92 lines
5.5 KiB
Markdown
92 lines
5.5 KiB
Markdown
# Harness-Guided AITuner Progress
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## Goal
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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.
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## Paper Alignment
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- Prompt structure now includes an explicit `[Harnesses]` section aligned with paper Figure 12.
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- The harness uses the paper's L-C-A workload model:
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- L: prompt length percentiles and tail ratio.
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- C: prefix/KV-cache reuse estimated from repeated `hash_ids` blocks when available.
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- A: request rate, 1-second QPS burst ratio, and interarrival CV.
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- Knob rules follow the paper's Figure 13 style:
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- map active bottleneck to a knob family;
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- probe adjacent legal choices;
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- enforce guard conditions to avoid harmful side effects;
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- prefer stopping over weak exploratory proposals after convergence.
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## Local Implementation Log
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- Added `src/aituner/harness.py`.
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- Builds structured harness context for prompt injection.
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- Adds TP, max-num-seqs, max-num-batched-tokens, chunked-prefill, and memory-utilization harnesses when those knobs are tunable.
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- Extracts compact recent trial diagnostics from result JSON files.
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- Adds a convergence guard based on recent completed trial performance.
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- Extended `src/aituner/trace.py`.
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- `summarize_window` now reports L-C-A features.
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- `TraceRequest` now carries optional metadata for `hash_ids`, turn, parent chat id, and trace type.
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- Extended `src/aituner/llm.py`.
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- Prompt now includes tested config signatures and the structured harness section.
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- Prompt schema now asks for `should_stop`.
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- Extended `src/aituner/spec.py`.
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- `Proposal` accepts optional `should_stop`.
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- Extended `src/aituner/cli.py`.
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- `study tune` honors `should_stop=true` by recording the proposal and not launching another GPU trial.
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- Extended `tests/test_core_flow.py`.
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- Prompt includes harness context.
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- Trace summary includes new L-C-A fields.
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- Proposal parsing accepts `should_stop`.
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- CLI does not launch a trial for a stop proposal.
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## Local Verification
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- `python3 -m compileall -q src tests`: passed.
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- `PYTHONPATH=src python3 -m unittest tests.test_core_flow`: passed, 59 tests.
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- `pytest -q` and `python3 -m pytest -q`: not runnable locally because `pytest` is not installed.
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## Remote Experiment Log
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### 2026-04-25 16:30-16:45 CST
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- Pushed commit `2c5e9af` to `origin/main` and pulled it on `dash0`.
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- Remote prompt check command:
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- `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`
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- Harness profile for `chat_w20260311_1000`, after applying the 0-8k filter:
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- L: p50 1992, p95 7628, p99 8102, tail ratio 3.83, regime `moderate_tail_prefill_sensitive`.
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- C: repeated token ratio estimate 0.191, repeated block ratio 0.189, multi-turn ratio 0.160, regime `low_prefix_reuse`.
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- A: request rate 29.52 req/s, p95 1s QPS 40, burst ratio 1.36, regime `smooth`.
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- Active harnesses: `tensor-parallel-size` and `max-num-batched-tokens`, which matches a TTFT/prefill-sensitive 0-8k chat workload.
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- Remote `compileall` passed.
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- 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.
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### 2026-04-25 16:38-16:58 CST
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- Started real run in tmux session `aituner_harness_qwen27b_0_8k_20260425`.
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- Store root: `.aituner/harness-studies-20260425`.
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- First proposal followed the harness:
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- proposal: `tensor-parallel-size: 2`;
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- rationale: L profile is prefill-sensitive, prefix reuse is low, arrivals are smooth, so probe adjacent TP before runtime batching knobs.
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- First high-load probe at `sampling_u=0.03125` was infeasible:
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- request rate 0.895 req/s;
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- pass rate 0.145;
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- p95 TTFT 4063 ms and p95 TPOT 113 ms;
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- failed reasons included `tpot_ms>50.0` and `slo_pass_rate_unrecoverable`.
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- 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.
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- Action: stopped the run and freed GPUs. Updating `worker._replay_requests` to drain in-flight requests after early stop before the next probe starts.
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### 2026-04-25 17:00-17:12 CST
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- 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.
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- This is not aligned with the paper's agentic loop, which evaluates the initial configuration first and then searches from measured feedback.
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- 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.
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Remaining next steps:
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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`.
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2. Compare the first few iterations against the prior 12-iteration behavior:
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- best request rate per GPU should improve or reach the known good region in fewer trials;
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- proposals should follow the active bottleneck harness;
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- if the incumbent has converged, the LLM should emit `should_stop=true` instead of proposing a weak exploratory config.
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