update-golden-values

# Update golden values + signed per-model percentage summary End-to-end workflow for refreshing golden values from a GitHub Actions workflow run, reporting signed percentage changes per test/model environment, and writing a PR-ready summary. The skill orchestrates two scripts that already live in the repo: - `tests/test_utils/python_scripts/download_golden_values.py` — pulls artifacts from a workflow run and overwrites `tests/functional_tests/test_cases/**/golden_values_*.json`. - `tests/test_utils/python_scripts/compare_golden_values_kl.py` — diffs the working-tree goldens against `git HEAD` and reports per-metric `avg_rel_diff = mean((old − new) / old)`. (Filename keeps the legacy `_kl` suffix; the script no longer computes KL divergence.) ## Inputs to gather from the user 1. **GitHub Actions workflow run ID** (e.g. `25341543542`). It's the numeric ID in the run URL. 2. **Source**: should be `github` for this workflow. (`gitlab` is supported by the download script but uses a different env path.) 3. **Scope** — accept one of: - `only-failing` → run with `--only-failing` (download from failing/cancelled jobs only). Use this for "fix the broken tests" workflows. - `all` → run without `--only-failing` (download from every job that produced golden values). Use this when the user wants a full refresh. If the user doesn't specify, ask. Don't silently default. ## Workflow ``` - [ ] Step 1: Set up env (token + venv with deps) - [ ] Step 2: Reset prior golden-value edits - [ ] Step 3: Download goldens (scope = only-failing | all) - [ ] Step 4: Run relative-diff comparison + generate per-model percentage table - [ ] Step 5: Produce PR-ready summary ``` ### Step 1 — Environment The download script needs `GITHUB_TOKEN`. If the user has the `gh` CLI authenticated, derive it; do NOT export the token into a long-lived shell or commit it. ```bash # token (one-shot, scoped to the command) export GITHUB_TOKEN="$(gh auth token)" # python deps (the script imports click, gitlab, requests) python3 -m venv /tmp/gv_venv /tmp/gv_venv/bin/pip install --quiet click python-gitlab requests ``` Reuse `/tmp/gv_venv` if it already exists. The comparison script only depends on `click` (also in the venv). ### Step 2 — Reset prior edits (only if user re-runs) If the working tree already has prior golden-value modifications you want to discard before re-downloading: ```bash git checkout -- tests/functional_tests/test_cases/ git ls-files --others --exclude-standard tests/functional_tests/test_cases/ \ | while IFS= read -r f; do rm -f "$f"; done ``` Skip this step when the user explicitly wants to layer a new download on top of an in-progress branch. ### Step 3 — Download Build the command from the user-provided scope: ```bash # scope = only-failing (default for "fix broken tests") /tmp/gv_venv/bin/python tests/test_utils/python_scripts/download_golden_values.py \ --source github --pipeline-id <WORKFLOW_RUN_ID> --only-failing # scope = all (full refresh; omit the flag) /tmp/gv_venv/bin/python tests/test_utils/python_scripts/download_golden_values.py \ --source github --pipeline-id <WORKFLOW_RUN_ID> ``` When `--only-failing` is set, the GitHub path filters at `_fetch_and_filter_artifacts` on `matched_job["conclusion"] == "success"`, so only failing/cancelled jobs contribute artifacts. Without the flag, every job's golden-value artifact is pulled. Capture the final two log lines for the summary; they look like: ``` INFO:__main__:Total tests with golden values: <N> INFO:__main__:Total golden values found: <M> ``` ### Step 4 — Relative-diff comparison ```bash /tmp/gv_venv/bin/python tests/test_utils/python_scripts/compare_golden_values_kl.py \ --top 20 --csv /tmp/reldiff_summary.csv ``` The CSV holds one row per `(file, metric)` with four columns: `file, metric, n_steps, avg_rel_diff` - `n_steps` — count of shared steps that contributed (steps where `|old| < 1e-12` are skipped to avoid div-by-zero; NaN/inf are dropped). - `avg_rel_diff` — `mean((old − new) / old)`. **Signed**: positive = the new run is smaller than the old run at the typical step (e.g. loss decreased), negative = larger. Convert the raw ratio to a percentage for the report: `avg_rel_diff_pct = 100 × avg_rel_diff` Always include the `%` symbol and preserve the sign. Do not take the absolute value, produce magnitude-only statistics, or combine models into magnitude buckets. Generate one row per test/model environment instead: ```python import collections import csv from pathlib import Path rows = list(csv.DictReader(open('/tmp/reldiff_summary.csv'))) for r in rows: r['n_steps'] = int(r['n_steps']) r['avg_rel_diff_pct'] = 100 * float(r['avg_rel_diff']) by_file = collections.defaultdict(dict) for r in rows: by_file[r['file']][r['metric']] = { 'n_steps': r['n_steps'], 'pct': r['avg_rel_diff_pct'], } preferred_metrics = [ 'lm loss', 'mtp_1 loss', 'num-zeros', 'iteration-time', 'mem-allocated-bytes', 'mem-max-allocated-bytes', ] present_metrics = {r['metric'] for r in rows} metrics = [m for m in preferred_metrics if m in present_metrics] metrics.extend(sorted(present_metrics - set(metrics))) print('| Test / environment | Steps | ' + ' | '.join(f'`{m}` (%)' for m in metrics) + ' |') print('| --- | --: | ' + ' | '.join('--:' for _ in metrics) + ' |') for file_name in sorted(by_file): path = Path(file_name) test_name = path.parent.name environment = path.stem.removeprefix('golden_values_') values = by_file[file_name] steps = max(v['n_steps'] for v in values.values()) cells = [] for metric in metrics: if metric not in values: cells.append('—') continue pct = values[metric]['pct'] cells.append('`0.000000%`' if pct == 0 else f'`{pct:+.6f}%`') print(f'| `{test_name}` / {environment} | {steps} | ' + ' | '.join(cells) + ' |') print() print('`Steps` is the largest shared-step count for the row. State any metric-specific') print('difference, such as `iteration-time` having one fewer step after a leading NaN is filtered.') ``` ### Step 5 — Summary blurb Use this template verbatim, filling in `<…>` from steps 3–4. Drop sections that don't apply to the run. Pick the wording for the first line based on the scope used: - `only-failing` → "Refresh of golden values for failing functional tests from GitHub workflow run …" - `all` → "Full refresh of golden values from GitHub workflow run …" Match the `download_golden_values.py` command in the bullet list to the scope used (with or without `--only-failing`). ````markdown ### Summary <scope-appropriate sentence> from GitHub workflow run `<WORKFLOW_RUN_ID>`. **Golden value updates** - Re-ran `tests/test_utils/python_scripts/download_golden_values.py --source github --pipeline-id <WORKFLOW_RUN_ID> <--only-failing if scope=only-failing>`. - Updated **<N> golden-value files** under `tests/functional_tests/test_cases/`. ### Signed per-model relative differences Comparison covers <FILES_WITH_BASELINE> files across <NUM_METRICS> distinct metrics = **<TOTAL_ROWS> `(file, metric)` pairs**. The reported percentage is `100 × mean((old − new) / old)` over shared steps. Positive percentages mean the new run is lower; negative percentages mean it is higher. <INSERT THE GENERATED PER-MODEL MARKDOWN TABLE HERE> State when a metric uses fewer shared steps than the row's `Steps` value—for example, when a leading `iteration-time` NaN was filtered. **Interpretation** (apply only statements supported by the signed percentages) - `lm loss` changes between `-0.01%` and `+0.01%` generally match old goldens to numerical noise. - For `lm loss` or `num-zeros`, call out values below `-0.1%` or above `+0.1%` for review. - For `mem-allocated-bytes` or `mem-max-allocated-bytes`, call out values below `-0.01%` or above `+0.01%`; these metrics should normally remain close to `0%`. - Negative `iteration-time` percentages mean the new run was slower; positive percentages mean it was faster. Treat timing changes as scheduler/warmup noise unless they repeat. - Describe large `num-zeros` changes per model; do not hide them in a cross-model magnitude aggregate. ```` ## Reading the columns | column | meaning | | ------------------ | ----------------------------------------------------------------------------------------------------- | | `n_steps` | shared step indices used in the average (NaN/inf and steps with `\|old\| < 1e-12` are dropped). | | `avg_rel_diff` | raw ratio: `mean((old − new) / old)` over `n_steps`; positive = new < old, negative = new > old. | | `avg_rel_diff_pct` | report value: `100 × avg_rel_diff`; retain the sign and append `%` so the unit is unambiguous. | Keep the sign in every table cell and sort rows by test/model and environment. Do not rank or summarize the report using unsigned magnitudes. Triage rules of thumb: - `lm loss` changes from `-0.01%` through `+0.01%` are generally run-to-run noise. - Treat `mem-allocated-bytes` and `mem-max-allocated-bytes` values outside `-0.01%` through `+0.01%` as changes that require review. - A negative `iteration-time` percentage means the new run was slower; a positive percentage means it was faster. Treat timing changes as scheduler/warmup noise unless they repeat. - Focus reviewer attention on `lm loss` and `num-zeros` values outside `-0.1%` through `+0.1%`. ## Notes & gotchas - The download script's `_fetch_and_filter_artifacts` honors `--only-failing` only on the GitHub path. The Gitlab path applies it per-job inside `download_from_gitlab`. - A brand-new golden file (no `git HEAD` baseline) is silently skipped by the comparison script with a warning. Subtract these from the file count when reporting "files with baseline". - Steps where `|old|` is below `1e-12` are excluded from the average — division blows up there (think `num-zeros` step 0 on a dense model, or `mem-*` before allocation). If every shared step is excluded for a metric, that `(file, metric)` row is omitted entirely. - Functional-test metric collection removes leading `iteration-time` NaNs before artifacts are uploaded. The comparison script filters NaN/inf values at other steps, so other values for that metric still contribute. - The script's filename is `compare_golden_values_kl.py` for legacy reasons; it no longer computes KL divergence. The function and CSV column names reflect what it actually does (`avg_rel_diff`). - Never commit `GITHUB_TOKEN`, `RO_API_TOKEN`, or any value derived from `gh auth token`. If the user wants you to commit, only stage golden-value files and the optional CSV — not the env or the venv.