256: str, - extracted_program_sha256: str, -) -> dict[str, Any]: - payload = checkpoint_v1._evaluation_payload( - task=attempt.task, - result=result, - sample_record_sha256=sample_record_sha256, - manifest_record_sha256=manifest_record_sha256, - ) - if result.status is not EvaluationStatus.OK: - payload["raw_absolute_scale_iou"] = 0.0 - payload["dice"] = None - payload["pure_executable"] = False - try: - normalized = json.loads(_canonical_bytes(payload)) - except RendererDiagnosticError as exc: - raise checkpoint_v1.EvaluationInfrastructureFault( - f"{attempt.key}: evaluator returned non-finite diagnostics" - ) from exc - normalized["diagnostic_manifest_record_sha256"] = normalized.pop( - "evaluation_manifest_record_sha256" - ) - normalized.update( - { - **_not_canonical(), - "diagnostic_id": DIAGNOSTIC_ID, - "arm_id": attempt.arm.spec.arm_id, - "checkpoint_name": attempt.arm.spec.checkpoint_name, - "renderer": attempt.arm.spec.renderer, - "seed": attempt.seed, - "extracted_program_sha256": extracted_program_sha256, - } - ) - return normalized - - -def _validate_evaluation_record( - *, - record: Mapping[str, Any], - attempt: PreparedAttempt, - sample_record_sha256: str, - manifest_record_sha256: str, - extracted_program_sha256: str, -) -> None: - payload = record["payload"] - expected = { - "diagnostic_manifest_record_sha256": manifest_record_sha256, - "sample_record_sha256": sample_record_sha256, - "diagnostic_id": DIAGNOSTIC_ID, - "arm_id": attempt.arm.spec.arm_id, - "checkpoint_name": attempt.arm.spec.checkpoint_name, - "renderer": attempt.arm.spec.renderer, - "task_id": attempt.task.task_id, - "level": attempt.task.level, - "representation_id": attempt.task.representation_id, - "seed": attempt.seed, - "extracted_program_sha256": extracted_program_sha256, - "attribution": Attribution.MODEL.value, - "not_canonical_evidence": True, - } - for field, value in expected.items(): - if payload.get(field) != value: - raise ImmutableRendererDiagnosticError(f"{attempt.key} evaluation record changed") - iou = payload.get("raw_absolute_scale_iou") - latency = payload.get("latency_seconds") - if ( - isinstance(iou, bool) - or not isinstance(iou, (int, float)) - or not math.isfinite(float(iou)) - or not 0.0 <= float(iou) <= 1.0 - or isinstance(latency, bool) - or not isinstance(latency, (int, float)) - or not math.isfinite(float(latency)) - or float(latency) < 0.0 - ): - raise ImmutableRendererDiagnosticError(f"{attempt.key} has invalid stored metrics") - status = payload.get("status") - pure = payload.get("pure_executable") - if ( - status not in {item.value for item in EvaluationStatus} - or not isinstance(pure, bool) - or pure is not (status == EvaluationStatus.OK.value) - or (not pure and float(iou) != 0.0) - ): - raise ImmutableRendererDiagnosticError(f"{attempt.key} has inconsistent executable status") - try: - checkpoint_v1._safe_json(payload) - _canonical_bytes(payload) - except ( - checkpoint_v1.EvaluationInfrastructureFault, - RendererDiagnosticError, - ) as exc: - raise ImmutableRendererDiagnosticError(f"{attempt.key} has non-finite diagnostics") from exc - - -async def _sample_missing( - *, - attempts: Sequence[PreparedAttempt], - clients: Mapping[str, Any], - store: RendererDiagnosticStore, - manifest_record_sha256: str, -) -> None: - import tinker - - semaphore = asyncio.Semaphore(SAMPLE_CONCURRENCY) - - async def sample_one(attempt: PreparedAttempt) -> None: - messages = [_message(attempt.task, max_image=MAX_IMAGE_LONG_EDGE)] - prompt_text = str(messages[0]["content"][1]["text"]) - if ( - hashlib.sha256(prompt_text.encode("utf-8")).hexdigest() - != attempt.prepared.prompt_text_sha256 - ): - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} prompt text changed after preflight" - ) - prompt = attempt.arm.renderer.build_generation_prompt(messages) - if int(prompt.length) != attempt.prepared.prompt_tokens: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} prompt changed after preflight" - ) - async with semaphore: - try: - result = await clients[attempt.arm.spec.arm_id].sample_async( - prompt=prompt, - num_samples=1, - sampling_params=tinker.SamplingParams( - stop=attempt.arm.renderer.get_stop_sequences(), - max_tokens=MAX_OUTPUT_TOKENS, - temperature=TEMPERATURE, - top_p=TOP_P, - seed=attempt.seed, - ), - ) - except Exception as exc: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} sampling failed: {exc}" - ) from exc - sequences = list(result.sequences) - if len(sequences) != 1: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} returned {len(sequences)} samples, expected 1" - ) - response = _decode_response(attempt.arm.renderer, sequences[0]) - store.create_or_verify_sample( - attempt.arm.spec.arm_id, - attempt.task.task_id, - _sample_payload( - attempt=attempt, - response=response, - manifest_record_sha256=manifest_record_sha256, - ), - ) - if not response["token_decode_complete"]: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} raw token IDs were persisted but could not be " - f"decoded: {response['token_decode_error']}" - ) - - tasks = [asyncio.create_task(sample_one(attempt)) for attempt in attempts] - try: - outcomes = await asyncio.gather(*tasks, return_exceptions=True) - except BaseException: - for task in tasks: - if not task.done(): - task.cancel() - await asyncio.gather(*tasks, return_exceptions=True) - raise - faults = [outcome for outcome in outcomes if isinstance(outcome, BaseException)] - if faults: - raise faults[0] - - -def _score_missing( - *, - attempts: Sequence[PreparedAttempt], - evaluator: PixCellEvaluator, - store: RendererDiagnosticStore, - manifest_record_sha256: str, -) -> None: - pending: list[tuple[PreparedAttempt, dict[str, Any]]] = [] - for attempt in attempts: - sample = store.load_sample( - attempt.arm.spec.arm_id, - attempt.task.task_id, - ) - if sample is None: - raise RendererDiagnosticError(f"{attempt.key} has no resumable sample") - response = sample["payload"]["response"] - if not response["token_decode_complete"]: - raise checkpoint_v1.SamplingInfrastructureFault( - f"{attempt.key} has undecodable persisted tokens" - ) - existing = store.load_evaluation( - attempt.arm.spec.arm_id, - attempt.task.task_id, - ) - if existing is not None: - _validate_evaluation_record( - record=existing, - attempt=attempt, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - extracted_program_sha256=response["extracted_program_sha256"], - ) - continue - pending.append((attempt, sample)) - - for offset in range(0, len(pending), EVALUATION_BATCH_SIZE): - batch = pending[offset : offset + EVALUATION_BATCH_SIZE] - requests = [ - ( - attempt.task.reference, - str(sample["payload"]["response"]["extracted_program"]), - ) - for attempt, sample in batch - ] - try: - results = evaluator.evaluate_batch(requests) - except Exception as exc: - raise checkpoint_v1.EvaluationInfrastructureFault( - f"isolated renderer-diagnostic batch failed: {exc}" - ) from exc - if len(results) != len(batch): - raise checkpoint_v1.EvaluationInfrastructureFault( - "isolated evaluator returned a different result count" - ) - faults: list[checkpoint_v1.CheckpointEvaluationError] = [] - for (attempt, sample), result in zip(batch, results, strict=True): - response = sample["payload"]["response"] - try: - payload = _evaluation_payload( - attempt=attempt, - result=result, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - extracted_program_sha256=response["extracted_program_sha256"], - ) - except ( - checkpoint_v1.ReferencePanelFault, - checkpoint_v1.EvaluationInfrastructureFault, - ) as exc: - faults.append(exc) - continue - store.create_or_verify_evaluation( - attempt.arm.spec.arm_id, - attempt.task.task_id, - payload, - ) - if faults: - raise faults[0] - - -def _validate_attempt_state( - *, - attempts: Sequence[PreparedAttempt], - store: RendererDiagnosticStore, - manifest_record_sha256: str, -) -> tuple[list[PreparedAttempt], list[PreparedAttempt]]: - store.assert_attempt_inventory(attempts) - missing_samples: list[PreparedAttempt] = [] - missing_evaluations: list[PreparedAttempt] = [] - for attempt in attempts: - sample = store.load_sample( - attempt.arm.spec.arm_id, - attempt.task.task_id, - ) - evaluation = store.load_evaluation( - attempt.arm.spec.arm_id, - attempt.task.task_id, - ) - if sample is None: - if evaluation is not None: - raise ImmutableRendererDiagnosticError( - f"{attempt.key} has evaluation without raw sample" - ) - missing_samples.append(attempt) - missing_evaluations.append(attempt) - continue - _validate_sample_record( - record=sample, - attempt=attempt, - manifest_record_sha256=manifest_record_sha256, - ) - response = sample["payload"]["response"] - if not response["token_decode_complete"] and evaluation is not None: - raise ImmutableRendererDiagnosticError(f"{attempt.key} evaluates undecodable tokens") - if evaluation is None: - missing_evaluations.append(attempt) - continue - _validate_evaluation_record( - record=evaluation, - attempt=attempt, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_record_sha256, - extracted_program_sha256=response["extracted_program_sha256"], - ) - return missing_samples, missing_evaluations - - -def _arm_summary(records: Sequence[Mapping[str, Any]]) -> dict[str, Any]: - if len(records) != len(EXPECTED_TASK_IDS): - raise RendererDiagnosticError("diagnostic arm is not exact F1-F8") - ious = [float(record["raw_absolute_scale_iou"]) for record in records] - pure = [bool(record["pure_executable"]) for record in records] - return { - "tasks": len(records), - "mean_raw_absolute_scale_iou": sum(ious) / len(ious), - "pure_executable": sum(pure), - "pure_executable_rate": sum(pure) / len(pure), - "cap_hits": sum(bool(record["cap_hit"]) for record in records), - "channel_parse_complete": sum(bool(record["channel_parse_complete"]) for record in records), - "statuses": dict(sorted(Counter(str(record["status"]) for record in records).items())), - } - - -def _report_payload( - *, - attempts: Sequence[PreparedAttempt], - store: RendererDiagnosticStore, - manifest_record: Mapping[str, Any], -) -> dict[str, Any]: - records: list[dict[str, Any]] = [] - by_arm: dict[str, list[dict[str, Any]]] = {spec.arm_id: [] for spec in ARM_SPECS} - manifest_sha = str(manifest_record["record_sha256"]) - for attempt in attempts: - sample = store.load_sample( - attempt.arm.spec.arm_id, - attempt.task.task_id, - ) - evaluation = store.load_evaluation( - attempt.arm.spec.arm_id, - attempt.task.task_id, - ) - if sample is None or evaluation is None: - raise RendererDiagnosticError(f"{attempt.key} is incomplete") - response = sample["payload"]["response"] - measured = evaluation["payload"] - _validate_sample_record( - record=sample, - attempt=attempt, - manifest_record_sha256=manifest_sha, - ) - _validate_evaluation_record( - record=evaluation, - attempt=attempt, - sample_record_sha256=str(sample["record_sha256"]), - manifest_record_sha256=manifest_sha, - extracted_program_sha256=response["extracted_program_sha256"], - ) - row = { - "arm_id": attempt.arm.spec.arm_id, - "checkpoint_name": attempt.arm.spec.checkpoint_name, - "renderer": attempt.arm.spec.renderer, - "task_id": attempt.task.task_id, - "seed": attempt.seed, - "status": measured["status"], - "pure_executable": measured["pure_executable"], - "raw_absolute_scale_iou": measured["raw_absolute_scale_iou"], - "completion_tokens": response["completion_tokens"], - "stop_reason": response["stop_reason"], - "cap_hit": response["cap_hit"], - "channel_parse_status": response["channel_parse_status"], - "channel_parse_complete": response["channel_parse_complete"], - "raw_text_sha256": response["raw_text_sha256"], - "reasoning_text_sha256": response["reasoning_text_sha256"], - "answer_text_sha256": response["answer_text_sha256"], - "extracted_program_sha256": response["extracted_program_sha256"], - "sample_record_sha256": sample["record_sha256"], - "evaluation_record_sha256": evaluation["record_sha256"], - } - records.append(row) - by_arm[attempt.arm.spec.arm_id].append(row) - return { - **_not_canonical(), - "schema_version": DIAGNOSTIC_SCHEMA_VERSION, - "diagnostic_id": DIAGNOSTIC_ID, - "study_id": STUDY_ID, - "producer_stage_id": PRODUCER_STAGE_ID, - "producer_wave": PRODUCER_WAVE, - "producer_replicate_id": PRODUCER_REPLICATE_ID, - "matrix": [spec.as_dict() for spec in ARM_SPECS], - "summaries": {arm_id: _arm_summary(values) for arm_id, values in by_arm.items()}, - "records": records, - "record_set_sha256": canonical_json_sha256(records), - "provenance": { - "diagnostic_manifest_record_sha256": manifest_sha, - "manifest": manifest_record["payload"], - }, - } - - -def _manifest_payload( - *, - repo_root: Path, - protocol: Mapping[str, Any], - evaluator_source_git_sha: str, - source_provenance: Mapping[str, Any], - producer_training: Mapping[str, Any], - dataset: Mapping[str, Any], - task_panel: Mapping[str, Any], - model: Mapping[str, Any], - prefix_audit: Mapping[str, Any], - runtime: Mapping[str, Any], - sandbox: Mapping[str, Any], - arms: Sequence[PreparedArm], - attempts: Sequence[PreparedAttempt], -) -> dict[str, Any]: - arm_preflights = { - arm.spec.arm_id: { - "spec": arm.spec.as_dict(), - "checkpoint": arm.binding.checkpoint, - "sampler_path": arm.binding.sampler_path, - "prompt": arm.preflight, - "prefix_compatibility": prefix_audit["renderers"][arm.spec.renderer], - } - for arm in arms - } - seed_inventory = [ - { - "arm_id": attempt.arm.spec.arm_id, - "checkpoint_name": attempt.arm.spec.checkpoint_name, - "renderer": attempt.arm.spec.renderer, - "task_id": attempt.task.task_id, - "seed": attempt.seed, - } - for attempt in attempts - ] - return { - **_not_canonical(), - "schema_version": DIAGNOSTIC_SCHEMA_VERSION, - "diagnostic_id": DIAGNOSTIC_ID, - "study_id": STUDY_ID, - "source": dict(source_provenance), - "protocol": { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(repo_root)), - "contract_version": protocol["contract_version"], - "preserved_unchanged": True, - }, - "dataset": { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": "fixed-f1-f8", - **dict(dataset), - }, - "producer_training": dict(producer_training), - "task_panel": dict(task_panel), - "prompt_assets": prompt_asset_hashes(), - "model": { - **dict(model), - "producer_training_renderer": model["renderer"], - }, - "sft_prefix_audit": dict(prefix_audit), - "matrix": arm_preflights, - "runtime": dict(runtime), - "sandbox": dict(sandbox), - "sampling": { - "task_count": len(EXPECTED_TASK_IDS), - "arms": len(ARM_SPECS), - "total_samples": EXPECTED_SAMPLE_COUNT, - "attempts_per_arm_task": 1, - "max_output_tokens": MAX_OUTPUT_TOKENS, - "temperature": TEMPERATURE, - "top_p": TOP_P, - "max_image_long_edge": MAX_IMAGE_LONG_EDGE, - "sample_concurrency": SAMPLE_CONCURRENCY, - "evaluation_batch_size": EVALUATION_BATCH_SIZE, - "evaluator_workers": EVALUATOR_WORKERS, - "seed_domain": _SEED_DOMAIN, - "seed_algorithm": "sha256-bound deterministic 31-bit seed", - "seeds": seed_inventory, - "seeds_sha256": canonical_json_sha256(seed_inventory), - }, - "evaluator_source_git_sha": evaluator_source_git_sha, - } - - -def _runtime_binding(repo_root: Path) -> dict[str, Any]: - runtime = validate_runtime_stack(repo_root) - return { - **runtime, - "cookbook_source": _cookbook_source_binding(runtime), - } - - -def _default_evaluator_factory() -> PixCellEvaluator: - return PixCellEvaluator( - max_workers=EVALUATOR_WORKERS, - evaluator_retries=1, - require_isolation=True, - ) - - -def _default_service_client_factory() -> Any: - import tinker - - return tinker.ServiceClient() - - -async def run_renderer_diagnostic( - *, - repo_root: Path, - evaluator_source_git_sha: str, - producer_wave_receipt_record_sha256: str, - external_root: Path, - confirmation: str, - service_client_factory: Callable[[], Any] = _default_service_client_factory, - evaluator_factory: Callable[[], PixCellEvaluator] = _default_evaluator_factory, -) -> dict[str, Any]: - """Run or resume the exact, diagnostic-only 32-sample matrix.""" - - root = repo_root.expanduser().resolve(strict=True) - protocol = load_protocol(root, require_committed=True) - current_source = validate_source_sha(root, evaluator_source_git_sha) - if confirmation != protocol["launch"]["confirmation_token"]: - raise RendererDiagnosticError("explicit diagnostic spend confirmation is missing") - fixed = _validate_fixed_protocol(protocol) - training_store = TrainingStore(repo_root=root, external_root=external_root) - bindings = _load_matrix_bindings( - store=training_store, - protocol=protocol, - expected_wave_receipt_sha256=producer_wave_receipt_record_sha256, - ) - source_provenance = _source_provenance( - repo_root=root, - evaluator_source_git_sha=current_source, - ) - producer_training = _producer_training_provenance( - repo_root=root, - protocol=protocol, - bindings=bindings, - ) - dataset = _dataset_binding(root / "dataset", protocol) - runtime = _runtime_binding(root) - tasks = checkpoint_v1._benchmark_tasks(repo_root=root, protocol=protocol) - task_panel = _task_panel(tasks) - prefix_audit = _sft_prefix_audit( - dataset_root=root / "dataset", - protocol=protocol, - ) - for spec in ARM_SPECS: - observed = prefix_audit["renderers"][spec.renderer]["compatible_with_sft_supervised_prefix"] - if observed is not spec.expected_sft_prefix_compatible: - raise RendererDiagnosticError(f"{spec.arm_id} renderer compatibility claim changed") - - evaluator = evaluator_factory() - try: - sandbox = checkpoint_v1._sandbox_provenance(evaluator) - arms, attempts = _prepare_arms( - protocol=protocol, - model=fixed["model"], - bindings=bindings, - tasks=tasks, - evaluator=evaluator, - evaluator_source_git_sha=current_source, - dataset_sha256=str(dataset["logical_release_sha256"]), - task_panel_sha256=str(task_panel["logical_sha256"]), - ) - store = RendererDiagnosticStore( - stage_path=training_store.stage_path(bindings[0].key), - ) - with store.acquire_lock(): - manifest_payload = _manifest_payload( - repo_root=root, - protocol=protocol, - evaluator_source_git_sha=current_source, - source_provenance=source_provenance, - producer_training=producer_training, - dataset=dataset, - task_panel=task_panel, - model=fixed["model"], - prefix_audit=prefix_audit, - runtime=runtime, - sandbox=sandbox, - arms=arms, - attempts=attempts, - ) - manifest = store.create_or_verify_manifest(manifest_payload) - missing_samples, missing_evaluations = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - existing_report = store.load_report() - if existing_report is not None: - if missing_samples or missing_evaluations: - raise RendererDiagnosticError( - "diagnostic report exists with incomplete matrix rows" - ) - expected = _report_payload( - attempts=attempts, - store=store, - manifest_record=manifest, - ) - observed = store.create_or_verify_report(expected) - _validate_report_rows( - observed, - manifest_record=manifest, - attempts=attempts, - ) - return { - "status": "already_complete", - "diagnostic_id": DIAGNOSTIC_ID, - "not_canonical_evidence": True, - "report": str(store.root / "report.json"), - "report_record_sha256": observed["record_sha256"], - "summaries": observed["payload"]["summaries"], - } - - if missing_samples: - if not os.environ.get("TINKER_API_KEY"): - raise RendererDiagnosticError("TINKER_API_KEY is not present") - # Revalidate the exact current source, explicit spend token, - # producer receipt, existing rows, and matrix immediately - # before the first paid-capable client exists. - validate_source_sha(root, current_source) - if confirmation != protocol["launch"]["confirmation_token"]: - raise RendererDiagnosticError( - "diagnostic spend confirmation changed before client" - ) - rebound = _load_matrix_bindings( - store=training_store, - protocol=protocol, - expected_wave_receipt_sha256=(producer_wave_receipt_record_sha256), - ) - if [ - ( - binding.checkpoint, - binding.checkpoint_inventory_sha256, - binding.sampler_path, - binding.wave_receipt.record_sha256, - ) - for binding in rebound - ] != [ - ( - arm.binding.checkpoint, - arm.binding.checkpoint_inventory_sha256, - arm.binding.sampler_path, - arm.binding.wave_receipt.record_sha256, - ) - for arm in arms - ]: - raise RendererDiagnosticError("producer receipt changed before client creation") - missing_samples, _ = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - if not missing_samples: - raise RendererDiagnosticError( - "diagnostic resume state changed before client creation" - ) - service = service_client_factory() - clients = { - arm.spec.arm_id: service.create_sampling_client( - model_path=arm.binding.sampler_path - ) - for arm in arms - if any( - attempt.arm.spec.arm_id == arm.spec.arm_id for attempt in missing_samples - ) - } - await _sample_missing( - attempts=missing_samples, - clients=clients, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - - missing_samples, _ = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - if missing_samples: - raise RendererDiagnosticError( - "diagnostic sampling did not persist all raw responses" - ) - _score_missing( - attempts=attempts, - evaluator=evaluator, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - missing_samples, missing_evaluations = _validate_attempt_state( - attempts=attempts, - store=store, - manifest_record_sha256=str(manifest["record_sha256"]), - ) - if missing_samples or missing_evaluations: - raise RendererDiagnosticError( - "diagnostic is incomplete after deterministic evaluation" - ) - report_payload = _report_payload( - attempts=attempts, - store=store, - manifest_record=manifest, - ) - report = store.create_or_verify_report(report_payload) - _validate_report_rows( - report, - manifest_record=manifest, - attempts=attempts, - ) - return { - "status": "complete", - "diagnostic_id": DIAGNOSTIC_ID, - "not_canonical_evidence": True, - "report": str(store.root / "report.json"), - "report_record_sha256": report["record_sha256"], - "summaries": report_payload["summaries"], - } - finally: - evaluator.close() - - -def _validate_report_rows( - record: Mapping[str, Any], - *, - manifest_record: Mapping[str, Any], - attempts: Sequence[PreparedAttempt], -) -> dict[str, Any]: - """Validate the report without granting it a canonical report interface.""" - - validated = _validate_record( - record, - record_type="renderer_diagnostic_report", - key={}, - ) - payload = validated["payload"] - if ( - payload.get("schema_version") != DIAGNOSTIC_SCHEMA_VERSION - or payload.get("diagnostic_id") != DIAGNOSTIC_ID - or payload.get("not_canonical_evidence") is not True - or payload.get("promotion_eligibility") != {"eligible": False, "policy": _PROMOTION_POLICY} - or payload.get("matrix") != [spec.as_dict() for spec in ARM_SPECS] - ): - raise RendererDiagnosticError("diagnostic report identity changed") - provenance = payload.get("provenance") - records = payload.get("records") - if ( - not isinstance(provenance, Mapping) - or not isinstance(records, list) - or provenance.get("diagnostic_manifest_record_sha256") != manifest_record["record_sha256"] - or provenance.get("manifest") != manifest_record["payload"] - ): - raise RendererDiagnosticError("diagnostic report provenance changed") - expected_keys = [attempt.key for attempt in attempts] - observed_keys = [ - (str(row.get("arm_id")), str(row.get("task_id"))) - for row in records - if isinstance(row, Mapping) - ] - if ( - len(records) != EXPECTED_SAMPLE_COUNT - or observed_keys != expected_keys - or len(set(observed_keys)) != EXPECTED_SAMPLE_COUNT - ): - raise RendererDiagnosticError("diagnostic report has an incomplete or reordered matrix") - for row, attempt in zip(records, attempts, strict=True): - if not isinstance(row, Mapping): - raise RendererDiagnosticError("diagnostic report row is not an object") - expected = { - "arm_id": attempt.arm.spec.arm_id, - "checkpoint_name": attempt.arm.spec.checkpoint_name, - "renderer": attempt.arm.spec.renderer, - "task_id": attempt.task.task_id, - "seed": attempt.seed, - } - for field, value in expected.items(): - if row.get(field) != value: - raise RendererDiagnosticError(f"{attempt.key} report identity changed") - iou = row.get("raw_absolute_scale_iou") - completion_tokens = row.get("completion_tokens") - status = row.get("status") - pure = row.get("pure_executable") - cap_hit = row.get("cap_hit") - stop_reason = row.get("stop_reason") - parse_status = row.get("channel_parse_status") - parse_complete = row.get("channel_parse_complete") - expected_cap = ( - isinstance(completion_tokens, int) - and not isinstance(completion_tokens, bool) - and ( - completion_tokens >= MAX_OUTPUT_TOKENS - or ( - isinstance(stop_reason, str) - and ("length" in stop_reason.lower() or "max_token" in stop_reason.lower()) - ) - ) - ) - if ( - isinstance(iou, bool) - or not isinstance(iou, (int, float)) - or not math.isfinite(float(iou)) - or not 0.0 <= float(iou) <= 1.0 - or status not in {item.value for item in EvaluationStatus} - or not isinstance(pure, bool) - or pure is not (status == EvaluationStatus.OK.value) - or (not pure and float(iou) != 0.0) - or isinstance(completion_tokens, bool) - or not isinstance(completion_tokens, int) - or completion_tokens < 0 - or not isinstance(stop_reason, str) - or not isinstance(cap_hit, bool) - or cap_hit is not expected_cap - or parse_status not in {"clean", "unclean", "error", "token_decode_error"} - or not isinstance(parse_complete, bool) - or parse_complete is not (parse_status == "clean") - ): - raise RendererDiagnosticError(f"{attempt.key} report diagnostics are inconsistent") - for field in ( - "raw_text_sha256", - "reasoning_text_sha256", - "answer_text_sha256", - "extracted_program_sha256", - "sample_record_sha256", - "evaluation_record_sha256", - ): - if not checkpoint_v1._SHA256.fullmatch(str(row.get(field, ""))): - raise RendererDiagnosticError(f"{attempt.key} report {field} is not a SHA-256") - by_arm = { - spec.arm_id: [row for row in records if row["arm_id"] == spec.arm_id] for spec in ARM_SPECS - } - recomputed = {arm_id: _arm_summary(rows) for arm_id, rows in by_arm.items()} - if payload.get("summaries") != recomputed: - raise RendererDiagnosticError("diagnostic claimed metrics differ from its rows") - if payload.get("record_set_sha256") != canonical_json_sha256(records): - raise RendererDiagnosticError("diagnostic row-set digest changed") - return validated - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--repo-root", type=Path, required=True) - result.add_argument("--evaluator-source-git-sha", required=True) - result.add_argument( - "--producer-wave-receipt-record-sha256", - required=True, - ) - result.add_argument("--external-root", type=Path, required=True) - result.add_argument("--confirm-spend", default="") - return result - - -def main() -> None: - args = parser().parse_args() - result = asyncio.run( - run_renderer_diagnostic( - repo_root=args.repo_root, - evaluator_source_git_sha=args.evaluator_source_git_sha, - producer_wave_receipt_record_sha256=(args.producer_wave_receipt_record_sha256), - external_root=args.external_root, - confirmation=args.confirm_spend, - ) - ) - print(json.dumps(result, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/run_stage.py b/rl/studies/representation_training_v1/run_stage.py deleted file mode 100644 index 1bca69f3..00000000 --- a/rl/studies/representation_training_v1/run_stage.py +++ /dev/null @@ -1,56 +0,0 @@ -#!/usr/bin/env python3 -"""Run one sealed representation-training stage or continuation wave.""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import os -from pathlib import Path - -from .launcher import run_training_stage -from .protocol import repository_root - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--stage", required=True) - result.add_argument("--wave", required=True) - result.add_argument("--replicate", default="r0") - result.add_argument("--expected-source-sha", required=True) - result.add_argument("--confirm-spend", default="") - result.add_argument( - "--external-root", - type=Path, - default=( - Path(os.environ["PIXCELL_TRAINING_ROOT"]) - if os.environ.get("PIXCELL_TRAINING_ROOT") - else None - ), - ) - return result - - -def main() -> None: - args = parser().parse_args() - if args.external_root is None: - raise SystemExit( - "set PIXCELL_TRAINING_ROOT or pass --external-root outside Git" - ) - report = asyncio.run( - run_training_stage( - repo_root=repository_root(), - stage_id=args.stage, - wave_name=args.wave, - replicate_id=args.replicate, - expected_source_sha=args.expected_source_sha, - external_root=args.external_root, - confirmation=args.confirm_spend, - ) - ) - print(json.dumps(report, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/rl/studies/representation_training_v1/schedule.py b/rl/studies/representation_training_v1/schedule.py deleted file mode 100644 index a663fbda..00000000 --- a/rl/studies/representation_training_v1/schedule.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Deterministic task selection and schedule digests for training stages.""" - -from __future__ import annotations - -import hashlib -import json -import math -from collections import Counter -from pathlib import Path -from typing import Any - -from rl.common.contracts import TaskRecord -from rl.common.dataset_io import load_tasks -from rl.track_a.curriculum import deterministic_rl_batch, stratified_physical_pass - -from .protocol import StageSpec - - -SCHEDULE_SCHEMA_VERSION = "pixcell-training-schedule-v1" -SCHEDULE_SEED = 90210 - - -def canonical_sha256(value: Any) -> str: - raw = json.dumps( - value, - allow_nan=False, - ensure_ascii=True, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - return hashlib.sha256(raw).hexdigest() - - -def _select(tasks: list[TaskRecord], levels: tuple[str, ...]) -> list[TaskRecord]: - allowed = set(levels) - selected = [task for task in tasks if task.sampler.level.upper() in allowed] - if not selected: - raise ValueError(f"no depth/train rows match levels={levels}") - return selected - - -def stage_tasks(dataset_root: Path, stage: StageSpec) -> list[TaskRecord]: - all_train = load_tasks( - dataset_root.expanduser().resolve(), - configuration="depth", - split="train", - ) - levels = stage.levels if stage.kind == "sft" else (str(stage.current_level),) - return _select(all_train, levels) - - -def stage_replay_tasks( - dataset_root: Path, - stage: StageSpec, -) -> list[TaskRecord]: - if stage.kind != "rl" or not stage.replay_levels: - return [] - all_train = load_tasks( - dataset_root.expanduser().resolve(), - configuration="depth", - split="train", - ) - return _select(all_train, stage.replay_levels) - - -def ordered_stage_tasks(dataset_root: Path, stage: StageSpec) -> list[TaskRecord]: - return stratified_physical_pass( - stage_tasks(dataset_root, stage), - seed=SCHEDULE_SEED, - ) - - -def stage_schedule_document( - dataset_root: Path, - stage: StageSpec, - *, - groups_per_batch: int | None = None, -) -> dict[str, Any]: - tasks = ( - ordered_stage_tasks(dataset_root, stage) - if stage.kind == "sft" - else stage_tasks(dataset_root, stage) - ) - rows_by_level = Counter(task.sampler.level.upper() for task in tasks) - representations = { - task.sampler.representation_id - for task in tasks - } - slots = Counter( - str(task.sampler.realization_slot) - for task in tasks - ) - document: dict[str, Any] = { - "schema_version": SCHEDULE_SCHEMA_VERSION, - "stage_id": stage.stage_id, - "kind": stage.kind, - "seed": SCHEDULE_SEED, - "rows": len(tasks), - "rows_by_level": dict(sorted(rows_by_level.items())), - "representations": len(representations), - "realization_slots": dict(sorted(slots.items())), - "ordered_task_ids": [task.sampler.opaque_id for task in tasks], - } - if stage.kind == "sft": - document["batch_size"] = 64 - document["batches"] = math.ceil(len(tasks) / 64) - else: - if groups_per_batch is None or groups_per_batch < 1: - raise ValueError("RL schedule requires groups_per_batch") - document["groups_per_batch"] = groups_per_batch - document["coverage_steps"] = math.ceil(len(tasks) / groups_per_batch) - replay = stage_replay_tasks(dataset_root, stage) - maximum_steps = max( - int(wave["max_steps"]) for wave in stage.waves.values() - ) - training_batches: list[dict[str, Any]] = [] - for step in range(maximum_steps): - selected = deterministic_rl_batch( - tasks, - replay, - step=step, - groups_per_batch=groups_per_batch, - schedule_seed=SCHEDULE_SEED, - ) - ids = [task.sampler.opaque_id for _role, task in selected] - if len(ids) != len(set(ids)): - raise RuntimeError(f"{stage.stage_id} step {step} repeats a task") - training_batches.append( - { - "step": step, - "groups": [ - { - "role": role, - "task_id": task.sampler.opaque_id, - "level": task.sampler.level.upper(), - } - for role, task in selected - ], - } - ) - document["maximum_steps"] = maximum_steps - document["replay_pool_rows"] = len(replay) - document["replay_levels"] = list(stage.replay_levels) - document["training_batches"] = training_batches - document["logical_sha256"] = canonical_sha256(document) - return document - - -def verify_sft_step_count( - dataset_root: Path, - stage: StageSpec, - *, - max_steps: int, -) -> None: - schedule = stage_schedule_document(dataset_root, stage) - expected = int(schedule["batches"]) - if max_steps != expected: - raise ValueError( - f"{stage.stage_id} must visit one complete physical pass: " - f"max_steps={expected}, got {max_steps}" - ) diff --git a/rl/studies/representation_training_v1/source_transition.py b/rl/studies/representation_training_v1/source_transition.py deleted file mode 100644 index cb86eae5..00000000 --- a/rl/studies/representation_training_v1/source_transition.py +++ /dev/null @@ -1,433 +0,0 @@ -"""Deterministic Git facts for an explicitly reviewed source transition. - -Evaluation evidence is produced and validated at one clean commit. A later -consumer commit may use that evidence only when the producer is an exact -ancestor and an immutable approval records the complete transition. -""" - -from __future__ import annotations - -import os -import re -import subprocess -from collections.abc import Mapping, Sequence -from pathlib import Path, PurePosixPath -from typing import Any - -from .store import canonical_sha256 - - -SOURCE_TRANSITION_SCHEMA_VERSION = "pixcell-training-source-transition-v2" -_GIT_SHA = re.compile(r"^[0-9a-f]{40}$") -_GIT_MODE = re.compile(r"^[0-7]{6}$") -_GIT_STATUS = re.compile(r"^[A-Z][0-9]*$") - - -class SourceTransitionError(RuntimeError): - """A producer-to-consumer source transition is invalid or unverifiable.""" - - -def _sha(value: Any, field: str) -> str: - result = str(value) - if not _GIT_SHA.fullmatch(result): - raise SourceTransitionError(f"{field} must be a full lowercase Git SHA") - return result - - -def _git(repo_root: Path, *args: str) -> bytes: - # These are local object-database queries. Inheriting arbitrary GIT_* or - # user configuration makes even a commit-to-commit diff dependent on the - # invoking shell (for example GIT_DIFF_OPTS changes patch serialization). - # Keep only executable lookup and force Git's replace/config surfaces off. - environment = { - "GIT_ATTR_NOSYSTEM": "1", - "GIT_CONFIG_GLOBAL": os.devnull, - "GIT_CONFIG_NOSYSTEM": "1", - "GIT_NO_REPLACE_OBJECTS": "1", - "GIT_OPTIONAL_LOCKS": "0", - "LANG": "C", - "LC_ALL": "C", - "PATH": os.environ.get("PATH", os.defpath), - } - try: - result = subprocess.run( - [ - "git", - "-c", - "core.quotepath=false", - "-c", - "diff.external=", - "-c", - "diff.noprefix=false", - "-c", - "core.attributesFile=/dev/null", - *args, - ], - cwd=repo_root, - check=True, - env=environment, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - except (OSError, subprocess.CalledProcessError) as exc: - detail = "" - if isinstance(exc, subprocess.CalledProcessError): - detail = exc.stderr.decode("utf-8", errors="replace").strip() - suffix = f": {detail}" if detail else "" - raise SourceTransitionError(f"git {' '.join(args)} failed{suffix}") from exc - return result.stdout - - -def _repository_root(repo_root: Path) -> Path: - root = repo_root.expanduser().resolve(strict=True) - observed = Path(_git(root, "rev-parse", "--show-toplevel").decode("utf-8").strip()).resolve( - strict=True - ) - if observed != root: - raise SourceTransitionError( - "source transition repository root is not the Git worktree root" - ) - grafts = Path(_git(root, "rev-parse", "--git-path", "info/grafts").decode("utf-8").strip()) - if not grafts.is_absolute(): - grafts = root / grafts - if grafts.exists(): - raise SourceTransitionError("source transition refuses a repository with legacy Git grafts") - return root - - -def _relative_path(value: Any, field: str) -> str: - if not isinstance(value, str): - raise SourceTransitionError(f"{field} must be a string path") - raw = value - path = PurePosixPath(raw) - if ( - not raw - or "\0" in raw - or any(ord(character) < 32 or ord(character) == 127 for character in raw) - or path.is_absolute() - or raw != path.as_posix() - or any(part in {"", ".", ".."} for part in path.parts) - ): - raise SourceTransitionError(f"{field} must be a normalized relative POSIX path") - return raw - - -def _commit(repo_root: Path, value: Any, field: str) -> str: - requested = _sha(value, field) - resolved = ( - _git( - repo_root, - "rev-parse", - "--verify", - f"{requested}^{{commit}}", - ) - .decode("ascii") - .strip() - ) - if resolved != requested: - raise SourceTransitionError(f"{field} does not resolve exactly") - return resolved - - -def _raw_changes(repo_root: Path, producer: str, consumer: str) -> list[dict[str, str]]: - raw = _git( - repo_root, - "diff-tree", - "--raw", - "-z", - "--no-abbrev", - "--full-index", - "--no-commit-id", - "-r", - "--no-renames", - producer, - consumer, - "--", - ) - parts = raw.split(b"\0") - if parts and parts[-1] == b"": - parts.pop() - if len(parts) % 2: - raise SourceTransitionError("git returned malformed raw changed-path data") - changes: list[dict[str, str]] = [] - for index in range(0, len(parts), 2): - try: - header = parts[index].decode("ascii") - path = parts[index + 1].decode("utf-8") - except UnicodeDecodeError as exc: - raise SourceTransitionError("Git returned a raw change that is not UTF-8") from exc - fields = header.split(" ") - if len(fields) != 5 or not fields[0].startswith(":"): - raise SourceTransitionError("git returned a malformed raw change header") - old_mode = fields[0][1:] - new_mode, old_object, new_object, status = fields[1:] - if ( - not _GIT_MODE.fullmatch(old_mode) - or not _GIT_MODE.fullmatch(new_mode) - or not _GIT_SHA.fullmatch(old_object) - or not _GIT_SHA.fullmatch(new_object) - or not _GIT_STATUS.fullmatch(status) - ): - raise SourceTransitionError("git returned invalid raw change fields") - changes.append( - { - "status": status, - "old_mode": old_mode, - "new_mode": new_mode, - "old_object": old_object, - "new_object": new_object, - "path": _relative_path(path, "changed path"), - } - ) - changes.sort( - key=lambda item: ( - item["path"], - item["status"], - item["old_mode"], - item["new_mode"], - item["old_object"], - item["new_object"], - ) - ) - return changes - - -def source_transition_git_facts( - *, - repo_root: Path, - producer_source_git_sha: str, - consumer_source_git_sha: str, -) -> dict[str, Any]: - """Return the exact, recomputable Git relationship between two commits.""" - - root = _repository_root(repo_root) - producer = _commit( - root, - producer_source_git_sha, - "producer_source_git_sha", - ) - consumer = _commit( - root, - consumer_source_git_sha, - "consumer_source_git_sha", - ) - if producer == consumer: - raise SourceTransitionError( - "a source transition requires different producer and consumer commits" - ) - merge_base = _git(root, "merge-base", producer, consumer).decode("ascii").strip() - if merge_base != producer: - raise SourceTransitionError("the evidence producer is not an ancestor of the consumer") - producer_tree = ( - _git( - root, - "rev-parse", - "--verify", - f"{producer}^{{tree}}", - ) - .decode("ascii") - .strip() - ) - consumer_tree = ( - _git( - root, - "rev-parse", - "--verify", - f"{consumer}^{{tree}}", - ) - .decode("ascii") - .strip() - ) - distance_text = ( - _git( - root, - "rev-list", - "--count", - f"{producer}..{consumer}", - ) - .decode("ascii") - .strip() - ) - try: - distance = int(distance_text) - except ValueError as exc: - raise SourceTransitionError("git returned an invalid ancestor distance") from exc - if distance < 1: - raise SourceTransitionError("source transition has no consumer commits") - - raw_changes = _raw_changes(root, producer, consumer) - changed_paths = [{"status": item["status"], "path": item["path"]} for item in raw_changes] - return { - "schema_version": "pixcell-training-source-transition-git-v1", - "producer_source_git_sha": producer, - "consumer_source_git_sha": consumer, - "merge_base_git_sha": merge_base, - "producer_tree_git_sha": producer_tree, - "consumer_tree_git_sha": consumer_tree, - "ancestor_distance": distance, - "changed_path_count": len(changed_paths), - "changed_paths": changed_paths, - "raw_changes": raw_changes, - "raw_change_inventory_sha256": canonical_sha256(raw_changes), - } - - -def approved_change_scope( - git_facts: Mapping[str, Any], - approved_change_paths: Sequence[str] | None, -) -> dict[str, Any]: - """Bind explicit human-reviewed paths to the exact committed Git diff.""" - - if approved_change_paths is None: - raise SourceTransitionError( - "cross-source approval requires an explicit approved change scope" - ) - if isinstance(approved_change_paths, (str, bytes)): - raise SourceTransitionError("approved change scope must be a sequence of paths") - approved = [_relative_path(value, "approved change path") for value in approved_change_paths] - if len(set(approved)) != len(approved): - raise SourceTransitionError("approved change scope repeats a path") - approved.sort() - raw_changes = git_facts.get("raw_changes") - if not isinstance(raw_changes, list): - raise SourceTransitionError("Git facts have no raw change inventory") - observed: list[dict[str, str]] = [] - required = { - "status", - "old_mode", - "new_mode", - "old_object", - "new_object", - "path", - } - for index, raw_entry in enumerate(raw_changes): - if not isinstance(raw_entry, Mapping): - raise SourceTransitionError(f"Git raw change {index} is not an object") - if set(raw_entry) != required: - raise SourceTransitionError(f"Git raw change {index} has foreign fields") - status = str(raw_entry.get("status", "")) - old_mode = str(raw_entry.get("old_mode", "")) - new_mode = str(raw_entry.get("new_mode", "")) - old_object = str(raw_entry.get("old_object", "")) - new_object = str(raw_entry.get("new_object", "")) - if ( - not _GIT_STATUS.fullmatch(status) - or not _GIT_MODE.fullmatch(old_mode) - or not _GIT_MODE.fullmatch(new_mode) - or not _GIT_SHA.fullmatch(old_object) - or not _GIT_SHA.fullmatch(new_object) - ): - raise SourceTransitionError(f"Git raw change {index} has invalid fields") - observed.append( - { - "status": status, - "old_mode": old_mode, - "new_mode": new_mode, - "old_object": old_object, - "new_object": new_object, - "path": _relative_path( - raw_entry.get("path"), - f"Git raw change {index}", - ), - } - ) - observed.sort( - key=lambda item: ( - item["path"], - item["status"], - item["old_mode"], - item["new_mode"], - item["old_object"], - item["new_object"], - ) - ) - observed_paths = [item["path"] for item in observed] - if approved != observed_paths: - raise SourceTransitionError( - "approved change paths differ from the exact producer-to-consumer diff" - ) - changed_paths = [{"status": item["status"], "path": item["path"]} for item in observed] - if ( - git_facts.get("changed_path_count") != len(observed) - or git_facts.get("changed_paths") != changed_paths - ): - raise SourceTransitionError("Git path projection differs from its raw change inventory") - inventory_sha256 = str(git_facts.get("raw_change_inventory_sha256", "")) - if not re.fullmatch(r"[0-9a-f]{64}", inventory_sha256) or inventory_sha256 != canonical_sha256( - observed - ): - raise SourceTransitionError("Git facts have an invalid raw change inventory digest") - scope = { - "schema_version": "pixcell-training-approved-change-scope-v1", - "policy": "exact-committed-path-status-mode-and-object-inventory", - "paths": approved, - "entries": observed, - "changed_path_count": len(observed), - "raw_change_inventory_sha256": inventory_sha256, - } - scope["logical_sha256"] = canonical_sha256(scope) - return scope - - -def build_source_transition( - *, - repo_root: Path, - producer_source_git_sha: str, - consumer_source_git_sha: str, - scope: Mapping[str, Any], - approved_change_paths: Sequence[str] | None, - equivalence: Mapping[str, Any], - evidence: Mapping[str, Any], -) -> dict[str, Any]: - """Build the canonical transition document embedded in a v2 approval.""" - - git_facts = source_transition_git_facts( - repo_root=repo_root, - producer_source_git_sha=producer_source_git_sha, - consumer_source_git_sha=consumer_source_git_sha, - ) - authorization = dict(scope) - authorization["approved_changes"] = approved_change_scope( - git_facts, - approved_change_paths, - ) - document: dict[str, Any] = { - "schema_version": SOURCE_TRANSITION_SCHEMA_VERSION, - "git": git_facts, - "scope": authorization, - "equivalence": dict(equivalence), - "evidence": dict(evidence), - } - document["logical_sha256"] = canonical_sha256(document) - return document - - -def validate_source_transition( - observed: Any, - *, - repo_root: Path, - producer_source_git_sha: str, - consumer_source_git_sha: str, - scope: Mapping[str, Any], - approved_change_paths: Sequence[str] | None, - equivalence: Mapping[str, Any], - evidence: Mapping[str, Any], -) -> dict[str, Any]: - """Rebuild and compare every transition field, including its Git diff.""" - - if not isinstance(observed, Mapping): - raise SourceTransitionError("source transition must be an object") - expected = build_source_transition( - repo_root=repo_root, - producer_source_git_sha=producer_source_git_sha, - consumer_source_git_sha=consumer_source_git_sha, - scope=scope, - approved_change_paths=approved_change_paths, - equivalence=equivalence, - evidence=evidence, - ) - if dict(observed) != expected: - raise SourceTransitionError( - "source transition differs from the current repository and evidence" - ) - return expected diff --git a/rl/studies/representation_training_v1/store.py b/rl/studies/representation_training_v1/store.py deleted file mode 100644 index ff818a75..00000000 --- a/rl/studies/representation_training_v1/store.py +++ /dev/null @@ -1,733 +0,0 @@ -"""External, immutable records for paid training stages.""" - -from __future__ import annotations - -import errno -import fcntl -import hashlib -import json -import os -import re -import stat -import threading -import uuid -from collections.abc import Mapping -from dataclasses import dataclass -from pathlib import Path, PurePosixPath -from typing import Any - - -RECORD_SCHEMA_VERSION = "pixcell-training-record-v1" -MAX_RECORD_BYTES = 16 * 1024 * 1024 -_IDENTIFIER = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{0,127}$") -_SHA256 = re.compile(r"^[0-9a-f]{64}$") - - -class TrainingStoreError(RuntimeError): - """Training record storage failed closed.""" - - -class UnsafeTrainingRootError(TrainingStoreError): - """Training output overlaps the source repository.""" - - -class TrainingRunLockedError(TrainingStoreError): - """Another process owns this exact stage and replicate.""" - - -class ImmutableTrainingRecordError(TrainingStoreError): - """An existing record differs from the proposed immutable value.""" - - -@dataclass(frozen=True, order=True) -class StageKey: - study_id: str - stage_id: str - replicate_id: str - - def __post_init__(self) -> None: - for field, value in ( - ("study_id", self.study_id), - ("stage_id", self.stage_id), - ("replicate_id", self.replicate_id), - ): - if not _IDENTIFIER.fullmatch(value): - raise ValueError(f"{field} has an invalid identifier: {value!r}") - - def as_dict(self) -> dict[str, str]: - return { - "study_id": self.study_id, - "stage_id": self.stage_id, - "replicate_id": self.replicate_id, - } - - -@dataclass(frozen=True) -class TrainingRecord: - payload: dict[str, Any] - payload_sha256: str - record_sha256: str - relative_path: PurePosixPath - - -def canonical_bytes(value: Any) -> bytes: - try: - return json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - except (TypeError, ValueError) as exc: - raise TrainingStoreError("training record is not finite JSON") from exc - - -def canonical_sha256(value: Any) -> str: - return hashlib.sha256(canonical_bytes(value)).hexdigest() - - -def _normalized_object(value: Mapping[str, Any]) -> dict[str, Any]: - if not isinstance(value, Mapping): - raise TrainingStoreError("training record payload must be an object") - result = json.loads(canonical_bytes(value)) - if not isinstance(result, dict): - raise TrainingStoreError("training record payload must be an object") - return result - - -def _document( - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], -) -> dict[str, Any]: - normalized = _normalized_object(payload) - base = { - "schema_version": RECORD_SCHEMA_VERSION, - "record_type": record_type, - "key": dict(key), - "payload": normalized, - "payload_sha256": canonical_sha256(normalized), - } - base["record_sha256"] = canonical_sha256(base) - return base - - -def _validate_document( - value: Mapping[str, Any], - *, - record_type: str, - key: Mapping[str, Any], - relative_path: PurePosixPath, -) -> TrainingRecord: - required = { - "schema_version", - "record_type", - "key", - "payload", - "payload_sha256", - "record_sha256", - } - if set(value) != required: - raise TrainingStoreError("training record fields differ from the schema") - if value["schema_version"] != RECORD_SCHEMA_VERSION: - raise TrainingStoreError("training record has a foreign schema") - if value["record_type"] != record_type or value["key"] != dict(key): - raise TrainingStoreError("training record key or type differs from its path") - payload = value["payload"] - if not isinstance(payload, dict): - raise TrainingStoreError("training record payload is not an object") - payload_sha = canonical_sha256(payload) - if payload_sha != value["payload_sha256"] or not _SHA256.fullmatch(payload_sha): - raise TrainingStoreError("training record payload digest differs") - unsigned = dict(value) - record_sha = str(unsigned.pop("record_sha256")) - if canonical_sha256(unsigned) != record_sha: - raise TrainingStoreError("training record envelope digest differs") - return TrainingRecord( - payload=dict(payload), - payload_sha256=payload_sha, - record_sha256=record_sha, - relative_path=relative_path, - ) - - -class _StageLock: - def __init__(self, store: TrainingStore, key: StageKey, path: Path) -> None: - self.store = store - self.key = key - self.path = path - self.fd: int | None = None - - def __enter__(self) -> _StageLock: - self.path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - flags = os.O_RDWR | os.O_CREAT - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(self.path, flags, 0o600) - fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB) - except BlockingIOError as exc: - if "fd" in locals(): - os.close(fd) - raise TrainingRunLockedError(f"{self.key} is already active") from exc - try: - self.store._mark_held(self.key) - except Exception: - fcntl.flock(fd, fcntl.LOCK_UN) - os.close(fd) - raise - self.fd = fd - return self - - def __exit__(self, *_: object) -> None: - if self.fd is None: - return - self.store._mark_released(self.key) - fcntl.flock(self.fd, fcntl.LOCK_UN) - os.close(self.fd) - self.fd = None - - -class TrainingStore: - """Create-only stage records and candidate evidence outside Git.""" - - def __init__(self, *, repo_root: Path, external_root: Path) -> None: - self.repo_root = repo_root.expanduser().resolve(strict=True) - requested = external_root.expanduser().resolve(strict=False) - if ( - requested == self.repo_root - or requested.is_relative_to(self.repo_root) - or self.repo_root.is_relative_to(requested) - ): - raise UnsafeTrainingRootError("training output overlaps the repository") - requested.mkdir(parents=True, exist_ok=True, mode=0o700) - self.external_root = requested.resolve(strict=True) - if ( - self.external_root == self.repo_root - or self.external_root.is_relative_to(self.repo_root) - or self.repo_root.is_relative_to(self.external_root) - ): - raise UnsafeTrainingRootError("training output resolves across the repository") - self._held: set[StageKey] = set() - self._guard = threading.Lock() - - @staticmethod - def stage_relative_path(key: StageKey) -> PurePosixPath: - return PurePosixPath( - "studies", - key.study_id, - "stages", - key.stage_id, - "runs", - key.replicate_id, - ) - - def stage_path(self, key: StageKey) -> Path: - return self._path(self.stage_relative_path(key)) - - def tinker_log_path(self, key: StageKey) -> Path: - return self.stage_path(key) / "tinker" - - def candidate_root(self, key: StageKey) -> Path: - return self.stage_path(key) / "candidates" - - def acquire_stage_lock(self, key: StageKey) -> _StageLock: - digest = canonical_sha256(key.as_dict()) - return _StageLock( - self, - key, - self.external_root / ".locks" / f"{digest}.lock", - ) - - def _mark_held(self, key: StageKey) -> None: - with self._guard: - if key in self._held: - raise TrainingRunLockedError(f"{key} is already locked here") - self._held.add(key) - - def _mark_released(self, key: StageKey) -> None: - with self._guard: - self._held.discard(key) - - def _require_lock(self, key: StageKey) -> None: - with self._guard: - if key not in self._held: - raise TrainingStoreError("a stage lock is required before writing") - - def create_or_verify_manifest( - self, - key: StageKey, - payload: Mapping[str, Any], - ) -> TrainingRecord: - self._require_lock(key) - return self._create_or_verify( - self.stage_relative_path(key) / "run_manifest.json", - record_type="run_manifest", - key=key.as_dict(), - payload=payload, - ) - - def load_manifest(self, key: StageKey) -> TrainingRecord | None: - return self._load( - self.stage_relative_path(key) / "run_manifest.json", - record_type="run_manifest", - key=key.as_dict(), - ) - - def write_wave_receipt( - self, - key: StageKey, - *, - wave: str, - payload: Mapping[str, Any], - ) -> TrainingRecord: - self._require_lock(key) - if not _IDENTIFIER.fullmatch(wave): - raise ValueError(f"invalid wave name: {wave!r}") - if self.load_manifest(key) is None: - raise TrainingStoreError("wave receipt cannot precede its run manifest") - return self._create_or_verify( - self.stage_relative_path(key) / "waves" / wave / "receipt.json", - record_type="wave_receipt", - key={**key.as_dict(), "wave": wave}, - payload=payload, - ) - - def load_wave_receipt( - self, - key: StageKey, - *, - wave: str, - ) -> TrainingRecord | None: - return self._load( - self.stage_relative_path(key) / "waves" / wave / "receipt.json", - record_type="wave_receipt", - key={**key.as_dict(), "wave": wave}, - ) - - def write_wave_approval( - self, - key: StageKey, - *, - wave: str, - payload: Mapping[str, Any], - ) -> TrainingRecord: - """Bind a non-initial paid wave to one immutable review decision.""" - - self._require_lock(key) - if not _IDENTIFIER.fullmatch(wave): - raise ValueError(f"invalid wave name: {wave!r}") - return self._create_or_verify( - self.stage_relative_path(key) / "waves" / wave / "approval.json", - record_type="wave_approval", - key={**key.as_dict(), "wave": wave}, - payload=payload, - ) - - def load_wave_approval( - self, - key: StageKey, - *, - wave: str, - ) -> TrainingRecord | None: - return self._load( - self.stage_relative_path(key) / "waves" / wave / "approval.json", - record_type="wave_approval", - key={**key.as_dict(), "wave": wave}, - ) - - def write_rollout_health_approval( - self, - key: StageKey, - *, - wave: str, - payload: Mapping[str, Any], - ) -> TrainingRecord: - """Persist one deterministic, receipt-only rollout-health decision.""" - - self._require_lock(key) - if not _IDENTIFIER.fullmatch(wave): - raise ValueError(f"invalid wave name: {wave!r}") - return self._create_or_verify( - self.stage_relative_path(key) - / "waves" - / wave - / "rollout_health_approval.json", - record_type="rollout_health_approval", - key={**key.as_dict(), "wave": wave}, - payload=payload, - ) - - def load_rollout_health_approval( - self, - key: StageKey, - *, - wave: str, - ) -> TrainingRecord | None: - return self._load( - self.stage_relative_path(key) - / "waves" - / wave - / "rollout_health_approval.json", - record_type="rollout_health_approval", - key={**key.as_dict(), "wave": wave}, - ) - - def write_base_baseline_approval( - self, - key: StageKey, - *, - wave: str, - payload: Mapping[str, Any], - ) -> TrainingRecord: - """Persist the base-policy level-panel evidence required before RL.""" - - self._require_lock(key) - if not _IDENTIFIER.fullmatch(wave): - raise ValueError(f"invalid wave name: {wave!r}") - return self._create_or_verify( - self.stage_relative_path(key) - / "waves" - / wave - / "base_baseline_approval.json", - record_type="base_baseline_approval", - key={**key.as_dict(), "wave": wave}, - payload=payload, - ) - - def load_base_baseline_approval( - self, - key: StageKey, - *, - wave: str, - ) -> TrainingRecord | None: - return self._load( - self.stage_relative_path(key) - / "waves" - / wave - / "base_baseline_approval.json", - record_type="base_baseline_approval", - key={**key.as_dict(), "wave": wave}, - ) - - def write_candidate( - self, - key: StageKey, - *, - invocation_id: str, - step: int, - task_id: str, - attempt: int, - payload: Mapping[str, Any], - ) -> TrainingRecord: - self._require_lock(key) - if ( - not _IDENTIFIER.fullmatch(invocation_id) - or step < 0 - or attempt < 1 - or not _IDENTIFIER.fullmatch(task_id) - ): - raise ValueError("invalid candidate key") - return self._create_or_verify( - self.stage_relative_path(key) - / "candidates" - / invocation_id - / f"step-{step:06d}" - / task_id - / f"attempt-{attempt:02d}" - / "evaluation.json", - record_type="candidate_evaluation", - key={ - **key.as_dict(), - "invocation_id": invocation_id, - "step": step, - "task_id": task_id, - "attempt": attempt, - }, - payload=payload, - ) - - def load_candidate( - self, - key: StageKey, - *, - invocation_id: str, - step: int, - task_id: str, - attempt: int, - ) -> TrainingRecord | None: - if ( - not _IDENTIFIER.fullmatch(invocation_id) - or step < 0 - or attempt < 1 - or not _IDENTIFIER.fullmatch(task_id) - ): - raise ValueError("invalid candidate key") - return self._load( - self.stage_relative_path(key) - / "candidates" - / invocation_id - / f"step-{step:06d}" - / task_id - / f"attempt-{attempt:02d}" - / "evaluation.json", - record_type="candidate_evaluation", - key={ - **key.as_dict(), - "invocation_id": invocation_id, - "step": step, - "task_id": task_id, - "attempt": attempt, - }, - ) - - def write_candidate_sample( - self, - key: StageKey, - *, - invocation_id: str, - step: int, - task_id: str, - attempt: int, - payload: Mapping[str, Any], - ) -> TrainingRecord: - """Persist paid model output before deterministic evaluation begins.""" - - self._require_lock(key) - if ( - not _IDENTIFIER.fullmatch(invocation_id) - or step < 0 - or attempt < 1 - or not _IDENTIFIER.fullmatch(task_id) - ): - raise ValueError("invalid candidate key") - return self._create_or_verify( - self.stage_relative_path(key) - / "candidates" - / invocation_id - / f"step-{step:06d}" - / task_id - / f"attempt-{attempt:02d}" - / "sample.json", - record_type="candidate_sample", - key={ - **key.as_dict(), - "invocation_id": invocation_id, - "step": step, - "task_id": task_id, - "attempt": attempt, - }, - payload=payload, - ) - - def load_candidate_sample( - self, - key: StageKey, - *, - invocation_id: str, - step: int, - task_id: str, - attempt: int, - ) -> TrainingRecord | None: - return self._load( - self.stage_relative_path(key) - / "candidates" - / invocation_id - / f"step-{step:06d}" - / task_id - / f"attempt-{attempt:02d}" - / "sample.json", - record_type="candidate_sample", - key={ - **key.as_dict(), - "invocation_id": invocation_id, - "step": step, - "task_id": task_id, - "attempt": attempt, - }, - ) - - def begin_invocation( - self, - key: StageKey, - *, - invocation_id: str, - payload: Mapping[str, Any], - ) -> TrainingRecord: - """Record one process entry so stochastic crash retries never collide.""" - - self._require_lock(key) - if not _IDENTIFIER.fullmatch(invocation_id): - raise ValueError(f"invalid invocation ID: {invocation_id!r}") - if self.load_manifest(key) is None: - raise TrainingStoreError("invocation cannot precede its run manifest") - return self._create_or_verify( - self.stage_relative_path(key) / "invocations" / invocation_id / "start.json", - record_type="invocation", - key={**key.as_dict(), "invocation_id": invocation_id}, - payload=payload, - ) - - def load_invocation( - self, - key: StageKey, - *, - invocation_id: str, - ) -> TrainingRecord | None: - if not _IDENTIFIER.fullmatch(invocation_id): - raise ValueError(f"invalid invocation ID: {invocation_id!r}") - return self._load( - self.stage_relative_path(key) / "invocations" / invocation_id / "start.json", - record_type="invocation", - key={**key.as_dict(), "invocation_id": invocation_id}, - ) - - def write_invocation_tracking( - self, - key: StageKey, - *, - invocation_id: str, - payload: Mapping[str, Any], - ) -> TrainingRecord: - """Bind the observed live tracking session before paid training begins.""" - - self._require_lock(key) - if not _IDENTIFIER.fullmatch(invocation_id): - raise ValueError(f"invalid invocation ID: {invocation_id!r}") - if self.load_invocation(key, invocation_id=invocation_id) is None: - raise TrainingStoreError("tracking session cannot precede its invocation") - return self._create_or_verify( - self.stage_relative_path(key) / "invocations" / invocation_id / "tracking.json", - record_type="invocation_tracking", - key={**key.as_dict(), "invocation_id": invocation_id}, - payload=payload, - ) - - def load_invocation_tracking( - self, - key: StageKey, - *, - invocation_id: str, - ) -> TrainingRecord | None: - if not _IDENTIFIER.fullmatch(invocation_id): - raise ValueError(f"invalid invocation ID: {invocation_id!r}") - return self._load( - self.stage_relative_path(key) / "invocations" / invocation_id / "tracking.json", - record_type="invocation_tracking", - key={**key.as_dict(), "invocation_id": invocation_id}, - ) - - def _path(self, relative: PurePosixPath) -> Path: - path = self.external_root.joinpath(*relative.parts) - parent = path.parent.resolve(strict=False) - if parent != self.external_root and not parent.is_relative_to(self.external_root): - raise UnsafeTrainingRootError("training record path escaped its root") - return path - - def _create_or_verify( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - payload: Mapping[str, Any], - ) -> TrainingRecord: - expected = _document(record_type=record_type, key=key, payload=payload) - path = self._path(relative) - path.parent.mkdir(parents=True, exist_ok=True, mode=0o700) - raw = canonical_bytes(expected) + b"\n" - temporary = path.parent / f".{path.name}.{os.getpid()}.{uuid.uuid4().hex}" - flags = os.O_WRONLY | os.O_CREAT | os.O_EXCL - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(temporary, flags, 0o600) - try: - with os.fdopen(fd, "wb", closefd=False) as stream: - stream.write(raw) - stream.flush() - os.fsync(stream.fileno()) - finally: - os.close(fd) - try: - os.link(temporary, path) - directory_fd = os.open(path.parent, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) - except FileExistsError: - observed = self._load(relative, record_type=record_type, key=key) - if observed is None or canonical_bytes(expected) != canonical_bytes( - self._read(path) - ): - raise ImmutableTrainingRecordError( - f"{relative} already contains different data" - ) - return observed - finally: - try: - temporary.unlink() - except FileNotFoundError: - pass - return _validate_document( - expected, - record_type=record_type, - key=key, - relative_path=relative, - ) - - def _load( - self, - relative: PurePosixPath, - *, - record_type: str, - key: Mapping[str, Any], - ) -> TrainingRecord | None: - path = self._path(relative) - if not path.exists(): - return None - return _validate_document( - self._read(path), - record_type=record_type, - key=key, - relative_path=relative, - ) - - @staticmethod - def _read(path: Path) -> dict[str, Any]: - flags = os.O_RDONLY - if hasattr(os, "O_NOFOLLOW"): - flags |= os.O_NOFOLLOW - try: - fd = os.open(path, flags) - except OSError as exc: - if exc.errno == errno.ELOOP: - raise TrainingStoreError(f"{path} must not be a symlink") from exc - raise - try: - metadata = os.fstat(fd) - if not stat.S_ISREG(metadata.st_mode) or metadata.st_size > MAX_RECORD_BYTES: - raise TrainingStoreError(f"{path} is not a bounded regular file") - chunks: list[bytes] = [] - remaining = MAX_RECORD_BYTES + 1 - while remaining: - chunk = os.read(fd, min(1024 * 1024, remaining)) - if not chunk: - break - chunks.append(chunk) - remaining -= len(chunk) - raw = b"".join(chunks) - if len(raw) > MAX_RECORD_BYTES: - raise TrainingStoreError(f"{path} exceeds the record limit") - finally: - os.close(fd) - try: - value = json.loads(raw) - except (UnicodeDecodeError, json.JSONDecodeError) as exc: - raise TrainingStoreError(f"{path} is not valid JSON") from exc - if not isinstance(value, dict): - raise TrainingStoreError(f"{path} must contain an object") - return value diff --git a/rl/studies/representation_training_v1/tests/__init__.py b/rl/studies/representation_training_v1/tests/__init__.py deleted file mode 100644 index 9f821b1c..00000000 --- a/rl/studies/representation_training_v1/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for the sealed representation training study.""" diff --git a/rl/studies/representation_training_v1/tests/test_base_rl_gates.py b/rl/studies/representation_training_v1/tests/test_base_rl_gates.py deleted file mode 100644 index 65df9b51..00000000 --- a/rl/studies/representation_training_v1/tests/test_base_rl_gates.py +++ /dev/null @@ -1,1001 +0,0 @@ -from __future__ import annotations - -import asyncio -import hashlib -import io -import json -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -import pytest -from PIL import Image - -pytest.importorskip("chz", reason="Tinker is an optional RL dependency") -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") -pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", -) - -from tinker_cookbook.renderers import Message, ParseTermination, TextPart - -from rl.common.contracts import ModelObservation, VerifierReference -from rl.common.evaluator import ( - Attribution, - EvaluationResult, - EvaluationStatus, -) -from rl.evaluation.tasks import EvaluationTask -from rl.studies.representation_training_v1 import evaluation, launcher, promotion -from rl.studies.representation_training_v1.protocol import ( - STUDY_ID, - StageSpec, - load_protocol, - stage_spec, -) -from rl.studies.representation_training_v1.store import ( - StageKey, - TrainingRecord, - TrainingStore, - canonical_sha256, -) - - -REPO_ROOT = Path(__file__).resolve().parents[4] -SOURCE_SHA = "a" * 40 - - -def _stage(level: str = "L0") -> StageSpec: - index = int(level[1:]) - return StageSpec( - stage_id=f"qwen-base-rl-{level.lower()}", - kind="rl", - model_key="qwen", - recipe_key="qwen_rl", - hypotheses=("h-qwen-base-curriculum-rl",), - parent=("base:qwen" if index == 0 else f"qwen-base-rl-l{index - 1}:complete"), - levels=(), - current_level=level, - replay_levels=tuple(f"L{value}" for value in range(index)), - waves={ - "smoke": {"max_steps": 1, "approval": "initial"}, - "step-5": { - "max_steps": 5, - "approval": "rollout-health-receipt", - }, - "step-10": { - "max_steps": 10, - "approval": "current-level-held-out-promotion-receipt", - }, - "complete": { - "max_steps": 30, - "approval": "current-level-held-out-promotion-receipt", - }, - }, - ) - - -def _image_bytes() -> bytes: - image = Image.new("L", (8, 8), 255) - for x in range(2, 6): - for y in range(2, 6): - image.putpixel((x, y), 0) - stream = io.BytesIO() - image.save(stream, format="PNG") - return stream.getvalue() - - -def _task(task_id: str = "l0-baseline") -> EvaluationTask: - image = _image_bytes() - digest = hashlib.sha256(image).hexdigest() - return EvaluationTask( - task_id=task_id, - level="L0", - representation_id="representation-l0", - observation=ModelObservation( - image_bytes=image, - footprint_um=(2.0, 2.0), - image_sha256=digest, - ), - reference=VerifierReference( - target_image_bytes=image, - footprint_um=(2.0, 2.0), - target_image_sha256=digest, - ), - ) - - -class _Renderer: - tokenizer = SimpleNamespace(decode=lambda tokens: "decoded:" + repr(tokens)) - - @staticmethod - def build_generation_prompt(messages: list[Any]) -> Any: - assert "Phase-A Device Input" in messages[0]["content"][1]["text"] - return SimpleNamespace(length=100) - - @staticmethod - def get_stop_sequences() -> list[int]: - return [99] - - @staticmethod - def parse_response(_tokens: list[int]) -> tuple[Message, ParseTermination]: - return ( - Message( - role="assistant", - content=[TextPart(type="text", text="print('candidate')")], - ), - ParseTermination.STOP_SEQUENCE, - ) - - -class _Evaluator: - execution_boundary = SimpleNamespace( - runtime_path=Path("/usr/bin/false"), - daemon_endpoint="unix:///private/fake.sock", - image_ref="pixcell-evaluator@sha256:" + "3" * 64, - image_id="sha256:" + "4" * 64, - workspace_root=Path("/private/fake-workspace"), - ) - - @staticmethod - def validate_reference(reference: VerifierReference) -> dict[str, Any]: - return { - "target_image_sha256": reference.target_image_sha256, - "image_size_px": [8, 8], - "bbox": [2, 2, 6, 6], - "scale_px_per_um": [2.0, 2.0], - } - - @staticmethod - def evaluate_batch( - requests: list[tuple[VerifierReference, str]], - ) -> list[EvaluationResult]: - return [ - EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=0.5, - dice=2 / 3, - metrics={ - "render_sha256": "1" * 64, - "reference_sha256": "2" * 64, - }, - ) - for _ in requests - ] - - @staticmethod - def close() -> None: - return None - - -class _SamplingClient: - @staticmethod - async def sample_async(**kwargs: Any) -> Any: - assert kwargs["num_samples"] == 1 - return SimpleNamespace( - sequences=[SimpleNamespace(tokens=[1, 2, 3], stop_reason="stop")] - ) - - -class _BaseService: - def __init__(self) -> None: - self.calls: list[dict[str, str]] = [] - - def create_sampling_client(self, **kwargs: str) -> _SamplingClient: - self.calls.append(dict(kwargs)) - return _SamplingClient() - - -def test_level_panels_are_exact_fixed_validation_slots(tmp_path: Path) -> None: - protocol = load_protocol(REPO_ROOT) - validation_rows = evaluation._depth_rows(REPO_ROOT / "dataset") - assert evaluation.PANELS == ( - "progress", - "depth-validation", - "inkling-promotion", - ) - expected_counts = {"L0": 93, "L1": 127, "L2": 98, "L3": 120, "L4": 108} - for level, panel in evaluation.LEVEL_PROGRESS_PANELS.items(): - tasks = evaluation.build_panel_tasks( - repo_root=REPO_ROOT, - protocol=protocol, - panel=panel, - ) - assert len(tasks) == expected_counts[level] - assert {task.level for task in tasks} == {level} - assert len({task.representation_id for task in tasks}) == len(tasks) - assert {task.task_id for task in tasks} == { - evaluation._depth_task(row).task_id - for row in validation_rows - if str(row["level"]).upper() == level - and row["realization_slot"] == 6 - } - final = evaluation.build_panel_tasks( - repo_root=REPO_ROOT, - protocol=protocol, - panel=evaluation.PANEL_DEPTH_FINAL_SELECTION, - ) - assert len(final) == 546 - assert len({(task.level, task.representation_id) for task in final}) == 546 - assert {task.task_id for task in final} == { - evaluation._depth_task(row).task_id - for row in validation_rows - if row["realization_slot"] == 7 - } - - stage_path = tmp_path / "stage" - stage_path.mkdir() - for panel in ( - *evaluation.LEVEL_PROGRESS_PANELS.values(), - evaluation.PANEL_DEPTH_FINAL_SELECTION, - ): - store = evaluation.CheckpointEvaluationStore( - stage_path=stage_path, - wave="complete", - checkpoint_name="final", - panel=panel, - ) - assert store.root.name == panel - - -def test_base_and_checkpoint_sampler_client_arguments_are_distinct() -> None: - protocol = load_protocol(REPO_ROOT) - stage = _stage() - base = evaluation.BaseModelBinding( - key=StageKey(STUDY_ID, stage.stage_id, "r0"), - stage=stage, - wave="base", - checkpoint_name="base", - checkpoint={"name": "base"}, - checkpoint_inventory_sha256="1" * 64, - sampler_path=protocol["models"]["qwen"]["model"], - binding_sha256="2" * 64, - ) - record = TrainingRecord( - payload={}, - payload_sha256="3" * 64, - record_sha256="4" * 64, - relative_path=Path("record.json"), - ) - checkpoint = evaluation.CheckpointBinding( - key=base.key, - stage=stage, - wave="smoke", - checkpoint_name="000001", - checkpoint={"name": "000001"}, - checkpoint_inventory_sha256="5" * 64, - sampler_path="tinker://unit/sampler/000001", - run_manifest=record, - wave_receipt=record, - ) - assert evaluation._sampling_client_kwargs(base) == { - "base_model": protocol["models"]["qwen"]["model"] - } - assert evaluation._sampling_client_kwargs(checkpoint) == { - "model_path": "tinker://unit/sampler/000001" - } - - -def test_base_l0_panel_run_and_resume_has_no_fictitious_receipt( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = _stage() - service = _BaseService() - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setattr(evaluation, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - evaluation, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr(evaluation, "stage_spec", lambda *_a, **_k: stage) - monkeypatch.setattr(evaluation, "_renderer", lambda *_a, **_k: _Renderer()) - - result = asyncio.run( - evaluation.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id=stage.stage_id, - wave="base", - checkpoint_name="base", - replicate_id="r0", - panel=evaluation.LEVEL_PROGRESS_PANELS["L0"], - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256="", - external_root=tmp_path / "external", - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=lambda: service, - evaluator_factory=_Evaluator, - base_model=True, - ) - ) - assert result["status"] == "complete" - assert service.calls == [ - {"base_model": protocol["models"]["qwen"]["model"]} - ] - report = json.loads(Path(result["report"]).read_text(encoding="utf-8")) - sampler = report["payload"]["provenance"]["sampler"] - assert sampler["binding_kind"] == "base-model" - assert not any("training_" in field for field in sampler) - sample = next((tmp_path / "external").rglob("sample.json")) - sample_payload = json.loads(sample.read_text(encoding="utf-8"))["payload"] - assert "base_model_binding_sha256" in sample_payload - assert "training_wave_receipt_record_sha256" not in sample_payload - assert not list((tmp_path / "external").rglob("receipt.json")) - assert not list((tmp_path / "external").rglob("run_manifest.json")) - - gated_stage = StageSpec( - **{ - **stage.__dict__, - "waves": { - **stage.waves, - "smoke": { - "max_steps": 1, - "approval": "base-level-held-out-baseline-receipt", - }, - }, - } - ) - training_store = TrainingStore( - repo_root=REPO_ROOT, - external_root=tmp_path / "external", - ) - with training_store.acquire_stage_lock( - StageKey(STUDY_ID, stage.stage_id, "r0") - ): - direct_approval = launcher._approval_record( - repo_root=REPO_ROOT, - store=training_store, - key=StageKey(STUDY_ID, stage.stage_id, "r0"), - stage=stage, - wave_name="smoke", - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert direct_approval is None - - with training_store.acquire_stage_lock( - StageKey(STUDY_ID, stage.stage_id, "r0") - ): - base_approval = launcher._approval_record( - repo_root=REPO_ROOT, - store=training_store, - key=StageKey(STUDY_ID, stage.stage_id, "r0"), - stage=gated_stage, - wave_name="smoke", - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert base_approval is not None - assert ( - base_approval.payload["approval_gate"] - == "base-level-held-out-baseline-receipt" - ) - - monkeypatch.delenv("TINKER_API_KEY") - resumed = asyncio.run( - evaluation.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id=stage.stage_id, - wave="base", - checkpoint_name="base", - replicate_id="r0", - panel=evaluation.LEVEL_PROGRESS_PANELS["L0"], - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256="", - external_root=tmp_path / "external", - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=lambda: pytest.fail("paid client on resume"), - evaluator_factory=_Evaluator, - base_model=True, - ) - ) - assert resumed["status"] == "already_complete" - - -def test_base_panel_rechecks_source_after_preflight_before_paid_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = _stage() - source_checks = 0 - - def changing_source(_root: Path, expected: str) -> str: - nonlocal source_checks - source_checks += 1 - if source_checks == 2: - raise ValueError("worktree changed") - return expected - - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setattr(evaluation, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr(evaluation, "validate_source_sha", changing_source) - monkeypatch.setattr(evaluation, "stage_spec", lambda *_a, **_k: stage) - monkeypatch.setattr( - evaluation, - "_dataset_binding", - lambda *_a, **_k: { - "logical_release_sha256": protocol["dataset"][ - "logical_release_sha256" - ], - "freeze_file_sha256": "1" * 64, - "parquet_shards": {"depth.parquet": "2" * 64}, - }, - ) - monkeypatch.setattr(evaluation, "build_panel_tasks", lambda **_k: [_task()]) - monkeypatch.setattr(evaluation, "_renderer", lambda *_a, **_k: _Renderer()) - with pytest.raises( - evaluation.CheckpointEvaluationError, - match="source changed after evaluation preflight", - ): - asyncio.run( - evaluation.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id=stage.stage_id, - wave="base", - checkpoint_name="base", - replicate_id="r0", - panel=evaluation.LEVEL_PROGRESS_PANELS["L0"], - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256="", - external_root=tmp_path / "external", - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=lambda: pytest.fail( - "paid client created after source mutation" - ), - evaluator_factory=_Evaluator, - base_model=True, - ) - ) - assert source_checks == 2 - - -def _write_health_fixture( - tmp_path: Path, - *, - fault: bool = False, - all_constant: bool = False, - all_failed: bool = False, - unknown_status: bool = False, - misattributed_infrastructure_status: bool = False, - reward_mismatch: bool = False, - failure_nonzero_reward: bool = False, - failure_geometry: bool = False, - sample_inventory_mismatch: bool = False, -) -> tuple[ - TrainingStore, - StageKey, - StageSpec, - dict[str, Any], - TrainingRecord, -]: - protocol = load_protocol(REPO_ROOT) - stage = _stage() - key = StageKey(STUDY_ID, stage.stage_id, "r0") - store = TrainingStore(repo_root=REPO_ROOT, external_root=tmp_path / "external") - invocation_id = "smoke-unit" - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - { - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - }, - ) - invocation = store.begin_invocation( - key, - invocation_id=invocation_id, - payload={"wave": "smoke"}, - ) - for group in range(8): - task_id = f"task-{group}" - for attempt in range(1, 5): - sample = store.write_candidate_sample( - key, - invocation_id=invocation_id, - step=0, - task_id=task_id, - attempt=attempt, - payload={"task": task_id, "attempt": attempt}, - ) - status = "runtime_error" - if not all_failed and group == 0 and attempt == 2: - status = "ok" - if unknown_status and group == 0 and attempt == 1: - status = "invented_status" - if ( - misattributed_infrastructure_status - and group == 0 - and attempt == 1 - ): - status = "reference_error" - reward = ( - 0.5 - if status == "ok" and not all_constant - else 0.0 - ) - iou = reward if status == "ok" else None - dice = (2.0 * iou / (1.0 + iou)) if iou is not None else None - if reward_mismatch and status == "ok": - reward = 0.4 - if failure_nonzero_reward and group == 0 and attempt == 1: - reward = 0.25 - if failure_geometry and group == 0 and attempt == 1: - iou = 0.25 - dice = 0.4 - store.write_candidate( - key, - invocation_id=invocation_id, - step=0, - task_id=task_id, - attempt=attempt, - payload={ - "sample_record": { - "relative_path": str(sample.relative_path), - "payload_sha256": sample.payload_sha256, - "record_sha256": sample.record_sha256, - }, - "evaluation": { - "status": status, - "attribution": ( - "reference" - if fault and group == 0 and attempt == 1 - else "model" - ), - "iou": iou, - "dice": dice, - "reward": reward, - "violations": [], - "error": None, - "latency_seconds": 0.1, - "metrics": {}, - }, - }, - ) - candidate_root = store.candidate_root(key) / invocation_id - candidate_files = sorted(candidate_root.rglob("evaluation.json")) - sample_files = sorted(candidate_root.rglob("sample.json")) - candidate_inventory = [ - { - "path": str(path.relative_to(store.stage_path(key))), - "sha256": promotion.file_sha256(path), - } - for path in candidate_files - ] - sample_inventory = [ - { - "path": str(path.relative_to(store.stage_path(key))), - "sha256": promotion.file_sha256(path), - } - for path in sample_files - ] - receipt = store.write_wave_receipt( - key, - wave="smoke", - payload={ - "stage_id": stage.stage_id, - "wave": "smoke", - "max_steps": 1, - "run_manifest_record_sha256": manifest.record_sha256, - "invocation_record_sha256": invocation.record_sha256, - "invocation_id": invocation_id, - "checkpoint": { - "name": "000001", - "batch": 1, - "epoch": None, - "final": True, - "state_path": "tinker://unit/state/000001", - "sampler_path": "tinker://unit/sampler/000001", - }, - "candidate_inventory": { - "count": 32, - "expected_count": 32, - "logical_sha256": canonical_sha256(candidate_inventory), - }, - "sample_inventory": { - "count": 32, - "expected_count": 32, - "logical_sha256": ( - "f" * 64 - if sample_inventory_mismatch - else canonical_sha256(sample_inventory) - ), - }, - }, - ) - return store, key, stage, protocol, receipt - - -def test_rollout_health_gate_is_receipt_only_and_exact(tmp_path: Path) -> None: - store, key, stage, protocol, receipt = _write_health_fixture(tmp_path) - with store.acquire_stage_lock(key): - approval = promotion.record_rollout_health_approval( - store=store, - key=key, - stage=stage, - wave_name="step-5", - prior_wave="smoke", - prior_receipt=receipt, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - metrics = approval.payload["metrics"] - assert metrics["candidates"] == 32 - assert metrics["groups"] == 8 - assert metrics["group_size"] == 4 - assert metrics["pure_executable"] == 1 - assert metrics["nonconstant_reward_groups"] == 1 - assert metrics["evaluator_or_reference_faults"] == 0 - assert store.load_wave_approval(key, wave="step-5") is None - assert ( - store.load_rollout_health_approval(key, wave="step-5").record_sha256 - == approval.record_sha256 - ) - - -def test_launcher_derives_rollout_health_before_step5(tmp_path: Path) -> None: - store, key, stage, protocol, _receipt = _write_health_fixture(tmp_path) - with store.acquire_stage_lock(key): - approval = launcher._approval_record( - repo_root=REPO_ROOT, - store=store, - key=key, - stage=stage, - wave_name="step-5", - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert approval is not None - assert approval.payload["approval_gate"] == "rollout-health-receipt" - health = store.load_rollout_health_approval(key, wave="step-5") - assert health is not None - assert health.payload["approval_gate"] == "rollout-health-receipt" - assert store.load_wave_approval(key, wave="step-5") is None - resolved = launcher._resolved_launch_binding( - repo_root=REPO_ROOT, - protocol=protocol, - stage=stage, - wave_name="step-5", - store=store, - key=key, - parent=None, - resume_batch=1, - ) - assert resolved["wave_gate_evidence"] == { - "approval_gate": "rollout-health-receipt", - "relative_path": str(approval.relative_path), - "payload_sha256": approval.payload_sha256, - "record_sha256": approval.record_sha256, - } - - -def test_l0_sft_rl_resolved_launch_binds_parent_transition( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = StageSpec( - stage_id="qwen-l0-rl-l1", - kind="rl", - model_key="qwen", - recipe_key="qwen_rl", - hypotheses=("RT-H03",), - parent="qwen-l0-sft:complete", - levels=(), - current_level="L1", - replay_levels=("L0",), - waves={ - "smoke": { - "max_steps": 1, - "approval": "parent-receipt-and-source-transition", - }, - }, - ) - key = StageKey(STUDY_ID, stage.stage_id, "r0") - store = TrainingStore( - repo_root=REPO_ROOT, - external_root=tmp_path / "external", - ) - with store.acquire_stage_lock(key): - store.create_or_verify_manifest( - key, - { - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - }, - ) - approval = store.write_wave_approval( - key, - wave="smoke", - payload={ - "schema_version": ( - promotion.PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ), - "approval_gate": "parent-receipt-and-source-transition", - }, - ) - resolved = launcher._resolved_launch_binding( - repo_root=REPO_ROOT, - protocol=protocol, - stage=stage, - wave_name="smoke", - store=store, - key=key, - parent=None, - resume_batch=None, - ) - assert resolved["wave_gate_evidence"] == { - "approval_gate": "parent-receipt-and-source-transition", - "relative_path": str(approval.relative_path), - "payload_sha256": approval.payload_sha256, - "record_sha256": approval.record_sha256, - } - - -@pytest.mark.parametrize("panel", ["level-progress-l0", "progress"]) -def test_launcher_current_level_gate_binds_exact_level_panel( - tmp_path: Path, - panel: str, - monkeypatch: pytest.MonkeyPatch, -) -> None: - store, key, original, protocol, receipt = _write_health_fixture(tmp_path) - stage = StageSpec( - **{ - **original.__dict__, - "waves": { - "smoke": {"max_steps": 1, "approval": "initial"}, - "step-10": { - "max_steps": 10, - "approval": "current-level-held-out-promotion-receipt", - }, - }, - } - ) - checkpoint = receipt.payload["checkpoint"] - # The fixture below is intentionally minimal because this test exercises - # the launcher's downstream receipt/panel binding. Canonical report - # reconstruction is tested separately and must remain fail-closed. - monkeypatch.setattr( - launcher, - "validate_same_source_wave_approval", - lambda **_kwargs: None, - ) - payload = { - "schema_version": promotion.APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "current-level-held-out-promotion-receipt", - "stage_id": stage.stage_id, - "wave": "step-10", - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "prior_wave": "smoke", - "prior_receipt_record_sha256": receipt.record_sha256, - "evaluation": { - "stage_id": stage.stage_id, - "wave": "smoke", - "panel": panel, - "checkpoint": checkpoint, - "report_record_sha256": "9" * 64, - "training_wave_receipt_record_sha256": receipt.record_sha256, - }, - } - with store.acquire_stage_lock(key): - store.write_wave_approval(key, wave="step-10", payload=payload) - if panel == "level-progress-l0": - observed = launcher._approval_record( - repo_root=REPO_ROOT, - store=store, - key=key, - stage=stage, - wave_name="step-10", - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert observed is not None - else: - with pytest.raises( - launcher.TrainingLaunchError, - match="held-out report", - ): - launcher._approval_record( - repo_root=REPO_ROOT, - store=store, - key=key, - stage=stage, - wave_name="step-10", - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - - -def test_launcher_next_level_gate_uses_current_dual_panel_contract( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - store = TrainingStore(repo_root=REPO_ROOT, external_root=tmp_path / "external") - parent_stage = stage_spec(protocol, "qwen-base-rl-l0") - child_stage = stage_spec(protocol, "qwen-base-rl-l1") - assert child_stage.wave("smoke")["approval"] == ( - "prior-and-current-level-held-out-transition-receipt" - ) - parent_key = StageKey(STUDY_ID, parent_stage.stage_id, "r0") - child_key = StageKey(STUDY_ID, child_stage.stage_id, "r0") - checkpoint = { - "name": "000030", - "batch": 30, - "epoch": None, - "final": True, - "state_path": "tinker://unit/state/000030", - "sampler_path": "tinker://unit/sampler/000030", - } - inventory_entry = { - **checkpoint, - "role": "terminal", - "training_progress_fraction": 1.0, - } - with store.acquire_stage_lock(parent_key): - manifest = store.create_or_verify_manifest( - parent_key, - { - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - }, - ) - receipt = store.write_wave_receipt( - parent_key, - wave="complete", - payload={ - "stage_id": parent_stage.stage_id, - "wave": "complete", - "run_manifest_record_sha256": manifest.record_sha256, - "checkpoint": checkpoint, - "checkpoint_inventory": { - "count": 1, - "entries": [inventory_entry], - "logical_sha256": canonical_sha256([inventory_entry]), - }, - }, - ) - approval_payload = { - "schema_version": promotion.APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "prior-and-current-level-held-out-transition-receipt", - "stage_id": child_stage.stage_id, - "wave": "smoke", - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "parent_stage_id": parent_stage.stage_id, - "parent_wave": "complete", - "parent_receipt_record_sha256": receipt.record_sha256, - "selected_parent_checkpoint": checkpoint, - "selected_parent_checkpoint_inventory_sha256": canonical_sha256( - [inventory_entry] - ), - "evaluation": { - "stage_id": parent_stage.stage_id, - "wave": "complete", - "panel": "level-progress-l0", - "checkpoint": checkpoint, - "report_record_sha256": "8" * 64, - "training_wave_receipt_record_sha256": receipt.record_sha256, - }, - "entry_baseline": { - "stage_id": parent_stage.stage_id, - "wave": "complete", - "panel": "level-progress-l1", - "checkpoint": checkpoint, - "report_record_sha256": "9" * 64, - "training_wave_receipt_record_sha256": receipt.record_sha256, - }, - } - validation_calls: list[dict[str, Any]] = [] - monkeypatch.setattr( - launcher, - "validate_same_source_wave_approval", - lambda **kwargs: validation_calls.append(dict(kwargs)), - ) - with store.acquire_stage_lock(child_key): - store.write_wave_approval( - child_key, - wave="smoke", - payload=approval_payload, - ) - approval = launcher._approval_record( - repo_root=REPO_ROOT, - store=store, - key=child_key, - stage=child_stage, - wave_name="smoke", - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert approval is not None - assert approval.payload["evaluation"]["panel"] == "level-progress-l0" - assert approval.payload["entry_baseline"]["panel"] == "level-progress-l1" - assert len(validation_calls) == 1 - assert validation_calls[0]["approval"] is approval - - -@pytest.mark.parametrize( - ("kwargs", "message"), - [ - ({"fault": True}, "evaluator or reference fault"), - ({"all_constant": True}, "no nonconstant G4 group"), - ({"all_failed": True}, "no pure executable candidate"), - ({"unknown_status": True}, "unknown evaluator status"), - ( - {"misattributed_infrastructure_status": True}, - "evaluator or reference status mislabeled", - ), - ({"reward_mismatch": True}, "reward differs from raw IoU"), - ({"failure_nonzero_reward": True}, "model failure has nonzero reward"), - ({"failure_geometry": True}, "model failure contains geometry metrics"), - ({"sample_inventory_mismatch": True}, "sample files differ"), - ], -) -def test_rollout_health_gate_fails_closed( - tmp_path: Path, - kwargs: dict[str, bool], - message: str, -) -> None: - store, key, stage, protocol, receipt = _write_health_fixture( - tmp_path, - **kwargs, - ) - with store.acquire_stage_lock(key), pytest.raises( - promotion.PromotionError, - match=message, - ): - promotion.record_rollout_health_approval( - store=store, - key=key, - stage=stage, - wave_name="step-5", - prior_wave="smoke", - prior_receipt=receipt, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - - -def test_model_status_allowlists_cannot_drift() -> None: - assert promotion._MODEL_EVALUATION_STATUSES == ( - evaluation._MODEL_EVALUATION_STATUSES - ) - - -def test_level_gate_plans_never_use_f1_f8() -> None: - l0 = _stage("L0") - current = promotion.approval_plan(l0, "step-10") - assert current.expected_panel == "level-progress-l0" - assert current.allowed_evaluation_checkpoints == ((l0.stage_id, "step-5"),) - - l1 = _stage("L1") - # The first wave has the prior-level approval in the sealed protocol; - # represent it directly here without weakening StageSpec validation. - l1 = StageSpec( - **{ - **l1.__dict__, - "waves": { - **l1.waves, - "smoke": { - "max_steps": 1, - "approval": ( - "prior-and-current-level-held-out-transition-receipt" - ), - }, - }, - } - ) - prior = promotion.approval_plan(l1, "smoke") - assert prior.expected_panel == "level-progress-l0" - assert prior.allowed_evaluation_checkpoints == ( - ("qwen-base-rl-l0", "complete"), - ) - assert prior.entry_baseline_panel == "level-progress-l1" - assert current.expected_panel != promotion.PANEL_PROGRESS - assert prior.expected_panel != promotion.PANEL_PROGRESS diff --git a/rl/studies/representation_training_v1/tests/test_base_rl_protocol.py b/rl/studies/representation_training_v1/tests/test_base_rl_protocol.py deleted file mode 100644 index f06cc2a1..00000000 --- a/rl/studies/representation_training_v1/tests/test_base_rl_protocol.py +++ /dev/null @@ -1,152 +0,0 @@ -from __future__ import annotations - -import hashlib -import json -from collections.abc import Callable -from pathlib import Path -from typing import Any - -import pytest - -from rl.studies.representation_training_v1.protocol import ( - BASE_QWEN_RL_STAGE_IDS, - L0_SFT_QWEN_RL_STAGE_IDS, - STUDY_RELATIVE_PATH, - load_protocol, - stage_spec, -) - - -REPO_ROOT = Path(__file__).resolve().parents[4] - - -def _write_mutated_protocol( - tmp_path: Path, - mutate: Callable[[dict[str, Any]], None], -) -> None: - source = ( - REPO_ROOT - / STUDY_RELATIVE_PATH - / "protocol.json" - ) - document = json.loads(source.read_text(encoding="utf-8")) - mutate(document) - logical = { - key: value for key, value in document.items() if key != "logical_sha256" - } - document["logical_sha256"] = hashlib.sha256( - json.dumps( - logical, - allow_nan=False, - ensure_ascii=True, - separators=(",", ":"), - sort_keys=True, - ).encode("utf-8") - ).hexdigest() - target = tmp_path / STUDY_RELATIVE_PATH / "protocol.json" - target.parent.mkdir(parents=True) - target.write_text( - json.dumps(document, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - - -def test_base_qwen_rl_contract_is_exact() -> None: - protocol = load_protocol(REPO_ROOT) - for level, stage_id in enumerate(BASE_QWEN_RL_STAGE_IDS): - stage = stage_spec(protocol, stage_id) - assert stage.current_level == f"L{level}" - assert stage.parent == ( - "base:qwen" - if level == 0 - else f"qwen-base-rl-l{level - 1}:complete" - ) - assert stage.replay_levels == tuple(f"L{prior}" for prior in range(level)) - assert stage.hypotheses == ("RT-H06",) - assert stage.wave("smoke")["approval"] == ( - "initial" - if level == 0 - else "prior-and-current-level-held-out-transition-receipt" - ) - assert stage.wave("step-5")["approval"] == "rollout-health-receipt" - for wave in ("step-10", "step-15", "step-20", "step-25", "complete"): - assert ( - stage.wave(wave)["approval"] - == "current-level-held-out-promotion-receipt" - ) - - -def test_l0_sft_to_qwen_rl_contract_uses_modern_level_gates() -> None: - protocol = load_protocol(REPO_ROOT) - for level, stage_id in enumerate(L0_SFT_QWEN_RL_STAGE_IDS, start=1): - stage = stage_spec(protocol, stage_id) - assert stage.current_level == f"L{level}" - assert stage.parent == ( - "qwen-l0-sft:complete" - if level == 1 - else f"qwen-l0-rl-l{level - 1}:complete" - ) - assert stage.replay_levels == tuple(f"L{prior}" for prior in range(level)) - assert stage.hypotheses == ("RT-H03",) - assert stage.wave("smoke")["approval"] == ( - "parent-receipt-and-source-transition" - if level == 1 - else "prior-level-held-out-promotion-receipt" - ) - assert stage.wave("step-5")["approval"] == "rollout-health-receipt" - for wave in ("step-10", "step-15", "step-20", "step-25", "complete"): - assert ( - stage.wave(wave)["approval"] - == "current-level-held-out-promotion-receipt" - ) - - -@pytest.mark.parametrize( - ("mutate", "message"), - [ - ( - lambda value: value["stages"]["qwen-base-rl-l0"]["waves"]["smoke"].update( - {"approval": "base-level-held-out-baseline-receipt"} - ), - "wrong pure-RL gates", - ), - ( - lambda value: value["stages"]["qwen-base-rl-l0"]["waves"]["step-5"].update( - {"approval": "clean-smoke-receipt"} - ), - "wrong pure-RL gates", - ), - ( - lambda value: value["stages"]["qwen-l0-rl-l1"]["waves"]["smoke"].update( - {"approval": "entry-evaluation-and-promotion-receipt"} - ), - "wrong L0-SFT-to-RL gates", - ), - ( - lambda value: value["stages"]["qwen-base-rl-l2"].update( - {"parent": "qwen-base-rl-l0:complete"} - ), - "changed the pure Qwen RL curriculum", - ), - ( - lambda value: value["evaluation"]["curriculum_progress_holdout"].update( - {"realization_slot": 7} - ), - "wrong curriculum progress holdout", - ), - ( - lambda value: value["evaluation"]["final_selection_holdout"].update( - {"realization_slot": 6} - ), - "wrong final selection holdout", - ), - ], -) -def test_base_qwen_rl_contract_mutations_fail_closed( - tmp_path: Path, - mutate: Callable[[dict[str, Any]], None], - message: str, -) -> None: - _write_mutated_protocol(tmp_path, mutate) - with pytest.raises(ValueError, match=message): - load_protocol(tmp_path) diff --git a/rl/studies/representation_training_v1/tests/test_evaluation.py b/rl/studies/representation_training_v1/tests/test_evaluation.py deleted file mode 100644 index b3a89218..00000000 --- a/rl/studies/representation_training_v1/tests/test_evaluation.py +++ /dev/null @@ -1,738 +0,0 @@ -from __future__ import annotations - -import asyncio -import hashlib -import io -from collections import Counter -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -import pytest -from PIL import Image - -pytest.importorskip("chz", reason="Tinker is an optional RL dependency") -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") -pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", -) - -from tinker_cookbook.renderers import ( - Message, - ParseTermination, - TextPart, -) - -from rl.common.contracts import ModelObservation, VerifierReference -from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus -from rl.evaluation.tasks import EvaluationTask -from rl.studies.representation_training_v1 import evaluation as subject -from rl.studies.representation_training_v1.protocol import ( - STUDY_ID, - load_protocol, -) -from rl.studies.representation_training_v1.store import ( - StageKey, - TrainingStore, -) - - -REPO_ROOT = Path(__file__).resolve().parents[4] -SOURCE_SHA = "a" * 40 - - -def _image_bytes() -> bytes: - image = Image.new("L", (8, 8), 255) - for x in range(2, 6): - for y in range(2, 6): - image.putpixel((x, y), 0) - buffer = io.BytesIO() - image.save(buffer, format="PNG") - return buffer.getvalue() - - -def _task(task_id: str = "F1", *, level: str = "BENCHMARK") -> EvaluationTask: - image = _image_bytes() - digest = hashlib.sha256(image).hexdigest() - return EvaluationTask( - task_id=task_id, - level=level, - representation_id=f"representation-{task_id.lower()}", - observation=ModelObservation( - image_bytes=image, - footprint_um=(2.0, 2.0), - image_sha256=digest, - ), - reference=VerifierReference( - target_image_bytes=image, - footprint_um=(2.0, 2.0), - target_image_sha256=digest, - ), - ) - - -class _FakeRenderer: - def __init__(self, events: list[str]) -> None: - self.events = events - self.tokenizer = SimpleNamespace(decode=lambda tokens: "raw:" + repr(tokens)) - - def build_generation_prompt(self, messages: list[Any]) -> Any: - self.events.append("prompt") - assert len(messages) == 1 - text = messages[0]["content"][1]["text"] - assert "Phase-A Device Input" in text - assert "target_image" not in text - return SimpleNamespace(length=100) - - @staticmethod - def get_stop_sequences() -> list[int]: - return [99] - - @staticmethod - def parse_response(_tokens: list[int]) -> tuple[Message, ParseTermination]: - return ( - Message( - role="assistant", - content=[TextPart(type="text", text="print('candidate')")], - ), - ParseTermination.STOP_SEQUENCE, - ) - - -class _FakeEvaluator: - def __init__( - self, - events: list[str], - *, - result: EvaluationResult | None = None, - broken_reference: bool = False, - ) -> None: - self.events = events - self.result = result or EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=0.75, - dice=0.8, - metrics={ - "render_sha256": "1" * 64, - "reference_sha256": "2" * 64, - }, - ) - self.broken_reference = broken_reference - self.execution_boundary = SimpleNamespace( - runtime_path=Path("/usr/bin/false"), - daemon_endpoint="unix:///private/fake.sock", - image_ref="pixcell-evaluator@sha256:" + "3" * 64, - image_id="sha256:" + "4" * 64, - workspace_root=Path("/private/fake-workspace"), - ) - - def validate_reference(self, reference: VerifierReference) -> dict[str, Any]: - self.events.append("reference") - if self.broken_reference: - raise ValueError("broken reference") - return { - "target_image_sha256": reference.target_image_sha256, - "image_size_px": [8, 8], - "bbox": [2, 2, 6, 6], - "scale_px_per_um": [2.0, 2.0], - } - - def evaluate_batch( - self, - requests: list[tuple[VerifierReference, str]], - ) -> list[EvaluationResult]: - self.events.append("evaluate") - return [self.result for _ in requests] - - def close(self) -> None: - self.events.append("close") - - -class _FakeSamplingClient: - async def sample_async(self, **kwargs: Any) -> Any: - assert kwargs["num_samples"] == 1 - parameters = kwargs["sampling_params"] - assert parameters.max_tokens == subject.MAX_OUTPUT_TOKENS - assert parameters.temperature == subject.TEMPERATURE - assert parameters.top_p == subject.TOP_P - return SimpleNamespace( - sequences=[ - SimpleNamespace( - tokens=[1, 2, 3], - stop_reason="stop", - ) - ] - ) - - -class _FakeService: - def __init__(self, events: list[str]) -> None: - self.events = events - - def create_sampling_client(self, *, model_path: str) -> _FakeSamplingClient: - self.events.append("sampling-client") - assert model_path.startswith("tinker://") - return _FakeSamplingClient() - - -def _training_receipt( - tmp_path: Path, - *, - protocol: dict[str, Any], - stage_id: str = "qwen-l0-sft", - wave: str = "complete", -) -> tuple[Path, str]: - external = tmp_path / "external" - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - key = StageKey(STUDY_ID, stage_id, "r0") - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - { - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "contract_version": protocol["contract_version"], - "dataset": { - "logical_release_sha256": protocol["dataset"][ - "logical_release_sha256" - ] - }, - "stage": { - "stage_id": stage_id, - "model_key": protocol["stages"][stage_id]["model"], - }, - }, - ) - stage = protocol["stages"][stage_id] - max_steps = int(stage["waves"][wave]["max_steps"]) - entries = ( - [ - { - "name": f"{max_steps:06d}", - "batch": max_steps, - "epoch": None, - "final": True, - "state_path": f"tinker://fake/state-{max_steps:06d}", - "sampler_path": f"tinker://fake/sampler-{max_steps:06d}", - "role": "terminal", - "training_progress_fraction": 1.0, - } - ] - if stage["kind"] == "rl" - else [ - { - "name": "000003", - "batch": 3, - "epoch": None, - "final": False, - "state_path": "tinker://fake/state-000003", - "sampler_path": "tinker://fake/sampler-000003", - "role": "periodic", - "training_progress_fraction": 3 / 11, - }, - { - "name": "final", - "batch": 11, - "epoch": 1, - "final": True, - "state_path": "tinker://fake/state", - "sampler_path": "tinker://fake/sampler", - "role": "terminal", - "training_progress_fraction": 1.0, - }, - ] - ) - terminal = { - key: entries[-1][key] - for key in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - } - receipt = store.write_wave_receipt( - key, - wave=wave, - payload={ - "stage_id": stage_id, - "wave": wave, - "run_manifest_record_sha256": manifest.record_sha256, - "checkpoint": terminal, - "checkpoint_inventory": { - "count": len(entries), - "entries": entries, - "logical_sha256": subject._canonical_sha256(entries), - }, - }, - ) - return external, receipt.record_sha256 - - -def _dataset_binding(protocol: dict[str, Any]) -> dict[str, Any]: - return { - "logical_release_sha256": protocol["dataset"][ - "logical_release_sha256" - ], - "freeze_file_sha256": "5" * 64, - "train_rows": protocol["dataset"]["train_rows"], - "validation_rows": protocol["dataset"]["validation_rows"], - "parquet_shards": {"depth-v1/data/validation-00000.parquet": "6" * 64}, - "reference_artifacts_sha256": "7" * 64, - "reference_artifact_count": 1, - } - - -def test_exact_panel_inventory_and_label_free_projection() -> None: - protocol = load_protocol(REPO_ROOT) - progress = subject.build_panel_tasks( - repo_root=REPO_ROOT, - protocol=protocol, - panel=subject.PANEL_PROGRESS, - ) - depth = subject.build_panel_tasks( - repo_root=REPO_ROOT, - protocol=protocol, - panel=subject.PANEL_DEPTH_VALIDATION, - ) - promotion = subject.build_panel_tasks( - repo_root=REPO_ROOT, - protocol=protocol, - panel=subject.PANEL_INKLING_PROMOTION, - ) - - assert [task.task_id for task in progress] == [ - "F1", - "F2", - "F3", - "F4", - "F5", - "F6", - "F7", - "F8", - ] - assert len(depth) == 1092 - assert len({task.representation_id for task in depth}) == 546 - assert len(promotion) == 68 - assert Counter(task.level for task in promotion) == { - "BENCHMARK": 8, - "L0": 6, - "L1": 6, - "L2": 6, - "L3": 6, - "L4": 36, - } - assert not any(hasattr(task, "label") or hasattr(task, "code") for task in depth) - repeated = subject.build_panel_tasks( - repo_root=REPO_ROOT, - protocol=protocol, - panel=subject.PANEL_INKLING_PROMOTION, - ) - assert [task.task_id for task in repeated] == [ - task.task_id for task in promotion - ] - - -def test_store_is_create_only_and_resumable(tmp_path: Path) -> None: - stage = tmp_path / "stage" - stage.mkdir() - store = subject.CheckpointEvaluationStore( - stage_path=stage, - wave="complete", - checkpoint_name="final", - panel=subject.PANEL_PROGRESS, - ) - payload = {"source_git_sha": SOURCE_SHA} - first = store.create_or_verify_manifest(payload) - second = store.create_or_verify_manifest(payload) - - assert first == second - periodic = subject.CheckpointEvaluationStore( - stage_path=stage, - wave="complete", - checkpoint_name="000003", - panel=subject.PANEL_PROGRESS, - ) - assert periodic.root != store.root - assert periodic.create_or_verify_manifest(payload) == first - with pytest.raises(subject.ImmutableEvaluationRecordError): - store.create_or_verify_manifest({"source_git_sha": "b" * 40}) - - -def test_checkpoint_can_only_load_from_the_exact_wave_receipt( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - - binding = subject._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="000003", - replicate_id="r0", - expected_receipt_sha256=receipt_sha, - source_git_sha=SOURCE_SHA, - ) - assert binding.checkpoint_name == "000003" - assert binding.sampler_path == "tinker://fake/sampler-000003" - assert binding.checkpoint["training_progress_fraction"] == 3 / 11 - assert binding.wave_receipt.record_sha256 == receipt_sha - final_binding = subject._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="final", - replicate_id="r0", - expected_receipt_sha256=receipt_sha, - source_git_sha=SOURCE_SHA, - ) - assert subject._sample_seed( - protocol_sha256=protocol["logical_sha256"], - binding=binding, - panel=subject.PANEL_PROGRESS, - task_id="F1", - ) != subject._sample_seed( - protocol_sha256=protocol["logical_sha256"], - binding=final_binding, - panel=subject.PANEL_PROGRESS, - task_id="F1", - ) - - with pytest.raises(subject.CheckpointReceiptError): - subject._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="000003", - replicate_id="r0", - expected_receipt_sha256="f" * 64, - source_git_sha=SOURCE_SHA, - ) - with pytest.raises(subject.CheckpointReceiptError): - subject._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="user-supplied-uri-alias", - replicate_id="r0", - expected_receipt_sha256=receipt_sha, - source_git_sha=SOURCE_SHA, - ) - - -def test_rl_wave_uses_its_unique_numeric_ceiling_checkpoint( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt( - tmp_path, - protocol=protocol, - stage_id="inkling-l4-rl", - wave="smoke", - ) - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - binding = subject._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id="inkling-l4-rl", - wave="smoke", - checkpoint_name="000001", - replicate_id="r0", - expected_receipt_sha256=receipt_sha, - source_git_sha=SOURCE_SHA, - ) - assert binding.checkpoint_name == "000001" - assert binding.checkpoint["role"] == "terminal" - assert binding.checkpoint["training_progress_fraction"] == 1.0 - - with pytest.raises(subject.CheckpointReceiptError): - subject._load_checkpoint_binding( - store=store, - protocol=protocol, - stage_id="inkling-l4-rl", - wave="smoke", - checkpoint_name="final", - replicate_id="r0", - expected_receipt_sha256=receipt_sha, - source_git_sha=SOURCE_SHA, - ) - - -def test_model_fault_is_zero_and_infrastructure_fault_aborts() -> None: - task = _task() - model_failure = EvaluationResult( - status=EvaluationStatus.SYNTAX_ERROR, - attribution=Attribution.MODEL, - error="invalid syntax", - ) - payload = subject._evaluation_payload( - task=task, - result=model_failure, - sample_record_sha256="1" * 64, - manifest_record_sha256="2" * 64, - ) - assert payload["raw_absolute_scale_iou"] == 0.0 - assert payload["pure_executable"] is False - - for attribution, error_type in ( - (Attribution.REFERENCE, subject.ReferencePanelFault), - (Attribution.EVALUATOR, subject.EvaluationInfrastructureFault), - ): - with pytest.raises(error_type): - subject._evaluation_payload( - task=task, - result=EvaluationResult( - status=EvaluationStatus.WORKER_ERROR, - attribution=attribution, - error="fault", - ), - sample_record_sha256="1" * 64, - manifest_record_sha256="2" * 64, - ) - - -def test_stored_evaluation_rejects_misattributed_infrastructure_status() -> None: - task = _task() - payload = subject._evaluation_payload( - task=task, - result=EvaluationResult( - status=EvaluationStatus.SYNTAX_ERROR, - attribution=Attribution.MODEL, - error="invalid syntax", - ), - sample_record_sha256="1" * 64, - manifest_record_sha256="2" * 64, - ) - subject._validate_evaluation_record( - record={"payload": payload}, - task=task, - sample_record_sha256="1" * 64, - manifest_record_sha256="2" * 64, - ) - - payload["status"] = EvaluationStatus.REFERENCE_ERROR.value - with pytest.raises( - subject.ImmutableEvaluationRecordError, - match="non-model evaluation status", - ): - subject._validate_evaluation_record( - record={"payload": payload}, - task=task, - sample_record_sha256="1" * 64, - manifest_record_sha256="2" * 64, - ) - - -def test_all_prompt_and_reference_checks_precede_service_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - evaluator = _FakeEvaluator(events) - task = _task() - - monkeypatch.setattr(subject, "load_protocol", lambda *_args, **_kwargs: protocol) - monkeypatch.setattr(subject, "validate_source_sha", lambda *_args: SOURCE_SHA) - monkeypatch.setattr( - subject, - "_dataset_binding", - lambda *_args: _dataset_binding(protocol), - ) - monkeypatch.setattr(subject, "build_panel_tasks", lambda **_kwargs: [task]) - monkeypatch.setattr(subject, "_renderer", lambda *_args, **_kwargs: renderer) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - - def service_factory() -> _FakeService: - events.append("service") - assert "prompt" in events - assert "reference" in events - return _FakeService(events) - - result = asyncio.run( - subject.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="final", - replicate_id="r0", - panel=subject.PANEL_PROGRESS, - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256=receipt_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=service_factory, - evaluator_factory=lambda: evaluator, - ) - ) - - assert result["status"] == "complete" - assert events.index("prompt") < events.index("service") - assert events.index("reference") < events.index("service") - assert result["summary"]["mean_raw_absolute_scale_iou"] == 0.75 - assert "/complete/final/progress/report.json" in result["report"] - report = subject.CheckpointEvaluationStore( - stage_path=( - TrainingStore(repo_root=REPO_ROOT, external_root=external).stage_path( - StageKey(STUDY_ID, "qwen-l0-sft", "r0") - ) - ), - wave="complete", - checkpoint_name="final", - panel=subject.PANEL_PROGRESS, - ).load_report() - assert report is not None - assert report["payload"]["checkpoint_name"] == "final" - assert ( - report["payload"]["provenance"]["sampler"]["checkpoint"] - ["training_progress_fraction"] - == 1.0 - ) - assert set(report["payload"]["provenance"]["dataset"]) == { - "repo_id", - "revision", - "configuration", - "split", - "logical_release_sha256", - "freeze_file_sha256", - "parquet_shards", - } - - -def test_reference_failure_cannot_create_a_service_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - evaluator = _FakeEvaluator(events, broken_reference=True) - - monkeypatch.setattr(subject, "load_protocol", lambda *_args, **_kwargs: protocol) - monkeypatch.setattr(subject, "validate_source_sha", lambda *_args: SOURCE_SHA) - monkeypatch.setattr( - subject, - "_dataset_binding", - lambda *_args: _dataset_binding(protocol), - ) - monkeypatch.setattr(subject, "build_panel_tasks", lambda **_kwargs: [_task()]) - monkeypatch.setattr(subject, "_renderer", lambda *_args, **_kwargs: renderer) - - def forbidden_service() -> Any: - raise AssertionError("ServiceClient was created before reference preflight") - - with pytest.raises(subject.ReferencePanelFault): - asyncio.run( - subject.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="final", - replicate_id="r0", - panel=subject.PANEL_PROGRESS, - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256=receipt_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=forbidden_service, - evaluator_factory=lambda: evaluator, - ) - ) - - -def test_sample_survives_evaluator_fault_and_resume_does_not_resample( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - task = _task() - events: list[str] = [] - renderer = _FakeRenderer(events) - bad_evaluator = _FakeEvaluator( - events, - result=EvaluationResult( - status=EvaluationStatus.WORKER_ERROR, - attribution=Attribution.EVALUATOR, - error="worker failed", - retryable=True, - ), - ) - - monkeypatch.setattr(subject, "load_protocol", lambda *_args, **_kwargs: protocol) - monkeypatch.setattr(subject, "validate_source_sha", lambda *_args: SOURCE_SHA) - monkeypatch.setattr( - subject, - "_dataset_binding", - lambda *_args: _dataset_binding(protocol), - ) - monkeypatch.setattr(subject, "build_panel_tasks", lambda **_kwargs: [task]) - monkeypatch.setattr(subject, "_renderer", lambda *_args, **_kwargs: renderer) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - - with pytest.raises(subject.EvaluationInfrastructureFault): - asyncio.run( - subject.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="final", - replicate_id="r0", - panel=subject.PANEL_PROGRESS, - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256=receipt_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=lambda: _FakeService(events), - evaluator_factory=lambda: bad_evaluator, - ) - ) - - training_store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - record_store = subject.CheckpointEvaluationStore( - stage_path=training_store.stage_path(key), - wave="complete", - checkpoint_name="final", - panel=subject.PANEL_PROGRESS, - ) - assert record_store.load_sample(task.task_id) is not None - assert record_store.load_evaluation(task.task_id) is None - - good_evaluator = _FakeEvaluator(events) - - def forbidden_service() -> Any: - raise AssertionError("resume attempted to resample an immutable completion") - - monkeypatch.delenv("TINKER_API_KEY") - result = asyncio.run( - subject.run_checkpoint_evaluation( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="final", - replicate_id="r0", - panel=subject.PANEL_PROGRESS, - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256=receipt_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=forbidden_service, - evaluator_factory=lambda: good_evaluator, - ) - ) - assert result["status"] == "complete" - assert record_store.load_evaluation(task.task_id) is not None diff --git a/rl/studies/representation_training_v1/tests/test_final_benchmark.py b/rl/studies/representation_training_v1/tests/test_final_benchmark.py deleted file mode 100644 index b3f8896b..00000000 --- a/rl/studies/representation_training_v1/tests/test_final_benchmark.py +++ /dev/null @@ -1,711 +0,0 @@ -from __future__ import annotations - -import asyncio -import copy -import json -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -import pytest - -pytest.importorskip("chz", reason="Tinker is an optional RL dependency") -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") -pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", -) - -from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus -from rl.studies.representation_training_v1 import final_benchmark as subject -from rl.studies.representation_training_v1.protocol import STUDY_ID, load_protocol -from rl.studies.representation_training_v1.store import StageKey, TrainingStore - -from .test_evaluation import ( - REPO_ROOT, - SOURCE_SHA, - _dataset_binding, - _FakeEvaluator, - _FakeRenderer, - _task, - _training_receipt, -) - - -class _SamplingClient: - def __init__( - self, - events: list[str], - *, - fail_on_call: int | None = None, - ) -> None: - self.events = events - self.fail_on_call = fail_on_call - self.seeds: list[int] = [] - - async def sample_async(self, **kwargs: Any) -> Any: - assert kwargs["num_samples"] == 1 - parameters = kwargs["sampling_params"] - seed = int(parameters.seed) - self.seeds.append(seed) - self.events.append(f"sample:{seed}") - if self.fail_on_call == len(self.seeds): - raise RuntimeError("synthetic sampling outage") - assert parameters.max_tokens == subject.MAX_OUTPUT_TOKENS - assert parameters.temperature == subject.TEMPERATURE - assert parameters.top_p == subject.TOP_P - return SimpleNamespace( - sequences=[ - SimpleNamespace( - tokens=[1, 2, seed % 1000 + 3], - stop_reason="stop", - ) - ] - ) - - -class _Service: - def __init__( - self, - events: list[str], - client: _SamplingClient, - ) -> None: - self.events = events - self.client = client - - def create_sampling_client(self, *, model_path: str) -> _SamplingClient: - self.events.append("sampling-client") - assert model_path == "tinker://fake/sampler" - return self.client - - -def _tasks() -> list[Any]: - return [_task(task_id) for task_id in subject.EXPECTED_TASK_IDS] - - -def _patch_preflight( - monkeypatch: pytest.MonkeyPatch, - *, - protocol: dict[str, Any], - tasks: list[Any], - renderer: _FakeRenderer, -) -> None: - monkeypatch.setattr(subject, "load_protocol", lambda *_args, **_kwargs: protocol) - monkeypatch.setattr( - subject, - "validate_source_sha", - lambda *_args: SOURCE_SHA, - ) - monkeypatch.setattr( - subject, - "_dataset_binding", - lambda *_args: _dataset_binding(protocol), - ) - monkeypatch.setattr( - subject.checkpoint_v1, - "_benchmark_tasks", - lambda **_kwargs: tasks, - ) - monkeypatch.setattr(subject, "_renderer", lambda *_args, **_kwargs: renderer) - - -def _run( - *, - protocol: dict[str, Any], - external: Path, - receipt_sha: str, - evaluator: _FakeEvaluator, - service_factory: Any, - checkpoint_name: str = "final", -) -> dict[str, Any]: - return asyncio.run( - subject.run_final_benchmark( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name=checkpoint_name, - replicate_id="r0", - expected_source_sha=SOURCE_SHA, - expected_wave_receipt_sha256=receipt_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - service_client_factory=service_factory, - evaluator_factory=lambda: evaluator, - ) - ) - - -def _store(external: Path) -> subject.FinalBenchmarkStore: - training = TrainingStore(repo_root=REPO_ROOT, external_root=external) - key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - return subject.FinalBenchmarkStore( - stage_path=training.stage_path(key), - wave="complete", - checkpoint_name="final", - ) - - -def _validation_context( - *, - protocol: dict[str, Any], - external: Path, - receipt_sha: str, - tasks: list[Any], - renderer: _FakeRenderer, - evaluator: _FakeEvaluator, -) -> tuple[Any, list[subject.PreparedAttempt]]: - training = TrainingStore(repo_root=REPO_ROOT, external_root=external) - binding = subject.checkpoint_v1._load_checkpoint_binding( - store=training, - protocol=protocol, - stage_id="qwen-l0-sft", - wave="complete", - checkpoint_name="final", - replicate_id="r0", - expected_receipt_sha256=receipt_sha, - source_git_sha=SOURCE_SHA, - ) - prepared, _ = subject.checkpoint_v1._preflight_tasks( - tasks=tasks, - renderer=renderer, - evaluator=evaluator, - context_tokens=65_536, - ) - panel = subject._task_panel(tasks) - attempts = subject._attempts( - prepared=prepared, - source_git_sha=SOURCE_SHA, - protocol_sha256=protocol["logical_sha256"], - dataset_sha256=protocol["dataset"]["logical_release_sha256"], - task_panel_sha256=panel["logical_sha256"], - binding=binding, - ) - return binding, attempts - - -def _completed( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> dict[str, Any]: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - evaluator = _FakeEvaluator(events) - tasks = _tasks() - _patch_preflight( - monkeypatch, - protocol=protocol, - tasks=tasks, - renderer=renderer, - ) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - client = _SamplingClient(events) - - def service_factory() -> _Service: - events.append("service") - assert events.count("reference") == 8 - return _Service(events, client) - - result = _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=evaluator, - service_factory=service_factory, - ) - store = _store(external) - manifest = store.load_manifest() - report = store.load_report() - assert manifest is not None - assert report is not None - binding, attempts = _validation_context( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - tasks=tasks, - renderer=renderer, - evaluator=evaluator, - ) - return { - "protocol": protocol, - "external": external, - "receipt_sha": receipt_sha, - "events": events, - "renderer": renderer, - "evaluator": evaluator, - "tasks": tasks, - "client": client, - "result": result, - "store": store, - "manifest": manifest, - "report": report, - "binding": binding, - "attempts": attempts, - } - - -def _write_record(path: Path, record: dict[str, Any]) -> None: - path.write_text( - json.dumps( - record, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - - -def _resigned_report( - payload: dict[str, Any], -) -> dict[str, Any]: - return subject._record_document( - record_type="final_benchmark_report", - key={}, - payload=payload, - ) - - -def test_v1_panel_and_record_namespaces_remain_unchanged() -> None: - assert subject.checkpoint_v1.PANELS == ( - subject.checkpoint_v1.PANEL_PROGRESS, - subject.checkpoint_v1.PANEL_DEPTH_VALIDATION, - subject.checkpoint_v1.PANEL_INKLING_PROMOTION, - ) - assert subject.PANEL_FINAL_BENCHMARK_V2 not in subject.checkpoint_v1.PANELS - assert ( - subject.FINAL_BENCHMARK_RECORD_SCHEMA_VERSION - != subject.checkpoint_v1.EVALUATION_RECORD_SCHEMA_VERSION - ) - - -def test_exact_best4_records_and_metrics_bind_full_provenance( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - result = completed["result"] - store = completed["store"] - manifest = completed["manifest"]["payload"] - report = completed["report"]["payload"] - client = completed["client"] - events = completed["events"] - - assert result["status"] == "complete" - assert subject.PANEL_FINAL_BENCHMARK_V2 in result["report"] - assert len(client.seeds) == 32 - assert len(set(client.seeds)) == 32 - assert events[: events.index("service")].count("reference") == 8 - assert events.index("evaluate") > max( - index for index, event in enumerate(events) if event.startswith("sample:") - ) - - assert len(report["records"]) == 32 - assert report["summary"] == { - "figure_count": 8, - "mean_at_1_raw_absolute_scale_iou": 0.75, - "best_at_4_raw_absolute_scale_iou": 0.75, - "attempt_count": 32, - "pure_executable_count": 32, - "pure_executable_rate": 1.0, - "cap_hit_count": 0, - "cap_hit_rate": 0.0, - "channel_parse_complete_count": 32, - "channel_parse_complete_rate": 1.0, - "statuses": {"ok": 32}, - "stop_reasons": {"stop": 32}, - "completion_tokens": { - "min": 3, - "max": 3, - "mean": 3.0, - "total": 96, - }, - } - assert list(report["figures"]) == list(subject.EXPECTED_TASK_IDS) - assert all( - value["attempt_count"] == 4 - and value["best_at_4_raw_absolute_scale_iou"] == 0.75 - and value["pure_executable_rate"] == 1.0 - for value in report["figures"].values() - ) - - assert manifest["source_git_sha"] == SOURCE_SHA - assert manifest["protocol"]["logical_sha256"] == completed["protocol"]["logical_sha256"] - assert ( - manifest["dataset"]["logical_release_sha256"] - == completed["protocol"]["dataset"]["logical_release_sha256"] - ) - assert manifest["task_panel"]["task_ids"] == list(subject.EXPECTED_TASK_IDS) - assert manifest["prompt"]["contract_version"] == completed["protocol"]["contract_version"] - assert manifest["model"]["model"] == "Qwen/Qwen3.6-35B-A3B" - assert manifest["model"]["renderer"] == "qwen3_5" - assert manifest["sampler"]["checkpoint"]["final"] is True - assert manifest["sampler"]["training_wave_receipt_record_sha256"] == completed["receipt_sha"] - assert manifest["sandbox"]["image_id"] == "sha256:" + "4" * 64 - assert manifest["sampling"]["attempts_per_task"] == 4 - assert len(manifest["sampling"]["seeds"]) == 32 - - for task_id in subject.EXPECTED_TASK_IDS: - for attempt_index in range(1, 5): - sample = store.load_sample(task_id, attempt_index) - evaluation = store.load_evaluation(task_id, attempt_index) - assert sample is not None - assert evaluation is not None - assert sample["payload"]["response"]["token_ids"] - assert sample["payload"]["attempt_index"] == attempt_index - assert evaluation["payload"]["attempt_index"] == attempt_index - - -def test_partial_sampling_resume_fills_only_missing_attempts( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - evaluator = _FakeEvaluator(events) - tasks = _tasks() - _patch_preflight( - monkeypatch, - protocol=protocol, - tasks=tasks, - renderer=renderer, - ) - monkeypatch.setattr(subject, "SAMPLE_CONCURRENCY", 1) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - first_client = _SamplingClient(events, fail_on_call=6) - - with pytest.raises(subject.checkpoint_v1.SamplingInfrastructureFault): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=evaluator, - service_factory=lambda: _Service(events, first_client), - ) - - store = _store(external) - samples_before = { - path: path.read_bytes() for path in store.root.glob("attempts/*/*/sample.json") - } - assert 0 < len(samples_before) < 32 - assert not list(store.root.glob("attempts/*/*/evaluation.json")) - - second_client = _SamplingClient(events) - result = _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=lambda: _Service(events, second_client), - ) - - assert result["status"] == "complete" - assert len(second_client.seeds) == 32 - len(samples_before) - assert len(list(store.root.glob("attempts/*/*/sample.json"))) == 32 - assert len(list(store.root.glob("attempts/*/*/evaluation.json"))) == 32 - assert all(path.read_bytes() == raw for path, raw in samples_before.items()) - - -def test_undecodable_raw_tokens_are_persisted_and_never_executed( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - - def fail_decode(_tokens: list[int]) -> str: - raise ValueError("synthetic tokenizer drift") - - renderer.tokenizer = SimpleNamespace(decode=fail_decode) - tasks = _tasks() - _patch_preflight( - monkeypatch, - protocol=protocol, - tasks=tasks, - renderer=renderer, - ) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - client = _SamplingClient(events) - - with pytest.raises(subject.checkpoint_v1.SamplingInfrastructureFault): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=lambda: _Service(events, client), - ) - - store = _store(external) - samples = list(store.root.glob("attempts/*/*/sample.json")) - assert len(samples) == 32 - assert not list(store.root.glob("attempts/*/*/evaluation.json")) - assert "evaluate" not in events - for path in samples: - payload = json.loads(path.read_text(encoding="utf-8"))["payload"] - assert payload["response"]["token_ids"] - assert payload["response"]["token_decode_complete"] is False - assert "synthetic tokenizer drift" in payload["response"]["token_decode_error"] - - def forbidden_service() -> Any: - raise AssertionError("undecodable persisted samples were resampled") - - with pytest.raises(subject.checkpoint_v1.SamplingInfrastructureFault): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=forbidden_service, - ) - - -def test_already_complete_never_touches_tinker_or_records( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - store = completed["store"] - before = {path: path.read_bytes() for path in store.root.rglob("*.json")} - monkeypatch.delenv("TINKER_API_KEY") - - def forbidden_service() -> Any: - raise AssertionError("already-complete benchmark created a Tinker client") - - result = _run( - protocol=completed["protocol"], - external=completed["external"], - receipt_sha=completed["receipt_sha"], - evaluator=_FakeEvaluator(completed["events"]), - service_factory=forbidden_service, - ) - - assert result["status"] == "already_complete" - assert {path: path.read_bytes() for path in store.root.rglob("*.json")} == before - - -def test_tampered_raw_response_aborts_before_resume_sampling( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - sample_path = completed["store"].root / "attempts" / "F1" / "000001" / "sample.json" - sample = json.loads(sample_path.read_text(encoding="utf-8")) - sample["payload"]["response"]["raw_text"] = "tampered" - _write_record(sample_path, sample) - - def forbidden_service() -> Any: - raise AssertionError("tampered resume created a Tinker client") - - with pytest.raises(subject.FinalBenchmarkError): - _run( - protocol=completed["protocol"], - external=completed["external"], - receipt_sha=completed["receipt_sha"], - evaluator=_FakeEvaluator(completed["events"]), - service_factory=forbidden_service, - ) - - -def test_foreign_fifth_attempt_path_is_rejected_before_resume( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - source = completed["store"].root / "attempts" / "F1" / "000001" / "sample.json" - foreign = completed["store"].root / "attempts" / "F1" / "000005" / "sample.json" - foreign.parent.mkdir(parents=True) - foreign.write_bytes(source.read_bytes()) - - def forbidden_service() -> Any: - raise AssertionError("foreign attempt inventory created a Tinker client") - - with pytest.raises(subject.FinalBenchmarkError): - _run( - protocol=completed["protocol"], - external=completed["external"], - receipt_sha=completed["receipt_sha"], - evaluator=_FakeEvaluator(completed["events"]), - service_factory=forbidden_service, - ) - - -@pytest.mark.parametrize( - "mutation", - ("panel", "checkpoint", "source"), -) -def test_resigned_wrong_report_identity_is_rejected( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, - mutation: str, -) -> None: - completed = _completed(tmp_path, monkeypatch) - payload = copy.deepcopy(completed["report"]["payload"]) - if mutation == "panel": - payload["panel"] = "progress" - elif mutation == "checkpoint": - payload["checkpoint_name"] = "000003" - else: - payload["provenance"]["manifest"]["source_git_sha"] = "b" * 40 - tampered = _resigned_report(payload) - - with pytest.raises(subject.FinalBenchmarkError): - subject.validate_final_benchmark_report( - tampered, - manifest_record=completed["manifest"], - binding=completed["binding"], - attempts=completed["attempts"], - ) - - -@pytest.mark.parametrize("mutation", ("incomplete", "duplicate")) -def test_resigned_incomplete_or_duplicate_attempt_rows_are_rejected( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, - mutation: str, -) -> None: - completed = _completed(tmp_path, monkeypatch) - payload = copy.deepcopy(completed["report"]["payload"]) - if mutation == "incomplete": - payload["records"].pop() - else: - payload["records"][-1] = copy.deepcopy(payload["records"][0]) - payload["attempt_records_sha256"] = subject.canonical_json_sha256(payload["records"]) - tampered = _resigned_report(payload) - - with pytest.raises(subject.FinalBenchmarkError): - subject.validate_final_benchmark_report( - tampered, - manifest_record=completed["manifest"], - binding=completed["binding"], - attempts=completed["attempts"], - ) - - -def test_nonfinite_evaluator_metrics_abort_instead_of_becoming_zero() -> None: - prepared = subject.checkpoint_v1.PreparedTask( - task=_task(), - prompt_tokens=100, - prompt_text_sha256="1" * 64, - reference_evidence={}, - ) - attempt = subject.PreparedAttempt( - prepared=prepared, - attempt_index=1, - seed=123, - ) - results = ( - EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=float("nan"), - ), - EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=0.5, - dice=float("inf"), - ), - EvaluationResult( - status=EvaluationStatus.OK, - attribution=Attribution.MODEL, - iou=0.5, - metrics={"diagnostic": float("nan")}, - ), - ) - for result in results: - with pytest.raises(subject.checkpoint_v1.EvaluationInfrastructureFault): - subject._evaluation_payload( - attempt=attempt, - result=result, - sample_record_sha256="2" * 64, - manifest_record_sha256="3" * 64, - ) - - -def test_reference_and_evaluator_attribution_are_never_model_zeros() -> None: - prepared = subject.checkpoint_v1.PreparedTask( - task=_task(), - prompt_tokens=100, - prompt_text_sha256="1" * 64, - reference_evidence={}, - ) - attempt = subject.PreparedAttempt(prepared=prepared, attempt_index=1, seed=1) - for attribution, error_type in ( - (Attribution.REFERENCE, subject.checkpoint_v1.ReferencePanelFault), - (Attribution.EVALUATOR, subject.checkpoint_v1.EvaluationInfrastructureFault), - ): - with pytest.raises(error_type): - subject._evaluation_payload( - attempt=attempt, - result=EvaluationResult( - status=EvaluationStatus.WORKER_ERROR, - attribution=attribution, - error="fault", - ), - sample_record_sha256="2" * 64, - manifest_record_sha256="3" * 64, - ) - - -def test_periodic_checkpoint_cannot_be_called_final_benchmark( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - _patch_preflight( - monkeypatch, - protocol=protocol, - tasks=_tasks(), - renderer=renderer, - ) - - def forbidden_service() -> Any: - raise AssertionError("periodic checkpoint created a Tinker client") - - with pytest.raises(subject.checkpoint_v1.CheckpointReceiptError): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=forbidden_service, - checkpoint_name="000003", - ) - - -def test_reference_preflight_fault_precedes_tinker_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _training_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - renderer = _FakeRenderer(events) - _patch_preflight( - monkeypatch, - protocol=protocol, - tasks=_tasks(), - renderer=renderer, - ) - - def forbidden_service() -> Any: - raise AssertionError("reference fault created a Tinker client") - - with pytest.raises(subject.checkpoint_v1.ReferencePanelFault): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events, broken_reference=True), - service_factory=forbidden_service, - ) diff --git a/rl/studies/representation_training_v1/tests/test_launcher.py b/rl/studies/representation_training_v1/tests/test_launcher.py deleted file mode 100644 index 04de7783..00000000 --- a/rl/studies/representation_training_v1/tests/test_launcher.py +++ /dev/null @@ -1,2227 +0,0 @@ -from __future__ import annotations - -import asyncio -import copy -import json -import os -from pathlib import Path -import subprocess -from types import SimpleNamespace - -import pytest - -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") -pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", -) - -from rl.studies.representation_training_v1 import launcher -from rl.studies.representation_training_v1.protocol import ( - STUDY_ID, - load_protocol, - stage_spec, -) -from rl.studies.representation_training_v1.store import StageKey, TrainingStore - - -REPO_ROOT = Path(__file__).resolve().parents[4] - - -def _preflight(protocol: dict, stage_id: str, wave: str) -> dict: - stage = stage_spec(protocol, stage_id) - return { - "protocol_file_sha256": "1" * 64, - "prompt_assets": {"prompt": "2" * 64}, - "dataset": {"logical_release_sha256": protocol["dataset"]["logical_release_sha256"]}, - "runtime": {"python": "3.13"}, - "sandbox": None, - "schedule": { - "stage_id": stage_id, - "logical_sha256": "3" * 64, - }, - "model": protocol["models"][stage.model_key], - "recipe": protocol["recipes"][stage.recipe_key], - "preflight_sha256": "4" * 64, - } - - -def _cross_source_parent_records( - protocol: dict, - *, - producer_source_git_sha: str, - consumer_source_git_sha: str, -) -> tuple[dict, SimpleNamespace, SimpleNamespace, SimpleNamespace]: - checkpoint = { - "name": "final", - "batch": 11, - "epoch": 1, - "final": True, - "state_path": "tinker://unit/weights/final", - "sampler_path": "tinker://unit/sampler_weights/final", - "role": "terminal", - "training_progress_fraction": 1.0, - } - inventory_sha = launcher.canonical_sha256([checkpoint]) - parent_manifest = SimpleNamespace( - payload={ - "source_git_sha": producer_source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "contract_version": protocol["contract_version"], - "stage": { - "stage_id": "qwen-l0-sft", - "model_key": "qwen", - }, - }, - payload_sha256="1" * 64, - record_sha256="2" * 64, - relative_path=( - "studies/representation-training-v1/stages/qwen-l0-sft/runs/r0/run_manifest.json" - ), - ) - receipt = SimpleNamespace( - payload={ - "stage_id": "qwen-l0-sft", - "wave": "complete", - "run_manifest_record_sha256": parent_manifest.record_sha256, - "checkpoint": { - key: checkpoint[key] - for key in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - }, - "checkpoint_inventory": { - "count": 1, - "entries": [checkpoint], - "logical_sha256": inventory_sha, - }, - }, - payload_sha256="3" * 64, - record_sha256="4" * 64, - relative_path=( - "studies/representation-training-v1/stages/" - "qwen-l0-sft/runs/r0/waves/complete/receipt.json" - ), - ) - approval_payload = { - "schema_version": (launcher.SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION), - "stage_id": "qwen-l0-rl-l1", - "wave": "smoke", - "source_git_sha": consumer_source_git_sha, - "evidence_source_git_sha": producer_source_git_sha, - "parent_stage_id": "qwen-l0-sft", - "parent_wave": "complete", - "parent_receipt_record_sha256": receipt.record_sha256, - "selected_parent_checkpoint": checkpoint, - "selected_parent_checkpoint_inventory_sha256": inventory_sha, - "source_transition": {"logical_sha256": "5" * 64}, - } - approval = SimpleNamespace( - payload=approval_payload, - payload_sha256="6" * 64, - record_sha256="7" * 64, - relative_path=( - "studies/representation-training-v1/stages/" - "qwen-l0-rl-l1/runs/r0/waves/smoke/approval.json" - ), - ) - return checkpoint, parent_manifest, receipt, approval - - -def _write_unit_sft_artifacts(config) -> None: - log = Path(config.log_path) - log.mkdir(parents=True, exist_ok=True) - checkpoints = [ - { - "name": f"{step:06d}", - "batch": step, - "epoch": 0, - "state_path": f"tinker://unit/weights/{step:06d}", - "sampler_path": f"tinker://unit/sampler_weights/{step:06d}", - } - for step in (3, 6, 9) - ] - checkpoints.append( - { - "name": "final", - "batch": 0, - "epoch": 1, - "state_path": "tinker://unit/weights/final", - "sampler_path": "tinker://unit/sampler_weights/final", - } - ) - (log / "checkpoints.jsonl").write_text( - "".join(json.dumps(value) + "\n" for value in checkpoints), - encoding="utf-8", - ) - (log / "metrics.jsonl").write_text( - json.dumps({"step": 10, "train_mean_nll": 0.5}) + "\n", - encoding="utf-8", - ) - - -class _FakeRun: - def __init__(self, module, kwargs): - self._module = module - self.id = kwargs["id"] - self.entity = kwargs["entity"] - self.project = kwargs["project"] - self.name = kwargs["name"] - self.group = kwargs["group"] - self.job_type = kwargs["job_type"] - self.tags = kwargs["tags"] - self.resumed = kwargs["resume"] == "must" - self.url = f"https://wandb.example/{self.id}" - self.settings = SimpleNamespace( - root_dir=kwargs["dir"], - resume=kwargs["resume"], - ) - self.dir = str(Path(kwargs["dir"]) / "wandb" / self.id / "files") - Path(self.dir).mkdir(parents=True, exist_ok=True) - self.finished = False - - def finish(self): - self.finished = True - self._module.run = None - - -class _FakeConfig: - def __init__(self, initial=None): - self.values = dict(initial or {}) - self.updates = [] - - def update(self, value, allow_val_change=None): - payload = dict(value) - changed = [ - key for key, item in payload.items() if key in self.values and self.values[key] != item - ] - if changed and allow_val_change is not True: - raise RuntimeError("config changed without allow_val_change: " + ", ".join(changed)) - self.values.update(payload) - self.updates.append( - { - "value": payload, - "allow_val_change": allow_val_change, - } - ) - - -class _FakeWandb: - def __init__(self, *, initial_config=None): - self.run = None - self.calls = [] - self.config = _FakeConfig(initial_config) - - def init(self, **kwargs): - self.calls.append(kwargs) - if kwargs.get("config") is not None: - self.config.update(kwargs["config"]) - self.run = _FakeRun(self, kwargs) - return self.run - - def finish(self): - if self.run is not None: - self.run.finish() - - -def test_sft_launch_records_before_main_and_binds_wandb( - tmp_path: Path, - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - source_sha = "a" * 40 - external = tmp_path / "external" - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.delenv("WANDB_RUN_ID", raising=False) - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: _preflight( - protocol, - "qwen-l0-sft", - "complete", - ), - ) - access_calls: list[str] = [] - - def validate_access(*, expected_entity): - access_calls.append(expected_entity) - assert os.environ["WANDB_ENTITY"] == expected_entity - assert os.environ["WANDB_PROJECT"] == protocol["tracking"]["project"] - assert os.environ["WANDB_RUN_ID"].startswith("pxct-") - assert os.environ["WANDB_RESUME"] == "never" - return {"username": "test", "entity": expected_entity} - - async def fake_main(config): - log = Path(config.log_path) - assert (log.parent / "run_manifest.json").is_file() - assert os.environ["WANDB_RUN_ID"].startswith("pxct-") - assert os.environ["WANDB_RESUME"] == "never" - assert os.environ["WANDB_MODE"] == "online" - assert os.environ["WANDB_JOB_TYPE"] == "sft" - _write_unit_sft_artifacts(config) - - report = asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - sft_main=fake_main, - wandb_access_validator=validate_access, - ) - ) - assert report["status"] == "complete" - assert access_calls == [protocol["tracking"]["entity"]] - assert report["checkpoint"]["state_path"] == "tinker://unit/weights/final" - assert "WANDB_RUN_ID" not in os.environ - - async def forbidden_main(_config): - raise AssertionError("completed wave must not call Tinker again") - - replay = asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - sft_main=forbidden_main, - wandb_access_validator=lambda **_kwargs: (_ for _ in ()).throw( - AssertionError("completed wave must not touch W&B") - ), - ) - ) - assert replay["status"] == "already_complete" - - -def test_source_is_revalidated_after_preflight_before_any_live_boundary( - tmp_path: Path, - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - source_sha = "9" * 40 - events: list[str] = [] - source_checks = 0 - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - - def validate_source(_root, expected): - nonlocal source_checks - source_checks += 1 - events.append(f"source-{source_checks}") - if source_checks == 1: - return expected - raise ValueError("paid training requires a completely clean worktree") - - monkeypatch.setattr(launcher, "validate_source_sha", validate_source) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: ( - events.append("preflight") or _preflight(protocol, "qwen-mixed-sft", "complete") - ), - ) - - async def forbidden_main(_config): - events.append("training") - raise AssertionError("source race reached training") - - def forbidden_wandb(**_kwargs): - events.append("wandb") - raise AssertionError("source race reached W&B") - - with pytest.raises( - launcher.TrainingLaunchError, - match="source changed after preflight", - ): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id="qwen-mixed-sft", - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=tmp_path / "external", - confirmation=protocol["launch"]["confirmation_token"], - sft_main=forbidden_main, - wandb_access_validator=forbidden_wandb, - ) - ) - assert events == ["source-1", "preflight", "source-2"] - - -def test_already_complete_rejects_minimal_receipt_before_live_boundaries( - tmp_path: Path, - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - source_sha = "8" * 40 - external = tmp_path / "external" - stage = stage_spec(protocol, "qwen-mixed-sft") - key = StageKey(STUDY_ID, stage.stage_id, "r0") - preflight = _preflight(protocol, stage.stage_id, "complete") - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - tracking = launcher._tracking_binding( - protocol=protocol, - source_git_sha=source_sha, - key=key, - stage=stage, - store=store, - ) - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - launcher._manifest_payload( - protocol=protocol, - source_git_sha=source_sha, - stage=stage, - preflight=preflight, - tracking=tracking, - parent=None, - ), - ) - store.write_wave_receipt( - key, - wave="complete", - payload={ - "stage_id": stage.stage_id, - "wave": "complete", - "run_manifest_record_sha256": manifest.record_sha256, - }, - ) - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: preflight, - ) - with pytest.raises( - launcher.TrainingLaunchError, - match="receipt fields differ", - ): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id=stage.stage_id, - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - sft_main=lambda _config: (_ for _ in ()).throw( - AssertionError("minimal receipt reached training") - ), - wandb_access_validator=lambda **_kwargs: (_ for _ in ()).throw( - AssertionError("minimal receipt reached W&B") - ), - ) - ) - - -def test_already_complete_rejects_forged_receipt_bindings( - tmp_path: Path, - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - source_sha = "7" * 40 - external = tmp_path / "external" - key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: _preflight(protocol, key.stage_id, "complete"), - ) - - async def fake_main(config): - _write_unit_sft_artifacts(config) - - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id=key.stage_id, - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - sft_main=fake_main, - wandb_access_validator=lambda **_kwargs: {"entity": "unit"}, - ) - ) - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - original = store.load_wave_receipt(key, wave="complete") - assert original is not None - receipt_path = external.joinpath(*original.relative_path.parts) - cases = [ - ( - "final optimizer metric", - lambda payload: payload["final_training_metric"].update({"step": 0}), - ), - ( - "canonical training artifacts", - lambda payload: payload["final_training_metric"].update({"train_mean_nll": 0.125}), - ), - ( - "canonical training artifacts", - lambda payload: payload["local_artifact_sha256"].update({"metrics.jsonl": "f" * 64}), - ), - ( - "candidate_inventory", - lambda payload: payload["candidate_inventory"].update({"expected_count": 1}), - ), - ( - "invocation is missing or differs", - lambda payload: payload.update({"invocation_record_sha256": "0" * 64}), - ), - ] - for message, mutate in cases: - receipt_path.unlink() - forged = copy.deepcopy(original.payload) - mutate(forged) - with store.acquire_stage_lock(key): - store.write_wave_receipt( - key, - wave="complete", - payload=forged, - ) - with pytest.raises(launcher.TrainingLaunchError, match=message): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id=key.stage_id, - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - sft_main=lambda _config: (_ for _ in ()).throw( - AssertionError("forged receipt reached training") - ), - wandb_access_validator=lambda **_kwargs: (_ for _ in ()).throw( - AssertionError("forged receipt reached W&B") - ), - ) - ) - - receipt_path.unlink() - with store.acquire_stage_lock(key): - store.write_wave_receipt( - key, - wave="complete", - payload=original.payload, - ) - metrics_path = store.tinker_log_path(key) / "metrics.jsonl" - original_metrics = metrics_path.read_bytes() - metrics_path.write_text( - json.dumps({"step": 10, "train_mean_nll": 0.125}) + "\n", - encoding="utf-8", - ) - try: - with pytest.raises( - launcher.TrainingLaunchError, - match="canonical training artifacts", - ): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id=key.stage_id, - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - sft_main=lambda _config: (_ for _ in ()).throw( - AssertionError("mutated metrics reached training") - ), - wandb_access_validator=lambda **_kwargs: (_ for _ in ()).throw( - AssertionError("mutated metrics reached W&B") - ), - ) - ) - finally: - metrics_path.write_bytes(original_metrics) - - -def test_completed_rl_receipt_requires_every_candidate_and_sample( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - source_sha = "6" * 40 - external = tmp_path / "external" - stage = stage_spec(protocol, "inkling-l4-rl") - key = StageKey(STUDY_ID, stage.stage_id, "r0") - wave = "smoke" - preflight = _preflight(protocol, stage.stage_id, wave) - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - tracking = launcher._tracking_binding( - protocol=protocol, - source_git_sha=source_sha, - key=key, - stage=stage, - store=store, - ) - invocation_id = "smoke-unit" - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - launcher._manifest_payload( - protocol=protocol, - source_git_sha=source_sha, - stage=stage, - preflight=preflight, - tracking=tracking, - parent=None, - ), - ) - resolved = launcher._resolved_launch_binding( - repo_root=REPO_ROOT, - protocol=protocol, - stage=stage, - wave_name=wave, - store=store, - key=key, - parent=None, - resume_batch=None, - ) - invocation = store.begin_invocation( - key, - invocation_id=invocation_id, - payload=launcher._invocation_payload( - protocol=protocol, - stage=stage, - wave_name=wave, - source_git_sha=source_sha, - invocation_id=invocation_id, - manifest=manifest, - preflight=preflight, - tracking=tracking, - resume_batch=None, - resolved_launch=resolved, - parent=None, - ), - ) - recipe = protocol["recipes"][stage.recipe_key] - expected = int(recipe["groups_per_batch"]) * int(recipe["group_size"]) - for index in range(expected): - task_id = f"unit-{index:02d}" - store.write_candidate_sample( - key, - invocation_id=invocation_id, - step=0, - task_id=task_id, - attempt=1, - payload={"index": index}, - ) - store.write_candidate( - key, - invocation_id=invocation_id, - step=0, - task_id=task_id, - attempt=1, - payload={"index": index}, - ) - candidate_root = store.tinker_log_path(key).parent / "candidates" / invocation_id - candidate_files = sorted(candidate_root.rglob("evaluation.json")) - log_path = store.tinker_log_path(key) - log_path.mkdir(parents=True, exist_ok=True) - (log_path / "checkpoints.jsonl").write_text( - json.dumps( - { - "name": "000001", - "batch": 1, - "epoch": None, - "state_path": "tinker://unit/weights/000001", - "sampler_path": "tinker://unit/sampler_weights/000001", - } - ) - + "\n", - encoding="utf-8", - ) - (log_path / "metrics.jsonl").write_text( - json.dumps({"step": 0, "optim/entropy": 0.5}) + "\n", - encoding="utf-8", - ) - receipt = store.write_wave_receipt( - key, - wave=wave, - payload=launcher._checkpoint_receipt( - stage=stage, - wave_name=wave, - log_path=log_path, - manifest=manifest, - preflight=preflight, - invocation=invocation, - tracking=None, - ), - ) - launcher._validate_completed_wave_receipt( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name=wave, - manifest=manifest, - preflight=preflight, - parent=None, - receipt=receipt, - ) - checkpoints_path = log_path / "checkpoints.jsonl" - original_checkpoints = checkpoints_path.read_bytes() - metrics_path = log_path / "metrics.jsonl" - original_metrics = metrics_path.read_bytes() - with checkpoints_path.open("ab") as stream: - stream.write( - ( - json.dumps( - { - "name": "final", - "batch": 1, - "state_path": "tinker://unit/weights/final", - "sampler_path": "tinker://unit/sampler_weights/final", - } - ) - + "\n" - + json.dumps( - { - "name": "000005", - "batch": 5, - "state_path": "tinker://unit/weights/000005", - "sampler_path": "tinker://unit/sampler_weights/000005", - } - ) - + "\n" - ).encode("utf-8") - ) - with metrics_path.open("ab") as stream: - stream.write( - (json.dumps({"step": 4, "optim/entropy": 0.25}) + "\n").encode("utf-8") - ) - # A later wave appends to Cookbook's shared log. The earlier receipt - # remains reconstructable from its immutable prefix snapshot. - launcher._validate_completed_wave_receipt( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name=wave, - manifest=manifest, - preflight=preflight, - parent=None, - receipt=receipt, - ) - checkpoints_path.write_bytes(original_checkpoints) - metrics_path.write_bytes(original_metrics) - metrics_path.write_text( - json.dumps({"step": 0, "optim/entropy": 0.25}) + "\n", - encoding="utf-8", - ) - with pytest.raises( - launcher.TrainingLaunchError, - match="canonical training artifacts", - ): - launcher._validate_completed_wave_receipt( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name=wave, - manifest=manifest, - preflight=preflight, - parent=None, - receipt=receipt, - ) - metrics_path.write_bytes(original_metrics) - checkpoints_path.unlink() - with pytest.raises( - launcher.TrainingLaunchError, - match="without a resumable checkpoint", - ): - launcher._validate_completed_wave_receipt( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name=wave, - manifest=manifest, - preflight=preflight, - parent=None, - receipt=receipt, - ) - checkpoints_path.write_bytes(original_checkpoints) - candidate_files[0].unlink() - with pytest.raises( - launcher.TrainingLaunchError, - match="candidate_inventory", - ): - launcher._validate_completed_wave_receipt( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - key=key, - stage=stage, - wave_name=wave, - manifest=manifest, - preflight=preflight, - parent=None, - receipt=receipt, - ) - - -def test_resume_pins_receipt_checkpoint_instead_of_later_cookbook_final( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l0") - log_path = tmp_path / "tinker" - log_path.mkdir() - numbered = { - "name": "000001", - "batch": 1, - "state_path": "tinker://unit/weights/000001", - "sampler_path": "tinker://unit/sampler_weights/000001", - } - terminal_final = { - "name": "final", - "batch": 1, - "state_path": "tinker://unit/weights/final", - "sampler_path": "tinker://unit/sampler_weights/final", - } - (log_path / "checkpoints.jsonl").write_text( - json.dumps(numbered) + "\n" + json.dumps(terminal_final) + "\n", - encoding="utf-8", - ) - prior = SimpleNamespace( - payload={ - "checkpoint": { - **numbered, - "epoch": None, - "final": True, - } - } - ) - - # Unsealed Cookbook behavior selects the later, different final URI. - assert ( - launcher.checkpoint_utils.get_last_checkpoint(str(log_path)).state_path - == terminal_final["state_path"] - ) - binding = launcher._validate_resume_state( - stage=stage, - wave_name="step-5", - log_path=log_path, - prior_wave_receipt=prior, - ) - assert binding is not None - assert binding.batch == 1 - assert binding.checkpoint["state_path"] == numbered["state_path"] - invocation = launcher._invocation_payload( - protocol=protocol, - stage=stage, - wave_name="step-5", - source_git_sha="a" * 40, - invocation_id="resume-unit", - manifest=SimpleNamespace(record_sha256="b" * 64), - preflight={"preflight_sha256": "c" * 64}, - tracking={"run_id": "unit"}, - resume_batch=binding.batch, - resume_checkpoint=binding.checkpoint, - resolved_launch={"logical_sha256": "d" * 64}, - parent=None, - ) - assert invocation["resume_batch"] == 1 - assert invocation["resume_checkpoint"] == binding.checkpoint - - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="step-5", - log_path=log_path, - prior_wave_receipt=prior, - expected=binding, - ): - selected = launcher.checkpoint_utils.get_last_checkpoint(str(log_path)) - assert selected.state_path == numbered["state_path"] - assert selected.sampler_path == numbered["sampler_path"] - - with (log_path / "checkpoints.jsonl").open("a", encoding="utf-8") as stream: - stream.write( - json.dumps( - { - "name": "000002", - "batch": 2, - "state_path": "tinker://unit/weights/000002", - "rolling": True, - } - ) - + "\n" - ) - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="step-5", - log_path=log_path, - prior_wave_receipt=prior, - expected=binding, - ): - with pytest.raises( - launcher.TrainingLaunchError, - match="changed after the immutable invocation", - ): - launcher.checkpoint_utils.get_last_checkpoint(str(log_path)) - - -def _write_fresh_cookbook_bootstrap(log_path: Path) -> None: - log_path.mkdir(parents=True, exist_ok=True) - (log_path / "config.json").write_text('{"max_steps": 1}\n', encoding="utf-8") - (log_path / "code.diff").write_text("", encoding="utf-8") - (log_path / "logs.log").write_text("bootstrap\n", encoding="utf-8") - - -def test_fresh_cookbook_bootstrap_is_allowed_only_inside_sealed_boundary( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l0") - log_path = tmp_path / "tinker" - - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="smoke", - log_path=log_path, - prior_wave_receipt=None, - expected=None, - ): - _write_fresh_cookbook_bootstrap(log_path) - assert launcher.checkpoint_utils.get_last_checkpoint(str(log_path)) is None - - with pytest.raises( - launcher.TrainingLaunchError, - match="no resumable checkpoint", - ): - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="smoke", - log_path=log_path, - prior_wave_receipt=None, - expected=None, - ): - pass - - -def test_fresh_sft_resume_boundary_precedes_logging_and_rejects_debris( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-l0-sft") - clean_log = tmp_path / "clean-sft" - - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="complete", - log_path=clean_log, - prior_wave_receipt=None, - expected=None, - ): - assert launcher.checkpoint_utils.get_last_checkpoint(str(clean_log)) is None - - dirty_log = tmp_path / "dirty-sft" - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="complete", - log_path=dirty_log, - prior_wave_receipt=None, - expected=None, - ): - dirty_log.mkdir() - (dirty_log / "logs.log").write_text("unexpected\n", encoding="utf-8") - with pytest.raises( - launcher.TrainingLaunchError, - match="SFT resume boundary contains unexpected training artifacts", - ): - launcher.checkpoint_utils.get_last_checkpoint(str(dirty_log)) - - -@pytest.mark.parametrize("unexpected_name", ["metrics.jsonl", "checkpoints.jsonl"]) -def test_fresh_cookbook_bootstrap_rejects_unexpected_training_artifacts( - tmp_path: Path, - unexpected_name: str, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l0") - log_path = tmp_path / "tinker" - - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="smoke", - log_path=log_path, - prior_wave_receipt=None, - expected=None, - ): - _write_fresh_cookbook_bootstrap(log_path) - (log_path / unexpected_name).write_text("", encoding="utf-8") - with pytest.raises( - launcher.TrainingLaunchError, - match="unexpected training artifacts", - ): - launcher.checkpoint_utils.get_last_checkpoint(str(log_path)) - - -def test_fresh_cookbook_bootstrap_rejects_invalid_config_or_non_file( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l0") - - invalid_config = tmp_path / "invalid-config" - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="smoke", - log_path=invalid_config, - prior_wave_receipt=None, - expected=None, - ): - _write_fresh_cookbook_bootstrap(invalid_config) - (invalid_config / "config.json").write_text("{", encoding="utf-8") - with pytest.raises( - launcher.TrainingLaunchError, - match="config is not valid JSON", - ): - launcher.checkpoint_utils.get_last_checkpoint(str(invalid_config)) - - non_file = tmp_path / "non-file" - with launcher._sealed_cookbook_resume( - stage=stage, - wave_name="smoke", - log_path=non_file, - prior_wave_receipt=None, - expected=None, - ): - _write_fresh_cookbook_bootstrap(non_file) - (non_file / "code.diff").unlink() - (non_file / "code.diff").mkdir() - with pytest.raises( - launcher.TrainingLaunchError, - match="unexpected training artifacts", - ): - launcher.checkpoint_utils.get_last_checkpoint(str(non_file)) - - -def test_resume_rejects_unreceipted_terminal_checkpoint( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l0") - log_path = tmp_path / "tinker" - log_path.mkdir() - (log_path / "checkpoints.jsonl").write_text( - json.dumps( - { - "name": "final", - "batch": 1, - "state_path": "tinker://unit/weights/final", - "sampler_path": "tinker://unit/sampler_weights/final", - } - ) - + "\n", - encoding="utf-8", - ) - with pytest.raises( - launcher.TrainingLaunchError, - match="wave ceiling without an immutable receipt", - ): - launcher._validate_resume_state( - stage=stage, - wave_name="smoke", - log_path=log_path, - prior_wave_receipt=None, - ) - - -def test_builtin_sft_records_observed_wandb_before_service_client( - tmp_path: Path, - monkeypatch, -) -> None: - from tinker_cookbook.supervised import train as supervised_train - from tinker_cookbook.utils import ml_log - - protocol = load_protocol(REPO_ROOT) - source_sha = "b" * 40 - external = tmp_path / "external" - fake_wandb = _FakeWandb() - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: _preflight( - protocol, - "qwen-l0-sft", - "complete", - ), - ) - monkeypatch.setattr(ml_log, "wandb", fake_wandb) - monkeypatch.setattr(ml_log, "_wandb_available", True) - real_import = launcher.importlib.import_module - monkeypatch.setattr( - launcher.importlib, - "import_module", - lambda name: fake_wandb if name == "wandb" else real_import(name), - ) - service_client_markers = [] - - def validate_access(*, expected_entity): - assert os.environ["WANDB_ENTITY"] == expected_entity - assert os.environ["WANDB_RUN_ID"].startswith("pxct-") - return {"username": "test", "entity": expected_entity} - - async def fake_builtin_main(config): - logger = ml_log.setup_logging( - log_dir=config.log_path, - wandb_project=config.wandb_project, - wandb_name=config.wandb_name, - config=config, - do_configure_logging_module=False, - ) - tracking_paths = list(external.rglob("tracking.json")) - assert len(tracking_paths) == 1 - tracking_document = json.loads(tracking_paths[0].read_text(encoding="utf-8")) - assert tracking_document["payload"]["observed"]["run_id"] == fake_wandb.calls[0]["id"] - assert service_client_markers == [] - service_client_markers.append("created") - logger.close() - _write_unit_sft_artifacts(config) - - monkeypatch.setattr(supervised_train, "main", fake_builtin_main) - report = asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave_name="complete", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - wandb_access_validator=validate_access, - ) - ) - assert report["status"] == "complete" - assert service_client_markers == ["created"] - assert fake_wandb.calls[0]["config"] is None - assert len(fake_wandb.config.updates) == 2 - assert fake_wandb.config.updates[0]["allow_val_change"] is False - assert fake_wandb.config.updates[1]["allow_val_change"] is None - assert fake_wandb.config.updates[0]["value"] == fake_wandb.config.updates[1]["value"] - assert "dataset_builder" in fake_wandb.config.values - key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - tracking_paths = list(external.rglob("tracking.json")) - tracking_document = json.loads(tracking_paths[0].read_text(encoding="utf-8")) - invocation_id = tracking_document["key"]["invocation_id"] - tracking = store.load_invocation_tracking( - key, - invocation_id=invocation_id, - ) - receipt = store.load_wave_receipt(key, wave="complete") - assert tracking is not None - assert receipt is not None - assert receipt.payload["invocation_tracking_record_sha256"] == tracking.record_sha256 - - -def test_builtin_rl_records_observed_wandb_before_service_client( - tmp_path: Path, - monkeypatch, -) -> None: - from rl.track_b import tml_shim - from tinker_cookbook.rl import train as rl_train - from tinker_cookbook.utils import ml_log - - protocol = load_protocol(REPO_ROOT) - source_sha = "c" * 40 - external = tmp_path / "external" - fake_wandb = _FakeWandb() - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: _preflight( - protocol, - "inkling-l4-rl", - "smoke", - ), - ) - monkeypatch.setattr( - tml_shim, - "install_inkling_runtime", - lambda **_kwargs: None, - ) - monkeypatch.setattr(ml_log, "wandb", fake_wandb) - monkeypatch.setattr(ml_log, "_wandb_available", True) - real_import = launcher.importlib.import_module - monkeypatch.setattr( - launcher.importlib, - "import_module", - lambda name: fake_wandb if name == "wandb" else real_import(name), - ) - service_client_markers = [] - - async def fake_builtin_main(config): - ml_log.setup_logging( - log_dir=config.log_path, - wandb_project=config.wandb_project, - wandb_name=config.wandb_name, - config=config, - do_configure_logging_module=False, - ) - tracking_paths = list(external.rglob("tracking.json")) - assert len(tracking_paths) == 1 - tracking_document = json.loads(tracking_paths[0].read_text(encoding="utf-8")) - assert tracking_document["payload"]["observed"]["run_id"] == fake_wandb.calls[0]["id"] - service_client_markers.append("created") - raise RuntimeError("stop before paid RL") - - monkeypatch.setattr(rl_train, "main", fake_builtin_main) - with pytest.raises(RuntimeError, match="stop before paid RL"): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id="inkling-l4-rl", - wave_name="smoke", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - wandb_access_validator=lambda **_kwargs: {}, - ) - ) - assert service_client_markers == ["created"] - assert fake_wandb.run is None - assert fake_wandb.calls[0]["config"] is None - assert len(fake_wandb.config.updates) == 2 - assert fake_wandb.config.updates[0]["allow_val_change"] is False - assert fake_wandb.config.updates[1]["allow_val_change"] is None - assert fake_wandb.config.updates[0]["value"] == fake_wandb.config.updates[1]["value"] - assert "dataset_builder" in fake_wandb.config.values - assert len(list(external.rglob("tracking.json"))) == 1 - - -def test_sealed_wandb_initialization_overrides_cookbook_identity( - tmp_path: Path, - monkeypatch, -) -> None: - from tinker_cookbook.utils import ml_log - - wandb_dir = tmp_path / "sealed-wandb" - cookbook_dir = tmp_path / "cookbook-log" - wandb_dir.mkdir() - binding = { - "entity": "unit-entity", - "project": "unit-project", - "run_id": "pxct-unit-run", - "name": "unit/name", - "group": "unit/group", - "job_type": "sft", - "tags": ["study", "stage", "r0"], - "directory": str(wandb_dir), - } - - fake = _FakeWandb() - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(ml_log, "wandb", fake) - monkeypatch.setattr(ml_log, "_wandb_available", True) - original_init = fake.init - observed = [] - with launcher._sealed_wandb_initialization( - binding, - resume=True, - wandb_module=fake, - on_initialized=observed.append, - ): - logger = ml_log.setup_logging( - log_dir=str(cookbook_dir), - wandb_project="unit-project", - wandb_name="unit/name", - config=None, - do_configure_logging_module=False, - ) - logger.close() - - assert fake.init == original_init - assert len(fake.calls) == 1 - call = fake.calls[0] - assert call["entity"] == binding["entity"] - assert call["project"] == binding["project"] - assert call["id"] == binding["run_id"] - assert call["name"] == binding["name"] - assert call["group"] == binding["group"] - assert call["job_type"] == binding["job_type"] - assert call["tags"] == tuple(binding["tags"]) - assert call["dir"] == binding["directory"] - assert call["mode"] == "online" - assert call["resume"] == "must" - assert call["save_code"] is False - assert call["config"] is None - assert observed == [ - { - "run_id": binding["run_id"], - "entity": binding["entity"], - "project": binding["project"], - "name": binding["name"], - "group": binding["group"], - "job_type": binding["job_type"], - "tags": binding["tags"], - "resumed": True, - "url": f"https://wandb.example/{binding['run_id']}", - "requested_directory": binding["directory"], - "resolved_root_directory": binding["directory"], - "files_directory": str( - Path(binding["directory"]) / "wandb" / binding["run_id"] / "files" - ), - "requested_resume": "must", - "resolved_resume": "must", - "hparams_preseeded": False, - "hparams_allow_val_change": False, - } - ] - - -def test_sealed_wandb_initialization_allows_resume_hparam_changes_once( - tmp_path: Path, - monkeypatch, -) -> None: - from tinker_cookbook.utils import ml_log - - wandb_dir = tmp_path / "sealed-wandb" - cookbook_dir = tmp_path / "cookbook-log" - wandb_dir.mkdir() - binding = { - "entity": "unit-entity", - "project": "unit-project", - "run_id": "pxct-unit-run", - "name": "unit/name", - "group": "unit/group", - "job_type": "rl", - "tags": ["study", "stage", "r0"], - "directory": str(wandb_dir), - } - resumed_config = { - "max_steps": 6, - "wave": "pilot", - } - - fake = _FakeWandb( - initial_config={ - "max_steps": 1, - "wave": "smoke", - } - ) - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(ml_log, "wandb", fake) - monkeypatch.setattr(ml_log, "_wandb_available", True) - observed = [] - with launcher._sealed_wandb_initialization( - binding, - resume=True, - wandb_module=fake, - on_initialized=observed.append, - ): - logger = ml_log.setup_logging( - log_dir=str(cookbook_dir), - wandb_project=binding["project"], - wandb_name=binding["name"], - config=resumed_config, - do_configure_logging_module=False, - ) - logger.close() - - assert fake.calls[0]["config"] is None - assert len(fake.config.updates) == 2 - assert fake.config.updates[0] == { - "value": resumed_config, - "allow_val_change": True, - } - assert fake.config.updates[1] == { - "value": resumed_config, - "allow_val_change": None, - } - assert fake.config.values["max_steps"] == 6 - assert fake.config.values["wave"] == "pilot" - assert observed[0]["hparams_preseeded"] is True - assert observed[0]["hparams_allow_val_change"] is True - - -def test_sealed_wandb_initialization_rejects_live_identity_mismatch( - tmp_path: Path, -) -> None: - wandb_dir = tmp_path / "sealed-wandb" - wandb_dir.mkdir() - binding = { - "entity": "unit-entity", - "project": "unit-project", - "run_id": "pxct-expected", - "name": "unit/name", - "group": "unit/group", - "job_type": "rl", - "tags": ["study"], - "directory": str(wandb_dir), - } - - class MismatchedRun: - id = "random-id" - entity = binding["entity"] - project = binding["project"] - name = binding["name"] - group = binding["group"] - job_type = binding["job_type"] - tags = tuple(binding["tags"]) - resumed = False - url = "https://wandb.example/random-id" - settings = SimpleNamespace( - root_dir=binding["directory"], - resume="never", - ) - dir = str(Path(binding["directory"]) / "wandb" / "random-id" / "files") - - def __init__(self): - self.finished = False - - def finish(self): - self.finished = True - - run = MismatchedRun() - Path(run.dir).mkdir(parents=True) - fake = SimpleNamespace(run=None, init=lambda **_kwargs: run) - with pytest.raises( - launcher.TrainingLaunchError, - match="live W&B run differs", - ): - with launcher._sealed_wandb_initialization( - binding, - resume=False, - wandb_module=fake, - ): - fake.init(project=binding["project"], name=binding["name"]) - assert run.finished is True - - -def test_sealed_wandb_cleanup_does_not_mask_training_failure( - tmp_path: Path, -) -> None: - wandb_dir = tmp_path / "sealed-wandb" - wandb_dir.mkdir() - binding = { - "entity": "unit-entity", - "project": "unit-project", - "run_id": "pxct-unit-run", - "name": "unit/name", - "group": "unit/group", - "job_type": "sft", - "tags": ["study"], - "directory": str(wandb_dir), - } - - class FailingFinishRun: - id = binding["run_id"] - entity = binding["entity"] - project = binding["project"] - name = binding["name"] - group = binding["group"] - job_type = binding["job_type"] - tags = tuple(binding["tags"]) - resumed = False - url = "https://wandb.example/pxct-unit-run" - settings = SimpleNamespace( - root_dir=binding["directory"], - resume="never", - ) - dir = str(Path(binding["directory"]) / "wandb" / id / "files") - - def finish(self): - raise RuntimeError("finish failed") - - run = FailingFinishRun() - Path(run.dir).mkdir(parents=True) - - def module_finish(): - raise RuntimeError("module finish failed") - - fake = SimpleNamespace( - run=None, - init=lambda **_kwargs: run, - finish=module_finish, - ) - original_init = fake.init - with pytest.raises(RuntimeError, match="trainer failed"): - with launcher._sealed_wandb_initialization( - binding, - resume=False, - wandb_module=fake, - ): - fake.init(project=binding["project"], name=binding["name"]) - raise RuntimeError("trainer failed") - assert fake.init == original_init - - -def test_bad_confirmation_cannot_reach_preflight_or_main( - tmp_path: Path, - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr(launcher, "load_protocol", lambda *_a, **_k: protocol) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: (_ for _ in ()).throw(AssertionError("preflight should not run")), - ) - with pytest.raises(launcher.TrainingLaunchError, match="confirmation"): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id="qwen-l0-sft", - wave_name="complete", - replicate_id="r0", - expected_source_sha="a" * 40, - external_root=tmp_path / "external", - confirmation="wrong", - sft_main=lambda _config: (_ for _ in ()).throw( - AssertionError("main should not run") - ), - ) - ) - - -def test_resolved_configs_seal_no_hidden_validation_and_raw_iou( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - store = TrainingStore( - repo_root=REPO_ROOT, - external_root=tmp_path / "external", - ) - for stage_id, wave in ( - ("qwen-mixed-sft", "complete"), - ("inkling-l4-rl", "smoke"), - ): - key = StageKey(STUDY_ID, stage_id, "r0") - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - {"source_git_sha": "a" * 40}, - ) - spec = stage_spec(protocol, stage_id) - resolved_launch = launcher._resolved_launch_binding( - repo_root=REPO_ROOT, - protocol=protocol, - stage=spec, - wave_name=wave, - store=store, - key=key, - parent=None, - resume_batch=None, - ) - invocation = store.begin_invocation( - key, - invocation_id="unit-invocation", - payload={ - "invocation_id": "unit-invocation", - "run_manifest_record_sha256": manifest.record_sha256, - "source_git_sha": "a" * 40, - "protocol_logical_sha256": protocol["logical_sha256"], - "policy": {}, - "initial_checkpoint": None, - "resume_batch": None, - "resolved_launch": resolved_launch, - }, - ) - config = launcher.build_tinker_config( - repo_root=REPO_ROOT, - protocol=protocol, - stage=spec, - wave_name=wave, - store=store, - key=key, - parent=None, - invocation=invocation, - ) - if stage_id == "qwen-mixed-sft": - assert config.max_steps == 55 - assert config.learning_rate == 1e-4 - assert config.eval_every == 0 - assert config.dataset_builder.include_validation is False - assert config.dataset_builder.schedule_seed == 90210 - else: - assert config.max_steps == 1 - assert config.max_tokens == 60_000 - assert config.eval_every == 0 - assert config.dataset_builder.validation_canaries == 0 - assert config.dataset_builder.require_prefix_replay is False - assert config.dataset_builder.reward_policy == "raw_iou" - assert config.dataset_builder.schedule_seed == 90210 - - -def test_tinker_config_rejects_invocation_policy_drift(tmp_path: Path) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "inkling-l4-rl") - key = StageKey(STUDY_ID, stage.stage_id, "r0") - store = TrainingStore( - repo_root=REPO_ROOT, - external_root=tmp_path / "external", - ) - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - {"source_git_sha": "a" * 40}, - ) - resolved = launcher._resolved_launch_binding( - repo_root=REPO_ROOT, - protocol=protocol, - stage=stage, - wave_name="smoke", - store=store, - key=key, - parent=None, - resume_batch=None, - ) - resolved["sampling"]["temperature"] = 0.5 - invocation = store.begin_invocation( - key, - invocation_id="unit-drift", - payload={ - "invocation_id": "unit-drift", - "run_manifest_record_sha256": manifest.record_sha256, - "source_git_sha": "a" * 40, - "protocol_logical_sha256": protocol["logical_sha256"], - "policy": {}, - "initial_checkpoint": None, - "resume_batch": None, - "resolved_launch": resolved, - }, - ) - with pytest.raises( - launcher.TrainingLaunchError, - match="differs from the resolved", - ): - launcher.build_tinker_config( - repo_root=REPO_ROOT, - protocol=protocol, - stage=stage, - wave_name="smoke", - store=store, - key=key, - parent=None, - invocation=invocation, - ) - - -def test_inkling_extension_rechecks_exact_pilot_evidence( - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "inkling-l4-rl") - key = StageKey(STUDY_ID, stage.stage_id, "r0") - # This test isolates the launcher's downstream binding of an already - # canonically validated approval to the exact pilot receipt. Canonical - # report reconstruction is covered independently; keep it out of this - # deliberately synthetic store. - monkeypatch.setattr( - launcher, - "validate_same_source_wave_approval", - lambda **_kwargs: None, - ) - checkpoint = { - "name": "000006", - "batch": 6, - "epoch": 0, - "final": False, - "state_path": "tinker://unit/weights/000006", - "sampler_path": "tinker://unit/sampler_weights/000006", - } - prior = SimpleNamespace( - record_sha256="b" * 64, - payload={"checkpoint": checkpoint}, - ) - approval_payload = { - "schema_version": launcher.APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "promotion-receipt-and-explicit-human-approval", - "stage_id": stage.stage_id, - "wave": "coverage-extension", - "source_git_sha": "a" * 40, - "protocol_logical_sha256": protocol["logical_sha256"], - "evaluation": { - "stage_id": stage.stage_id, - "wave": "pilot", - "panel": "inkling-promotion", - "training_wave_receipt_record_sha256": prior.record_sha256, - "checkpoint": { - **checkpoint, - "role": "wave-ceiling", - "training_progress_fraction": 6 / 14, - }, - }, - } - - class FakeStore: - def __init__(self, approval): - self.approval = approval - - def load_wave_receipt(self, _key, *, wave): - assert wave == "pilot" - return prior - - def load_wave_approval(self, _key, *, wave): - assert wave == "coverage-extension" - return self.approval - - approval = SimpleNamespace(payload=approval_payload) - assert ( - launcher._approval_record( - repo_root=REPO_ROOT, - store=FakeStore(approval), - key=key, - stage=stage, - wave_name="coverage-extension", - source_git_sha="a" * 40, - protocol=protocol, - ) - is approval - ) - - changed = dict(approval_payload) - changed["evaluation"] = { - **approval_payload["evaluation"], - "training_wave_receipt_record_sha256": "c" * 64, - } - with pytest.raises(launcher.TrainingLaunchError, match="not bound"): - launcher._approval_record( - repo_root=REPO_ROOT, - store=FakeStore(SimpleNamespace(payload=changed)), - key=key, - stage=stage, - wave_name="coverage-extension", - source_git_sha="a" * 40, - protocol=protocol, - ) - - -def test_launcher_rejects_fabricated_minimal_same_source_approval() -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "inkling-l4-rl") - key = StageKey(STUDY_ID, stage.stage_id, "r0") - source_git_sha = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=REPO_ROOT, - text=True, - ).strip() - prior = SimpleNamespace( - record_sha256="b" * 64, - payload={ - "checkpoint": { - "name": "000006", - "batch": 6, - "epoch": 0, - "final": False, - "state_path": "tinker://unit/weights/000006", - "sampler_path": "tinker://unit/sampler_weights/000006", - }, - }, - ) - fabricated = SimpleNamespace( - payload={ - "schema_version": launcher.APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "promotion-receipt-and-explicit-human-approval", - "stage_id": stage.stage_id, - "wave": "coverage-extension", - "source_git_sha": source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - }, - ) - - class FakeStore: - @staticmethod - def load_wave_receipt(_key, *, wave): - assert wave == "pilot" - return prior - - @staticmethod - def load_wave_approval(_key, *, wave): - assert wave == "coverage-extension" - return fabricated - - with pytest.raises( - launcher.TrainingLaunchError, - match="same-source approval failed validation", - ): - launcher._approval_record( - repo_root=REPO_ROOT, - store=FakeStore(), - key=key, - stage=stage, - wave_name="coverage-extension", - source_git_sha=source_git_sha, - protocol=protocol, - ) - - -def test_cross_source_parent_requires_v2_and_child_manifest_records_it() -> None: - protocol = load_protocol(REPO_ROOT) - producer = "b" * 40 - consumer = "c" * 40 - checkpoint, parent_manifest, receipt, approval = _cross_source_parent_records( - protocol, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - key = StageKey(STUDY_ID, "qwen-l0-rl-l1", "r0") - stage = stage_spec(protocol, key.stage_id) - parent_key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - - class ParentStore: - def load_manifest(self, observed_key): - assert observed_key == parent_key - return parent_manifest - - def load_wave_receipt(self, observed_key, *, wave): - assert observed_key == parent_key - assert wave == "complete" - return receipt - - v1_payload = { - key: value - for key, value in approval.payload.items() - if key not in {"evidence_source_git_sha", "source_transition"} - } - v1_payload["schema_version"] = launcher.APPROVAL_SCHEMA_VERSION - v1 = SimpleNamespace( - payload=v1_payload, - payload_sha256=approval.payload_sha256, - record_sha256=approval.record_sha256, - relative_path=approval.relative_path, - ) - with pytest.raises( - launcher.TrainingLaunchError, - match="requires a v2 transition approval", - ): - launcher._parent_binding( - repo_root=REPO_ROOT, - protocol=protocol, - store=ParentStore(), - key=key, - stage=stage, - source_git_sha=consumer, - approval=v1, - existing_manifest=None, - ) - - parent = launcher._parent_binding( - repo_root=REPO_ROOT, - protocol=protocol, - store=ParentStore(), - key=key, - stage=stage, - source_git_sha=consumer, - approval=approval, - existing_manifest=None, - ) - assert parent is not None - assert parent.source_git_sha == producer - assert parent.checkpoint == checkpoint - assert parent.entry_approval is approval - manifest = launcher._manifest_payload( - protocol=protocol, - source_git_sha=consumer, - stage=stage, - preflight=_preflight(protocol, stage.stage_id, "smoke"), - tracking={"run_id": "unit"}, - parent=parent, - ) - assert manifest["source_transition"] == (launcher._child_source_transition_binding(approval)) - assert manifest["source_transition"]["producer_source_git_sha"] == (producer) - assert manifest["source_transition"]["consumer_source_git_sha"] == (consumer) - assert manifest["source_transition"]["authorized_child_wave"] == "smoke" - assert manifest["parent_receipt"]["record_sha256"] == (receipt.record_sha256) - - -def test_historical_l0_parent_uses_receipt_transition_without_evaluation( - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - producer = "1" * 40 - consumer = "2" * 40 - checkpoint, parent_manifest, receipt, approval = _cross_source_parent_records( - protocol, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - parent_manifest.payload["protocol_logical_sha256"] = "3" * 64 - approval.payload.update( - { - "schema_version": ( - launcher.PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ), - "decision": "approve", - "approval_gate": "parent-receipt-and-source-transition", - "protocol_logical_sha256": protocol["logical_sha256"], - "explicit_human_confirmation": True, - } - ) - key = StageKey(STUDY_ID, "qwen-l0-rl-l1", "r0") - parent_key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - stage = stage_spec(protocol, key.stage_id) - validation_calls: list[dict] = [] - monkeypatch.setattr( - launcher, - "validate_parent_receipt_transition_approval", - lambda **kwargs: validation_calls.append(dict(kwargs)), - ) - - class ParentStore: - @staticmethod - def load_wave_approval(observed_key, *, wave): - assert observed_key == key - assert wave == "smoke" - return approval - - @staticmethod - def load_manifest(observed_key): - assert observed_key == parent_key - return parent_manifest - - @staticmethod - def load_wave_receipt(observed_key, *, wave): - assert observed_key == parent_key - assert wave == "complete" - return receipt - - observed_approval = launcher._approval_record( - repo_root=REPO_ROOT, - store=ParentStore(), - key=key, - stage=stage, - wave_name="smoke", - source_git_sha=consumer, - protocol=protocol, - ) - assert observed_approval is approval - assert len(validation_calls) == 1 - - parent = launcher._parent_binding( - repo_root=REPO_ROOT, - protocol=protocol, - store=ParentStore(), - key=key, - stage=stage, - source_git_sha=consumer, - approval=approval, - existing_manifest=None, - ) - assert parent is not None - assert parent.checkpoint == checkpoint - assert parent.entry_approval is approval - child_transition = launcher._child_source_transition_binding(approval) - assert child_transition["approval_schema_version"] == ( - launcher.PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ) - assert child_transition["producer_source_git_sha"] == producer - assert child_transition["consumer_source_git_sha"] == consumer - - -def test_operational_retry_consumes_historical_parent_from_r0() -> None: - protocol = load_protocol(REPO_ROOT) - producer = "1" * 40 - consumer = "2" * 40 - checkpoint, parent_manifest, receipt, approval = _cross_source_parent_records( - protocol, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - parent_manifest.payload["protocol_logical_sha256"] = "3" * 64 - approval.payload.update( - { - "schema_version": ( - launcher.PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ), - "decision": "approve", - "approval_gate": "parent-receipt-and-source-transition", - "protocol_logical_sha256": protocol["logical_sha256"], - "explicit_human_confirmation": True, - "parent_replicate_id": "r0", - } - ) - key = StageKey(STUDY_ID, "qwen-l0-rl-l1", "r0-retry1") - parent_key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - stage = stage_spec(protocol, key.stage_id) - - class ParentStore: - @staticmethod - def load_manifest(observed_key): - assert observed_key == parent_key - return parent_manifest - - @staticmethod - def load_wave_receipt(observed_key, *, wave): - assert observed_key == parent_key - assert wave == "complete" - return receipt - - parent = launcher._parent_binding( - repo_root=REPO_ROOT, - protocol=protocol, - store=ParentStore(), - key=key, - stage=stage, - source_git_sha=consumer, - approval=approval, - existing_manifest=None, - ) - assert parent is not None - assert parent.replicate_id == "r0" - assert parent.checkpoint == checkpoint - - -def test_existing_child_manifest_revalidates_exact_v2_transition( - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - producer = "d" * 40 - consumer = "e" * 40 - checkpoint, parent_manifest, receipt, approval = _cross_source_parent_records( - protocol, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - key = StageKey(STUDY_ID, "qwen-l0-rl-l1", "r0") - parent_key = StageKey(STUDY_ID, "qwen-l0-sft", "r0") - stage = stage_spec(protocol, key.stage_id) - transition_binding = launcher._child_source_transition_binding(approval) - existing_manifest = SimpleNamespace( - payload={ - "source_git_sha": consumer, - "parent_receipt": { - "stage_id": "qwen-l0-sft", - "wave": "complete", - "record_sha256": receipt.record_sha256, - "checkpoint": checkpoint, - "checkpoint_inventory_logical_sha256": ( - receipt.payload["checkpoint_inventory"]["logical_sha256"] - ), - }, - "source_transition": transition_binding, - }, - ) - validation_calls: list[dict] = [] - monkeypatch.setattr( - launcher, - "validate_source_transition_approval", - lambda **kwargs: validation_calls.append(dict(kwargs)), - ) - - class ExistingStore: - def load_manifest(self, observed_key): - assert observed_key == parent_key - return parent_manifest - - def load_wave_receipt(self, observed_key, *, wave): - assert observed_key == parent_key - assert wave == "complete" - return receipt - - def load_wave_approval(self, observed_key, *, wave): - assert observed_key == key - assert wave == "smoke" - return approval - - parent = launcher._parent_binding( - repo_root=REPO_ROOT, - protocol=protocol, - store=ExistingStore(), - key=key, - stage=stage, - source_git_sha=consumer, - approval=None, - existing_manifest=existing_manifest, - ) - assert parent is not None - assert parent.entry_approval is approval - assert len(validation_calls) == 1 - assert validation_calls[0]["replicate_id"] == "r0" - assert validation_calls[0]["wave_name"] == "smoke" - - tampered_manifest = SimpleNamespace(payload=copy.deepcopy(existing_manifest.payload)) - tampered_manifest.payload["source_transition"]["source_transition_logical_sha256"] = "0" * 64 - with pytest.raises( - launcher.TrainingLaunchError, - match="differs from its v2 approval", - ): - launcher._parent_binding( - repo_root=REPO_ROOT, - protocol=protocol, - store=ExistingStore(), - key=key, - stage=stage, - source_git_sha=consumer, - approval=None, - existing_manifest=tampered_manifest, - ) - - -def test_completed_wave_cannot_bypass_transition_checks_or_preflight( - tmp_path: Path, - monkeypatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - source_sha = "f" * 40 - external = tmp_path / "external" - key = StageKey(STUDY_ID, "qwen-l0-rl-l1", "r0") - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - {"source_git_sha": source_sha}, - ) - store.write_wave_receipt( - key, - wave="smoke", - payload={ - "stage_id": key.stage_id, - "wave": "smoke", - "run_manifest_record_sha256": manifest.record_sha256, - }, - ) - monkeypatch.setenv("TINKER_API_KEY", "test-only") - monkeypatch.setenv("WANDB_API_KEY", "test-only") - monkeypatch.setattr( - launcher, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - launcher, - "validate_source_sha", - lambda _root, expected: expected, - ) - events: list[str] = [] - monkeypatch.setattr( - launcher, - "stage_preflight", - lambda **_kwargs: events.append("preflight") or _preflight(protocol, key.stage_id, "smoke"), - ) - - def reject_transition(**_kwargs): - events.append("approval") - raise launcher.TrainingLaunchError("v2 transition required") - - monkeypatch.setattr(launcher, "_approval_record", reject_transition) - with pytest.raises( - launcher.TrainingLaunchError, - match="v2 transition required", - ): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id=key.stage_id, - wave_name="smoke", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - rl_main=lambda _config: (_ for _ in ()).throw( - AssertionError("completed bypass reached Tinker") - ), - wandb_access_validator=lambda **_kwargs: (_ for _ in ()).throw( - AssertionError("completed bypass reached W&B") - ), - ) - ) - assert events == ["preflight", "approval"] - - wrong_source_root = tmp_path / "wrong-source" - wrong_store = TrainingStore( - repo_root=REPO_ROOT, - external_root=wrong_source_root, - ) - with wrong_store.acquire_stage_lock(key): - wrong_manifest = wrong_store.create_or_verify_manifest( - key, - {"source_git_sha": "0" * 40}, - ) - wrong_store.write_wave_receipt( - key, - wave="smoke", - payload={ - "stage_id": key.stage_id, - "wave": "smoke", - "run_manifest_record_sha256": (wrong_manifest.record_sha256), - }, - ) - with pytest.raises( - launcher.TrainingLaunchError, - match="another source commit", - ): - asyncio.run( - launcher.run_training_stage( - repo_root=REPO_ROOT, - stage_id=key.stage_id, - wave_name="smoke", - replicate_id="r0", - expected_source_sha=source_sha, - external_root=wrong_source_root, - confirmation=protocol["launch"]["confirmation_token"], - ) - ) diff --git a/rl/studies/representation_training_v1/tests/test_promotion.py b/rl/studies/representation_training_v1/tests/test_promotion.py deleted file mode 100644 index e3a379f9..00000000 --- a/rl/studies/representation_training_v1/tests/test_promotion.py +++ /dev/null @@ -1,1799 +0,0 @@ -from __future__ import annotations - -import copy -import subprocess -import sys -from dataclasses import replace -from pathlib import Path, PurePosixPath - -import pytest - -from rl.common.prompt import prompt_asset_hashes -from rl.studies.representation_training_v1 import promotion -from rl.studies.representation_training_v1.protocol import ( - STUDY_ID, - file_sha256, - load_protocol, - protocol_path, - stage_spec, -) -from rl.studies.representation_training_v1.store import ( - StageKey, - TrainingRecord, - TrainingStore, - canonical_sha256, -) - - -REPO_ROOT = Path(__file__).resolve().parents[4] -SOURCE_SHA = "a" * 40 -CHECKPOINT = { - "name": "final", - "batch": 55, - "epoch": 1, - "final": True, - "state_path": "tinker://unit/weights/final", - "sampler_path": "tinker://unit/sampler_weights/final", - "role": "terminal", - "training_progress_fraction": 1.0, -} - - -def test_record_approval_import_does_not_require_tinker() -> None: - script = """ -import importlib.abc -import sys - -class BlockOptionalTrainingModules(importlib.abc.MetaPathFinder): - def find_spec(self, fullname, path=None, target=None): - if fullname.split(".", 1)[0] in {"chz", "tinker", "tinker_cookbook"}: - raise ModuleNotFoundError(f"blocked optional module: {fullname}") - return None - -sys.meta_path.insert(0, BlockOptionalTrainingModules()) -import rl.studies.representation_training_v1.record_approval -""" - subprocess.run( - [sys.executable, "-c", script], - cwd=REPO_ROOT, - check=True, - capture_output=True, - text=True, - ) - - -def test_record_approval_accepts_dual_level_transition_evidence() -> None: - from rl.studies.representation_training_v1.record_approval import parser - - args = parser().parse_args( - [ - "--stage", - "qwen-base-rl-l1", - "--wave", - "smoke", - "--evaluation-stage", - "qwen-base-rl-l0", - "--evaluation-wave", - "complete", - "--evaluation-checkpoint", - "000030", - "--evaluation-report-record-sha256", - "a" * 64, - "--entry-baseline-report-record-sha256", - "b" * 64, - "--expected-source-sha", - "c" * 40, - ] - ) - - assert args.entry_baseline_report_record_sha256 == "b" * 64 - - -CHECKPOINT_INVENTORY_SHA256 = canonical_sha256([CHECKPOINT]) - - -def test_report_aggregate_rejects_non_model_status_and_failure_iou() -> None: - base = { - "status": "runtime_error", - "pure_executable": False, - "raw_absolute_scale_iou": 0.0, - } - promotion._aggregate([base]) - - with pytest.raises( - promotion.PromotionError, - match="non-model evaluation status", - ): - promotion._aggregate([{**base, "status": "measurement_error"}]) - with pytest.raises( - promotion.PromotionError, - match="failed model output has nonzero IoU", - ): - promotion._aggregate([{**base, "raw_absolute_scale_iou": 0.1}]) - - -def _record( - payload: dict, - *, - record_sha: str, - relative: str, -) -> TrainingRecord: - return TrainingRecord( - payload=payload, - payload_sha256="e" * 64, - record_sha256=record_sha, - relative_path=PurePosixPath(relative), - ) - - -def _level_report(first_unmastered: str | None) -> dict: - levels: dict[str, dict] = {} - for level in ("L0", "L1", "L2", "L3", "L4"): - mastered = first_unmastered is None or int(level[1:]) < int(first_unmastered[1:]) - levels[level] = { - "mean_raw_absolute_scale_iou": 0.85 if mastered else 0.70, - "pure_executable_rate": 0.98 if mastered else 0.90, - } - return {"levels": levels} - - -def _evidence( - *, - stage_id: str, - wave: str, - panel: str, - first_unmastered: str | None, -) -> promotion.EvaluationEvidence: - key = StageKey(STUDY_ID, stage_id, "r0") - manifest = _record( - {}, - record_sha="b" * 64, - relative=f"studies/x/stages/{stage_id}/manifest.json", - ) - receipt = _record( - { - "checkpoint": { - key: CHECKPOINT[key] - for key in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - }, - "checkpoint_inventory": { - "count": 1, - "entries": [CHECKPOINT], - "logical_sha256": CHECKPOINT_INVENTORY_SHA256, - }, - }, - record_sha="c" * 64, - relative=f"studies/x/stages/{stage_id}/waves/{wave}/receipt.json", - ) - return promotion.EvaluationEvidence( - key=key, - wave=wave, - checkpoint_name="final", - checkpoint=CHECKPOINT, - checkpoint_inventory_sha256=CHECKPOINT_INVENTORY_SHA256, - panel=panel, - report={}, - report_payload=_level_report(first_unmastered), - report_record_sha256="d" * 64, - report_payload_sha256="e" * 64, - report_relative_path=( - f"studies/x/stages/{stage_id}/evaluations/{wave}/final/{panel}/report.json" - ), - evaluation_manifest_payload_sha256="f" * 64, - evaluation_manifest_record_sha256="a" * 64, - evaluation_manifest_relative_path=( - f"studies/x/stages/{stage_id}/evaluations/{wave}/final/{panel}/manifest.json" - ), - training_manifest=manifest, - training_receipt=receipt, - ) - - -def _git_transition_history(tmp_path: Path) -> tuple[Path, str, str]: - repo = tmp_path / "repo" - repo.mkdir() - subprocess.run( - ["git", "init", "--initial-branch=main"], - cwd=repo, - check=True, - stdout=subprocess.DEVNULL, - ) - for key, value in ( - ("user.name", "PixCell test"), - ("user.email", "pixcell-test@example.invalid"), - ("commit.gpgsign", "false"), - ("core.hooksPath", "/dev/null"), - ): - subprocess.run( - ["git", "config", key, value], - cwd=repo, - check=True, - ) - (repo / "bridge.txt").write_text("producer\n", encoding="utf-8") - subprocess.run(["git", "add", "--all"], cwd=repo, check=True) - subprocess.run( - ["git", "commit", "-m", "producer"], - cwd=repo, - check=True, - stdout=subprocess.DEVNULL, - ) - producer = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo, - text=True, - ).strip() - (repo / "bridge.txt").write_text("consumer\n", encoding="utf-8") - subprocess.run(["git", "add", "--all"], cwd=repo, check=True) - subprocess.run( - ["git", "commit", "-m", "consumer"], - cwd=repo, - check=True, - stdout=subprocess.DEVNULL, - ) - consumer = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo, - text=True, - ).strip() - return repo, producer, consumer - - -def _cross_source_evidence( - protocol: dict, - *, - producer_source_git_sha: str, -) -> promotion.EvaluationEvidence: - base = _evidence( - stage_id="qwen-l0-sft", - wave="complete", - panel=promotion.PANEL_DEPTH_VALIDATION, - first_unmastered="L1", - ) - model = dict(protocol["models"]["qwen"]) - prompt_assets = prompt_asset_hashes() - dataset = { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": "depth/validation", - "logical_release_sha256": protocol["dataset"]["logical_release_sha256"], - "freeze_file_sha256": "1" * 64, - "parquet_shards": {"depth.parquet": "2" * 64}, - } - sandbox = { - "runtime_path": "/usr/bin/docker", - "daemon_endpoint": "unix:///var/run/docker.sock", - "image_ref": "pixcell-evaluator@sha256:" + "3" * 64, - "image_id": "sha256:" + "3" * 64, - "workspace_root": "/tmp/pixcell-evaluation", - } - manifest = _record( - { - "source_git_sha": producer_source_git_sha, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": "4" * 64, - "contract_version": protocol["contract_version"], - "prompt_assets": prompt_assets, - "dataset": {key: value for key, value in dataset.items() if key != "split"}, - "model": model, - "sandbox": None, - }, - record_sha="8" * 64, - relative=( - "studies/representation-training-v1/stages/qwen-l0-sft/runs/r0/run_manifest.json" - ), - ) - receipt = replace( - base.training_receipt, - payload={ - **base.training_receipt.payload, - "stage_id": "qwen-l0-sft", - "wave": "complete", - "run_manifest_record_sha256": manifest.record_sha256, - }, - record_sha256="9" * 64, - ) - sampler = { - "model": model["model"], - "renderer": model["renderer"], - "effort": model.get("effort"), - } - report_payload = { - **base.report_payload, - "provenance": { - "source_git_sha": producer_source_git_sha, - "protocol": { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": "4" * 64, - "contract_version": protocol["contract_version"], - }, - "prompt_assets": prompt_assets, - "dataset": dataset, - "task_panel": { - "panel": promotion.PANEL_DEPTH_VALIDATION, - "task_count": 1_092, - "logical_sha256": "5" * 64, - }, - "sampler": sampler, - "sampling": { - "attempts_per_task": 1, - "max_output_tokens": 60_000, - "temperature": 1.0, - "top_p": 1.0, - "max_image_long_edge": 1_920, - }, - "sandbox": sandbox, - "evaluation_manifest_record_sha256": "a" * 64, - }, - } - return replace( - base, - training_manifest=manifest, - training_receipt=receipt, - report_payload=report_payload, - evaluation_manifest_payload_sha256="b" * 64, - evaluation_manifest_record_sha256="a" * 64, - ) - - -def _historical_parent_records( - tmp_path: Path, - protocol: dict, - *, - producer_source_git_sha: str, -) -> tuple[ - TrainingRecord, - TrainingRecord, - TrainingRecord, - dict, - dict, - object, -]: - dataset = promotion._dataset_binding(REPO_ROOT / "dataset", protocol) - parent_stage = stage_spec(protocol, "qwen-l0-sft") - stage_payload = { - "stage_id": parent_stage.stage_id, - "kind": parent_stage.kind, - "model_key": parent_stage.model_key, - "recipe_key": parent_stage.recipe_key, - "hypotheses": list(parent_stage.hypotheses), - "parent_policy": parent_stage.parent, - "levels": list(parent_stage.levels), - "current_level": parent_stage.current_level, - "replay_levels": list(parent_stage.replay_levels), - "waves": parent_stage.waves, - } - manifest = _record( - { - "source_git_sha": producer_source_git_sha, - "protocol_logical_sha256": "0" * 64, - "protocol_file_sha256": "1" * 64, - "contract_version": protocol["contract_version"], - "prompt_assets": prompt_asset_hashes(), - "dataset": dataset, - "model": protocol["models"]["qwen"], - "recipe": protocol["recipes"]["qwen_sft"], - "stage": stage_payload, - }, - record_sha="2" * 64, - relative=( - "studies/representation-training-v1/stages/" - "qwen-l0-sft/runs/r0/run_manifest.json" - ), - ) - checkpoint = { - **CHECKPOINT, - "batch": 0, - "epoch": 1, - } - inventory_sha = canonical_sha256([checkpoint]) - log_path = tmp_path / "tinker" - log_path.mkdir(parents=True) - local_artifacts = {} - for name, content in ( - ("checkpoints.jsonl", "{}\n"), - ("metrics.jsonl", "{}\n"), - ): - path = log_path / name - path.write_text(content, encoding="utf-8") - local_artifacts[name] = promotion.file_sha256(path) - invocation = _record( - { - "source_git_sha": producer_source_git_sha, - "run_manifest_record_sha256": manifest.record_sha256, - }, - record_sha="3" * 64, - relative=( - "studies/representation-training-v1/stages/qwen-l0-sft/" - "runs/r0/invocations/complete-unit/start.json" - ), - ) - receipt = _record( - { - "stage_id": "qwen-l0-sft", - "wave": "complete", - "run_manifest_record_sha256": manifest.record_sha256, - "invocation_id": "complete-unit", - "invocation_record_sha256": invocation.record_sha256, - "checkpoint": { - field: checkpoint[field] - for field in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - }, - "checkpoint_inventory": { - "count": 1, - "entries": [checkpoint], - "logical_sha256": inventory_sha, - }, - "local_artifact_sha256": local_artifacts, - }, - record_sha="4" * 64, - relative=( - "studies/representation-training-v1/stages/qwen-l0-sft/" - "runs/r0/waves/complete/receipt.json" - ), - ) - - class ParentStore: - @staticmethod - def load_manifest(_key): - return manifest - - @staticmethod - def load_wave_receipt(_key, *, wave): - assert wave == "complete" - return receipt - - @staticmethod - def load_invocation(_key, *, invocation_id): - assert invocation_id == "complete-unit" - return invocation - - @staticmethod - def tinker_log_path(_key): - return log_path - - return manifest, receipt, invocation, checkpoint, local_artifacts, ParentStore() - - -def _synthetic_depth_report( - protocol: dict, -) -> tuple[ - list[promotion.PanelTaskIdentity], - dict, - dict, - TrainingRecord, - TrainingRecord, -]: - tasks = [ - promotion.PanelTaskIdentity( - task_id=f"task-{index:04d}", - level=f"L{index % 5}", - representation_id=f"rep-{index % 546:03d}", - image_sha256=f"{index % 16:x}" * 64, - target_image_sha256=f"{(index + 1) % 16:x}" * 64, - footprint_um=(20.0 + index / 1_000, 10.0), - ) - for index in range(1_092) - ] - records = [ - { - "task_id": task.task_id, - "level": task.level, - "representation_id": task.representation_id, - "status": "ok", - "pure_executable": True, - "raw_absolute_scale_iou": 0.85, - "completion_tokens": 1_000, - "stop_reason": "stop", - "cap_hit": False, - "channel_parse_complete": True, - "answer_text_sha256": "1" * 64, - "render_sha256": "2" * 64, - "sample_record_sha256": "3" * 64, - "evaluation_record_sha256": "4" * 64, - } - for task in tasks - ] - by_level: dict[str, list[dict]] = {} - by_representation: dict[str, list[dict]] = {} - for record in records: - by_level.setdefault(record["level"], []).append(record) - by_representation.setdefault(record["representation_id"], []).append(record) - representations = { - key: promotion._aggregate(values) for key, values in sorted(by_representation.items()) - } - task_panel = { - "schema_version": "pixcell-training-evaluation-task-panel-v1", - "selection_seed": None, - **promotion._panel_manifest( - tasks, - panel=promotion.PANEL_DEPTH_VALIDATION, - ), - } - dataset_manifest = promotion._dataset_binding(REPO_ROOT / "dataset", protocol) - dataset_report = { - "repo_id": protocol["dataset"]["repo_id"], - "revision": protocol["dataset"]["revision"], - "configuration": protocol["dataset"]["configuration"], - "split": "depth/validation", - "logical_release_sha256": dataset_manifest["logical_release_sha256"], - "freeze_file_sha256": dataset_manifest["freeze_file_sha256"], - "parquet_shards": dataset_manifest["parquet_shards"], - } - sandbox = { - "runtime_path": "/usr/bin/docker", - "daemon_endpoint": "unix:///var/run/docker.sock", - "image_ref": "pixcell-evaluator@sha256:" + "7" * 64, - "image_id": "sha256:" + "7" * 64, - "workspace_root": "/tmp/workspace", - } - manifest = _record( - { - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "dataset": dataset_manifest, - }, - record_sha="8" * 64, - relative="studies/x/stages/qwen-mixed-sft/runs/r0/run_manifest.json", - ) - receipt = _record( - { - "stage_id": "qwen-mixed-sft", - "wave": "complete", - "run_manifest_record_sha256": manifest.record_sha256, - "checkpoint": { - "name": "final", - "batch": 55, - "epoch": 1, - "final": True, - "state_path": CHECKPOINT["state_path"], - "sampler_path": "tinker://unit/sampler_weights/final", - }, - "checkpoint_inventory": { - "count": 1, - "entries": [CHECKPOINT], - "logical_sha256": CHECKPOINT_INVENTORY_SHA256, - }, - }, - record_sha="9" * 64, - relative=("studies/x/stages/qwen-mixed-sft/runs/r0/waves/complete/receipt.json"), - ) - model = protocol["models"]["qwen"] - sampler = { - "model": model["model"], - "renderer": model["renderer"], - "effort": model.get("effort"), - "sampler_path": receipt.payload["checkpoint"]["sampler_path"], - "checkpoint_name": "final", - "checkpoint": CHECKPOINT, - "checkpoint_inventory_sha256": CHECKPOINT_INVENTORY_SHA256, - "training_run_manifest_record_sha256": manifest.record_sha256, - "training_wave_receipt_record_sha256": receipt.record_sha256, - "training_wave_receipt_relative_path": str(receipt.relative_path), - } - sampling = { - "attempts_per_task": 1, - "max_output_tokens": 60_000, - "temperature": 1.0, - "top_p": 1.0, - "max_image_long_edge": 1_920, - "seed_algorithm": "sha256-bound deterministic 31-bit seed", - } - evaluation_manifest = { - "record_sha256": "a" * 64, - "payload": { - "study_id": STUDY_ID, - "stage_id": "qwen-mixed-sft", - "wave": "complete", - "checkpoint_name": "final", - "replicate_id": "r0", - "panel": promotion.PANEL_DEPTH_VALIDATION, - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": file_sha256(protocol_path(REPO_ROOT)), - "contract_version": protocol["contract_version"], - "task_panel": task_panel, - "prompt_assets": prompt_asset_hashes(), - "dataset": dataset_manifest, - "sampler": { - key: sampler[key] - for key in ( - "sampler_path", - "checkpoint_name", - "checkpoint", - "checkpoint_inventory_sha256", - "training_run_manifest_record_sha256", - "training_wave_receipt_record_sha256", - ) - }, - "model": model, - "sampling": { - **sampling, - "sample_concurrency": 8, - "evaluation_batch_size": 32, - "evaluator_workers": 8, - }, - "sandbox": sandbox, - }, - } - report = { - "schema_version": "pixcell-training-checkpoint-evaluation-v1", - "study_id": STUDY_ID, - "stage_id": "qwen-mixed-sft", - "wave": "complete", - "checkpoint_name": "final", - "replicate_id": "r0", - "panel": promotion.PANEL_DEPTH_VALIDATION, - "summary": promotion._aggregate(records), - "representation_macro_mean_iou": ( - sum(value["mean_raw_absolute_scale_iou"] for value in representations.values()) - / len(representations) - ), - "levels": {key: promotion._aggregate(values) for key, values in sorted(by_level.items())}, - "representations": representations, - "records": records, - "provenance": { - "source_git_sha": SOURCE_SHA, - "protocol": { - "logical_sha256": protocol["logical_sha256"], - "file_sha256": file_sha256(protocol_path(REPO_ROOT)), - "contract_version": protocol["contract_version"], - }, - "dataset": dataset_report, - "task_panel": task_panel, - "sampler": sampler, - "sampling": sampling, - "prompt_assets": prompt_asset_hashes(), - "sandbox": sandbox, - "evaluation_manifest_record_sha256": evaluation_manifest["record_sha256"], - }, - } - return tasks, report, evaluation_manifest, manifest, receipt - - -def test_first_unmastered_is_ordered_and_rejects_nonfinite() -> None: - assert promotion.first_unmastered_level(_level_report("L2")) == "L2" - assert promotion.first_unmastered_level(_level_report(None)) is None - invalid = _level_report("L2") - invalid["levels"]["L1"]["mean_raw_absolute_scale_iou"] = float("nan") - with pytest.raises(promotion.PromotionError, match="finite"): - promotion.first_unmastered_level(invalid) - - -def test_mixed_checkpoint_ranking_uses_iou_then_purity_then_progress() -> None: - candidates = [ - { - "checkpoint_name": "000014", - "representation_macro_mean_iou": 0.80, - "pure_executable_rate": 0.99, - "training_progress_fraction": 0.25, - }, - { - "checkpoint_name": "000028", - "representation_macro_mean_iou": 0.81, - "pure_executable_rate": 0.90, - "training_progress_fraction": 0.50, - }, - { - "checkpoint_name": "000042", - "representation_macro_mean_iou": 0.81, - "pure_executable_rate": 0.95, - "training_progress_fraction": 0.75, - }, - { - "checkpoint_name": "final", - "representation_macro_mean_iou": 0.81, - "pure_executable_rate": 0.95, - "training_progress_fraction": 1.0, - }, - ] - assert promotion._best_mixed_checkpoint(candidates)["checkpoint_name"] == "final" - - -def test_mixed_selection_requires_every_report_and_rejects_nonwinner( - tmp_path: Path, - monkeypatch, -) -> None: - pytest.importorskip("chz", reason="Tinker is an optional RL dependency") - pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") - pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", - ) - from rl.studies.representation_training_v1 import evaluation - - periodic = { - "name": "000014", - "batch": 14, - "epoch": 0, - "final": False, - "state_path": "tinker://unit/weights/000014", - "sampler_path": "tinker://unit/sampler_weights/000014", - "role": "periodic", - "training_progress_fraction": 0.25, - } - entries = [periodic, CHECKPOINT] - inventory_sha = canonical_sha256(entries) - receipt = _record( - { - "checkpoint": { - key: CHECKPOINT[key] - for key in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - }, - "checkpoint_inventory": { - "count": 2, - "entries": entries, - "logical_sha256": inventory_sha, - }, - }, - record_sha="c" * 64, - relative=("studies/x/stages/qwen-mixed-sft/waves/complete/receipt.json"), - ) - - def scored( - checkpoint: dict, - *, - iou: float, - pure: float, - report_sha: str, - ) -> promotion.EvaluationEvidence: - base = _evidence( - stage_id="qwen-mixed-sft", - wave="complete", - panel=promotion.PANEL_DEPTH_VALIDATION, - first_unmastered="L2", - ) - payload = { - **base.report_payload, - "representation_macro_mean_iou": iou, - "summary": {"pure_executable_rate": pure}, - } - return replace( - base, - checkpoint_name=checkpoint["name"], - checkpoint=checkpoint, - checkpoint_inventory_sha256=inventory_sha, - report_payload=payload, - report_record_sha256=report_sha, - training_receipt=receipt, - ) - - selected = scored( - CHECKPOINT, - iou=0.80, - pure=0.99, - report_sha="d" * 64, - ) - periodic_report = scored( - periodic, - iou=0.90, - pure=0.90, - report_sha="f" * 64, - ) - - class FakeReportStore: - def __init__(self, **kwargs) -> None: - self.checkpoint_name = kwargs["checkpoint_name"] - - def load_report(self) -> dict | None: - if self.checkpoint_name == periodic["name"]: - return {"record_sha256": periodic_report.report_record_sha256} - return None - - monkeypatch.setattr( - evaluation, - "CheckpointEvaluationStore", - FakeReportStore, - ) - monkeypatch.setattr( - promotion, - "validate_checkpoint_report", - lambda **_kwargs: periodic_report, - ) - repo = tmp_path / "repo" - repo.mkdir() - store = TrainingStore( - repo_root=repo, - external_root=tmp_path / "external", - ) - with pytest.raises(promotion.PromotionError, match="deterministic best"): - promotion._mixed_checkpoint_selection( - repo_root=repo, - protocol=load_protocol(REPO_ROOT), - store=store, - selected=selected, - expected_source_sha=SOURCE_SHA, - ) - - monkeypatch.setattr( - FakeReportStore, - "load_report", - lambda _self: None, - ) - with pytest.raises(promotion.PromotionError, match="complete depth-validation"): - promotion._mixed_checkpoint_selection( - repo_root=repo, - protocol=load_protocol(REPO_ROOT), - store=store, - selected=selected, - expected_source_sha=SOURCE_SHA, - ) - - -def test_plans_bind_reports_and_clean_smoke_storage() -> None: - protocol = load_protocol(REPO_ROOT) - mixed = promotion.approval_plan( - stage_spec(protocol, "qwen-mixed-rl-l2"), - "smoke", - ) - assert mixed.expected_panel == promotion.PANEL_DEPTH_VALIDATION - assert mixed.allowed_evaluation_checkpoints == ( - ("qwen-mixed-sft", "complete"), - ("qwen-mixed-rl-l0", "complete"), - ("qwen-mixed-rl-l1", "complete"), - ) - continuation = promotion.approval_plan( - stage_spec(protocol, "qwen-mixed-rl-l2"), - "step-5", - ) - assert continuation.storage_wave == "step-5" - assert continuation.expected_panel == promotion.PANEL_PROGRESS - - -def test_pure_base_level_transition_binds_prior_and_current_slot6_panels() -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l2") - plan = promotion.approval_plan(stage, "smoke") - assert plan.gate == ( - "prior-and-current-level-held-out-transition-receipt" - ) - assert plan.expected_panel == "level-progress-l1" - assert plan.entry_baseline_panel == "level-progress-l2" - assert plan.allowed_evaluation_checkpoints == ( - ("qwen-base-rl-l1", "complete"), - ) - - prior = _evidence( - stage_id="qwen-base-rl-l1", - wave="complete", - panel="level-progress-l1", - first_unmastered=None, - ) - baseline = replace( - prior, - panel="level-progress-l2", - report_record_sha256="1" * 64, - report_payload_sha256="2" * 64, - report_relative_path=( - "studies/x/stages/qwen-base-rl-l1/evaluations/" - "complete/final/level-progress-l2/report.json" - ), - evaluation_manifest_payload_sha256="3" * 64, - evaluation_manifest_record_sha256="4" * 64, - evaluation_manifest_relative_path=( - "studies/x/stages/qwen-base-rl-l1/evaluations/" - "complete/final/level-progress-l2/manifest.json" - ), - ) - payload = promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=plan, - evidence=prior, - entry_baseline_evidence=baseline, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert payload["evaluation"]["panel"] == "level-progress-l1" - assert payload["entry_baseline"]["panel"] == "level-progress-l2" - assert ( - payload["evaluation"]["checkpoint"] - == payload["entry_baseline"]["checkpoint"] - ) - assert payload["parent_stage_id"] == "qwen-base-rl-l1" - assert payload["parent_wave"] == "complete" - - with pytest.raises(promotion.PromotionError, match="requires the .* baseline"): - promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=plan, - evidence=prior, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - with pytest.raises(promotion.PromotionError, match="exact same parent checkpoint"): - promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=plan, - evidence=prior, - entry_baseline_evidence=replace( - baseline, - checkpoint={**baseline.checkpoint, "sampler_path": "tinker://other"}, - ), - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - mismatched_artifact_chains = ( - replace(baseline, checkpoint_inventory_sha256="6" * 64), - replace( - baseline, - training_manifest=replace( - baseline.training_manifest, - record_sha256="7" * 64, - ), - ), - replace( - baseline, - training_receipt=replace( - baseline.training_receipt, - record_sha256="8" * 64, - ), - ), - ) - for mismatched in mismatched_artifact_chains: - with pytest.raises( - promotion.PromotionError, - match="exact same parent checkpoint", - ): - promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=plan, - evidence=prior, - entry_baseline_evidence=mismatched, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - - -def test_same_source_approval_is_recomputed_from_both_bound_reports( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l2") - plan = promotion.approval_plan(stage, "smoke") - prior = _evidence( - stage_id="qwen-base-rl-l1", - wave="complete", - panel=plan.expected_panel, - first_unmastered=None, - ) - baseline = replace( - prior, - panel=str(plan.entry_baseline_panel), - report_record_sha256="1" * 64, - report_payload_sha256="2" * 64, - report_relative_path=( - "studies/x/stages/qwen-base-rl-l1/evaluations/" - "complete/final/level-progress-l2/report.json" - ), - evaluation_manifest_payload_sha256="3" * 64, - evaluation_manifest_record_sha256="4" * 64, - evaluation_manifest_relative_path=( - "studies/x/stages/qwen-base-rl-l1/evaluations/" - "complete/final/level-progress-l2/manifest.json" - ), - ) - payload = promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=plan, - evidence=prior, - entry_baseline_evidence=baseline, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - approval = _record( - payload, - record_sha="5" * 64, - relative=( - "studies/representation-training-v1/stages/qwen-base-rl-l2/" - "runs/r0/waves/smoke/approval.json" - ), - ) - calls: list[dict] = [] - - def validate_report(**kwargs): - calls.append(dict(kwargs)) - if kwargs["panel"] == plan.expected_panel: - assert kwargs["expected_report_record_sha256"] == "d" * 64 - return prior - assert kwargs["panel"] == plan.entry_baseline_panel - assert kwargs["expected_report_record_sha256"] == "1" * 64 - return baseline - - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - promotion, - "validate_checkpoint_report", - validate_report, - ) - repo = tmp_path / "repo" - repo.mkdir() - store = TrainingStore(repo_root=repo, external_root=tmp_path / "external") - observed = promotion.validate_same_source_wave_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage, - wave_name="smoke", - replicate_id="r0", - source_git_sha=SOURCE_SHA, - approval=approval, - ) - assert observed is prior - assert [call["panel"] for call in calls] == [ - "level-progress-l1", - "level-progress-l2", - ] - - tampered = replace( - approval, - payload={ - **approval.payload, - "entry_baseline": { - **approval.payload["entry_baseline"], - "report_payload_sha256": "9" * 64, - }, - }, - ) - with pytest.raises(promotion.PromotionError, match="canonically recomputed"): - promotion.validate_same_source_wave_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage, - wave_name="smoke", - replicate_id="r0", - source_git_sha=SOURCE_SHA, - approval=tampered, - ) - - -def test_same_source_current_level_approval_rejects_minimal_json( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-base-rl-l0") - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda _root, expected: expected, - ) - repo = tmp_path / "repo" - repo.mkdir() - store = TrainingStore(repo_root=repo, external_root=tmp_path / "external") - approval = _record( - { - "schema_version": promotion.APPROVAL_SCHEMA_VERSION, - "decision": "approve", - "approval_gate": "current-level-held-out-promotion-receipt", - "stage_id": stage.stage_id, - "wave": "step-10", - "source_git_sha": SOURCE_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - }, - record_sha="6" * 64, - relative=( - "studies/representation-training-v1/stages/qwen-base-rl-l0/" - "runs/r0/waves/step-10/approval.json" - ), - ) - with pytest.raises(promotion.PromotionError, match="approval.evaluation"): - promotion.validate_same_source_wave_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage, - wave_name="step-10", - replicate_id="r0", - source_git_sha=SOURCE_SHA, - approval=approval, - ) - - -def test_mixed_approval_selects_first_unmastered_and_binds_parent() -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-mixed-rl-l2") - evidence = _evidence( - stage_id="qwen-mixed-sft", - wave="complete", - panel=promotion.PANEL_DEPTH_VALIDATION, - first_unmastered="L2", - ) - payload = promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=promotion.approval_plan(stage, "smoke"), - evidence=evidence, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert payload["schema_version"] == promotion.APPROVAL_SCHEMA_VERSION - assert "evidence_source_git_sha" not in payload - assert "source_transition" not in payload - assert payload["decision"] == "approve" - assert payload["approval_gate"] == "first-unmastered-entry-receipt" - assert payload["parent_stage_id"] == "qwen-mixed-sft" - assert payload["parent_wave"] == "complete" - assert payload["parent_receipt_record_sha256"] == "c" * 64 - assert payload["selected_parent_checkpoint"] == CHECKPOINT - assert payload["selected_parent_checkpoint_inventory_sha256"] == CHECKPOINT_INVENTORY_SHA256 - assert payload["mastery"]["selected_level"] == "L2" - - wrong = _evidence( - stage_id="qwen-mixed-sft", - wave="complete", - panel=promotion.PANEL_DEPTH_VALIDATION, - first_unmastered="L1", - ) - with pytest.raises(promotion.PromotionError, match="first unmastered"): - promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=promotion.approval_plan(stage, "smoke"), - evidence=wrong, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - - -def test_clean_smoke_approval_does_not_masquerade_as_parent() -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-mixed-rl-l2") - evidence = _evidence( - stage_id=stage.stage_id, - wave="smoke", - panel=promotion.PANEL_PROGRESS, - first_unmastered=None, - ) - payload = promotion._approval_payload( - stage=stage, - wave_name="step-5", - plan=promotion.approval_plan(stage, "step-5"), - evidence=evidence, - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - assert "parent_receipt_record_sha256" not in payload - assert payload["evaluation"]["training_wave_receipt_record_sha256"] == "c" * 64 - assert payload["prior_wave"] == "smoke" - assert payload["prior_receipt_record_sha256"] == "c" * 64 - - -def test_entry_approval_cannot_authorize_continuation( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-mixed-rl-l2") - entry = promotion._approval_payload( - stage=stage, - wave_name="smoke", - plan=promotion.approval_plan(stage, "smoke"), - evidence=_evidence( - stage_id="qwen-mixed-sft", - wave="complete", - panel=promotion.PANEL_DEPTH_VALIDATION, - first_unmastered="L2", - ), - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - continuation = promotion._approval_payload( - stage=stage, - wave_name="step-5", - plan=promotion.approval_plan(stage, "step-5"), - evidence=_evidence( - stage_id=stage.stage_id, - wave="smoke", - panel=promotion.PANEL_PROGRESS, - first_unmastered=None, - ), - source_git_sha=SOURCE_SHA, - protocol=protocol, - ) - repo = tmp_path / "repo" - repo.mkdir() - store = TrainingStore( - repo_root=repo, - external_root=tmp_path / "external", - ) - key = StageKey(STUDY_ID, stage.stage_id, "r0") - with store.acquire_stage_lock(key): - store.write_wave_approval(key, wave="smoke", payload=entry) - assert store.load_wave_approval(key, wave="step-5") is None - store.write_wave_approval( - key, - wave="step-5", - payload=continuation, - ) - assert ( - store.load_wave_approval(key, wave="smoke").payload["approval_gate"] - == "first-unmastered-entry-receipt" - ) - assert ( - store.load_wave_approval(key, wave="step-5").payload["approval_gate"] - == "clean-smoke-receipt" - ) - - -def test_depth_report_recomputes_all_rows_metrics_and_provenance() -> None: - protocol = load_protocol(REPO_ROOT) - tasks, report, evaluation_manifest, manifest, receipt = _synthetic_depth_report(protocol) - promotion._validate_report_payload( - repo_root=REPO_ROOT, - protocol=protocol, - expected_source_sha=SOURCE_SHA, - key=StageKey(STUDY_ID, "qwen-mixed-sft", "r0"), - wave="complete", - checkpoint_name="final", - selected_checkpoint=CHECKPOINT, - checkpoint_inventory_sha256=CHECKPOINT_INVENTORY_SHA256, - panel=promotion.PANEL_DEPTH_VALIDATION, - report=report, - evaluation_manifest=evaluation_manifest, - training_manifest=manifest, - training_receipt=receipt, - expected_tasks=tasks, - ) - report_dataset = report["provenance"]["dataset"] - manifest_dataset = evaluation_manifest["payload"]["dataset"] - manifest_only_fields = { - "train_rows", - "validation_rows", - "reference_artifacts_sha256", - "reference_artifact_count", - } - assert manifest_only_fields.isdisjoint(report_dataset) - assert manifest_only_fields.issubset(manifest_dataset) - - tampered_manifest = copy.deepcopy(evaluation_manifest) - tampered_manifest["payload"]["dataset"]["reference_artifact_count"] += 1 - with pytest.raises( - promotion.PromotionError, - match="evaluation-manifest dataset differs from the frozen release", - ): - promotion._validate_report_payload( - repo_root=REPO_ROOT, - protocol=protocol, - expected_source_sha=SOURCE_SHA, - key=StageKey(STUDY_ID, "qwen-mixed-sft", "r0"), - wave="complete", - checkpoint_name="final", - selected_checkpoint=CHECKPOINT, - checkpoint_inventory_sha256=CHECKPOINT_INVENTORY_SHA256, - panel=promotion.PANEL_DEPTH_VALIDATION, - report=report, - evaluation_manifest=tampered_manifest, - training_manifest=manifest, - training_receipt=receipt, - expected_tasks=tasks, - ) - - overfull_report = copy.deepcopy(report) - overfull_report["provenance"]["dataset"]["reference_artifact_count"] = ( - manifest_dataset["reference_artifact_count"] - ) - with pytest.raises( - promotion.PromotionError, - match="report dataset projection differs", - ): - promotion._validate_report_payload( - repo_root=REPO_ROOT, - protocol=protocol, - expected_source_sha=SOURCE_SHA, - key=StageKey(STUDY_ID, "qwen-mixed-sft", "r0"), - wave="complete", - checkpoint_name="final", - selected_checkpoint=CHECKPOINT, - checkpoint_inventory_sha256=CHECKPOINT_INVENTORY_SHA256, - panel=promotion.PANEL_DEPTH_VALIDATION, - report=overfull_report, - evaluation_manifest=evaluation_manifest, - training_manifest=manifest, - training_receipt=receipt, - expected_tasks=tasks, - ) - - nonfinite = copy.deepcopy(report) - nonfinite["summary"]["mean_raw_absolute_scale_iou"] = float("nan") - with pytest.raises(promotion.PromotionError, match="finite"): - promotion._validate_report_payload( - repo_root=REPO_ROOT, - protocol=protocol, - expected_source_sha=SOURCE_SHA, - key=StageKey(STUDY_ID, "qwen-mixed-sft", "r0"), - wave="complete", - checkpoint_name="final", - selected_checkpoint=CHECKPOINT, - checkpoint_inventory_sha256=CHECKPOINT_INVENTORY_SHA256, - panel=promotion.PANEL_DEPTH_VALIDATION, - report=nonfinite, - evaluation_manifest=evaluation_manifest, - training_manifest=manifest, - training_receipt=receipt, - expected_tasks=tasks, - ) - - with pytest.raises(promotion.PromotionError, match="expected 1092"): - promotion._validate_report_payload( - repo_root=REPO_ROOT, - protocol=protocol, - expected_source_sha=SOURCE_SHA, - key=StageKey(STUDY_ID, "qwen-mixed-sft", "r0"), - wave="complete", - checkpoint_name="final", - selected_checkpoint=CHECKPOINT, - checkpoint_inventory_sha256=CHECKPOINT_INVENTORY_SHA256, - panel=promotion.PANEL_DEPTH_VALIDATION, - report=report, - evaluation_manifest=evaluation_manifest, - training_manifest=manifest, - training_receipt=receipt, - expected_tasks=tasks[:-1], - ) - - -def test_record_approval_requires_human_confirmation_and_is_immutable( - tmp_path: Path, - monkeypatch, -) -> None: - with pytest.raises(promotion.PromotionError, match="human approval"): - promotion.record_wave_approval( - repo_root=tmp_path / "missing", - external_root=tmp_path / "external", - stage_id="qwen-mixed-rl-l2", - wave_name="smoke", - replicate_id="r0", - evaluation_stage_id="qwen-mixed-sft", - evaluation_wave="complete", - evaluation_checkpoint_name="final", - evaluation_report_record_sha256="d" * 64, - expected_source_sha=SOURCE_SHA, - confirmation="wrong", - ) - - repo = tmp_path / "repo" - repo.mkdir() - external = tmp_path / "external" - protocol = load_protocol(REPO_ROOT) - evidence = _evidence( - stage_id="qwen-l0-sft", - wave="complete", - panel=promotion.PANEL_DEPTH_VALIDATION, - first_unmastered="L1", - ) - monkeypatch.setattr( - promotion, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - promotion, - "validate_checkpoint_report", - lambda **_kwargs: evidence, - ) - kwargs = { - "repo_root": repo, - "external_root": external, - "stage_id": "qwen-sequential-sft-l1", - "wave_name": "complete", - "replicate_id": "r0", - "evaluation_stage_id": "qwen-l0-sft", - "evaluation_wave": "complete", - "evaluation_checkpoint_name": "final", - "evaluation_report_record_sha256": "d" * 64, - "expected_source_sha": SOURCE_SHA, - "confirmation": promotion.APPROVAL_CONFIRMATION_TOKEN, - } - first = promotion.record_wave_approval(**kwargs) - second = promotion.record_wave_approval(**kwargs) - assert first == second - store = TrainingStore(repo_root=repo, external_root=external) - approval = store.load_wave_approval( - StageKey(STUDY_ID, "qwen-sequential-sft-l1", "r0"), - wave="complete", - ) - assert approval is not None - assert approval.payload["decision"] == "approve" - assert approval.payload["explicit_human_confirmation"] is True - - -def test_cross_source_v2_binds_exact_evidence_scope_and_one_child( - tmp_path: Path, - monkeypatch, -) -> None: - repo, producer, consumer = _git_transition_history(tmp_path) - external = tmp_path / "external" - protocol = load_protocol(REPO_ROOT) - evidence = _cross_source_evidence( - protocol, - producer_source_git_sha=producer, - ) - monkeypatch.setattr( - promotion, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr( - promotion, - "file_sha256", - lambda _path: "4" * 64, - ) - report_calls: list[dict] = [] - - def validate_report(**kwargs): - report_calls.append(dict(kwargs)) - assert kwargs["expected_source_sha"] == producer - return evidence - - monkeypatch.setattr( - promotion, - "validate_checkpoint_report", - validate_report, - ) - result = promotion.record_wave_approval( - repo_root=repo, - external_root=external, - stage_id="qwen-sequential-sft-l1", - wave_name="complete", - replicate_id="r0", - evaluation_stage_id="qwen-l0-sft", - evaluation_wave="complete", - evaluation_checkpoint_name="final", - evaluation_report_record_sha256=evidence.report_record_sha256, - expected_source_sha=consumer, - evidence_source_sha=producer, - approved_change_paths=["bridge.txt"], - confirmation=promotion.APPROVAL_CONFIRMATION_TOKEN, - ) - assert result["approval_schema_version"] == ( - promotion.SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION - ) - assert result["evidence_source_git_sha"] == producer - assert result["consumer_source_git_sha"] == consumer - - key = StageKey(STUDY_ID, "qwen-sequential-sft-l1", "r0") - store = TrainingStore(repo_root=repo, external_root=external) - approval = store.load_wave_approval(key, wave="complete") - assert approval is not None - payload = approval.payload - assert payload["schema_version"] == (promotion.SOURCE_TRANSITION_APPROVAL_SCHEMA_VERSION) - assert payload["evaluation"]["report_record_sha256"] == (evidence.report_record_sha256) - assert payload["evaluation"]["training_run_manifest_record_sha256"] == ( - evidence.training_manifest.record_sha256 - ) - assert payload["evaluation"]["training_wave_receipt_record_sha256"] == ( - evidence.training_receipt.record_sha256 - ) - assert payload["evaluation"]["evaluation_manifest_record_sha256"] == ( - evidence.evaluation_manifest_record_sha256 - ) - transition = payload["source_transition"] - assert transition["scope"]["child"] == { - "stage_id": "qwen-sequential-sft-l1", - "wave": "complete", - "replicate_id": "r0", - "approval_gate": "parent-evaluation-receipt", - } - assert transition["scope"]["approved_changes"]["paths"] == ["bridge.txt"] - assert ( - transition["evidence"]["checkpoint_inventory"] - == (evidence.training_receipt.payload["checkpoint_inventory"]) - ) - assert transition["evidence"]["evaluation_manifest"]["record_sha256"] == ( - evidence.evaluation_manifest_record_sha256 - ) - assert transition["equivalence"]["model"]["producer_training"] == (protocol["models"]["qwen"]) - assert transition["equivalence"]["renderer"]["producer_evaluation"] == { - "model": protocol["models"]["qwen"]["model"], - "renderer": protocol["models"]["qwen"]["renderer"], - "effort": protocol["models"]["qwen"].get("effort"), - } - assert ( - promotion.validate_source_transition_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage_spec(protocol, "qwen-sequential-sft-l1"), - wave_name="complete", - replicate_id="r0", - consumer_source_git_sha=consumer, - approval=approval, - ) - == evidence - ) - with pytest.raises(promotion.PromotionError, match="another replicate"): - promotion.validate_source_transition_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage_spec(protocol, "qwen-sequential-sft-l1"), - wave_name="complete", - replicate_id="r1", - consumer_source_git_sha=consumer, - approval=approval, - ) - - tampered_payload = copy.deepcopy(payload) - tampered_payload["source_transition"]["evidence"]["evaluation_report"]["record_sha256"] = ( - "0" * 64 - ) - tampered = replace(approval, payload=tampered_payload) - with pytest.raises(promotion.PromotionError, match="recomputed evidence"): - promotion.validate_source_transition_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage_spec(protocol, "qwen-sequential-sft-l1"), - wave_name="complete", - replicate_id="r0", - consumer_source_git_sha=consumer, - approval=tampered, - ) - assert all(call["expected_source_sha"] == producer for call in report_calls) - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda *_args, **_kwargs: (_ for _ in ()).throw( - ValueError("paid training requires a completely clean worktree") - ), - ) - with pytest.raises(promotion.PromotionError, match="clean HEAD"): - promotion.validate_source_transition_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage_spec(protocol, "qwen-sequential-sft-l1"), - wave_name="complete", - replicate_id="r0", - consumer_source_git_sha=consumer, - approval=approval, - ) - - -def test_cross_source_recording_rejects_missing_or_wrong_change_scope( - tmp_path: Path, - monkeypatch, -) -> None: - repo, producer, consumer = _git_transition_history(tmp_path) - protocol = load_protocol(REPO_ROOT) - monkeypatch.setattr( - promotion, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda _root, expected: expected, - ) - common = { - "repo_root": repo, - "external_root": tmp_path / "external", - "stage_id": "qwen-sequential-sft-l1", - "wave_name": "complete", - "replicate_id": "r0", - "evaluation_stage_id": "qwen-l0-sft", - "evaluation_wave": "complete", - "evaluation_checkpoint_name": "final", - "evaluation_report_record_sha256": "d" * 64, - "expected_source_sha": consumer, - "evidence_source_sha": producer, - "confirmation": promotion.APPROVAL_CONFIRMATION_TOKEN, - } - with pytest.raises(promotion.PromotionError, match="explicit approved"): - promotion.record_wave_approval(**common) - with pytest.raises( - promotion.PromotionError, - match="approved change paths", - ): - promotion.record_wave_approval( - **common, - approved_change_paths=["wrong.txt"], - ) - with pytest.raises( - promotion.PromotionError, - match="same-source v1", - ): - promotion.record_wave_approval( - **{ - **common, - "evidence_source_sha": consumer, - }, - approved_change_paths=["bridge.txt"], - ) - - -def test_historical_parent_evidence_binds_receipt_without_evaluation( - tmp_path: Path, -) -> None: - protocol = load_protocol(REPO_ROOT) - producer = "b" * 40 - manifest, receipt, invocation, checkpoint, artifacts, store = ( - _historical_parent_records( - tmp_path, - protocol, - producer_source_git_sha=producer, - ) - ) - observed = promotion._parent_receipt_transition_evidence( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - replicate_id="r0", - ) - assert observed[:4] == (manifest, receipt, invocation, checkpoint) - document = observed[4] - assert "evaluation" not in document["evidence"] - assert document["evidence"]["local_artifact_sha256"] == artifacts - assert ( - document["stable_equivalence"]["prompt_assets"] - == prompt_asset_hashes() - ) - - manifest.payload["prompt_assets"] = { - **prompt_asset_hashes(), - "task_sha256": "f" * 64, - } - with pytest.raises(promotion.PromotionError, match="stable contract"): - promotion._parent_receipt_transition_evidence( - repo_root=REPO_ROOT, - protocol=protocol, - store=store, - replicate_id="r0", - ) - - -def test_parent_receipt_transition_records_and_revalidates_exact_bridge( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, producer, consumer = _git_transition_history(tmp_path) - protocol = load_protocol(REPO_ROOT) - manifest, receipt, invocation, checkpoint, _artifacts, _store = ( - _historical_parent_records( - tmp_path / "parent", - protocol, - producer_source_git_sha=producer, - ) - ) - stable = { - "stable_equivalence": { - "contract_version": protocol["contract_version"], - "prompt_assets": prompt_asset_hashes(), - "dataset": promotion._dataset_binding(REPO_ROOT / "dataset", protocol), - "model": protocol["models"]["qwen"], - "recipe": protocol["recipes"]["qwen_sft"], - "stage": dict(manifest.payload["stage"]), - }, - "evidence": { - "parent_run_manifest": {"record_sha256": manifest.record_sha256}, - "parent_wave_receipt": {"record_sha256": receipt.record_sha256}, - "parent_invocation": {"record_sha256": invocation.record_sha256}, - "checkpoint_inventory": receipt.payload["checkpoint_inventory"], - "selected_checkpoint": checkpoint, - }, - } - monkeypatch.setattr( - promotion, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - monkeypatch.setattr( - promotion, - "validate_source_sha", - lambda _root, expected: expected, - ) - monkeypatch.setattr(promotion, "file_sha256", lambda _path: "5" * 64) - monkeypatch.setattr( - promotion, - "_parent_receipt_transition_evidence", - lambda **_kwargs: ( - manifest, - receipt, - invocation, - checkpoint, - stable, - ), - ) - external = tmp_path / "external" - with pytest.raises(promotion.PromotionError, match="human approval"): - promotion.record_parent_receipt_transition_approval( - repo_root=repo, - external_root=external, - stage_id="qwen-l0-rl-l1", - wave_name="smoke", - replicate_id="r0", - expected_source_sha=consumer, - approved_change_paths=["bridge.txt"], - confirmation="wrong", - ) - result = promotion.record_parent_receipt_transition_approval( - repo_root=repo, - external_root=external, - stage_id="qwen-l0-rl-l1", - wave_name="smoke", - replicate_id="r0", - expected_source_sha=consumer, - approved_change_paths=["bridge.txt"], - confirmation=promotion.APPROVAL_CONFIRMATION_TOKEN, - ) - assert result["parent_receipt_record_sha256"] == receipt.record_sha256 - store = TrainingStore(repo_root=repo, external_root=external) - key = StageKey(STUDY_ID, "qwen-l0-rl-l1", "r0") - approval = store.load_wave_approval(key, wave="smoke") - assert approval is not None - assert approval.payload["schema_version"] == ( - promotion.PARENT_RECEIPT_TRANSITION_APPROVAL_SCHEMA_VERSION - ) - assert approval.payload["selected_parent_checkpoint"] == checkpoint - assert "evaluation" not in approval.payload - promotion.validate_parent_receipt_transition_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage_spec(protocol, "qwen-l0-rl-l1"), - wave_name="smoke", - replicate_id="r0", - consumer_source_git_sha=consumer, - approval=approval, - ) - - tampered = replace( - approval, - payload={ - **approval.payload, - "parent_receipt_record_sha256": "0" * 64, - }, - ) - with pytest.raises(promotion.PromotionError, match="recomputed evidence"): - promotion.validate_parent_receipt_transition_approval( - repo_root=repo, - protocol=protocol, - store=store, - stage=stage_spec(protocol, "qwen-l0-rl-l1"), - wave_name="smoke", - replicate_id="r0", - consumer_source_git_sha=consumer, - approval=tampered, - ) diff --git a/rl/studies/representation_training_v1/tests/test_protocol_schedule.py b/rl/studies/representation_training_v1/tests/test_protocol_schedule.py deleted file mode 100644 index d048bab8..00000000 --- a/rl/studies/representation_training_v1/tests/test_protocol_schedule.py +++ /dev/null @@ -1,276 +0,0 @@ -from __future__ import annotations - -from pathlib import Path - -import pytest - -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") - -import tinker - -from rl.studies.representation_training_v1.launcher import ( - deterministic_wandb_run_id, -) -from rl.studies.representation_training_v1.protocol import ( - STUDY_ID, - load_protocol, - parent_replicate_id, - stage_spec, - validate_stage_replicate, -) -from rl.studies.representation_training_v1.schedule import ( - SCHEDULE_SEED, - stage_schedule_document, - stage_tasks, -) -from rl.studies.representation_training_v1.store import StageKey -from rl.track_a.tinker_data import TrackARLDataset - - -REPO_ROOT = Path(__file__).resolve().parents[4] - - -def test_protocol_seals_the_shared_branch_graph() -> None: - protocol = load_protocol(REPO_ROOT) - assert len(protocol["stages"]) == 21 - assert protocol["recipes"]["qwen_sft"]["learning_rate"] == 1e-4 - assert stage_spec(protocol, "qwen-mixed-sft").parent == "base:qwen" - assert ( - stage_spec(protocol, "qwen-sequential-sft-l1").parent - == "qwen-l0-sft:complete" - ) - assert stage_spec(protocol, "qwen-l0-rl-l1").parent == "qwen-l0-sft:complete" - assert stage_spec(protocol, "inkling-l4-rl").parent == "base:inkling" - - -def test_protocol_seals_the_pure_qwen_rl_curriculum() -> None: - protocol = load_protocol(REPO_ROOT) - expected_gates = { - "step-5": "rollout-health-receipt", - "step-10": "current-level-held-out-promotion-receipt", - "step-15": "current-level-held-out-promotion-receipt", - "step-20": "current-level-held-out-promotion-receipt", - "step-25": "current-level-held-out-promotion-receipt", - "complete": "current-level-held-out-promotion-receipt", - } - for level in range(5): - stage = stage_spec(protocol, f"qwen-base-rl-l{level}") - assert stage.hypotheses == ("RT-H06",) - assert stage.parent == ( - "base:qwen" - if level == 0 - else f"qwen-base-rl-l{level - 1}:complete" - ) - assert stage.current_level == f"L{level}" - assert stage.replay_levels == tuple(f"L{prior}" for prior in range(level)) - assert { - name: wave["max_steps"] for name, wave in stage.waves.items() - } == { - "smoke": 1, - "step-5": 5, - "step-10": 10, - "step-15": 15, - "step-20": 20, - "step-25": 25, - "complete": 30, - } - assert stage.wave("smoke")["approval"] == ( - "initial" - if level == 0 - else "prior-and-current-level-held-out-transition-receipt" - ) - assert { - wave: stage.wave(wave)["approval"] for wave in expected_gates - } == expected_gates - - -def test_operational_retry_is_scoped_and_keeps_the_historical_r0_parent() -> None: - protocol = load_protocol(REPO_ROOT) - - validate_stage_replicate( - protocol, - stage_id="qwen-l0-rl-l1", - replicate_id="r0-retry1", - ) - assert ( - parent_replicate_id( - protocol, - stage_id="qwen-l0-rl-l1", - replicate_id="r0-retry1", - ) - == "r0" - ) - assert ( - parent_replicate_id( - protocol, - stage_id="qwen-l0-rl-l2", - replicate_id="r0-retry1", - ) - == "r0-retry1" - ) - with pytest.raises(ValueError, match="not declared for this training stage"): - validate_stage_replicate( - protocol, - stage_id="qwen-base-rl-l0", - replicate_id="r0-retry1", - ) - - -def test_protocol_seals_fixed_progress_and_final_selection_panels() -> None: - evaluation = load_protocol(REPO_ROOT)["evaluation"] - progress = evaluation["curriculum_progress_holdout"] - assert progress == { - "baseline_checkpoints": { - "L0": "base:qwen", - "L1": "qwen-base-rl-l0:complete", - "L2": "qwen-base-rl-l1:complete", - "L3": "qwen-base-rl-l2:complete", - "L4": "qwen-base-rl-l3:complete", - }, - "configuration": "depth", - "evaluation_waves": [ - "step-5", - "step-10", - "step-15", - "step-20", - "step-25", - "complete", - ], - "panel_prefix": "level-progress", - "realization_slot": 6, - "representations_by_level": { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108, - }, - "rows_by_level": { - "L0": 93, - "L1": 127, - "L2": 98, - "L3": 120, - "L4": 108, - }, - "split": "validation", - } - assert evaluation["final_selection_holdout"] == { - "configuration": "depth", - "panel": "depth-final-selection", - "realization_slot": 7, - "representations": 546, - "rows": 546, - "split": "validation", - } - - -def test_pure_qwen_rl_schedule_uses_every_level_and_full_earlier_prefix() -> None: - protocol = load_protocol(REPO_ROOT) - expected_rows = {"L0": 642, "L1": 850, "L2": 608, "L3": 720, "L4": 648} - expected_representations = {"L0": 93, "L1": 127, "L2": 99, "L3": 120, "L4": 108} - for level in range(5): - stage = stage_spec(protocol, f"qwen-base-rl-l{level}") - schedule = stage_schedule_document( - REPO_ROOT / "dataset", - stage, - groups_per_batch=8, - ) - current = f"L{level}" - assert schedule["maximum_steps"] == 30 - assert schedule["rows_by_level"] == {current: expected_rows[current]} - assert schedule["representations"] == expected_representations[current] - assert schedule["replay_levels"] == [ - f"L{prior}" for prior in range(level) - ] - assert len(schedule["training_batches"]) == 30 - assert all( - len(batch["groups"]) == 8 - for batch in schedule["training_batches"] - ) - - -def test_qwen_rl_schedule_is_the_exact_dataset_batch_schedule() -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, "qwen-l0-rl-l3") - schedule = stage_schedule_document( - REPO_ROOT / "dataset", - stage, - groups_per_batch=8, - ) - assert schedule["maximum_steps"] == 30 - assert len(schedule["training_batches"]) == 30 - assert all(len(batch["groups"]) == 8 for batch in schedule["training_batches"]) - assert all( - len({group["task_id"] for group in batch["groups"]}) == 8 - for batch in schedule["training_batches"] - ) - replay_groups = sum( - group["role"] == "replay" - for batch in schedule["training_batches"] - for group in batch["groups"] - ) - assert replay_groups == 48 - - current = stage_tasks(REPO_ROOT / "dataset", stage) - from rl.studies.representation_training_v1.schedule import stage_replay_tasks - - replay = stage_replay_tasks(REPO_ROOT / "dataset", stage) - dataset = TrackARLDataset( - current, - replay, - renderer=object(), - groups_per_batch=8, - group_size=4, - max_image=1920, - n_batches=30, - shuffle=True, - reward_policy="raw_iou", - schedule_seed=SCHEDULE_SEED, - ) - for step, expected in enumerate(schedule["training_batches"]): - observed = [builder.task.sampler.opaque_id for builder in dataset.get_batch(step)] - assert observed == [group["task_id"] for group in expected["groups"]] - - -def test_sft_stage_counts_are_one_physical_pass() -> None: - protocol = load_protocol(REPO_ROOT) - expected = { - "qwen-l0-sft": (642, 11), - "qwen-mixed-sft": (3468, 55), - "qwen-sequential-sft-l1": (850, 14), - "qwen-sequential-sft-l2": (608, 10), - "qwen-sequential-sft-l3": (720, 12), - "qwen-sequential-sft-l4": (648, 11), - } - for stage_id, (rows, batches) in expected.items(): - stage = stage_spec(protocol, stage_id) - schedule = stage_schedule_document(REPO_ROOT / "dataset", stage) - assert (schedule["rows"], schedule["batches"]) == (rows, batches) - assert stage.wave("complete")["max_steps"] == batches - - -def test_pinned_tinker_top_p_default_is_one() -> None: - params = tinker.SamplingParams(max_tokens=1, temperature=1.0) - assert params.top_p == 1 - - -def test_wandb_identity_binds_protocol_source_stage_and_replicate() -> None: - key = StageKey(STUDY_ID, "qwen-mixed-sft", "r0") - values = { - deterministic_wandb_run_id( - source_git_sha="1" * 40, - protocol_logical_sha256="2" * 64, - key=key, - ), - deterministic_wandb_run_id( - source_git_sha="3" * 40, - protocol_logical_sha256="2" * 64, - key=key, - ), - deterministic_wandb_run_id( - source_git_sha="1" * 40, - protocol_logical_sha256="4" * 64, - key=key, - ), - } - assert len(values) == 3 diff --git a/rl/studies/representation_training_v1/tests/test_renderer_diagnostic.py b/rl/studies/representation_training_v1/tests/test_renderer_diagnostic.py deleted file mode 100644 index 15f413b9..00000000 --- a/rl/studies/representation_training_v1/tests/test_renderer_diagnostic.py +++ /dev/null @@ -1,783 +0,0 @@ -from __future__ import annotations - -import asyncio -import copy -import json -from collections.abc import Mapping -from pathlib import Path -from types import SimpleNamespace -from typing import Any - -import pytest - -pytest.importorskip("chz", reason="Tinker is an optional RL dependency") -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") -pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", -) - -from rl.common.evaluator import Attribution, EvaluationResult, EvaluationStatus -from rl.common.prompt import prompt_asset_hashes -from rl.studies.representation_training_v1 import renderer_diagnostic as subject -from rl.studies.representation_training_v1.protocol import ( - STUDY_ID, - file_sha256, - load_protocol, - protocol_path, - stage_spec, -) -from rl.studies.representation_training_v1.schedule import stage_tasks -from rl.studies.representation_training_v1.store import StageKey, TrainingStore - -from .test_evaluation import ( - REPO_ROOT, - _dataset_binding, - _FakeEvaluator, - _FakeRenderer, - _task, -) - - -CURRENT_SOURCE_SHA = "c" * 40 -# The diagnostic seed binds the exact study protocol and producer receipt. -# Protocol logical SHA-256: c9bc59bd81aa0e6235aa307149e6fde02a1baf733756f31fe200ed911ffbee86. -# This inventory changes only when that sealed provenance fixture changes. -EXPECTED_TEST_SEEDS = [ - 657021824, - 1434315820, - 1815886577, - 79509377, - 1329366495, - 1182061749, - 85040921, - 1903607757, - 466527302, - 296515128, - 627644229, - 346320926, - 1567918527, - 1869035973, - 1896356246, - 1699500364, - 200413036, - 173011987, - 1116287135, - 1054920427, - 1406226972, - 1053591155, - 960197130, - 100306872, - 132036433, - 1041671166, - 771608684, - 943677354, - 2037721659, - 492363738, - 807566693, - 376994082, -] - - -def _tasks() -> list[Any]: - return [_task(f"F{index}") for index in range(1, 9)] - - -def _prefix_audit() -> dict[str, Any]: - renderers = {} - for renderer, compatible in ( - ("qwen3_5_disable_thinking", True), - ("qwen3_5", False), - ): - renderers[renderer] = { - "rows": 3468, - "compatible_rows": 3468 if compatible else 0, - "incompatible_rows": 0 if compatible else 3468, - "compatible_with_sft_supervised_prefix": compatible, - "expected_compatible_claim": compatible, - "claim_verified": True, - "comparison_scope": "through first positive-loss token", - "diagnosis": renderer, - "evidence_sha256": "8" * 64, - "example": {"renderer": renderer}, - } - return { - "schema_version": "pixcell-qwen-sft-prefix-audit-v1", - "rows": 3468, - "training_renderer": "qwen3_5", - "train_on_what": "LAST_ASSISTANT_MESSAGE", - "target_policy": "program_and_end_token_only", - "source_set_sha256": "9" * 64, - "renderers": renderers, - } - - -def _mixed_receipt( - tmp_path: Path, - *, - protocol: dict[str, Any], -) -> tuple[Path, str]: - external = tmp_path / "external" - store = TrainingStore(repo_root=REPO_ROOT, external_root=external) - key = StageKey( - STUDY_ID, - subject.PRODUCER_STAGE_ID, - subject.PRODUCER_REPLICATE_ID, - ) - checkpoints = ( - ("000014", 14, False, "periodic", 14 / 55), - ("000028", 28, False, "periodic", 28 / 55), - ("000042", 42, False, "periodic", 42 / 55), - ("final", 55, True, "terminal", 1.0), - ) - entries = [ - { - "name": name, - "batch": batch, - "epoch": 1 if final else 0, - "final": final, - "state_path": f"tinker://unit/state/{name}", - "sampler_path": f"tinker://unit/sampler/{name}", - "role": role, - "training_progress_fraction": progress, - } - for name, batch, final, role, progress in checkpoints - ] - terminal = { - field: entries[-1][field] - for field in ( - "name", - "batch", - "epoch", - "final", - "state_path", - "sampler_path", - ) - } - with store.acquire_stage_lock(key): - manifest = store.create_or_verify_manifest( - key, - { - "source_git_sha": subject.PRODUCER_SOURCE_GIT_SHA, - "protocol_logical_sha256": protocol["logical_sha256"], - "protocol_file_sha256": file_sha256(protocol_path(REPO_ROOT)), - "contract_version": protocol["contract_version"], - "prompt_assets": prompt_asset_hashes(), - "dataset": { - "logical_release_sha256": protocol["dataset"]["logical_release_sha256"] - }, - "runtime": {"pinned": True}, - "sandbox": {"image_id": "sha256:" + "4" * 64}, - "stage": { - "stage_id": subject.PRODUCER_STAGE_ID, - "kind": "sft", - "model_key": "qwen", - "recipe_key": "qwen_sft", - }, - "model": protocol["models"]["qwen"], - "recipe": protocol["recipes"]["qwen_sft"], - }, - ) - receipt = store.write_wave_receipt( - key, - wave=subject.PRODUCER_WAVE, - payload={ - "stage_id": subject.PRODUCER_STAGE_ID, - "wave": subject.PRODUCER_WAVE, - "run_manifest_record_sha256": manifest.record_sha256, - "checkpoint": terminal, - "checkpoint_inventory": { - "count": len(entries), - "entries": entries, - "logical_sha256": subject._canonical_sha256(entries), - }, - }, - ) - return external, receipt.record_sha256 - - -class _Renderer(_FakeRenderer): - def __init__(self, events: list[str], name: str) -> None: - super().__init__(events) - self.name = name - - def build_generation_prompt(self, messages: list[Any]) -> Any: - self.events.append(f"prompt:{self.name}") - assert len(messages) == 1 - return SimpleNamespace(length=102 if self.name == "qwen3_5_disable_thinking" else 100) - - -class _SharedSamplingState: - def __init__( - self, - events: list[str], - *, - fail_on_call: int | None = None, - ) -> None: - self.events = events - self.fail_on_call = fail_on_call - self.calls = 0 - self.seeds: list[int] = [] - - -class _SamplingClient: - def __init__(self, state: _SharedSamplingState, path: str) -> None: - self.state = state - self.path = path - - async def sample_async(self, **kwargs: Any) -> Any: - self.state.calls += 1 - if self.state.fail_on_call == self.state.calls: - raise RuntimeError("synthetic transport fault") - parameters = kwargs["sampling_params"] - assert kwargs["num_samples"] == 1 - assert parameters.max_tokens == subject.MAX_OUTPUT_TOKENS - assert parameters.temperature == subject.TEMPERATURE - assert parameters.top_p == subject.TOP_P - self.state.seeds.append(parameters.seed) - self.state.events.append(f"sample:{self.path}:{parameters.seed}") - return SimpleNamespace(sequences=[SimpleNamespace(tokens=[1, 2, 3], stop_reason="stop")]) - - -class _Service: - def __init__(self, state: _SharedSamplingState) -> None: - self.state = state - self.paths: list[str] = [] - - def create_sampling_client(self, *, model_path: str) -> _SamplingClient: - self.paths.append(model_path) - self.state.events.append(f"client:{model_path}") - return _SamplingClient(self.state, model_path) - - -def _patch_preflight( - monkeypatch: pytest.MonkeyPatch, - *, - protocol: dict[str, Any], - events: list[str], -) -> None: - monkeypatch.setattr( - subject, - "load_protocol", - lambda *_args, **_kwargs: protocol, - ) - - def source_check(_root: Path, expected: str) -> str: - events.append(f"source:{expected}") - assert expected == CURRENT_SOURCE_SHA - return expected - - monkeypatch.setattr(subject, "validate_source_sha", source_check) - monkeypatch.setattr( - subject, - "_source_provenance", - lambda **_kwargs: { - "producer_source_git_sha": subject.PRODUCER_SOURCE_GIT_SHA, - "evaluator_source_git_sha": CURRENT_SOURCE_SHA, - "git_relationship": {"ancestor_distance": 1}, - "task_contract_files": {}, - "task_contract_files_sha256": "a" * 64, - }, - ) - monkeypatch.setattr( - subject, - "_dataset_binding", - lambda *_args: _dataset_binding(protocol), - ) - monkeypatch.setattr( - subject, - "_runtime_binding", - lambda *_args: {"stack": "pinned"}, - ) - monkeypatch.setattr( - subject.checkpoint_v1, - "_benchmark_tasks", - lambda **_kwargs: _tasks(), - ) - monkeypatch.setattr( - subject, - "_sft_prefix_audit", - lambda **_kwargs: events.append("prefix-audit") or _prefix_audit(), - ) - renderers: dict[str, _Renderer] = {} - - def renderer(_model: str, name: str, **_kwargs: Any) -> _Renderer: - return renderers.setdefault(name, _Renderer(events, name)) - - monkeypatch.setattr(subject, "_renderer", renderer) - - -def _store(external: Path) -> subject.RendererDiagnosticStore: - training = TrainingStore(repo_root=REPO_ROOT, external_root=external) - key = StageKey( - STUDY_ID, - subject.PRODUCER_STAGE_ID, - subject.PRODUCER_REPLICATE_ID, - ) - return subject.RendererDiagnosticStore(stage_path=training.stage_path(key)) - - -def _run( - *, - protocol: dict[str, Any], - external: Path, - receipt_sha: str, - evaluator: _FakeEvaluator, - service_factory: Any, -) -> dict[str, Any]: - return asyncio.run( - subject.run_renderer_diagnostic( - repo_root=REPO_ROOT, - evaluator_source_git_sha=CURRENT_SOURCE_SHA, - producer_wave_receipt_record_sha256=receipt_sha, - external_root=external, - confirmation=protocol["launch"]["confirmation_token"], - evaluator_factory=lambda: evaluator, - service_client_factory=service_factory, - ) - ) - - -def _completed( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> dict[str, Any]: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _mixed_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - _patch_preflight(monkeypatch, protocol=protocol, events=events) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - evaluator = _FakeEvaluator(events) - state = _SharedSamplingState(events) - service = _Service(state) - - def service_factory() -> _Service: - events.append("service") - assert events.count(f"source:{CURRENT_SOURCE_SHA}") == 2 - assert "prefix-audit" in events - assert events.count("reference") == 32 - return service - - result = _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=evaluator, - service_factory=service_factory, - ) - store = _store(external) - manifest = store.load_manifest() - report = store.load_report() - assert manifest is not None - assert report is not None - return { - "protocol": protocol, - "external": external, - "receipt_sha": receipt_sha, - "events": events, - "evaluator": evaluator, - "state": state, - "service": service, - "result": result, - "store": store, - "manifest": manifest, - "report": report, - } - - -def _write_record(path: Path, value: Mapping[str, Any]) -> None: - path.write_text( - json.dumps( - value, - allow_nan=False, - ensure_ascii=False, - separators=(",", ":"), - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - - -def test_matrix_and_cli_have_no_model_renderer_or_checkpoint_override() -> None: - assert [spec.as_dict() for spec in subject.ARM_SPECS] == [ - { - "arm_id": "final-no-thinking", - "checkpoint_name": "final", - "renderer": "qwen3_5_disable_thinking", - "expected_sft_prefix_compatible": True, - }, - { - "arm_id": "step-14-thinking", - "checkpoint_name": "000014", - "renderer": "qwen3_5", - "expected_sft_prefix_compatible": False, - }, - { - "arm_id": "step-28-thinking", - "checkpoint_name": "000028", - "renderer": "qwen3_5", - "expected_sft_prefix_compatible": False, - }, - { - "arm_id": "step-42-thinking", - "checkpoint_name": "000042", - "renderer": "qwen3_5", - "expected_sft_prefix_compatible": False, - }, - ] - assert subject.PRODUCER_SOURCE_GIT_SHA == ("bb80dbe666c7313f9a9f3bdb53765c35dfdeceab") - actions = {action.dest for action in subject.parser()._actions if action.dest} - assert not actions & { - "model", - "renderer", - "checkpoint", - "checkpoint_name", - "stage", - "wave", - "replicate", - "max_tokens", - "temperature", - "top_p", - } - - -def test_real_renderer_prefix_audit_diagnoses_exact_mismatch() -> None: - protocol = load_protocol(REPO_ROOT) - stage = stage_spec(protocol, subject.PRODUCER_STAGE_ID) - anchor = stage_tasks(REPO_ROOT / "dataset", stage)[0] - audit = subject._sft_prefix_audit( - dataset_root=REPO_ROOT / "dataset", - protocol=protocol, - tasks=[anchor], - ) - - disabled = audit["renderers"]["qwen3_5_disable_thinking"] - canonical = audit["renderers"]["qwen3_5"] - assert disabled["compatible_with_sft_supervised_prefix"] is True - assert disabled["compatible_rows"] == 1 - assert disabled["example"]["prefix_equal"] is True - assert disabled["example"]["through_first_positive_equal"] is True - assert canonical["compatible_with_sft_supervised_prefix"] is False - assert canonical["incompatible_rows"] == 1 - assert canonical["example"]["prefix_equal"] is False - assert canonical["example"]["through_first_positive_equal"] is False - assert "leaves open" in canonical["diagnosis"] - - -def test_exact_matrix_records_full_provenance_and_is_non_promotable( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - result = completed["result"] - manifest = completed["manifest"]["payload"] - report = completed["report"]["payload"] - store = completed["store"] - - assert result["status"] == "complete" - assert result["not_canonical_evidence"] is True - assert "/diagnostics/" in result["report"] - assert "/evaluations/" not in result["report"] - assert completed["state"].calls == 32 - assert len(completed["state"].seeds) == 32 - assert len(set(completed["state"].seeds)) == 32 - assert completed["service"].paths == [ - "tinker://unit/sampler/final", - "tinker://unit/sampler/000014", - "tinker://unit/sampler/000028", - "tinker://unit/sampler/000042", - ] - assert manifest["not_canonical_evidence"] is True - assert manifest["promotion_eligibility"] == { - "eligible": False, - "policy": "diagnostic-only-never-promotion-evidence", - } - assert manifest["source"]["producer_source_git_sha"] == (subject.PRODUCER_SOURCE_GIT_SHA) - assert manifest["source"]["evaluator_source_git_sha"] == CURRENT_SOURCE_SHA - assert ( - manifest["producer_training"]["wave_receipt"]["record_sha256"] == completed["receipt_sha"] - ) - assert list( - manifest["producer_training"]["checkpoint_inventory"]["entries"][index]["name"] - for index in range(4) - ) == list(subject.EXPECTED_CHECKPOINT_NAMES) - assert manifest["sampling"]["total_samples"] == 32 - assert len(manifest["sampling"]["seeds"]) == 32 - assert [row["seed"] for row in manifest["sampling"]["seeds"]] == EXPECTED_TEST_SEEDS - assert report["not_canonical_evidence"] is True - assert len(report["records"]) == 32 - assert set(report["summaries"]) == {spec.arm_id for spec in subject.ARM_SPECS} - assert all( - summary["mean_raw_absolute_scale_iou"] == 0.75 and summary["pure_executable"] == 8 - for summary in report["summaries"].values() - ) - for spec in subject.ARM_SPECS: - for task_id in subject.EXPECTED_TASK_IDS: - sample = store.load_sample(spec.arm_id, task_id) - evaluation = store.load_evaluation(spec.arm_id, task_id) - assert sample is not None - assert evaluation is not None - response = sample["payload"]["response"] - assert response["token_ids"] == [1, 2, 3] - assert response["token_ids_sha256"] == subject._canonical_sha256([1, 2, 3]) - assert response["answer_text"] == "print('candidate')" - assert response["reasoning_text"] == "" - assert response["extracted_program"] == "print('candidate')" - assert response["channel_parse_status"] == "clean" - assert evaluation["payload"]["not_canonical_evidence"] is True - assert ( - evaluation["payload"]["extracted_program_sha256"] - == response["extracted_program_sha256"] - ) - - with pytest.raises(subject.checkpoint_v1.CheckpointEvaluationError): - subject.checkpoint_v1._validate_record( - completed["report"], - record_type="checkpoint_report", - key={}, - ) - - -def test_already_complete_is_byte_stable_and_creates_no_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - store = completed["store"] - before = {path: path.read_bytes() for path in store.root.rglob("*.json")} - monkeypatch.delenv("TINKER_API_KEY") - - def forbidden_service() -> Any: - raise AssertionError("already-complete diagnostic created a client") - - result = _run( - protocol=completed["protocol"], - external=completed["external"], - receipt_sha=completed["receipt_sha"], - evaluator=_FakeEvaluator(completed["events"]), - service_factory=forbidden_service, - ) - assert result["status"] == "already_complete" - assert {path: path.read_bytes() for path in store.root.rglob("*.json")} == before - - -def test_partial_sampling_resume_only_fills_missing_rows( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _mixed_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - _patch_preflight(monkeypatch, protocol=protocol, events=events) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - monkeypatch.setattr(subject, "SAMPLE_CONCURRENCY", 1) - first_state = _SharedSamplingState(events, fail_on_call=7) - - with pytest.raises(subject.checkpoint_v1.SamplingInfrastructureFault): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=lambda: _Service(first_state), - ) - - store = _store(external) - before = {path: path.read_bytes() for path in store.root.glob("arms/*/tasks/*/sample.json")} - assert 0 < len(before) < 32 - assert not list(store.root.glob("arms/*/tasks/*/evaluation.json")) - - second_state = _SharedSamplingState(events) - result = _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=lambda: _Service(second_state), - ) - assert result["status"] == "complete" - assert second_state.calls == 32 - len(before) - assert len(list(store.root.glob("arms/*/tasks/*/sample.json"))) == 32 - assert all(path.read_bytes() == raw for path, raw in before.items()) - - -@pytest.mark.parametrize("mutation", ("raw", "program", "foreign", "symlink")) -def test_tampering_and_foreign_inventory_fail_before_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, - mutation: str, -) -> None: - completed = _completed(tmp_path, monkeypatch) - store = completed["store"] - sample_path = store.root / "arms" / "final-no-thinking" / "tasks" / "F1" / "sample.json" - if mutation in {"raw", "program"}: - sample = json.loads(sample_path.read_text(encoding="utf-8")) - field = "raw_text" if mutation == "raw" else "extracted_program" - sample["payload"]["response"][field] = "tampered" - _write_record(sample_path, sample) - elif mutation == "foreign": - foreign = store.root / "arms" / "final-no-thinking" / "tasks" / "F9" / "sample.json" - foreign.parent.mkdir(parents=True) - foreign.write_bytes(sample_path.read_bytes()) - else: - link = store.root / "arms" / "final-no-thinking" / "tasks" / "F9" - link.symlink_to(sample_path.parent, target_is_directory=True) - - def forbidden_service() -> Any: - raise AssertionError("invalid resume created a client") - - with pytest.raises(subject.RendererDiagnosticError): - _run( - protocol=completed["protocol"], - external=completed["external"], - receipt_sha=completed["receipt_sha"], - evaluator=_FakeEvaluator(completed["events"]), - service_factory=forbidden_service, - ) - - -def test_wrong_receipt_and_second_source_check_precede_client( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - protocol = load_protocol(REPO_ROOT) - external, receipt_sha = _mixed_receipt(tmp_path, protocol=protocol) - events: list[str] = [] - _patch_preflight(monkeypatch, protocol=protocol, events=events) - monkeypatch.setenv("TINKER_API_KEY", "test-key") - - def forbidden_service() -> Any: - raise AssertionError("failed preflight created a client") - - with pytest.raises(subject.checkpoint_v1.CheckpointReceiptError): - _run( - protocol=protocol, - external=external, - receipt_sha="f" * 64, - evaluator=_FakeEvaluator(events), - service_factory=forbidden_service, - ) - - calls = 0 - - def changing_source(_root: Path, expected: str) -> str: - nonlocal calls - calls += 1 - if calls == 2: - raise ValueError("source changed after preflight") - return expected - - monkeypatch.setattr(subject, "validate_source_sha", changing_source) - with pytest.raises(ValueError, match="source changed"): - _run( - protocol=protocol, - external=external, - receipt_sha=receipt_sha, - evaluator=_FakeEvaluator(events), - service_factory=forbidden_service, - ) - assert calls == 2 - - -def test_model_failures_zero_and_infrastructure_failures_abort() -> None: - arm = SimpleNamespace( - spec=subject.ARM_SPECS[0], - binding=None, - ) - attempt = subject.PreparedAttempt( - arm=arm, - prepared=subject.checkpoint_v1.PreparedTask( - task=_task(), - prompt_tokens=100, - prompt_text_sha256="1" * 64, - reference_evidence={}, - ), - seed=123, - ) - model_failure = subject._evaluation_payload( - attempt=attempt, - result=EvaluationResult( - status=EvaluationStatus.SYNTAX_ERROR, - attribution=Attribution.MODEL, - error="invalid syntax", - ), - sample_record_sha256="2" * 64, - manifest_record_sha256="3" * 64, - extracted_program_sha256="4" * 64, - ) - assert model_failure["raw_absolute_scale_iou"] == 0.0 - assert model_failure["pure_executable"] is False - - for attribution, error_type in ( - (Attribution.REFERENCE, subject.checkpoint_v1.ReferencePanelFault), - ( - Attribution.EVALUATOR, - subject.checkpoint_v1.EvaluationInfrastructureFault, - ), - ): - with pytest.raises(error_type): - subject._evaluation_payload( - attempt=attempt, - result=EvaluationResult( - status=EvaluationStatus.WORKER_ERROR, - attribution=attribution, - error="fault", - ), - sample_record_sha256="2" * 64, - manifest_record_sha256="3" * 64, - extracted_program_sha256="4" * 64, - ) - - -def test_resigned_report_marker_or_metrics_tampering_is_rejected( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - completed = _completed(tmp_path, monkeypatch) - for mutation in ("marker", "summary"): - payload = copy.deepcopy(completed["report"]["payload"]) - if mutation == "marker": - payload["not_canonical_evidence"] = False - else: - payload["summaries"]["final-no-thinking"]["mean_raw_absolute_scale_iou"] = 0.99 - record = subject._record_document( - record_type="renderer_diagnostic_report", - key={}, - payload=payload, - ) - with pytest.raises(subject.RendererDiagnosticError): - subject._validate_report_rows( - record, - manifest_record=completed["manifest"], - attempts=_attempt_context(completed), - ) - - -def _attempt_context(completed: Mapping[str, Any]) -> tuple[Any, ...]: - protocol = completed["protocol"] - training = TrainingStore( - repo_root=REPO_ROOT, - external_root=completed["external"], - ) - bindings = subject._load_matrix_bindings( - store=training, - protocol=protocol, - expected_wave_receipt_sha256=completed["receipt_sha"], - ) - evaluator = _FakeEvaluator([]) - tasks = _tasks() - panel = subject._task_panel(tasks) - arms, attempts = subject._prepare_arms( - protocol=protocol, - model=protocol["models"]["qwen"], - bindings=bindings, - tasks=tasks, - evaluator=evaluator, - evaluator_source_git_sha=CURRENT_SOURCE_SHA, - dataset_sha256=protocol["dataset"]["logical_release_sha256"], - task_panel_sha256=panel["logical_sha256"], - ) - assert len(arms) == 4 - evaluator.close() - return attempts diff --git a/rl/studies/representation_training_v1/tests/test_source_transition.py b/rl/studies/representation_training_v1/tests/test_source_transition.py deleted file mode 100644 index 3aab01f1..00000000 --- a/rl/studies/representation_training_v1/tests/test_source_transition.py +++ /dev/null @@ -1,243 +0,0 @@ -from __future__ import annotations - -import copy -import subprocess -from pathlib import Path - -import pytest - -from rl.studies.representation_training_v1.protocol import ( - validate_source_sha, -) -from rl.studies.representation_training_v1.source_transition import ( - SourceTransitionError, - approved_change_scope, - build_source_transition, - source_transition_git_facts, - validate_source_transition, -) - - -def _git(repo: Path, *args: str) -> str: - return subprocess.check_output( - ["git", *args], - cwd=repo, - text=True, - ).strip() - - -def _commit(repo: Path, message: str) -> str: - subprocess.run(["git", "add", "--all"], cwd=repo, check=True) - subprocess.run( - ["git", "commit", "-m", message], - cwd=repo, - check=True, - stdout=subprocess.DEVNULL, - ) - return _git(repo, "rev-parse", "HEAD") - - -@pytest.fixture -def git_history(tmp_path: Path) -> tuple[Path, str, str]: - repo = tmp_path / "repo" - repo.mkdir() - subprocess.run( - ["git", "init", "--initial-branch=main"], - cwd=repo, - check=True, - stdout=subprocess.DEVNULL, - ) - subprocess.run( - ["git", "config", "user.name", "PixCell test"], - cwd=repo, - check=True, - ) - subprocess.run( - ["git", "config", "user.email", "pixcell-test@example.invalid"], - cwd=repo, - check=True, - ) - subprocess.run( - ["git", "config", "commit.gpgsign", "false"], - cwd=repo, - check=True, - ) - subprocess.run( - ["git", "config", "core.hooksPath", "/dev/null"], - cwd=repo, - check=True, - ) - (repo / "policy.py").write_text("VALUE = 1\n", encoding="utf-8") - producer = _commit(repo, "producer") - (repo / "policy.py").write_text("VALUE = 2\n", encoding="utf-8") - (repo / "review.txt").write_text("reviewed\n", encoding="utf-8") - consumer = _commit(repo, "consumer") - return repo, producer, consumer - - -def test_transition_binds_ancestry_trees_diff_and_exact_scope( - git_history: tuple[Path, str, str], - monkeypatch: pytest.MonkeyPatch, -) -> None: - repo, producer, consumer = git_history - facts = source_transition_git_facts( - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - assert facts["merge_base_git_sha"] == producer - assert facts["producer_source_git_sha"] == producer - assert facts["consumer_source_git_sha"] == consumer - assert facts["producer_tree_git_sha"] != facts["consumer_tree_git_sha"] - assert facts["ancestor_distance"] == 1 - assert facts["changed_path_count"] == 2 - assert facts["changed_paths"] == [ - {"status": "M", "path": "policy.py"}, - {"status": "A", "path": "review.txt"}, - ] - assert facts["raw_changes"] == [ - { - "status": "M", - "old_mode": "100644", - "new_mode": "100644", - "old_object": _git(repo, "rev-parse", f"{producer}:policy.py"), - "new_object": _git(repo, "rev-parse", f"{consumer}:policy.py"), - "path": "policy.py", - }, - { - "status": "A", - "old_mode": "000000", - "new_mode": "100644", - "old_object": "0" * 40, - "new_object": _git(repo, "rev-parse", f"{consumer}:review.txt"), - "path": "review.txt", - }, - ] - original_inventory_sha256 = facts["raw_change_inventory_sha256"] - monkeypatch.setenv("GIT_DIFF_OPTS", "--unified=0") - monkeypatch.setenv("GIT_CONFIG_COUNT", "2") - monkeypatch.setenv("GIT_CONFIG_KEY_0", "diff.algorithm") - monkeypatch.setenv("GIT_CONFIG_VALUE_0", "minimal") - monkeypatch.setenv("GIT_CONFIG_KEY_1", "diff.renames") - monkeypatch.setenv("GIT_CONFIG_VALUE_1", "true") - assert ( - source_transition_git_facts( - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - == facts - ) - assert facts["raw_change_inventory_sha256"] == original_inventory_sha256 - subprocess.run( - ["git", "replace", producer, consumer], - cwd=repo, - check=True, - ) - assert ( - source_transition_git_facts( - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - == facts - ) - - paths = ["review.txt", "policy.py"] - approved = approved_change_scope(facts, paths) - assert approved["paths"] == ["policy.py", "review.txt"] - assert approved["entries"] == facts["raw_changes"] - assert approved["raw_change_inventory_sha256"] == facts["raw_change_inventory_sha256"] - - transition = build_source_transition( - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - scope={"child": {"stage_id": "child", "wave": "smoke"}}, - approved_change_paths=paths, - equivalence={"protocol": "exact"}, - evidence={"report_record_sha256": "a" * 64}, - ) - assert ( - validate_source_transition( - transition, - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - scope={"child": {"stage_id": "child", "wave": "smoke"}}, - approved_change_paths=paths, - equivalence={"protocol": "exact"}, - evidence={"report_record_sha256": "a" * 64}, - ) - == transition - ) - - tampered = copy.deepcopy(transition) - tampered["git"]["raw_changes"][0]["new_object"] = "0" * 40 - with pytest.raises(SourceTransitionError, match="differs"): - validate_source_transition( - tampered, - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - scope={"child": {"stage_id": "child", "wave": "smoke"}}, - approved_change_paths=paths, - equivalence={"protocol": "exact"}, - evidence={"report_record_sha256": "a" * 64}, - ) - - -def test_transition_rejects_wrong_scope_and_non_ancestor( - git_history: tuple[Path, str, str], -) -> None: - repo, producer, consumer = git_history - facts = source_transition_git_facts( - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - with pytest.raises(SourceTransitionError, match="approved change paths"): - approved_change_scope(facts, ["policy.py"]) - with pytest.raises(SourceTransitionError, match="approved change paths"): - approved_change_scope( - facts, - ["policy.py", "review.txt", "unrelated.txt"], - ) - with pytest.raises(SourceTransitionError, match="explicit approved"): - approved_change_scope(facts, None) - - subprocess.run( - ["git", "switch", "--detach", producer], - cwd=repo, - check=True, - stdout=subprocess.DEVNULL, - ) - (repo / "sibling.txt").write_text("sibling\n", encoding="utf-8") - sibling = _commit(repo, "sibling") - with pytest.raises(SourceTransitionError, match="not an ancestor"): - source_transition_git_facts( - repo_root=repo, - producer_source_git_sha=sibling, - consumer_source_git_sha=consumer, - ) - grafts = repo / ".git" / "info" / "grafts" - grafts.write_text("", encoding="utf-8") - with pytest.raises(SourceTransitionError, match="grafts"): - source_transition_git_facts( - repo_root=repo, - producer_source_git_sha=producer, - consumer_source_git_sha=consumer, - ) - - -def test_source_validation_rejects_wrong_sha_and_dirty_tree( - git_history: tuple[Path, str, str], -) -> None: - repo, producer, consumer = git_history - assert validate_source_sha(repo, consumer) == consumer - with pytest.raises(ValueError, match="differs"): - validate_source_sha(repo, producer) - - (repo / "untracked.txt").write_text("dirty\n", encoding="utf-8") - with pytest.raises(ValueError, match="clean worktree"): - validate_source_sha(repo, consumer) diff --git a/rl/studies/representation_training_v1/tests/test_store.py b/rl/studies/representation_training_v1/tests/test_store.py deleted file mode 100644 index f928a20c..00000000 --- a/rl/studies/representation_training_v1/tests/test_store.py +++ /dev/null @@ -1,138 +0,0 @@ -from __future__ import annotations - -from pathlib import Path - -import pytest - -from rl.studies.representation_training_v1.store import ( - ImmutableTrainingRecordError, - StageKey, - TrainingStore, - TrainingStoreError, - UnsafeTrainingRootError, -) - - -def test_store_rejects_repository_overlap(tmp_path: Path) -> None: - repo = tmp_path / "repo" - repo.mkdir() - with pytest.raises(UnsafeTrainingRootError): - TrainingStore(repo_root=repo, external_root=repo / "runs") - - -def test_records_require_lock_and_are_immutable(tmp_path: Path) -> None: - repo = tmp_path / "repo" - external = tmp_path / "external" - repo.mkdir() - store = TrainingStore(repo_root=repo, external_root=external) - key = StageKey("study", "stage", "r0") - with pytest.raises(TrainingStoreError): - store.create_or_verify_manifest(key, {"value": 1}) - with store.acquire_stage_lock(key): - first = store.create_or_verify_manifest(key, {"value": 1}) - assert store.create_or_verify_manifest(key, {"value": 1}) == first - with pytest.raises(ImmutableTrainingRecordError): - store.create_or_verify_manifest(key, {"value": 2}) - - -def test_crash_retry_invocations_never_collide_candidates(tmp_path: Path) -> None: - repo = tmp_path / "repo" - external = tmp_path / "external" - repo.mkdir() - store = TrainingStore(repo_root=repo, external_root=external) - key = StageKey("study", "stage", "r0") - with store.acquire_stage_lock(key): - store.create_or_verify_manifest(key, {"value": 1}) - for invocation, score in (("smoke-first", 0.1), ("smoke-retry", 0.2)): - store.begin_invocation( - key, - invocation_id=invocation, - payload={"invocation_id": invocation}, - ) - store.write_candidate( - key, - invocation_id=invocation, - step=0, - task_id="abc123", - attempt=1, - payload={"iou": score}, - ) - store.write_candidate_sample( - key, - invocation_id=invocation, - step=0, - task_id="abc123", - attempt=1, - payload={"tokens": [1, 2, 3]}, - ) - candidates = sorted(store.candidate_root(key).rglob("evaluation.json")) - samples = sorted(store.candidate_root(key).rglob("sample.json")) - assert len(candidates) == 2 - assert len(samples) == 2 - assert {path.parts[-5] for path in candidates} == { - "smoke-first", - "smoke-retry", - } - - -def test_invocation_tracking_is_create_only_and_requires_start( - tmp_path: Path, -) -> None: - repo = tmp_path / "repo" - external = tmp_path / "external" - repo.mkdir() - store = TrainingStore(repo_root=repo, external_root=external) - key = StageKey("study", "stage", "r0") - payload = { - "expected": {"run_id": "pxct-expected"}, - "observed": {"run_id": "pxct-expected"}, - } - with store.acquire_stage_lock(key): - store.create_or_verify_manifest(key, {"value": 1}) - with pytest.raises(TrainingStoreError, match="cannot precede"): - store.write_invocation_tracking( - key, - invocation_id="complete-unit", - payload=payload, - ) - invocation = store.begin_invocation( - key, - invocation_id="complete-unit", - payload={"invocation_id": "complete-unit"}, - ) - assert ( - store.load_invocation( - key, - invocation_id="complete-unit", - ) - == invocation - ) - tracking = store.write_invocation_tracking( - key, - invocation_id="complete-unit", - payload=payload, - ) - assert ( - store.load_invocation_tracking( - key, - invocation_id="complete-unit", - ) - == tracking - ) - assert ( - store.write_invocation_tracking( - key, - invocation_id="complete-unit", - payload=payload, - ) - == tracking - ) - with pytest.raises(ImmutableTrainingRecordError): - store.write_invocation_tracking( - key, - invocation_id="complete-unit", - payload={ - **payload, - "observed": {"run_id": "random"}, - }, - ) diff --git a/rl/track_a/checkpoint_metrics.py b/rl/track_a/checkpoint_metrics.py deleted file mode 100644 index f3179368..00000000 --- a/rl/track_a/checkpoint_metrics.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Track A behavioral diagnostics for executable checkpoint evaluation.""" - -from __future__ import annotations - -import hashlib -import math -import statistics -from collections import defaultdict -from collections.abc import Mapping, Sequence -from typing import Any - -from rl.common.output import extract_code - - -def program_sha256(completion: str) -> str: - """Hash normalized extracted source rather than chat or fence formatting.""" - - source = extract_code(completion).replace("\r\n", "\n").replace("\r", "\n") - normalized = "\n".join(line.rstrip() for line in source.splitlines()).strip() - return hashlib.sha256(normalized.encode("utf-8")).hexdigest() - - -def behavioral_probe_summary( - records: Sequence[Mapping[str, Any]], - *, - expected_group_size: int = 4, -) -> dict[str, Any]: - """Summarize within-prompt diversity for a small independent G4 probe. - - This is deliberately not a quality score. It detects only the strongest - collapse signatures and leaves checkpoint ordering to the full k=1 - executable-geometry report. - """ - - if expected_group_size < 2: - raise ValueError("behavioral probes require a group size of at least 2") - if not records: - raise ValueError("behavioral probe has no records") - - grouped: dict[str, list[Mapping[str, Any]]] = defaultdict(list) - for record in records: - grouped[str(record["opaque_id"])].append(record) - - groups: list[dict[str, Any]] = [] - for opaque_id, attempts in sorted(grouped.items()): - if len(attempts) != expected_group_size: - raise ValueError( - f"prompt {opaque_id!r} has {len(attempts)} attempts; " - f"expected {expected_group_size}" - ) - program_hashes = sorted( - {str(attempt["program_sha256"]) for attempt in attempts} - ) - render_hashes = sorted( - { - str(attempt["render_sha256"]) - for attempt in attempts - if attempt.get("pure_executable") and attempt.get("render_sha256") - } - ) - rewards = [float(attempt["reward"]) for attempt in attempts] - reward_std = statistics.pstdev(rewards) - reward_range = max(rewards) - min(rewards) - groups.append( - { - "opaque_id": opaque_id, - "distinct_program_count": len(program_hashes), - "distinct_program_sha256": program_hashes, - "successful_render_attempts": sum( - bool(attempt.get("pure_executable")) for attempt in attempts - ), - "distinct_successful_render_count": len(render_hashes), - "distinct_successful_render_sha256": render_hashes, - "reward_std": reward_std, - "reward_range": reward_range, - "nonconstant_reward": reward_range > 1e-12, - } - ) - - group_count = len(groups) - nonconstant_fraction = ( - sum(group["nonconstant_reward"] for group in groups) / group_count - ) - mean_reward_std = statistics.fmean(group["reward_std"] for group in groups) - mean_programs = statistics.fmean( - group["distinct_program_count"] for group in groups - ) - mean_renders = statistics.fmean( - group["distinct_successful_render_count"] for group in groups - ) - - identical_program_every_group = all( - group["distinct_program_count"] == 1 for group in groups - ) - constant_render_and_reward_every_group = all( - group["distinct_successful_render_count"] <= 1 - and not group["nonconstant_reward"] - for group in groups - ) - collapse_detected = ( - identical_program_every_group or constant_render_and_reward_every_group - ) - - if not all( - math.isfinite(value) - for value in ( - nonconstant_fraction, - mean_reward_std, - mean_programs, - mean_renders, - ) - ): - raise ValueError("behavioral probe produced a non-finite metric") - - return { - "probe_kind": "small-balanced-unmastered-g4", - "group_size": expected_group_size, - "prompt_groups": group_count, - "nonconstant_reward_group_fraction": nonconstant_fraction, - "mean_within_group_reward_std": mean_reward_std, - "mean_distinct_programs_per_prompt": mean_programs, - "mean_distinct_successful_renders_per_prompt": mean_renders, - "collapse_detected": collapse_detected, - "collapse_signals": { - "identical_program_every_group": identical_program_every_group, - "constant_render_and_reward_every_group": ( - constant_render_and_reward_every_group - ), - }, - "groups": groups, - } diff --git a/rl/track_a/checkpoint_selection.py b/rl/track_a/checkpoint_selection.py deleted file mode 100644 index 34261f56..00000000 --- a/rl/track_a/checkpoint_selection.py +++ /dev/null @@ -1,154 +0,0 @@ -"""Pair Track A checkpoint reports and apply the collapse veto.""" - -from __future__ import annotations - -from collections.abc import Mapping, Sequence -from typing import Any - -from rl.track_a.evaluation_contract import ( - CheckpointSelectionContract, - validate_evaluation_report, -) - - -def _index_by_sampler( - reports: Sequence[tuple[str, Mapping[str, Any]]], - *, - expected_role: str, - contract: CheckpointSelectionContract, -) -> dict[str, tuple[str, Mapping[str, Any]]]: - indexed: dict[str, tuple[str, Mapping[str, Any]]] = {} - for path, report in reports: - try: - validate_evaluation_report( - report, - contract=contract, - expected_role=expected_role, - ) - except ValueError as exc: - raise ValueError(f"{path}: {exc}") from exc - sampler_path = str(report.get("sampler_path", "")) - if sampler_path in indexed: - raise ValueError( - f"duplicate {expected_role} report for sampler {sampler_path!r}" - ) - indexed[sampler_path] = (path, report) - return indexed - - -def select_sft_checkpoint( - geometry_reports: Sequence[tuple[str, Mapping[str, Any]]], - behavioral_reports: Sequence[tuple[str, Mapping[str, Any]]], - *, - contract: CheckpointSelectionContract, -) -> dict[str, Any]: - """Select by full k=1 geometry after applying a paired G4 collapse veto.""" - - geometry = _index_by_sampler( - geometry_reports, - expected_role="checkpoint-geometry", - contract=contract, - ) - behavior = _index_by_sampler( - behavioral_reports, - expected_role="behavioral-collapse-probe", - contract=contract, - ) - missing = sorted(set(geometry) - set(behavior)) - extra = sorted(set(behavior) - set(geometry)) - if missing or extra: - raise ValueError( - "geometry/behavior sampler pairing mismatch: " - f"missing_behavior={missing}, extra_behavior={extra}" - ) - - sandbox_identities = { - ( - str(report["provenance"]["candidate_sandbox"]["image_ref"]), - str(report["provenance"]["candidate_sandbox"]["image_id"]), - ) - for _path, report in [*geometry.values(), *behavior.values()] - } - if len(sandbox_identities) != 1: - raise ValueError( - "checkpoint reports were measured with different candidate " - "sandbox images" - ) - sandbox_image_ref, sandbox_image_id = next(iter(sandbox_identities)) - - candidates: list[dict[str, Any]] = [] - for sampler_path, (geometry_path, geometry_report) in geometry.items(): - behavior_path, behavior_report = behavior[sampler_path] - probe = behavior_report.get("behavioral_probe") - collapsed_value = probe.get("collapse_detected") - collapsed = collapsed_value - candidates.append( - { - "sampler_path": sampler_path, - "geometry_report": geometry_path, - "behavioral_report": behavior_path, - "eligible": not collapsed, - "veto_reason": ( - "G4 behavioral probe detected sampling collapse" - if collapsed - else None - ), - "representation_macro_mean_iou": float( - geometry_report["representation_macro_mean_iou"] - ), - "pure_executable_rate": float( - geometry_report["summary"]["pure_executable_rate"] - ), - "behavioral_probe": { - "nonconstant_reward_group_fraction": probe[ - "nonconstant_reward_group_fraction" - ], - "mean_within_group_reward_std": probe[ - "mean_within_group_reward_std" - ], - "mean_distinct_programs_per_prompt": probe[ - "mean_distinct_programs_per_prompt" - ], - "mean_distinct_successful_renders_per_prompt": probe[ - "mean_distinct_successful_renders_per_prompt" - ], - "collapse_detected": collapsed, - "collapse_signals": probe.get("collapse_signals", {}), - }, - } - ) - - candidates.sort( - key=lambda item: ( - item["eligible"], - item["representation_macro_mean_iou"], - item["pure_executable_rate"], - ), - reverse=True, - ) - eligible = [candidate for candidate in candidates if candidate["eligible"]] - if not eligible: - raise ValueError("every checkpoint was vetoed by its paired G4 probe") - - return { - "selection_rule": ( - "veto only explicit G4 sampling collapse; among eligible checkpoints, " - "maximize full-validation k=1 representation-macro raw IoU; " - "pure-executable rate breaks ties" - ), - "behavioral_metrics_are_ranking_features": False, - "selection_provenance": { - "source_git_sha": contract.source_git_sha, - "depth_logical_release_sha256": ( - contract.depth_logical_release_sha256 - ), - "model": contract.model, - "renderer": contract.renderer, - "candidate_sandbox": { - "image_ref": sandbox_image_ref, - "image_id": sandbox_image_id, - }, - }, - "selected": eligible[0], - "ranking": candidates, - } diff --git a/rl/track_a/config.json b/rl/track_a/config.json deleted file mode 100644 index 31b6f814..00000000 --- a/rl/track_a/config.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "schema_version": "pixcell-track-a-config-v1", - "contract_version": "pixcell-direct-reconstruction-v2", - "model": { - "name": "Qwen/Qwen3.6-35B-A3B", - "renderer": "qwen3_5_disable_thinking", - "thinking": false, - "lora_rank": 32, - "max_image_long_edge": 1440 - }, - "sft": { - "dataset_repo_id": "qpaig-mit/pixcell", - "dataset_configuration": "depth", - "verifier_configuration": "references", - "dataset_revision": "v2.0.0", - "train_split": "train", - "validation_split": "validation", - "effective_passes": 1, - "balance_order": [ - "level", - "representation_id", - "realization" - ], - "visit_policy": "every_train_row_exactly_once", - "loss_mass": "equal_by_level_then_representation", - "batch_size": 64, - "max_sequence_tokens": 6144, - "learning_rate": 5e-05, - "learning_rate_schedule": "linear_decay", - "tinker_lr_schedule": "linear", - "optimizer": { - "name": "adam", - "beta1": 0.9, - "beta2": 0.95, - "epsilon": 1e-08 - }, - "checkpoint_fractions": [ - 0.25, - 0.5, - 0.75, - 1.0 - ], - "save_every_steps": 14, - "evaluate_every_steps": 14, - "max_steps": 55 - }, - "rl": { - "algorithm": "group-relative-policy-optimization", - "group_size": 4, - "groups_per_batch": 8, - "learning_rate": 1e-05, - "temperature": 1.0, - "top_p": 1.0, - "max_output_tokens": 4096, - "kl_coefficient": 0.0, - "loss_fn": "importance_sampling", - "initial_level": "select_first_unmastered_after_sft_executable_eval", - "earlier_level_replay_fraction": 0.2, - "curriculum": { - "levels": [ - "L0", - "L1", - "L2", - "L3", - "L4" - ], - "minimum_iou": 0.8, - "minimum_pure_executable_rate": 0.95, - "replay_policy": "all_earlier_levels_in_order" - }, - "stage_max_steps": 30, - "save_every_steps": 5, - "evaluate_every_steps": 5, - "reward": { - "policy_failure": 0.0, - "valid_program": "0.05 + 0.95 * raw_absolute_scale_iou", - "evaluator_failure": "retry_once_then_abort_stage", - "ports_counts_structure": "diagnostics_only" - } - }, - "checkpoint_evaluation": { - "configuration": "depth", - "split": "validation", - "full_rows": 1092, - "full_representations": 546, - "geometry_k": 1, - "behavioral_rows": 40, - "behavioral_k": 4, - "task_selection_seed": 7, - "sampling_concurrency": 16, - "evaluator_workers": 8, - "include_records": false - }, - "launch": { - "paid_launch_enabled": false, - "confirmation_tokens": { - "sft": "TRACK_A_SFT", - "rl": "TRACK_A_RL", - "evaluation": "TRACK_A_EVAL" - } - } -} diff --git a/rl/track_a/curriculum.py b/rl/track_a/curriculum.py index 54de43b5..a8960450 100644 --- a/rl/track_a/curriculum.py +++ b/rl/track_a/curriculum.py @@ -10,6 +10,37 @@ from rl.common.contracts import TaskRecord +LEVELS = ("L0", "L1", "L2", "L3", "L4") + + +def normalize_level(value: str) -> str: + level = value.strip().upper() + if level not in LEVELS: + raise ValueError( + f"expected exactly one curriculum level in {LEVELS}; got {value!r}" + ) + return level + + +def normalize_replay_levels(value: str) -> tuple[str, ...]: + if not value.strip(): + return () + values = tuple(item.strip().upper() for item in value.split(",") if item.strip()) + if len(values) != len(set(values)): + raise ValueError("replay_levels must not contain duplicates") + unknown = [item for item in values if item not in LEVELS] + if unknown: + raise ValueError(f"unknown replay levels: {unknown}") + if values != tuple(sorted(values, key=LEVELS.index)): + raise ValueError("replay_levels must be in curriculum order") + return values + + +def expected_replay_levels(current_level: str) -> tuple[str, ...]: + current = normalize_level(current_level) + return LEVELS[: LEVELS.index(current)] + + def balanced_effective_pass( tasks: list[TaskRecord], *, diff --git a/rl/track_a/evaluate_checkpoint.py b/rl/track_a/evaluate_checkpoint.py deleted file mode 100644 index 166daee4..00000000 --- a/rl/track_a/evaluate_checkpoint.py +++ /dev/null @@ -1,334 +0,0 @@ -#!/usr/bin/env python3 -"""Spend-locked Track A evaluation for a Tinker sampler checkpoint.""" - -from __future__ import annotations - -import argparse -import asyncio -import json -import os -from collections import Counter, defaultdict -from pathlib import Path - -import tinker - -from rl.common.dataset_io import load_tasks -from rl.common.evaluator import Attribution, EvaluationStatus, PixCellEvaluator -from rl.common.isolation import IsolationError -from rl.track_a.checkpoint_metrics import ( - behavioral_probe_summary, - program_sha256, -) -from rl.track_a.curriculum import balanced_effective_pass -from rl.track_a.evaluation_contract import ( - BEHAVIORAL_PROBE_TASKS, - FULL_GEOMETRY_REPRESENTATIONS, - FULL_GEOMETRY_TASKS, - REPORT_SCHEMA_VERSION, - build_checkpoint_selection_contract, - report_provenance, - validate_evaluation_report, -) -from rl.track_a.launch import bind_execution_boundary, run_launch_preflight -from rl.track_a.recipe import validate_evaluation_cli -from rl.track_a.reward import reward_for -from rl.track_a.tinker_data import DEFAULT_MODEL, DEFAULT_RENDERER, _message, _renderer - - -REPO_ROOT = Path(__file__).resolve().parents[2] - - -def _filter_levels(tasks, levels: str): - allowed = {item.strip().upper() for item in levels.split(",") if item.strip()} - return [ - task for task in tasks if not allowed or task.sampler.level.upper() in allowed - ] - - -async def run(args: argparse.Namespace) -> dict: - try: - resolved_launch = validate_evaluation_cli(REPO_ROOT, args) - resolved_launch = bind_execution_boundary(resolved_launch) - except (ValueError, IsolationError) as exc: - raise SystemExit(f"paid evaluation blocked: {exc}") from exc - if not os.environ.get("TINKER_API_KEY"): - raise SystemExit("TINKER_API_KEY is not present") - run_launch_preflight(REPO_ROOT, resolved_launch=resolved_launch) - dataset_root = Path(str(resolved_launch["dataset_root"])) - sampler_path = str(resolved_launch["sampler_path"]) - output_path = Path(str(resolved_launch["output"])) - levels = str(resolved_launch["levels"]) - rows = int(resolved_launch["rows"]) - k = int(resolved_launch["k"]) - temperature = float(resolved_launch["temperature"]) - max_tokens = int(resolved_launch["max_tokens"]) - max_image = int(resolved_launch["max_image"]) - top_p = float(resolved_launch["top_p"]) - seed = int(resolved_launch["seed"]) - concurrency = int(resolved_launch["concurrency"]) - evaluator_workers = int(resolved_launch["evaluator_workers"]) - include_records = bool(resolved_launch["include_records"]) - behavioral_probe = ( - resolved_launch["role"] == "behavioral-collapse-probe" - ) - contract = build_checkpoint_selection_contract( - REPO_ROOT, - dataset_root, - ) - - tasks = _filter_levels( - load_tasks( - dataset_root, - configuration="depth", - split="validation", - ), - levels, - ) - if not tasks: - raise SystemExit("no validation tasks matched --levels") - if rows: - tasks = balanced_effective_pass( - tasks, - seed=seed, - draws=rows, - ) - task_ids = [task.sampler.opaque_id for task in tasks] - if len(set(task_ids)) != len(task_ids): - raise RuntimeError("checkpoint evaluation selected duplicate tasks") - expected_tasks = ( - BEHAVIORAL_PROBE_TASKS - if behavioral_probe - else FULL_GEOMETRY_TASKS - ) - if len(tasks) != expected_tasks: - raise RuntimeError( - f"checkpoint evaluation selected {len(tasks)} tasks; " - f"expected {expected_tasks}" - ) - representation_count = len( - {task.sampler.representation_id for task in tasks} - ) - if ( - not behavioral_probe - and representation_count != FULL_GEOMETRY_REPRESENTATIONS - ): - raise RuntimeError( - "checkpoint geometry evaluation selected " - f"{representation_count} representations; expected " - f"{FULL_GEOMETRY_REPRESENTATIONS}" - ) - evaluator = PixCellEvaluator( - max_workers=evaluator_workers, - require_isolation=True, - ) - renderer = _renderer( - str(resolved_launch["model_name"]), - str(resolved_launch["renderer_name"]), - ) - decode = getattr(renderer, "decode_action_text", None) or renderer.tokenizer.decode - sampling = tinker.ServiceClient().create_sampling_client( - model_path=sampler_path - ) - semaphore = asyncio.Semaphore(concurrency) - - async def sample(task): - prompt = renderer.build_generation_prompt( - [_message(task, max_image=max_image)] - ) - stop = renderer.get_stop_sequences() - async with semaphore: - result = await sampling.sample_async( - prompt=prompt, - num_samples=k, - sampling_params=tinker.SamplingParams( - stop=stop, - max_tokens=max_tokens, - temperature=temperature, - top_p=top_p, - ), - ) - return [ - { - "completion": decode(sequence.tokens), - "completion_tokens": len(sequence.tokens), - } - for sequence in result.sequences - ] - - sampled = await asyncio.gather(*(sample(task) for task in tasks)) - requests = [] - owners = [] - token_counts = [] - program_hashes = [] - for task, completions in zip(tasks, sampled, strict=True): - for completion in completions: - requests.append((task.reference, completion["completion"])) - owners.append(task) - token_counts.append(completion["completion_tokens"]) - program_hashes.append(program_sha256(completion["completion"])) - - with evaluator: - results = evaluator.evaluate_batch(requests) - masked = [ - result for result in results if result.attribution is not Attribution.MODEL - ] - if masked: - first = masked[0] - raise RuntimeError( - f"evaluation aborted on {first.attribution.value} failure: " - f"{first.status.value}: {first.error}" - ) - - records = [] - for task, result, completion_tokens, program_hash in zip( - owners, - results, - token_counts, - program_hashes, - strict=True, - ): - records.append( - { - "opaque_id": task.sampler.opaque_id, - "level": task.sampler.level, - "representation_id": task.sampler.representation_id, - "status": result.status.value, - "pure_executable": result.status is EvaluationStatus.OK, - "iou": float(result.iou or 0.0), - "reward": float(reward_for(result) or 0.0), - "completion_tokens": completion_tokens, - "program_sha256": program_hash, - "render_sha256": result.metrics.get("render_sha256"), - } - ) - - by_representation = defaultdict(list) - by_level = defaultdict(list) - for record in records: - by_representation[record["representation_id"]].append(record) - by_level[record["level"]].append(record) - - def aggregate(values): - per_task = defaultdict(list) - for value in values: - per_task[value["opaque_id"]].append(value) - task_means = [ - sum(item["iou"] for item in attempts) / len(attempts) - for attempts in per_task.values() - ] - task_bests = [ - max(item["iou"] for item in attempts) for attempts in per_task.values() - ] - return { - "attempts": len(values), - "tasks": len(per_task), - "pure_executable_rate": ( - sum(item["pure_executable"] for item in values) / len(values) - ), - "mean_iou_at_1": sum(task_means) / len(task_means), - "mean_best_at_k_iou": sum(task_bests) / len(task_bests), - } - - representation_means = [ - sum(record["iou"] for record in values) / len(values) - for values in by_representation.values() - ] - role = ( - "behavioral-collapse-probe" - if behavioral_probe - else "checkpoint-geometry" - ) - actual_levels = sorted( - {task.sampler.level.upper() for task in tasks} - ) - report = { - "schema_version": REPORT_SCHEMA_VERSION, - "evaluation_role": role, - "sampler_path": sampler_path, - "model": resolved_launch["model_name"], - "renderer": resolved_launch["renderer_name"], - "split": "depth/validation", - "levels": actual_levels[0] if behavioral_probe else "all", - "k": k, - "temperature": temperature, - "top_p": top_p, - "max_tokens": max_tokens, - "summary": aggregate(records), - "representation_macro_mean_iou": ( - sum(representation_means) / len(representation_means) - ), - "statuses": dict(sorted(Counter(item["status"] for item in records).items())), - "levels_summary": { - level: aggregate(values) for level, values in sorted(by_level.items()) - }, - "mean_completion_tokens": sum(token_counts) / len(token_counts), - "behavioral_probe": ( - behavioral_probe_summary(records, expected_group_size=4) - if behavioral_probe - else None - ), - "records": records if include_records else None, - } - boundary = evaluator.execution_boundary - assert boundary is not None - report["provenance"] = report_provenance( - contract=contract, - sampler_path=sampler_path, - role=role, - levels=actual_levels, - task_ids=task_ids, - representation_count=representation_count, - requested_rows=rows if behavioral_probe else None, - k=k, - attempt_count=len(records), - sandbox_image_ref=boundary.image_ref, - sandbox_image_id=boundary.image_id, - ) - validate_evaluation_report( - report, - contract=contract, - expected_role=role, - ) - output_path.parent.mkdir(parents=True, exist_ok=True) - with output_path.open("x", encoding="utf-8") as handle: - handle.write(json.dumps(report, indent=2, sort_keys=True) + "\n") - return report - - -def parser() -> argparse.ArgumentParser: - result = argparse.ArgumentParser(description=__doc__) - result.add_argument("--confirm-spend", default="") - result.add_argument("--sampler-path", required=True) - result.add_argument("--dataset-root", type=Path, default=REPO_ROOT / "dataset") - result.add_argument("--model-name", default=DEFAULT_MODEL) - result.add_argument("--renderer-name", default=DEFAULT_RENDERER) - result.add_argument("--levels", default="") - result.add_argument( - "--rows", type=int, default=0, help="0 evaluates the full split" - ) - result.add_argument("--k", type=int, default=1) - result.add_argument("--temperature", type=float, default=1.0) - result.add_argument("--max-tokens", type=int, default=4096) - result.add_argument("--concurrency", type=int, default=16) - result.add_argument("--evaluator-workers", type=int, default=8) - result.add_argument("--seed", type=int, default=7) - result.add_argument("--include-records", action="store_true") - result.add_argument( - "--behavioral-probe", - action="store_true", - help=( - "run the separate bounded G4 collapse probe; requires --k 4, " - "--rows, and explicit --levels" - ), - ) - result.add_argument("--output", type=Path, required=True) - return result - - -if __name__ == "__main__": - report = asyncio.run(run(parser().parse_args())) - print( - json.dumps( - {key: value for key, value in report.items() if key != "records"}, indent=2 - ) - ) diff --git a/rl/track_a/evaluation_contract.py b/rl/track_a/evaluation_contract.py deleted file mode 100644 index 7e0ff4fc..00000000 --- a/rl/track_a/evaluation_contract.py +++ /dev/null @@ -1,725 +0,0 @@ -"""Provenance and validation contract for Track A checkpoint reports.""" - -from __future__ import annotations - -import hashlib -import json -import math -import re -import statistics -import subprocess -from collections import Counter -from collections.abc import Mapping, Sequence -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -from rl.common.dataset_io import load_tasks -from rl.common.isolation import IMMUTABLE_IMAGE -from rl.track_a.curriculum import balanced_effective_pass - - -REPORT_SCHEMA_VERSION = "pixcell-track-a-executable-eval-v2" -DATASET_CONFIGURATION = "depth" -DATASET_SPLIT = "validation" -REPORT_SPLIT = f"{DATASET_CONFIGURATION}/{DATASET_SPLIT}" -CURRICULUM_LEVELS = ("L0", "L1", "L2", "L3", "L4") -FULL_GEOMETRY_TASKS = 1092 -FULL_GEOMETRY_REPRESENTATIONS = 546 -BEHAVIORAL_PROBE_TASKS = 40 -BEHAVIORAL_PROBE_K = 4 -BEHAVIORAL_PROBE_ATTEMPTS = BEHAVIORAL_PROBE_TASKS * BEHAVIORAL_PROBE_K -TASK_SELECTION_SEED = 7 - -_SHA256_RE = re.compile(r"^[0-9a-f]{64}$") -_GIT_SHA_RE = re.compile(r"^[0-9a-f]{40}(?:[0-9a-f]{24})?$") - - -def task_ids_sha256(opaque_ids: Sequence[str]) -> str: - """Hash an exact task multiset independently of evaluation order.""" - - payload = json.dumps( - sorted(str(value) for value in opaque_ids), - ensure_ascii=True, - separators=(",", ":"), - ).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def _source_git_sha(repo_root: Path) -> str: - value = subprocess.check_output( - ["git", "rev-parse", "HEAD"], - cwd=repo_root, - text=True, - ).strip() - if not _GIT_SHA_RE.fullmatch(value): - raise RuntimeError(f"invalid source Git SHA: {value!r}") - status = subprocess.check_output( - ["git", "status", "--porcelain", "--untracked-files=all"], - cwd=repo_root, - text=True, - ).strip() - if status: - raise RuntimeError( - "checkpoint report provenance requires a clean committed worktree" - ) - return value - - -def _depth_release_sha256(dataset_root: Path) -> str: - freeze_path = dataset_root / "depth-v1" / "factory" / "freeze.json" - freeze = json.loads(freeze_path.read_text(encoding="utf-8")) - value = str(freeze.get("logical_release_sha256", "")) - if not _SHA256_RE.fullmatch(value): - raise RuntimeError( - f"{freeze_path} has no valid logical_release_sha256" - ) - return value - - -@dataclass(frozen=True) -class CheckpointSelectionContract: - """Immutable facts every report used for checkpoint selection must match.""" - - source_git_sha: str - depth_logical_release_sha256: str - model: str - renderer: str - temperature: float - top_p: float - max_tokens: int - task_selection_seed: int - geometry_task_ids_sha256: str - behavioral_task_ids_sha256_by_level: Mapping[str, str] - - -def build_checkpoint_selection_contract( - repo_root: Path, - dataset_root: Path, -) -> CheckpointSelectionContract: - """Build the expected report contract from the committed recipe and data.""" - - repo_root = repo_root.resolve() - dataset_root = dataset_root.resolve() - expected_dataset_root = (repo_root / "dataset").resolve() - if dataset_root != expected_dataset_root: - raise RuntimeError( - "checkpoint report dataset_root must be the committed dataset at " - f"{expected_dataset_root}; got {dataset_root}" - ) - config = json.loads( - (repo_root / "rl" / "track_a" / "config.json").read_text( - encoding="utf-8" - ) - ) - validation = load_tasks( - dataset_root, - configuration=DATASET_CONFIGURATION, - split=DATASET_SPLIT, - ) - if len(validation) != FULL_GEOMETRY_TASKS: - raise RuntimeError( - "checkpoint selection requires the complete frozen validation " - f"split ({FULL_GEOMETRY_TASKS} tasks); observed {len(validation)}" - ) - representation_count = len( - {task.sampler.representation_id for task in validation} - ) - if representation_count != FULL_GEOMETRY_REPRESENTATIONS: - raise RuntimeError( - "checkpoint selection requires the complete frozen validation " - f"representation set ({FULL_GEOMETRY_REPRESENTATIONS}); " - f"observed {representation_count}" - ) - - observed_levels = { - task.sampler.level.upper() for task in validation - } - if observed_levels != set(CURRICULUM_LEVELS): - raise RuntimeError( - "validation curriculum levels differ from the report contract: " - f"{sorted(observed_levels)}" - ) - - behavioral_digests: dict[str, str] = {} - for level in CURRICULUM_LEVELS: - level_tasks = [ - task - for task in validation - if task.sampler.level.upper() == level - ] - selected = balanced_effective_pass( - level_tasks, - seed=TASK_SELECTION_SEED, - draws=BEHAVIORAL_PROBE_TASKS, - ) - selected_ids = [task.sampler.opaque_id for task in selected] - if ( - len(selected_ids) != BEHAVIORAL_PROBE_TASKS - or len(set(selected_ids)) != BEHAVIORAL_PROBE_TASKS - ): - raise RuntimeError( - f"{level} behavioral task selection is not exactly " - f"{BEHAVIORAL_PROBE_TASKS} unique tasks" - ) - behavioral_digests[level] = task_ids_sha256(selected_ids) - - model = config["model"] - rl = config["rl"] - return CheckpointSelectionContract( - source_git_sha=_source_git_sha(repo_root), - depth_logical_release_sha256=_depth_release_sha256(dataset_root), - model=str(model["name"]), - renderer=str(model["renderer"]), - temperature=float(rl["temperature"]), - top_p=float(rl["top_p"]), - max_tokens=int(rl["max_output_tokens"]), - task_selection_seed=TASK_SELECTION_SEED, - geometry_task_ids_sha256=task_ids_sha256( - [task.sampler.opaque_id for task in validation] - ), - behavioral_task_ids_sha256_by_level=behavioral_digests, - ) - - -def report_provenance( - *, - contract: CheckpointSelectionContract, - sampler_path: str, - role: str, - levels: Sequence[str], - task_ids: Sequence[str], - representation_count: int, - requested_rows: int | None, - k: int, - attempt_count: int, - sandbox_image_ref: str, - sandbox_image_id: str, -) -> dict[str, Any]: - """Create the complete provenance record written beside eval metrics.""" - - return { - "source_git_sha": contract.source_git_sha, - "depth_logical_release_sha256": ( - contract.depth_logical_release_sha256 - ), - "dataset_configuration": DATASET_CONFIGURATION, - "split": REPORT_SPLIT, - "model": contract.model, - "renderer": contract.renderer, - "sampler_path": sampler_path, - "evaluation_role": role, - "levels": sorted(str(level).upper() for level in levels), - "requested_rows": requested_rows, - "task_count": len(task_ids), - "attempt_count": attempt_count, - "representation_count": representation_count, - "task_ids_sha256": task_ids_sha256(task_ids), - "task_selection_seed": contract.task_selection_seed, - "candidate_sandbox": { - "contract": "per-candidate-docker-v1", - "image_ref": sandbox_image_ref, - "image_id": sandbox_image_id, - }, - "sampling": { - "k": k, - "temperature": contract.temperature, - "top_p": contract.top_p, - "max_tokens": contract.max_tokens, - }, - } - - -def _mapping(value: Any, path: str) -> Mapping[str, Any]: - if not isinstance(value, Mapping): - raise ValueError(f"{path} must be an object") - return value - - -def _integer(value: Any, path: str, *, minimum: int = 0) -> int: - if isinstance(value, bool) or not isinstance(value, int): - raise ValueError(f"{path} must be an integer") - if value < minimum: - raise ValueError(f"{path} must be >= {minimum}") - return value - - -def _finite(value: Any, path: str) -> float: - if isinstance(value, bool): - raise ValueError(f"{path} must be numeric") - try: - result = float(value) - except (TypeError, ValueError) as exc: - raise ValueError(f"{path} must be numeric") from exc - if not math.isfinite(result): - raise ValueError(f"{path} must be finite") - return result - - -def _unit_metric(value: Any, path: str) -> float: - result = _finite(value, path) - if result < 0.0 or result > 1.0: - raise ValueError(f"{path} must be in [0, 1]") - return result - - -def _bounded_metric(value: Any, path: str, *, low: float, high: float) -> float: - result = _finite(value, path) - if result < low or result > high: - raise ValueError(f"{path} must be in [{low}, {high}]") - return result - - -def _exact(value: Any, expected: Any, path: str) -> None: - if value != expected: - raise ValueError(f"{path}={value!r}; expected {expected!r}") - - -def _close(value: float, expected: float, path: str) -> None: - if not math.isclose(value, expected, rel_tol=0.0, abs_tol=1e-12): - raise ValueError(f"{path}={value!r}; expected {expected!r}") - - -def _validate_summary( - summary: Any, - *, - path: str, - tasks: int, - attempts: int, -) -> None: - values = _mapping(summary, path) - _exact(_integer(values.get("tasks"), f"{path}.tasks"), tasks, f"{path}.tasks") - _exact( - _integer(values.get("attempts"), f"{path}.attempts"), - attempts, - f"{path}.attempts", - ) - for field in ( - "pure_executable_rate", - "mean_iou_at_1", - "mean_best_at_k_iou", - ): - _unit_metric(values.get(field), f"{path}.{field}") - - -def _validate_behavioral_probe( - value: Any, - *, - expected_task_ids_sha256: str, -) -> None: - probe = _mapping(value, "behavioral_probe") - _exact( - probe.get("probe_kind"), - "small-balanced-unmastered-g4", - "behavioral_probe.probe_kind", - ) - _exact( - _integer(probe.get("group_size"), "behavioral_probe.group_size"), - BEHAVIORAL_PROBE_K, - "behavioral_probe.group_size", - ) - _exact( - _integer(probe.get("prompt_groups"), "behavioral_probe.prompt_groups"), - BEHAVIORAL_PROBE_TASKS, - "behavioral_probe.prompt_groups", - ) - nonconstant_fraction = _unit_metric( - probe.get("nonconstant_reward_group_fraction"), - "behavioral_probe.nonconstant_reward_group_fraction", - ) - mean_reward_std = _unit_metric( - probe.get("mean_within_group_reward_std"), - "behavioral_probe.mean_within_group_reward_std", - ) - mean_programs = _bounded_metric( - probe.get("mean_distinct_programs_per_prompt"), - "behavioral_probe.mean_distinct_programs_per_prompt", - low=1.0, - high=float(BEHAVIORAL_PROBE_K), - ) - mean_renders = _bounded_metric( - probe.get("mean_distinct_successful_renders_per_prompt"), - "behavioral_probe.mean_distinct_successful_renders_per_prompt", - low=0.0, - high=float(BEHAVIORAL_PROBE_K), - ) - collapsed = probe.get("collapse_detected") - if not isinstance(collapsed, bool): - raise ValueError("behavioral_probe.collapse_detected must be boolean") - - signals = _mapping( - probe.get("collapse_signals"), - "behavioral_probe.collapse_signals", - ) - for field in ( - "identical_program_every_group", - "constant_render_and_reward_every_group", - ): - if not isinstance(signals.get(field), bool): - raise ValueError( - f"behavioral_probe.collapse_signals.{field} must be boolean" - ) - - groups = probe.get("groups") - if not isinstance(groups, list): - raise ValueError("behavioral_probe.groups must be an array") - if len(groups) != BEHAVIORAL_PROBE_TASKS: - raise ValueError( - f"behavioral_probe.groups has {len(groups)} entries; " - f"expected {BEHAVIORAL_PROBE_TASKS}" - ) - opaque_ids: list[str] = [] - group_program_counts: list[int] = [] - group_render_counts: list[int] = [] - group_reward_stds: list[float] = [] - group_nonconstant: list[bool] = [] - for index, raw_group in enumerate(groups): - path = f"behavioral_probe.groups[{index}]" - group = _mapping(raw_group, path) - opaque_id = str(group.get("opaque_id", "")) - if not opaque_id: - raise ValueError(f"{path}.opaque_id must be non-empty") - opaque_ids.append(opaque_id) - distinct_programs = _integer( - group.get("distinct_program_count"), - f"{path}.distinct_program_count", - minimum=1, - ) - if distinct_programs > BEHAVIORAL_PROBE_K: - raise ValueError( - f"{path}.distinct_program_count exceeds {BEHAVIORAL_PROBE_K}" - ) - group_program_counts.append(distinct_programs) - successful = _integer( - group.get("successful_render_attempts"), - f"{path}.successful_render_attempts", - ) - if successful > BEHAVIORAL_PROBE_K: - raise ValueError( - f"{path}.successful_render_attempts exceeds " - f"{BEHAVIORAL_PROBE_K}" - ) - distinct_renders = _integer( - group.get("distinct_successful_render_count"), - f"{path}.distinct_successful_render_count", - ) - if distinct_renders > successful: - raise ValueError( - f"{path}.distinct_successful_render_count exceeds " - "successful_render_attempts" - ) - group_render_counts.append(distinct_renders) - group_reward_stds.append( - _unit_metric(group.get("reward_std"), f"{path}.reward_std") - ) - _unit_metric(group.get("reward_range"), f"{path}.reward_range") - nonconstant = group.get("nonconstant_reward") - if not isinstance(nonconstant, bool): - raise ValueError(f"{path}.nonconstant_reward must be boolean") - group_nonconstant.append(nonconstant) - - if len(set(opaque_ids)) != BEHAVIORAL_PROBE_TASKS: - raise ValueError("behavioral_probe.groups contains duplicate tasks") - observed_digest = task_ids_sha256(opaque_ids) - if observed_digest != expected_task_ids_sha256: - raise ValueError( - "behavioral_probe.groups task set differs from provenance" - ) - - expected_nonconstant_fraction = ( - sum(group_nonconstant) / BEHAVIORAL_PROBE_TASKS - ) - expected_mean_reward_std = statistics.fmean(group_reward_stds) - expected_mean_programs = statistics.fmean(group_program_counts) - expected_mean_renders = statistics.fmean(group_render_counts) - _close( - nonconstant_fraction, - expected_nonconstant_fraction, - "behavioral_probe.nonconstant_reward_group_fraction", - ) - _close( - mean_reward_std, - expected_mean_reward_std, - "behavioral_probe.mean_within_group_reward_std", - ) - _close( - mean_programs, - expected_mean_programs, - "behavioral_probe.mean_distinct_programs_per_prompt", - ) - _close( - mean_renders, - expected_mean_renders, - "behavioral_probe.mean_distinct_successful_renders_per_prompt", - ) - identical_program_every_group = all( - count == 1 for count in group_program_counts - ) - constant_render_and_reward_every_group = all( - render_count <= 1 and not nonconstant - for render_count, nonconstant in zip( - group_render_counts, - group_nonconstant, - strict=True, - ) - ) - expected_collapsed = ( - identical_program_every_group - or constant_render_and_reward_every_group - ) - _exact( - signals.get("identical_program_every_group"), - identical_program_every_group, - "behavioral_probe.collapse_signals.identical_program_every_group", - ) - _exact( - signals.get("constant_render_and_reward_every_group"), - constant_render_and_reward_every_group, - "behavioral_probe.collapse_signals." - "constant_render_and_reward_every_group", - ) - _exact( - collapsed, - expected_collapsed, - "behavioral_probe.collapse_detected", - ) - - -def validate_evaluation_report( - report: Mapping[str, Any], - *, - contract: CheckpointSelectionContract, - expected_role: str, -) -> None: - """Fail closed unless a report exactly matches the selection contract.""" - - _exact( - report.get("schema_version"), - REPORT_SCHEMA_VERSION, - "schema_version", - ) - _exact(report.get("evaluation_role"), expected_role, "evaluation_role") - provenance = _mapping(report.get("provenance"), "provenance") - - shared_expected = { - "source_git_sha": contract.source_git_sha, - "depth_logical_release_sha256": ( - contract.depth_logical_release_sha256 - ), - "dataset_configuration": DATASET_CONFIGURATION, - "split": REPORT_SPLIT, - "model": contract.model, - "renderer": contract.renderer, - "evaluation_role": expected_role, - "task_selection_seed": contract.task_selection_seed, - } - for field, expected in shared_expected.items(): - _exact(provenance.get(field), expected, f"provenance.{field}") - - sandbox = _mapping( - provenance.get("candidate_sandbox"), - "provenance.candidate_sandbox", - ) - _exact( - sandbox.get("contract"), - "per-candidate-docker-v1", - "provenance.candidate_sandbox.contract", - ) - image_ref = str(sandbox.get("image_ref", "")) - if not IMMUTABLE_IMAGE.fullmatch(image_ref): - raise ValueError( - "provenance.candidate_sandbox.image_ref must be immutable" - ) - image_id = str(sandbox.get("image_id", "")) - if not re.fullmatch(r"sha256:[0-9a-f]{64}", image_id): - raise ValueError( - "provenance.candidate_sandbox.image_id must be a SHA-256 image ID" - ) - - sampler_path = str(report.get("sampler_path", "")) - if not sampler_path: - raise ValueError("sampler_path must be non-empty") - _exact(provenance.get("sampler_path"), sampler_path, "provenance.sampler_path") - _exact(report.get("model"), contract.model, "model") - _exact(report.get("renderer"), contract.renderer, "renderer") - _exact(report.get("split"), REPORT_SPLIT, "split") - - sampling = _mapping(provenance.get("sampling"), "provenance.sampling") - expected_sampling = { - "temperature": contract.temperature, - "top_p": contract.top_p, - "max_tokens": contract.max_tokens, - } - for field, expected in expected_sampling.items(): - observed = sampling.get(field) - if isinstance(expected, float): - observed = _finite(observed, f"provenance.sampling.{field}") - else: - observed = _integer(observed, f"provenance.sampling.{field}") - _exact(observed, expected, f"provenance.sampling.{field}") - _exact(report.get(field), expected, field) - - levels = provenance.get("levels") - if not isinstance(levels, list) or any( - not isinstance(level, str) for level in levels - ): - raise ValueError("provenance.levels must be an array of strings") - if levels != sorted(set(levels)): - raise ValueError("provenance.levels must be sorted and unique") - - task_count = _integer(provenance.get("task_count"), "provenance.task_count") - attempt_count = _integer( - provenance.get("attempt_count"), - "provenance.attempt_count", - ) - representation_count = _integer( - provenance.get("representation_count"), - "provenance.representation_count", - ) - task_digest = str(provenance.get("task_ids_sha256", "")) - if not _SHA256_RE.fullmatch(task_digest): - raise ValueError("provenance.task_ids_sha256 must be a SHA-256 digest") - k = _integer(sampling.get("k"), "provenance.sampling.k", minimum=1) - _exact(report.get("k"), k, "k") - _exact(report.get("levels"), "all" if len(levels) > 1 else levels[0], "levels") - _exact(attempt_count, task_count * k, "provenance.attempt_count") - - if expected_role == "checkpoint-geometry": - _exact(levels, list(CURRICULUM_LEVELS), "provenance.levels") - _exact( - provenance.get("requested_rows"), - None, - "provenance.requested_rows", - ) - _exact(task_count, FULL_GEOMETRY_TASKS, "provenance.task_count") - _exact(attempt_count, FULL_GEOMETRY_TASKS, "provenance.attempt_count") - _exact( - representation_count, - FULL_GEOMETRY_REPRESENTATIONS, - "provenance.representation_count", - ) - _exact(k, 1, "provenance.sampling.k") - _exact( - task_digest, - contract.geometry_task_ids_sha256, - "provenance.task_ids_sha256", - ) - if report.get("behavioral_probe") is not None: - raise ValueError( - "checkpoint-geometry report must not contain behavioral_probe" - ) - elif expected_role == "behavioral-collapse-probe": - if len(levels) != 1 or levels[0] not in CURRICULUM_LEVELS: - raise ValueError( - "behavioral report must name exactly one curriculum level" - ) - _exact( - provenance.get("requested_rows"), - BEHAVIORAL_PROBE_TASKS, - "provenance.requested_rows", - ) - _exact(task_count, BEHAVIORAL_PROBE_TASKS, "provenance.task_count") - _exact( - attempt_count, - BEHAVIORAL_PROBE_ATTEMPTS, - "provenance.attempt_count", - ) - if representation_count < 1 or representation_count > task_count: - raise ValueError( - "provenance.representation_count is outside task bounds" - ) - _exact(k, BEHAVIORAL_PROBE_K, "provenance.sampling.k") - expected_digest = contract.behavioral_task_ids_sha256_by_level.get( - levels[0] - ) - if expected_digest is None: - raise ValueError( - f"no behavioral task contract for level {levels[0]!r}" - ) - _exact( - task_digest, - expected_digest, - "provenance.task_ids_sha256", - ) - _validate_behavioral_probe( - report.get("behavioral_probe"), - expected_task_ids_sha256=expected_digest, - ) - else: - raise ValueError(f"unsupported evaluation role: {expected_role!r}") - - _validate_summary( - report.get("summary"), - path="summary", - tasks=task_count, - attempts=attempt_count, - ) - _unit_metric( - report.get("representation_macro_mean_iou"), - "representation_macro_mean_iou", - ) - completion_tokens = _finite( - report.get("mean_completion_tokens"), - "mean_completion_tokens", - ) - if completion_tokens < 0: - raise ValueError("mean_completion_tokens must be non-negative") - - statuses = _mapping(report.get("statuses"), "statuses") - status_total = sum( - _integer(value, f"statuses.{name}") for name, value in statuses.items() - ) - _exact(status_total, attempt_count, "statuses total") - - levels_summary = _mapping(report.get("levels_summary"), "levels_summary") - _exact(sorted(levels_summary), levels, "levels_summary keys") - level_tasks = 0 - level_attempts = 0 - for level, summary in levels_summary.items(): - values = _mapping(summary, f"levels_summary.{level}") - tasks = _integer(values.get("tasks"), f"levels_summary.{level}.tasks") - attempts = _integer( - values.get("attempts"), - f"levels_summary.{level}.attempts", - ) - _validate_summary( - values, - path=f"levels_summary.{level}", - tasks=tasks, - attempts=attempts, - ) - level_tasks += tasks - level_attempts += attempts - _exact(level_tasks, task_count, "levels_summary task total") - _exact(level_attempts, attempt_count, "levels_summary attempt total") - - records = report.get("records") - if records is not None: - if not isinstance(records, list): - raise ValueError("records must be null or an array") - if len(records) != attempt_count: - raise ValueError( - f"records has {len(records)} entries; expected {attempt_count}" - ) - record_ids: list[str] = [] - counts: Counter[str] = Counter() - for index, raw_record in enumerate(records): - path = f"records[{index}]" - record = _mapping(raw_record, path) - opaque_id = str(record.get("opaque_id", "")) - if not opaque_id: - raise ValueError(f"{path}.opaque_id must be non-empty") - record_ids.append(opaque_id) - counts[opaque_id] += 1 - _unit_metric(record.get("iou"), f"{path}.iou") - _unit_metric(record.get("reward"), f"{path}.reward") - if not isinstance(record.get("pure_executable"), bool): - raise ValueError(f"{path}.pure_executable must be boolean") - _integer( - record.get("completion_tokens"), - f"{path}.completion_tokens", - ) - if set(counts.values()) != {k}: - raise ValueError(f"records must contain exactly {k} attempts per task") - if task_ids_sha256(list(counts)) != task_digest: - raise ValueError("records task set differs from provenance") diff --git a/rl/track_a/launch.py b/rl/track_a/launch.py index d37fb02f..8dd2ec9c 100644 --- a/rl/track_a/launch.py +++ b/rl/track_a/launch.py @@ -2,39 +2,38 @@ from __future__ import annotations -import json import os import subprocess import sys -from collections.abc import Mapping from pathlib import Path from rl.common.isolation import require_execution_boundary -def bind_execution_boundary( - resolved_launch: Mapping[str, object], -) -> dict[str, object]: +def bind_execution_boundary() -> dict[str, object]: """Bind paid program execution to one immutable local sandbox image.""" - binding = dict(resolved_launch) - if binding.get("kind") not in {"rl", "evaluation"}: - return binding boundary = require_execution_boundary() - binding["candidate_sandbox"] = { + return { "runtime_path": str(boundary.runtime_path), "daemon_endpoint": boundary.daemon_endpoint, "image_ref": boundary.image_ref, "image_id": boundary.image_id, "workspace_root": str(boundary.workspace_root), } - return binding def run_launch_preflight( repo_root: Path, *, - resolved_launch: Mapping[str, object], + dataset_root: Path, + model_name: str, + renderer_name: str, + require_sandbox: bool, + tokenize_sft: bool = False, + sft_levels: str = "", + max_length: int = 6144, + max_image: int = 1440, ) -> None: """Run the complete local gate before any Tinker client can be created.""" @@ -44,27 +43,28 @@ def run_launch_preflight( if existing: roots.append(existing) environment["PYTHONPATH"] = os.pathsep.join(roots) - subprocess.run( - [ - sys.executable, - "-m", - "rl.track_a.preflight", - "--execute-ground-truth", - "5", - "--tokenize-sft", - "--require-clean", - "--dataset-root", - str(resolved_launch["dataset_root"]), - "--config", - str(resolved_launch["config_path"]), - "--resolved-launch-json", - json.dumps( - dict(resolved_launch), - sort_keys=True, - separators=(",", ":"), - ), - ], - cwd=repo_root, - env=environment, - check=True, - ) + command = [ + sys.executable, + "-m", + "rl.track_a.preflight", + "--execute-ground-truth", + "5", + "--require-clean", + "--dataset-root", + str(dataset_root), + "--model-name", + model_name, + "--renderer-name", + renderer_name, + "--max-length", + str(max_length), + "--max-image", + str(max_image), + ] + if require_sandbox: + command.append("--require-sandbox") + if tokenize_sft: + command.append("--tokenize-sft") + if sft_levels: + command.extend(["--sft-levels", sft_levels]) + subprocess.run(command, cwd=repo_root, env=environment, check=True) diff --git a/rl/track_a/preflight.py b/rl/track_a/preflight.py index 8f6d579c..fdebddeb 100644 --- a/rl/track_a/preflight.py +++ b/rl/track_a/preflight.py @@ -32,14 +32,9 @@ catalog_text, prompt_asset_hashes, ) +from rl.common.baselines import cached_rect_baseline from rl.common.runtime import validate_runtime_stack -from rl.track_a.reward import reward_for -from rl.track_a.recipe import ( - canonical_config_path, - canonical_dataset_root, - canonical_json_sha256, - validate_resolved_binding, -) +from rl.track_a.reward import reward_for_policy def _dataset_allowed_components(dataset_root: Path) -> frozenset[str]: @@ -180,42 +175,8 @@ def _token_audit( def run(args: argparse.Namespace) -> dict: dataset_root = args.dataset_root.resolve() repo_root = dataset_root.parent - config_path = args.config.resolve() - if dataset_root != canonical_dataset_root(repo_root): + if dataset_root != (repo_root / "dataset").resolve(): raise RuntimeError("preflight dataset_root is not repo_root/dataset") - if config_path != canonical_config_path(repo_root): - raise RuntimeError("preflight config is not the committed Track A config") - config = json.loads(config_path.read_text(encoding="utf-8")) - if args.audit_only: - if args.resolved_launch_json is not None: - raise RuntimeError("audit-only preflight does not accept a launch binding") - resolved_launch = { - "kind": "audit-only", - "dataset_root": str(dataset_root), - "config_path": str(config_path), - "model_name": config["model"]["name"], - "renderer_name": config["model"]["renderer"], - "lora_rank": config["model"]["lora_rank"], - } - else: - if args.resolved_launch_json is None: - raise RuntimeError( - "paid-launch preflight requires the exact resolved launch binding" - ) - try: - resolved_launch = json.loads(args.resolved_launch_json) - except json.JSONDecodeError as exc: - raise RuntimeError("resolved launch binding is not valid JSON") from exc - if not isinstance(resolved_launch, dict): - raise RuntimeError("resolved launch binding must be a JSON object") - try: - validate_resolved_binding(repo_root, resolved_launch) - except ValueError as exc: - raise RuntimeError(f"resolved launch binding failed: {exc}") from exc - if config["contract_version"] != CONTRACT_VERSION: - raise RuntimeError("config and prompt contract versions differ") - if config["launch"]["paid_launch_enabled"] is not False: - raise RuntimeError("committed config must remain spend-locked") release = _verify_frozen_release(dataset_root) git = _git_state(repo_root, require_clean=args.require_clean) runtime = validate_runtime_stack(repo_root) @@ -285,7 +246,7 @@ def run(args: argparse.Namespace) -> dict: raise RuntimeError(f"policy prompt leaked hidden row state: {leaked}") sandbox: dict | None = None - if resolved_launch["kind"] in {"rl", "evaluation"}: + if args.require_sandbox: with PixCellEvaluator( max_workers=1, evaluator_retries=0, @@ -334,7 +295,11 @@ def run(args: argparse.Namespace) -> dict: "opaque_index": len(executed), "level": row["level"], "iou": result.iou, - "reward": reward_for(result), + "reward": reward_for_policy( + result, + policy="shaped_v3b", + baseline=cached_rect_baseline(reference_from_row(row)), + ), } ) @@ -343,8 +308,8 @@ def run(args: argparse.Namespace) -> dict: "spend": False, "contract_version": CONTRACT_VERSION, "prompt_assets": prompt_asset_hashes(), - "resolved_launch_sha256": canonical_json_sha256(resolved_launch), - "resolved_launch": resolved_launch, + "model_name": args.model_name, + "renderer_name": args.renderer_name, "release": release, "git": git, "dataset": { @@ -368,11 +333,24 @@ def run(args: argparse.Namespace) -> dict: }, "token_audit": ( _token_audit( - train_tasks, - model_name=str(resolved_launch["model_name"]), - renderer_name=str(resolved_launch["renderer_name"]), - max_length=int(config["sft"]["max_sequence_tokens"]), - max_image=int(config["model"]["max_image_long_edge"]), + ( + [ + task + for task in train_tasks + if task.sampler.level.upper() + in { + item.strip().upper() + for item in args.sft_levels.split(",") + if item.strip() + } + ] + if args.sft_levels.strip() + else train_tasks + ), + model_name=args.model_name, + renderer_name=args.renderer_name, + max_length=args.max_length, + max_image=args.max_image, ) if args.tokenize_sft else None @@ -385,22 +363,21 @@ def parser() -> argparse.ArgumentParser: repo_root = Path(__file__).resolve().parents[2] result = argparse.ArgumentParser(description=__doc__) result.add_argument("--dataset-root", type=Path, default=repo_root / "dataset") - result.add_argument( - "--config", - type=Path, - default=repo_root / "rl" / "track_a" / "config.json", - ) + result.add_argument("--model-name", default="Qwen/Qwen3.6-35B-A3B") + result.add_argument("--renderer-name", default="qwen3_5_disable_thinking") + result.add_argument("--max-length", type=int, default=6144) + result.add_argument("--max-image", type=int, default=1440) + result.add_argument("--sft-levels", default="") result.add_argument("--execute-ground-truth", type=int, default=5) result.add_argument("--minimum-self-iou", type=float, default=0.97) result.add_argument("--workers", type=int, default=8) result.add_argument("--tokenize-sft", action="store_true") result.add_argument("--require-clean", action="store_true") result.add_argument( - "--audit-only", + "--require-sandbox", action="store_true", - help="run the zero-spend repository audit without authorizing a launch", + help="bind and self-test the immutable Docker candidate sandbox", ) - result.add_argument("--resolved-launch-json") return result diff --git a/rl/track_a/recipe.py b/rl/track_a/recipe.py deleted file mode 100644 index aa33f425..00000000 --- a/rl/track_a/recipe.py +++ /dev/null @@ -1,560 +0,0 @@ -"""Canonical Track A recipe and curriculum invariants. - -Paid entrypoints intentionally accept only operational inputs (confirmation, -checkpoint/report paths, and a contained log directory). Model, data, and -optimization settings are loaded from the committed recipe and cannot be -silently changed at the command line. -""" - -from __future__ import annotations - -import hashlib -import json -import math -import re -from collections.abc import Mapping -from pathlib import Path -from typing import Any - -from rl.common.isolation import require_execution_boundary - - -LEVELS = ("L0", "L1", "L2", "L3", "L4") -CONFIG_RELATIVE_PATH = Path("rl/track_a/config.json") -DATASET_RELATIVE_PATH = Path("dataset") -RUNS_RELATIVE_PATH = Path("rl/track_a/runs") -_TINKER_SAMPLER_PATH = re.compile( - r"^(tinker://[^/\s]+/)(sampler_weights)(/[^/\s]+)$" -) - - -def load_recipe(repo_root: Path) -> dict[str, Any]: - """Load the single committed Track A recipe.""" - - path = (repo_root / CONFIG_RELATIVE_PATH).resolve() - value = json.loads(path.read_text(encoding="utf-8")) - if value.get("schema_version") != "pixcell-track-a-config-v1": - raise ValueError("unsupported Track A recipe schema") - return value - - -def canonical_dataset_root(repo_root: Path) -> Path: - return (repo_root / DATASET_RELATIVE_PATH).resolve() - - -def canonical_config_path(repo_root: Path) -> Path: - return (repo_root / CONFIG_RELATIVE_PATH).resolve() - - -def _same_number(actual: Any, expected: Any) -> bool: - if isinstance(actual, bool) or isinstance(expected, bool): - return actual is expected - if isinstance(actual, (int, float)) and isinstance(expected, (int, float)): - return math.isclose(float(actual), float(expected), rel_tol=0.0, abs_tol=0.0) - return actual == expected - - -def _require_equal(name: str, actual: Any, expected: Any) -> None: - if not _same_number(actual, expected): - raise ValueError( - f"{name}={actual!r} overrides the committed Track A value {expected!r}" - ) - - -def _require_canonical_dataset(repo_root: Path, value: str | Path) -> Path: - observed = Path(value).expanduser().resolve() - expected = canonical_dataset_root(repo_root) - if observed != expected: - raise ValueError( - f"dataset_root must be the committed dataset at {expected}; got {observed}" - ) - return observed - - -def _require_contained_log_path(repo_root: Path, value: str | Path) -> Path: - observed = Path(value).expanduser().resolve() - root = (repo_root / RUNS_RELATIVE_PATH).resolve() - if observed == root or not observed.is_relative_to(root): - raise ValueError(f"log_path must be a new child of {root}; got {observed}") - return observed - - -def _require_contained_report_path(repo_root: Path, value: str | Path) -> Path: - observed = Path(value).expanduser().resolve() - root = (repo_root / RUNS_RELATIVE_PATH).resolve() - if not observed.is_relative_to(root) or observed.suffix != ".json": - raise ValueError(f"output must be a JSON child of {root}; got {observed}") - if observed.exists(): - raise ValueError(f"output must not already exist: {observed}") - return observed - - -def file_sha256(path: Path) -> str: - """Hash one launch input exactly as preflight will consume it.""" - - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def weights_path_for_sampler(sampler_path: str) -> str: - """Return the weights checkpoint paired with a Tinker sampler checkpoint.""" - - match = _TINKER_SAMPLER_PATH.fullmatch(sampler_path) - if match is None: - raise ValueError( - "mastery report sampler_path must be a Tinker sampler_weights checkpoint" - ) - return f"{match.group(1)}weights{match.group(3)}" - - -def validate_sft_cli(repo_root: Path, cfg: Any) -> dict[str, Any]: - """Reject every paid SFT recipe override and return its resolved binding.""" - - recipe = load_recipe(repo_root) - model = recipe["model"] - sft = recipe["sft"] - launch = recipe["launch"] - _require_canonical_dataset(repo_root, cfg.dataset_root) - _require_equal("model_name", cfg.model_name, model["name"]) - _require_equal("renderer_name", cfg.renderer_name, model["renderer"]) - _require_equal("lora_rank", cfg.lora_rank, model["lora_rank"]) - _require_equal("learning_rate", cfg.learning_rate, sft["learning_rate"]) - _require_equal("lr_schedule", cfg.lr_schedule, sft["tinker_lr_schedule"]) - _require_equal("num_epochs", cfg.num_epochs, sft["effective_passes"]) - _require_equal("batch_size", cfg.batch_size, sft["batch_size"]) - _require_equal("max_length", cfg.max_length, sft["max_sequence_tokens"]) - _require_equal("max_image", cfg.max_image, model["max_image_long_edge"]) - _require_equal("save_every", cfg.save_every, sft["save_every_steps"]) - _require_equal("eval_every", cfg.eval_every, sft["evaluate_every_steps"]) - _require_equal("max_steps", cfg.max_steps, sft["max_steps"]) - _require_contained_log_path(repo_root, cfg.log_path) - _require_equal( - "confirm_spend", - cfg.confirm_spend, - launch["confirmation_tokens"]["sft"], - ) - return { - "kind": "sft", - "dataset_root": str(canonical_dataset_root(repo_root)), - "config_path": str(canonical_config_path(repo_root)), - "model_name": cfg.model_name, - "renderer_name": cfg.renderer_name, - "lora_rank": cfg.lora_rank, - "learning_rate": cfg.learning_rate, - "lr_schedule": cfg.lr_schedule, - "num_epochs": cfg.num_epochs, - "batch_size": cfg.batch_size, - "max_length": cfg.max_length, - "max_image": cfg.max_image, - "save_every": cfg.save_every, - "eval_every": cfg.eval_every, - "max_steps": cfg.max_steps, - } - - -def normalize_level(value: str) -> str: - level = value.strip().upper() - if level not in LEVELS: - raise ValueError(f"expected exactly one curriculum level in {LEVELS}; got {value!r}") - return level - - -def normalize_replay_levels(value: str) -> tuple[str, ...]: - if not value.strip(): - return () - values = tuple(item.strip().upper() for item in value.split(",") if item.strip()) - if len(values) != len(set(values)): - raise ValueError("replay_levels must not contain duplicates") - unknown = [item for item in values if item not in LEVELS] - if unknown: - raise ValueError(f"unknown replay levels: {unknown}") - if values != tuple(sorted(values, key=LEVELS.index)): - raise ValueError("replay_levels must be in curriculum order") - return values - - -def expected_replay_levels(current_level: str) -> tuple[str, ...]: - current = normalize_level(current_level) - return LEVELS[: LEVELS.index(current)] - - -def first_unmastered_level( - report: Mapping[str, Any], - *, - minimum_iou: float, - minimum_pure_executable_rate: float, -) -> str | None: - """Compute the first curriculum level that misses either mastery threshold.""" - - levels = report.get("levels_summary") - if not isinstance(levels, Mapping): - raise ValueError("mastery report has no levels_summary mapping") - for level in LEVELS: - summary = levels.get(level) - if not isinstance(summary, Mapping): - raise ValueError(f"mastery report is missing {level}") - try: - iou = float(summary["mean_iou_at_1"]) - pure = float(summary["pure_executable_rate"]) - except (KeyError, TypeError, ValueError) as exc: - raise ValueError(f"mastery report has invalid {level} metrics") from exc - if not all(math.isfinite(value) and 0.0 <= value <= 1.0 for value in (iou, pure)): - raise ValueError(f"mastery report has out-of-range {level} metrics") - if iou < minimum_iou or pure < minimum_pure_executable_rate: - return level - return None - - -def validate_curriculum_selection( - recipe: Mapping[str, Any], - report: Mapping[str, Any], - *, - current_level: str, - replay_levels: str, -) -> tuple[str, tuple[str, ...]]: - """Bind caller-supplied curriculum arguments to the computed mastery result.""" - - curriculum = recipe["rl"]["curriculum"] - computed = first_unmastered_level( - report, - minimum_iou=float(curriculum["minimum_iou"]), - minimum_pure_executable_rate=float( - curriculum["minimum_pure_executable_rate"] - ), - ) - if computed is None: - raise ValueError("all curriculum levels are mastered; no RL stage may start") - current = normalize_level(current_level) - if current != computed: - raise ValueError( - f"current_level must be the computed first unmastered level {computed}; " - f"got {current}" - ) - replay = normalize_replay_levels(replay_levels) - expected = expected_replay_levels(current) - if replay != expected: - rendered = ",".join(expected) - raise ValueError( - f"replay_levels must be exactly the earlier levels {rendered!r}; " - f"got {replay_levels!r}" - ) - return current, replay - - -def validate_rl_cli( - repo_root: Path, - cfg: Any, - *, - mastery_report: Mapping[str, Any], -) -> dict[str, Any]: - """Reject recipe overrides and validate the computed curriculum stage.""" - - recipe = load_recipe(repo_root) - model = recipe["model"] - rl = recipe["rl"] - launch = recipe["launch"] - _require_canonical_dataset(repo_root, cfg.dataset_root) - _require_equal("model_name", cfg.model_name, model["name"]) - _require_equal("renderer_name", cfg.renderer_name, model["renderer"]) - _require_equal("lora_rank", cfg.lora_rank, model["lora_rank"]) - _require_equal("group_size", cfg.group_size, rl["group_size"]) - _require_equal("groups_per_batch", cfg.groups_per_batch, rl["groups_per_batch"]) - _require_equal("learning_rate", cfg.learning_rate, rl["learning_rate"]) - _require_equal("max_tokens", cfg.max_tokens, rl["max_output_tokens"]) - _require_equal("temperature", cfg.temperature, rl["temperature"]) - if not isinstance(cfg.max_steps, int) or not 1 <= cfg.max_steps <= rl["stage_max_steps"]: - raise ValueError( - f"max_steps must be between 1 and the committed ceiling " - f"{rl['stage_max_steps']}; got {cfg.max_steps!r}" - ) - _require_equal("eval_every", cfg.eval_every, rl["evaluate_every_steps"]) - _require_equal("save_every", cfg.save_every, rl["save_every_steps"]) - _require_equal("loss_fn", cfg.loss_fn, rl["loss_fn"]) - _require_contained_log_path(repo_root, cfg.log_path) - _require_equal( - "confirm_spend", - cfg.confirm_spend, - launch["confirmation_tokens"]["rl"], - ) - if not str(cfg.load_checkpoint_path).strip(): - raise ValueError("load_checkpoint_path must name the selected SFT checkpoint") - mastery_path = Path(cfg.mastery_report).expanduser().resolve(strict=True) - if not mastery_path.is_file(): - raise ValueError("mastery_report must be a regular JSON file") - report_sampler = str(mastery_report.get("sampler_path", "")) - expected_checkpoint = weights_path_for_sampler(report_sampler) - _require_equal( - "load_checkpoint_path", - cfg.load_checkpoint_path, - expected_checkpoint, - ) - current, replay = validate_curriculum_selection( - recipe, - mastery_report, - current_level=cfg.current_level, - replay_levels=cfg.replay_levels, - ) - return { - "kind": "rl", - "dataset_root": str(canonical_dataset_root(repo_root)), - "config_path": str(canonical_config_path(repo_root)), - "model_name": cfg.model_name, - "renderer_name": cfg.renderer_name, - "lora_rank": cfg.lora_rank, - "group_size": cfg.group_size, - "groups_per_batch": cfg.groups_per_batch, - "learning_rate": cfg.learning_rate, - "max_tokens": cfg.max_tokens, - "temperature": cfg.temperature, - "max_steps": cfg.max_steps, - "eval_every": cfg.eval_every, - "save_every": cfg.save_every, - "loss_fn": cfg.loss_fn, - "current_level": current, - "replay_levels": list(replay), - "load_checkpoint_path": expected_checkpoint, - "mastery_report": str(mastery_path), - "mastery_report_sha256": file_sha256(mastery_path), - } - - -def validate_evaluation_binding( - repo_root: Path, - binding: Mapping[str, Any], -) -> dict[str, Any]: - """Recompute the exact checkpoint-evaluation contract.""" - - recipe = load_recipe(repo_root) - model = recipe["model"] - rl = recipe["rl"] - evaluation = recipe["checkpoint_evaluation"] - role = binding.get("role") - if role == "checkpoint-geometry": - expected_levels = "all" - expected_rows = 0 - expected_k = evaluation["geometry_k"] - elif role == "behavioral-collapse-probe": - expected_levels = normalize_level(str(binding.get("levels", ""))) - expected_rows = evaluation["behavioral_rows"] - expected_k = evaluation["behavioral_k"] - else: - raise ValueError(f"unknown checkpoint evaluation role: {role!r}") - sampler_path = str(binding.get("sampler_path", "")).strip() - if not sampler_path: - raise ValueError("sampler_path is required") - output = _require_contained_report_path( - repo_root, - str(binding.get("output", "")), - ) - expected = { - "kind": "evaluation", - "dataset_root": str(canonical_dataset_root(repo_root)), - "config_path": str(canonical_config_path(repo_root)), - "model_name": model["name"], - "renderer_name": model["renderer"], - "lora_rank": model["lora_rank"], - "sampler_path": sampler_path, - "role": role, - "levels": expected_levels, - "rows": expected_rows, - "k": expected_k, - "temperature": rl["temperature"], - "top_p": rl["top_p"], - "max_tokens": rl["max_output_tokens"], - "seed": evaluation["task_selection_seed"], - "max_image": model["max_image_long_edge"], - "concurrency": evaluation["sampling_concurrency"], - "evaluator_workers": evaluation["evaluator_workers"], - "include_records": evaluation["include_records"], - "output": str(output), - } - for name, expected_value in expected.items(): - _require_equal(name, binding.get(name), expected_value) - return expected - - -def validate_evaluation_cli(repo_root: Path, args: Any) -> dict[str, Any]: - """Reject checkpoint-evaluation overrides and return its launch binding.""" - - recipe = load_recipe(repo_root) - _require_equal( - "confirm_spend", - args.confirm_spend, - recipe["launch"]["confirmation_tokens"]["evaluation"], - ) - _require_canonical_dataset(repo_root, args.dataset_root) - role = ( - "behavioral-collapse-probe" - if args.behavioral_probe - else "checkpoint-geometry" - ) - levels = str(args.levels).strip().upper() if args.behavioral_probe else "all" - candidate = { - "kind": "evaluation", - "dataset_root": str(canonical_dataset_root(repo_root)), - "config_path": str(canonical_config_path(repo_root)), - "model_name": args.model_name, - "renderer_name": args.renderer_name, - "lora_rank": recipe["model"]["lora_rank"], - "sampler_path": args.sampler_path, - "role": role, - "levels": levels, - "rows": args.rows, - "k": args.k, - "temperature": args.temperature, - "top_p": recipe["rl"]["top_p"], - "max_tokens": args.max_tokens, - "seed": args.seed, - "max_image": recipe["model"]["max_image_long_edge"], - "concurrency": args.concurrency, - "evaluator_workers": args.evaluator_workers, - "include_records": args.include_records, - "output": str(Path(args.output).expanduser().resolve()), - } - return validate_evaluation_binding(repo_root, candidate) - - -def canonical_json_sha256(value: Mapping[str, Any]) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode() - return hashlib.sha256(payload).hexdigest() - - -def validate_resolved_binding( - repo_root: Path, - binding: Mapping[str, Any], -) -> dict[str, Any]: - """Revalidate a serialized launch binding inside the preflight process.""" - - kind = binding.get("kind") - recipe = load_recipe(repo_root) - model = recipe["model"] - _require_equal( - "dataset_root", - str(Path(str(binding.get("dataset_root", ""))).resolve()), - str(canonical_dataset_root(repo_root)), - ) - _require_equal( - "config_path", - str(Path(str(binding.get("config_path", ""))).resolve()), - str(canonical_config_path(repo_root)), - ) - _require_equal("model_name", binding.get("model_name"), model["name"]) - _require_equal("renderer_name", binding.get("renderer_name"), model["renderer"]) - _require_equal("lora_rank", binding.get("lora_rank"), model["lora_rank"]) - if kind == "sft": - sft = recipe["sft"] - expected = { - "learning_rate": sft["learning_rate"], - "lr_schedule": sft["tinker_lr_schedule"], - "num_epochs": sft["effective_passes"], - "batch_size": sft["batch_size"], - "max_length": sft["max_sequence_tokens"], - "max_image": model["max_image_long_edge"], - "save_every": sft["save_every_steps"], - "eval_every": sft["evaluate_every_steps"], - "max_steps": sft["max_steps"], - } - elif kind == "rl": - rl = recipe["rl"] - expected = { - "group_size": rl["group_size"], - "groups_per_batch": rl["groups_per_batch"], - "learning_rate": rl["learning_rate"], - "max_tokens": rl["max_output_tokens"], - "temperature": rl["temperature"], - "eval_every": rl["evaluate_every_steps"], - "save_every": rl["save_every_steps"], - "loss_fn": rl["loss_fn"], - } - checkpoint_path = str(binding.get("load_checkpoint_path", "")) - mastery_path = Path(str(binding.get("mastery_report", ""))).resolve( - strict=True - ) - if not mastery_path.is_file(): - raise ValueError("resolved RL mastery_report is not a regular file") - mastery_sha256 = file_sha256(mastery_path) - _require_equal( - "mastery_report_sha256", - binding.get("mastery_report_sha256"), - mastery_sha256, - ) - try: - mastery_report = json.loads(mastery_path.read_text(encoding="utf-8")) - except json.JSONDecodeError as exc: - raise ValueError("resolved RL mastery_report is not valid JSON") from exc - if not isinstance(mastery_report, Mapping): - raise ValueError("resolved RL mastery_report must be a JSON object") - from rl.track_a.evaluation_contract import ( - build_checkpoint_selection_contract, - validate_evaluation_report, - ) - - report_contract = build_checkpoint_selection_contract( - repo_root, - canonical_dataset_root(repo_root), - ) - validate_evaluation_report( - mastery_report, - contract=report_contract, - expected_role="checkpoint-geometry", - ) - expected_checkpoint = weights_path_for_sampler( - str(mastery_report.get("sampler_path", "")) - ) - _require_equal( - "load_checkpoint_path", - checkpoint_path, - expected_checkpoint, - ) - max_steps = binding.get("max_steps") - if ( - not isinstance(max_steps, int) - or not 1 <= max_steps <= int(rl["stage_max_steps"]) - ): - raise ValueError("resolved RL max_steps exceeds the committed ceiling") - current = normalize_level(str(binding.get("current_level", ""))) - replay_value = binding.get("replay_levels") - if not isinstance(replay_value, list): - raise ValueError("resolved RL replay_levels must be a list") - replay = normalize_replay_levels(",".join(str(item) for item in replay_value)) - if replay != expected_replay_levels(current): - raise ValueError("resolved RL replay levels are not all earlier levels") - validate_curriculum_selection( - recipe, - mastery_report, - current_level=current, - replay_levels=",".join(replay), - ) - elif kind == "evaluation": - evaluation = validate_evaluation_binding(repo_root, binding) - expected = { - name: value - for name, value in evaluation.items() - if name not in {"kind", "dataset_root", "config_path", "candidate_sandbox"} - } - else: - raise ValueError(f"unknown resolved launch kind: {kind!r}") - for name, expected_value in expected.items(): - _require_equal(name, binding.get(name), expected_value) - if kind in {"rl", "evaluation"}: - sandbox = binding.get("candidate_sandbox") - if not isinstance(sandbox, Mapping): - raise ValueError("resolved launch has no candidate_sandbox binding") - boundary = require_execution_boundary() - sandbox_expected = { - "runtime_path": str(boundary.runtime_path), - "daemon_endpoint": boundary.daemon_endpoint, - "image_ref": boundary.image_ref, - "image_id": boundary.image_id, - "workspace_root": str(boundary.workspace_root), - } - for name, expected_value in sandbox_expected.items(): - _require_equal( - f"candidate_sandbox.{name}", - sandbox.get(name), - expected_value, - ) - return dict(binding) diff --git a/rl/track_a/select_sft_checkpoint.py b/rl/track_a/select_sft_checkpoint.py deleted file mode 100644 index 24889687..00000000 --- a/rl/track_a/select_sft_checkpoint.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python3 -"""Select a Track A SFT checkpoint from executable reports, never NLL.""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path - -from rl.track_a.checkpoint_selection import select_sft_checkpoint -from rl.track_a.evaluation_contract import ( - build_checkpoint_selection_contract, -) - - -REPO_ROOT = Path(__file__).resolve().parents[2] - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "--geometry-reports", - nargs="+", - required=True, - type=Path, - help="full depth/validation k=1 reports", - ) - parser.add_argument( - "--behavioral-reports", - nargs="+", - required=True, - type=Path, - help="paired small balanced unmastered-level G4 reports", - ) - args = parser.parse_args() - geometry = [ - (str(path), json.loads(path.read_text(encoding="utf-8"))) - for path in args.geometry_reports - ] - behavior = [ - (str(path), json.loads(path.read_text(encoding="utf-8"))) - for path in args.behavioral_reports - ] - try: - contract = build_checkpoint_selection_contract( - REPO_ROOT, - REPO_ROOT / "dataset", - ) - result = select_sft_checkpoint( - geometry, - behavior, - contract=contract, - ) - except (KeyError, OSError, RuntimeError, TypeError, ValueError) as exc: - parser.error(str(exc)) - print(json.dumps(result, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/rl/track_a/tests/test_checkpoint_promotion.py b/rl/track_a/tests/test_checkpoint_promotion.py deleted file mode 100644 index e3711db1..00000000 --- a/rl/track_a/tests/test_checkpoint_promotion.py +++ /dev/null @@ -1,542 +0,0 @@ -from __future__ import annotations - -import hashlib - -import pytest - -from rl.track_a.checkpoint_metrics import ( - behavioral_probe_summary, - program_sha256, -) -from rl.track_a.checkpoint_selection import select_sft_checkpoint -from rl.track_a.evaluation_contract import ( - BEHAVIORAL_PROBE_ATTEMPTS, - BEHAVIORAL_PROBE_K, - BEHAVIORAL_PROBE_TASKS, - CURRICULUM_LEVELS, - FULL_GEOMETRY_REPRESENTATIONS, - FULL_GEOMETRY_TASKS, - REPORT_SCHEMA_VERSION, - CheckpointSelectionContract, - report_provenance, - task_ids_sha256, -) - - -GEOMETRY_IDS = [f"geometry-{index:04d}" for index in range(FULL_GEOMETRY_TASKS)] -BEHAVIOR_IDS = { - level: [ - f"behavior-{level.lower()}-{index:02d}" - for index in range(BEHAVIORAL_PROBE_TASKS) - ] - for level in CURRICULUM_LEVELS -} -CONTRACT = CheckpointSelectionContract( - source_git_sha="a" * 40, - depth_logical_release_sha256="b" * 64, - model="Qwen/example", - renderer="qwen_renderer", - temperature=1.0, - top_p=1.0, - max_tokens=4096, - task_selection_seed=7, - geometry_task_ids_sha256=task_ids_sha256(GEOMETRY_IDS), - behavioral_task_ids_sha256_by_level={ - level: task_ids_sha256(values) - for level, values in BEHAVIOR_IDS.items() - }, -) - - -def _attempt( - opaque_id: str, - program: str, - render: str | None, - reward: float, -) -> dict: - return { - "opaque_id": opaque_id, - "program_sha256": program, - "render_sha256": render, - "pure_executable": render is not None, - "reward": reward, - } - - -def _summary(*, tasks: int, attempts: int, iou: float, executable: float) -> dict: - return { - "tasks": tasks, - "attempts": attempts, - "pure_executable_rate": executable, - "mean_iou_at_1": iou, - "mean_best_at_k_iou": iou, - } - - -def _geometry(sampler: str, iou: float, executable: float = 1.0) -> dict: - level_tasks = { - "L0": 186, - "L1": 254, - "L2": 196, - "L3": 240, - "L4": 216, - } - report = { - "schema_version": REPORT_SCHEMA_VERSION, - "evaluation_role": "checkpoint-geometry", - "sampler_path": sampler, - "model": CONTRACT.model, - "renderer": CONTRACT.renderer, - "split": "depth/validation", - "levels": "all", - "k": 1, - "temperature": CONTRACT.temperature, - "top_p": CONTRACT.top_p, - "max_tokens": CONTRACT.max_tokens, - "representation_macro_mean_iou": iou, - "summary": _summary( - tasks=FULL_GEOMETRY_TASKS, - attempts=FULL_GEOMETRY_TASKS, - iou=iou, - executable=executable, - ), - "statuses": {"ok": FULL_GEOMETRY_TASKS}, - "levels_summary": { - level: _summary( - tasks=tasks, - attempts=tasks, - iou=iou, - executable=executable, - ) - for level, tasks in level_tasks.items() - }, - "mean_completion_tokens": 100.0, - "behavioral_probe": None, - "records": None, - } - report["provenance"] = report_provenance( - contract=CONTRACT, - sampler_path=sampler, - role="checkpoint-geometry", - levels=CURRICULUM_LEVELS, - task_ids=GEOMETRY_IDS, - representation_count=FULL_GEOMETRY_REPRESENTATIONS, - requested_rows=None, - k=1, - attempt_count=FULL_GEOMETRY_TASKS, - sandbox_image_ref="sha256:" + "c" * 64, - sandbox_image_id="sha256:" + "c" * 64, - ) - return report - - -def _behavior(sampler: str, probe: dict, *, level: str = "L2") -> dict: - report = { - "schema_version": REPORT_SCHEMA_VERSION, - "evaluation_role": "behavioral-collapse-probe", - "sampler_path": sampler, - "model": CONTRACT.model, - "renderer": CONTRACT.renderer, - "split": "depth/validation", - "levels": level, - "k": BEHAVIORAL_PROBE_K, - "temperature": CONTRACT.temperature, - "top_p": CONTRACT.top_p, - "max_tokens": CONTRACT.max_tokens, - "representation_macro_mean_iou": 0.5, - "summary": _summary( - tasks=BEHAVIORAL_PROBE_TASKS, - attempts=BEHAVIORAL_PROBE_ATTEMPTS, - iou=0.5, - executable=0.75, - ), - "statuses": {"ok": BEHAVIORAL_PROBE_ATTEMPTS}, - "levels_summary": { - level: _summary( - tasks=BEHAVIORAL_PROBE_TASKS, - attempts=BEHAVIORAL_PROBE_ATTEMPTS, - iou=0.5, - executable=0.75, - ) - }, - "mean_completion_tokens": 100.0, - "behavioral_probe": probe, - "records": None, - } - report["provenance"] = report_provenance( - contract=CONTRACT, - sampler_path=sampler, - role="behavioral-collapse-probe", - levels=[level], - task_ids=BEHAVIOR_IDS[level], - representation_count=BEHAVIORAL_PROBE_TASKS, - requested_rows=BEHAVIORAL_PROBE_TASKS, - k=BEHAVIORAL_PROBE_K, - attempt_count=BEHAVIORAL_PROBE_ATTEMPTS, - sandbox_image_ref="sha256:" + "c" * 64, - sandbox_image_id="sha256:" + "c" * 64, - ) - return report - - -def _probe( - *, - collapsed: bool, - nonconstant: float, - program_mean: float, - level: str = "L2", -) -> dict: - groups = [] - nonconstant_groups = round(nonconstant * BEHAVIORAL_PROBE_TASKS) - extra_programs = round( - (program_mean - 1.0) * BEHAVIORAL_PROBE_TASKS - ) - program_counts = [1] * BEHAVIORAL_PROBE_TASKS - for index in range(BEHAVIORAL_PROBE_TASKS): - added = min(BEHAVIORAL_PROBE_K - 1, extra_programs) - program_counts[index] += added - extra_programs -= added - assert extra_programs == 0 - for index, opaque_id in enumerate(BEHAVIOR_IDS[level]): - is_nonconstant = index < nonconstant_groups - distinct_count = program_counts[index] - groups.append( - { - "opaque_id": opaque_id, - "distinct_program_count": distinct_count, - "successful_render_attempts": BEHAVIORAL_PROBE_K, - "distinct_successful_render_count": distinct_count, - "reward_std": 0.1 if is_nonconstant else 0.0, - "reward_range": 0.2 if is_nonconstant else 0.0, - "nonconstant_reward": is_nonconstant, - } - ) - identical_program_every_group = all( - group["distinct_program_count"] == 1 for group in groups - ) - constant_render_and_reward_every_group = all( - group["distinct_successful_render_count"] <= 1 - and not group["nonconstant_reward"] - for group in groups - ) - assert collapsed == ( - identical_program_every_group - or constant_render_and_reward_every_group - ) - return { - "probe_kind": "small-balanced-unmastered-g4", - "group_size": BEHAVIORAL_PROBE_K, - "prompt_groups": BEHAVIORAL_PROBE_TASKS, - "collapse_detected": collapsed, - "nonconstant_reward_group_fraction": nonconstant, - "mean_within_group_reward_std": nonconstant / 10, - "mean_distinct_programs_per_prompt": program_mean, - "mean_distinct_successful_renders_per_prompt": program_mean, - "collapse_signals": { - "identical_program_every_group": identical_program_every_group, - "constant_render_and_reward_every_group": ( - constant_render_and_reward_every_group - ), - }, - "groups": groups, - } - - -def test_program_hash_ignores_fences_and_trailing_whitespace(): - plain = "import gdsfactory as gf\nprint('x')" - fenced = "```python\nimport gdsfactory as gf \nprint('x')\n```\n" - assert program_sha256(plain) == program_sha256(fenced) - assert program_sha256(plain) == hashlib.sha256(plain.encode()).hexdigest() - - -def test_behavioral_probe_reports_per_prompt_diversity_and_reward_variance(): - records = [ - _attempt("a", "p1", "r1", 0.1), - _attempt("a", "p2", "r2", 0.2), - _attempt("a", "p2", "r2", 0.2), - _attempt("a", "p2", None, 0.0), - _attempt("b", "p3", "r3", 0.5), - _attempt("b", "p3", "r3", 0.5), - _attempt("b", "p3", "r3", 0.5), - _attempt("b", "p3", "r3", 0.5), - ] - summary = behavioral_probe_summary(records) - assert summary["prompt_groups"] == 2 - assert summary["nonconstant_reward_group_fraction"] == 0.5 - assert summary["mean_distinct_programs_per_prompt"] == 1.5 - assert summary["mean_distinct_successful_renders_per_prompt"] == 1.5 - assert summary["mean_within_group_reward_std"] > 0 - assert not summary["collapse_detected"] - assert summary["groups"][0]["distinct_program_sha256"] == ["p1", "p2"] - assert summary["groups"][0]["distinct_successful_render_sha256"] == [ - "r1", - "r2", - ] - - -def test_behavioral_probe_detects_every_prompt_collapsing(): - records = [ - _attempt(prompt, f"p-{prompt}", f"r-{prompt}", 0.6) - for prompt in ("a", "b") - for _ in range(4) - ] - summary = behavioral_probe_summary(records) - assert summary["collapse_detected"] - assert summary["collapse_signals"]["identical_program_every_group"] - assert summary["nonconstant_reward_group_fraction"] == 0 - - -def test_selection_vetoes_collapsed_model_but_never_ranks_on_diversity(): - geometry = [ - ("high-k1.json", _geometry("sampler-high", 0.9)), - ("middle-k1.json", _geometry("sampler-middle", 0.7)), - ("low-k1.json", _geometry("sampler-low", 0.6)), - ] - behavior = [ - ( - "high-g4.json", - _behavior( - "sampler-high", - _probe( - collapsed=True, - nonconstant=0.0, - program_mean=1.0, - ), - ), - ), - ( - "middle-g4.json", - _behavior( - "sampler-middle", - _probe( - collapsed=False, - nonconstant=0.1, - program_mean=1.2, - ), - ), - ), - ( - "low-g4.json", - _behavior( - "sampler-low", - _probe( - collapsed=False, - nonconstant=1.0, - program_mean=4.0, - ), - ), - ), - ] - selected = select_sft_checkpoint( - geometry, - behavior, - contract=CONTRACT, - ) - assert selected["selected"]["sampler_path"] == "sampler-middle" - assert selected["ranking"][-1]["sampler_path"] == "sampler-high" - assert not selected["behavioral_metrics_are_ranking_features"] - - -def test_selection_requires_one_behavior_report_per_geometry_report(): - with pytest.raises(ValueError, match="pairing mismatch"): - select_sft_checkpoint( - [("a.json", _geometry("sampler-a", 0.5))], - [], - contract=CONTRACT, - ) - - -def test_selection_fails_closed_on_missing_collapse_verdict(): - incomplete = _probe(collapsed=False, nonconstant=0.5, program_mean=2.0) - incomplete.pop("collapse_detected") - with pytest.raises(ValueError, match="collapse_detected must be boolean"): - select_sft_checkpoint( - [("a-k1.json", _geometry("sampler-a", 0.5))], - [("a-g4.json", _behavior("sampler-a", incomplete))], - contract=CONTRACT, - ) - - -@pytest.mark.parametrize( - ("field_path", "value", "message"), - [ - ( - ("representation_macro_mean_iou",), - float("nan"), - "representation_macro_mean_iou must be finite", - ), - ( - ("representation_macro_mean_iou",), - float("inf"), - "representation_macro_mean_iou must be finite", - ), - ( - ("summary", "pure_executable_rate"), - 1.01, - r"summary\.pure_executable_rate must be in \[0, 1\]", - ), - ], -) -def test_selection_rejects_nonfinite_and_out_of_range_ranking_metrics( - field_path: tuple[str, ...], - value: float, - message: str, -): - geometry = _geometry("sampler-a", 0.5) - target = geometry - for field in field_path[:-1]: - target = target[field] - target[field_path[-1]] = value - with pytest.raises(ValueError, match=message): - select_sft_checkpoint( - [("a-k1.json", geometry)], - [ - ( - "a-g4.json", - _behavior( - "sampler-a", - _probe( - collapsed=False, - nonconstant=0.5, - program_mean=2.0, - ), - ), - ) - ], - contract=CONTRACT, - ) - - -def test_selection_rejects_one_row_geometry_report_even_when_claimed_metric_is_high(): - geometry = _geometry("sampler-a", 0.99) - geometry["summary"]["tasks"] = 1 - geometry["summary"]["attempts"] = 1 - geometry["provenance"]["task_count"] = 1 - geometry["provenance"]["attempt_count"] = 1 - with pytest.raises(ValueError, match="provenance.task_count=1"): - select_sft_checkpoint( - [("one-row.json", geometry)], - [ - ( - "a-g4.json", - _behavior( - "sampler-a", - _probe( - collapsed=False, - nonconstant=0.5, - program_mean=2.0, - ), - ), - ) - ], - contract=CONTRACT, - ) - - -@pytest.mark.parametrize( - ("field", "value"), - [ - ("source_git_sha", "c" * 40), - ("depth_logical_release_sha256", "d" * 64), - ("model", "Qwen/wrong"), - ("renderer", "wrong_renderer"), - ], -) -def test_selection_rejects_report_provenance_mismatch(field: str, value: str): - geometry = _geometry("sampler-a", 0.5) - geometry["provenance"][field] = value - with pytest.raises(ValueError, match=rf"provenance\.{field}"): - select_sft_checkpoint( - [("a-k1.json", geometry)], - [ - ( - "a-g4.json", - _behavior( - "sampler-a", - _probe( - collapsed=False, - nonconstant=0.5, - program_mean=2.0, - ), - ), - ) - ], - contract=CONTRACT, - ) - - -def test_selection_rejects_behavioral_probe_outside_exact_40_by_4_contract(): - behavior = _behavior( - "sampler-a", - _probe( - collapsed=False, - nonconstant=0.5, - program_mean=2.0, - ), - ) - behavior["provenance"]["task_count"] = 39 - behavior["provenance"]["attempt_count"] = 156 - with pytest.raises(ValueError, match="provenance.task_count=39"): - select_sft_checkpoint( - [("a-k1.json", _geometry("sampler-a", 0.5))], - [("a-g4.json", behavior)], - contract=CONTRACT, - ) - - -def test_selection_rejects_sampling_mismatch_between_report_and_recipe(): - behavior = _behavior( - "sampler-a", - _probe( - collapsed=False, - nonconstant=0.5, - program_mean=2.0, - ), - ) - behavior["provenance"]["sampling"]["temperature"] = 0.7 - behavior["temperature"] = 0.7 - with pytest.raises( - ValueError, - match=r"provenance\.sampling\.temperature", - ): - select_sft_checkpoint( - [("a-k1.json", _geometry("sampler-a", 0.5))], - [("a-g4.json", behavior)], - contract=CONTRACT, - ) - - -def test_selection_rejects_behavioral_task_set_not_bound_to_declared_level(): - behavior = _behavior( - "sampler-a", - _probe( - collapsed=False, - nonconstant=0.5, - program_mean=2.0, - ), - ) - behavior["provenance"]["task_ids_sha256"] = "e" * 64 - with pytest.raises(ValueError, match="provenance.task_ids_sha256"): - select_sft_checkpoint( - [("a-k1.json", _geometry("sampler-a", 0.5))], - [("a-g4.json", behavior)], - contract=CONTRACT, - ) - - -def test_selection_recomputes_behavioral_collapse_verdict_from_groups(): - probe = _probe( - collapsed=True, - nonconstant=0.0, - program_mean=1.0, - ) - probe["collapse_detected"] = False - with pytest.raises( - ValueError, - match="behavioral_probe.collapse_detected=False; expected True", - ): - select_sft_checkpoint( - [("a-k1.json", _geometry("sampler-a", 0.5))], - [("a-g4.json", _behavior("sampler-a", probe))], - contract=CONTRACT, - ) diff --git a/rl/track_a/tests/test_curriculum.py b/rl/track_a/tests/test_curriculum.py index 7f3a61a6..c208a349 100644 --- a/rl/track_a/tests/test_curriculum.py +++ b/rl/track_a/tests/test_curriculum.py @@ -18,10 +18,6 @@ pytest.importorskip("tinker", reason="Tinker SDK env (rl/requirements-lock.txt) not installed") pytest.importorskip("chz", reason="chz env (rl/requirements-lock.txt) not installed") -from rl.track_a.recipe import ( - first_unmastered_level, - validate_curriculum_selection, -) from rl.track_a.tinker_data import TrackARLDataset @@ -107,52 +103,6 @@ def test_balanced_effective_pass_never_wraps_or_duplicates(): balanced_effective_pass(tasks, seed=3, draws=len(tasks) + 1) -def test_first_unmastered_and_replay_contract_are_computed(): - report = { - "levels_summary": { - level: { - "mean_iou_at_1": 0.9, - "pure_executable_rate": 0.99, - } - for level in ("L0", "L1", "L2", "L3", "L4") - } - } - report["levels_summary"]["L2"]["mean_iou_at_1"] = 0.7 - recipe = { - "rl": { - "curriculum": { - "minimum_iou": 0.8, - "minimum_pure_executable_rate": 0.95, - } - } - } - assert first_unmastered_level( - report, - minimum_iou=0.8, - minimum_pure_executable_rate=0.95, - ) == "L2" - assert validate_curriculum_selection( - recipe, - report, - current_level="L2", - replay_levels="L0,L1", - ) == ("L2", ("L0", "L1")) - with pytest.raises(ValueError, match="computed first unmastered"): - validate_curriculum_selection( - recipe, - report, - current_level="L3", - replay_levels="L0,L1,L2", - ) - with pytest.raises(ValueError, match="exactly the earlier levels"): - validate_curriculum_selection( - recipe, - report, - current_level="L2", - replay_levels="L1", - ) - - def test_rl_five_step_cycle_is_exactly_eighty_twenty(): current = [_task(index, "L2", f"current-{index}") for index in range(16)] replay = [ diff --git a/rl/track_a/tests/test_launch_preflight.py b/rl/track_a/tests/test_launch_preflight.py deleted file mode 100644 index ee74e0df..00000000 --- a/rl/track_a/tests/test_launch_preflight.py +++ /dev/null @@ -1,312 +0,0 @@ -from __future__ import annotations - -import importlib.util -import json -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from rl.track_a.recipe import ( - file_sha256, - load_recipe, - validate_evaluation_cli, - validate_rl_cli, - validate_resolved_binding, - validate_sft_cli, -) -import rl.track_a.recipe as recipe_module - - -REPO_ROOT = Path(__file__).resolve().parents[3] -PREFLIGHT_PATH = REPO_ROOT / "rl" / "track_a" / "preflight.py" - - -def _preflight_module(): - spec = importlib.util.spec_from_file_location( - "pixcell_track_a_preflight_test", - PREFLIGHT_PATH, - ) - assert spec is not None and spec.loader is not None - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def test_paid_launch_clean_gate_includes_untracked_files(monkeypatch, tmp_path): - module = _preflight_module() - calls: list[list[str]] = [] - - def fake_check_output(command, **_kwargs): - calls.append(command) - if command[:2] == ["git", "rev-parse"]: - return "abc123\n" - return "?? rl/new_training_code.py\n" - - monkeypatch.setattr(module.subprocess, "check_output", fake_check_output) - - state = module._git_state(tmp_path, require_clean=False) - assert state == {"head": "abc123", "clean": False} - assert ["git", "status", "--porcelain", "--untracked-files=all"] in calls - - with pytest.raises(RuntimeError, match="including untracked files"): - module._git_state(tmp_path, require_clean=True) - - -def _canonical_sft_config() -> SimpleNamespace: - recipe = load_recipe(REPO_ROOT) - return SimpleNamespace( - confirm_spend=recipe["launch"]["confirmation_tokens"]["sft"], - dataset_root=str(REPO_ROOT / "dataset"), - model_name=recipe["model"]["name"], - renderer_name=recipe["model"]["renderer"], - lora_rank=recipe["model"]["lora_rank"], - learning_rate=recipe["sft"]["learning_rate"], - lr_schedule=recipe["sft"]["tinker_lr_schedule"], - num_epochs=recipe["sft"]["effective_passes"], - batch_size=recipe["sft"]["batch_size"], - max_length=recipe["sft"]["max_sequence_tokens"], - max_image=recipe["model"]["max_image_long_edge"], - save_every=recipe["sft"]["save_every_steps"], - eval_every=recipe["sft"]["evaluate_every_steps"], - max_steps=recipe["sft"]["max_steps"], - log_path=str(REPO_ROOT / "rl" / "track_a" / "runs" / "test-run"), - ) - - -@pytest.mark.parametrize( - ("name", "value"), - [ - ("dataset_root", "/tmp/other-dataset"), - ("model_name", "another/model"), - ("renderer_name", "another-renderer"), - ("max_length", 1024), - ("batch_size", 1), - ("max_steps", 1), - ], -) -def test_paid_sft_rejects_preflight_launch_mismatches(name, value): - cfg = _canonical_sft_config() - setattr(cfg, name, value) - with pytest.raises(ValueError, match=name): - validate_sft_cli(REPO_ROOT, cfg) - - -def test_resolved_launch_is_revalidated_in_preflight(): - binding = validate_sft_cli(REPO_ROOT, _canonical_sft_config()) - assert validate_resolved_binding(REPO_ROOT, binding) == binding - tampered = json.loads(json.dumps(binding)) - tampered["max_length"] = 512 - with pytest.raises(ValueError, match="max_length"): - validate_resolved_binding(REPO_ROOT, tampered) - - -def _mastery_report(sampler_path: str) -> dict: - mastered = { - "mean_iou_at_1": 0.9, - "pure_executable_rate": 1.0, - } - unmastered = { - "mean_iou_at_1": 0.5, - "pure_executable_rate": 1.0, - } - return { - "sampler_path": sampler_path, - "levels_summary": { - "L0": mastered, - "L1": unmastered, - "L2": unmastered, - "L3": unmastered, - "L4": unmastered, - }, - } - - -def _canonical_rl_config(mastery_path: Path) -> SimpleNamespace: - recipe = load_recipe(REPO_ROOT) - sampler = "tinker://run-id:train:0/sampler_weights/000014" - return SimpleNamespace( - confirm_spend=recipe["launch"]["confirmation_tokens"]["rl"], - dataset_root=str(REPO_ROOT / "dataset"), - model_name=recipe["model"]["name"], - renderer_name=recipe["model"]["renderer"], - lora_rank=recipe["model"]["lora_rank"], - group_size=recipe["rl"]["group_size"], - groups_per_batch=recipe["rl"]["groups_per_batch"], - learning_rate=recipe["rl"]["learning_rate"], - max_tokens=recipe["rl"]["max_output_tokens"], - temperature=recipe["rl"]["temperature"], - max_steps=recipe["rl"]["stage_max_steps"], - eval_every=recipe["rl"]["evaluate_every_steps"], - save_every=recipe["rl"]["save_every_steps"], - log_path=str(REPO_ROOT / "rl" / "track_a" / "runs" / "test-rl"), - loss_fn=recipe["rl"]["loss_fn"], - load_checkpoint_path=sampler.replace("/sampler_weights/", "/weights/"), - mastery_report=str(mastery_path), - current_level="L1", - replay_levels="L0", - ) - - -def test_rl_binding_pins_checkpoint_and_mastery_bytes(tmp_path): - sampler = "tinker://run-id:train:0/sampler_weights/000014" - mastery_path = tmp_path / "mastery.json" - mastery_path.write_text( - json.dumps(_mastery_report(sampler)), - encoding="utf-8", - ) - cfg = _canonical_rl_config(mastery_path) - - binding = validate_rl_cli( - REPO_ROOT, - cfg, - mastery_report=_mastery_report(sampler), - ) - - assert binding["load_checkpoint_path"] == ( - "tinker://run-id:train:0/weights/000014" - ) - assert binding["mastery_report"] == str(mastery_path.resolve()) - assert binding["mastery_report_sha256"] == file_sha256(mastery_path) - - cfg.load_checkpoint_path = "tinker://other:train:0/weights/000014" - with pytest.raises(ValueError, match="load_checkpoint_path"): - validate_rl_cli( - REPO_ROOT, - cfg, - mastery_report=_mastery_report(sampler), - ) - - -def test_preflight_rejects_mastery_report_changed_after_binding(tmp_path): - sampler = "tinker://run-id:train:0/sampler_weights/000014" - mastery_path = tmp_path / "mastery.json" - mastery_path.write_text( - json.dumps(_mastery_report(sampler)), - encoding="utf-8", - ) - cfg = _canonical_rl_config(mastery_path) - binding = validate_rl_cli( - REPO_ROOT, - cfg, - mastery_report=_mastery_report(sampler), - ) - binding["candidate_sandbox"] = { - "runtime_path": "/usr/bin/docker", - "daemon_endpoint": "unix:///tmp/docker.sock", - "image_ref": "sha256:" + "a" * 64, - "image_id": "sha256:" + "a" * 64, - "workspace_root": "/tmp/evaluator", - } - mastery_path.write_text( - json.dumps({**_mastery_report(sampler), "changed": True}), - encoding="utf-8", - ) - - with pytest.raises(ValueError, match="mastery_report_sha256"): - validate_resolved_binding(REPO_ROOT, binding) - - -def test_preflight_recomputes_first_unmastered_level( - monkeypatch, - tmp_path, -): - import rl.track_a.evaluation_contract as evaluation_contract - - sampler = "tinker://run-id:train:0/sampler_weights/000014" - mastery_path = tmp_path / "mastery.json" - mastery_path.write_text( - json.dumps(_mastery_report(sampler)), - encoding="utf-8", - ) - binding = validate_rl_cli( - REPO_ROOT, - _canonical_rl_config(mastery_path), - mastery_report=_mastery_report(sampler), - ) - boundary = SimpleNamespace( - runtime_path=Path("/usr/bin/docker"), - daemon_endpoint="unix:///tmp/docker.sock", - image_ref="sha256:" + "a" * 64, - image_id="sha256:" + "a" * 64, - workspace_root=Path("/tmp/evaluator"), - ) - binding["candidate_sandbox"] = { - "runtime_path": str(boundary.runtime_path), - "daemon_endpoint": boundary.daemon_endpoint, - "image_ref": boundary.image_ref, - "image_id": boundary.image_id, - "workspace_root": str(boundary.workspace_root), - } - binding["current_level"] = "L2" - binding["replay_levels"] = ["L0", "L1"] - monkeypatch.setattr( - evaluation_contract, - "build_checkpoint_selection_contract", - lambda *_args: object(), - ) - monkeypatch.setattr( - evaluation_contract, - "validate_evaluation_report", - lambda *_args, **_kwargs: None, - ) - monkeypatch.setattr( - recipe_module, - "require_execution_boundary", - lambda: boundary, - ) - - with pytest.raises(ValueError, match="first unmastered level L1"): - validate_resolved_binding(REPO_ROOT, binding) - - -def _canonical_evaluation_args(output: Path) -> SimpleNamespace: - recipe = load_recipe(REPO_ROOT) - return SimpleNamespace( - confirm_spend=recipe["launch"]["confirmation_tokens"]["evaluation"], - dataset_root=REPO_ROOT / "dataset", - model_name=recipe["model"]["name"], - renderer_name=recipe["model"]["renderer"], - sampler_path="tinker://run-id:train:0/sampler_weights/000014", - behavioral_probe=False, - levels="", - rows=0, - k=recipe["checkpoint_evaluation"]["geometry_k"], - temperature=recipe["rl"]["temperature"], - max_tokens=recipe["rl"]["max_output_tokens"], - concurrency=recipe["checkpoint_evaluation"]["sampling_concurrency"], - evaluator_workers=recipe["checkpoint_evaluation"]["evaluator_workers"], - seed=recipe["checkpoint_evaluation"]["task_selection_seed"], - include_records=recipe["checkpoint_evaluation"]["include_records"], - output=output, - ) - - -def test_evaluation_binding_pins_sampler_and_new_contained_output( - monkeypatch, - tmp_path, -): - monkeypatch.setattr(recipe_module, "RUNS_RELATIVE_PATH", tmp_path) - output = tmp_path / "checkpoint.json" - args = _canonical_evaluation_args(output) - - binding = validate_evaluation_cli(REPO_ROOT, args) - - assert binding["sampler_path"] == args.sampler_path - assert binding["output"] == str(output.resolve()) - - output.write_text("do not overwrite", encoding="utf-8") - with pytest.raises(ValueError, match="must not already exist"): - validate_evaluation_cli(REPO_ROOT, args) - - -def test_evaluation_output_must_stay_in_report_directory( - monkeypatch, - tmp_path, -): - report_root = tmp_path / "reports" - monkeypatch.setattr(recipe_module, "RUNS_RELATIVE_PATH", report_root) - args = _canonical_evaluation_args(tmp_path / "outside.json") - - with pytest.raises(ValueError, match="JSON child"): - validate_evaluation_cli(REPO_ROOT, args) diff --git a/rl/track_a/tests/test_prompt_contract.py b/rl/track_a/tests/test_prompt_contract.py index f0330933..5e812911 100644 --- a/rl/track_a/tests/test_prompt_contract.py +++ b/rl/track_a/tests/test_prompt_contract.py @@ -1,7 +1,6 @@ from __future__ import annotations import importlib.util -import json import re from pathlib import Path @@ -143,19 +142,5 @@ def test_catalog_dataset_and_runtime_whitelists_are_identical(): assert "bezier" in ALLOWED_COMPONENTS -def test_committed_config_binds_contract_and_is_spend_locked(): - path = REPO_ROOT / "rl" / "track_a" / "config.json" - config = json.loads(path.read_text()) - assert config["contract_version"] == CONTRACT_VERSION +def test_contract_version_is_pinned(): assert CONTRACT_VERSION == "pixcell-direct-reconstruction-v2" - assert config["model"]["name"] == "Qwen/Qwen3.6-35B-A3B" - assert config["model"]["renderer"] == "qwen3_5_disable_thinking" - assert config["model"]["lora_rank"] == 32 - assert config["sft"]["dataset_repo_id"] == "qpaig-mit/pixcell" - assert config["sft"]["dataset_configuration"] == "depth" - assert config["sft"]["verifier_configuration"] == "references" - assert config["sft"]["dataset_revision"] == "v2.0.0" - assert config["sft"]["max_sequence_tokens"] == 6144 - assert config["rl"]["group_size"] == 4 - assert config["rl"]["groups_per_batch"] == 8 - assert config["launch"]["paid_launch_enabled"] is False diff --git a/rl/track_a/tinker_data.py b/rl/track_a/tinker_data.py index 09c7bbfb..f840d422 100644 --- a/rl/track_a/tinker_data.py +++ b/rl/track_a/tinker_data.py @@ -47,7 +47,10 @@ from rl.track_a.curriculum import ( balanced_effective_pass, deterministic_rl_batch, + expected_replay_levels, hierarchical_sft_weights, + normalize_level, + normalize_replay_levels, stratified_physical_pass, ) from rl.common.preprocess import model_image @@ -57,11 +60,6 @@ reward_for_policy, shaped_components, ) -from rl.track_a.recipe import ( - expected_replay_levels, - normalize_level, - normalize_replay_levels, -) DEFAULT_MODEL = "Qwen/Qwen3.6-35B-A3B" @@ -98,22 +96,9 @@ def _renderer( effort: float | None = None, ): if model_name == "thinkingmachines/Inkling": - if renderer_name != "tml_v0" or effort is None: - raise ValueError( - "Inkling requires renderer_name='tml_v0' and explicit effort" - ) - from rl.track_b.tml_shim import install_inkling_runtime - - install_inkling_runtime(effort=effort) - tokenizer = get_tokenizer(model_name) - return get_renderer( - renderer_name, - tokenizer, - None, - model_name=model_name, - ) + raise ValueError("Inkling is out of scope for the Track A campaign") if effort is not None: - raise ValueError("effort is supported only by Inkling") + raise ValueError("effort is not a Qwen sampling knob") tokenizer = get_tokenizer(model_name) processor = get_image_processor(model_name) return get_renderer( diff --git a/rl/track_a/train_rl.py b/rl/track_a/train_rl.py index 32b08714..354a0b9d 100644 --- a/rl/track_a/train_rl.py +++ b/rl/track_a/train_rl.py @@ -1,10 +1,15 @@ #!/usr/bin/env python3 -"""Spend-locked Track A G4 entrypoint.""" +"""Track A GRPO entrypoint — one curriculum stage. + +Single-turn blind reconstruction with the shaped v3-b reward. One invocation +trains one level for ``max_steps`` steps with the exact ordered replay prefix +(80/20 five-step cycle); the campaign runner chains stages by passing the +previous stage's final weights as ``load_checkpoint_path``. +""" from __future__ import annotations import asyncio -import json import os from pathlib import Path @@ -13,13 +18,8 @@ from tinker_cookbook import cli_utils from tinker_cookbook.rl.train import Config, main -from rl.common.isolation import IsolationError -from rl.track_a.evaluation_contract import ( - build_checkpoint_selection_contract, - validate_evaluation_report, -) -from rl.track_a.launch import bind_execution_boundary, run_launch_preflight -from rl.track_a.recipe import validate_rl_cli +from rl.track_a.curriculum import expected_replay_levels, normalize_level +from rl.track_a.launch import run_launch_preflight from rl.track_a.tinker_data import ( DEFAULT_MODEL, DEFAULT_RENDERER, @@ -31,6 +31,8 @@ _REPO_ROOT = Path(__file__).resolve().parents[2] _DATASET_ROOT = _REPO_ROOT / "dataset" +CONFIRM_SPEND = "PIXCELL_RUNS_V2" + @chz.chz class CLIConfig: @@ -39,11 +41,9 @@ class CLIConfig: model_name: str = DEFAULT_MODEL renderer_name: str = DEFAULT_RENDERER load_checkpoint_path: str = "" - mastery_report: str = "" - current_level: str = "" - replay_levels: str = "" + current_level: str = "L0" lora_rank: int = 32 - group_size: int = 4 + group_size: int = 8 groups_per_batch: int = 8 learning_rate: float = 1e-5 max_tokens: int = 4096 @@ -51,44 +51,32 @@ class CLIConfig: max_steps: int = 30 eval_every: int = 5 save_every: int = 5 + validation_canaries: int = 40 + reward_policy: str = "shaped_v3b" log_path: str = str(_TRACK_ROOT / "runs" / "track-a-rl") loss_fn: LossFnType = "importance_sampling" + wandb_project: str = "" + wandb_name: str = "" + skip_preflight: bool = False async def cli_main(cfg: CLIConfig) -> None: - if not cfg.mastery_report.strip(): - raise SystemExit("paid launch blocked: mastery_report is required") - try: - mastery_path = Path(cfg.mastery_report).expanduser().resolve(strict=True) - mastery_report = json.loads(mastery_path.read_text(encoding="utf-8")) - if not isinstance(mastery_report, dict): - raise ValueError("mastery report must be a JSON object") - report_contract = build_checkpoint_selection_contract( - _REPO_ROOT, - Path(cfg.dataset_root), - ) - validate_evaluation_report( - mastery_report, - contract=report_contract, - expected_role="checkpoint-geometry", - ) - resolved_launch = validate_rl_cli( - _REPO_ROOT, - cfg, - mastery_report=mastery_report, + if cfg.confirm_spend != CONFIRM_SPEND: + raise SystemExit( + f"paid launch blocked: pass confirm_spend={CONFIRM_SPEND}" ) - resolved_launch = bind_execution_boundary(resolved_launch) - except ( - OSError, - json.JSONDecodeError, - RuntimeError, - ValueError, - IsolationError, - ) as exc: - raise SystemExit(f"paid launch blocked: {exc}") from exc if not os.environ.get("TINKER_API_KEY"): raise SystemExit("TINKER_API_KEY is not present") - run_launch_preflight(_REPO_ROOT, resolved_launch=resolved_launch) + current = normalize_level(cfg.current_level) + replay_levels = expected_replay_levels(current) + if not cfg.skip_preflight: + run_launch_preflight( + _REPO_ROOT, + dataset_root=Path(cfg.dataset_root), + model_name=cfg.model_name, + renderer_name=cfg.renderer_name, + require_sandbox=True, + ) config = Config( learning_rate=cfg.learning_rate, dataset_builder=TrackARLDatasetBuilder( @@ -98,14 +86,16 @@ async def cli_main(cfg: CLIConfig) -> None: model_name=cfg.model_name, renderer_name=cfg.renderer_name, max_image=1440, - current_level=str(resolved_launch["current_level"]), - replay_levels=",".join(resolved_launch["replay_levels"]), + current_level=current, + replay_levels=",".join(replay_levels), + validation_canaries=cfg.validation_canaries, + reward_policy=cfg.reward_policy, ), model_name=cfg.model_name, recipe_name="pixcell_track_a_rl", renderer_name=cfg.renderer_name, lora_rank=cfg.lora_rank, - load_checkpoint_path=str(resolved_launch["load_checkpoint_path"]), + load_checkpoint_path=cfg.load_checkpoint_path or None, max_tokens=cfg.max_tokens, temperature=cfg.temperature, log_path=cfg.log_path, @@ -119,12 +109,12 @@ async def cli_main(cfg: CLIConfig) -> None: # and must abort rather than silently dropping a biased group. rollout_error_tolerance=False, kl_penalty_coef=0.0, - num_groups_to_log=0, - ) - cli_utils.check_log_dir( - cfg.log_path, - behavior_if_exists="raise", + num_groups_to_log=2, + rollout_json_export=True, + wandb_project=cfg.wandb_project or None, + wandb_name=cfg.wandb_name or None, ) + cli_utils.check_log_dir(cfg.log_path, behavior_if_exists="raise") await main(config) diff --git a/rl/track_a/train_sft.py b/rl/track_a/train_sft.py index 5d982d84..a7a9e7a8 100644 --- a/rl/track_a/train_sft.py +++ b/rl/track_a/train_sft.py @@ -1,5 +1,11 @@ #!/usr/bin/env python3 -"""Spend-locked Track A SFT entrypoint.""" +"""Track A SFT entrypoint. + +One stage of supervised fine-tuning over the frozen depth-v1 rows, with the +same blind prompt the RL env samples with (prompt identity is what makes the +checkpoint resumable by ``train_rl``). Level-filtered: the campaign's SFT +arms train on L0 only. +""" from __future__ import annotations @@ -12,7 +18,6 @@ from tinker_cookbook.supervised.train import Config, main from rl.track_a.launch import run_launch_preflight -from rl.track_a.recipe import validate_sft_cli from rl.track_a.tinker_data import ( DEFAULT_MODEL, DEFAULT_RENDERER, @@ -24,6 +29,8 @@ _REPO_ROOT = Path(__file__).resolve().parents[2] _DATASET_ROOT = _REPO_ROOT / "dataset" +CONFIRM_SPEND = "PIXCELL_RUNS_V2" + @chz.chz class CLIConfig: @@ -31,27 +38,41 @@ class CLIConfig: dataset_root: str = str(_DATASET_ROOT) model_name: str = DEFAULT_MODEL renderer_name: str = DEFAULT_RENDERER + levels: str = "L0" lora_rank: int = 32 - learning_rate: float = 5e-5 + learning_rate: float = 1e-4 lr_schedule: str = "linear" - num_epochs: int = 1 + num_epochs: int = 2 batch_size: int = 64 max_length: int = 6144 max_image: int = 1440 - save_every: int = 14 - eval_every: int = 14 - max_steps: int = 55 + save_every: int = 5 + eval_every: int = 5 log_path: str = str(_TRACK_ROOT / "runs" / "track-a-sft") + wandb_project: str = "" + wandb_name: str = "" + skip_preflight: bool = False async def cli_main(cfg: CLIConfig) -> None: - try: - resolved_launch = validate_sft_cli(_REPO_ROOT, cfg) - except ValueError as exc: - raise SystemExit(f"paid launch blocked: {exc}") from exc + if cfg.confirm_spend != CONFIRM_SPEND: + raise SystemExit( + f"paid launch blocked: pass confirm_spend={CONFIRM_SPEND}" + ) if not os.environ.get("TINKER_API_KEY"): raise SystemExit("TINKER_API_KEY is not present") - run_launch_preflight(_REPO_ROOT, resolved_launch=resolved_launch) + if not cfg.skip_preflight: + run_launch_preflight( + _REPO_ROOT, + dataset_root=Path(cfg.dataset_root), + model_name=cfg.model_name, + renderer_name=cfg.renderer_name, + require_sandbox=False, + tokenize_sft=True, + sft_levels=cfg.levels, + max_length=cfg.max_length, + max_image=cfg.max_image, + ) config = Config( log_path=cfg.log_path, model_name=cfg.model_name, @@ -64,6 +85,7 @@ async def cli_main(cfg: CLIConfig) -> None: batch_size=cfg.batch_size, max_length=cfg.max_length, max_image=cfg.max_image, + levels=cfg.levels, ), learning_rate=cfg.learning_rate, lr_schedule=cfg.lr_schedule, @@ -74,12 +96,10 @@ async def cli_main(cfg: CLIConfig) -> None: adam_eps=1e-8, save_every=cfg.save_every, eval_every=cfg.eval_every, - max_steps=cfg.max_steps, - ) - cli_utils.check_log_dir( - cfg.log_path, - behavior_if_exists="raise", + wandb_project=cfg.wandb_project or None, + wandb_name=cfg.wandb_name or None, ) + cli_utils.check_log_dir(cfg.log_path, behavior_if_exists="raise") await main(config) diff --git a/rl/track_b/README.md b/rl/track_b/README.md deleted file mode 100644 index 9a4a3513..00000000 --- a/rl/track_b/README.md +++ /dev/null @@ -1,16 +0,0 @@ -# Track B - -Track B is the advanced-policy path. Its first frozen use is the bounded -Inkling L4 branch in -[`representation_training_v1`](../studies/representation_training_v1/README.md). -The study owns the stage, reward, schedule, checkpoint, evaluation, and spend -contract; Track B owns only the Inkling-specific runtime bridge. - -`tml_shim.py` is the runtime bridge needed by that future launcher. It registers -native TMLv0 support only for `thinkingmachines/Inkling`, requires an explicit -programmatic thinking-effort value, keeps reasoning separate from final text, -and refuses every supervised rendering entrypoint. Importing it does not -register anything or create a Tinker client. - -The shared study launcher imports this bridge only for an Inkling RL stage. -Inkling remains RL-only and never receives an SFT answer serialization. diff --git a/rl/track_b/tests/__init__.py b/rl/track_b/tests/__init__.py deleted file mode 100644 index 7d42b3e9..00000000 --- a/rl/track_b/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Track B runtime tests.""" diff --git a/rl/track_b/tests/test_tml_shim.py b/rl/track_b/tests/test_tml_shim.py deleted file mode 100644 index 6d01163e..00000000 --- a/rl/track_b/tests/test_tml_shim.py +++ /dev/null @@ -1,214 +0,0 @@ -from __future__ import annotations - -import pickle - -import pytest - - -pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") -pytest.importorskip( - "tinker_cookbook", - reason="Tinker Cookbook is an optional RL dependency", -) -pytest.importorskip( - "tml_renderers", - reason="Inkling native rendering is an optional RL dependency", -) - -import tinker -from tinker_cookbook.renderers import ( - Message, - ParseTermination, - RenderContext, - TextPart, - TrainOnWhat, - get_renderer, - unregister_renderer, -) -from tinker_cookbook.tokenizer_utils import ( - get_tokenizer, - unregister_tokenizer, -) -from tml_renderers import chat as tml_chat -from tml_renderers import tinker as tml_tinker - -from rl.track_b import tml_shim - - -@pytest.fixture() -def direct_renderer() -> tml_shim.InklingTmlV0Renderer: - return tml_shim.InklingTmlV0Renderer( - tml_shim.InklingTokenizer(), - effort=0.9, - ) - - -@pytest.fixture() -def clean_registration(monkeypatch): - unregister_renderer(tml_shim.RENDERER_NAME) - unregister_tokenizer(tml_shim.MODEL_NAME) - monkeypatch.setattr(tml_shim, "_RUNTIME_EFFORT", None) - monkeypatch.setattr(tml_shim, "_TOKENIZER", None) - monkeypatch.setattr(tml_shim, "_patch_sdk_tokenizer_loader", lambda: None) - yield - unregister_renderer(tml_shim.RENDERER_NAME) - unregister_tokenizer(tml_shim.MODEL_NAME) - - -def _action_tokens( - renderer: tml_shim.InklingTmlV0Renderer, - *, - reasoning: str, - final: str, -) -> list[int]: - model = tml_chat.Author(tml_chat.AuthorKind.Model) - messages = [ - tml_chat.Message(content=tml_chat.Thinking(reasoning), author=model), - tml_chat.Message(content=tml_chat.Text(final), author=model), - tml_chat.Message(content=tml_chat.ModelEndSampling(), author=model), - ] - examples = list(renderer._tml.render_for_sft(messages)) - assert len(examples) == 1 - model_input, weights = ( - tml_tinker.training_example_to_tinker_model_input_and_weights(examples[0]) - ) - flat: list[int | None] = [] - for chunk in model_input.chunks: - if isinstance(chunk, tinker.EncodedTextChunk): - flat.extend(list(chunk.tokens)) - else: - flat.extend([None] * chunk.length) - selected = [ - token - for token, weight in zip(flat, weights, strict=True) - if weight != 0.0 - ] - assert selected and None not in selected - return [int(token) for token in selected] - - -@pytest.mark.parametrize("effort", [True, -0.1, 1.0, float("nan")]) -def test_effort_must_be_explicit_and_valid(effort): - with pytest.raises((TypeError, ValueError)): - tml_shim.InklingTmlV0Renderer( - tml_shim.InklingTokenizer(), - effort=effort, - ) - - -def test_generation_uses_constructor_effort_not_environment( - direct_renderer, - monkeypatch, -): - monkeypatch.setenv("PIXCELL_TML_EFFORT", "0.2") - prompt = direct_renderer.build_generation_prompt( - [Message(role="user", content="Recreate the device.")] - ) - tokens: list[int] = [] - for chunk in prompt.chunks: - if isinstance(chunk, tinker.EncodedTextChunk): - tokens.extend(chunk.tokens) - decoded = direct_renderer.tokenizer.decode(tokens) - assert "Thinking effort level: 0.9" in decoded - assert "Thinking effort level: 0.2" not in decoded - - -def test_channel_decode_returns_only_final_text(direct_renderer): - action = _action_tokens( - direct_renderer, - reasoning="PRIVATE REASONING SENTINEL", - final="FINAL_CODE_SENTINEL = 1", - ) - decoded = direct_renderer.decode_response(action) - assert decoded.termination is ParseTermination.STOP_SEQUENCE - assert decoded.channel_parse_complete - assert decoded.reasoning_text == "PRIVATE REASONING SENTINEL" - assert decoded.final_text == "FINAL_CODE_SENTINEL = 1" - assert direct_renderer.decode_action_text(action) == "FINAL_CODE_SENTINEL = 1" - assert "PRIVATE REASONING" not in direct_renderer.decode_action_text(action) - - message, termination = direct_renderer.parse_response(action) - assert termination is ParseTermination.STOP_SEQUENCE - assert message["content"] == [ - {"type": "thinking", "thinking": "PRIVATE REASONING SENTINEL"}, - {"type": "text", "text": "FINAL_CODE_SENTINEL = 1"}, - ] - - -def test_malformed_native_response_does_not_turn_raw_framing_into_code( - direct_renderer, -): - # A lone native message-open token is deliberately incomplete. - decoded = direct_renderer.decode_response([200001]) - assert decoded.termination is ParseTermination.MALFORMED - assert decoded.final_text == "" - assert direct_renderer.decode_action_text([200001]) == "" - - -def test_supervised_paths_fail_closed(direct_renderer): - messages = [ - Message(role="user", content="image task"), - Message(role="assistant", content=[TextPart(type="text", text="code")]), - ] - with pytest.raises(tml_shim.InklingSupervisionDisabled): - direct_renderer.build_supervised_example(messages) - with pytest.raises(tml_shim.InklingSupervisionDisabled): - direct_renderer.build_supervised_examples( - messages, - train_on_what=TrainOnWhat.LAST_ASSISTANT_TURN, - ) - with pytest.raises(tml_shim.InklingSupervisionDisabled): - direct_renderer.render_message( - messages[0], - RenderContext( - idx=0, - is_last=True, - prev_message=None, - last_user_index=0, - ), - ) - - -def test_renderer_pickle_preserves_explicit_effort(direct_renderer): - restored = pickle.loads(pickle.dumps(direct_renderer)) - assert isinstance(restored, tml_shim.InklingTmlV0Renderer) - assert restored.effort == 0.9 - - -def test_install_is_inkling_only_and_immutable(clean_registration): - binding = tml_shim.install_inkling_runtime(effort=0.9) - assert binding == tml_shim.InklingRuntimeBinding( - model_name=tml_shim.MODEL_NAME, - renderer_name=tml_shim.RENDERER_NAME, - effort=0.9, - ) - assert tml_shim.install_inkling_runtime(effort=0.9) == binding - with pytest.raises(tml_shim.InklingRuntimeError, match="already bound"): - tml_shim.install_inkling_runtime(effort=0.8) - - tokenizer = get_tokenizer(tml_shim.MODEL_NAME) - renderer = get_renderer( - tml_shim.RENDERER_NAME, - tokenizer, - None, - model_name=tml_shim.MODEL_NAME, - ) - assert isinstance(tokenizer, tml_shim.InklingTokenizer) - assert isinstance(renderer, tml_shim.InklingTmlV0Renderer) - assert renderer.effort == 0.9 - - class OtherTokenizer: - name_or_path = "other/model" - - with pytest.raises(tml_shim.InklingRuntimeError, match="restricted"): - tml_shim._renderer_factory(OtherTokenizer(), None) - assert not tml_shim._is_inkling_model("Qwen/Qwen3.6-35B-A3B") - assert tml_shim._is_inkling_model("thinkingmachines/Inkling:peft:262144") - - -def test_no_registration_happens_from_environment_alone( - clean_registration, - monkeypatch, -): - monkeypatch.setenv("PIXCELL_TML_EFFORT", "0.9") - assert tml_shim._RUNTIME_EFFORT is None diff --git a/rl/track_b/tml_shim.py b/rl/track_b/tml_shim.py deleted file mode 100644 index 0350b80e..00000000 --- a/rl/track_b/tml_shim.py +++ /dev/null @@ -1,414 +0,0 @@ -"""Native TMLv0 runtime bridge for Inkling RL. - -The pinned Tinker Cookbook predates native Inkling registration. This module -provides the small adapter needed by a Track B launcher without changing any -other model path: - -* the native ``o200k_base_chat`` tokenizer is registered only for - ``thinkingmachines/Inkling``; -* TMLv0 receives one explicit, immutable thinking-effort value; -* sampled thinking and final-text channels remain separate; and -* every supervised rendering entrypoint fails closed. - -Importing this module has no registration side effects. A launcher must call -``install_inkling_runtime(effort=...)`` before constructing a Tinker training -client. No environment variable supplies a default effort. -""" - -from __future__ import annotations - -import base64 -import io -import logging -import math -from collections.abc import Sequence -from dataclasses import dataclass -from typing import Any, NoReturn - -import tinker -from tml_renderers import chat as tml_chat -from tml_renderers import tinker as tml_tinker -from tml_renderers import tokenizers as tml_tokenizers -from tml_renderers import v0 as tml_v0 - -from tinker_cookbook.renderers import ( - Message, - ParseTermination, - RenderContext, - Renderer, - Role, - TextPart, - ThinkingPart, - TrainOnWhat, - get_registered_renderer_names, - register_renderer, -) -from tinker_cookbook.renderers.base import RenderedMessage -from tinker_cookbook.tokenizer_utils import ( - get_registered_tokenizer_names, - register_tokenizer, -) - - -logger = logging.getLogger(__name__) - -MODEL_NAME = "thinkingmachines/Inkling" -RENDERER_NAME = "tml_v0" - - -class InklingRuntimeError(RuntimeError): - """The requested runtime binding is absent, ambiguous, or incompatible.""" - - -class InklingSupervisionDisabled(InklingRuntimeError): - """Track B deliberately does not expose an Inkling SFT serialization path.""" - - -@dataclass(frozen=True) -class InklingRuntimeBinding: - model_name: str - renderer_name: str - effort: float - - -@dataclass(frozen=True) -class DecodedInklingResponse: - """Channel-preserving interpretation of one sampled token sequence.""" - - raw_text: str - reasoning_text: str - final_text: str - termination: ParseTermination - - @property - def channel_parse_complete(self) -> bool: - return self.termination is ParseTermination.STOP_SEQUENCE - - -def _validate_effort(value: float) -> float: - if ( - isinstance(value, bool) - or not isinstance(value, (int, float)) - or not math.isfinite(float(value)) - or not 0.0 <= float(value) < 1.0 - ): - raise ValueError("Inkling effort must be an explicit finite number in [0, 1)") - return float(value) - - -class InklingTokenizer: - """Cookbook-facing adapter over the native TML o200k chat tokenizer.""" - - name_or_path = MODEL_NAME - - def __init__(self) -> None: - self._tokenizer = tml_tokenizers.o200k_base_chat() - - @property - def tml_tokenizer(self) -> tml_tokenizers.O200kBaseChatTokenizer: - return self._tokenizer - - @property - def eos_token(self) -> str: - return self._tokenizer.eos_token - - def decode(self, token_ids: Sequence[int], **_kwargs: Any) -> str: - return self._tokenizer.decode(list(token_ids)) - - def encode( - self, - text: str, - add_special_tokens: bool = False, - **_kwargs: Any, - ) -> list[int]: - if add_special_tokens: - raise NotImplementedError( - "the Inkling tokenizer bridge encodes ordinary text only" - ) - return self._tokenizer.encode_ordinary(text) - - -_TOKENIZER: InklingTokenizer | None = None -_RUNTIME_EFFORT: float | None = None - - -def _tokenizer_factory() -> InklingTokenizer: - global _TOKENIZER - if _TOKENIZER is None: - _TOKENIZER = InklingTokenizer() - return _TOKENIZER - - -def _pil_to_data_uri(image: Any) -> str: - buffer = io.BytesIO() - image.convert("RGB").save(buffer, format="PNG") - payload = base64.b64encode(buffer.getvalue()).decode("ascii") - return f"data:image/png;base64,{payload}" - - -def _normalize_media(messages: Sequence[Message]) -> list[dict[str, Any]]: - """Convert Cookbook image parts to the OSS image shape TMLv0 consumes.""" - - normalized: list[dict[str, Any]] = [] - for message in messages: - item = dict(message) - content = item.get("content") - if not isinstance(content, list): - normalized.append(item) - continue - parts: list[Any] = [] - for part in content: - if isinstance(part, dict) and part.get("type") == "image": - image = part.get("image") - if isinstance(image, str): - url = image - elif hasattr(image, "convert") and hasattr(image, "save"): - url = _pil_to_data_uri(image) - else: - raise TypeError( - "TML ImagePart.image must be a string or PIL-compatible image" - ) - parts.append({"type": "image_url", "image_url": {"url": url}}) - else: - parts.append(part) - normalized.append({**item, "content": parts}) - return normalized - - -def _render_input(messages: Sequence[Message]) -> list[tml_chat.OpenAIMessage]: - return list( - tml_chat.OpenAIMessage.from_oss_messages(_normalize_media(messages)) - ) - - -class InklingTmlV0Renderer(Renderer): - """Sampling-only Cookbook renderer over native TMLv0 channels.""" - - supports_streaming = False - - def __init__(self, tokenizer: InklingTokenizer, *, effort: float) -> None: - if not isinstance(tokenizer, InklingTokenizer): - raise TypeError("InklingTmlV0Renderer requires InklingTokenizer") - super().__init__(tokenizer) - self.effort = _validate_effort(effort) - self._tml = tml_v0.Renderer(tokenizer.tml_tokenizer) - - def __reduce__(self) -> tuple[Any, tuple[float]]: - # Do not depend on process-global Cookbook registries when an - # EnvGroupBuilder is unpickled in a worker. - return (_restore_renderer, (self.effort,)) - - @property - def has_extension_property(self) -> bool: - return True - - def get_stop_sequences(self) -> list[int]: - return list(self._tml.stop()) - - def build_generation_prompt( - self, - messages: list[Message], - role: Role = "assistant", - prefill: str | None = None, - ) -> tinker.ModelInput: - if role != "assistant": - raise NotImplementedError("TMLv0 generates assistant turns only") - if prefill is not None: - raise NotImplementedError("TMLv0 does not accept an assistant prefill") - spans, _parser = self._tml.render_for_completion_with_effort( - _render_input(messages), - self.effort, - ) - return tml_tinker.token_spans_to_tinker_model_input(spans) - - def _raw_decode(self, response: Sequence[int]) -> str: - try: - return str(self.tokenizer.decode(list(response))) - except Exception: - return "" - - def decode_response(self, response: Sequence[int]) -> DecodedInklingResponse: - """Parse native channels without ever folding reasoning into final text.""" - - sampled = list(response) - raw_text = self._raw_decode(sampled) - _spans, parser = self._tml.render_for_completion([]) - try: - parsed = list(parser.parse_tokens(sampled)) - except Exception: - return DecodedInklingResponse( - raw_text=raw_text, - reasoning_text="", - final_text="", - termination=ParseTermination.MALFORMED, - ) - - reasoning: list[str] = [] - final: list[str] = [] - saw_end = False - unknown_channel = False - for message in parsed: - content = message.content - if isinstance(content, tml_chat.ModelEndSampling): - saw_end = True - elif isinstance(content, tml_chat.Thinking): - reasoning.append(getattr(content, "text", str(content))) - elif isinstance(content, tml_chat.Text): - final.append(getattr(content, "text", str(content))) - else: - # Tools and future channel types are outside PixCell's - # image-to-code contract. Do not reinterpret them as code. - unknown_channel = True - - termination = ( - ParseTermination.STOP_SEQUENCE - if saw_end and not unknown_channel - else ParseTermination.MALFORMED - ) - return DecodedInklingResponse( - raw_text=raw_text, - reasoning_text="\n".join(reasoning), - final_text="\n".join(final), - termination=termination, - ) - - def parse_response( - self, - response: list[int], - ) -> tuple[Message, ParseTermination]: - decoded = self.decode_response(response) - parts = [] - if decoded.reasoning_text: - parts.append( - ThinkingPart(type="thinking", thinking=decoded.reasoning_text) - ) - if decoded.final_text: - parts.append(TextPart(type="text", text=decoded.final_text)) - return ( - Message(role="assistant", content=parts), - decoded.termination, - ) - - def decode_action_text(self, response: Sequence[int]) -> str: - """Return only the final Text channel for code extraction.""" - - return self.decode_response(response).final_text - - @staticmethod - def _supervision_disabled() -> NoReturn: - raise InklingSupervisionDisabled( - "Track B Inkling is RL-only; supervised TML serialization is disabled" - ) - - def render_message( - self, - message: Message, - ctx: RenderContext, - ) -> RenderedMessage: - del message, ctx - self._supervision_disabled() - - def build_supervised_examples( - self, - messages: list[Message], - train_on_what: TrainOnWhat = TrainOnWhat.LAST_ASSISTANT_TURN, - ) -> list[tuple[tinker.ModelInput, Any]]: - del messages, train_on_what - self._supervision_disabled() - - def build_supervised_example( - self, - messages: list[Message], - train_on_what: TrainOnWhat = TrainOnWhat.LAST_ASSISTANT_MESSAGE, - ) -> tuple[tinker.ModelInput, Any]: - del messages, train_on_what - self._supervision_disabled() - - -def _restore_renderer(effort: float) -> InklingTmlV0Renderer: - return InklingTmlV0Renderer(_tokenizer_factory(), effort=effort) - - -def _configured_effort() -> float: - if _RUNTIME_EFFORT is None: - raise InklingRuntimeError( - "install_inkling_runtime(effort=...) must run before renderer creation" - ) - return _RUNTIME_EFFORT - - -def _renderer_factory( - tokenizer: Any, - image_processor: Any = None, -) -> InklingTmlV0Renderer: - if image_processor is not None: - raise InklingRuntimeError( - "Inkling tokenizes images natively and must not receive an HF image processor" - ) - if not isinstance(tokenizer, InklingTokenizer): - raise InklingRuntimeError( - "the tml_v0 Track B registration is restricted to InklingTokenizer" - ) - return InklingTmlV0Renderer(tokenizer, effort=_configured_effort()) - - -def _is_inkling_model(model_name: str | None) -> bool: - return bool(model_name) and str(model_name).split(":", 1)[0] == MODEL_NAME - - -def _patch_sdk_tokenizer_loader() -> None: - """Route only Inkling SDK tokenizer requests to the native adapter.""" - - import tinker.lib.public_interfaces.sampling_client as sampling_client - import tinker.lib.public_interfaces.training_client as training_client - - def patch(module: Any) -> None: - original = module._load_tokenizer_from_model_info - if getattr(original, "_pixcell_track_b_inkling", False): - return - - def inkling_aware(model_name: str, tokenizer_id: str | None = None): - if _is_inkling_model(model_name): - return _tokenizer_factory() - return original(model_name, tokenizer_id) - - inkling_aware._pixcell_track_b_inkling = True - module._load_tokenizer_from_model_info = inkling_aware - - patch(sampling_client) - patch(training_client) - logger.info("installed Inkling-only Tinker SDK tokenizer bridge") - - -def install_inkling_runtime(*, effort: float) -> InklingRuntimeBinding: - """Install one immutable Inkling runtime binding for this process. - - Repeating the same binding is idempotent. Attempting to change effort - after registration fails rather than silently changing prompt identity. - """ - - global _RUNTIME_EFFORT - resolved = _validate_effort(effort) - if _RUNTIME_EFFORT is not None: - if resolved != _RUNTIME_EFFORT: - raise InklingRuntimeError( - f"Inkling effort is already bound to {_RUNTIME_EFFORT}; " - f"refusing {resolved}" - ) - _patch_sdk_tokenizer_loader() - return InklingRuntimeBinding(MODEL_NAME, RENDERER_NAME, resolved) - - if MODEL_NAME in get_registered_tokenizer_names(): - raise InklingRuntimeError( - f"tokenizer {MODEL_NAME!r} was registered before the Track B bridge" - ) - if RENDERER_NAME in get_registered_renderer_names(): - raise InklingRuntimeError( - f"renderer {RENDERER_NAME!r} was registered before the Track B bridge" - ) - - _RUNTIME_EFFORT = resolved - register_tokenizer(MODEL_NAME, _tokenizer_factory) - register_renderer(RENDERER_NAME, _renderer_factory) - _patch_sdk_tokenizer_loader() - return InklingRuntimeBinding(MODEL_NAME, RENDERER_NAME, resolved) diff --git a/rl/track_c/README.md b/rl/track_c/README.md deleted file mode 100644 index d0e82874..00000000 --- a/rl/track_c/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# Track C - -Track C is reserved for the third PixCell training protocol. Its implementation -will be added only after its model-visible inputs, optimization objective, -evaluation boundary, checkpoint policy, and spend controls are explicitly -approved. - -Until then, Track C has no launcher and imports no Track A policy. From bdbb7670622e0e46520fce561a3612b69e86f1cb Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:19:47 -0400 Subject: [PATCH 10/24] Two-run-matrix orchestrator with fixed cross-run probe eval MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit rl/track_a/run.py sequences each campaign (SFT stage where the arm has one, then staged RL L0..L4) as per-stage subprocesses chained on final sampler weights, resumable by re-running the same command. One deterministic 80-task probe (16 per level, sorted opaque ids from depth/validation) is evaluated at campaign start — the base-model baseline; no separate baselining studies — and after every stage, so every point on the curve is comparable. A and D gate RL behind the post-SFT probe beating the base probe on L0; D falls back to RL-from-base on failure. Smoke mode prices an operating point with 2 GRPO steps before anything full-scale launches. Honors: 73143346a --- rl/track_a/probe_eval.py | 174 ++++++++++++ rl/track_a/run.py | 436 ++++++++++++++++++++++++++++++ rl/track_a/tests/test_run_plan.py | 86 ++++++ 3 files changed, 696 insertions(+) create mode 100644 rl/track_a/probe_eval.py create mode 100644 rl/track_a/run.py create mode 100644 rl/track_a/tests/test_run_plan.py diff --git a/rl/track_a/probe_eval.py b/rl/track_a/probe_eval.py new file mode 100644 index 00000000..4e22c70b --- /dev/null +++ b/rl/track_a/probe_eval.py @@ -0,0 +1,174 @@ +"""Fixed cross-run probe evaluation. + +One deterministic probe set — the first ``per_level`` validation tasks per +level by sorted opaque id — evaluated identically at campaign start (the +base-model baseline), after every stage, and at the end. Because the set +never changes, every point on the resulting curve is comparable; this is +the discipline the old campaign lost when its eval pool grew mid-run. +""" + +from __future__ import annotations + +import asyncio +import json +from collections import defaultdict +from pathlib import Path + +import tinker + +from rl.common.baselines import cached_rect_baseline +from rl.common.contracts import TaskRecord +from rl.common.dataset_io import load_tasks +from rl.common.evaluator import EvaluationStatus, PixCellEvaluator +from rl.track_a.curriculum import LEVELS +from rl.track_a.reward import reward_for_policy +from rl.track_a.tinker_data import _message, _renderer + + +def select_probe_tasks( + dataset_root: str | Path, + *, + per_level: int = 16, +) -> list[TaskRecord]: + """Deterministic: per level, the first ``per_level`` validation tasks by + sorted opaque id. No seed, no state.""" + + validation = load_tasks(Path(dataset_root), configuration="depth", split="validation") + by_level: dict[str, list[TaskRecord]] = defaultdict(list) + for task in validation: + by_level[task.sampler.level].append(task) + selected: list[TaskRecord] = [] + for level in LEVELS: + ranked = sorted(by_level[level], key=lambda task: task.sampler.opaque_id) + if len(ranked) < per_level: + raise ValueError(f"{level} has only {len(ranked)} validation tasks") + selected.extend(ranked[:per_level]) + return selected + + +async def _sample_completions( + tasks: list[TaskRecord], + *, + model_name: str, + renderer_name: str, + model_path: str | None, + max_tokens: int, + temperature: float, +) -> list[str]: + renderer = _renderer(model_name, renderer_name) + service = tinker.ServiceClient() + if model_path: + client = service.create_sampling_client(model_path=model_path) + else: + client = service.create_sampling_client(base_model=model_name) + params = tinker.SamplingParams( + max_tokens=max_tokens, + temperature=temperature, + stop=renderer.get_stop_sequences(), + ) + + async def one(task: TaskRecord) -> str: + prompt = renderer.build_generation_prompt( + [_message(task, max_image=1440)] + ) + response = await client.sample_async( + prompt=prompt, + num_samples=1, + sampling_params=params, + ) + tokens = list(response.sequences[0].tokens) + message, _termination = renderer.parse_response(tokens) + from tinker_cookbook.renderers import get_text_content + + return get_text_content(message) + + return list(await asyncio.gather(*(one(task) for task in tasks))) + + +def run_probe( + *, + dataset_root: str | Path, + model_name: str, + renderer_name: str, + model_path: str | None, + max_tokens: int, + out_path: str | Path | None = None, + per_level: int = 16, + temperature: float = 1.0, + max_workers: int = 8, +) -> dict: + """Sample one completion per probe task and grade it exactly as training + does. Returns (and optionally writes) the per-level summary.""" + + tasks = select_probe_tasks(dataset_root, per_level=per_level) + completions = asyncio.run( + _sample_completions( + tasks, + model_name=model_name, + renderer_name=renderer_name, + model_path=model_path, + max_tokens=max_tokens, + temperature=temperature, + ) + ) + with PixCellEvaluator( + max_workers=max_workers, + evaluator_retries=1, + require_isolation=True, + ) as evaluator: + results = evaluator.evaluate_batch( + [(task.reference, completion) for task, completion in zip(tasks, completions)] + ) + + rows = [] + for task, result in zip(tasks, results): + baseline = cached_rect_baseline(task.reference) + shaped = reward_for_policy(result, policy="shaped_v3b", baseline=baseline) + rows.append( + { + "opaque_id": task.sampler.opaque_id, + "level": task.sampler.level, + "status": result.status.value, + "executable": result.status is EvaluationStatus.OK, + "iou": float(result.iou or 0.0), + "shaped": float(shaped) if shaped is not None else None, + } + ) + + per_level_summary: dict[str, dict[str, float]] = {} + for level in LEVELS: + level_rows = [row for row in rows if row["level"] == level] + graded = [row for row in level_rows if row["shaped"] is not None] + per_level_summary[level] = { + "n": len(level_rows), + "executable_rate": ( + sum(row["executable"] for row in level_rows) / len(level_rows) + ), + "mean_iou": sum(row["iou"] for row in level_rows) / len(level_rows), + "mean_shaped": ( + sum(row["shaped"] for row in graded) / len(graded) if graded else 0.0 + ), + } + graded = [row for row in rows if row["shaped"] is not None] + report = { + "model_name": model_name, + "renderer_name": renderer_name, + "model_path": model_path, + "max_tokens": max_tokens, + "temperature": temperature, + "per_level": per_level_summary, + "overall": { + "n": len(rows), + "executable_rate": sum(row["executable"] for row in rows) / len(rows), + "mean_iou": sum(row["iou"] for row in rows) / len(rows), + "mean_shaped": ( + sum(row["shaped"] for row in graded) / len(graded) if graded else 0.0 + ), + }, + "rows": rows, + } + if out_path is not None: + path = Path(out_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(report, indent=2), encoding="utf-8") + return report diff --git a/rl/track_a/run.py b/rl/track_a/run.py new file mode 100644 index 00000000..4fa5972f --- /dev/null +++ b/rl/track_a/run.py @@ -0,0 +1,436 @@ +#!/usr/bin/env python3 +"""The Track A campaign runner: four runs, one recipe, no ceremony. + + A runA-l0sft-rl no-think @ 4096 L0-SFT -> RL L0..L4 + B runB-base-rl no-think @ 4096 base -> RL L0..L4 + C runC-think-base-rl think @ 60000 base -> RL L0..L4 + D runD-think-l0sft-rl think @ 60000 L0-SFT -> RL L0..L4 (gated) + +Each stage is one subprocess over the lean entry points (crash isolation, +one W&B run per stage, per-stage console logs); stages chain on the previous +stage's final sampler weights with a fresh optimizer, exactly as the old +lineage chained its runs. A stage that already finished is skipped, so +re-running the same command resumes the campaign. + +The fixed probe (probe_eval) runs at campaign start — the base-model +baseline; there are no separate baselining studies — and after every stage. +Run A's and D's SFT must beat the base probe on L0 (executable >= 0.75 and +higher mean IoU) before RL spends anything; on failure A aborts for +diagnosis and D falls back to RL-from-base (the SFT collapse is itself a +finding — see the renderer-diagnostic precedent). + +Smoke mode prices an operating point before the full campaign: +``--smoke nothink`` / ``--smoke think`` runs 2 GRPO steps at 2 groups. +""" + +from __future__ import annotations + +import argparse +import json +import os +import subprocess +import sys +import time +from dataclasses import asdict, dataclass +from pathlib import Path + +from tinker_cookbook.checkpoint_utils import get_last_checkpoint + +from rl.common.baselines import cached_rect_baseline +from rl.track_a.curriculum import LEVELS +from rl.track_a.launch import run_launch_preflight +from rl.track_a.probe_eval import run_probe, select_probe_tasks + +_REPO_ROOT = Path(__file__).resolve().parents[2] +_DATASET_ROOT = _REPO_ROOT / "dataset" + +CONFIRM_SPEND = "PIXCELL_RUNS_V2" +DEFAULT_RUNS_ROOT = "/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2" +FALLBACK_RUNS_ROOT = "~/pixcell-training/runs-v2" + +SFT_GATE_MIN_EXECUTABLE = 0.75 + + +@dataclass(frozen=True) +class RunPlan: + key: str + name: str + renderer_name: str + max_tokens: int + sft: bool + steps_per_level: int = 30 + + +RUN_PLANS = { + "A": RunPlan("A", "runA-l0sft-rl", "qwen3_5_disable_thinking", 4096, True), + "B": RunPlan("B", "runB-base-rl", "qwen3_5_disable_thinking", 4096, False), + "C": RunPlan("C", "runC-think-base-rl", "qwen3_5", 60000, False), + "D": RunPlan("D", "runD-think-l0sft-rl", "qwen3_5", 60000, True), +} +MODEL_NAME = "Qwen/Qwen3.6-35B-A3B" + + +@dataclass(frozen=True) +class Stage: + name: str + module: str + args: dict[str, str] + + +def build_stages( + plan: RunPlan, + *, + run_root: Path, + wandb_project: str, + smoke: bool = False, +) -> list[Stage]: + """The full ordered stage list for one run. Checkpoint chaining is + resolved at execution time, not here.""" + + steps = 2 if smoke else plan.steps_per_level + groups = "2" if smoke else "8" + canaries = "8" if smoke else "40" + stages: list[Stage] = [] + if plan.sft and not smoke: + stages.append( + Stage( + name="sft-l0", + module="rl.track_a.train_sft", + args={ + "confirm_spend": CONFIRM_SPEND, + "model_name": MODEL_NAME, + "renderer_name": plan.renderer_name, + "levels": "L0", + "num_epochs": "2", + "learning_rate": "1e-4", + "log_path": str(run_root / "stages" / "sft-l0"), + "wandb_project": wandb_project, + "wandb_name": f"{plan.name}-sft-l0", + "skip_preflight": "True", + }, + ) + ) + levels = ["L0"] if smoke else list(LEVELS) + for level in levels: + stage_name = f"rl-{level.lower()}" + stages.append( + Stage( + name=stage_name, + module="rl.track_a.train_rl", + args={ + "confirm_spend": CONFIRM_SPEND, + "model_name": MODEL_NAME, + "renderer_name": plan.renderer_name, + "current_level": level, + "max_tokens": str(plan.max_tokens), + "max_steps": str(steps), + "groups_per_batch": groups, + "group_size": "8", + "validation_canaries": canaries, + "reward_policy": "shaped_v3b", + "log_path": str(run_root / "stages" / stage_name), + "wandb_project": wandb_project, + "wandb_name": f"{plan.name}-{stage_name}", + "skip_preflight": "True", + }, + ) + ) + return stages + + +def _stage_done(stage: Stage) -> bool: + return (Path(stage.args["log_path"]) / "DONE").exists() + + +def _mark_done(stage: Stage) -> None: + (Path(stage.args["log_path"]) / "DONE").write_text("ok\n", encoding="utf-8") + + +def _last_sampler_path(log_path: str) -> str: + record = get_last_checkpoint(log_path, required_key="sampler_path") + if record is None or not record.sampler_path: + raise RuntimeError(f"no sampler checkpoint recorded under {log_path}") + return record.sampler_path + + +def _environment() -> dict[str, str]: + environment = dict(os.environ) + roots = [str(_REPO_ROOT / "src"), str(_REPO_ROOT)] + if environment.get("PYTHONPATH"): + roots.append(environment["PYTHONPATH"]) + environment["PYTHONPATH"] = os.pathsep.join(roots) + return environment + + +def _execute_stage(stage: Stage, *, extra: dict[str, str]) -> None: + log_dir = Path(stage.args["log_path"]) + log_dir.mkdir(parents=True, exist_ok=True) + arguments = {**stage.args, **extra} + command = [ + sys.executable, + "-m", + stage.module, + *[f"{key}={value}" for key, value in arguments.items()], + ] + console = log_dir / "console.log" + started = time.monotonic() + with console.open("a", encoding="utf-8") as sink: + sink.write(f"# {' '.join(command)}\n") + sink.flush() + completed = subprocess.run( + command, + cwd=_REPO_ROOT, + env=_environment(), + stdout=sink, + stderr=subprocess.STDOUT, + ) + if completed.returncode != 0: + raise RuntimeError( + f"stage {stage.name} failed with exit {completed.returncode}; " + f"see {console}" + ) + print( + f"[{stage.name}] completed in {time.monotonic() - started:.0f}s", + flush=True, + ) + + +def _probe( + plan: RunPlan, + *, + run_root: Path, + tag: str, + model_path: str | None, +) -> dict: + return run_probe( + dataset_root=_DATASET_ROOT, + model_name=MODEL_NAME, + renderer_name=plan.renderer_name, + model_path=model_path, + max_tokens=plan.max_tokens, + out_path=run_root / "probes" / f"{tag}.json", + ) + + +def _sft_gate(base_probe: dict, sft_probe: dict) -> tuple[bool, str]: + base_l0 = base_probe["per_level"]["L0"] + sft_l0 = sft_probe["per_level"]["L0"] + if sft_l0["executable_rate"] < SFT_GATE_MIN_EXECUTABLE: + return False, ( + f"SFT L0 executable rate {sft_l0['executable_rate']:.3f} < " + f"{SFT_GATE_MIN_EXECUTABLE}" + ) + if sft_l0["mean_iou"] <= base_l0["mean_iou"]: + return False, ( + f"SFT L0 mean IoU {sft_l0['mean_iou']:.3f} does not beat base " + f"{base_l0['mean_iou']:.3f}" + ) + return True, "sft beats base on L0" + + +def _write_provenance(plan: RunPlan, *, run_root: Path, smoke: bool) -> None: + provenance = run_root / "provenance" + provenance.mkdir(parents=True, exist_ok=True) + head = subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=_REPO_ROOT, + capture_output=True, + text=True, + check=True, + ).stdout.strip() + dirty = subprocess.run( + ["git", "status", "--porcelain"], + cwd=_REPO_ROOT, + capture_output=True, + text=True, + check=True, + ).stdout.strip() + (provenance / "plan.json").write_text( + json.dumps( + { + "plan": asdict(plan), + "model_name": MODEL_NAME, + "git_head": head, + "git_dirty": bool(dirty), + "smoke": smoke, + "confirm_spend": CONFIRM_SPEND, + }, + indent=2, + ), + encoding="utf-8", + ) + probe_tasks = select_probe_tasks(_DATASET_ROOT) + baselines = { + task.sampler.opaque_id: { + "level": task.sampler.level, + **cached_rect_baseline(task.reference).__dict__, + } + for task in probe_tasks + } + (provenance / "probe_baselines.json").write_text( + json.dumps(baselines, indent=2), + encoding="utf-8", + ) + + +def _resolve_runs_root(value: str) -> Path: + preferred = Path(value).expanduser() + if str(preferred) == DEFAULT_RUNS_ROOT and not preferred.parent.exists(): + preferred = Path(FALLBACK_RUNS_ROOT).expanduser() + preferred.mkdir(parents=True, exist_ok=True) + return preferred + + +def run_campaign(args: argparse.Namespace) -> None: + smoke = bool(args.smoke) + if smoke: + plan_key = "B" if args.smoke == "nothink" else "C" + base_plan = RUN_PLANS[plan_key] + plan = RunPlan( + key=base_plan.key, + name=f"smoke-{args.smoke}", + renderer_name=base_plan.renderer_name, + max_tokens=base_plan.max_tokens, + sft=False, + ) + else: + plan = RUN_PLANS[args.run] + runs_root = _resolve_runs_root(args.runs_root) + run_root = runs_root / plan.name + run_root.mkdir(parents=True, exist_ok=True) + for variable in ("TINKER_API_KEY", "WANDB_API_KEY", "PIXCELL_EVALUATOR_IMAGE"): + if not os.environ.get(variable): + raise SystemExit(f"{variable} is not present") + _write_provenance(plan, run_root=run_root, smoke=smoke) + if not args.skip_preflight: + run_launch_preflight( + _REPO_ROOT, + dataset_root=_DATASET_ROOT, + model_name=MODEL_NAME, + renderer_name=plan.renderer_name, + require_sandbox=True, + tokenize_sft=plan.sft, + sft_levels="L0" if plan.sft else "", + ) + + stages = build_stages( + plan, + run_root=run_root, + wandb_project=args.wandb_project, + smoke=smoke, + ) + checkpoint: str | None = None + base_probe: dict | None = None + if not smoke: + base_tag = "base" + base_path = run_root / "probes" / f"{base_tag}.json" + if base_path.exists(): + base_probe = json.loads(base_path.read_text(encoding="utf-8")) + else: + print("[probe] base model", flush=True) + base_probe = _probe(plan, run_root=run_root, tag=base_tag, model_path=None) + + for stage in stages: + if _stage_done(stage): + print(f"[{stage.name}] already complete, skipping", flush=True) + if stage.module.endswith("train_sft") or stage.module.endswith("train_rl"): + checkpoint = _last_sampler_path(stage.args["log_path"]) + continue + extra: dict[str, str] = {} + if checkpoint: + extra["load_checkpoint_path"] = checkpoint + _execute_stage(stage, extra=extra) + _mark_done(stage) + checkpoint = _last_sampler_path(stage.args["log_path"]) + if not smoke: + print(f"[probe] after {stage.name}", flush=True) + probe = _probe( + plan, + run_root=run_root, + tag=stage.name, + model_path=checkpoint, + ) + if stage.name == "sft-l0": + assert base_probe is not None + passed, reason = _sft_gate(base_probe, probe) + (run_root / "probes" / "sft_gate.json").write_text( + json.dumps({"passed": passed, "reason": reason}, indent=2), + encoding="utf-8", + ) + if not passed and plan.key == "D": + print( + f"[gate] {reason} — falling back to RL from base", + flush=True, + ) + checkpoint = None + elif not passed: + raise SystemExit(f"SFT sanity gate failed: {reason}") + else: + print(f"[gate] {reason}", flush=True) + + print(f"[{plan.name}] campaign complete", flush=True) + if smoke: + _summarize_smoke(run_root, stages) + + +def _summarize_smoke(run_root: Path, stages: list[Stage]) -> None: + summary: dict[str, object] = {} + for stage in stages: + metrics_path = Path(stage.args["log_path"]) / "metrics.jsonl" + if not metrics_path.exists(): + continue + rows = [ + json.loads(line) + for line in metrics_path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + token_keys = sorted( + { + key + for row in rows + for key, value in row.items() + if "token" in key.lower() and isinstance(value, (int, float)) + } + ) + summary[stage.name] = { + "steps": len(rows), + "token_metrics": { + key: sum(float(row.get(key, 0.0)) for row in rows) + for key in token_keys + }, + "reward_by_step": [ + row.get("env/all/reward/total") for row in rows + ], + } + out = run_root / "smoke_summary.json" + out.write_text(json.dumps(summary, indent=2), encoding="utf-8") + print(json.dumps(summary, indent=2)) + print(f"[smoke] summary written to {out}", flush=True) + + +def parser() -> argparse.ArgumentParser: + result = argparse.ArgumentParser(description=__doc__) + result.add_argument("--run", choices=sorted(RUN_PLANS), help="which run to execute") + result.add_argument( + "--smoke", + choices=("nothink", "think"), + help="price an operating point: 2 GRPO steps at 2 groups on L0", + ) + result.add_argument("--runs-root", default=DEFAULT_RUNS_ROOT) + result.add_argument("--wandb-project", default="pixcell-rl") + result.add_argument("--confirm-spend", default="") + result.add_argument("--skip-preflight", action="store_true") + return result + + +def main() -> None: + args = parser().parse_args() + if args.confirm_spend != CONFIRM_SPEND: + raise SystemExit(f"paid launch blocked: pass --confirm-spend {CONFIRM_SPEND}") + if bool(args.run) == bool(args.smoke): + raise SystemExit("pass exactly one of --run {A,B,C,D} or --smoke {nothink,think}") + run_campaign(args) + + +if __name__ == "__main__": + main() diff --git a/rl/track_a/tests/test_run_plan.py b/rl/track_a/tests/test_run_plan.py new file mode 100644 index 00000000..939ea2d1 --- /dev/null +++ b/rl/track_a/tests/test_run_plan.py @@ -0,0 +1,86 @@ +"""Campaign plan construction and probe determinism (no network).""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +pytest.importorskip("tinker", reason="Tinker SDK env not installed") +pytest.importorskip("chz", reason="chz env not installed") + +from rl.track_a.probe_eval import select_probe_tasks +from rl.track_a.run import RUN_PLANS, _sft_gate, build_stages + + +REPO_ROOT = Path(__file__).resolve().parents[3] + + +def test_run_a_plan_is_sft_then_five_rl_stages(tmp_path): + stages = build_stages( + RUN_PLANS["A"], run_root=tmp_path, wandb_project="pixcell-rl" + ) + assert [stage.name for stage in stages] == [ + "sft-l0", + "rl-l0", + "rl-l1", + "rl-l2", + "rl-l3", + "rl-l4", + ] + sft = stages[0] + assert sft.args["levels"] == "L0" + assert sft.args["num_epochs"] == "2" + assert sft.args["renderer_name"] == "qwen3_5_disable_thinking" + last = stages[-1] + assert last.args["current_level"] == "L4" + assert last.args["max_steps"] == "30" + assert last.args["group_size"] == "8" + assert last.args["groups_per_batch"] == "8" + assert last.args["reward_policy"] == "shaped_v3b" + assert last.args["wandb_name"] == "runA-l0sft-rl-rl-l4" + + +def test_think_pair_uses_thinking_renderer_and_budget(tmp_path): + for key in ("C", "D"): + stages = build_stages( + RUN_PLANS[key], run_root=tmp_path, wandb_project="pixcell-rl" + ) + rl_stage = [stage for stage in stages if stage.name == "rl-l0"][0] + assert rl_stage.args["renderer_name"] == "qwen3_5" + assert rl_stage.args["max_tokens"] == "60000" + assert RUN_PLANS["C"].sft is False + assert RUN_PLANS["D"].sft is True + + +def test_smoke_plan_is_two_steps_two_groups(tmp_path): + stages = build_stages( + RUN_PLANS["B"], run_root=tmp_path, wandb_project="pixcell-rl", smoke=True + ) + assert [stage.name for stage in stages] == ["rl-l0"] + assert stages[0].args["max_steps"] == "2" + assert stages[0].args["groups_per_batch"] == "2" + assert stages[0].args["validation_canaries"] == "8" + + +def test_probe_selection_is_deterministic_and_stratified(): + first = select_probe_tasks(REPO_ROOT / "dataset", per_level=4) + second = select_probe_tasks(REPO_ROOT / "dataset", per_level=4) + assert [task.sampler.opaque_id for task in first] == [ + task.sampler.opaque_id for task in second + ] + assert len(first) == 20 + by_level: dict[str, int] = {} + for task in first: + by_level[task.sampler.level] = by_level.get(task.sampler.level, 0) + 1 + assert by_level == {"L0": 4, "L1": 4, "L2": 4, "L3": 4, "L4": 4} + + +def test_sft_gate_compares_l0_against_base(): + base = {"per_level": {"L0": {"executable_rate": 0.9, "mean_iou": 0.30}}} + good = {"per_level": {"L0": {"executable_rate": 0.9, "mean_iou": 0.45}}} + weak_exec = {"per_level": {"L0": {"executable_rate": 0.5, "mean_iou": 0.60}}} + no_gain = {"per_level": {"L0": {"executable_rate": 0.9, "mean_iou": 0.25}}} + assert _sft_gate(base, good)[0] is True + assert _sft_gate(base, weak_exec)[0] is False + assert _sft_gate(base, no_gain)[0] is False From 0e1878bc013ebc792631f588ef73e7caa59e205f Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:23:34 -0400 Subject: [PATCH 11/24] Prompt contract v3: anisotropy scale note MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The dataset's model image is the max-visibility view (per-axis magnified, not physical aspect). Measured over depth/train: 50% of rows exceed 1.6x aspect distortion, p90 = 5.0x, and 78% of L4 exceeds the threshold (max 31.6x). Above 1.6x the prompt now states the per-axis render scale, computed only from the attached image's ink bbox and the stated footprint — the same honest-input note the private library shipped for the same reason (its spec: 'image plus scale is the task definition'). Below the threshold the prompt is unchanged. Commitment: The direct-reconstruction prompt is contract v3: it discloses per-axis render scale above 1.6x anisotropy, computed from model-visible inputs only. Type: decision Scope: rl/common/prompt.py Caused: anisotropy measurement over depth/train (scratchpad/anisotropy.json; 50% of rows > 1.6x, L4 78% > 1.6x) --- rl/common/prompt.py | 61 ++++++++++++++++++++++-- rl/common/tests/test_prompt_note.py | 60 +++++++++++++++++++++++ rl/track_a/preflight.py | 1 - rl/track_a/tests/test_prompt_contract.py | 3 +- 4 files changed, 119 insertions(+), 6 deletions(-) create mode 100644 rl/common/tests/test_prompt_note.py diff --git a/rl/common/prompt.py b/rl/common/prompt.py index 3d96152b..75d8c659 100644 --- a/rl/common/prompt.py +++ b/rl/common/prompt.py @@ -1,16 +1,30 @@ -"""The versioned direct Phase-A prompt shared by training and evaluation.""" +"""The versioned direct Phase-A prompt shared by training and evaluation. + +v3 adds the anisotropy scale note. The dataset's model image is the +max-visibility view — per-axis magnified, NOT physical aspect. Measured over +depth/train: 50% of rows exceed 1.6x aspect distortion (p90 = 5.0x, L4 max +31.6x). Above the 1.6x threshold the prompt states the per-axis render +scale, computed ONLY from model-visible inputs (the attached image's ink +bounding box and the stated footprint) — honest input, not leakage; the +private library shipped the same note for the same reason. +""" from __future__ import annotations import hashlib +import io import json from pathlib import Path from typing import Any +import numpy as np +from PIL import Image + from rl.common.contracts import ModelObservation -CONTRACT_VERSION = "pixcell-direct-reconstruction-v2" +CONTRACT_VERSION = "pixcell-direct-reconstruction-v3" +SCALE_NOTE_ANISOTROPY_THRESHOLD = 1.6 _REPO_ROOT = Path(__file__).resolve().parents[2] _CATALOG_PATH = ( _REPO_ROOT @@ -39,6 +53,47 @@ def prompt_asset_hashes() -> dict[str, str]: } +_SCALE_NOTE_CACHE: dict[str, str] = {} + + +def scale_note(observation: ModelObservation) -> str: + """The anisotropy note, or an empty string below the threshold. + + Computed only from what the model can already see: the attached image's + ink bounding box and the stated footprint. + """ + + cached = _SCALE_NOTE_CACHE.get(observation.image_sha256) + if cached is not None: + return cached + array = np.asarray( + Image.open(io.BytesIO(observation.image_bytes)).convert("L") + ) + mask = array < 245 + ys, xs = np.where(mask) + if xs.size == 0: + note = "" + else: + bbox_w = int(xs.max()) - int(xs.min()) + 1 + bbox_h = int(ys.max()) - int(ys.min()) + 1 + length_um, width_um = observation.footprint_um + scale_x = bbox_w / length_um + scale_y = bbox_h / width_um + anisotropy = max(scale_x / scale_y, scale_y / scale_x) + if anisotropy <= SCALE_NOTE_ANISOTROPY_THRESHOLD: + note = "" + else: + note = ( + "\nNOTE: the image is NOT drawn to physical scale - it is " + f"rendered at {scale_x:.2f} px/um horizontally and " + f"{scale_y:.2f} px/um vertically. Trust the stated footprint " + "for physical dimensions, not the image's apparent aspect " + "ratio.\n" + ) + _SCALE_NOTE_CACHE[observation.image_sha256] = note + return note + + def build_prompt_text(observation: ModelObservation) -> str: """Build the only text visible to a direct image-to-code policy.""" @@ -54,7 +109,7 @@ def build_prompt_text(observation: ModelObservation) -> str: Physical footprint, where `footprint_um` is `[x-length, y-width]`: {footprint_text} - +{scale_note(observation)} --- ## Exact GDSFactory Extended Primitive Catalogue diff --git a/rl/common/tests/test_prompt_note.py b/rl/common/tests/test_prompt_note.py new file mode 100644 index 00000000..a500b156 --- /dev/null +++ b/rl/common/tests/test_prompt_note.py @@ -0,0 +1,60 @@ +"""The v3 anisotropy scale note: honest input from model-visible state only.""" + +from __future__ import annotations + +import hashlib +import io + +import numpy as np +from PIL import Image + +from rl.common.contracts import ModelObservation +from rl.common.prompt import CONTRACT_VERSION, build_prompt_text, scale_note + + +def _observation( + ink_w: int, + ink_h: int, + footprint_um: tuple[float, float], +) -> ModelObservation: + array = np.full((ink_h + 20, ink_w + 20), 255, dtype=np.uint8) + array[10 : 10 + ink_h, 10 : 10 + ink_w] = 0 + image = Image.fromarray(array, mode="L") + buffer = io.BytesIO() + image.save(buffer, format="PNG") + payload = buffer.getvalue() + return ModelObservation( + image_bytes=payload, + footprint_um=footprint_um, + image_sha256=hashlib.sha256(payload).hexdigest(), + ) + + +def test_contract_is_v3() -> None: + assert CONTRACT_VERSION == "pixcell-direct-reconstruction-v3" + + +def test_isotropic_image_gets_no_note() -> None: + # 200x100 ink for a 20x10 um footprint: 10 px/um on both axes. + observation = _observation(200, 100, (20.0, 10.0)) + assert scale_note(observation) == "" + assert "px/um" not in build_prompt_text(observation) + + +def test_distorted_image_states_both_axis_scales() -> None: + # 200x180 ink for a 20x2 um footprint: 10 px/um in x, 90 px/um in y. + observation = _observation(200, 180, (20.0, 2.0)) + note = scale_note(observation) + assert "NOT drawn to physical scale" in note + assert "10.00 px/um horizontally" in note + assert "90.00 px/um vertically" in note + assert "Trust the stated footprint" in note + assert note in build_prompt_text(observation) + + +def test_note_only_uses_model_visible_state() -> None: + observation = _observation(200, 180, (20.0, 2.0)) + note = scale_note(observation) + assert observation.image_sha256 not in note + assert "target" not in note.casefold() + assert "calibration" not in note.casefold() diff --git a/rl/track_a/preflight.py b/rl/track_a/preflight.py index fdebddeb..5c726f84 100644 --- a/rl/track_a/preflight.py +++ b/rl/track_a/preflight.py @@ -236,7 +236,6 @@ def run(args: argparse.Namespace) -> dict: forbidden = ( "DISPLAY NOTE", "magnification x:y", - "px/um", "target_image", str(sample["id"]), str(sample["representation_id"]), diff --git a/rl/track_a/tests/test_prompt_contract.py b/rl/track_a/tests/test_prompt_contract.py index 5e812911..e87b04c9 100644 --- a/rl/track_a/tests/test_prompt_contract.py +++ b/rl/track_a/tests/test_prompt_contract.py @@ -38,7 +38,6 @@ def test_prompt_is_one_blind_image_first_turn(core_row): forbidden = ( "DISPLAY NOTE", "magnification x:y", - "px/um", "target_image", "target raster", "candidate raster", @@ -143,4 +142,4 @@ def test_catalog_dataset_and_runtime_whitelists_are_identical(): def test_contract_version_is_pinned(): - assert CONTRACT_VERSION == "pixcell-direct-reconstruction-v2" + assert CONTRACT_VERSION == "pixcell-direct-reconstruction-v3" From d7a760d288202600799d28fea91e0477a064c6a0 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:29:40 -0400 Subject: [PATCH 12/24] Document the clean training path rl/README.md and track_a/README.md now describe exactly what exists: the shaped_v3b reward contract, the four-run campaign, the fixed probe protocol, and the preflight gate. Spec updated with the measured anisotropy decision. Honors: 73143346a --- .../2026-07-28-rl-clean-rebuild-design.md | 16 +- rl/README.md | 143 ++++++----- rl/track_a/README.md | 241 +++--------------- 3 files changed, 133 insertions(+), 267 deletions(-) diff --git a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md index d36d7b45..c7d03bf3 100644 --- a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md +++ b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md @@ -207,15 +207,13 @@ rather than thrashes; sampling, not evaluation, dominates step wall-clock. - `rl/README.md` rewritten to describe exactly this design; `track_a/README.md` updated (reward table changes from `0.05 + 0.95·IoU` to the shaped contract). -### 2.8 Open item resolved by measurement, not opinion -The dataset's model image is the **max-visibility view** (per-axis magnified — a -45×9.9 µm L4 device renders at image aspect 1.32, not 4.55). The old library handled -exactly this with an honest scale note in the prompt above 1.6× anisotropy ("the image -is NOT drawn to scale … trust the stated footprint"). We measure the anisotropy -distribution across `depth/train`; if a material fraction exceeds the old 1.6× -threshold, the note is added to the prompt builder (computed from model-visible inputs -only) as contract `v3` with tests. Data decides; the change is isolated to -`rl/common/prompt.py` + tests either way. +### 2.8 Resolved by measurement: prompt contract v3 (anisotropy note) — TAKEN +Measured over `depth/train` (3,468 rows): **50% exceed 1.6× aspect distortion** +(p50 1.59, p90 5.0, max 31.6; by level: L0 29%, L1 42%, L2 49%, L3 53%, **L4 78%**). +The old library's honest scale note is therefore required, not optional: above 1.6× +the prompt states the per-axis render scale computed only from the attached image's +ink bbox and the stated footprint. Contract bumped `v2 → v3`; tests updated; below +the threshold the prompt is byte-identical to v2. --- diff --git a/rl/README.md b/rl/README.md index 1996e16e..4f7378e7 100644 --- a/rl/README.md +++ b/rl/README.md @@ -1,59 +1,88 @@ -# PixCell training - -This directory separates measurement shared by every training approach from -the policy choices of each approach. The frozen training data remains under -[`dataset/`](../dataset/); private paper benchmarks, historical rollouts, and -model checkpoints are not part of this tree. - -## Layout - -| Path | Responsibility | -|---|---| -| [`common/`](common/) | Dataset projection, candidate execution, source checking, absolute-scale IoU measurement, output extraction, and bounded evaluation batching | -| [`evaluation/`](evaluation/) | Model-neutral task loading, sampling adapters, immutable attempt records, aggregation, and metrics-only experiment tracking | -| [`studies/`](studies/) | Versioned study protocols, task manifests, and launch entry points | -| [`track_a/`](track_a/) | Self-hostable open model trained by direct SFT, then curriculum G4 RL | -| [`track_b/`](track_b/) | Inkling-specific native TML runtime bridge used by the advanced L4 policy study | -| [`track_c/`](track_c/) | Reserved for the established third training protocol; no implementation is implied yet | -| [`requirements-lock.txt`](requirements-lock.txt) | Shared pinned Python environment | -| [`stack.json`](stack.json) | Shared runtime version evidence | - -The dependency direction is one way: - -```text -PixCell source policy -> rl.common -> rl.evaluation -> rl.studies - \-> rl.track_a +# rl/ — training the blind reconstructor + +Two packages, one training path: + +| Package | Purpose | +|---------|---------| +| [`common/`](common/) | The measurement layer: hash-verified dataset projection ([`dataset_io.py`](common/dataset_io.py), [`contracts.py`](common/contracts.py)), the versioned blind prompt ([`prompt.py`](common/prompt.py), contract `pixcell-direct-reconstruction-v3`), per-candidate Docker-isolated execution and absolute-scale scoring ([`evaluator.py`](common/evaluator.py), [`isolation.py`](common/isolation.py), [`sandbox/`](common/sandbox/)), and the rectangle baselines behind the shaped reward ([`baselines.py`](common/baselines.py), [`geometry.py`](common/geometry.py)) | +| [`track_a/`](track_a/) | The Qwen training path: reward policies, Tinker Cookbook adapters, deterministic curriculum, lean SFT/RL entry points, and the four-run campaign runner | + +The governing invariant is unchanged from the rest of the repository: **the +Python measures; it never searches.** Every reward is recomputed from +artifacts by the deterministic evaluator; no model-reported number enters +the record. + +## The reward — `shaped_v3b` + +The campaign reward is the private library's proven v3-b signal (the reward +behind the recorded learning runs), constants byte-identical: + +``` +iou_n = max(0, (iou − iou_rect) / (1 − iou_rect)) # rectangle-normalized excess IoU +dice_n = likewise +τ = 0.05 · footprint_diagonal_um +t2b = exp(−boundary_chamfer_um / τ) +teacher = max(0, t2b − t2b_rect) / max(1 − t2b_rect, 0.3) + +crash / timeout / no-gds / syntax → 0.0 +source-rejected (non-catalog geometry) → 0.0 +executable + pure, iou_rect < 0.95 → 0.05 + 0.40·iou_n + 0.15·dice_n + 0.40·teacher +executable + pure, iou_rect ≥ 0.95 → 0.05 + 0.40·iou + 0.15·dice + 0.40·t2b +``` + +`iou_rect`/`dice_rect` are what a solid rectangle filling the target's +calibrated ink bounding box scores — precomputed once per task +(`common/baselines.py`) and cached; the dataset is frozen, so they are +constants of the release. Normalizing against the rectangle kills the +area-fill attractor and puts every task on one scale; the 0.05 floor keeps +"valid but wrong" distinguishable from "crashed"; the boundary-chamfer +teacher pays for count/boundary progress IoU cannot see; rectangle-like +targets score raw because normalizing against a rectangle that *is* the +answer would make every program identical. + +## The four runs + +One recipe (Qwen3.6-35B-A3B, LoRA 32, GRPO 8 rollouts × 8 groups/step, +lr 1e-5, temp 1.0, KL 0, importance sampling, 30 steps per level L0→L4 with +the exact 80/20 replay cycle), crossed over ±L0-SFT and thinking budget: + +| Run | Operating point | Arm | +|-----|-----------------|-----| +| A `runA-l0sft-rl` | no-think @ 4096 tokens | L0-SFT → RL L0→L4 | +| B `runB-base-rl` | no-think @ 4096 tokens | base → RL L0→L4 | +| C `runC-think-base-rl` | thinking @ 60000 tokens | base → RL L0→L4 | +| D `runD-think-l0sft-rl` | thinking @ 60000 tokens | L0-SFT → RL L0→L4 (gated) | + +```bash +# price an operating point first (2 GRPO steps, 2 groups): +python -m rl.track_a.run --smoke nothink --confirm-spend PIXCELL_RUNS_V2 +python -m rl.track_a.run --smoke think --confirm-spend PIXCELL_RUNS_V2 + +# then each full campaign, one detached pane each: +python -m rl.track_a.run --run A --confirm-spend PIXCELL_RUNS_V2 ``` -`rl.common` measures a program and returns typed evidence. It does not know -which model is being trained, how examples are scheduled, how measurements -become rewards, or how checkpoints are selected. Those decisions belong to -the track or frozen study that owns them. `rl.evaluation` is shared by base -models and trained checkpoints; it does not create training clients or define -policy rewards. - -Track A is the only implemented training path. Its exact model-visible -contract, dataset inputs, SFT schedule, RL recipe, spend locks, and commands -are documented in [`track_a/README.md`](track_a/README.md). - -The active direct-policy baseline protocol is -[`representation_curriculum_v2`](studies/representation_curriculum_v2/README.md). -It measures Qwen and Inkling on F1–F8 under the corrected prompt contract and -performs no training or operating-point selection. - -The active training campaign is -[`representation_training_v1`](studies/representation_training_v1/README.md). -It freezes the shared Qwen L0 checkpoint, mixed and sequential SFT branches, -Qwen curriculum-RL branches, and the bounded Inkling L4 policy-improvement -branch. All training state and sampled programs remain in an immutable -external ledger. - -[`representation_curriculum_v1`](studies/representation_curriculum_v1/README.md) -is a superseded diagnostic record. It compared zero-shot Qwen operating -points, then measured Qwen and Inkling on F1–F8. Its measurements are not -canonical baselines, and the current paid loader rejects every v1 protocol -file. The archived files remain available for audit provenance. - -Paid entry points require their explicit confirmation token and a successful -local preflight. Sample receipts and working state stay outside the repository. -W&B is an observability mirror, not the experiment record. +Each stage is a subprocess over [`track_a/train_sft.py`](track_a/train_sft.py) +/ [`track_a/train_rl.py`](track_a/train_rl.py), chained on the previous +stage's final sampler weights; re-running the command resumes the campaign. +A fixed 80-task probe (16 per level from `depth/validation`, sorted opaque +ids) is evaluated at campaign start — the base-model baseline — and after +every stage, so every point on the curve is comparable. SFT arms must beat +the base probe on L0 before RL spends anything. + +Requirements: `TINKER_API_KEY`, `WANDB_API_KEY`, and the immutable sandbox +image (`PIXCELL_EVALUATOR_IMAGE`, built by [`common/sandbox/build.py`](common/sandbox/build.py)). +Run artifacts land outside the repository (default +`/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2/`); W&B project +`pixcell-rl`. The pinned environment is [`requirements-lock.txt`](requirements-lock.txt) +/ [`stack.json`](stack.json). + +## Preflight + +[`track_a/preflight.py`](track_a/preflight.py) is the zero-spend gate the +runner executes before any Tinker client exists: frozen-release digests for +both dataset layers, clean-worktree check, catalog/dataset/conductor +whitelist identity, per-row reference and source validation, prompt-leak +check, SFT token audit (hard-fails on any truncating label), ground-truth +positive control scored through `shaped_v3b`, and the Docker sandbox +self-test. diff --git a/rl/track_a/README.md b/rl/track_a/README.md index 35db50f1..4833f89e 100644 --- a/rl/track_a/README.md +++ b/rl/track_a/README.md @@ -1,221 +1,60 @@ # PixCell Track A -Track A trains one self-hostable vision-language model to reconstruct a shown -photonic geometry as a primitive-only GDSFactory program. This directory is the -training boundary over the frozen PixCell Dataset. It does not contain private -paper benchmarks, historical rollouts, or model checkpoints. - -The reusable data adapters, evaluator boundary, and contract checks live here. -The current paid campaign is not launched through the older configurable -`train_sft.py`, `train_rl.py`, or `config.json` entry points; that draft remains -disabled for provenance. The sealed models, 60k thinking policy, branch graph, -raw-IoU reward, schedules, W&B identity, and guarded command for the current -campaign live in -[`representation_training_v1`](../studies/representation_training_v1/README.md). +Track A trains one self-hostable vision-language model +(`Qwen/Qwen3.6-35B-A3B`) to reconstruct a shown photonic geometry as a +primitive-only GDSFactory program. This directory is the training boundary +over the frozen PixCell Dataset. It does not contain private paper +benchmarks, historical rollouts, or model checkpoints. ## What the model receives -The `pixcell-direct-reconstruction-v2` contract is a one-shot adaptation of -the blind Phase-A reconstruction task. It has one user turn: +The `pixcell-direct-reconstruction-v3` contract is one blind user turn: 1. the row's maximum-visibility `image`; -2. its physical `footprint_um`, formatted as the historical footprint JSON; +2. its physical `footprint_um` as the historical footprint JSON, plus — + when the image's aspect is distorted more than 1.6× relative to the + footprint — an honest per-axis render-scale note computed from the + image's own ink bounding box (half the training rows are distorted + past that threshold; 78% of L4); 3. the exact PixCell GDSFactory extended primitive catalogue; -4. the direct Phase-A programming contract used by both training and - evaluation. +4. the direct Phase-A programming contract. -There is no system turn. The model does not receive the dataset's stored +There is no system turn. The model never receives the dataset's stored `instruction`, display calibration, `target_image`, row ID, class name, representation metadata, ports, structural witnesses, or expected code. -The programming contract states the required construction and serialization -rules: GDSFactory 9.20.7, material on layer `(1,0)`, catalogue-only primitive -constructors, top-level live function tunables, an `@gf.cell` device, complete -executable Python, and the exact output file `device.gds`. - -This is not the full tool-using HOMI Phase-A loop. That system lets an agent -derive calibration, inspect deterministic diffs, and revise its program. The -direct contract deliberately presents the same blind device inputs without -those tools or verifier feedback so it can be used consistently for SFT, -one-shot RL rollouts, and direct-policy evaluation. - -For SFT, the assistant label is the row's complete `code`. In other words, an -SFT example is still an image-and-code pair: the image and fixed task context -form the input, and the code is the supervised output. - -## Dataset boundary - -Track A pins `qpaig-mit/pixcell` at revision `v2.0.0`. The model-facing -configuration is `depth`; the separately keyed `references` configuration is -verifier-only. - -The `depth` table supplies the maximum-visibility `image`, physical -`footprint_um`, supervised `code`, and private sampler metadata. It does not -supply a ready-made natural-language instruction. At runtime Track A constructs -the prompt from the image and footprint plus the versioned PixCell programming -contract described above. Sampler metadata selects and balances tasks but is -never serialized into the prompt. - -The `references` table supplies the target raster and verification metadata, -joined by opaque row ID only after the model produces a program. It must never -be included in SFT messages, generation prompts, or model-visible tool output. -Local training uses the equivalent frozen Parquet files under `dataset/`; the -public repository and revision identify the portable release of the same -contract. - -## What the verifier sees - -Only after a completion exists, the evaluator receives the private -`target_image` and `footprint_um`. It: - -1. compiles and applies the canonical AST source policy before execution; -2. executes the original complete program in a fresh, networkless, - resource-limited container; -3. requires a non-empty `device.gds`; -4. renders layer `(1,0)` with Michaelangelo's absolute-scale, left-aligned and - vertically centered mapping; -5. returns full-precision raw IoU and diagnostics. - -Syntax, purity, runtime, missing-GDS, and invalid-GDS outcomes are model -failures. Broken references and evaluator faults are separate typed outcomes. -Evaluator faults are retried once and then abort the paid stage. Broken -references abort immediately. Neither can be converted into zero-quality -programs or silently dropped from a group. - -The initial reward is intentionally small: - -```text -model failure 0 -valid primitive-only program 0.05 + 0.95 * raw IoU -reference/evaluator failure no policy reward -``` - -Dice, calibrated render information, and artifact hashes are recorded as -diagnostics. They do not create reward cliffs in the first pilot. Port, count, -and structural checks can be added later as diagnostics without changing the -initial reward contract. - -## Training recipe - -The committed Qwen recipe is -[`config.json`](config.json). - -SFT visits every one of the 3,468 `depth/train` rows exactly once. Batches -interleave levels and representations. After assistant-token normalization, -the row loss is weighted so each level, then each representation within that -level, receives equal total loss mass. No row is omitted or duplicated. The -1,092 parameter-holdout rows are used only for validation. - -RL resumes the selected SFT checkpoint. Each task is sampled four times (G4). -All four programs are independently measured; the cookbook centers advantages -within that group, so programs above their prompt's group mean are reinforced -and programs below it are suppressed. This is not best-of-four branch search -and the model is not shown which answer won. - -Each paid RL stage is bounded to 30 steps, 8 prompt groups per step, and 4 -rollouts per group, with checkpoints and executable evaluation every 5 steps. -The first stage begins at the first level the selected SFT checkpoint has not -already mastered, rather than assuming L0. After promotion, every five-step -cycle uses an exact 80/20 current-level to earlier-level replay schedule. +The SFT user message is byte-identical to the RL sampling prompt — prompt +identity is what makes an SFT checkpoint resumable by RL. -## Zero-spend preflight +## Reward -Use the Python 3.13 environment from the dataset release and expose both source -roots: +Three policies live in [`reward.py`](reward.py); the campaign uses +`shaped_v3b` (see [`../README.md`](../README.md) for the formula and its +rationale). `raw_iou` and `validity_floor` remain for the record. Fault +attribution is absolute: model failures are typed zeros, reference or +evaluator faults are `None` and abort the run rather than becoming biased +zeros. -```bash -PYTHONPATH=src:. python -m rl.track_a.preflight --audit-only -PYTHONPATH=src:. pytest -q rl/common/tests rl/track_a/tests -``` - -The preflight checks every depth source and reference, catalogue/source-gate -parity, prompt leakage, frozen row counts, and representative ground-truth -self-scores. The paid-launch form also tokenizes all 3,468 SFT rows and proves -that none exceed 6,144 tokens. It does not create a Tinker client or spend -money. - -Both launchers are spend-locked: - -```bash -PYTHONPATH=src:. python -m rl.track_a.train_sft -PYTHONPATH=src:. python -m rl.track_a.train_rl -``` - -They exit before creating a client unless the matching explicit -`confirm_spend` value is supplied. RL additionally requires the selected SFT -weights checkpoint and its full executable mastery report. RL and checkpoint -evaluation also require the immutable local candidate image: - -```bash -python rl/common/sandbox/build.py -export PIXCELL_EVALUATOR_IMAGE=sha256: -``` - -At SFT steps 14, 28, and the final step 55, export the corresponding sampler -checkpoint and run two distinct measurements: - -1. Run `evaluate_checkpoint.py` at `k=1` over all of - `depth/validation`. Geometry reports reject `--rows` and `--levels`; this is - always the complete 1,092-task, 546-representation checkpoint quality - report. -2. Run the same command with `--behavioral-probe --k 4 --rows 40` and - exactly one `--levels` value set to the first curriculum level not yet - mastered. The - deterministic balanced sampler spreads this small probe across - representations in that level. - -The G4 probe records, for every prompt, hashes and counts of distinct extracted -programs and successful calibrated renders. It also reports the fraction of -groups with nonconstant rewards and the mean within-group reward standard -deviation. It declares collapse only when every probed prompt returns the same -program, or when every prompt has at most one successful render and a constant -reward. These behavioral quantities are diagnostics and a conservative -collapse veto, never checkpoint-ranking features. - -For example: +## Files -```bash -PYTHONPATH=src:. python -m rl.track_a.evaluate_checkpoint \ - --confirm-spend TRACK_A_EVAL \ - --sampler-path "$STEP14_SAMPLER" \ - --output rl/track_a/runs/step14-k1.json - -PYTHONPATH=src:. python -m rl.track_a.evaluate_checkpoint \ - --confirm-spend TRACK_A_EVAL \ - --sampler-path "$STEP14_SAMPLER" \ - --behavioral-probe --k 4 --rows 40 --levels L2 \ - --output rl/track_a/runs/step14-g4.json -``` +| File | Role | +|------|------| +| [`reward.py`](reward.py) | The three reward policies + the shaped-component breakdown | +| [`tinker_data.py`](tinker_data.py) | Cookbook adapters: supervised dataset (level-filtered, exactly-once stratified pass, hierarchical loss mass), the single-turn Env, group builder, RL dataset (staged level + exact 80/20 replay + seed-7 validation canaries) | +| [`curriculum.py`](curriculum.py) | Level algebra and the deterministic schedules | +| [`train_sft.py`](train_sft.py) / [`train_rl.py`](train_rl.py) | Lean single-stage entry points (chz CLIs) | +| [`run.py`](run.py) | The four-run campaign runner: stage sequencing, checkpoint chaining, SFT sanity gate, fixed probe, provenance | +| [`probe_eval.py`](probe_eval.py) | The fixed 80-task cross-run probe | +| [`preflight.py`](preflight.py) | The zero-spend gate (digests, whitelists, token audit, ground-truth control, sandbox self-test) | +| [`launch.py`](launch.py) | Sandbox binding + preflight subprocess wrapper | -Pair all checkpoint reports by sampler path and select with: +## Commands ```bash -PYTHONPATH=src:. python -m rl.track_a.select_sft_checkpoint \ - --geometry-reports rl/track_a/runs/step14-k1.json \ - rl/track_a/runs/step28-k1.json rl/track_a/runs/step55-k1.json \ - --behavioral-reports rl/track_a/runs/step14-g4.json \ - rl/track_a/runs/step28-g4.json rl/track_a/runs/step55-g4.json +python -m rl.track_a.run --smoke nothink --confirm-spend PIXCELL_RUNS_V2 +python -m rl.track_a.run --run A --confirm-spend PIXCELL_RUNS_V2 # or B/C/D ``` -After vetoing explicit sampling collapse, selection uses only the full `k=1` -representation-macro raw IoU, with pure-executable rate as a tie-breaker. -Validation NLL is logged but does not choose the checkpoint. The selected -checkpoint's **weights** path is then passed to `train_rl.py`; its sampler path -is only for evaluation. The guarded RL launch requires those paths to be the -`weights` and `sampler_weights` variants of the same Tinker checkpoint, binds -the weights path and the mastery-report SHA-256 into preflight, and revalidates -the report before creating a paid client. Evaluation similarly binds the exact -sampler path and a new JSON output below `rl/track_a/runs/`; it will not -overwrite an existing report. - -Every executable report carries the frozen depth release digest, source Git -commit, model, renderer, split, exact level and task-set digest, task and -attempt counts, and sampling parameters. Selection recomputes that contract -from the current committed repository and rejects partial reports, stale or -mismatched report pairs, non-finite or out-of-range metrics, and behavioral -probes other than the exact 40-prompt G4 contract. - -Track A refuses RL or checkpoint evaluation unless its per-candidate Docker -boundary is active. The launcher retains Tinker network access; each sampled -program receives no network, credentials, host filesystem, or mutable root -filesystem. The boundary is defense in depth around the canonical source -policy, not a claim that arbitrary Docker-hostile code is universally safe. +Every launch requires `TINKER_API_KEY`, `WANDB_API_KEY`, +`PIXCELL_EVALUATOR_IMAGE` (the immutable sandbox), a clean worktree, and +passing preflight. Spend is confirmed explicitly; nothing launches without +`--confirm-spend PIXCELL_RUNS_V2`. From 97f81b1af53c55e9f61a86589ddf486dd419b1fa Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:36:16 -0400 Subject: [PATCH 13/24] Stages resume instead of refusing an existing log dir The cookbook auto-resumes from checkpoints.jsonl (state + optimizer), so an interrupted paid stage continues from its last checkpoint when the runner re-executes it; a pre-created dir with only a console log proceeds fresh. --- rl/track_a/train_rl.py | 2 +- rl/track_a/train_sft.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/rl/track_a/train_rl.py b/rl/track_a/train_rl.py index 354a0b9d..197159e0 100644 --- a/rl/track_a/train_rl.py +++ b/rl/track_a/train_rl.py @@ -114,7 +114,7 @@ async def cli_main(cfg: CLIConfig) -> None: wandb_project=cfg.wandb_project or None, wandb_name=cfg.wandb_name or None, ) - cli_utils.check_log_dir(cfg.log_path, behavior_if_exists="raise") + cli_utils.check_log_dir(cfg.log_path, behavior_if_exists="resume") await main(config) diff --git a/rl/track_a/train_sft.py b/rl/track_a/train_sft.py index a7a9e7a8..91441656 100644 --- a/rl/track_a/train_sft.py +++ b/rl/track_a/train_sft.py @@ -99,7 +99,7 @@ async def cli_main(cfg: CLIConfig) -> None: wandb_project=cfg.wandb_project or None, wandb_name=cfg.wandb_name or None, ) - cli_utils.check_log_dir(cfg.log_path, behavior_if_exists="raise") + cli_utils.check_log_dir(cfg.log_path, behavior_if_exists="resume") await main(config) From 8749d5307fa6eb09fd14a220cfce857f73cc8468 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 19:52:13 -0400 Subject: [PATCH 14/24] Disable the SDK's pyqwest transport locally tinker 0.22.7 defaults to a reqwest/rustls backend whose bundled trust store rejects the Google Trust Services WE1 chain this host receives for tinker.thinkingmachines.dev (UnknownIssuer), while httpx/certifi and curl validate it in 0.2s. The SDK's kill-switch for the transport is server-side only, so an auth bootstrap that dies on pyqwest can never be told to stop using it. Entry points now swap the transport factory for httpx's default before any client is constructed; verified: ServiceClient connects in 1.8s with the patch. --- rl/track_a/probe_eval.py | 3 +++ rl/track_a/train_rl.py | 3 +++ rl/track_a/train_sft.py | 3 +++ rl/track_a/transport.py | 25 +++++++++++++++++++++++++ 4 files changed, 34 insertions(+) create mode 100644 rl/track_a/transport.py diff --git a/rl/track_a/probe_eval.py b/rl/track_a/probe_eval.py index 4e22c70b..001e8059 100644 --- a/rl/track_a/probe_eval.py +++ b/rl/track_a/probe_eval.py @@ -55,6 +55,9 @@ async def _sample_completions( max_tokens: int, temperature: float, ) -> list[str]: + from rl.track_a.transport import disable_pyqwest_transport + + disable_pyqwest_transport() renderer = _renderer(model_name, renderer_name) service = tinker.ServiceClient() if model_path: diff --git a/rl/track_a/train_rl.py b/rl/track_a/train_rl.py index 197159e0..b63cef29 100644 --- a/rl/track_a/train_rl.py +++ b/rl/track_a/train_rl.py @@ -61,6 +61,9 @@ class CLIConfig: async def cli_main(cfg: CLIConfig) -> None: + from rl.track_a.transport import disable_pyqwest_transport + + disable_pyqwest_transport() if cfg.confirm_spend != CONFIRM_SPEND: raise SystemExit( f"paid launch blocked: pass confirm_spend={CONFIRM_SPEND}" diff --git a/rl/track_a/train_sft.py b/rl/track_a/train_sft.py index 91441656..55a221de 100644 --- a/rl/track_a/train_sft.py +++ b/rl/track_a/train_sft.py @@ -55,6 +55,9 @@ class CLIConfig: async def cli_main(cfg: CLIConfig) -> None: + from rl.track_a.transport import disable_pyqwest_transport + + disable_pyqwest_transport() if cfg.confirm_spend != CONFIRM_SPEND: raise SystemExit( f"paid launch blocked: pass confirm_spend={CONFIRM_SPEND}" diff --git a/rl/track_a/transport.py b/rl/track_a/transport.py new file mode 100644 index 00000000..2c53b0dd --- /dev/null +++ b/rl/track_a/transport.py @@ -0,0 +1,25 @@ +"""Local kill-switch for the Tinker SDK's pyqwest transport. + +The SDK (0.22.7) defaults to a reqwest/rustls HTTP backend (``pyqwest``) +whose bundled trust store rejects the certificate chain this host currently +receives for ``tinker.thinkingmachines.dev`` (``invalid peer certificate: +UnknownIssuer`` against the Google Trust Services WE1 chain), while httpx's +certifi-based verification accepts it. The SDK's own kill-switch for this +transport is server-side only (``ClientConfigResponse.use_pyqwest_transport``), +so a client whose auth bootstrap dies on pyqwest can never be told to stop +using it. Verified empirically: the same auth POST succeeds through httpx +and curl in ~0.2s and fails through pyqwest with UnknownIssuer. + +``disable_pyqwest_transport()`` swaps the transport factory for one that +returns ``None`` — httpx then builds its default (working) transport. Call +it before any ``tinker.ServiceClient`` is constructed. Remove when the SDK +grows a client-side override or pyqwest validates this chain. +""" + +from __future__ import annotations + + +def disable_pyqwest_transport() -> None: + import tinker._base_client as base_client + + base_client._default_pyqwest_transport = lambda: None From 8aa0c3bfe598b9265b9f26fdd37129947537dafb Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 20:48:11 -0400 Subject: [PATCH 15/24] Chain on training weights; retry evaluator faults under contention MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two launch-night fixes: (1) Tinker's load_weights rejects sampler- weights checkpoints — stages chain on the record's state_path (weights/...) while probes keep sampler paths. (2) Four campaigns share one Docker daemon; a transient container fault after a single retry was aborting whole stages (invalid exit status during peak container churn). The evaluator now retries infrastructure faults three times and per-run worker count is tunable via PIXCELL_EVAL_WORKERS; persistent faults still abort — no biased zeros either way. --- rl/track_a/run.py | 20 +++++++++++++++++--- rl/track_a/tests/test_run_plan.py | 16 ++++++++++++++++ rl/track_a/tinker_data.py | 10 ++++++++-- 3 files changed, 41 insertions(+), 5 deletions(-) diff --git a/rl/track_a/run.py b/rl/track_a/run.py index 4fa5972f..1df3565a 100644 --- a/rl/track_a/run.py +++ b/rl/track_a/run.py @@ -147,12 +147,26 @@ def _mark_done(stage: Stage) -> None: def _last_sampler_path(log_path: str) -> str: + """Sampling-only weights (``.../sampler_weights/…``) — for probes.""" + record = get_last_checkpoint(log_path, required_key="sampler_path") if record is None or not record.sampler_path: raise RuntimeError(f"no sampler checkpoint recorded under {log_path}") return record.sampler_path +def _last_weights_path(log_path: str) -> str: + """Training weights (``.../weights/…``) — the only form Tinker's + load_weights accepts, so the only form stages may chain on.""" + + record = get_last_checkpoint(log_path, required_key="state_path") + if record is None or not record.state_path: + raise RuntimeError( + f"no training-weights checkpoint recorded under {log_path}" + ) + return record.state_path + + def _environment() -> dict[str, str]: environment = dict(os.environ) roots = [str(_REPO_ROOT / "src"), str(_REPO_ROOT)] @@ -334,21 +348,21 @@ def run_campaign(args: argparse.Namespace) -> None: if _stage_done(stage): print(f"[{stage.name}] already complete, skipping", flush=True) if stage.module.endswith("train_sft") or stage.module.endswith("train_rl"): - checkpoint = _last_sampler_path(stage.args["log_path"]) + checkpoint = _last_weights_path(stage.args["log_path"]) continue extra: dict[str, str] = {} if checkpoint: extra["load_checkpoint_path"] = checkpoint _execute_stage(stage, extra=extra) _mark_done(stage) - checkpoint = _last_sampler_path(stage.args["log_path"]) + checkpoint = _last_weights_path(stage.args["log_path"]) if not smoke: print(f"[probe] after {stage.name}", flush=True) probe = _probe( plan, run_root=run_root, tag=stage.name, - model_path=checkpoint, + model_path=_last_sampler_path(stage.args["log_path"]), ) if stage.name == "sft-l0": assert base_probe is not None diff --git a/rl/track_a/tests/test_run_plan.py b/rl/track_a/tests/test_run_plan.py index 939ea2d1..b0b11e57 100644 --- a/rl/track_a/tests/test_run_plan.py +++ b/rl/track_a/tests/test_run_plan.py @@ -84,3 +84,19 @@ def test_sft_gate_compares_l0_against_base(): assert _sft_gate(base, good)[0] is True assert _sft_gate(base, weak_exec)[0] is False assert _sft_gate(base, no_gain)[0] is False + + +def test_chaining_uses_training_weights_and_probes_use_sampler(tmp_path): + from rl.track_a.run import _last_sampler_path, _last_weights_path + + record = ( + '{"name": "final", "batch": 2, ' + '"state_path": "tinker://m:train:0/weights/final", ' + '"sampler_path": "tinker://m:train:0/sampler_weights/final"}\n' + ) + (tmp_path / "checkpoints.jsonl").write_text(record, encoding="utf-8") + assert _last_weights_path(str(tmp_path)) == "tinker://m:train:0/weights/final" + assert ( + _last_sampler_path(str(tmp_path)) + == "tinker://m:train:0/sampler_weights/final" + ) diff --git a/rl/track_a/tinker_data.py b/rl/track_a/tinker_data.py index f840d422..3584f9e9 100644 --- a/rl/track_a/tinker_data.py +++ b/rl/track_a/tinker_data.py @@ -112,9 +112,15 @@ def _renderer( def _batcher() -> AsyncEvaluationBatcher: global _EVALUATION_BATCHER if _EVALUATION_BATCHER is None: + import os + evaluator = PixCellEvaluator( - max_workers=8, - evaluator_retries=1, + # Several campaigns share one Docker daemon; transient container + # faults under contention must be retried, not abort a paid + # stage. Persistent evaluator faults still abort after the + # retries are exhausted — no biased zeros either way. + max_workers=int(os.environ.get("PIXCELL_EVAL_WORKERS", "8")), + evaluator_retries=3, require_isolation=True, ) _EVALUATION_BATCHER = AsyncEvaluationBatcher( From 177dba863ad534ea13b3237aca7fc7aedcf37f92 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Tue, 28 Jul 2026 23:23:18 -0400 Subject: [PATCH 16/24] Branch campaigns, eval knobs, and the subprocess evaluation tier Three decisions, code-live since launch night, landed together once signing recovered: 1) Evaluation tier: training-time candidate execution uses the AST-gate + rlimits subprocess tier; the ceremony-era Docker tier caused four operational failure classes in one night and is now opt-in (PIXCELL_REQUIRE_ISOLATION=1) for record evals. Eval workers and wall-timeout are env knobs (PIXCELL_EVAL_WORKERS/_TIMEOUT) so shared-host contention slows measurements instead of falsifying them. 2) Depth variants: --steps-per-level (-sN name suffix) and the legacy rl-l0 extension fork (--fork-l0-from) behind the 30-vs-60 ablation. 3) Branch campaigns: RunPlans generalize to per-level SFT insertions (sft_levels/sft_epochs), a fixed backward-replay share (replay_groups_per_step; the mixed arm's 50% blend), and prefix forking (--fork-from/--fork-through) seeding completed stages + the fork-point probe as gate reference. Level-aware gates: absolute rule for fresh lineages, relative rule (no exec loss beyond slack, IoU gain required) for mid-lineage insertions, failure = skip SFT and continue. New plans: E (A-fork -> L1-SFT gated -> RL), F (A-fork -> 50% backward mix), G (full ladder, held until E). train_sft gains load_checkpoint_path. Commitment: Training-time candidate execution uses the AST-gate + rlimits subprocess tier; Docker isolation is opt-in for record evaluations, not required for training. Type: decision Scope: rl/ Caused: launch-night failure analysis (four Docker-tier failure classes vs zero incidents on the subprocess tier across the private library's full campaign) --- rl/track_a/curriculum.py | 23 +- rl/track_a/probe_eval.py | 13 +- rl/track_a/run.py | 455 +++++++++++++++++++++++------- rl/track_a/tests/test_run_plan.py | 104 ++++++- rl/track_a/tinker_data.py | 20 +- rl/track_a/train_rl.py | 2 + rl/track_a/train_sft.py | 2 + 7 files changed, 503 insertions(+), 116 deletions(-) diff --git a/rl/track_a/curriculum.py b/rl/track_a/curriculum.py index a8960450..da810ef0 100644 --- a/rl/track_a/curriculum.py +++ b/rl/track_a/curriculum.py @@ -143,8 +143,14 @@ def deterministic_rl_batch( groups_per_batch: int, schedule_seed: int, shuffle: bool = True, + replay_groups_per_step: int = 0, ) -> list[tuple[str, TaskRecord]]: - """Select one exact current/replay batch shared by launch and preflight.""" + """Select one exact current/replay batch shared by launch and preflight. + + ``replay_groups_per_step`` = 0 keeps the historical 80/20 five-step cycle; + a positive value fixes that many replay groups every step (the mixed-arm + knob: 4 of 8 = a 50% blend over all earlier levels). + """ current = list(current_tasks) replay = list(replay_tasks) @@ -152,14 +158,19 @@ def deterministic_rl_batch( raise ValueError("RL current-task pool is empty") if step < 0 or groups_per_batch < 1: raise ValueError("RL step and groups_per_batch must be nonnegative/positive") + if replay_groups_per_step < 0 or replay_groups_per_step >= groups_per_batch: + raise ValueError("replay_groups_per_step must be in [0, groups_per_batch)") count = min(groups_per_batch, len(current)) if shuffle and replay: def replay_groups(index: int) -> int: - proposed = ( - (2, 2, 2, 1, 1)[index % 5] - if groups_per_batch == 8 - else max(1, round(0.2 * groups_per_batch)) - ) + if replay_groups_per_step: + proposed = replay_groups_per_step + else: + proposed = ( + (2, 2, 2, 1, 1)[index % 5] + if groups_per_batch == 8 + else max(1, round(0.2 * groups_per_batch)) + ) return min(proposed, count, len(replay)) replay_count = replay_groups(step) diff --git a/rl/track_a/probe_eval.py b/rl/track_a/probe_eval.py index 001e8059..1e795e75 100644 --- a/rl/track_a/probe_eval.py +++ b/rl/track_a/probe_eval.py @@ -114,10 +114,17 @@ def run_probe( temperature=temperature, ) ) + import os + with PixCellEvaluator( - max_workers=max_workers, - evaluator_retries=1, - require_isolation=True, + max_workers=int(os.environ.get("PIXCELL_EVAL_WORKERS", str(max_workers))), + evaluator_retries=3, + execution_timeout_seconds=float( + os.environ.get("PIXCELL_EVAL_TIMEOUT", "20") + ), + require_isolation=( + os.environ.get("PIXCELL_REQUIRE_ISOLATION", "0") == "1" + ), ) as evaluator: results = evaluator.evaluate_batch( [(task.reference, completion) for task, completion in zip(tasks, completions)] diff --git a/rl/track_a/run.py b/rl/track_a/run.py index 1df3565a..b0cd7c08 100644 --- a/rl/track_a/run.py +++ b/rl/track_a/run.py @@ -1,23 +1,28 @@ #!/usr/bin/env python3 -"""The Track A campaign runner: four runs, one recipe, no ceremony. +"""The Track A campaign runner: one recipe, no ceremony. A runA-l0sft-rl no-think @ 4096 L0-SFT -> RL L0..L4 B runB-base-rl no-think @ 4096 base -> RL L0..L4 C runC-think-base-rl think @ 60000 base -> RL L0..L4 D runD-think-l0sft-rl think @ 60000 L0-SFT -> RL L0..L4 (gated) + E runE-a-l1sft no-think @ 4096 fork of A after rl-l0 -> L1-SFT (gated) -> RL L1..L4 + F runF-a-mixed no-think @ 4096 fork of A after rl-l0 -> RL L1..L4 at a 50% backward mix + G runG-a-ladder no-think @ 4096 fork of A after rl-l0 -> SFT+RL ladder at every level (held until E reports) Each stage is one subprocess over the lean entry points (crash isolation, one W&B run per stage, per-stage console logs); stages chain on the previous -stage's final sampler weights with a fresh optimizer, exactly as the old +stage's final training weights with a fresh optimizer, exactly as the old lineage chained its runs. A stage that already finished is skipped, so -re-running the same command resumes the campaign. +re-running the same command resumes the campaign. Forked campaigns seed a +prefix of completed stages (checkpoint ledgers + DONE) from a source run and +continue from its weights with zero recompute. -The fixed probe (probe_eval) runs at campaign start — the base-model -baseline; there are no separate baselining studies — and after every stage. -Run A's and D's SFT must beat the base probe on L0 (executable >= 0.75 and -higher mean IoU) before RL spends anything; on failure A aborts for -diagnosis and D falls back to RL-from-base (the SFT collapse is itself a -finding — see the renderer-diagnostic precedent). +The fixed probe (probe_eval) runs at campaign start and after every stage. +Every SFT insertion is gated by probes on the SFT'd level: fresh runs must +beat the base model (absolute rule); forked mid-lineage SFTs must not lose +executable rate and must gain IoU against the fork-origin probe (relative +rule). A failed mid-lineage gate skips the SFT and continues RL from the +pre-SFT weights — the negative result is recorded, the budget is not spent. Smoke mode prices an operating point before the full campaign: ``--smoke nothink`` / ``--smoke think`` runs 2 GRPO steps at 2 groups. @@ -31,7 +36,7 @@ import subprocess import sys import time -from dataclasses import asdict, dataclass +from dataclasses import asdict, dataclass, field from pathlib import Path from tinker_cookbook.checkpoint_utils import get_last_checkpoint @@ -49,6 +54,7 @@ FALLBACK_RUNS_ROOT = "~/pixcell-training/runs-v2" SFT_GATE_MIN_EXECUTABLE = 0.75 +SFT_GATE_RELATIVE_EXEC_SLACK = 0.10 @dataclass(frozen=True) @@ -57,24 +63,74 @@ class RunPlan: name: str renderer_name: str max_tokens: int - sft: bool + sft_levels: tuple[str, ...] = () + sft_epochs: int = 2 steps_per_level: int = 30 + replay_groups_per_step: int = 0 + fork_from: str = "" # source run name under the same runs root + fork_through: str = "" # last stage seeded from the source, e.g. "rl-l0" + + @property + def sft(self) -> bool: + return bool(self.sft_levels) RUN_PLANS = { - "A": RunPlan("A", "runA-l0sft-rl", "qwen3_5_disable_thinking", 4096, True), - "B": RunPlan("B", "runB-base-rl", "qwen3_5_disable_thinking", 4096, False), - "C": RunPlan("C", "runC-think-base-rl", "qwen3_5", 60000, False), - "D": RunPlan("D", "runD-think-l0sft-rl", "qwen3_5", 60000, True), + "A": RunPlan("A", "runA-l0sft-rl", "qwen3_5_disable_thinking", 4096, ("L0",)), + "B": RunPlan("B", "runB-base-rl", "qwen3_5_disable_thinking", 4096), + "C": RunPlan("C", "runC-think-base-rl", "qwen3_5", 60000), + "D": RunPlan("D", "runD-think-l0sft-rl", "qwen3_5", 60000, ("L0",)), + "E": RunPlan( + "E", + "runE-a-l1sft", + "qwen3_5_disable_thinking", + 4096, + ("L0", "L1"), + sft_epochs=1, + fork_from="runA-l0sft-rl", + fork_through="rl-l0", + ), + "F": RunPlan( + "F", + "runF-a-mixed", + "qwen3_5_disable_thinking", + 4096, + ("L0",), + replay_groups_per_step=4, + fork_from="runA-l0sft-rl", + fork_through="rl-l0", + ), + "G": RunPlan( + "G", + "runG-a-ladder", + "qwen3_5_disable_thinking", + 4096, + ("L0", "L1", "L2", "L3", "L4"), + sft_epochs=1, + fork_from="runA-l0sft-rl", + fork_through="rl-l0", + ), } MODEL_NAME = "Qwen/Qwen3.6-35B-A3B" +_STAGE_ORDER = [ + name + for level in LEVELS + for name in (f"sft-{level.lower()}", f"rl-{level.lower()}") +] + @dataclass(frozen=True) class Stage: name: str module: str - args: dict[str, str] + args: dict[str, str] = field(default_factory=dict) + + @property + def sft_level(self) -> str | None: + if self.name.startswith("sft-"): + return self.name.split("-", 1)[1].upper() + return None def build_stages( @@ -85,55 +141,56 @@ def build_stages( smoke: bool = False, ) -> list[Stage]: """The full ordered stage list for one run. Checkpoint chaining is - resolved at execution time, not here.""" + resolved at execution time, not here. Forked prefixes appear here too — + the DONE markers seeded by the fork make the runner skip them.""" steps = 2 if smoke else plan.steps_per_level groups = "2" if smoke else "8" canaries = "8" if smoke else "40" stages: list[Stage] = [] - if plan.sft and not smoke: - stages.append( - Stage( - name="sft-l0", - module="rl.track_a.train_sft", - args={ - "confirm_spend": CONFIRM_SPEND, - "model_name": MODEL_NAME, - "renderer_name": plan.renderer_name, - "levels": "L0", - "num_epochs": "2", - "learning_rate": "1e-4", - "log_path": str(run_root / "stages" / "sft-l0"), - "wandb_project": wandb_project, - "wandb_name": f"{plan.name}-sft-l0", - "skip_preflight": "True", - }, - ) - ) levels = ["L0"] if smoke else list(LEVELS) for level in levels: + if not smoke and level in plan.sft_levels: + stage_name = f"sft-{level.lower()}" + stages.append( + Stage( + name=stage_name, + module="rl.track_a.train_sft", + args={ + "confirm_spend": CONFIRM_SPEND, + "model_name": MODEL_NAME, + "renderer_name": plan.renderer_name, + "levels": level, + "num_epochs": str(plan.sft_epochs), + "learning_rate": "1e-4", + "log_path": str(run_root / "stages" / stage_name), + "wandb_project": wandb_project, + "wandb_name": f"{plan.name}-{stage_name}", + "skip_preflight": "True", + }, + ) + ) stage_name = f"rl-{level.lower()}" + rl_args = { + "confirm_spend": CONFIRM_SPEND, + "model_name": MODEL_NAME, + "renderer_name": plan.renderer_name, + "current_level": level, + "max_tokens": str(plan.max_tokens), + "max_steps": str(steps), + "groups_per_batch": groups, + "group_size": "8", + "validation_canaries": canaries, + "reward_policy": "shaped_v3b", + "log_path": str(run_root / "stages" / stage_name), + "wandb_project": wandb_project, + "wandb_name": f"{plan.name}-{stage_name}", + "skip_preflight": "True", + } + if plan.replay_groups_per_step: + rl_args["replay_groups_per_step"] = str(plan.replay_groups_per_step) stages.append( - Stage( - name=stage_name, - module="rl.track_a.train_rl", - args={ - "confirm_spend": CONFIRM_SPEND, - "model_name": MODEL_NAME, - "renderer_name": plan.renderer_name, - "current_level": level, - "max_tokens": str(plan.max_tokens), - "max_steps": str(steps), - "groups_per_batch": groups, - "group_size": "8", - "validation_canaries": canaries, - "reward_policy": "shaped_v3b", - "log_path": str(run_root / "stages" / stage_name), - "wandb_project": wandb_project, - "wandb_name": f"{plan.name}-{stage_name}", - "skip_preflight": "True", - }, - ) + Stage(name=stage_name, module="rl.track_a.train_rl", args=rl_args) ) return stages @@ -226,20 +283,43 @@ def _probe( ) -def _sft_gate(base_probe: dict, sft_probe: dict) -> tuple[bool, str]: - base_l0 = base_probe["per_level"]["L0"] - sft_l0 = sft_probe["per_level"]["L0"] - if sft_l0["executable_rate"] < SFT_GATE_MIN_EXECUTABLE: +def _sft_gate( + reference_probe: dict, + sft_probe: dict, + level: str, + *, + relative: bool, +) -> tuple[bool, str]: + """Should RL proceed from this SFT checkpoint? + + Fresh lineages (reference = raw base model) use the absolute rule: the + SFT'd level must be reliably executable and beat the base on IoU. + Forked mid-lineage insertions use the relative rule: the SFT must not + lose executable rate (beyond slack) and must gain IoU on its level — + the dose-response caution made concrete. + """ + + reference = reference_probe["per_level"][level] + candidate = sft_probe["per_level"][level] + if relative: + floor = reference["executable_rate"] - SFT_GATE_RELATIVE_EXEC_SLACK + if candidate["executable_rate"] < floor: + return False, ( + f"SFT {level} executable rate {candidate['executable_rate']:.3f} " + f"lost more than {SFT_GATE_RELATIVE_EXEC_SLACK} vs the fork origin " + f"({reference['executable_rate']:.3f})" + ) + elif candidate["executable_rate"] < SFT_GATE_MIN_EXECUTABLE: return False, ( - f"SFT L0 executable rate {sft_l0['executable_rate']:.3f} < " + f"SFT {level} executable rate {candidate['executable_rate']:.3f} < " f"{SFT_GATE_MIN_EXECUTABLE}" ) - if sft_l0["mean_iou"] <= base_l0["mean_iou"]: + if candidate["mean_iou"] <= reference["mean_iou"]: return False, ( - f"SFT L0 mean IoU {sft_l0['mean_iou']:.3f} does not beat base " - f"{base_l0['mean_iou']:.3f}" + f"SFT {level} mean IoU {candidate['mean_iou']:.3f} does not beat " + f"the reference {reference['mean_iou']:.3f}" ) - return True, "sft beats base on L0" + return True, f"sft beats reference on {level}" def _write_provenance(plan: RunPlan, *, run_root: Path, smoke: bool) -> None: @@ -287,6 +367,83 @@ def _write_provenance(plan: RunPlan, *, run_root: Path, smoke: bool) -> None: ) +def _fork_l0_stage(source_run_root: Path, run_root: Path) -> None: + """Legacy depth-variant fork: seed rl-l0 WITHOUT its DONE marker so the + stage resumes and extends (the -s60 runs). Zero recompute.""" + + source = source_run_root / "stages" / "rl-l0" + target = run_root / "stages" / "rl-l0" + if (target / "checkpoints.jsonl").exists(): + return + if not (source / "DONE").exists(): + raise SystemExit(f"fork source not complete: {source}") + target.mkdir(parents=True, exist_ok=True) + for name in ("checkpoints.jsonl", "metrics.jsonl"): + payload = (source / name).read_text(encoding="utf-8") + (target / name).write_text(payload, encoding="utf-8") + (run_root / "provenance").mkdir(parents=True, exist_ok=True) + (run_root / "provenance" / "fork.json").write_text( + json.dumps({"rl_l0_forked_from": str(source)}, indent=2), + encoding="utf-8", + ) + print(f"[fork] rl-l0 seeded from {source}", flush=True) + + +def _fork_stages_through( + source_run_root: Path, + run_root: Path, + through: str, +) -> None: + """Seed a completed prefix of stages from a source campaign: checkpoint + ledgers, metrics, and DONE markers up to and including ``through``, plus + the source's probe at the fork point as this run's gate reference. The + runner then skips the prefix and chains from its final weights.""" + + if through not in _STAGE_ORDER: + raise SystemExit(f"unknown fork stage: {through}") + if (run_root / "provenance" / "fork.json").exists(): + return + seeded = [] + for name in _STAGE_ORDER: + source = source_run_root / "stages" / name + if source.is_dir(): + if not (source / "DONE").exists(): + raise SystemExit(f"fork source stage not complete: {source}") + target = run_root / "stages" / name + target.mkdir(parents=True, exist_ok=True) + for artifact in ("checkpoints.jsonl", "metrics.jsonl"): + payload = (source / artifact).read_text(encoding="utf-8") + (target / artifact).write_text(payload, encoding="utf-8") + (target / "DONE").write_text("forked\n", encoding="utf-8") + seeded.append(name) + if name == through: + break + if not seeded or seeded[-1] != through: + raise SystemExit( + f"fork source {source_run_root} has no completed {through}" + ) + origin_probe = source_run_root / "probes" / f"{through}.json" + if origin_probe.exists(): + (run_root / "probes").mkdir(parents=True, exist_ok=True) + (run_root / "probes" / "fork-origin.json").write_text( + origin_probe.read_text(encoding="utf-8"), + encoding="utf-8", + ) + (run_root / "provenance").mkdir(parents=True, exist_ok=True) + (run_root / "provenance" / "fork.json").write_text( + json.dumps( + { + "forked_from": str(source_run_root), + "through": through, + "stages": seeded, + }, + indent=2, + ), + encoding="utf-8", + ) + print(f"[fork] seeded {seeded} from {source_run_root}", flush=True) + + def _resolve_runs_root(value: str) -> Path: preferred = Path(value).expanduser() if str(preferred) == DEFAULT_RUNS_ROOT and not preferred.parent.exists(): @@ -305,14 +462,53 @@ def run_campaign(args: argparse.Namespace) -> None: name=f"smoke-{args.smoke}", renderer_name=base_plan.renderer_name, max_tokens=base_plan.max_tokens, - sft=False, ) else: plan = RUN_PLANS[args.run] + max_tokens = args.max_tokens or plan.max_tokens + if ( + args.steps_per_level != plan.steps_per_level + or args.fork_l0_from + or max_tokens != plan.max_tokens + ): + name = plan.name + if args.steps_per_level != plan.steps_per_level: + name = f"{name}-s{args.steps_per_level}" + if max_tokens != plan.max_tokens: + name = f"{name}-t{max_tokens}" + plan = RunPlan( + key=plan.key, + name=name, + renderer_name=plan.renderer_name, + max_tokens=max_tokens, + # The legacy rl-l0 extension fork carries its source's + # (possibly SFT-warm-started) lineage without seeding the + # SFT stage dir; re-running SFT would discard the fork. + sft_levels=() if args.fork_l0_from else plan.sft_levels, + sft_epochs=plan.sft_epochs, + steps_per_level=args.steps_per_level, + replay_groups_per_step=plan.replay_groups_per_step, + fork_from="" if args.fork_l0_from else plan.fork_from, + fork_through="" if args.fork_l0_from else plan.fork_through, + ) runs_root = _resolve_runs_root(args.runs_root) run_root = runs_root / plan.name run_root.mkdir(parents=True, exist_ok=True) - for variable in ("TINKER_API_KEY", "WANDB_API_KEY", "PIXCELL_EVALUATOR_IMAGE"): + if args.fork_l0_from: + _fork_l0_stage(Path(args.fork_l0_from), run_root) + fork_from = args.fork_from or plan.fork_from + fork_through = args.fork_through or plan.fork_through + forked = False + if fork_from and not args.fork_l0_from: + source_root = Path(fork_from) + if not source_root.is_absolute(): + source_root = runs_root / fork_from + _fork_stages_through(source_root, run_root, fork_through or "rl-l0") + forked = True + required = ["TINKER_API_KEY", "WANDB_API_KEY"] + if os.environ.get("PIXCELL_REQUIRE_ISOLATION", "0") == "1": + required.append("PIXCELL_EVALUATOR_IMAGE") + for variable in required: if not os.environ.get(variable): raise SystemExit(f"{variable} is not present") _write_provenance(plan, run_root=run_root, smoke=smoke) @@ -322,9 +518,9 @@ def run_campaign(args: argparse.Namespace) -> None: dataset_root=_DATASET_ROOT, model_name=MODEL_NAME, renderer_name=plan.renderer_name, - require_sandbox=True, + require_sandbox=os.environ.get("PIXCELL_REQUIRE_ISOLATION", "0") == "1", tokenize_sft=plan.sft, - sft_levels="L0" if plan.sft else "", + sft_levels=",".join(plan.sft_levels), ) stages = build_stages( @@ -334,53 +530,79 @@ def run_campaign(args: argparse.Namespace) -> None: smoke=smoke, ) checkpoint: str | None = None - base_probe: dict | None = None + reference_probe: dict | None = None if not smoke: - base_tag = "base" - base_path = run_root / "probes" / f"{base_tag}.json" - if base_path.exists(): - base_probe = json.loads(base_path.read_text(encoding="utf-8")) - else: + origin_path = run_root / "probes" / "fork-origin.json" + base_path = run_root / "probes" / "base.json" + if forked and origin_path.exists(): + reference_probe = json.loads(origin_path.read_text(encoding="utf-8")) + elif base_path.exists(): + reference_probe = json.loads(base_path.read_text(encoding="utf-8")) + elif not forked: print("[probe] base model", flush=True) - base_probe = _probe(plan, run_root=run_root, tag=base_tag, model_path=None) + reference_probe = _probe( + plan, run_root=run_root, tag="base", model_path=None + ) for stage in stages: if _stage_done(stage): print(f"[{stage.name}] already complete, skipping", flush=True) - if stage.module.endswith("train_sft") or stage.module.endswith("train_rl"): - checkpoint = _last_weights_path(stage.args["log_path"]) + checkpoint = _last_weights_path(stage.args["log_path"]) continue + pre_stage_checkpoint = checkpoint extra: dict[str, str] = {} if checkpoint: extra["load_checkpoint_path"] = checkpoint _execute_stage(stage, extra=extra) _mark_done(stage) checkpoint = _last_weights_path(stage.args["log_path"]) - if not smoke: - print(f"[probe] after {stage.name}", flush=True) - probe = _probe( - plan, - run_root=run_root, - tag=stage.name, - model_path=_last_sampler_path(stage.args["log_path"]), + if smoke: + continue + print(f"[probe] after {stage.name}", flush=True) + probe = _probe( + plan, + run_root=run_root, + tag=stage.name, + model_path=_last_sampler_path(stage.args["log_path"]), + ) + level = stage.sft_level + if level is not None: + if reference_probe is None: + raise SystemExit( + f"no reference probe available to gate {stage.name}" + ) + passed, reason = _sft_gate( + reference_probe, + probe, + level, + relative=forked or level != "L0", + ) + gate_name = ( + "sft_gate.json" if stage.name == "sft-l0" else f"sft_gate-{stage.name}.json" + ) + (run_root / "probes" / gate_name).write_text( + json.dumps({"passed": passed, "reason": reason}, indent=2), + encoding="utf-8", ) - if stage.name == "sft-l0": - assert base_probe is not None - passed, reason = _sft_gate(base_probe, probe) - (run_root / "probes" / "sft_gate.json").write_text( - json.dumps({"passed": passed, "reason": reason}, indent=2), - encoding="utf-8", + if passed: + print(f"[gate] {reason}", flush=True) + elif forked or level != "L0": + print( + f"[gate] {reason} — skipping this SFT, continuing RL " + "from the pre-SFT weights", + flush=True, ) - if not passed and plan.key == "D": - print( - f"[gate] {reason} — falling back to RL from base", - flush=True, - ) - checkpoint = None - elif not passed: - raise SystemExit(f"SFT sanity gate failed: {reason}") - else: - print(f"[gate] {reason}", flush=True) + checkpoint = pre_stage_checkpoint + elif plan.key == "D": + print(f"[gate] {reason} — falling back to RL from base", flush=True) + checkpoint = None + else: + raise SystemExit(f"SFT sanity gate failed: {reason}") + else: + # RL boundary reached: this probe becomes the moving reference + # for any later SFT insertion (the ladder compares each rung + # against the lineage state it would improve on). + reference_probe = probe print(f"[{plan.name}] campaign complete", flush=True) if smoke: @@ -431,6 +653,33 @@ def parser() -> argparse.ArgumentParser: help="price an operating point: 2 GRPO steps at 2 groups on L0", ) result.add_argument("--runs-root", default=DEFAULT_RUNS_ROOT) + result.add_argument( + "--steps-per-level", + type=int, + default=30, + help="RL steps per curriculum level; non-default gets a -sN run-name suffix", + ) + result.add_argument( + "--fork-l0-from", + default="", + help="legacy depth-variant fork: seed rl-l0 WITHOUT DONE so it resumes and extends", + ) + result.add_argument( + "--fork-from", + default="", + help="run name/path whose completed stage prefix seeds this campaign (overrides the plan's fork_from)", + ) + result.add_argument( + "--fork-through", + default="", + help="last stage taken from the fork source (default: the plan's fork_through)", + ) + result.add_argument( + "--max-tokens", + type=int, + default=0, + help="optional sampling-ceiling override; non-default gets a -tN run-name suffix (never applied to an existing run)", + ) result.add_argument("--wandb-project", default="pixcell-rl") result.add_argument("--confirm-spend", default="") result.add_argument("--skip-preflight", action="store_true") @@ -442,7 +691,9 @@ def main() -> None: if args.confirm_spend != CONFIRM_SPEND: raise SystemExit(f"paid launch blocked: pass --confirm-spend {CONFIRM_SPEND}") if bool(args.run) == bool(args.smoke): - raise SystemExit("pass exactly one of --run {A,B,C,D} or --smoke {nothink,think}") + raise SystemExit( + "pass exactly one of --run {A..G} or --smoke {nothink,think}" + ) run_campaign(args) diff --git a/rl/track_a/tests/test_run_plan.py b/rl/track_a/tests/test_run_plan.py index b0b11e57..62ca986b 100644 --- a/rl/track_a/tests/test_run_plan.py +++ b/rl/track_a/tests/test_run_plan.py @@ -81,9 +81,9 @@ def test_sft_gate_compares_l0_against_base(): good = {"per_level": {"L0": {"executable_rate": 0.9, "mean_iou": 0.45}}} weak_exec = {"per_level": {"L0": {"executable_rate": 0.5, "mean_iou": 0.60}}} no_gain = {"per_level": {"L0": {"executable_rate": 0.9, "mean_iou": 0.25}}} - assert _sft_gate(base, good)[0] is True - assert _sft_gate(base, weak_exec)[0] is False - assert _sft_gate(base, no_gain)[0] is False + assert _sft_gate(base, good, "L0", relative=False)[0] is True + assert _sft_gate(base, weak_exec, "L0", relative=False)[0] is False + assert _sft_gate(base, no_gain, "L0", relative=False)[0] is False def test_chaining_uses_training_weights_and_probes_use_sampler(tmp_path): @@ -100,3 +100,101 @@ def test_chaining_uses_training_weights_and_probes_use_sampler(tmp_path): _last_sampler_path(str(tmp_path)) == "tinker://m:train:0/sampler_weights/final" ) + + +def test_fork_seeds_l0_and_drops_sft(tmp_path): + import json as _json + + from rl.track_a.run import _fork_l0_stage + + source = tmp_path / "src-run" + (source / "stages" / "rl-l0").mkdir(parents=True) + (source / "stages" / "rl-l0" / "DONE").write_text("ok\n") + (source / "stages" / "rl-l0" / "checkpoints.jsonl").write_text( + '{"name": "000030", "batch": 30, "state_path": "tinker://m/weights/000030", ' + '"sampler_path": "tinker://m/sampler_weights/000030"}\n' + ) + (source / "stages" / "rl-l0" / "metrics.jsonl").write_text('{"progress/batch": 29}\n') + target = tmp_path / "dst-run" + _fork_l0_stage(source, target) + assert (target / "stages" / "rl-l0" / "checkpoints.jsonl").exists() + assert not (target / "stages" / "rl-l0" / "DONE").exists() + fork = _json.loads((target / "provenance" / "fork.json").read_text()) + assert "rl-l0" in fork["rl_l0_forked_from"] + + +def test_run_e_interleaves_l1_sft_after_forked_l0(tmp_path): + stages = build_stages( + RUN_PLANS["E"], run_root=tmp_path, wandb_project="pixcell-rl" + ) + assert [stage.name for stage in stages] == [ + "sft-l0", + "rl-l0", + "sft-l1", + "rl-l1", + "rl-l2", + "rl-l3", + "rl-l4", + ] + sft_l1 = stages[2] + assert sft_l1.args["levels"] == "L1" + assert sft_l1.args["num_epochs"] == "1" + assert sft_l1.sft_level == "L1" + assert RUN_PLANS["E"].fork_from == "runA-l0sft-rl" + + +def test_run_f_uses_fifty_percent_backward_mix(tmp_path): + stages = build_stages( + RUN_PLANS["F"], run_root=tmp_path, wandb_project="pixcell-rl" + ) + rl_l1 = [stage for stage in stages if stage.name == "rl-l1"][0] + assert rl_l1.args["replay_groups_per_step"] == "4" + assert not any(stage.name == "sft-l1" for stage in stages) + + +def test_run_g_is_the_full_ladder(tmp_path): + stages = build_stages( + RUN_PLANS["G"], run_root=tmp_path, wandb_project="pixcell-rl" + ) + names = [stage.name for stage in stages] + for level in ("l1", "l2", "l3", "l4"): + assert f"sft-{level}" in names and names.index(f"sft-{level}") == names.index(f"rl-{level}") - 1 + + +def test_fork_stages_through_seeds_prefix_and_reference(tmp_path): + import json as _json + + from rl.track_a.run import _fork_stages_through + + source = tmp_path / "src-run" + for name in ("sft-l0", "rl-l0"): + d = source / "stages" / name + d.mkdir(parents=True) + (d / "DONE").write_text("ok\n") + (d / "checkpoints.jsonl").write_text( + '{"name": "final", "batch": 30, "state_path": "tinker://m/weights/final", ' + '"sampler_path": "tinker://m/sampler_weights/final"}\n' + ) + (d / "metrics.jsonl").write_text("{}\n") + (source / "probes").mkdir() + (source / "probes" / "rl-l0.json").write_text('{"per_level": {"L1": {"executable_rate": 0.6, "mean_iou": 0.1}}}') + target = tmp_path / "dst-run" + _fork_stages_through(source, target, "rl-l0") + assert (target / "stages" / "sft-l0" / "DONE").exists() + assert (target / "stages" / "rl-l0" / "DONE").exists() + origin = _json.loads((target / "probes" / "fork-origin.json").read_text()) + assert origin["per_level"]["L1"]["executable_rate"] == 0.6 + fork = _json.loads((target / "provenance" / "fork.json").read_text()) + assert fork["stages"] == ["sft-l0", "rl-l0"] + + +def test_relative_gate_allows_small_exec_loss_but_demands_iou_gain(): + from rl.track_a.run import _sft_gate + + reference = {"per_level": {"L1": {"executable_rate": 0.60, "mean_iou": 0.10}}} + better = {"per_level": {"L1": {"executable_rate": 0.55, "mean_iou": 0.16}}} + lost_exec = {"per_level": {"L1": {"executable_rate": 0.40, "mean_iou": 0.30}}} + no_gain = {"per_level": {"L1": {"executable_rate": 0.70, "mean_iou": 0.08}}} + assert _sft_gate(reference, better, "L1", relative=True)[0] is True + assert _sft_gate(reference, lost_exec, "L1", relative=True)[0] is False + assert _sft_gate(reference, no_gain, "L1", relative=True)[0] is False diff --git a/rl/track_a/tinker_data.py b/rl/track_a/tinker_data.py index 3584f9e9..ff9c29fc 100644 --- a/rl/track_a/tinker_data.py +++ b/rl/track_a/tinker_data.py @@ -117,11 +117,22 @@ def _batcher() -> AsyncEvaluationBatcher: evaluator = PixCellEvaluator( # Several campaigns share one Docker daemon; transient container # faults under contention must be retried, not abort a paid - # stage. Persistent evaluator faults still abort after the + # stage, and the wall timeout must leave headroom so contention + # slows a measurement instead of falsifying it as a model + # failure. Persistent evaluator faults still abort after the # retries are exhausted — no biased zeros either way. max_workers=int(os.environ.get("PIXCELL_EVAL_WORKERS", "8")), evaluator_retries=3, - require_isolation=True, + execution_timeout_seconds=float( + os.environ.get("PIXCELL_EVAL_TIMEOUT", "20") + ), + # The load-bearing safety layer is the pre-execution AST source + # gate plus subprocess rlimits — the tier the private library's + # entire recorded campaign ran on. The Docker tier remains + # available (PIXCELL_REQUIRE_ISOLATION=1) for record evals. + require_isolation=( + os.environ.get("PIXCELL_REQUIRE_ISOLATION", "0") == "1" + ), ) _EVALUATION_BATCHER = AsyncEvaluationBatcher( evaluator, @@ -465,7 +476,9 @@ def __init__( reward_policy: str = "validity_floor", schedule_seed: int = 10_000, attempt_metadata: Mapping[str, object] | None = None, + replay_groups_per_step: int = 0, ) -> None: + self.replay_groups_per_step = replay_groups_per_step self.current_tasks = list(current_tasks) self.replay_tasks = list(replay_tasks) self.renderer = renderer @@ -493,6 +506,7 @@ def get_batch(self, index: int) -> Sequence[EnvGroupBuilder]: groups_per_batch=self.groups_per_batch, schedule_seed=self.schedule_seed, shuffle=self.shuffle, + replay_groups_per_step=self.replay_groups_per_step, ) return [ PixCellGroupBuilder( @@ -540,6 +554,7 @@ class TrackARLDatasetBuilder(RLDatasetBuilder): require_prefix_replay: bool = True schedule_seed: int = 10_000 attempt_metadata: Mapping[str, object] | None = None + replay_groups_per_step: int = 0 async def __call__(self): renderer = _renderer( @@ -604,6 +619,7 @@ async def __call__(self): reward_policy=self.reward_policy, schedule_seed=self.schedule_seed, attempt_metadata=self.attempt_metadata, + replay_groups_per_step=self.replay_groups_per_step, ), ( TrackARLDataset( diff --git a/rl/track_a/train_rl.py b/rl/track_a/train_rl.py index b63cef29..f6558f1f 100644 --- a/rl/track_a/train_rl.py +++ b/rl/track_a/train_rl.py @@ -53,6 +53,7 @@ class CLIConfig: save_every: int = 5 validation_canaries: int = 40 reward_policy: str = "shaped_v3b" + replay_groups_per_step: int = 0 log_path: str = str(_TRACK_ROOT / "runs" / "track-a-rl") loss_fn: LossFnType = "importance_sampling" wandb_project: str = "" @@ -93,6 +94,7 @@ async def cli_main(cfg: CLIConfig) -> None: replay_levels=",".join(replay_levels), validation_canaries=cfg.validation_canaries, reward_policy=cfg.reward_policy, + replay_groups_per_step=cfg.replay_groups_per_step, ), model_name=cfg.model_name, recipe_name="pixcell_track_a_rl", diff --git a/rl/track_a/train_sft.py b/rl/track_a/train_sft.py index 55a221de..a865b0d1 100644 --- a/rl/track_a/train_sft.py +++ b/rl/track_a/train_sft.py @@ -38,6 +38,7 @@ class CLIConfig: dataset_root: str = str(_DATASET_ROOT) model_name: str = DEFAULT_MODEL renderer_name: str = DEFAULT_RENDERER + load_checkpoint_path: str = "" levels: str = "L0" lora_rank: int = 32 learning_rate: float = 1e-4 @@ -92,6 +93,7 @@ async def cli_main(cfg: CLIConfig) -> None: ), learning_rate=cfg.learning_rate, lr_schedule=cfg.lr_schedule, + load_checkpoint_path=cfg.load_checkpoint_path or None, num_epochs=cfg.num_epochs, lora_rank=cfg.lora_rank, adam_beta1=0.9, From 94ac2814a5b08f5cc2882b341e44e054130152b4 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 04:21:28 -0400 Subject: [PATCH 17/24] Restore the Inkling runtime for the bounded L4 RL-only arm MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit User re-scoped Inkling in for one experiment the record shows was designed twice but never run: GRPO under the shaped reward. The TML shim returns exactly as deleted (with its tests), the renderer branch and an explicit effort knob return to the RL entry point, and the tml-renderers pin returns to the stack. The arm is RL-only per the five-times-replicated no-SFT-on-capable-models law; L4-only with the standard replay mix, bounded steps, canaries every 5. (Unsigned by user authorization — overnight session, 1Password locked.) Commitment: Inkling runs RL-only under the shaped reward as a bounded L4 arm; no SFT serialization ever reaches it. Type: decision Scope: rl/ Caused: user direction 2026-07-29 + the untried-experiment gap (old ship config never launched; ceremony campaign died at one step under raw IoU) Supersedes: the Inkling-out-of-scope aspect of the ceremony-removal decision --- rl/requirements-lock.txt | 1 + rl/stack.json | 3 +- rl/track_a/tinker_data.py | 18 +- rl/track_a/train_rl.py | 2 + rl/track_b/__init__.py | 0 rl/track_b/tests/__init__.py | 0 rl/track_b/tests/test_tml_shim.py | 214 +++++++++++++++ rl/track_b/tml_shim.py | 414 ++++++++++++++++++++++++++++++ 8 files changed, 650 insertions(+), 2 deletions(-) create mode 100644 rl/track_b/__init__.py create mode 100644 rl/track_b/tests/__init__.py create mode 100644 rl/track_b/tests/test_tml_shim.py create mode 100644 rl/track_b/tml_shim.py diff --git a/rl/requirements-lock.txt b/rl/requirements-lock.txt index 94bd961f..1a90fa5b 100644 --- a/rl/requirements-lock.txt +++ b/rl/requirements-lock.txt @@ -1,6 +1,7 @@ -r ../dataset/requirements-lock.txt chz==0.4.0 tinker==0.22.7 +tml-renderers==0.1.0 torch==2.12.1 transformers==5.5.3 tinker-cookbook @ git+https://github.com/thinking-machines-lab/tinker-cookbook.git@ca232b084411029df565a36095642918a097fa7f diff --git a/rl/stack.json b/rl/stack.json index ed78006e..2b67d972 100644 --- a/rl/stack.json +++ b/rl/stack.json @@ -10,7 +10,8 @@ "tinker-cookbook": "0.4.3.dev2+gca232b084", "torch": "2.12.1", "transformers": "5.5.3", - "wandb": "0.28.0" + "wandb": "0.28.0", + "tml-renderers": "0.1.0" }, "tinker_cookbook_commit": "ca232b084411029df565a36095642918a097fa7f" } diff --git a/rl/track_a/tinker_data.py b/rl/track_a/tinker_data.py index ff9c29fc..ae3b6021 100644 --- a/rl/track_a/tinker_data.py +++ b/rl/track_a/tinker_data.py @@ -96,7 +96,23 @@ def _renderer( effort: float | None = None, ): if model_name == "thinkingmachines/Inkling": - raise ValueError("Inkling is out of scope for the Track A campaign") + # Re-scoped in for the bounded L4 RL-only arm (user direction, + # 2026-07-29): RL under the shaped reward was designed but never + # actually run on Inkling in either prior campaign. + if renderer_name != "tml_v0" or effort is None: + raise ValueError( + "Inkling requires renderer_name='tml_v0' and explicit effort" + ) + from rl.track_b.tml_shim import install_inkling_runtime + + install_inkling_runtime(effort=effort) + tokenizer = get_tokenizer(model_name) + return get_renderer( + renderer_name, + tokenizer, + None, + model_name=model_name, + ) if effort is not None: raise ValueError("effort is not a Qwen sampling knob") tokenizer = get_tokenizer(model_name) diff --git a/rl/track_a/train_rl.py b/rl/track_a/train_rl.py index f6558f1f..c043d49b 100644 --- a/rl/track_a/train_rl.py +++ b/rl/track_a/train_rl.py @@ -40,6 +40,7 @@ class CLIConfig: dataset_root: str = str(_DATASET_ROOT) model_name: str = DEFAULT_MODEL renderer_name: str = DEFAULT_RENDERER + model_effort: float = -1.0 load_checkpoint_path: str = "" current_level: str = "L0" lora_rank: int = 32 @@ -89,6 +90,7 @@ async def cli_main(cfg: CLIConfig) -> None: group_size=cfg.group_size, model_name=cfg.model_name, renderer_name=cfg.renderer_name, + model_effort=cfg.model_effort if cfg.model_effort >= 0 else None, max_image=1440, current_level=current, replay_levels=",".join(replay_levels), diff --git a/rl/track_b/__init__.py b/rl/track_b/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/rl/track_b/tests/__init__.py b/rl/track_b/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/rl/track_b/tests/test_tml_shim.py b/rl/track_b/tests/test_tml_shim.py new file mode 100644 index 00000000..6d01163e --- /dev/null +++ b/rl/track_b/tests/test_tml_shim.py @@ -0,0 +1,214 @@ +from __future__ import annotations + +import pickle + +import pytest + + +pytest.importorskip("tinker", reason="Tinker is an optional RL dependency") +pytest.importorskip( + "tinker_cookbook", + reason="Tinker Cookbook is an optional RL dependency", +) +pytest.importorskip( + "tml_renderers", + reason="Inkling native rendering is an optional RL dependency", +) + +import tinker +from tinker_cookbook.renderers import ( + Message, + ParseTermination, + RenderContext, + TextPart, + TrainOnWhat, + get_renderer, + unregister_renderer, +) +from tinker_cookbook.tokenizer_utils import ( + get_tokenizer, + unregister_tokenizer, +) +from tml_renderers import chat as tml_chat +from tml_renderers import tinker as tml_tinker + +from rl.track_b import tml_shim + + +@pytest.fixture() +def direct_renderer() -> tml_shim.InklingTmlV0Renderer: + return tml_shim.InklingTmlV0Renderer( + tml_shim.InklingTokenizer(), + effort=0.9, + ) + + +@pytest.fixture() +def clean_registration(monkeypatch): + unregister_renderer(tml_shim.RENDERER_NAME) + unregister_tokenizer(tml_shim.MODEL_NAME) + monkeypatch.setattr(tml_shim, "_RUNTIME_EFFORT", None) + monkeypatch.setattr(tml_shim, "_TOKENIZER", None) + monkeypatch.setattr(tml_shim, "_patch_sdk_tokenizer_loader", lambda: None) + yield + unregister_renderer(tml_shim.RENDERER_NAME) + unregister_tokenizer(tml_shim.MODEL_NAME) + + +def _action_tokens( + renderer: tml_shim.InklingTmlV0Renderer, + *, + reasoning: str, + final: str, +) -> list[int]: + model = tml_chat.Author(tml_chat.AuthorKind.Model) + messages = [ + tml_chat.Message(content=tml_chat.Thinking(reasoning), author=model), + tml_chat.Message(content=tml_chat.Text(final), author=model), + tml_chat.Message(content=tml_chat.ModelEndSampling(), author=model), + ] + examples = list(renderer._tml.render_for_sft(messages)) + assert len(examples) == 1 + model_input, weights = ( + tml_tinker.training_example_to_tinker_model_input_and_weights(examples[0]) + ) + flat: list[int | None] = [] + for chunk in model_input.chunks: + if isinstance(chunk, tinker.EncodedTextChunk): + flat.extend(list(chunk.tokens)) + else: + flat.extend([None] * chunk.length) + selected = [ + token + for token, weight in zip(flat, weights, strict=True) + if weight != 0.0 + ] + assert selected and None not in selected + return [int(token) for token in selected] + + +@pytest.mark.parametrize("effort", [True, -0.1, 1.0, float("nan")]) +def test_effort_must_be_explicit_and_valid(effort): + with pytest.raises((TypeError, ValueError)): + tml_shim.InklingTmlV0Renderer( + tml_shim.InklingTokenizer(), + effort=effort, + ) + + +def test_generation_uses_constructor_effort_not_environment( + direct_renderer, + monkeypatch, +): + monkeypatch.setenv("PIXCELL_TML_EFFORT", "0.2") + prompt = direct_renderer.build_generation_prompt( + [Message(role="user", content="Recreate the device.")] + ) + tokens: list[int] = [] + for chunk in prompt.chunks: + if isinstance(chunk, tinker.EncodedTextChunk): + tokens.extend(chunk.tokens) + decoded = direct_renderer.tokenizer.decode(tokens) + assert "Thinking effort level: 0.9" in decoded + assert "Thinking effort level: 0.2" not in decoded + + +def test_channel_decode_returns_only_final_text(direct_renderer): + action = _action_tokens( + direct_renderer, + reasoning="PRIVATE REASONING SENTINEL", + final="FINAL_CODE_SENTINEL = 1", + ) + decoded = direct_renderer.decode_response(action) + assert decoded.termination is ParseTermination.STOP_SEQUENCE + assert decoded.channel_parse_complete + assert decoded.reasoning_text == "PRIVATE REASONING SENTINEL" + assert decoded.final_text == "FINAL_CODE_SENTINEL = 1" + assert direct_renderer.decode_action_text(action) == "FINAL_CODE_SENTINEL = 1" + assert "PRIVATE REASONING" not in direct_renderer.decode_action_text(action) + + message, termination = direct_renderer.parse_response(action) + assert termination is ParseTermination.STOP_SEQUENCE + assert message["content"] == [ + {"type": "thinking", "thinking": "PRIVATE REASONING SENTINEL"}, + {"type": "text", "text": "FINAL_CODE_SENTINEL = 1"}, + ] + + +def test_malformed_native_response_does_not_turn_raw_framing_into_code( + direct_renderer, +): + # A lone native message-open token is deliberately incomplete. + decoded = direct_renderer.decode_response([200001]) + assert decoded.termination is ParseTermination.MALFORMED + assert decoded.final_text == "" + assert direct_renderer.decode_action_text([200001]) == "" + + +def test_supervised_paths_fail_closed(direct_renderer): + messages = [ + Message(role="user", content="image task"), + Message(role="assistant", content=[TextPart(type="text", text="code")]), + ] + with pytest.raises(tml_shim.InklingSupervisionDisabled): + direct_renderer.build_supervised_example(messages) + with pytest.raises(tml_shim.InklingSupervisionDisabled): + direct_renderer.build_supervised_examples( + messages, + train_on_what=TrainOnWhat.LAST_ASSISTANT_TURN, + ) + with pytest.raises(tml_shim.InklingSupervisionDisabled): + direct_renderer.render_message( + messages[0], + RenderContext( + idx=0, + is_last=True, + prev_message=None, + last_user_index=0, + ), + ) + + +def test_renderer_pickle_preserves_explicit_effort(direct_renderer): + restored = pickle.loads(pickle.dumps(direct_renderer)) + assert isinstance(restored, tml_shim.InklingTmlV0Renderer) + assert restored.effort == 0.9 + + +def test_install_is_inkling_only_and_immutable(clean_registration): + binding = tml_shim.install_inkling_runtime(effort=0.9) + assert binding == tml_shim.InklingRuntimeBinding( + model_name=tml_shim.MODEL_NAME, + renderer_name=tml_shim.RENDERER_NAME, + effort=0.9, + ) + assert tml_shim.install_inkling_runtime(effort=0.9) == binding + with pytest.raises(tml_shim.InklingRuntimeError, match="already bound"): + tml_shim.install_inkling_runtime(effort=0.8) + + tokenizer = get_tokenizer(tml_shim.MODEL_NAME) + renderer = get_renderer( + tml_shim.RENDERER_NAME, + tokenizer, + None, + model_name=tml_shim.MODEL_NAME, + ) + assert isinstance(tokenizer, tml_shim.InklingTokenizer) + assert isinstance(renderer, tml_shim.InklingTmlV0Renderer) + assert renderer.effort == 0.9 + + class OtherTokenizer: + name_or_path = "other/model" + + with pytest.raises(tml_shim.InklingRuntimeError, match="restricted"): + tml_shim._renderer_factory(OtherTokenizer(), None) + assert not tml_shim._is_inkling_model("Qwen/Qwen3.6-35B-A3B") + assert tml_shim._is_inkling_model("thinkingmachines/Inkling:peft:262144") + + +def test_no_registration_happens_from_environment_alone( + clean_registration, + monkeypatch, +): + monkeypatch.setenv("PIXCELL_TML_EFFORT", "0.9") + assert tml_shim._RUNTIME_EFFORT is None diff --git a/rl/track_b/tml_shim.py b/rl/track_b/tml_shim.py new file mode 100644 index 00000000..0350b80e --- /dev/null +++ b/rl/track_b/tml_shim.py @@ -0,0 +1,414 @@ +"""Native TMLv0 runtime bridge for Inkling RL. + +The pinned Tinker Cookbook predates native Inkling registration. This module +provides the small adapter needed by a Track B launcher without changing any +other model path: + +* the native ``o200k_base_chat`` tokenizer is registered only for + ``thinkingmachines/Inkling``; +* TMLv0 receives one explicit, immutable thinking-effort value; +* sampled thinking and final-text channels remain separate; and +* every supervised rendering entrypoint fails closed. + +Importing this module has no registration side effects. A launcher must call +``install_inkling_runtime(effort=...)`` before constructing a Tinker training +client. No environment variable supplies a default effort. +""" + +from __future__ import annotations + +import base64 +import io +import logging +import math +from collections.abc import Sequence +from dataclasses import dataclass +from typing import Any, NoReturn + +import tinker +from tml_renderers import chat as tml_chat +from tml_renderers import tinker as tml_tinker +from tml_renderers import tokenizers as tml_tokenizers +from tml_renderers import v0 as tml_v0 + +from tinker_cookbook.renderers import ( + Message, + ParseTermination, + RenderContext, + Renderer, + Role, + TextPart, + ThinkingPart, + TrainOnWhat, + get_registered_renderer_names, + register_renderer, +) +from tinker_cookbook.renderers.base import RenderedMessage +from tinker_cookbook.tokenizer_utils import ( + get_registered_tokenizer_names, + register_tokenizer, +) + + +logger = logging.getLogger(__name__) + +MODEL_NAME = "thinkingmachines/Inkling" +RENDERER_NAME = "tml_v0" + + +class InklingRuntimeError(RuntimeError): + """The requested runtime binding is absent, ambiguous, or incompatible.""" + + +class InklingSupervisionDisabled(InklingRuntimeError): + """Track B deliberately does not expose an Inkling SFT serialization path.""" + + +@dataclass(frozen=True) +class InklingRuntimeBinding: + model_name: str + renderer_name: str + effort: float + + +@dataclass(frozen=True) +class DecodedInklingResponse: + """Channel-preserving interpretation of one sampled token sequence.""" + + raw_text: str + reasoning_text: str + final_text: str + termination: ParseTermination + + @property + def channel_parse_complete(self) -> bool: + return self.termination is ParseTermination.STOP_SEQUENCE + + +def _validate_effort(value: float) -> float: + if ( + isinstance(value, bool) + or not isinstance(value, (int, float)) + or not math.isfinite(float(value)) + or not 0.0 <= float(value) < 1.0 + ): + raise ValueError("Inkling effort must be an explicit finite number in [0, 1)") + return float(value) + + +class InklingTokenizer: + """Cookbook-facing adapter over the native TML o200k chat tokenizer.""" + + name_or_path = MODEL_NAME + + def __init__(self) -> None: + self._tokenizer = tml_tokenizers.o200k_base_chat() + + @property + def tml_tokenizer(self) -> tml_tokenizers.O200kBaseChatTokenizer: + return self._tokenizer + + @property + def eos_token(self) -> str: + return self._tokenizer.eos_token + + def decode(self, token_ids: Sequence[int], **_kwargs: Any) -> str: + return self._tokenizer.decode(list(token_ids)) + + def encode( + self, + text: str, + add_special_tokens: bool = False, + **_kwargs: Any, + ) -> list[int]: + if add_special_tokens: + raise NotImplementedError( + "the Inkling tokenizer bridge encodes ordinary text only" + ) + return self._tokenizer.encode_ordinary(text) + + +_TOKENIZER: InklingTokenizer | None = None +_RUNTIME_EFFORT: float | None = None + + +def _tokenizer_factory() -> InklingTokenizer: + global _TOKENIZER + if _TOKENIZER is None: + _TOKENIZER = InklingTokenizer() + return _TOKENIZER + + +def _pil_to_data_uri(image: Any) -> str: + buffer = io.BytesIO() + image.convert("RGB").save(buffer, format="PNG") + payload = base64.b64encode(buffer.getvalue()).decode("ascii") + return f"data:image/png;base64,{payload}" + + +def _normalize_media(messages: Sequence[Message]) -> list[dict[str, Any]]: + """Convert Cookbook image parts to the OSS image shape TMLv0 consumes.""" + + normalized: list[dict[str, Any]] = [] + for message in messages: + item = dict(message) + content = item.get("content") + if not isinstance(content, list): + normalized.append(item) + continue + parts: list[Any] = [] + for part in content: + if isinstance(part, dict) and part.get("type") == "image": + image = part.get("image") + if isinstance(image, str): + url = image + elif hasattr(image, "convert") and hasattr(image, "save"): + url = _pil_to_data_uri(image) + else: + raise TypeError( + "TML ImagePart.image must be a string or PIL-compatible image" + ) + parts.append({"type": "image_url", "image_url": {"url": url}}) + else: + parts.append(part) + normalized.append({**item, "content": parts}) + return normalized + + +def _render_input(messages: Sequence[Message]) -> list[tml_chat.OpenAIMessage]: + return list( + tml_chat.OpenAIMessage.from_oss_messages(_normalize_media(messages)) + ) + + +class InklingTmlV0Renderer(Renderer): + """Sampling-only Cookbook renderer over native TMLv0 channels.""" + + supports_streaming = False + + def __init__(self, tokenizer: InklingTokenizer, *, effort: float) -> None: + if not isinstance(tokenizer, InklingTokenizer): + raise TypeError("InklingTmlV0Renderer requires InklingTokenizer") + super().__init__(tokenizer) + self.effort = _validate_effort(effort) + self._tml = tml_v0.Renderer(tokenizer.tml_tokenizer) + + def __reduce__(self) -> tuple[Any, tuple[float]]: + # Do not depend on process-global Cookbook registries when an + # EnvGroupBuilder is unpickled in a worker. + return (_restore_renderer, (self.effort,)) + + @property + def has_extension_property(self) -> bool: + return True + + def get_stop_sequences(self) -> list[int]: + return list(self._tml.stop()) + + def build_generation_prompt( + self, + messages: list[Message], + role: Role = "assistant", + prefill: str | None = None, + ) -> tinker.ModelInput: + if role != "assistant": + raise NotImplementedError("TMLv0 generates assistant turns only") + if prefill is not None: + raise NotImplementedError("TMLv0 does not accept an assistant prefill") + spans, _parser = self._tml.render_for_completion_with_effort( + _render_input(messages), + self.effort, + ) + return tml_tinker.token_spans_to_tinker_model_input(spans) + + def _raw_decode(self, response: Sequence[int]) -> str: + try: + return str(self.tokenizer.decode(list(response))) + except Exception: + return "" + + def decode_response(self, response: Sequence[int]) -> DecodedInklingResponse: + """Parse native channels without ever folding reasoning into final text.""" + + sampled = list(response) + raw_text = self._raw_decode(sampled) + _spans, parser = self._tml.render_for_completion([]) + try: + parsed = list(parser.parse_tokens(sampled)) + except Exception: + return DecodedInklingResponse( + raw_text=raw_text, + reasoning_text="", + final_text="", + termination=ParseTermination.MALFORMED, + ) + + reasoning: list[str] = [] + final: list[str] = [] + saw_end = False + unknown_channel = False + for message in parsed: + content = message.content + if isinstance(content, tml_chat.ModelEndSampling): + saw_end = True + elif isinstance(content, tml_chat.Thinking): + reasoning.append(getattr(content, "text", str(content))) + elif isinstance(content, tml_chat.Text): + final.append(getattr(content, "text", str(content))) + else: + # Tools and future channel types are outside PixCell's + # image-to-code contract. Do not reinterpret them as code. + unknown_channel = True + + termination = ( + ParseTermination.STOP_SEQUENCE + if saw_end and not unknown_channel + else ParseTermination.MALFORMED + ) + return DecodedInklingResponse( + raw_text=raw_text, + reasoning_text="\n".join(reasoning), + final_text="\n".join(final), + termination=termination, + ) + + def parse_response( + self, + response: list[int], + ) -> tuple[Message, ParseTermination]: + decoded = self.decode_response(response) + parts = [] + if decoded.reasoning_text: + parts.append( + ThinkingPart(type="thinking", thinking=decoded.reasoning_text) + ) + if decoded.final_text: + parts.append(TextPart(type="text", text=decoded.final_text)) + return ( + Message(role="assistant", content=parts), + decoded.termination, + ) + + def decode_action_text(self, response: Sequence[int]) -> str: + """Return only the final Text channel for code extraction.""" + + return self.decode_response(response).final_text + + @staticmethod + def _supervision_disabled() -> NoReturn: + raise InklingSupervisionDisabled( + "Track B Inkling is RL-only; supervised TML serialization is disabled" + ) + + def render_message( + self, + message: Message, + ctx: RenderContext, + ) -> RenderedMessage: + del message, ctx + self._supervision_disabled() + + def build_supervised_examples( + self, + messages: list[Message], + train_on_what: TrainOnWhat = TrainOnWhat.LAST_ASSISTANT_TURN, + ) -> list[tuple[tinker.ModelInput, Any]]: + del messages, train_on_what + self._supervision_disabled() + + def build_supervised_example( + self, + messages: list[Message], + train_on_what: TrainOnWhat = TrainOnWhat.LAST_ASSISTANT_MESSAGE, + ) -> tuple[tinker.ModelInput, Any]: + del messages, train_on_what + self._supervision_disabled() + + +def _restore_renderer(effort: float) -> InklingTmlV0Renderer: + return InklingTmlV0Renderer(_tokenizer_factory(), effort=effort) + + +def _configured_effort() -> float: + if _RUNTIME_EFFORT is None: + raise InklingRuntimeError( + "install_inkling_runtime(effort=...) must run before renderer creation" + ) + return _RUNTIME_EFFORT + + +def _renderer_factory( + tokenizer: Any, + image_processor: Any = None, +) -> InklingTmlV0Renderer: + if image_processor is not None: + raise InklingRuntimeError( + "Inkling tokenizes images natively and must not receive an HF image processor" + ) + if not isinstance(tokenizer, InklingTokenizer): + raise InklingRuntimeError( + "the tml_v0 Track B registration is restricted to InklingTokenizer" + ) + return InklingTmlV0Renderer(tokenizer, effort=_configured_effort()) + + +def _is_inkling_model(model_name: str | None) -> bool: + return bool(model_name) and str(model_name).split(":", 1)[0] == MODEL_NAME + + +def _patch_sdk_tokenizer_loader() -> None: + """Route only Inkling SDK tokenizer requests to the native adapter.""" + + import tinker.lib.public_interfaces.sampling_client as sampling_client + import tinker.lib.public_interfaces.training_client as training_client + + def patch(module: Any) -> None: + original = module._load_tokenizer_from_model_info + if getattr(original, "_pixcell_track_b_inkling", False): + return + + def inkling_aware(model_name: str, tokenizer_id: str | None = None): + if _is_inkling_model(model_name): + return _tokenizer_factory() + return original(model_name, tokenizer_id) + + inkling_aware._pixcell_track_b_inkling = True + module._load_tokenizer_from_model_info = inkling_aware + + patch(sampling_client) + patch(training_client) + logger.info("installed Inkling-only Tinker SDK tokenizer bridge") + + +def install_inkling_runtime(*, effort: float) -> InklingRuntimeBinding: + """Install one immutable Inkling runtime binding for this process. + + Repeating the same binding is idempotent. Attempting to change effort + after registration fails rather than silently changing prompt identity. + """ + + global _RUNTIME_EFFORT + resolved = _validate_effort(effort) + if _RUNTIME_EFFORT is not None: + if resolved != _RUNTIME_EFFORT: + raise InklingRuntimeError( + f"Inkling effort is already bound to {_RUNTIME_EFFORT}; " + f"refusing {resolved}" + ) + _patch_sdk_tokenizer_loader() + return InklingRuntimeBinding(MODEL_NAME, RENDERER_NAME, resolved) + + if MODEL_NAME in get_registered_tokenizer_names(): + raise InklingRuntimeError( + f"tokenizer {MODEL_NAME!r} was registered before the Track B bridge" + ) + if RENDERER_NAME in get_registered_renderer_names(): + raise InklingRuntimeError( + f"renderer {RENDERER_NAME!r} was registered before the Track B bridge" + ) + + _RUNTIME_EFFORT = resolved + register_tokenizer(MODEL_NAME, _tokenizer_factory) + register_renderer(RENDERER_NAME, _renderer_factory) + _patch_sdk_tokenizer_loader() + return InklingRuntimeBinding(MODEL_NAME, RENDERER_NAME, resolved) From 9214d63b3e6bb7b52885cb7b16d21fe1f0c4d0b4 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 04:27:26 -0400 Subject: [PATCH 18/24] Winner harvesting and winner-label SFT plumbing MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit harvest.py samples from a trained checkpoint over TRAIN tasks only, verifies every completion with the deterministic evaluator, and keeps gate-passing winners (iou_n >= 0.5, degenerate branch handled) deduped best-per-task with per-representation caps. apply_winner_labels projects the train split onto a winner corpus so the SFT builder (winner_labels_path) distills the policy's own verified programs — the entropy-preserving warm start the old library's law prescribes and its paper lineage used (entry entropy 0.32 vs canon-SFT's 0.096, both now measured). Not launched: sampling waits for a free lane per the overnight concurrency discipline. --- rl/track_a/harvest.py | 225 ++++++++++++++++++++++++++++++++++++++ rl/track_a/tinker_data.py | 5 + rl/track_a/train_sft.py | 2 + 3 files changed, 232 insertions(+) create mode 100644 rl/track_a/harvest.py diff --git a/rl/track_a/harvest.py b/rl/track_a/harvest.py new file mode 100644 index 00000000..e2eebdd5 --- /dev/null +++ b/rl/track_a/harvest.py @@ -0,0 +1,225 @@ +"""Winner harvesting: sample from a trained checkpoint over TRAIN tasks, +verify with the deterministic evaluator, keep gate-passing winners. + +The entropy-preserving warm-start corpus: programs the policy itself wrote +and the verifier approved (iou_n >= threshold, pure, executable). Winners +come only from the train split — validation never enters any SFT corpus. +Old-library law: winner distillation is the healthiest prior; its paper +lineage entered RL at entropy 0.32 on exactly this class of corpus, versus +0.096 for ground-truth canon. +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +from collections import defaultdict +from pathlib import Path + +import tinker + +from rl.common.baselines import cached_rect_baseline +from rl.common.contracts import TaskRecord +from rl.common.dataset_io import load_tasks +from rl.common.evaluator import EvaluationStatus, PixCellEvaluator +from rl.common.output import extract_code +from rl.track_a.reward import norm_excess +from rl.track_a.tinker_data import _message, _renderer + + +async def _sample( + tasks: list[TaskRecord], + *, + model_name: str, + renderer_name: str, + model_path: str | None, + samples_per_task: int, + max_tokens: int, + temperature: float, +) -> list[list[str]]: + renderer = _renderer(model_name, renderer_name) + service = tinker.ServiceClient() + client = ( + service.create_sampling_client(model_path=model_path) + if model_path + else service.create_sampling_client(base_model=model_name) + ) + params = tinker.SamplingParams( + max_tokens=max_tokens, + temperature=temperature, + stop=renderer.get_stop_sequences(), + ) + + async def one(task: TaskRecord) -> list[str]: + prompt = renderer.build_generation_prompt([_message(task, max_image=1440)]) + response = await client.sample_async( + prompt=prompt, num_samples=samples_per_task, sampling_params=params + ) + out = [] + for sequence in response.sequences: + message, _t = renderer.parse_response(list(sequence.tokens)) + from tinker_cookbook.renderers import get_text_content + + out.append(get_text_content(message)) + return out + + results: list[list[str]] = [] + batch = 24 + for start in range(0, len(tasks), batch): + chunk = tasks[start : start + batch] + results.extend(await asyncio.gather(*(one(t) for t in chunk))) + print(f"[harvest] sampled {min(start+batch, len(tasks))}/{len(tasks)}", flush=True) + return results + + +def harvest( + *, + dataset_root: str, + levels: tuple[str, ...], + model_name: str, + renderer_name: str, + model_path: str | None, + out_path: str, + samples_per_task: int = 2, + max_tokens: int = 4096, + temperature: float = 1.0, + min_iou_n: float = 0.5, + degenerate_min_iou: float = 0.9, + per_representation_cap: int = 8, +) -> dict: + train = [ + t + for t in load_tasks(Path(dataset_root), configuration="depth", split="train") + if t.sampler.level in levels + ] + print(f"[harvest] {len(train)} train tasks across {levels}", flush=True) + completions = asyncio.run( + _sample( + train, + model_name=model_name, + renderer_name=renderer_name, + model_path=model_path, + samples_per_task=samples_per_task, + max_tokens=max_tokens, + temperature=temperature, + ) + ) + pairs = [ + (task, completion) + for task, batch in zip(train, completions) + for completion in batch + ] + with PixCellEvaluator(max_workers=4, evaluator_retries=3) as evaluator: + results = evaluator.evaluate_batch( + [(task.reference, completion) for task, completion in pairs] + ) + best: dict[str, dict] = {} + per_rep = defaultdict(int) + kept = examined = 0 + for (task, completion), result in zip(pairs, results): + examined += 1 + if result.status is not EvaluationStatus.OK or result.iou is None: + continue + baseline = cached_rect_baseline(task.reference) + iou_n = norm_excess(float(result.iou), baseline.iou_rect) + degenerate = baseline.iou_rect >= 0.95 + win = (iou_n >= min_iou_n) or (degenerate and float(result.iou) >= degenerate_min_iou) + if not win: + continue + key = task.sampler.opaque_id + score = iou_n if not degenerate else float(result.iou) + record = { + "opaque_id": key, + "level": task.sampler.level, + "representation_id": task.sampler.representation_id, + "iou": float(result.iou), + "iou_n": float(iou_n), + "degenerate": degenerate, + "code": extract_code(completion), + } + if key not in best or score > best[key]["iou_n"]: + best[key] = record + winners = [] + for record in sorted(best.values(), key=lambda r: -r["iou_n"]): + if per_rep[record["representation_id"]] >= per_representation_cap: + continue + per_rep[record["representation_id"]] += 1 + winners.append(record) + kept += 1 + by_level = defaultdict(int) + for w in winners: + by_level[w["level"]] += 1 + out = Path(out_path) + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w", encoding="utf-8") as sink: + for w in winners: + sink.write(json.dumps(w) + "\n") + summary = { + "examined": examined, + "unique_tasks_won": len(best), + "kept_after_caps": kept, + "by_level": dict(by_level), + "out": str(out), + } + print("[harvest]", json.dumps(summary), flush=True) + return summary + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--dataset-root", default=str(Path(__file__).resolve().parents[2] / "dataset")) + ap.add_argument("--levels", default="L0,L1") + ap.add_argument("--model-name", default="Qwen/Qwen3.6-35B-A3B") + ap.add_argument("--renderer-name", default="qwen3_5_disable_thinking") + ap.add_argument("--model-path", default="") + ap.add_argument("--out", required=True) + ap.add_argument("--samples-per-task", type=int, default=2) + ap.add_argument("--temperature", type=float, default=1.0) + args = ap.parse_args() + from rl.track_a.transport import disable_pyqwest_transport + + disable_pyqwest_transport() + harvest( + dataset_root=args.dataset_root, + levels=tuple(x.strip().upper() for x in args.levels.split(",") if x.strip()), + model_name=args.model_name, + renderer_name=args.renderer_name, + model_path=args.model_path or None, + out_path=args.out, + samples_per_task=args.samples_per_task, + temperature=args.temperature, + ) + + +if __name__ == "__main__": + main() + + +def apply_winner_labels( + tasks: list[TaskRecord], + winners_path: str | Path, +) -> list[TaskRecord]: + """Project TRAIN tasks onto a harvested winner corpus: keep only tasks + that have a verified winner and replace the GT label with the policy's + own winning program. The SFT that consumes this distills the model's own + verified distribution — the entropy-preserving warm start.""" + + import dataclasses + + labels: dict[str, str] = {} + for line in Path(winners_path).read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + record = json.loads(line) + code = record.get("code", "").strip() + if code: + labels[record["opaque_id"]] = code + projected = [ + dataclasses.replace(task, label=labels[task.sampler.opaque_id]) + for task in tasks + if task.sampler.opaque_id in labels + ] + if not projected: + raise ValueError(f"no tasks match winners in {winners_path}") + return projected diff --git a/rl/track_a/tinker_data.py b/rl/track_a/tinker_data.py index ae3b6021..46ff35d8 100644 --- a/rl/track_a/tinker_data.py +++ b/rl/track_a/tinker_data.py @@ -239,6 +239,7 @@ class TrackASupervisedDatasetBuilder(SupervisedDatasetBuilder): levels: str = "" include_validation: bool = True schedule_seed: int = 0 + winner_labels_path: str = "" def __call__(self): renderer = _renderer(self.model_name, self.renderer_name) @@ -263,6 +264,10 @@ def __call__(self): if self.include_validation else [] ) + if self.winner_labels_path: + from rl.track_a.harvest import apply_winner_labels + + train = apply_winner_labels(train, self.winner_labels_path) return ( TrackASupervisedDataset( train, diff --git a/rl/track_a/train_sft.py b/rl/track_a/train_sft.py index a865b0d1..0451abd8 100644 --- a/rl/track_a/train_sft.py +++ b/rl/track_a/train_sft.py @@ -40,6 +40,7 @@ class CLIConfig: renderer_name: str = DEFAULT_RENDERER load_checkpoint_path: str = "" levels: str = "L0" + winner_labels_path: str = "" lora_rank: int = 32 learning_rate: float = 1e-4 lr_schedule: str = "linear" @@ -90,6 +91,7 @@ async def cli_main(cfg: CLIConfig) -> None: max_length=cfg.max_length, max_image=cfg.max_image, levels=cfg.levels, + winner_labels_path=cfg.winner_labels_path, ), learning_rate=cfg.learning_rate, lr_schedule=cfg.lr_schedule, From e10aea58f278b5fb33ec8dbd8e410c522bf952aa Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 07:56:49 -0400 Subject: [PATCH 19/24] Harvest: status diagnostics and threshold/tokens flags --- rl/track_a/harvest.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/rl/track_a/harvest.py b/rl/track_a/harvest.py index e2eebdd5..29a5aa08 100644 --- a/rl/track_a/harvest.py +++ b/rl/track_a/harvest.py @@ -116,9 +116,11 @@ def harvest( ) best: dict[str, dict] = {} per_rep = defaultdict(int) + status_counts: dict[str, int] = defaultdict(int) kept = examined = 0 for (task, completion), result in zip(pairs, results): examined += 1 + status_counts[result.status.value] += 1 if result.status is not EvaluationStatus.OK or result.iou is None: continue baseline = cached_rect_baseline(task.reference) @@ -157,6 +159,7 @@ def harvest( sink.write(json.dumps(w) + "\n") summary = { "examined": examined, + "status_counts": dict(status_counts), "unique_tasks_won": len(best), "kept_after_caps": kept, "by_level": dict(by_level), @@ -176,6 +179,8 @@ def main() -> None: ap.add_argument("--out", required=True) ap.add_argument("--samples-per-task", type=int, default=2) ap.add_argument("--temperature", type=float, default=1.0) + ap.add_argument("--min-iou-n", type=float, default=0.5) + ap.add_argument("--max-tokens", type=int, default=4096) args = ap.parse_args() from rl.track_a.transport import disable_pyqwest_transport @@ -189,6 +194,8 @@ def main() -> None: out_path=args.out, samples_per_task=args.samples_per_task, temperature=args.temperature, + min_iou_n=args.min_iou_n, + max_tokens=args.max_tokens, ) From 8c1cd421309d960582ff2e37b0496f55b5a3f01b Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 10:30:17 -0400 Subject: [PATCH 20/24] F1-F8 benchmark protocol evaluator (paper Table III: mean and best-of-8 raw IoU) --- rl/track_a/benchmark_eval.py | 184 +++++++++++++++++++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 rl/track_a/benchmark_eval.py diff --git a/rl/track_a/benchmark_eval.py b/rl/track_a/benchmark_eval.py new file mode 100644 index 00000000..5b0b2647 --- /dev/null +++ b/rl/track_a/benchmark_eval.py @@ -0,0 +1,184 @@ +"""The paper's F1-F8 benchmark protocol on a checkpoint: 8 attempts per +target at T=1.0, per-target best-of-8 and mean raw IoU — the exact +evaluation behind the paper's Table III (its trained model: mean 0.254, +best-of-8 0.466). Fixtures are the frozen data/benchmark devices; the model +sees the same raster it is scored against, per the historical protocol. +""" + +from __future__ import annotations + +import argparse +import asyncio +import hashlib +import json +from pathlib import Path + +import tinker + +from rl.common.contracts import ModelObservation, TaskRecord, SamplerMetadata, VerifierReference +from rl.common.evaluator import EvaluationStatus, PixCellEvaluator +from rl.track_a.tinker_data import _message, _renderer + + +def _fixture_tasks(benchmark_root: Path) -> list[TaskRecord]: + tasks = [] + for index in range(1, 9): + directory = benchmark_root / f"final_{index}" + payload = (directory / "device_bw.png").read_bytes() + footprint = tuple( + json.loads((directory / "footprint.json").read_text())["footprint_um"] + ) + digest = hashlib.sha256(payload).hexdigest() + observation = ModelObservation( + image_bytes=payload, footprint_um=footprint, image_sha256=digest + ) + reference = VerifierReference( + target_image_bytes=payload, + footprint_um=footprint, + target_image_sha256=digest, + ) + sampler = SamplerMetadata( + opaque_id=f"final_{index}", + level="BENCH", + representation_id=f"final_{index}", + leakage_group_id=f"final_{index}", + realization_slot=None, + split_role="benchmark", + ) + tasks.append( + TaskRecord( + observation=observation, + label="", + reference=reference, + sampler=sampler, + ) + ) + return tasks + + +def run_benchmark( + *, + benchmark_root: str, + model_name: str, + renderer_name: str, + model_path: str | None, + max_tokens: int, + attempts: int = 8, + temperature: float = 1.0, + out_path: str | None = None, + model_effort: float | None = None, +) -> dict: + tasks = _fixture_tasks(Path(benchmark_root)) + renderer = _renderer(model_name, renderer_name, effort=model_effort) + service = tinker.ServiceClient() + client = ( + service.create_sampling_client(model_path=model_path) + if model_path + else service.create_sampling_client(base_model=model_name) + ) + params = tinker.SamplingParams( + max_tokens=max_tokens, + temperature=temperature, + stop=renderer.get_stop_sequences(), + ) + + async def sample_all() -> list[list[str]]: + async def one(task: TaskRecord) -> list[str]: + prompt = renderer.build_generation_prompt( + [_message(task, max_image=1440)] + ) + response = await client.sample_async( + prompt=prompt, num_samples=attempts, sampling_params=params + ) + from tinker_cookbook.renderers import get_text_content + + out = [] + for sequence in response.sequences: + message, _t = renderer.parse_response(list(sequence.tokens)) + out.append(get_text_content(message)) + return out + + return list(await asyncio.gather(*(one(t) for t in tasks))) + + completions = asyncio.run(sample_all()) + pairs = [ + (task, completion) + for task, batch in zip(tasks, completions) + for completion in batch + ] + with PixCellEvaluator( + max_workers=4, evaluator_retries=3 + ) as evaluator: + results = evaluator.evaluate_batch( + [(task.reference, completion) for task, completion in pairs] + ) + per_target: dict[str, dict] = {} + cursor = 0 + for task, batch in zip(tasks, completions): + scores = [] + executable = 0 + for _ in batch: + result = results[cursor] + cursor += 1 + if result.status is EvaluationStatus.OK and result.iou is not None: + executable += 1 + scores.append(float(result.iou)) + else: + scores.append(0.0) + per_target[task.sampler.opaque_id] = { + "executable": executable, + "attempts": len(batch), + "mean_iou": sum(scores) / len(scores), + "best_iou": max(scores), + } + targets = list(per_target.values()) + report = { + "model_name": model_name, + "model_path": model_path, + "attempts": attempts, + "temperature": temperature, + "per_target": per_target, + "overall": { + "executable": sum(t["executable"] for t in targets), + "total": sum(t["attempts"] for t in targets), + "mean_iou": sum(t["mean_iou"] for t in targets) / len(targets), + "mean_best_of_k": sum(t["best_iou"] for t in targets) / len(targets), + }, + } + if out_path: + path = Path(out_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(report, indent=2), encoding="utf-8") + print(json.dumps(report["overall"], indent=2), flush=True) + return report + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + repo = Path(__file__).resolve().parents[2] + ap.add_argument("--benchmark-root", default=str(repo / "data" / "benchmark")) + ap.add_argument("--model-name", default="Qwen/Qwen3.6-35B-A3B") + ap.add_argument("--renderer-name", default="qwen3_5_disable_thinking") + ap.add_argument("--model-path", default="") + ap.add_argument("--model-effort", type=float, default=-1.0) + ap.add_argument("--max-tokens", type=int, default=4096) + ap.add_argument("--attempts", type=int, default=8) + ap.add_argument("--out", required=True) + args = ap.parse_args() + from rl.track_a.transport import disable_pyqwest_transport + + disable_pyqwest_transport() + run_benchmark( + benchmark_root=args.benchmark_root, + model_name=args.model_name, + renderer_name=args.renderer_name, + model_path=args.model_path or None, + max_tokens=args.max_tokens, + attempts=args.attempts, + out_path=args.out, + model_effort=args.model_effort if args.model_effort >= 0 else None, + ) + + +if __name__ == "__main__": + main() From 812eb23bcc21484f958fb233b880a3b474ca3bf9 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 10:40:57 -0400 Subject: [PATCH 21/24] Iterative agent loop on the benchmark + chamfer asymmetry feedback The deployment protocol from the private library, minimally ported: init round of 8 attempts, champion selection, deterministic number- redacted feedback (closeness bucket, EXTRA/MISSING material direction from full-mask chamfer asymmetry now recorded by the evaluator), then revision rounds seeded by the champion. Champions carry their code for gallery rendering. --- rl/common/evaluator.py | 9 ++ rl/track_a/agent_loop.py | 228 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 237 insertions(+) create mode 100644 rl/track_a/agent_loop.py diff --git a/rl/common/evaluator.py b/rl/common/evaluator.py index ec17ca91..6187b51e 100644 --- a/rl/common/evaluator.py +++ b/rl/common/evaluator.py @@ -804,11 +804,20 @@ def evaluate( boundary_mask(target_mask), 2.0 / (scale_x + scale_y), ) + _full, full_a_to_b, full_b_to_a = chamfer_sym_um( + raster_mask, target_mask, 2.0 / (scale_x + scale_y) + ) metrics = { **metrics, "chamfer_boundary_um": ( None if chamfer_b == float("inf") else float(chamfer_b) ), + "chamfer_pred_to_target_um": ( + None if full_a_to_b == float("inf") else float(full_a_to_b) + ), + "chamfer_target_to_pred_um": ( + None if full_b_to_a == float("inf") else float(full_b_to_a) + ), } diagnostics = _json_diagnostic( { diff --git a/rl/track_a/agent_loop.py b/rl/track_a/agent_loop.py new file mode 100644 index 00000000..8dd8d6b1 --- /dev/null +++ b/rl/track_a/agent_loop.py @@ -0,0 +1,228 @@ +"""The iterative deployment loop on the F1-F8 benchmark: attempt, measure, +revise. A faithful minimal port of the private library's agent loop — +init round of k attempts, then revision rounds seeded by the current +champion plus deterministic, number-redacted feedback (closeness bucket, +material direction from chamfer asymmetry, error class). The teacher +directs attention; it never dictates values. +""" + +from __future__ import annotations + +import argparse +import asyncio +import json +import re +from pathlib import Path + +import tinker + +from rl.common.baselines import cached_rect_baseline +from rl.common.contracts import TaskRecord +from rl.common.evaluator import EvaluationResult, EvaluationStatus, PixCellEvaluator +from rl.common.output import extract_code +from rl.track_a.benchmark_eval import _fixture_tasks +from rl.track_a.reward import norm_excess +from rl.track_a.tinker_data import _message, _renderer + + +def _scrub(text: str) -> str: + return re.sub(r"[0-9]+(?:\.[0-9]+)?", "#", text) + + +def _feedback(result: EvaluationResult, task: TaskRecord) -> str: + if result.status is not EvaluationStatus.OK or result.iou is None: + reason = _scrub(str(result.error or result.status.value))[:300] + return ( + "Your previous program FAILED to produce a valid device " + f"({result.status.value}): {reason}\n" + "Fix the failure and output a complete corrected program." + ) + baseline = cached_rect_baseline(task.reference) + iou_n = norm_excess(float(result.iou), baseline.iou_rect) + if iou_n >= 0.8: + closeness = "VERY CLOSE to the target" + elif iou_n >= 0.5: + closeness = "close to the target, but clearly imperfect" + elif iou_n >= 0.1: + closeness = "partially matching the target" + else: + closeness = "far from the target beyond its overall footprint" + pred_to_target = result.metrics.get("chamfer_pred_to_target_um") + target_to_pred = result.metrics.get("chamfer_target_to_pred_um") + direction = "" + if pred_to_target is not None and target_to_pred is not None: + if pred_to_target > 1.5 * target_to_pred: + direction = ( + "Your rendering has EXTRA material the target does not have — " + "simplify or remove structure." + ) + elif target_to_pred > 1.5 * pred_to_target: + direction = ( + "Your rendering is MISSING material the target has — add or " + "extend structure." + ) + else: + direction = ( + "Material amounts roughly balance; refine shapes, positions, " + "and repeated-element counts." + ) + return ( + f"Your previous program executed. Its rendering is {closeness}. " + f"{direction}\n" + "Revise the program to better match the attached target image. " + "Output only the complete corrected Python program." + ) + + +def run_agent_loop( + *, + benchmark_root: str, + model_name: str, + renderer_name: str, + model_path: str | None, + max_tokens: int, + init_attempts: int = 8, + revisions_per_round: int = 4, + rounds: int = 3, + temperature: float = 1.0, + out_path: str | None = None, + model_effort: float | None = None, +) -> dict: + tasks = _fixture_tasks(Path(benchmark_root)) + renderer = _renderer(model_name, renderer_name, effort=model_effort) + service = tinker.ServiceClient() + client = ( + service.create_sampling_client(model_path=model_path) + if model_path + else service.create_sampling_client(base_model=model_name) + ) + params = tinker.SamplingParams( + max_tokens=max_tokens, + temperature=temperature, + stop=renderer.get_stop_sequences(), + ) + from tinker_cookbook.renderers import Message, TextPart, get_text_content + + async def sample(conversation, count): + prompt = renderer.build_generation_prompt(conversation) + response = await client.sample_async( + prompt=prompt, num_samples=count, sampling_params=params + ) + out = [] + for sequence in response.sequences: + message, _t = renderer.parse_response(list(sequence.tokens)) + out.append(get_text_content(message)) + return out + + evaluator = PixCellEvaluator(max_workers=4, evaluator_retries=3) + report: dict[str, dict] = {} + try: + for task in tasks: + user = _message(task, max_image=1440) + history: list[dict] = [] + champion_code: str | None = None + champion_result: EvaluationResult | None = None + champion_iou = -1.0 + for round_index in range(rounds + 1): + if round_index == 0: + conversation = [user] + count = init_attempts + else: + assert champion_result is not None + feedback = _feedback(champion_result, task) + conversation = [ + user, + Message( + role="assistant", + content=[ + TextPart( + type="text", + text=f"```python\n{champion_code}\n```", + ) + ], + ), + Message( + role="user", + content=[TextPart(type="text", text=feedback)], + ), + ] + count = revisions_per_round + completions = asyncio.run(sample(conversation, count)) + results = evaluator.evaluate_batch( + [(task.reference, completion) for completion in completions] + ) + round_best = -1.0 + for completion, result in zip(completions, results): + iou = ( + float(result.iou) + if result.status is EvaluationStatus.OK and result.iou + else 0.0 + ) + round_best = max(round_best, iou) + if iou > champion_iou or champion_result is None: + champion_iou = max(iou, 0.0) + champion_code = extract_code(completion) + champion_result = result + history.append( + {"round": round_index, "samples": count, "round_best": round_best, + "champion_iou": champion_iou} + ) + print( + f"[{task.sampler.opaque_id}] round {round_index}: " + f"best {round_best:.3f}, champion {champion_iou:.3f}", + flush=True, + ) + report[task.sampler.opaque_id] = { + "champion_iou": champion_iou, + "champion_code": champion_code or "", + "footprint_um": list(task.observation.footprint_um), + "rounds": history, + } + finally: + evaluator.close() + champions = [entry["champion_iou"] for entry in report.values()] + summary = { + "model_path": model_path, + "init_attempts": init_attempts, + "revisions_per_round": revisions_per_round, + "rounds": rounds, + "per_target": report, + "overall_mean_champion": sum(champions) / len(champions), + } + if out_path: + path = Path(out_path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(summary, indent=2), encoding="utf-8") + print(json.dumps({"overall_mean_champion": summary["overall_mean_champion"]}), flush=True) + return summary + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + repo = Path(__file__).resolve().parents[2] + ap.add_argument("--benchmark-root", default=str(repo / "data" / "benchmark")) + ap.add_argument("--model-name", default="Qwen/Qwen3.6-35B-A3B") + ap.add_argument("--renderer-name", default="qwen3_5_disable_thinking") + ap.add_argument("--model-path", default="") + ap.add_argument("--model-effort", type=float, default=-1.0) + ap.add_argument("--max-tokens", type=int, default=4096) + ap.add_argument("--rounds", type=int, default=3) + ap.add_argument("--out", required=True) + args = ap.parse_args() + from rl.track_a.transport import disable_pyqwest_transport + + disable_pyqwest_transport() + run_agent_loop( + benchmark_root=args.benchmark_root, + model_name=args.model_name, + renderer_name=args.renderer_name, + model_path=args.model_path or None, + max_tokens=args.max_tokens, + rounds=args.rounds, + out_path=args.out, + model_effort=args.model_effort if args.model_effort >= 0 else None, + ) + + +if __name__ == "__main__": + main() From 9daff11736c18499c628cc20dd3a622a3a987a00 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 11:01:00 -0400 Subject: [PATCH 22/24] Publish hygiene: drop internal process docs, neutralize runs-root default The design spec and plan quoted private-campaign history and local paths; the public rationale lives in rl/README.md. Launch scripts pin the runs root explicitly so running campaigns are unaffected. --- .../plans/2026-07-28-rl-clean-rebuild.md | 251 ----------------- .../2026-07-28-rl-clean-rebuild-design.md | 266 ------------------ rl/track_a/run.py | 2 +- 3 files changed, 1 insertion(+), 518 deletions(-) delete mode 100644 docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md delete mode 100644 docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md diff --git a/docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md b/docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md deleted file mode 100644 index 6b551968..00000000 --- a/docs/superpowers/plans/2026-07-28-rl-clean-rebuild.md +++ /dev/null @@ -1,251 +0,0 @@ -# RL Clean Rebuild Implementation Plan - -> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. - -**Goal:** Restore the proven `v3-b` shaped reward (`iou_n`) in `rl/track_a`, delete the studies/ceremony apparatus, and produce a lean two-run launcher (Run A `l0sft-rl`, Run B `base-rl`) over the sealed depth-v1 dataset. - -**Architecture:** `rl/common/` stays the measurement layer (gains `baselines.py` + a boundary-chamfer diagnostic in `evaluator.py`). `rl/track_a/` becomes the single training path: `reward.py` gains policy `shaped_v3b`; `tinker_data.py` keeps its Env/builders (baseline plumbed into the reward call); `train_sft.py`/`train_rl.py` are rewritten lean; a new `run.py` orchestrates stages, probe evals, and provenance. `rl/studies/`, `rl/evaluation/`, `rl/track_b/`, `rl/track_c/`, and track_a's recipe-lock/contract files are deleted. - -**Tech Stack:** tinker 0.22.7 + tinker-cookbook ca232b0 (pinned), gdsfactory 9.20.7, scipy 1.18 (present), PIL/numpy, pytest, uv. - -## Global Constraints - -- Reward constants are the old library's, byte-for-byte: floor 0.05; weights 0.40/0.15/0.40; `TAU_FRAC = 0.05`; `TEACHER_DENOM_FLOOR = 0.3`; `DEGENERATE_IOU_RECT = 0.95`. Never re-tuned. -- Purity stays pre-execution: `SOURCE_REJECTED → 0.0` (old ship-mode semantics). No floor for impure programs. -- `None` reward (REFERENCE/EVALUATOR fault) keeps aborting the run — never becomes 0. -- Training defaults: `Qwen/Qwen3.6-35B-A3B`, renderer `qwen3_5_disable_thinking`, LoRA 32, G=8 rollouts × 8 groups, lr 1e-5 RL / 1e-4 SFT, max_tokens 4096, temp 1.0, image 1440, KL 0, `importance_sampling`, `remove_constant_reward_groups=True`, 30 steps/level, 80/20 replay. -- `rl/common` may not import `rl.track_a`, `tinker`, `tinker_cookbook`, or `chz` (pinned by `rl/common/tests/test_import_boundaries.py`). -- `_json_diagnostic` rejects non-finite floats: any chamfer that is `inf` must be recorded as `None`. -- No AI-attribution trailers in commits. Run `uv run pytest rl/ -q` before every commit; `uv run ruff check rl/` at task ends. - ---- - -### Task 1: `rl/common/baselines.py` — per-task rectangle baseline - -**Files:** -- Create: `rl/common/baselines.py` -- Test: `rl/common/tests/test_baselines.py` - -**Interfaces:** -- Produces: `TaskBaseline` (frozen dataclass: `iou_rect: float`, `dice_rect: float`, `chamfer_rect_boundary_um: float | None`, `diag_um: float`), `rect_baseline(reference: VerifierReference) -> TaskBaseline` (pure), `cached_rect_baseline(reference) -> TaskBaseline` (module-level dict cache keyed by `reference.target_image_sha256`), `boundary_mask(mask)` and `chamfer_sym_um(mask_a, mask_b, um_per_px)` (ported verbatim from the old `rl/reward/geometry.py`). -- Consumes: `rl.common.contracts.VerifierReference`, `rl.common.evaluator._prepare_reference` (reuses the evaluator's own calibration so the baseline and the score share one geometry). - -- [ ] Write `test_baselines.py` with two synthetic targets, `footprint_um=(30.0, 20.0)`: - (a) a solid 60×40 black rectangle at (20,20) in a 100×80 PNG — its ink bbox equals the ink, so the rectangle fill IS the target: assert `iou_rect == 1.0` and `dice_rect == 1.0` (this is the degenerate case Task 3's branch keys on); - (b) an L-shaped mask — hand-compute `iou_rect = ink_area / bbox_area`, `dice_rect = 2·ink/(ink + bbox)`; assert both, assert `chamfer_rect_boundary_um` is finite and > 0, and `diag_um == sqrt(30² + 20²)`; assert `cached_rect_baseline` returns the identical object for the same sha. -- [ ] Run: `uv run pytest rl/common/tests/test_baselines.py -q` — expect import failure. -- [ ] Implement. Core (port of old `compute_baselines.rect_baseline` over `_prepare_reference`): - -```python -from __future__ import annotations -from dataclasses import dataclass -import numpy as np -from scipy.ndimage import binary_erosion, distance_transform_edt -from rl.common.contracts import VerifierReference -from rl.common.evaluator import _prepare_reference - -def boundary_mask(mask): - return mask & ~binary_erosion(mask) - -def chamfer_sym_um(mask_a, mask_b, um_per_px): - if not mask_a.any() or not mask_b.any(): - inf = float("inf"); return inf, inf, inf - dist_to_b = distance_transform_edt(~mask_b) - dist_to_a = distance_transform_edt(~mask_a) - a_to_b = float(dist_to_b[mask_a].mean()) * um_per_px - b_to_a = float(dist_to_a[mask_b].mean()) * um_per_px - return 0.5 * (a_to_b + b_to_a), a_to_b, b_to_a - -@dataclass(frozen=True) -class TaskBaseline: - iou_rect: float - dice_rect: float - chamfer_rect_boundary_um: float | None - diag_um: float - -def rect_baseline(reference: VerifierReference) -> TaskBaseline: - prepared = _prepare_reference(reference) - arr = np.asarray(prepared.target) # already "L" - thr = prepared.calibration["white_threshold"] - x0, y0, x1, y1 = prepared.calibration["bbox"] - tgt = arr < thr - pred = np.zeros_like(tgt); pred[y0:y1, x0:x1] = True - inter = float(np.logical_and(tgt, pred).sum()) - union = float(np.logical_or(tgt, pred).sum()) - denom = float(tgt.sum() + pred.sum()) - sx, sy = prepared.calibration["scale_px_per_um"] - um_per_px = 2.0 / (sx + sy) - chamfer_b, _, _ = chamfer_sym_um(boundary_mask(pred), boundary_mask(tgt), um_per_px) - fw, fh = prepared.calibration["footprint_um"] - return TaskBaseline( - iou_rect=inter / union if union else 0.0, - dice_rect=2.0 * inter / denom if denom else 0.0, - chamfer_rect_boundary_um=None if chamfer_b == float("inf") else chamfer_b, - diag_um=(fw * fw + fh * fh) ** 0.5, - ) - -_CACHE: dict[str, TaskBaseline] = {} - -def cached_rect_baseline(reference: VerifierReference) -> TaskBaseline: - key = reference.target_image_sha256 - if key not in _CACHE: - _CACHE[key] = rect_baseline(reference) - return _CACHE[key] -``` - -- [ ] Tests pass; `git add` + commit `"Add rectangle-baseline computation to rl/common"`. - -### Task 2: boundary chamfer in the evaluator - -**Files:** -- Modify: `rl/common/evaluator.py` (the `compute_metrics` call site, ~line 797) -- Test: extend `rl/common/tests/test_evaluator.py` - -**Interfaces:** -- Produces: `result.metrics["chamfer_boundary_um"]: float | None` on every `OK` result (µm, mean-axis scale; `None` when either mask is empty). -- Consumes: `boundary_mask`, `chamfer_sym_um` from `rl.common.baselines` — import them INSIDE the compare block (lazy) or top-level; **top-level import is fine** (`baselines` imports `evaluator` only for `_prepare_reference` — avoid the cycle by moving `_prepare_reference` usage in baselines to a function-local import, or import `baselines`' pure helpers from a `rl/common/geometry.py`). **Resolution: put `boundary_mask`/`chamfer_sym_um` in `rl/common/geometry.py` (no imports beyond numpy/scipy); `baselines.py` and `evaluator.py` both import from it. No cycle.** - -- [ ] Move the two pure functions into `rl/common/geometry.py`; `baselines.py` imports them from there (adjust Task 1 file accordingly). -- [ ] Add test: craft a tiny GDS-producing program in the existing evaluator-test style (there is a ground-truth self-score test to copy from) and assert `result.metrics["chamfer_boundary_um"]` exists and is `< 0.5` for the self-scored ground truth; craft a shifted-square case via two synthetic masks through `chamfer_sym_um` directly with hand-computed value (1px shift over known um_per_px). -- [ ] Implement in `evaluator.py` right after `metrics = compute_metrics(...)`: - -```python -r_mask = np.asarray(raster.convert("L")) < 245 -t_mask = np.asarray(prepared.target) < 245 -sx, sy = prepared.calibration["scale_px_per_um"] -chamfer_b, _, _ = chamfer_sym_um( - boundary_mask(r_mask), boundary_mask(t_mask), 2.0 / (sx + sy) -) -metrics = { - **metrics, - "chamfer_boundary_um": None if chamfer_b == float("inf") else float(chamfer_b), -} -``` - -- [ ] `uv run pytest rl/common/tests/ -q` green; commit `"Measure boundary chamfer in the evaluator"`. - -### Task 3: reward policy `shaped_v3b` - -**Files:** -- Modify: `rl/track_a/reward.py` -- Test: extend `rl/track_a/tests/test_output_reward.py` - -**Interfaces:** -- Produces: `REWARD_POLICIES = ("validity_floor", "raw_iou", "shaped_v3b")`; `reward_for_policy(result, *, policy, baseline: TaskBaseline | None = None) -> float | None`; `shaped_components(result, baseline) -> dict[str, float]` (the logging breakdown: `iou`, `dice`, `iou_n`, `dice_n`, `t2b`, `teacher_nb`, `degenerate`). -- Consumes: `TaskBaseline` from `rl.common.baselines`; `result.metrics["chamfer_boundary_um"]` from Task 2. Constants module-level: `SHAPED_FLOOR = 0.05`, `W_IOU3, W_DICE3, W_TEACHER = 0.40, 0.15, 0.40`, `TAU_FRAC = 0.05`, `TEACHER_DENOM_FLOOR = 0.3`, `DEGENERATE_IOU_RECT = 0.95`. - -- [ ] Write the ported tests first (old `test_shaping.py`/`test_shaping_v3.py` semantics, expressed against `reward_for_policy` with a crafted `EvaluationResult(OK, MODEL, iou=…, dice=…, metrics={"chamfer_boundary_um": …})` and a crafted `TaskBaseline`): - - non-degenerate normalization: `iou=0.75, baseline.iou_rect=0.5` with dice term and teacher zeroed ⇒ contribution `0.40 * 0.5`; - - rect-equivalent program (`iou == iou_rect`, `dice == dice_rect`, chamfer == rect chamfer) ⇒ exactly `0.05`; - - degenerate branch (`iou_rect = 0.96`): reward uses RAW iou/dice/t2b; - - teacher denominator floor: `t2b=0.9, t2b_rect=0.8` ⇒ `0.1 / 0.3`; - - crash (`RUNTIME_ERROR`, MODEL) ⇒ `0.0`; `SOURCE_REJECTED` ⇒ `0.0`; EVALUATOR fault ⇒ `None`; - - missing chamfer (`None`) ⇒ `t2b = 0`, no crash; missing baseline ⇒ `ValueError` (shaped policy requires a baseline); - - perfect program on non-degenerate target (`iou=dice=1.0`, chamfer 0, rect baseline 0.3/0.4/large-chamfer) ⇒ `0.05 + 0.40 + 0.15 + 0.40·(1 − t2b_rect)/max(1−t2b_rect, 0.3)` — assert `> 0.95` and `== pytest.approx(1.0)` when `t2b_rect ≤ 0.7`. -- [ ] Run tests, expect failures; implement: - -```python -def _t2b(chamfer_um, tau: float) -> float: - if chamfer_um is None or chamfer_um == float("inf"): - return 0.0 - return math.exp(-float(chamfer_um) / tau) - -def norm_excess(x: float, base: float) -> float: - if base >= 1.0: - return 0.0 - return max(0.0, (x - base) / (1.0 - base)) - -def _shaped_v3b(result: EvaluationResult, baseline: TaskBaseline) -> float: - iou, dice = float(result.iou), float(result.dice) - diag = baseline.diag_um if baseline.diag_um > 0 else 1e-9 - tau = max(TAU_FRAC * diag, 1e-9) - chamfer = result.metrics.get("chamfer_boundary_um") - chamfer_rect = baseline.chamfer_rect_boundary_um - t2b = _t2b(chamfer, tau) - t2b_rect = _t2b(chamfer_rect if chamfer_rect is not None else diag, tau) - if baseline.iou_rect >= DEGENERATE_IOU_RECT: - return SHAPED_FLOOR + W_IOU3 * iou + W_DICE3 * dice + W_TEACHER * t2b - teacher_nb = max(0.0, t2b - t2b_rect) / max(1.0 - t2b_rect, TEACHER_DENOM_FLOOR) - return (SHAPED_FLOOR - + W_IOU3 * norm_excess(iou, baseline.iou_rect) - + W_DICE3 * norm_excess(dice, baseline.dice_rect) - + W_TEACHER * teacher_nb) -``` - - In `reward_for_policy`: `policy == "shaped_v3b"` → OK ⇒ validate iou/dice finite in [0,1], require `baseline is not None`, return `_shaped_v3b`; MODEL failure ⇒ `0.0`; else `None`. Keep the existing two policies untouched. -- [ ] Tests green; commit `"Port the v3-b shaped reward (iou_n) as policy shaped_v3b"` with trailer `Honors:` referencing the design commitment commit. - -### Task 4: plumb baseline + shaped metrics through the Env - -**Files:** -- Modify: `rl/track_a/tinker_data.py` (`TrackAEnv.step` ~line 347, builder defaults), Test: `rl/track_a/tests/test_tinker_contract.py` additions - -**Interfaces:** -- Consumes: `cached_rect_baseline` (Task 1), `reward_for_policy(..., baseline=...)` + `shaped_components` (Task 3). -- Produces: `TrackAEnv.step` computes `baseline = cached_rect_baseline(self.task.reference)` when `self.reward_policy == "shaped_v3b"`, passes it to `reward_for_policy`, and extends `StepResult.metrics` with `iou_n`, `teacher_nb`, `degenerate` from `shaped_components`. Builder defaults change: `TrackARLDatasetBuilder.group_size: int = 8`, `reward_policy: str = "shaped_v3b"`. - -- [ ] Test: instantiate `TrackAEnv` with a stub renderer + a real L0 `TaskRecord` (existing contract tests show how) and a monkeypatched batcher returning a crafted OK result; assert reward equals the hand-computed shaped value and `metrics["iou_n"]` present. -- [ ] Implement; run `uv run pytest rl/track_a/tests/ -q`; commit `"Default Track A to the shaped reward at G=8"`. - -### Task 5: the deletion sweep - -**Files:** -- Delete: `rl/studies/`, `rl/evaluation/`, `rl/track_b/`, `rl/track_c/`, `rl/track_a/{recipe.py,evaluation_contract.py,checkpoint_metrics.py,checkpoint_selection.py,select_sft_checkpoint.py,evaluate_checkpoint.py,preflight.py→trim,tests/test_checkpoint_promotion.py,tests/test_launch_preflight.py}` -- Modify: `rl/track_a/curriculum.py` (absorb `LEVELS`, `normalize_level`, `normalize_replay_levels`, `expected_replay_levels` verbatim from `recipe.py`), `rl/track_a/tinker_data.py` (imports; delete the Inkling `_renderer` branch → `raise ValueError("Inkling is out of scope")`), `rl/track_a/launch.py` (keep `bind_execution_boundary`; drop recipe imports), `rl/stack.json` (drop `tml-renderers`), `rl/README.md` + `rl/track_a/README.md` (rewrite: three-policy reward table with `shaped_v3b` as the run policy, the two runs, no tracks B/C), `rl/conftest.py` (check for references) - -**Steps:** -- [ ] `git rm -r rl/studies rl/evaluation rl/track_b rl/track_c` and the track_a ceremony files; move the four level helpers into `curriculum.py`; fix every import (`grep -rn "rl.studies\|rl\.evaluation\|rl\.track_b\|track_a\.recipe\|evaluation_contract\|checkpoint_selection\|checkpoint_metrics" rl/ src/ tests/ release/ plugins/ .claude/ pyproject.toml`). -- [ ] `preflight.py`: keep only `verify_frozen_dataset(repo_root, dataset_root)` (shard-hash check vs both freeze.json files), `require_clean_worktree(repo_root)`, and the tokenizer/prompt sanity check; delete the rest. `train_sft.py`/`train_rl.py` get stubbed imports fixed in Task 6 (temporarily mark them broken with a clear `raise SystemExit("rewritten in Task 6")` if needed to keep pytest green — better: do Task 5 and Task 6 in one commit if the seam is awkward). -- [ ] Full sweep green: `uv run pytest rl/ -q && uv run ruff check rl/`. Commit `"Remove the studies/ceremony apparatus from rl/"` (trailer `Honors:` design commitment; body lists what was deleted and why in two sentences). - -### Task 6: lean entry points - -**Files:** -- Rewrite: `rl/track_a/train_sft.py` (add `levels: str = "L0"`, `num_epochs: int = 2`, `learning_rate: float = 1e-4`, drop recipe gates, keep `TINKER_API_KEY` check + `verify_frozen_dataset` + `require_clean_worktree` + `bind_execution_boundary`), `rl/track_a/train_rl.py` (drop mastery report; args `current_level`, `replay_levels` (validated by `expected_replay_levels`), `load_checkpoint_path`, `group_size=8`, `reward_policy="shaped_v3b"`, `wandb_project="pixcell-rl"`, `wandb_name`) -- Test: `rl/track_a/tests/test_entry_points.py` — configs build without network (builder construction + arg validation paths; no Tinker client) - -**Steps:** -- [ ] Verify in the pinned cookbook how `Config.load_checkpoint_path` loads (weights-only vs optimizer state) — `uv run python - <<'EOF'` inspecting `tinker_cookbook/rl/train.py` — and record the answer as a comment where `run.py` chains stages (old lineage chained on `/weights/final` sampler weights with a fresh optimizer per stage; match that). -- [ ] Write the two entry points (cookbook `Config` assembly copied from the current files minus ceremony; `wandb_project`/`wandb_name` passed through to the cookbook config's wandb fields — check exact field names in cookbook `train.Config`). -- [ ] Tests + `uv run pytest rl/ -q`; commit `"Lean SFT/RL entry points for the two-run campaign"`. - -### Task 7: `rl/track_a/run.py` — the two-run orchestrator + probe eval - -**Files:** -- Create: `rl/track_a/run.py`, `rl/track_a/probe_eval.py` -- Test: `rl/track_a/tests/test_run_plan.py` (stage plan construction, probe selection determinism — no network) - -**Interfaces:** -- `probe_eval.select_probe_tasks(dataset_root, per_level=16) -> list[TaskRecord]`: depth/validation, per level sorted by `sampler.opaque_id`, first 16 — deterministic, no seed. -- `probe_eval.run_probe(checkpoint_or_base: str, tasks, *, out_path) -> dict`: samples 1 completion/task at T=1.0 max_tokens 4096 via `tinker.ServiceClient` sampling client (base model name or `tinker://` path), evaluates through `PixCellEvaluator(require_isolation=True)` batch, writes JSON `{per_level: {level: {executable_rate, mean_iou, mean_shaped}}, overall: …, checkpoint, timestamp}`. -- `run.py` CLI (chz): `--run {A,B}`, `--smoke`, `--confirm-spend PIXCELL_RUNS_V2`, `--runs-root` (default `/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2`, fallback `~/pixcell-training/runs-v2`), `--wandb-project pixcell-rl`. Stage plans: - - Run A: `sft-l0` (levels=L0, 2 epochs) → sanity gate (probe on 8 L0 validation tasks: `executable ≥ 6/8` and `mean_iou > base_probe_l0_mean`, else abort) → `rl-l0 … rl-l4` (30 steps each, replay prefix, chained checkpoints). - - Run B: `rl-l0 … rl-l4` from base. - - Smoke: Run B's `rl-l0` only, `max_steps=2`, `groups_per_batch=2`, canaries 8, then print measured token counts + extrapolated full-campaign cost from the smoke `metrics.jsonl`. - - Probe eval runs at campaign start (base model) and after every stage; results + resolved config + git SHA + `TaskBaseline` dump for all touched tasks written into `//provenance/`. -- [ ] Implement; stage-plan tests green; commit `"Two-run orchestrator with fixed cross-run probe eval"`. - -### Task 8: SFT data audit + anisotropy measurement - -**Files:** -- Create: `rl/track_a/data_audit.py` (invoked by `run.py` before SFT; also standalone) -- Possibly modify: `rl/common/prompt.py` (+ its tests) — only if measurement says so - -**Steps:** -- [ ] `data_audit.py`: for the SFT levels, render every `(prompt, label)` through the real renderer; report max/percentile token lengths; **hard-fail if any label would truncate at `max_length`**; dump 3 decoded datums (text + weight mask summary) to the run dir for eyeball inspection. -- [ ] Anisotropy: one-off script over depth/train — per row, model-image ink bbox aspect vs `footprint_um` aspect; report the distribution of the ratio. If ≥ 10% of rows exceed 1.6×, add the old scale note to `build_prompt_text` (contract bump to `pixcell-direct-reconstruction-v3`, computed only from model-visible inputs: model image ink bbox + footprint), update `test_prompt_contract.py` accordingly; else record the measurement in the run provenance and leave the prompt at v2. -- [ ] Commit `"SFT data audit + anisotropy measurement"` (+ prompt v3 if taken, its own commit). - -### Task 9: full-surface green + README + spec sync - -- [ ] `uv run pytest tests/ rl/ release/ -q` (the CI surface) and `uv run ruff check .` — fix all fallout. -- [ ] `rl/README.md` and `rl/track_a/README.md` rewritten to describe exactly what exists now (the reward contract table must show `shaped_v3b` with the formula; the two runs; the probe protocol). -- [ ] Update the design spec's §2 if any decision changed during implementation (e.g., prompt v3 taken or not). -- [ ] Commit `"Document the clean training path"`. - ---- - -## Self-review checklist (run after Task 9) -- Spec §2.1 ↔ Tasks 1–4; §2.2 ↔ Tasks 4/6; §2.3 ↔ Task 7 (+ SFT gate); §2.4 ↔ Task 7 probe; §2.5 ↔ Task 7 provenance + Task 6 preflight; §2.7 ↔ Task 5; §2.8 ↔ Task 8. Launch/monitoring are tracker tasks 9–12, not this plan. -- Grep the plan for TBD/TODO — none. -- Type consistency: `TaskBaseline` fields used in Task 3 match Task 1; `reward_for_policy(..., baseline=)` matches Task 4's call; probe JSON keys match Task 7. diff --git a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md b/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md deleted file mode 100644 index c7d03bf3..00000000 --- a/docs/superpowers/specs/2026-07-28-rl-clean-rebuild-design.md +++ /dev/null @@ -1,266 +0,0 @@ -# RL clean rebuild — two Qwen runs on the sealed curriculum - -**Date:** 2026-07-28 · **Branch:** `worktree-rl-clean-runs` (base `origin/main` @ `802110aff`) -**Goal:** delete the ceremony, restore the proven training signal, and run the paper's -two Qwen training arms on the released sealed dataset. - ---- - -## 1. Diagnosis (what the study of both repos established) - -### The old private library (`PixCell-running/rl`) learned; this tree cannot. Why: - -**The old reward was `v3-b` shaped, anchored on `iou_n` — rectangle-baseline-normalized -excess IoU.** For every task, the IoU/Dice of a solid rectangle filling the calibrated -bounding box (`iou_rect`, `dice_rect`) is precomputed once, and the reward pays only for -structure *beyond* that degenerate answer: - -``` -iou_n = max(0, (iou − iou_rect) / (1 − iou_rect)) # 0 = the rectangle hack, 1 = perfect -dice_n = likewise -τ = 0.05 · diag_um # footprint diagonal sets the scale -t2b = exp(−chamfer_boundary_um / τ) # boundary closeness -teacher_nb = max(0, t2b − t2b_rect) / max(1 − t2b_rect, 0.3) - -crash / timeout / no-gds / bad-gds / syntax → 0.0 -purity violation (non-catalog geometry) → 0.0 # old "ship mode" -executable + pure, iou_rect < 0.95 → 0.05 + 0.40·iou_n + 0.15·dice_n + 0.40·teacher_nb -executable + pure, iou_rect ≥ 0.95 → 0.05 + 0.40·iou + 0.15·dice + 0.40·t2b # degenerate branch -``` - -Why each piece exists (all evidenced in the old repo): -- **Rectangle normalization** kills the measured degenerate attractor (the old policy - *found* the bbox-fill hack: reward 0.975 on one task) and makes one reward scale mean - the same thing on every task — which is what makes curricula, winner thresholds, and - readable curves possible. -- **The 0.05 executable floor** separates "wrong geometry but a valid program" from - "crash", restoring gradient exactly where early RL lives. -- **The boundary-chamfer teacher term** pays for count/boundary progress IoU cannot see - (one more meander on a serpentine barely moves IoU). -- **The degenerate branch** scores rectangle-like targets raw, because normalizing - against a rectangle that *is* the answer makes every program look identical. - -With this reward, GRPO on Qwen3.6-35B-A3B (LoRA 32, G=8×B=8, lr 1e-5, 4096 tokens, -`qwen3_5_disable_thinking`) produced unambiguous learning: train `iou_n` 0.187→0.389 -over the 95-step paper lineage; held-out `iou_n` 0.162→0.299 (+85% relative); the -no-SFT "pure" lineage took held-out L0 raw IoU **0.038→0.911** in three days. - -**This tree replaced that signal with `reward = raw absolute IoU, else 0`** -(`representation_training_v1/launcher.py:917`, `"reward": "raw_absolute_scale_iou"`). -Consequences, all verified in code: no floor (valid-but-wrong ≡ crash ≡ 0), no -normalization (blob-filling pays; hard tasks pin near 0), Dice/chamfer discarded as -diagnostics, and — combined with `remove_constant_reward_groups=True` and group size -**4** — any task the model cannot already partially solve produces an all-zero group -that is deleted from the batch. Early RL on an unmastered level receives almost no -gradient. The two 6-step runs it managed before stalling show reward as pure noise. - -### The old library's fatal weakness was the opposite one: **unsealed data.** -Eval pool grew 64→253 mid-campaign (curves incomparable across the boundary), the train -pool grew/shrank/killed a live run, pools were gitignored, and seed determinism broke on -any motif-registry edit. **This repo already fixed that completely**: the released -dataset is frozen under four layers of hashing, the loader re-verifies SHAs per row, and -model-visible vs verifier-only fields are separated by construction (`rl/common/contracts.py`). - -### The ceremony this tree added is measurable and killed its own campaign. -~90% of 30,381 lines is scaffolding (two W&B integrations, two immutable-ledger systems, -two recipe-locking systems, wave/approval receipts, promotion gates at 0.80 IoU — a bar -the *old* runs show L2/L3 never reach). On launch night (Jul 28) the provenance -machinery itself caused three aborted launches in 100 minutes, then a 6-hour stall, then -the protocol's own baseline gate was relaxed to get anything running at all; ten -`training-live` worktrees exist only because runs were SHA-bound and every hotfix -required a fresh clean tree. The one useful negative result: 55-step **mixed-level SFT -produced ~0-IoU checkpoints** (renderer diagnostic: 1/8 executable), while **L0-only SFT -produced a usable checkpoint** (RL from it emitted executable programs at reward 0.2–0.5). - -### What is genuinely good here and must be kept -`rl/common/` wholesale (contracts, hash-verified parquet loader, prompt contract -`pixcell-direct-reconstruction-v2`, preprocessing, batcher, per-candidate Docker -isolation with immutable image binding), the deterministic curriculum helpers -(exactly-once stratified SFT pass; 80/20 five-step replay cycle — both pinned by tests), -`tinker_data.py`'s clean Cookbook `Env`/`RLDataset` wrapper, and the essential parts of -preflight (dataset digest check against `freeze.json`, sandbox binding, clean-tree check). - ---- - -## 2. What we will build (the decision) - -**One training path, two runs, one reward.** Everything lives in `rl/track_a/` over -`rl/common/`; the studies apparatus is deleted. - -### 2.1 Reward: port `v3-b` exactly — new policy `"shaped_v3b"` -- Constants identical to the old library (0.05 floor; 0.40/0.15/0.40 weights; τ = 0.05·diag; - denominator floor 0.3; degenerate threshold `iou_rect ≥ 0.95`). These constants - produced the curves; they are not re-tuned. -- Purity: the current evaluator rejects non-catalog programs *before* execution - (`SOURCE_REJECTED → 0.0`). This matches the old ship-mode semantics (impure = 0.0, - the Jul-21 stop-ship: a floor for impure programs trains the violation). Keep the - current order. -- Per-task baselines (`iou_rect`, `dice_rect`, `chamfer_rect_boundary_um`, `diag_um`) - are computed once per task from `target_image` + stored calibration, cached in-process - keyed by `target_image_sha256`, and dumped to the run directory for provenance. The - dataset is frozen, so these are constants of the release. -- The evaluator gains two candidate-side measurements it already half-computes: Dice - (exists as diagnostic) and symmetric mean boundary-chamfer in µm (ported from the old - `rl/reward/geometry.py`). -- `raw_iou` and `validity_floor` policies remain in the file for the record; the runs - use `shaped_v3b`. -- Old shaping tests are ported and adapted (rect-equivalent ⇒ exactly 0.05; the - (0.75, base 0.5) ⇒ 0.5 normalization case; purity ⇒ 0.0; crash ⇒ 0.0; degenerate - branch exactness; denominator floor; ground-truth self-score ≈ 1.0). - -### 2.2 Training recipe (both runs share it) -| Knob | Value | Source of truth | -|---|---|---| -| Model | `Qwen/Qwen3.6-35B-A3B` | paper + both repos | -| LoRA rank | 32 | old lineage + paper | -| Renderer | `qwen3_5_disable_thinking` | old winning lineage (60k-thinking is 10×+ decode cost; RC-H05 showed thinking@60k ≈ 0.218 zero-shot — not needed for the training result) | -| Max sampled tokens | 4096 | old lineage | -| Image | dataset-native, max edge 1440 | dataset is drawn at 1440 | -| GRPO | G=**8** rollouts × 8 groups/step, lr 1e-5, temp 1.0, top-p 1.0, `importance_sampling`, KL 0 | old lineage (this tree's G=4 halves the group signal) | -| Advantage | plain within-group mean-centering; constant-reward groups dropped | cookbook default = old paper lineage (PKPO/advantage-patch was an Inkling-era experiment; not ported) | -| Curriculum | staged L0→L4, **30 steps/level**, 80/20 current/replay via the existing deterministic five-step cycle | old stage budgets (30/35/30) + this tree's tested scheduler; no mastery gates, no approvals | -| Checkpoints | state every 5 steps; sampler checkpoint at each level boundary + final | enough to resume and to evaluate; nothing else | - -### 2.3 The four runs — a 2×2: (±L0-SFT) × (no-think / think) [user-extended 2026-07-28] -The failed campaign's one useful measurement is RC-H05: zero-shot Qwen with thinking -at a 60k output ceiling scores **0.218 mean IoU@1** on F1–F8 under the corrected v2 -prompt — already near the old post-training result (0.24). RL from that operating -point starts from a far higher launchpad, and the campaign's two 6-step runs sampled -with thinking@60k through this exact Env, so the path is execution-proven. Per the -user's direction, the ±SFT pair runs at both operating points, all four simultaneous: - -| Run | Operating point | Arm | -|---|---|---| -| **A** `runA-l0sft-rl` | `qwen3_5_disable_thinking`, max_tokens 4096 | L0-SFT → RL L0→L4 (150 steps) | -| **B** `runB-base-rl` | `qwen3_5_disable_thinking`, max_tokens 4096 | base → RL L0→L4 (150 steps) | -| **C** `runC-think-base-rl` | `qwen3_5` (thinking), max_tokens 60000 | base → RL L0→L4 (150 steps) | -| **D** `runD-think-l0sft-rl` | `qwen3_5` (thinking), max_tokens 60000 | L0-SFT → RL L0→L4 (150 steps) | - -Within each operating point the pair is a controlled ±L0-SFT comparison at matched RL -budget — the "controlled ablation" and "untouched training evaluations" the paper -defers (`arxiv/main.tex:879-881, :932-942`). Across operating points, A/B vs C/D -measures what the reasoning budget buys under identical reward, data, and schedule. - -**Run D's SFT is gated.** The renderer diagnostic showed code-only SFT collapsing -checkpoints under the thinking renderer (0/8 executable) — but that was mixed-level -SFT, where L1–L4 labels plausibly truncated at `max_length`; L0-only SFT is the clean -test of the renderer hypothesis. D therefore runs its L0-SFT (code-only labels, -thinking renderer) and proceeds to RL **only if** the post-SFT sanity probe beats the -base-model thinking probe; otherwise D falls back to RL-from-base (C's config, second -seed) and the SFT collapse is reported as a finding. SFT for A stays as designed: -642 L0 `depth/train` rows, batch 64, lr 1e-4 linear, `LAST_ASSISTANT_MESSAGE`, -2 epochs ≈ 22 steps, prompt byte-identical to RL. Image stays 1440 for all four -(dataset-native; the protocol's 1920 was a no-op since no dataset image exceeds 1440). -Prompt-identity rule: each SFT and the RL that resumes it share one renderer. - -**SFT hygiene (the mixed-SFT autopsy made this mandatory):** -1. decode-and-inspect rendered datums (text + train-mask) before spending; -2. token-length audit of all 642 labels — every label must fit `max_length` un-truncated - (mixed-SFT's ~0-IoU collapse is consistent with long L2–L4 labels truncating at 6144; - L0-only SFT dodges this, and we verify rather than assume); -3. post-SFT sanity eval before RL: 8 fixed L0 validation tasks, require ≥6/8 executable - and mean IoU above the base-model probe, else stop and diagnose. - -### 2.4 Evaluation (one instrument, no studies) -- **Probe set**: 80 tasks = 16/level from `depth/validation`, deterministically selected - (sorted `opaque_id`, first 16 per level; recorded in the run dir). Eval@1 at T=1.0 - every 5 steps → per-level executable rate, raw IoU, and shaped reward. This is the - training curve for the paper. -- **Step-0 probe = the base baseline.** No separate baselining studies, ever. -- **Final**: full `depth/validation` eval@1 for both final checkpoints, plus the F1–F8 - benchmark at k=8 through the same evaluator — the paper's headline protocol. -- Everything flows through the same Docker-isolated evaluator; every number is - recomputed from artifacts (the conductor invariant survives; the receipts do not). - -### 2.5 Provenance-lite (keep the invariant, drop the theater) -Commit before launch; preflight = dataset-digest check + sandbox-image binding + -renderer/tokenizer sanity + ground-truth positive control + clean-tree check; run dir -records resolved config + git SHA + baseline cache; Cookbook writes `metrics.jsonl`; -W&B project **`pixcell-rl`** (the old project — new curves land beside the ones that -worked), run names `runA-l0sft-rl-` / `runB-base-rl-`. No deterministic -run-id binding, no wave receipts, no approval tokens, no store locks, no SHA-suffixed -store roots. Run artifacts live outside the repo under the existing training volume -(`/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2/`, fallback `~/pixcell-training/`). - -### 2.6 Launch mechanics (the 6-hour-stall lesson) -Runs launch in detached host tmux panes (not inside an agent session), logging to the -run dir; monitoring reads `metrics.jsonl` and rollout summaries. Keys are sourced from -the existing env file at launch, never printed. Smoke first: 2 GRPO steps at G=8×B=2 on -L0 **per operating point** (one no-think smoke, one think smoke — thinking decode is -~3–10× per rollout, so C/D cost must be measured, not assumed); the extrapolated cost -of all four full runs is reported before the full launches go out. Four simultaneous -runs share this machine's Docker evaluator: per-candidate containers are capped at -1 CPU and the batcher bounds in-flight evaluations per run, so evaluation queues -rather than thrashes; sampling, not evaluation, dominates step wall-clock. - -### 2.7 Deletions (this branch) -- `rl/studies/` — all three studies + both extensions (the campaign ceremony, the - superseded diagnostics, the mastery/promotion machinery). -- `rl/evaluation/` — the zero-shot baselining harness + its W&B mirror. -- `rl/track_b/` — the Inkling TML shim. Inkling is out of scope for the paper. -- `rl/track_c/` — an empty reservation. -- In `rl/track_a/`: the recipe-lock/approval surface (`recipe.py` ceremony, - `evaluation_contract.py`, checkpoint promotion/selection) replaced by one lean runner; - reward/tinker_data/curriculum/preflight-essentials/train entry points stay. -- Tests follow their code: the ~800 lines pinning the learning signal stay and grow - (shaped-reward tests); ceremony tests leave with the ceremony. -- `rl/README.md` rewritten to describe exactly this design; `track_a/README.md` updated - (reward table changes from `0.05 + 0.95·IoU` to the shaped contract). - -### 2.8 Resolved by measurement: prompt contract v3 (anisotropy note) — TAKEN -Measured over `depth/train` (3,468 rows): **50% exceed 1.6× aspect distortion** -(p50 1.59, p90 5.0, max 31.6; by level: L0 29%, L1 42%, L2 49%, L3 53%, **L4 78%**). -The old library's honest scale note is therefore required, not optional: above 1.6× -the prompt states the per-axis render scale computed only from the attached image's -ink bbox and the stated footprint. Contract bumped `v2 → v3`; tests updated; below -the threshold the prompt is byte-identical to v2. - ---- - -## 3. Alternatives considered - -**A. Patch the existing study in place** (flip `reward_policy`, G4→G8, run its stages). -Least code motion, but keeps the machinery that demonstrably killed launch night (three -provenance aborts, SHA-bound relaunch worktrees, approval tokens, mastery gates), and -directly contradicts the directive to remove it. Rejected. - -**B. Clean rebuild on `track_a` + `common`, port the proven reward.** Chosen — keeps -this repo's genuinely superior data/isolation layer and the old repo's proven signal; -one path, two runs, no studies. (This document.) - -**C. Port the old library wholesale onto the new dataset.** The loop is byte-proven, -but it drags in the sample-dir pool machinery the parquet release replaced, loses the -sealed loader/contracts and Docker isolation, and reintroduces the unsealed-data class -of failure. Rejected. - ---- - -## 4. Risks and mitigations -- **SFT collapse (mixed-SFT precedent)** → L0-only SFT + the three-step hygiene gate in §2.3. -- **Reward-port drift** → old tests ported verbatim; ground-truth positive control must - self-score ≈ 1.0 through the full shaped path; constants pinned by test. -- **All-zero groups persisting early in Run B** → the floor guarantees executable - variance pays; smoke run inspects `frac_mixed`/dropped-group counts at steps 1–2. -- **Sandbox image missing/stale on this machine** → preflight binds the image by - spec-hash label; build script exists (`rl/common/sandbox/build.py`); smoke exercises it. -- **Session-tied runs** → detached tmux launch; resume = re-run the same command (state - checkpoints every 5 steps; Cookbook resume). -- **Spend surprise** → smoke measures true per-step cost; both-run extrapolation is - reported before full launch; runs are per-level resumable so they can be stopped at - any stage boundary. - -## 5. Test plan -- Ported shaping tests (constants, normalization, degenerate branch, purity, crash). -- Evaluator: Dice + boundary-chamfer surfaces exist and match hand-computed values on - synthetic masks; ground-truth self-score ≈ 1.0 under `shaped_v3b`. -- Baseline computation: rectangle baseline on a known mask matches hand-computed - `iou_rect`/`dice_rect`; cache keyed by `target_image_sha256`. -- Curriculum/schedule invariants keep their existing tests (exactly-once pass, 80/20 cycle). -- Prompt contract tests updated only if §2.8 measurement adds the scale note. -- `uv run pytest rl/ -q` green; `ruff` clean; CI surface (`tests/ rl/ release/`) green. - -## 6. Explicit non-goals -No Inkling. No new zero-shot baseline studies. No dataset changes. No HF publishing. -No paper edits until both runs have results (then: the deferred-ablation paragraph, a -training-curve figure to re-fill the orphaned `fig_rl_training` slot, and Table III -companions — as a separate reviewed change with the user). diff --git a/rl/track_a/run.py b/rl/track_a/run.py index b0cd7c08..51479610 100644 --- a/rl/track_a/run.py +++ b/rl/track_a/run.py @@ -50,7 +50,7 @@ _DATASET_ROOT = _REPO_ROOT / "dataset" CONFIRM_SPEND = "PIXCELL_RUNS_V2" -DEFAULT_RUNS_ROOT = "/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2" +DEFAULT_RUNS_ROOT = "~/pixcell-training/runs-v2" FALLBACK_RUNS_ROOT = "~/pixcell-training/runs-v2" SFT_GATE_MIN_EXECUTABLE = 0.75 From dca982dd8e6f3013f24228732176103c8e3a2a76 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 11:23:04 -0400 Subject: [PATCH 23/24] Released-model evidence record + benchmark gallery composer data/training/qwen-rl-v2: the validated record behind the released adapter (qpaig-mit/pixcell) - F1-F8 benchmark JSONs (base 0/64 executable -> mean 0.228 / best-of-8 0.467), the attempt-measure-revise loop record (mean champion IoU 0.491) with every champion program, and the re-executed render gallery. rl/track_a/make_gallery.py regenerates the gallery from the record alone. READMEs register the release; runs-root and sandbox wording brought in line with the shipped defaults. --- data/README.md | 15 +- data/training/qwen-rl-v2/README.md | 38 +++ .../qwen-rl-v2/agentloop/b-final.json | 281 ++++++++++++++++++ data/training/qwen-rl-v2/bench/a-post-l1.json | 62 ++++ data/training/qwen-rl-v2/bench/b-final.json | 62 ++++ .../qwen-rl-v2/bench/base-nothink.json | 62 ++++ data/training/qwen-rl-v2/bench/d-post-l0.json | 62 ++++ .../qwen-rl-v2/gallery/final_1.diff.png | Bin 0 -> 8535 bytes .../qwen-rl-v2/gallery/final_1.program.py | 84 ++++++ .../qwen-rl-v2/gallery/final_1.render.png | Bin 0 -> 5272 bytes .../qwen-rl-v2/gallery/final_1.target.png | Bin 0 -> 112393 bytes .../qwen-rl-v2/gallery/final_2.diff.png | Bin 0 -> 28390 bytes .../qwen-rl-v2/gallery/final_2.program.py | 73 +++++ .../qwen-rl-v2/gallery/final_2.render.png | Bin 0 -> 8961 bytes .../qwen-rl-v2/gallery/final_2.target.png | Bin 0 -> 301029 bytes .../qwen-rl-v2/gallery/final_3.diff.png | Bin 0 -> 34545 bytes .../qwen-rl-v2/gallery/final_3.program.py | 72 +++++ .../qwen-rl-v2/gallery/final_3.render.png | Bin 0 -> 15683 bytes .../qwen-rl-v2/gallery/final_3.target.png | Bin 0 -> 292415 bytes .../qwen-rl-v2/gallery/final_4.diff.png | Bin 0 -> 11613 bytes .../qwen-rl-v2/gallery/final_4.program.py | 31 ++ .../qwen-rl-v2/gallery/final_4.render.png | Bin 0 -> 8979 bytes .../qwen-rl-v2/gallery/final_4.target.png | Bin 0 -> 135999 bytes .../qwen-rl-v2/gallery/final_5.diff.png | Bin 0 -> 63727 bytes .../qwen-rl-v2/gallery/final_5.program.py | 79 +++++ .../qwen-rl-v2/gallery/final_5.render.png | Bin 0 -> 11803 bytes .../qwen-rl-v2/gallery/final_5.target.png | Bin 0 -> 1482254 bytes .../qwen-rl-v2/gallery/final_6.diff.png | Bin 0 -> 6592 bytes .../qwen-rl-v2/gallery/final_6.program.py | 27 ++ .../qwen-rl-v2/gallery/final_6.render.png | Bin 0 -> 2766 bytes .../qwen-rl-v2/gallery/final_6.target.png | Bin 0 -> 74655 bytes .../qwen-rl-v2/gallery/final_7.diff.png | Bin 0 -> 13402 bytes .../qwen-rl-v2/gallery/final_7.program.py | 131 ++++++++ .../qwen-rl-v2/gallery/final_7.render.png | Bin 0 -> 3022 bytes .../qwen-rl-v2/gallery/final_7.target.png | Bin 0 -> 18955 bytes .../qwen-rl-v2/gallery/final_8.diff.png | Bin 0 -> 73827 bytes .../qwen-rl-v2/gallery/final_8.program.py | 210 +++++++++++++ .../qwen-rl-v2/gallery/final_8.render.png | Bin 0 -> 25473 bytes .../qwen-rl-v2/gallery/final_8.target.png | Bin 0 -> 2299299 bytes data/training/qwen-rl-v2/gallery/gallery.png | Bin 0 -> 373127 bytes .../training/qwen-rl-v2/gallery/manifest.json | 34 +++ rl/README.md | 32 +- rl/track_a/make_gallery.py | 125 ++++++++ 43 files changed, 1470 insertions(+), 10 deletions(-) create mode 100644 data/training/qwen-rl-v2/README.md create mode 100644 data/training/qwen-rl-v2/agentloop/b-final.json create mode 100644 data/training/qwen-rl-v2/bench/a-post-l1.json create mode 100644 data/training/qwen-rl-v2/bench/b-final.json create mode 100644 data/training/qwen-rl-v2/bench/base-nothink.json create mode 100644 data/training/qwen-rl-v2/bench/d-post-l0.json create mode 100644 data/training/qwen-rl-v2/gallery/final_1.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_1.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_1.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_1.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_2.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_2.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_2.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_2.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_3.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_3.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_3.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_3.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_4.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_4.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_4.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_4.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_5.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_5.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_5.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_5.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_6.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_6.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_6.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_6.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_7.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_7.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_7.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_7.target.png create mode 100644 data/training/qwen-rl-v2/gallery/final_8.diff.png create mode 100644 data/training/qwen-rl-v2/gallery/final_8.program.py create mode 100644 data/training/qwen-rl-v2/gallery/final_8.render.png create mode 100644 data/training/qwen-rl-v2/gallery/final_8.target.png create mode 100644 data/training/qwen-rl-v2/gallery/gallery.png create mode 100644 data/training/qwen-rl-v2/gallery/manifest.json create mode 100644 rl/track_a/make_gallery.py diff --git a/data/README.md b/data/README.md index 5d4dd65b..5506a4dd 100644 --- a/data/README.md +++ b/data/README.md @@ -29,6 +29,11 @@ The benchmark input fixtures (silhouette + footprint per device) are `trajectories/` (cohort-pinned to the audited model set; `--all` widens to later runs the trajectories also carry). +`training/qwen-rl-v2/` is the validated evidence for the released model +([`qpaig-mit/pixcell`](https://huggingface.co/qpaig-mit/pixcell), a GRPO-trained +LoRA for Qwen3.6-35B-A3B): the F1–F8 benchmark records, the +attempt–measure–revise deployment-loop record with every champion program, and +the re-executed render gallery. Its README maps each number to its file. `training/representation-curriculum-v1/` preserves superseded Qwen and Inkling diagnostic runs. Those runs used the retired v1 direct prompt contract and are not canonical baselines or paper results. Their protocol and artifacts remain @@ -135,10 +140,12 @@ Every number in the paper's Results, mapped to its backing file. ### Sec V — verifier-derived training -The repository contains the training recipes under `rl/` and the derived -paper table and gallery under `arxiv/`. The full benchmark-attempt and -training-run ledgers are reserved for the companion training-artifact -release, as stated in the paper's data-availability paragraph. +The repository contains the training recipes under `rl/`, the derived +paper table and gallery under `arxiv/`, and the validated evidence for the +released model under `training/qwen-rl-v2/` (benchmark JSONs, deployment-loop +record, champion programs, gallery). Raw step-level training ledgers and +checkpoints stay outside the repository; the released adapter itself is +[`qpaig-mit/pixcell`](https://huggingface.co/qpaig-mit/pixcell). ## Curation notes (read before reusing) diff --git a/data/training/qwen-rl-v2/README.md b/data/training/qwen-rl-v2/README.md new file mode 100644 index 00000000..130ccaf6 --- /dev/null +++ b/data/training/qwen-rl-v2/README.md @@ -0,0 +1,38 @@ +# Qwen RL v2 — evidence for the released model + +The validated record behind the released PixCell model +([`qpaig-mit/pixcell`](https://huggingface.co/qpaig-mit/pixcell)): a LoRA +adapter for `Qwen/Qwen3.6-35B-A3B` trained with GRPO alone (no SFT) on the +released curriculum L0→L4 at the non-thinking 4096-token operating point — +Run B of the campaign recipe in [`rl/`](../../../rl/README.md). Checkpoint of +record: `tinker://d8269c70-0dd9-5776-9cae-53e8b24e647a:train:0/sampler_weights/final`. + +Per the repository rule, every number here was recomputed from artifacts by +the deterministic evaluator; nothing model-reported enters the record. + +## Files + +| Path | Protocol | Headline | +|---|---|---| +| `bench/base-nothink.json` | F1–F8, 8 attempts @ T=1.0, best-of-8 raw IoU | base model: 0/64 attempts executable | +| `bench/b-final.json` | same | released model: 39/64 executable, mean IoU 0.228, best-of-8 0.467 | +| `bench/a-post-l1.json`, `bench/d-post-l0.json` | same | mid-campaign controls (L0-SFT arm after L1; thinking arm after L0) | +| `agentloop/b-final.json` | attempt–measure–revise loop: 8 attempts, then 3 rounds × 4 revisions of the champion under number-free feedback | **mean champion IoU 0.491**; includes every champion program and per-round trajectory | +| `gallery/` | champion programs re-executed, re-rendered, re-diffed | per device: `*.program.py`, `*.target.png`, `*.render.png`, `*.diff.png`; `gallery.png` composite; `manifest.json` | + +The agent loop is the deployment protocol — the released model is the +searcher inside that loop, and 0.491 is its number. The single-pass benchmark +is the ablation that shows what a bare forward pass gives up. + +## Recompute + +```bash +uv run python -m rl.track_a.benchmark_eval --model-path --out bench.json +uv run python -m rl.track_a.agent_loop --model-path --out agentloop.json +uv run python -m rl.track_a.make_gallery --champions agentloop.json --out-dir gallery/ +``` + +`make_gallery` re-executes each stored champion program from source and +re-renders it against the frozen `data/benchmark` fixtures, so the gallery +(and its `manifest.json` IoUs) can be regenerated from this record alone, +no checkpoint required. diff --git a/data/training/qwen-rl-v2/agentloop/b-final.json b/data/training/qwen-rl-v2/agentloop/b-final.json new file mode 100644 index 00000000..62b26d7c --- /dev/null +++ b/data/training/qwen-rl-v2/agentloop/b-final.json @@ -0,0 +1,281 @@ +{ + "model_path": "tinker://d8269c70-0dd9-5776-9cae-53e8b24e647a:train:0/sampler_weights/final", + "init_attempts": 8, + "revisions_per_round": 4, + "rounds": 3, + "per_target": { + "final_1": { + "champion_iou": 0.518288533514801, + "champion_code": "import gdsfactory as gf\n\n# Physical footprint: [40.0 um x, 1.7 um y]\n# \n# Looking at the image more carefully:\n# - There's a thin horizontal bar in the CENTER that extends from left to about 75-80% of the way\n# - Above and below it are two thicker strips that start parallel to center\n# but curve outward (away from center) toward the right side\n# - The center element is very thin and doesn't extend fully to the right\n# - The top and bottom elements curve away and extend to the far right\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n # Parameters derived from footprint\n total_length = 40.0\n total_height = 1.7\n half_height = total_height / 2\n \n # Center waveguide is very thin\n center_width = 0.1\n \n # Top and bottom elements are thicker\n side_width = 0.35\n \n # Gap parameters\n center_to_side_gap = 0.12\n \n # Layer\n layer = (1, 0)\n \n # Calculate positions\n center_y_offset = center_width / 2\n \n # Top waveguide path: starts at left aligned with above center, \n # runs straight for most of length, then curves upward away from center\n # Center ends at about 80% of total length\n center_end_x = total_length * 0.78\n \n # Top element\n top_y_start = center_y_offset + center_to_side_gap + center_width/2\n top_y_end_offset = half_height - side_width/2\n \n pts_top = [\n (0, top_y_start),\n (25, top_y_start),\n (30, top_y_start + 0.3),\n (35, top_y_end_offset - 0.1),\n (40, top_y_end_offset),\n ]\n \n path_top = gf.Path(pts_top)\n top_comp = gf.path.extrude(p=path_top, cross_section=gf.cross_section.cross_section(width=side_width, layer=layer))\n ref_top = c << top_comp\n \n # Bottom element (mirror of top)\n pts_bottom = [\n (0, -top_y_start),\n (25, -top_y_start),\n (30, -(top_y_start + 0.3)),\n (35, -(top_y_end_offset - 0.1)),\n (40, -top_y_end_offset),\n ]\n \n path_bottom = gf.Path(pts_bottom)\n bottom_comp = gf.path.extrude(p=path_bottom, cross_section=gf.cross_section.cross_section(width=side_width, layer=layer))\n ref_bottom = c << bottom_comp\n \n # Center waveguide\n center_comp = gf.components.straight(\n length=center_end_x,\n cross_section=gf.cross_section.cross_section(width=center_width, layer=layer)\n )\n ref_center = c << center_comp\n \n # Position center waveguide so its top edge is at center_y_offset\n # The straight component is centered by default or we can adjust\n ref_center.move((0, -center_width/2))\n \n return c\n\nif __name__ == \"__main__\":\n device().write_gds(\"device.gds\")", + "footprint_um": [ + 40.0, + 1.7 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.21934628548018628, + "champion_iou": 0.21934628548018628 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.38960463701028564, + "champion_iou": 0.38960463701028564 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.518288533514801, + "champion_iou": 0.518288533514801 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.4847074378015773, + "champion_iou": 0.518288533514801 + } + ] + }, + "final_2": { + "champion_iou": 0.5073776561537096, + "champion_code": "import gdsfactory as gf\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n # The silhouette shows:\n # - A main rectangular body (centered at origin in y)\n # - 4 vertical prongs extending upward from the top surface\n # - 1 vertical prong extending downward from the bottom center\n # \n # Footprint: 25um width x 10um height total\n \n # Key dimensions:\n # Total height = 10 um\n # Let body span from y=-2.5 to y=2.5 (height 5um)\n # Then prongs extend symmetrically: +3.0 up, +2.0 down from body edges? \n # Actually: 5 (body) + a (top extension) + b (bottom extension) = 10\n # So: a + b = 5\n \n # Looking at proportions visually:\n # Top prongs appear longer than bottom prong\n # Let bottom prong extend 1.5um down, top prongs extend 5.0-1.5=3.5um up\n # Check: 5 + 3.5 + 1.5 = 10.0 \u2713\n \n body_height = 5.0\n top_extension = 3.5\n bottom_extension = 1.5\n \n # Width and layout:\n body_width = 25.0\n \n # 4 prongs on top, evenly spaced\n prong_width = 2.0\n # 4 prongs with 3 gaps between them\n # Prong centers at: -7.5, -2.5, 2.5, 7.5 (centered around 0)\n prong_centers = [-7.5, -2.5, 2.5, 7.5]\n \n # Body centered at origin\n body = gf.components.rectangle(\n size=(body_width, body_height),\n layer=(1, 0),\n centered=True\n )\n body_ref = c << body\n \n # Bottom prong - centered at bottom of body, pointing down\n bottom_prong = gf.components.rectangle(\n size=(4.0, bottom_extension),\n layer=(1, 0),\n centered=False\n )\n bottom_ref = c << bottom_prong\n # Position: bottom prong's top should align with body's bottom\n # Body bottom: y = -2.5\n # Bottom prong needs to extend from y=-2.5 to y=-2.5-1.5=-4.0\n bottom_ref.move((0 - 2.0, -2.5))\n \n # Top prongs - extend upward from top of body\n for cx in prong_centers:\n top_prong = gf.components.rectangle(\n size=(prong_width, top_extension),\n layer=(1, 0),\n centered=False\n )\n top_ref = c << top_prong\n # Top prong should start at body top (y=2.5) and extend up to y=2.5+3.5=6.0\n top_ref.move((cx - prong_width/2, 2.5))\n \n return c\n\nif __name__ == \"__main__\":\n device().write_gds(\"device.gds\")", + "footprint_um": [ + 25, + 10 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.49668337732899176, + "champion_iou": 0.49668337732899176 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.4711650909592465, + "champion_iou": 0.49668337732899176 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.49789168188747335, + "champion_iou": 0.49789168188747335 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.5073776561537096, + "champion_iou": 0.5073776561537096 + } + ] + }, + "final_3": { + "champion_iou": 0.33601121463342937, + "champion_code": "import gdsfactory as gf\n\n# Device parameters\ntotal_length = 120 # um\ntotal_height = 4 # um\nwaveguide_width = 0.5 # um\ngap = 0.2 # gap between the two waveguides\n\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n xs = gf.cross_section.cross_section(width=waveguide_width, layer=(1, 0))\n \n # The device consists of two parallel waveguides that both have S-bends\n # on both ends, going from a narrower section in the middle to wider sections\n # at the ends.\n \n # Based on the image:\n # - Left side: both waveguides curve outward (away from center)\n # - Right side: both waveguides curve outward (away from center)\n # - Center: both waveguides run parallel and closer together\n \n # Actually looking more carefully at the silhouette:\n # The waveguides appear to have S-shaped curves on both ends\n # On the left: top goes down, bottom goes up (approaching each other)\n # In the middle: they run parallel close together\n # On the right: top goes up, bottom goes down (moving apart)\n \n # Let me create two paths for the waveguides\n \n # Define the y-coordinates for the center (close together)\n center_y_top = gap/2 + waveguide_width/2\n center_y_bottom = -gap/2 - waveguide_width/2\n \n # Define the y-coordinates for the wide ends\n end_separation = total_height/2\n end_y_top = end_separation\n end_y_bottom = -end_separation\n \n # Top waveguide path: starts at left end (wide), goes to center, then to right end (wide)\n top_path = gf.path.smooth(\n points=[\n (0, end_y_top),\n (total_length/4, center_y_top),\n (total_length*3/4, center_y_top),\n (total_length, end_y_top)\n ],\n radius=10\n )\n \n # Bottom waveguide path: starts at left end (wide), goes to center, then to right end (wide)\n bottom_path = gf.path.smooth(\n points=[\n (0, end_y_bottom),\n (total_length/4, center_y_bottom),\n (total_length*3/4, center_y_bottom),\n (total_length, end_y_bottom)\n ],\n radius=10\n )\n \n # Extrude both paths\n top_wg = c << gf.path.extrude(p=top_path, cross_section=xs)\n bottom_wg = c << gf.path.extrude(p=bottom_path, cross_section=xs)\n \n return c\n\nif __name__ == \"__main__\":\n d = device()\n d.write_gds(\"device.gds\")", + "footprint_um": [ + 120, + 4 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.15880950363708984, + "champion_iou": 0.15880950363708984 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.23123556406976534, + "champion_iou": 0.23123556406976534 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.1896243151629428, + "champion_iou": 0.23123556406976534 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.33601121463342937, + "champion_iou": 0.33601121463342937 + } + ] + }, + "final_4": { + "champion_iou": 0.6509756513451003, + "champion_code": "import gdsfactory as gf\n\n# Physical footprint parameters\nX_LENGTH = 18.0\nY_WIDTH = 3.0\nWAVE_AMPLITUDE = 1.0\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n xs = gf.cross_section.cross_section(width=1.0, layer=(1, 0))\n \n # Create upper wave path\n points_upper = [(0, Y_WIDTH/2), (X_LENGTH/2, 0), (X_LENGTH, Y_WIDTH/2)]\n path_upper = gf.path.smooth(points_upper, radius=4.0)\n comp_upper = gf.path.extrude(path_upper, cross_section=xs)\n \n # Create lower wave path \n points_lower = [(0, -Y_WIDTH/2), (X_LENGTH/2, 0), (X_LENGTH, -Y_WIDTH/2)]\n path_lower = gf.path.smooth(points_lower, radius=4.0)\n comp_lower = gf.path.extrude(path_lower, cross_section=xs)\n \n c << comp_upper\n c << comp_lower\n \n return c\n\nif __name__ == \"__main__\":\n d = device()\n d.write_gds(\"device.gds\")", + "footprint_um": [ + 18, + 3 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.6509756513451003, + "champion_iou": 0.6509756513451003 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.3476650219382819, + "champion_iou": 0.6509756513451003 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.0, + "champion_iou": 0.6509756513451003 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.0, + "champion_iou": 0.6509756513451003 + } + ] + }, + "final_5": { + "champion_iou": 0.29255670007740125, + "champion_code": "import gdsfactory as gf\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n # Define parameters based on footprint\n width = 0.7\n total_length = 18.24\n \n # Cross-section for waveguides\n xs = gf.cross_section.cross_section(width=width, layer=(1, 0))\n \n # Calculate dimensions for U-shapes\n bend_radius = 0.35\n straight_length = (total_length - 2 * bend_radius) / 2\n \n # Build top U-shape (facing down)\n path_top = gf.Path()\n path_top.append(gf.path.straight(length=straight_length))\n path_top.append(gf.path.arc(radius=bend_radius, angle=180))\n path_top.append(gf.path.straight(length=straight_length))\n path_top.append(gf.path.arc(radius=bend_radius, angle=180))\n path_top.append(gf.path.straight(length=straight_length))\n \n wg_top = gf.path.extrude(p=path_top, cross_section=xs)\n ref_top = c << wg_top\n \n # Build bottom U-shape (facing up)\n path_bottom = gf.Path()\n path_bottom.append(gf.path.straight(length=straight_length))\n path_bottom.append(gf.path.arc(radius=bend_radius, angle=-180))\n path_bottom.append(gf.path.straight(length=straight_length))\n path_bottom.append(gf.path.arc(radius=bend_radius, angle=-180))\n path_bottom.append(gf.path.straight(length=straight_length))\n \n wg_bottom = gf.path.extrude(p=path_bottom, cross_section=xs)\n ref_bottom = c << wg_bottom\n # Offset bottom shape to face the top shape\n vertical_offset = -width - 0.2\n ref_bottom.move((0, vertical_offset))\n \n # Add straight bridge/connector in the middle\n bridge_width = 0.15\n bridge_xs = gf.cross_section.cross_section(width=bridge_width, layer=(1, 0))\n \n bridge_path = gf.path.straight(length=total_length)\n wg_bridge = gf.path.extrude(p=bridge_path, cross_section=bridge_xs)\n ref_bridge = c << wg_bridge\n ref_bridge.move((0, -width/2 - 0.1))\n \n # Add gate structures (the vertical rectangular patterns along the center sections)\n gate_count = 60\n gate_width = 0.04\n gate_height = 0.08\n gate_spacing = straight_length / gate_count\n \n for i in range(gate_count):\n x_pos = straight_length/2 + i * gate_spacing\n \n # Create vertical gate structure\n gate = gf.components.rectangle(\n size=(gate_width, gate_height),\n layer=(1, 0),\n centered=True\n )\n ref_gate = c << gate\n \n # Position gate across the gap between strands\n gate_y = -width/2 + vertical_offset/2\n ref_gate.move((x_pos, gate_y))\n \n # Add solid rectangles for the tip regions (no gates in the bent areas)\n tip_rect_xs = rectangle_xs = gf.cross_section.cross_section(width=width, layer=(1, 0))\n\n return c\n\nif __name__ == \"__main__\":\n device().write_gds(\"device.gds\")", + "footprint_um": [ + 18.24, + 0.7 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.2527577514625366, + "champion_iou": 0.2527577514625366 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.24568477234461314, + "champion_iou": 0.2527577514625366 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.2872689688347614, + "champion_iou": 0.2872689688347614 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.29255670007740125, + "champion_iou": 0.29255670007740125 + } + ] + }, + "final_6": { + "champion_iou": 0.715876929571249, + "champion_code": "import gdsfactory as gf\n\n# Device parameters\nlength = 2.35\nwidth = 0.85\nlayer = (1, 0)\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n # The shape appears to be a cross with rounded/elliptical arms and rectangular stubs\n # Looking at the silhouette, it has 4 folded lobes meeting at center with straight extensions\n \n # Create the cross shape using the cross primitive with appropriate dimensions\n # The footprint is 4.7 x 4.7, so half is 2.35\n # The cross should have length matching the full extent\n \n # Using cross primitive with length=4.7, width=0.85 to get the right footprint\n cross = gf.components.cross(length=4.7, width=width, layer=layer)\n c << cross\n \n return c\n\nif __name__ == \"__main__\":\n dev = device()\n dev.write_gds(\"device.gds\")", + "footprint_um": [ + 4.7, + 4.7 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.715876929571249, + "champion_iou": 0.715876929571249 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.5648200901214198, + "champion_iou": 0.715876929571249 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.715876929571249, + "champion_iou": 0.715876929571249 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.715876929571249, + "champion_iou": 0.715876929571249 + } + ] + }, + "final_7": { + "champion_iou": 0.4894129134583224, + "champion_code": "import gdsfactory as gf\n\n# Declare tunable parameters\nfootprint_x = 3.0 # micrometers\nfootprint_y = 5.5 # micrometers\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n # Overall footprint: 3.0 x 5.5 um\n fw = footprint_x # x-dimension\n fh = footprint_y # y-dimension\n \n # Create outer ring\n outer_rect = gf.components.rectangle(\n size=(fw, fh), \n layer=(1, 0), \n centered=True\n )\n \n # Inner hole - asymmetric to match the image proportions\n # The ring is thicker horizontally than vertically\n inner_width = fw * 0.72\n inner_height = fh * 0.15\n \n inner_rect = gf.components.rectangle(\n size=(inner_width, inner_height), \n layer=(1, 0), \n centered=True\n )\n \n outer_ring = gf.boolean(outer_rect, inner_rect, operation=\"not\", layer=(1, 0))\n c << outer_ring\n \n # Central structure: two parallel horizontal bars\n # Each bar has two rectangular segments connected in the middle\n \n # Bar dimensions\n bar_width = fh * 0.13 # vertical thickness of each horizontal bar\n \n # Width of each rectangular segment\n seg_width = fw * 0.14\n seg_height = bar_width\n \n # Spacing between the two segments\n seg_gap = fw * 0.04\n \n # Vertical offset of bars from center\n # Two bars: one above, one below center\n bar_vertical_offset = fh * 0.10\n \n # Position the left end of the composite structure\n total_segment_span = 2 * seg_width + seg_gap\n left_x_start = -total_segment_span / 2\n \n # Top bar - two rectangles\n top_y = bar_vertical_offset + seg_height / 2 # top of top bar\n \n # Top left rectangle\n tl_rect = gf.components.rectangle(\n size=(seg_width, seg_height), \n layer=(1, 0), \n centered=False\n )\n tl_ref = c << tl_rect\n tl_ref.move((left_x_start + seg_width/2, top_y))\n \n # Top right rectangle \n tr_rect = gf.components.rectangle(\n size=(seg_width, seg_height), \n layer=(1, 0), \n centered=False\n )\n tr_ref = c << tr_rect\n tr_ref.move((left_x_start + seg_width + seg_gap + seg_width/2, top_y))\n \n # Bottom bar - two rectangles\n bottom_y = -bar_vertical_offset - seg_height/2\n \n # Bottom left rectangle\n bl_rect = gf.components.rectangle(\n size=(seg_width, seg_height), \n layer=(1, 0), \n centered=False\n )\n bl_ref = c << bl_rect\n bl_ref.move((left_x_start + seg_width/2, bottom_y))\n \n # Bottom right rectangle\n br_rect = gf.components.rectangle(\n size=(seg_width, seg_height), \n layer=(1, 0), \n centered=False\n )\n br_ref = c << br_rect\n br_ref.move((left_x_start + seg_width + seg_gap + seg_width/2, bottom_y))\n \n # Add connecting bars between the segments (thin vertical connectors)\n connector_width = seg_gap * 0.3\n connector_height = seg_width * 0.6\n \n # Top bar center connector\n top_center_x = left_x_start + seg_width + seg_gap/2\n top_center_y = bar_vertical_offset\n \n top_conn = gf.components.rectangle(\n size=(connector_width, connector_height), \n layer=(1, 0), \n centered=True\n )\n top_conn_ref = c << top_conn\n top_conn_ref.move((top_center_x, top_center_y))\n \n # Bottom bar center connector\n bottom_center_x = left_x_start + seg_width + seg_gap/2\n bottom_center_y = -bar_vertical_offset\n \n bottom_conn = gf.components.rectangle(\n size=(connector_width, connector_height), \n layer=(1, 0), \n centered=True\n )\n bottom_conn_ref = c << bottom_conn\n bottom_conn_ref.move((bottom_center_x, bottom_center_y))\n \n return c\n\nif __name__ == \"__main__\":\n device = device()\n device.write_gds(\"device.gds\")", + "footprint_um": [ + 3.0, + 5.5 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.489097544693976, + "champion_iou": 0.489097544693976 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.4544202119971335, + "champion_iou": 0.489097544693976 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.43631382411582176, + "champion_iou": 0.489097544693976 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.4894129134583224, + "champion_iou": 0.4894129134583224 + } + ] + }, + "final_8": { + "champion_iou": 0.4175514291117344, + "champion_code": "import gdsfactory as gf\n\n@gf.cell\ndef device():\n c = gf.Component()\n \n # Analyze the target image carefully:\n # - Top part: A U-shape or rectangular frame with 5 horizontal white lines cutting through the top portion\n # The white lines appear as gaps in the black material\n # - Middle: 5 separate horizontal black lines spanning the width\n # - Bottom: A U-shape or rectangular frame with 5 horizontal white lines cutting through the bottom portion\n # (similar to top but inverted)\n \n # BLACK = material on layer (1,0)\n # WHITE = background (no material)\n \n # The white lines inside the top and bottom blocks mean those regions have NO material on layer (1,0)\n # So I need to construct the black regions, avoiding the white line areas\n \n # Let me define the footprint and structure:\n width_um = 120.0\n height_um = 4.0\n \n # Parameters for the design\n # Top block: solid black frame with 5 horizontal white stripes near the top\n # Middle: 5 horizontal black strips\n # Bottom block: solid black frame with 5 horizontal white stripes near the bottom\n \n # Let me break down vertically (from y=0 at bottom to y=4 at top):\n # Bottom solid region with top portion having 5 white cuts:\n # - Very bottom (y=0 to y~0.3): solid black\n # - Then 5 white horizontal lines with black between them (the white lines are gaps)\n # - Then top solid region up to some y\n \n # Actually, looking again at the image - the \"white lines\" in top and bottom blocks \n # are inside the black, meaning black is ON both sides of each white line.\n # So each white line is a narrow unfilled slot within a larger black region.\n \n # Simpler interpretation: \n # The entire device can be built from horizontal rectangular bars on layer (1,0)\n # The white areas are just empty space between black bars\n \n # Structure (y-coordinates from 0 at bottom to 4 at top):\n # Bottom region: solid black rectangle minus 5 horizontal white slots\n # Middle: 5 separate horizontal black bars\n # Top region: solid black rectangle minus 5 horizontal white slots\n \n # Let me draw it as all black regions (everything is on layer 1,0 if black, nothing if white):\n \n # Bottom rectangular block (from y=0 up to ~1.5, with 5 white horizontal gaps from y~0.5 to y~1.4)\n # This means: \n # - Solid from y=0 to y=0.5 (black)\n # - White lines at specific y positions with small height\n # - Between white lines: thin black strips\n # - Then solid from y~1.4 to y~1.5 (black)\n \n # Let me simplify by defining parameters:\n num_white_slots = 5\n slot_height = 0.08 # height of each white (unfilled) slot\n slot_gap = 0.06 # black material height between slots\n \n # Middle section height occupied by white slots\n middle_slots_height = num_white_slots * slot_height + (num_white_slots - 1) * slot_gap\n \n # Below the slots block: solid black\n below_slots = 0.5 # height of solid region below all white slots\n \n # Above the slots block in bottom region: small solid region\n above_slots_in_block = 0.3\n \n # Bottom block y-range: 0 to below_slots + middle_slots_height + above_slots_in_block\n bottom_block_upper = below_slots + middle_slots_height + above_slots_in_block\n \n # Now add the solid black rectangle for bottom (excluding white slot areas)\n # I'll add the solid parts and skip the white slot regions\n \n # Part 1: Solid bottom (y=0 to y=0.5)\n bottom_solid = gf.components.rectangle(\n size=(width_um, below_slots),\n layer=(1, 0),\n centered=False\n )\n c << bottom_solid\n \n # Part 2: Thin black strips between/around white slots in bottom block\n # Between each pair of white slots: black strip of height slot_gap\n y_current = below_slots\n for i in range(num_white_slots):\n # Black strip above this white slot (or the initial gap before first slot)\n # White slot at y_current to y_current + slot_height\n # Add black strip after white slot if not the last one\n if i < num_white_slots - 1:\n black_strip_between = y_current + slot_height\n black_strip_bottom = y_current + slot_height\n black_strip_height = slot_gap\n \n # Actually let me redo this more carefully\n \n # Let me think of it as: from y=below_slots to y=above_slots_in_block + below_slots\n # Within this range, there are num_white_slots white slots of height slot_height,\n # separated by black strips of height slot_gap\n \n # White slot positions:\n white_slot_starts = []\n y_pos = below_slots\n for i in range(num_white_slots):\n white_slot_starts.append(y_pos)\n y_pos += slot_height + slot_gap\n \n # Now fill in the black regions:\n # Bottom solid already done (0 to below_slots)\n \n # Between white slots: black strips\n for i in range(num_white_slots - 1):\n # Black strip between slot i and slot i+1\n strip_start = white_slot_starts[i] + slot_height\n strip_height = slot_gap\n strip = gf.components.rectangle(\n size=(width_um, strip_height),\n layer=(1, 0),\n centered=False\n )\n ref = c << strip\n ref.move((0, strip_start))\n \n # Top strip of bottom block (above last white slot)\n top_strip_bottom = white_slot_starts[-1] + slot_height\n top_strip_height = above_slots_in_block\n if top_strip_height > 0:\n top_strip = gf.components.rectangle(\n size=(width_um, top_strip_height),\n layer=(1, 0),\n centered=False\n )\n ref = c << top_strip\n ref.move((0, top_strip_bottom))\n \n # Now middle section: 5 separate black bars (above the white slots of bottom block)\n # Middle bars start after the bottom block gap region\n middle_start_y = white_slot_starts[-1] + slot_height + above_slots_in_block\n middle_bar_height = 0.12\n middle_bar_gap = 0.08\n \n for i in range(5): # 5 middle bars\n bar_y = middle_start_y + i * (middle_bar_height + middle_bar_gap)\n bar = gf.components.rectangle(\n size=(width_um, middle_bar_height),\n layer=(1, 0),\n centered=False\n )\n ref = c << bar\n ref.move((0, bar_y))\n \n # Now top block: similar to bottom but at top\n # Top block solid region at top + white slots at bottom of top block\n \n # Leftover space after middle bars\n total_middle_bars_height = 5 * middle_bar_height + 4 * middle_bar_gap\n after_middle = middle_start_y + total_middle_bars_height\n \n # Top block: solid region from some y to 4.0, with white slots at its bottom edge\n top_block_solid_top = 3.5 # solid region from y=3.5 to y=4.0\n \n # White slots in top block (lower part of top block)\n top_block_slots_bottom = top_block_solid_top - (num_white_slots * slot_height + (num_white_slots - 1) * slot_gap)\n \n # Black strip below top block's white slots\n if top_block_slots_bottom > 0:\n bottom_strip_height = top_block_slots_bottom - after_middle\n if bottom_strip_height > 0:\n bottom_strip_2 = gf.components.rectangle(\n size=(width_um, bottom_strip_height),\n layer=(1, 0),\n centered=False\n )\n ref = c << bottom_strip_2\n ref.move((0, after_middle))\n \n # White slots in top block - skip these (they're white/background)\n # White slot positions in top block\n top_white_start = top_block_slots_bottom\n for i in range(num_white_slots):\n # White slot from top_white_start + i*(slot_height+slot_gap) to that + slot_height\n pass # Skip, don't add material here\n \n # Between white slots in top block: black strips\n for i in range(num_white_slots - 1):\n strip_start_2 = top_white_start + i * (slot_height + slot_gap) + slot_height\n strip = gf.components.rectangle(\n size=(width_um, slot_gap),\n layer=(1, 0),\n centered=False\n )\n ref = c << strip\n ref.move((0, strip_start_2))\n \n # Solid top region (already defined to go to y=4.0)\n top_solid = gf.components.rectangle(\n size=(width_um, top_block_solid_top - top_block_slots_bottom),\n layer=(1, 0),\n centered=False\n )\n ref = c << top_solid\n ref.move((0, top_block_slots_bottom))\n \n return c\n\nif __name__ == \"__main__\":\n dev = device()\n dev.write_gds(\"device.gds\")", + "footprint_um": [ + 120.0, + 4.0 + ], + "rounds": [ + { + "round": 0, + "samples": 8, + "round_best": 0.39029444269984703, + "champion_iou": 0.39029444269984703 + }, + { + "round": 1, + "samples": 4, + "round_best": 0.3807243430304511, + "champion_iou": 0.39029444269984703 + }, + { + "round": 2, + "samples": 4, + "round_best": 0.4175514291117344, + "champion_iou": 0.4175514291117344 + }, + { + "round": 3, + "samples": 4, + "round_best": 0.3683442339322844, + "champion_iou": 0.4175514291117344 + } + ] + } + }, + "overall_mean_champion": 0.4910063784832184 +} \ No newline at end of file diff --git a/data/training/qwen-rl-v2/bench/a-post-l1.json b/data/training/qwen-rl-v2/bench/a-post-l1.json new file mode 100644 index 00000000..bf2fd2cc --- /dev/null +++ b/data/training/qwen-rl-v2/bench/a-post-l1.json @@ -0,0 +1,62 @@ +{ + "model_name": "Qwen/Qwen3.6-35B-A3B", + "model_path": "tinker://da5c0ccb-55cb-547c-aa0a-b5169801186a:train:0/sampler_weights/final", + "attempts": 8, + "temperature": 1.0, + "per_target": { + "final_1": { + "executable": 2, + "attempts": 8, + "mean_iou": 0.04667048709503287, + "best_iou": 0.30297820032053774 + }, + "final_2": { + "executable": 0, + "attempts": 8, + "mean_iou": 0.0, + "best_iou": 0.0 + }, + "final_3": { + "executable": 0, + "attempts": 8, + "mean_iou": 0.0, + "best_iou": 0.0 + }, + "final_4": { + "executable": 0, + "attempts": 8, + "mean_iou": 0.0, + "best_iou": 0.0 + }, + "final_5": { + "executable": 0, + "attempts": 8, + "mean_iou": 0.0, + "best_iou": 0.0 + }, + "final_6": { + "executable": 7, + "attempts": 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+@gf.cell +def device(): + c = gf.Component() + + # The shape appears to be a cross with rounded/elliptical arms and rectangular stubs + # Looking at the silhouette, it has 4 folded lobes meeting at center with straight extensions + + # Create the cross shape using the cross primitive with appropriate dimensions + # The footprint is 4.7 x 4.7, so half is 2.35 + # The cross should have length matching the full extent + + # Using cross primitive with length=4.7, width=0.85 to get the right footprint + cross = gf.components.cross(length=4.7, width=width, layer=layer) + c << cross + + return c + +if __name__ == "__main__": + dev = device() + dev.write_gds("device.gds") \ No newline at end of file diff --git a/data/training/qwen-rl-v2/gallery/final_6.render.png b/data/training/qwen-rl-v2/gallery/final_6.render.png new file mode 100644 index 0000000000000000000000000000000000000000..9bbbff2ac4f5f511f09349378d0615cb2433f65c GIT binary patch literal 2766 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material on layer (1,0) + # WHITE = background (no material) + + # The white lines inside the top and bottom blocks mean those regions have NO material on layer (1,0) + # So I need to construct the black regions, avoiding the white line areas + + # Let me define the footprint and structure: + width_um = 120.0 + height_um = 4.0 + + # Parameters for the design + # Top block: solid black frame with 5 horizontal white stripes near the top + # Middle: 5 horizontal black strips + # Bottom block: solid black frame with 5 horizontal white stripes near the bottom + + # Let me break down vertically (from y=0 at bottom to y=4 at top): + # Bottom solid region with top portion having 5 white cuts: + # - Very bottom (y=0 to y~0.3): solid black + # - Then 5 white horizontal lines with black between them (the white lines are gaps) + # - Then top solid region up to some y + + # Actually, looking again at the image - the "white lines" in top and bottom blocks + # are inside the black, meaning black is ON both sides of each white line. + # So each white line is a narrow unfilled slot within a larger black region. + + # Simpler interpretation: + # The entire device can be built from horizontal rectangular bars on layer (1,0) + # The white areas are just empty space between black bars + + # Structure (y-coordinates from 0 at bottom to 4 at top): + # Bottom region: solid black rectangle minus 5 horizontal white slots + # Middle: 5 separate horizontal black bars + # Top region: solid black rectangle minus 5 horizontal white slots + + # Let me draw it as all black regions (everything is on layer 1,0 if black, nothing if white): + + # Bottom rectangular block (from y=0 up to ~1.5, with 5 white horizontal gaps from y~0.5 to y~1.4) + # This means: + # - Solid from y=0 to y=0.5 (black) + # - White lines at specific y positions with small height + # - Between white lines: thin black strips + # - Then solid from y~1.4 to y~1.5 (black) + + # Let me simplify by defining parameters: + num_white_slots = 5 + slot_height = 0.08 # height of each white (unfilled) slot + slot_gap = 0.06 # black material height between slots + + # Middle section height occupied by white slots + middle_slots_height = num_white_slots * slot_height + (num_white_slots - 1) * slot_gap + + # Below the slots block: solid black + below_slots = 0.5 # height of solid region below all white slots + + # Above the slots block in bottom region: small solid region + above_slots_in_block = 0.3 + + # Bottom block y-range: 0 to below_slots + middle_slots_height + above_slots_in_block + bottom_block_upper = below_slots + middle_slots_height + above_slots_in_block + + # Now add the solid black rectangle for bottom (excluding white slot areas) + # I'll add the solid parts and skip the white slot regions + + # Part 1: Solid bottom (y=0 to y=0.5) + bottom_solid = gf.components.rectangle( + size=(width_um, below_slots), + layer=(1, 0), + centered=False + ) + c << bottom_solid + + # Part 2: Thin black strips between/around white slots in bottom block + # Between each pair of white slots: black strip of height slot_gap + y_current = below_slots + for i in range(num_white_slots): + # Black strip above this white slot (or the initial gap before first slot) + # White slot at y_current to y_current + slot_height + # Add black strip after white slot if not the last one + if i < num_white_slots - 1: + black_strip_between = y_current + slot_height + black_strip_bottom = y_current + slot_height + black_strip_height = slot_gap + + # Actually let me redo this more carefully + + # Let me think of it as: from y=below_slots to y=above_slots_in_block + below_slots + # Within this range, there are num_white_slots white slots of height slot_height, + # separated by black strips of height slot_gap + + # White slot positions: + white_slot_starts = [] + y_pos = below_slots + for i in range(num_white_slots): + white_slot_starts.append(y_pos) + y_pos += slot_height + slot_gap + + # Now fill in the black regions: + # Bottom solid already done (0 to below_slots) + + # Between white slots: black strips + for i in range(num_white_slots - 1): + # Black strip between slot i and slot i+1 + strip_start = white_slot_starts[i] + slot_height + strip_height = slot_gap + strip = gf.components.rectangle( + size=(width_um, strip_height), + layer=(1, 0), + centered=False + ) + ref = c << strip + ref.move((0, strip_start)) + + # Top strip of bottom block (above last white slot) + top_strip_bottom = white_slot_starts[-1] + slot_height + top_strip_height = above_slots_in_block + if top_strip_height > 0: + top_strip = gf.components.rectangle( + size=(width_um, top_strip_height), + layer=(1, 0), + centered=False + ) + ref = c << top_strip + ref.move((0, top_strip_bottom)) + + # Now middle section: 5 separate black bars (above the white slots of bottom block) + # Middle bars start after the bottom block gap region + middle_start_y = white_slot_starts[-1] + slot_height + above_slots_in_block + middle_bar_height = 0.12 + middle_bar_gap = 0.08 + + for i in range(5): # 5 middle bars + bar_y = middle_start_y + i * (middle_bar_height + middle_bar_gap) + bar = gf.components.rectangle( + size=(width_um, middle_bar_height), + layer=(1, 0), + centered=False + ) + ref = c << bar + ref.move((0, bar_y)) + + # Now top block: similar to bottom but at top + # Top block solid region at top + white slots at bottom of top block + + # Leftover space after middle bars + total_middle_bars_height = 5 * middle_bar_height + 4 * middle_bar_gap + after_middle = middle_start_y + total_middle_bars_height + + # Top block: solid region from some y to 4.0, with white slots at its bottom edge + top_block_solid_top = 3.5 # solid region from y=3.5 to y=4.0 + + # White slots in top block (lower part of top block) + top_block_slots_bottom = top_block_solid_top - (num_white_slots * slot_height + (num_white_slots - 1) * slot_gap) + + # Black strip below top block's white slots + if top_block_slots_bottom > 0: + bottom_strip_height = top_block_slots_bottom - after_middle + if bottom_strip_height > 0: + bottom_strip_2 = gf.components.rectangle( + size=(width_um, bottom_strip_height), + layer=(1, 0), + centered=False + ) + ref = c << bottom_strip_2 + ref.move((0, after_middle)) + + # White slots in top block - skip these (they're white/background) + # White slot positions in top block + top_white_start = top_block_slots_bottom + for i in range(num_white_slots): + # White slot from top_white_start + i*(slot_height+slot_gap) to that + slot_height + pass # Skip, don't add material here + + # Between white slots in top block: black strips + for i in range(num_white_slots - 1): + strip_start_2 = top_white_start + i * (slot_height + slot_gap) + slot_height + strip = gf.components.rectangle( + size=(width_um, slot_gap), + layer=(1, 0), + centered=False + ) + ref = c << strip + ref.move((0, strip_start_2)) + + # Solid top region (already defined to go to y=4.0) + top_solid = gf.components.rectangle( + size=(width_um, top_block_solid_top - top_block_slots_bottom), + layer=(1, 0), + centered=False + ) + ref = c << top_solid + ref.move((0, top_block_slots_bottom)) + + return c + +if __name__ == "__main__": + dev = device() + dev.write_gds("device.gds") \ No newline at end of file diff --git a/data/training/qwen-rl-v2/gallery/final_8.render.png b/data/training/qwen-rl-v2/gallery/final_8.render.png new file mode 100644 index 0000000000000000000000000000000000000000..22100cba974dca07dec2e584cf951563b51993ad GIT binary patch literal 25473 zcmeI4T}V@57{}i^hvn1_MUBFx7eLON$F*qqj#BLSl>b1&0ZVcMuXswg@kXb!NJMMM^)rujz{tKTnj%1#8KIfeWAl1OjrwW>1HA zjl5%ez$+2_pq;r>qHaw}K1xw>Yby*~02Lq*kPDCwK?KkcGz1MnL(mX31Pwt$&=52P z4M9WD5Hti0K||0GGz1MnL(mX31PwPL4F^m-opO`P=60E2v{nmDn7d#$hx5EY7d-pM z%?){FGb1jqEHKt9o`&O|XzZSfhsp>+4Oe&7P;tP(1!1XzF@s`avW^!dPM)sVRKCoG zo@)9)lA-^w4|;L&r`Q>1{Y(#FOn!>8Al`xHNY12|YOM})m zOM~BqllaDTol>T|mqlWn%9z@&jKm$UwB5&jL)YJ4()wJt_Yi%;Fby%7|G)(gdZQYf 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+ "final_6": { + "champion_iou": 0.715876929571249, + "rendered": true + }, + "final_7": { + "champion_iou": 0.4894129134583224, + "rendered": true + }, + "final_8": { + "champion_iou": 0.4175514291117344, + "rendered": true + } +} \ No newline at end of file diff --git a/rl/README.md b/rl/README.md index 4f7378e7..317f0330 100644 --- a/rl/README.md +++ b/rl/README.md @@ -70,12 +70,32 @@ ids) is evaluated at campaign start — the base-model baseline — and after every stage, so every point on the curve is comparable. SFT arms must beat the base probe on L0 before RL spends anything. -Requirements: `TINKER_API_KEY`, `WANDB_API_KEY`, and the immutable sandbox -image (`PIXCELL_EVALUATOR_IMAGE`, built by [`common/sandbox/build.py`](common/sandbox/build.py)). -Run artifacts land outside the repository (default -`/Volumes/aadarwal_vx/QPG-MIT/PixCell-training/runs-v2/`); W&B project -`pixcell-rl`. The pinned environment is [`requirements-lock.txt`](requirements-lock.txt) -/ [`stack.json`](stack.json). +Requirements: `TINKER_API_KEY` and `WANDB_API_KEY`. Candidate programs +execute behind an AST source gate in a resource-limited subprocess by +default; set `PIXCELL_REQUIRE_ISOLATION=1` to demand the Docker tier +(image named by `PIXCELL_EVALUATOR_IMAGE`, built by +[`common/sandbox/build.py`](common/sandbox/build.py)). Run artifacts land +outside the repository (default `~/pixcell-training/runs-v2/`, override with +`--runs-root`); W&B project `pixcell-rl`. The pinned environment is +[`requirements-lock.txt`](requirements-lock.txt) / [`stack.json`](stack.json). + +## Result — the released model + +Run B ran to completion (150 GRPO steps, L0→L4) and is the released model: +[`qpaig-mit/pixcell`](https://huggingface.co/qpaig-mit/pixcell). On the +held-out F1–F8 benchmark (8 attempts at T=1.0) it scores mean IoU 0.228 and +best-of-8 0.467, from a base model that produced 0 executable attempts in 64. +Deployed as intended — inside the attempt–measure–revise loop +([`track_a/agent_loop.py`](track_a/agent_loop.py): 8 attempts, then 3 rounds +of 4 champion revisions under number-free verifier feedback) — it reaches +**mean champion IoU 0.491**. The validated evidence (benchmark records, loop +trajectories, champion programs, render gallery) is versioned in +[`data/training/qwen-rl-v2/`](../data/training/qwen-rl-v2/). + +The SFT-seeded arms (Run A and every fork from its checkpoints) entered RL +with low policy entropy and collapsed terminally within ~50–60 RL steps; the +thinking pair (C/D) was still mid-campaign at release time and is not part +of this record. ## Preflight diff --git a/rl/track_a/make_gallery.py b/rl/track_a/make_gallery.py new file mode 100644 index 00000000..9a341c93 --- /dev/null +++ b/rl/track_a/make_gallery.py @@ -0,0 +1,125 @@ +"""Compose the benchmark gallery from agent-loop champions: per target, the +frozen input raster beside the champion program's render at target +calibration, with the difference map in the paper's convention (maroon +overlap, pink candidate-only, black target-only).""" + +from __future__ import annotations + +import argparse +import json +import subprocess +import sys +import tempfile +from pathlib import Path + +import numpy as np +from PIL import Image + +from michaelangelo.core.compare_absolute import extract_polygons, render_gds_absolute +from rl.common.evaluator import _prepare_reference +from rl.track_a.benchmark_eval import _fixture_tasks + +OVERLAP = (128, 0, 0) +CANDIDATE_ONLY = (255, 146, 153) +TARGET_ONLY = (23, 25, 28) +BACKGROUND = (255, 255, 255) + + +def _render_champion(code: str, prepared) -> Image.Image | None: + with tempfile.TemporaryDirectory() as workdir: + script = Path(workdir) / "program.py" + script.write_text(code, encoding="utf-8") + proc = subprocess.run( + [sys.executable, str(script)], + cwd=workdir, + capture_output=True, + timeout=60, + ) + gds = Path(workdir) / "device.gds" + if proc.returncode != 0 or not gds.exists(): + return None + polygons = extract_polygons(gds) + if not polygons: + return None + raster, _info = render_gds_absolute(polygons, prepared.calibration) + return raster.convert("L") + + +def _diff(render: Image.Image, target: Image.Image) -> Image.Image: + r = np.asarray(render) < 245 + t = np.asarray(target) < 245 + out = np.full((*t.shape, 3), BACKGROUND, dtype=np.uint8) + out[r & t] = OVERLAP + out[r & ~t] = CANDIDATE_ONLY + out[~r & t] = TARGET_ONLY + return Image.fromarray(out) + + +def main() -> None: + ap = argparse.ArgumentParser(description=__doc__) + repo = Path(__file__).resolve().parents[2] + ap.add_argument("--benchmark-root", default=str(repo / "data" / "benchmark")) + ap.add_argument("--champions", required=True, help="agent-loop summary JSON") + ap.add_argument("--out-dir", required=True) + args = ap.parse_args() + summary = json.loads(Path(args.champions).read_text(encoding="utf-8")) + out = Path(args.out_dir) + out.mkdir(parents=True, exist_ok=True) + tasks = {t.sampler.opaque_id: t for t in _fixture_tasks(Path(args.benchmark_root))} + tiles = [] + manifest = {} + for name, entry in summary["per_target"].items(): + task = tasks[name] + prepared = _prepare_reference(task.reference) + target = prepared.target + render = _render_champion(entry["champion_code"], prepared) + (out / f"{name}.program.py").write_text( + entry["champion_code"], encoding="utf-8" + ) + target.save(out / f"{name}.target.png") + if render is not None: + render.save(out / f"{name}.render.png") + diff = _diff(render, target) + diff.save(out / f"{name}.diff.png") + manifest[name] = { + "champion_iou": entry["champion_iou"], + "rendered": render is not None, + } + row = [target.convert("RGB")] + if render is not None: + row.append(render.convert("RGB")) + row.append(_diff(render, target)) + height = 220 + scaled = [] + for image in row: + width = max(1, int(image.width * height / image.height)) + scaled.append(image.resize((width, height))) + strip = Image.new( + "RGB", + (sum(i.width for i in scaled) + 8 * (len(scaled) - 1), height), + BACKGROUND, + ) + x = 0 + for image in scaled: + strip.paste(image, (x, 0)) + x += image.width + 8 + tiles.append((name, entry["champion_iou"], strip)) + width = max(t[2].width for t in tiles) + 160 + total_height = sum(t[2].height + 16 for t in tiles) + sheet = Image.new("RGB", (width, total_height), BACKGROUND) + from PIL import ImageDraw + + draw = ImageDraw.Draw(sheet) + y = 0 + for name, iou, strip in tiles: + draw.text((4, y + 100), f"{name}\nIoU {iou:.3f}", fill=(23, 25, 28)) + sheet.paste(strip, (150, y)) + y += strip.height + 16 + sheet.save(out / "gallery.png") + (out / "manifest.json").write_text(json.dumps(manifest, indent=2), encoding="utf-8") + print(json.dumps(manifest, indent=2)) + print(f"gallery written to {out}/gallery.png", flush=True) + + +if __name__ == "__main__": + main() From 706032d11e0ed57799e8ff264f47af55cae987c1 Mon Sep 17 00:00:00 2001 From: Aadarsh Agarwal Date: Wed, 29 Jul 2026 11:36:35 -0400 Subject: [PATCH 24/24] Drop stored render gallery: regenerable from the record via make_gallery --- data/README.md | 9 +- data/training/qwen-rl-v2/README.md | 10 +- 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data/training/qwen-rl-v2/gallery/final_7.program.py delete mode 100644 data/training/qwen-rl-v2/gallery/final_7.render.png delete mode 100644 data/training/qwen-rl-v2/gallery/final_7.target.png delete mode 100644 data/training/qwen-rl-v2/gallery/final_8.diff.png delete mode 100644 data/training/qwen-rl-v2/gallery/final_8.program.py delete mode 100644 data/training/qwen-rl-v2/gallery/final_8.render.png delete mode 100644 data/training/qwen-rl-v2/gallery/final_8.target.png delete mode 100644 data/training/qwen-rl-v2/gallery/gallery.png delete mode 100644 data/training/qwen-rl-v2/gallery/manifest.json diff --git a/data/README.md b/data/README.md index 5506a4dd..d57127b6 100644 --- a/data/README.md +++ b/data/README.md @@ -31,9 +31,10 @@ later runs the trajectories also carry). `training/qwen-rl-v2/` is the validated evidence for the released model ([`qpaig-mit/pixcell`](https://huggingface.co/qpaig-mit/pixcell), a GRPO-trained -LoRA for Qwen3.6-35B-A3B): the F1–F8 benchmark records, the -attempt–measure–revise deployment-loop record with every champion program, and -the re-executed render gallery. Its README maps each number to its file. +LoRA for Qwen3.6-35B-A3B): the F1–F8 benchmark records and the +attempt–measure–revise deployment-loop record with every champion program; +the render gallery regenerates from it (`rl/track_a/make_gallery.py`). Its +README maps each number to its file. `training/representation-curriculum-v1/` preserves superseded Qwen and Inkling diagnostic runs. Those runs used the retired v1 direct prompt contract and are not canonical baselines or paper results. Their protocol and artifacts remain @@ -143,7 +144,7 @@ Every number in the paper's Results, mapped to its backing file. The repository contains the training recipes under `rl/`, the derived paper table and gallery under `arxiv/`, and the validated evidence for the released model under `training/qwen-rl-v2/` (benchmark JSONs, deployment-loop -record, champion programs, gallery). Raw step-level training ledgers and +record, champion programs). Raw step-level training ledgers and checkpoints stay outside the repository; the released adapter itself is [`qpaig-mit/pixcell`](https://huggingface.co/qpaig-mit/pixcell). diff --git a/data/training/qwen-rl-v2/README.md b/data/training/qwen-rl-v2/README.md index 130ccaf6..e465d019 100644 --- a/data/training/qwen-rl-v2/README.md +++ b/data/training/qwen-rl-v2/README.md @@ -18,7 +18,6 @@ the deterministic evaluator; nothing model-reported enters the record. | `bench/b-final.json` | same | released model: 39/64 executable, mean IoU 0.228, best-of-8 0.467 | | `bench/a-post-l1.json`, `bench/d-post-l0.json` | same | mid-campaign controls (L0-SFT arm after L1; thinking arm after L0) | | `agentloop/b-final.json` | attempt–measure–revise loop: 8 attempts, then 3 rounds × 4 revisions of the champion under number-free feedback | **mean champion IoU 0.491**; includes every champion program and per-round trajectory | -| `gallery/` | champion programs re-executed, re-rendered, re-diffed | per device: `*.program.py`, `*.target.png`, `*.render.png`, `*.diff.png`; `gallery.png` composite; `manifest.json` | The agent loop is the deployment protocol — the released model is the searcher inside that loop, and 0.491 is its number. The single-pass benchmark @@ -32,7 +31,8 @@ uv run python -m rl.track_a.agent_loop --model-path --out agent uv run python -m rl.track_a.make_gallery --champions agentloop.json --out-dir gallery/ ``` -`make_gallery` re-executes each stored champion program from source and -re-renders it against the frozen `data/benchmark` fixtures, so the gallery -(and its `manifest.json` IoUs) can be regenerated from this record alone, -no checkpoint required. +The render gallery (per device: target | champion render | difference map) +is not stored here: `make_gallery` re-executes each stored champion program +from source and re-renders it against the frozen `data/benchmark` fixtures, +so it regenerates from this record alone, no checkpoint required. 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portion - # (similar to top but inverted) - - # BLACK = material on layer (1,0) - # WHITE = background (no material) - - # The white lines inside the top and bottom blocks mean those regions have NO material on layer (1,0) - # So I need to construct the black regions, avoiding the white line areas - - # Let me define the footprint and structure: - width_um = 120.0 - height_um = 4.0 - - # Parameters for the design - # Top block: solid black frame with 5 horizontal white stripes near the top - # Middle: 5 horizontal black strips - # Bottom block: solid black frame with 5 horizontal white stripes near the bottom - - # Let me break down vertically (from y=0 at bottom to y=4 at top): - # Bottom solid region with top portion having 5 white cuts: - # - Very bottom (y=0 to y~0.3): solid black - # - Then 5 white horizontal lines with black between them (the white lines are gaps) - # - Then top solid region up to some y - - # Actually, looking again at the image - the "white lines" in top and bottom blocks - # are inside the black, meaning black is ON both sides of each white line. - # So each white line is a narrow unfilled slot within a larger black region. - - # Simpler interpretation: - # The entire device can be built from horizontal rectangular bars on layer (1,0) - # The white areas are just empty space between black bars - - # Structure (y-coordinates from 0 at bottom to 4 at top): - # Bottom region: solid black rectangle minus 5 horizontal white slots - # Middle: 5 separate horizontal black bars - # Top region: solid black rectangle minus 5 horizontal white slots - - # Let me draw it as all black regions (everything is on layer 1,0 if black, nothing if white): - - # Bottom rectangular block (from y=0 up to ~1.5, with 5 white horizontal gaps from y~0.5 to y~1.4) - # This means: - # - Solid from y=0 to y=0.5 (black) - # - White lines at specific y positions with small height - # - Between white lines: thin black strips - # - Then solid from y~1.4 to y~1.5 (black) - - # Let me simplify by defining parameters: - num_white_slots = 5 - slot_height = 0.08 # height of each white (unfilled) slot - slot_gap = 0.06 # black material height between slots - - # Middle section height occupied by white slots - middle_slots_height = num_white_slots * slot_height + (num_white_slots - 1) * slot_gap - - # Below the slots block: solid black - below_slots = 0.5 # height of solid region below all white slots - - # Above the slots block in bottom region: small solid region - above_slots_in_block = 0.3 - - # Bottom block y-range: 0 to below_slots + middle_slots_height + above_slots_in_block - bottom_block_upper = below_slots + middle_slots_height + above_slots_in_block - - # Now add the solid black rectangle for bottom (excluding white slot areas) - # I'll add the solid parts and skip the white slot regions - - # Part 1: Solid bottom (y=0 to y=0.5) - bottom_solid = gf.components.rectangle( - size=(width_um, below_slots), - layer=(1, 0), - centered=False - ) - c << bottom_solid - - # Part 2: Thin black strips between/around white slots in bottom block - # Between each pair of white slots: black strip of height slot_gap - y_current = below_slots - for i in range(num_white_slots): - # Black strip above this white slot (or the initial gap before first slot) - # White slot at y_current to y_current + slot_height - # Add black strip after white slot if not the last one - if i < num_white_slots - 1: - black_strip_between = y_current + slot_height - black_strip_bottom = y_current + slot_height - black_strip_height = slot_gap - - # Actually let me redo this more carefully - - # Let me think of it as: from y=below_slots to y=above_slots_in_block + below_slots - # Within this range, there are num_white_slots white slots of height slot_height, - # separated by black strips of height slot_gap - - # White slot positions: - white_slot_starts = [] - y_pos = below_slots - for i in range(num_white_slots): - white_slot_starts.append(y_pos) - y_pos += slot_height + slot_gap - - # Now fill in the black regions: - # Bottom solid already done (0 to below_slots) - - # Between white slots: black strips - for i in range(num_white_slots - 1): - # Black strip between slot i and slot i+1 - strip_start = white_slot_starts[i] + slot_height - strip_height = slot_gap - strip = gf.components.rectangle( - size=(width_um, strip_height), - layer=(1, 0), - centered=False - ) - ref = c << strip - ref.move((0, strip_start)) - - # Top strip of bottom block (above last white slot) - top_strip_bottom = white_slot_starts[-1] + slot_height - top_strip_height = above_slots_in_block - if top_strip_height > 0: - top_strip = gf.components.rectangle( - size=(width_um, top_strip_height), - layer=(1, 0), - centered=False - ) - ref = c << top_strip - ref.move((0, top_strip_bottom)) - - # Now middle section: 5 separate black bars (above the white slots of bottom block) - # Middle bars start after the bottom block gap region - middle_start_y = white_slot_starts[-1] + slot_height + above_slots_in_block - middle_bar_height = 0.12 - middle_bar_gap = 0.08 - - for i in range(5): # 5 middle bars - bar_y = middle_start_y + i * (middle_bar_height + middle_bar_gap) - bar = gf.components.rectangle( - size=(width_um, middle_bar_height), - layer=(1, 0), - centered=False - ) - ref = c << bar - ref.move((0, bar_y)) - - # Now top block: similar to bottom but at top - # Top block solid region at top + white slots at bottom of top block - - # Leftover space after middle bars - total_middle_bars_height = 5 * middle_bar_height + 4 * middle_bar_gap - after_middle = middle_start_y + total_middle_bars_height - - # Top block: solid region from some y to 4.0, with white slots at its bottom edge - top_block_solid_top = 3.5 # solid region from y=3.5 to y=4.0 - - # White slots in top block (lower part of top block) - top_block_slots_bottom = top_block_solid_top - (num_white_slots * slot_height + (num_white_slots - 1) * slot_gap) - - # Black strip below top block's white slots - if top_block_slots_bottom > 0: - bottom_strip_height = top_block_slots_bottom - after_middle - if bottom_strip_height > 0: - bottom_strip_2 = gf.components.rectangle( - size=(width_um, bottom_strip_height), - layer=(1, 0), - centered=False - ) - ref = c << bottom_strip_2 - ref.move((0, after_middle)) - - # White slots in top block - skip these (they're white/background) - # White slot positions in top block - top_white_start = top_block_slots_bottom - for i in range(num_white_slots): - # White slot from top_white_start + i*(slot_height+slot_gap) to that + slot_height - pass # Skip, don't add material here - - # Between white slots in top block: black strips - for i in range(num_white_slots - 1): - strip_start_2 = top_white_start + i * (slot_height + slot_gap) + slot_height - strip = gf.components.rectangle( - size=(width_um, slot_gap), - layer=(1, 0), - centered=False - ) - ref = c << strip - ref.move((0, strip_start_2)) - - # Solid top region (already defined to go to y=4.0) - top_solid 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"champion_iou": 0.715876929571249, - "rendered": true - }, - "final_7": { - "champion_iou": 0.4894129134583224, - "rendered": true - }, - "final_8": { - "champion_iou": 0.4175514291117344, - "rendered": true - } -} \ No newline at end of file diff --git a/rl/README.md b/rl/README.md index 317f0330..0e79c210 100644 --- a/rl/README.md +++ b/rl/README.md @@ -89,8 +89,9 @@ Deployed as intended — inside the attempt–measure–revise loop ([`track_a/agent_loop.py`](track_a/agent_loop.py): 8 attempts, then 3 rounds of 4 champion revisions under number-free verifier feedback) — it reaches **mean champion IoU 0.491**. The validated evidence (benchmark records, loop -trajectories, champion programs, render gallery) is versioned in -[`data/training/qwen-rl-v2/`](../data/training/qwen-rl-v2/). +trajectories, champion programs) is versioned in +[`data/training/qwen-rl-v2/`](../data/training/qwen-rl-v2/); the render +gallery regenerates from it via [`track_a/make_gallery.py`](track_a/make_gallery.py). The SFT-seeded arms (Run A and every fork from its checkpoints) entered RL with low policy entropy and collapsed terminally within ~50–60 RL steps; the

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a/data/training/qwen-rl-v2/gallery/final_4.program.py b/data/training/qwen-rl-v2/gallery/final_4.program.py deleted file mode 100644 index d65a6927..00000000 --- a/data/training/qwen-rl-v2/gallery/final_4.program.py +++ /dev/null @@ -1,31 +0,0 @@ -import gdsfactory as gf - -# Physical footprint parameters -X_LENGTH = 18.0 -Y_WIDTH = 3.0 -WAVE_AMPLITUDE = 1.0 - -@gf.cell -def device(): - c = gf.Component() - - xs = gf.cross_section.cross_section(width=1.0, layer=(1, 0)) - - # Create upper wave path - points_upper = [(0, Y_WIDTH/2), (X_LENGTH/2, 0), (X_LENGTH, Y_WIDTH/2)] - path_upper = gf.path.smooth(points_upper, radius=4.0) - comp_upper = gf.path.extrude(path_upper, cross_section=xs) - - # Create lower wave path - points_lower = [(0, -Y_WIDTH/2), (X_LENGTH/2, 0), (X_LENGTH, -Y_WIDTH/2)] - path_lower = gf.path.smooth(points_lower, radius=4.0) - comp_lower = gf.path.extrude(path_lower, cross_section=xs) - - c << comp_upper - c << comp_lower - - return c - -if __name__ == "__main__": - d 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a/data/training/qwen-rl-v2/gallery/final_4.program.py b/data/training/qwen-rl-v2/gallery/final_4.program.py new file mode 100644 index 00000000..d65a6927 --- /dev/null +++ b/data/training/qwen-rl-v2/gallery/final_4.program.py @@ -0,0 +1,31 @@ +import gdsfactory as gf + +# Physical footprint parameters +X_LENGTH = 18.0 +Y_WIDTH = 3.0 +WAVE_AMPLITUDE = 1.0 + +@gf.cell +def device(): + c = gf.Component() + + xs = gf.cross_section.cross_section(width=1.0, layer=(1, 0)) + + # Create upper wave path + points_upper = [(0, Y_WIDTH/2), (X_LENGTH/2, 0), (X_LENGTH, Y_WIDTH/2)] + path_upper = gf.path.smooth(points_upper, radius=4.0) + comp_upper = gf.path.extrude(path_upper, cross_section=xs) + + # Create lower wave path + points_lower = [(0, -Y_WIDTH/2), (X_LENGTH/2, 0), (X_LENGTH, -Y_WIDTH/2)] + path_lower = gf.path.smooth(points_lower, radius=4.0) + comp_lower = gf.path.extrude(path_lower, cross_section=xs) + + c << comp_upper + c << comp_lower + + return c + +if __name__ == "__main__": + d = 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