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aituner/docs/harness-tuning-progress.md

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Harness-Guided AITuner Progress

Goal

Improve AITuner convergence for the dash0 internal vLLM + Qwen3.5-27B 0-8k chat study. The prior 12-iteration run can still propose worse configs after finding good ones. The new harness should make config proposals bottleneck-directed and stop spending GPU trials once no adjacent harness-guided probe is justified.

Paper Alignment

  • Prompt structure now includes an explicit [Harnesses] section aligned with paper Figure 12.
  • The harness uses the paper's L-C-A workload model:
    • L: prompt length percentiles and tail ratio.
    • C: prefix/KV-cache reuse estimated from repeated hash_ids blocks when available.
    • A: request rate, 1-second QPS burst ratio, and interarrival CV.
  • Knob rules follow the paper's Figure 13 style:
    • map active bottleneck to a knob family;
    • probe adjacent legal choices;
    • enforce guard conditions to avoid harmful side effects;
    • prefer stopping over weak exploratory proposals after convergence.

Local Implementation Log

  • Added src/aituner/harness.py.
    • Builds structured harness context for prompt injection.
    • Adds TP, max-num-seqs, max-num-batched-tokens, chunked-prefill, and memory-utilization harnesses when those knobs are tunable.
    • Extracts compact recent trial diagnostics from result JSON files.
    • Adds a convergence guard based on recent completed trial performance.
  • Extended src/aituner/trace.py.
    • summarize_window now reports L-C-A features.
    • TraceRequest now carries optional metadata for hash_ids, turn, parent chat id, and trace type.
  • Extended src/aituner/llm.py.
    • Prompt now includes tested config signatures and the structured harness section.
    • Prompt schema now asks for should_stop.
  • Extended src/aituner/spec.py.
    • Proposal accepts optional should_stop.
  • Extended src/aituner/cli.py.
    • study tune honors should_stop=true by recording the proposal and not launching another GPU trial.
  • Extended tests/test_core_flow.py.
    • Prompt includes harness context.
    • Trace summary includes new L-C-A fields.
    • Proposal parsing accepts should_stop.
    • CLI does not launch a trial for a stop proposal.

Local Verification

  • python3 -m compileall -q src tests: passed.
  • PYTHONPATH=src python3 -m unittest tests.test_core_flow: passed, 59 tests.
  • pytest -q and python3 -m pytest -q: not runnable locally because pytest is not installed.

Remote Experiment Log

2026-04-25 16:30-16:45 CST

  • Pushed commit 2c5e9af to origin/main and pulled it on dash0.
  • Remote prompt check command:
    • PYTHONPATH=src python3 -m aituner.cli study prompt --study-root /tmp/aituner-harness-prompt-check/dash0-qwen27b-tight-slo-10min-run4-chat-0-8k --store-root /tmp/aituner-harness-prompt-check --prompt-name harness-check
  • Harness profile for chat_w20260311_1000, after applying the 0-8k filter:
    • L: p50 1992, p95 7628, p99 8102, tail ratio 3.83, regime moderate_tail_prefill_sensitive.
    • C: repeated token ratio estimate 0.191, repeated block ratio 0.189, multi-turn ratio 0.160, regime low_prefix_reuse.
    • A: request rate 29.52 req/s, p95 1s QPS 40, burst ratio 1.36, regime smooth.
    • Active harnesses: tensor-parallel-size and max-num-batched-tokens, which matches a TTFT/prefill-sensitive 0-8k chat workload.
  • Remote compileall passed.
  • Remote unittest discover initially exposed two pre-existing path-sensitive tests that hardcoded /home/gahow/phd/aituner; fixed them to derive REPO_ROOT from the test file path.

2026-04-25 16:38-16:58 CST

  • Started real run in tmux session aituner_harness_qwen27b_0_8k_20260425.
  • Store root: .aituner/harness-studies-20260425.
  • First proposal followed the harness:
    • proposal: tensor-parallel-size: 2;
    • rationale: L profile is prefill-sensitive, prefix reuse is low, arrivals are smooth, so probe adjacent TP before runtime batching knobs.
  • First high-load probe at sampling_u=0.03125 was infeasible:
    • request rate 0.895 req/s;
    • pass rate 0.145;
    • p95 TTFT 4063 ms and p95 TPOT 113 ms;
    • failed reasons included tpot_ms>50.0 and slo_pass_rate_unrecoverable.
  • Important implementation issue found: after an early-stopped probe, the worker returned while in-flight HTTP requests could continue occupying the engine, stalling/polluting the next binary-search probe.
  • Action: stopped the run and freed GPUs. Updating worker._replay_requests to drain in-flight requests after early stop before the next probe starts.

2026-04-25 17:00-17:12 CST

  • r2 confirmed that draining avoids immediate cross-probe pollution, but the first LLM trial still started from a speculative TP=2 edit without a measured incumbent.
  • This is not aligned with the paper's agentic loop, which evaluates the initial configuration first and then searches from measured feedback.
  • Action: update study tune so LLM-driven studies automatically materialize a baseline empty-patch trial first, unless --skip-baseline is passed. This should reduce early bad proposals because the first LLM edit will see real baseline bottleneck diagnostics and an incumbent request_rate_per_gpu.

Remaining next steps:

  1. Start a real harness-guided Qwen3.5-27B 0-8k chat tuning run from configs/examples/dash0_qwen27b_tight_slo_run4_0_8k.json.
  2. Compare the first few iterations against the prior 12-iteration behavior:
    • best request rate per GPU should improve or reach the known good region in fewer trials;
    • proposals should follow the active bottleneck harness;
    • if the incumbent has converged, the LLM should emit should_stop=true instead of proposing a weak exploratory config.