running-eval-suite

name

running-eval-suite

description

Run all reference benchmarks under benchmarks/eval/ and refresh the reference-table cells in benchmark_*.py. Auto-detects host hardware (H200/H100/H800/...). For each row's matching hw entry: replace cells in place. For new hardware or a new workload tag: append a new row to the section's table. Local-Pipeline-Result tables are skipped. One slash command runs everything end-to-end and auto-commits.

running-eval-suite

Scope

A contributor on any sglang-omni dev container runs /running-eval-suite to refresh every reference table cell in benchmarks/eval/benchmark_*.py that corresponds to the host's hardware:

1. Auto-detect host hardware (H200 / H100 / H800 / A100 / ...).
2. Run every row declared in models/<model>/config.yaml.
3. For each row, after the client succeeds:
   • If the file has a row whose Source column contains [<hw>, <workload>] matching this entry → replace its cells.
   • Otherwise → append a new row at the end of that section's table.
4. Local v1 Pipeline Result and similar Local-* sections are skipped entirely — those are contributors' personal experiments, not refresh targets.
5. Commit benchmarks/eval/ to a local commit. No push.

The skill never edits anything outside benchmarks/eval/. Cell rewrite is exact-string replacement on the data row line; the audit trail is plain git show HEAD.

Models

.claude/skills/running-eval-suite/models/<model>/config.yaml

Today: qwen3-omni (covers all 6 benchmarks/eval/*.py — Qwen3-Omni rows + S2-Pro TTS rows live in the same config; per-row hf_model_id distinguishes the model launched per row).

Prerequisites (the skill verifies, it does not create)

• A sglang-omni clone (benchmarks/eval/ reachable from the working dir).
• A venv with sglang + torch importable. Default candidates (first existing wins): /sgl-workspace/sglang-omni/omni/bin/python, /github/home/calibration/omni/bin/python, /github/home/omni/bin/python (legacy). Override with --venv-python <path> or $EVAL_VENV_PYTHON.
• HF model + datasets cached under HF_HOME=/github/home/.cache/huggingface (see auto_env in config.yaml). precheck reports ✓ / ✗ and prints the exact HF_ENDPOINT=https://hf-mirror.com huggingface-cli download … commands when something's missing.
• Free GPUs. The skill never kills another user's processes; if GPUs are busy precheck fails with the busy PID list and stops.
• auto_env from config.yaml is applied at startup (overrides shell) to match CI omni-setup: OMNI_CI_HOME, UV_INDEX_URL, torchinductor slice paths, etc. See tune-ci-thresholds skill for the full cache layout table. All benchmarks share the single omni venv and .github/scripts/ci_env.sh.
• HF_ENDPOINT defaults to https://hf-mirror.com (matches CI omni-setup).
• Before pytest or eval runs, run source .github/scripts/ci_env.sh so TORCHINDUCTOR_CACHE_DIR stays under ${OMNI_CI_HOME}/.torchinductor.

Two-terminal supervision (mandatory — always)

Same contract as tune-ci-thresholds — permanent, non-negotiable:

Tab	Role	Shows
A — Supervision	tail -f <log-path>	Detailed log — pytest, router, worker, HTTP, tracebacks
B — Job	run command / script	Progress summary only — START / PASS / FAIL, stage name

Verbose output goes to the log file (>> log 2>&1). Never tee on Tab B — that duplicates Tab A and breaks supervision.

Agent must spawn both tabs (Shell, block_until_ms: 0): Tab A first, Tab B second. Tell the user which is which.

Calibration (tune.py run): Tab A = newest _pytest/*/run*.log; Tab B = tune.py run on stdout (never >> run.log). See tune-ci-thresholds § Calibration (tune.py run) — always two tabs.

Eval suite commands:

Tab	Command
A — supervision	tail -f <run-dir>/run.log
B — job	cd /sgl-workspace/sglang-omni && python .claude/skills/running-eval-suite/runner.py run ... >> <run-dir>/run.log 2>&1

Full diagram, log-path table, and checklist: see tune-ci-thresholds § Two-terminal supervision (mandatory — always).

• First-time venv on the repro host: use .github/scripts/prepare_omni_venv.sh and .github/scripts/install_flashinfer_jit_cache.sh with OMNI_CI_HOME=/github/home/calibration (documented in tune-ci-thresholds).

If anything's off, precheck fails with an actionable message; fix it yourself and retry.

Invocation

• /running-eval-suite — runs everything end-to-end with defaults (model qwen3-omni, all benchmarks, 1 round, real apply, auto-commit).
• /running-eval-suite --benchmarks mmsu,omni_seedtts — run only those benchmark files. Use omni_seedtts / qwen_seedtts for Qwen3-Omni SeedTTS rows and tts_seedtts / s2pro_seedtts for S2-Pro rows.
• /running-eval-suite --rounds 3 — multi-round; only round 1 is used for apply (multi-round aggregation is a follow-up).
• /running-eval-suite --smoke 50 — --max-samples 50 injected; no edits applied. For benchmarks whose full set is already ≤50, the smoke flag is effectively a no-op (they run their full set anyway).
• /running-eval-suite --venv-python /usr/bin/python3 — override the default venv detection (also via $EVAL_VENV_PYTHON).
• /running-eval-suite --exclude-ids s2pro- — skip rows whose id contains a substring.
• /running-eval-suite --retry-failed-from <run-dir> — rerun rows that were not successfully edited in a previous run.
• /running-eval-suite --gpu-pool 0,1,2 — restrict server and ASR GPU allocation to a specific free GPU pool.

I never call AskUserQuestion. Defaults handle the full run; optional flags above are only for filtering, recovery, or explicit GPU placement. Errors halt the run with a printed reason. This matches the tune-ci-thresholds contract: type /running-eval-suite and walk away.

Steps I follow

1. Apply defaults to the invocation: model=qwen3-omni, benchmarks=all, rounds=1, smoke=null. No AskUserQuestion. If the user passed unknown flags, stop with the parsing error.

2. Run precheck. The runner creates <repo>/.eval-runs/<utc-ts>/ on first run; reuse the same <run-dir> for the rest of the invocation:

   python .claude/skills/running-eval-suite/runner.py \
     --model <M> precheck --benchmarks <names|all> \
     [--exclude-ids <substr>] [--gpu-pool <ids>] \
     --output-dir <run-dir>
   

   If exit code != 0, surface the ✗ lines and stop.

3. Run the benchmarks. The runner does the cell rewrites inline as each row finishes (no separate apply step):

   python .claude/skills/running-eval-suite/runner.py \
     --model <M> run --benchmarks <names|all> \
     --rounds <K> [--smoke <N>] \
     [--exclude-ids <substr>] [--gpu-pool <ids>] \
     --output-dir <run-dir>
   

   • For each row: launch server (process-group SIGTERM at end, never pkill -f), run client, capture result.json, then immediately edit the matching row in benchmarks/eval/benchmark_*.py (replace existing hw row OR append new hw row at end of the section's table; Local sections never touched).
   • Managed-router rows use router, 2-worker, ... Source workload tags. The first run on a hardware target appends new router-tagged rows next to the older direct/V1-pipeline rows; later runs replace those router rows in place.
   • Per-row state lands in <run-dir>/run-state.json. Stream the runner's stdout to the user so they can watch progress.
   • If a row fails (server boot timeout / client crash / locator mismatch / column missing / JSON path missing / etc.), that row's reason is recorded; the next row continues. No global halt.

4. After the run finishes, look at run-state.json's summary block (also printed to stdout): replaced count + appended count + failed runs + failed edits. If anything failed, surface those lines.

5. Auto-commit the refresh:

   git -C <repo> add benchmarks/eval/
   git -C <repo> diff --cached --quiet && (echo "no changes — nothing to commit"; exit 0)
   git -C <repo> commit -m "[Docs] Refresh reference benchmarks via /running-eval-suite (<utc-ts>)"
   

   No git push. If says there's nothing to commit (every row failed or was skipped), skip the commit and print a one-liner.

What I do not do

• Ask the user any questions. "Type the slash command and walk away" is the contract.
• Set up the container / venv / HF cache (covered by docs/contributing/running-eval-suite.md).
• Touch Local v1 Pipeline Result / Local Speech Pipeline tables inside benchmark_*.py — those are contributors' personal experiments.
• Kill other users' processes. Busy GPUs → precheck reports busy PID list → stop.
• Push. I commit locally so the user can git show HEAD, rebase/squash/amend, then push to their fork.
• Multi-round aggregation. --rounds K runs K rounds (useful for variance observation), but inline apply uses round 1 only. Cells whose existing Source tag references n=K mean are flagged in the preview but still updated; user can manually recompute the mean before pushing.

Files

.claude/skills/running-eval-suite/
├── SKILL.md
├── runner.py                                # CLI: precheck / run
└── models/
    └── qwen3-omni/
        └── config.yaml                      # 31 rows across all 6 benchmark .py files

Adding a new row

1. Identify the reference table row in some benchmarks/eval/benchmark_*.py. Note the section header above the table, the row's Config column substring, and the workload tag inside the row's Source column (the part inside the […] brackets, without the leading hardware token — e.g. router, 2-worker, full-set, c=8 from [H200, router, 2-worker, full-set, c=8]).
2. Append a new entry to models/<model>/config.yaml:

   - id: <unique-handle>          # informational; not used to select rows
     file: "benchmarks/eval/benchmark_omni_<name>.py"
     hf_model_id: "Qwen/Qwen3-Omni-30B-A3B-Instruct"   # optional, overrides config-level
     locate:
       section_substring: "Accuracy (accuracy)"
       config_substring: "modalities=text "
       source_workload: "router, 2-worker, full-set, c=8"
     # Server launch is normally inherited from default_server_profile or
     # server_profile_by_hf_model_id. Set server_profile only when a row needs
     # a different managed-router topology.
     server_profile: qwen3_omni_colocated_router
     client: "python benchmarks/eval/benchmark_omni_<name>.py --model qwen3-omni --port {port} --output-dir {output_dir} ..."
     result_json: "<benchmark>_results.json"
     cells:
       <col1>: { path: "<dotted.path>", format: "{v}" }
       <col2>: { paths: { v: ,   },   }
   

Adding a whole new model = drop in models/<new-model>/config.yaml mirroring qwen3-omni/config.yaml. No Python changes needed unless the new benchmark client emits result JSON in a structure that needs new helpers in runner.py.