update-golden-values

name	update-golden-values
description	Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), calculate signed per-model percentage changes, 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.

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.

# 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:

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:

# 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

/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:

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(  _  metrics) + )

 file_name  (by_file):
    path = Path(file_name)
    test_name = path.parent.name
    environment = path.stem.removeprefix()
    values = by_file[file_name]
    steps = (v[]  v  values.values())

    cells = []
     metric  metrics:
         metric   values:
            cells.append()
            
        pct = values[metric][]
        cells.append(  pct ==   )

    ( + .join(cells) + )

()
()
()

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).

### 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  for review.
 For  or , call out values below  or above ; these metrics should normally remain close to .
 Negative  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  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.