Gahow Wang
51a9e4a007
Phase 2 of the two-stop work. The L-C-A vector is a deterministic function of the trace's offered metadata, so the convergence of prefix-vs-full L-C-A (the paper's Fig. 9 curve) can be computed up front rather than monitored live, with identical result and no per-request overhead. - lca.find_convergence_prefix: earliest arrival-ordered prefix whose L and A family similarities reach tau and the slow C family reaches the stricter tau_c for stable_checks consecutive checkpoints. Self-similarity uses the raw log-feature vector (same window -> identical per-dim spread; RobustScaler is reserved for the cross-window Stop-C). If C never converges it reports the full set, which is the C-gate: no early stop on a cold/under-warmed cache. The checkpoint sims double as Phase 3 calibration data. - spec.AdaptiveStopSpec (trace.adaptive_stop), disabled by default until the thresholds are calibrated, so existing studies are unaffected. - worker._adaptive_replay_set truncates each probe's replay to the convergence prefix and records a certificate (converged, fraction, family similarity) into probe history and probe_details. Offered request_rate at the threshold is unchanged; only wall-clock replay shrinks. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
AITuner
AITuner is a small study orchestrator for OpenAI-compatible serving engines. It replays trace windows, searches for the highest feasible offered load under configured SLOs, and records enough trial context for LLM- or harness-guided configuration proposals.
Status
This repository is research tooling. Treat reported experiment numbers as valid
only when the matching study spec, trial artifacts, probe history, and
probe_details.jsonl files are available for audit.
Install
python3 -m pip install -e .
Test
The test suite uses the Python standard library unittest runner:
PYTHONPATH=src python3 -m unittest discover -s tests -v
If the package is installed in editable mode, PYTHONPATH=src is optional.
Basic Workflow
Initialize a study:
aituner study init --spec configs/examples/study.example.json
Run a local tuning loop:
aituner study tune --spec configs/examples/study.example.json --max-trials 2
Run a compare:
aituner compare run --spec configs/examples/compare.example.json
Remote experiment notes for this checkout live in AGENTS.md. The default
remote host is dash0, and code should be synchronized through Git before
remote runs.
Experiment Integrity
- Fixed-length replay requests are scored only when completion token usage is verifiable and matches the trace expectation.
- Each trial writes aggregate probe history and per-request probe details.
request_rate_per_gpuis the primary cross-topology metric:best_feasible_request_rate / (tensor_parallel_size * data_parallel_size).- Compare reports include failed and no-feasible window counts; do not interpret mean request rates without those counts.
- Bounded replays using
max_requests_per_probe,completion_tokens_override, orreplay_time_scaleare convergence tests for that bounded workload, not production benchmarks.
Configuration Notes
Example specs that use llm.endpoint.provider=codex resolve the endpoint from
the local Codex configuration unless llm.endpoint.base_url or
AITUNER_CODEX_BASE_URL is set. Public, reproducible examples should prefer an
explicit endpoint or omit the LLM endpoint and use proposal files.