--- name: update-golden-values description: Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with average normalized relative differences, and produce a PR-ready summary. Use when the user asks to update goldens for a CI run, refresh golden values from a workflow ID, or generate a golden-value diff summary for a PR description. when_to_use: User provides a GitHub Actions workflow run ID and asks to refresh golden values; user asks to update goldens for "failing tests only" or "all tests"; user asks for a per-metric relative-difference summary of the golden-value diff; user wants a PR description blurb after running download_golden_values.py. --- # Update golden values + relative-diff summary End-to-end workflow for refreshing golden values from a GitHub Actions workflow run, scoring the update with a per-metric average normalized relative difference, 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 + capture CSV - [ ] Step 5: Produce summary blurb ``` ### 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 --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 ``` 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: INFO:__main__:Total golden values found: ``` ### 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. Then derive aggregates from the CSV (do this in Python; do not paste raw CSV into the summary): ```python import csv, collections rows = list(csv.DictReader(open('/tmp/reldiff_summary.csv'))) for r in rows: r['n_steps'] = int(r['n_steps']) r['avg_rel_diff'] = float(r['avg_rel_diff']) r['abs'] = abs(r['avg_rel_diff']) by_metric = collections.defaultdict(list) for r in rows: by_metric[r['metric']].append(r['abs']) # headline numbers per metric (using |avg_rel_diff|) for m, vs in sorted(by_metric.items()): vs.sort() print(m, len(vs), 'median', vs[len(vs)//2], 'max', vs[-1]) # bucket counts across all rows, on |avg_rel_diff| buckets = [('==0', lambda x: x == 0), ('(0,1e-6)', lambda x: 0 < x < 1e-6), ('[1e-6,1e-4)', lambda x: 1e-6 <= x < 1e-4), ('[1e-4,1e-3)', lambda x: 1e-4 <= x < 1e-3), ('[1e-3,1e-2)', lambda x: 1e-3 <= x < 1e-2), ('[1e-2,1e-1)', lambda x: 1e-2 <= x < 1e-1), ('>=1e-1', lambda x: x >= 1e-1)] abs_all = [r['abs'] for r in rows] for label, pred in buckets: print(label, sum(1 for v in abs_all if pred(v))) ``` ### 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 from GitHub workflow run ``. **Golden value updates** - Re-ran `tests/test_utils/python_scripts/download_golden_values.py --source github --pipeline-id <--only-failing if scope=only-failing>`. - Updated ** golden-value files** under `tests/functional_tests/test_cases/`. ### Relative-difference summary Comparison covers files × metrics = ** `(file, metric)` pairs**. Per row: `avg_rel_diff = mean((old − new) / old)` over shared steps. **Per-metric headline numbers** (over `|avg_rel_diff|`) | metric | n | median \|avg_rel_diff\| | max \|avg_rel_diff\| | | ------------------------- | --: | -----------------------: | -------------------: | | `lm loss` | <…> | <…> | <…> | | `num-zeros` | <…> | <…> | <…> | | `iteration-time` | <…> | <…> | <…> | | `mem-allocated-bytes` | <…> | <…> | <…> | | `mem-max-allocated-bytes` | <…> | <…> | <…> | **Distribution of `|avg_rel_diff|` across all rows** | \|avg_rel_diff\| bucket | count | | ----------------------- | ----: | | `== 0` | <…> | | `(0, 1e-6)` | <…> | | `[1e-6, 1e-4)` | <…> | | `[1e-4, 1e-3)` | <…> | | `[1e-3, 1e-2)` | <…> | | `[1e-2, 1e-1)` | <…> | | `>= 1e-1` | <…> | **Interpretation** (apply only the bullets that match the data) - `lm loss` max `|avg_rel_diff|` / median — loss trajectories match old goldens to numerical noise (sub-1e-4 is within run-to-run variance). - `mem-*` metrics typically sit at `== 0` or `(0, 1e-6)`; flag any row that lands above `[1e-4, 1e-3)`. - `iteration-time` movement is dominated by warmup/scheduler noise; signed avg near zero means the run was simply jitterier, not slower or faster on average. - `num-zeros` shifts cluster on ``; within historical run-to-run variance. ```` ## 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` | `mean((old − new) / old)` over `n_steps`. Signed: positive = new < old, negative = new > old. | When sorting / filtering, the script ranks by `|avg_rel_diff|`. Keep the sign in the printed table so reviewers can see direction. Triage rules of thumb: - `lm loss` / `num-zeros` rows with `|avg_rel_diff|` ≲ 1e-4 are run-to-run noise. - `iteration-time` divergences are usually warmup/scheduler noise; a small signed mean near zero says the run was jitterier, not systematically faster or slower. - Focus reviewer attention on `lm loss` and `num-zeros` rows with `|avg_rel_diff|` ≥ ~1e-3. ## 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. - Some artifacts have a literal string `"nan"` in step 1 of `iteration-time`; the comparison script filters those out, so other steps for that metric still contribute. Don't flag `iteration-time` as a correctness problem unless something else also moved. - 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.

update-golden-values

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