84 lines
3.7 KiB
Markdown
84 lines
3.7 KiB
Markdown
# Profile-Driven Harness Implementation Log
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Date: 2026-05-12
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## Goal
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The harness should accelerate AITuner as a general tuning system, not as a collection of case-specific rules. The current implementation moves the harness toward a performance-engineering loop:
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1. extract a compact profile from each measured trial;
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2. rank bottleneck hypotheses from workload and probe evidence;
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3. generate generic candidate actions from a knob-effect model;
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4. score candidates by expected bottleneck relief, information gain, launch safety, and regression risk;
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5. block early stop while a high-value untested candidate remains.
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This is intended to apply across qwen3.5-27b chat, qwen3-235b prefill-only, qwen3-235b decode-only, and different SLOs without encoding model names, SLO constants, or known winning configs.
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## Code Changes
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- `src/aituner/harness.py`
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- Added `trial_profiles` to normalize trial topology, performance, probe failures, latency quantiles, and launch failure evidence.
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- Added `bottleneck_hypotheses`, a ranked list instead of a single active bottleneck label.
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- Added `candidate_actions`, generated from topology and runtime knob families.
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- Added `experiment_plan`, which selects the next high-score candidate or declares stop readiness.
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- Updated harness proposal generation to prefer the profile-driven next action before falling back to legacy deterministic proposal code.
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- Updated harness stop logic so convergence/validation stop is blocked when the planner still has a high-value untested candidate.
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- `tests/test_core_flow.py`
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- Added coverage that a strong TP=2 incumbent with TTFT pressure still selects an unmeasured TP=4 topology candidate.
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- Added coverage that decode-only TPOT pressure at max TP can prefer lowering `max-num-seqs` instead of blindly lowering TP.
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## Current Scoring Model
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The candidate score is intentionally generic:
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```text
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score = expected_bottleneck_relief * bottleneck_confidence
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+ information_gain
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+ launch_safety
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- regression_risk
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```
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Examples:
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- TTFT/prefill bottleneck: increasing TP and prefill batching candidates receive relief score.
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- Decode TPOT bottleneck: increasing TP is useful if a higher legal TP exists; if already at high TP, lowering decode concurrency can become the higher-value candidate.
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- Admission/queueing bottleneck: more DP or higher safe concurrency receives relief score.
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The scores are not tied to qwen27b/qwen235b or a fixed TPOT/TTFT threshold. They are tied to the measured bottleneck class and legal tunable space.
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## Verification
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Local:
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```bash
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python3 -m compileall -q src tests
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PYTHONPATH=src python3 -m unittest tests.test_core_flow
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```
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Result: `93` tests passed.
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## Next Experiment
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Run the same qwen3.5-27b chat 0-8k setup as the current ablation baseline:
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- workload: chat, input length 0-8k
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- SLO: TTFT p95 <= 4000ms, TPOT p95 <= 25ms, target pass rate 0.95
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- search: full range, `inherit_incumbent_floor=false`
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- budget: 12 total tuning iterations
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- LLM model: `gpt-5.4`
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- variant: harness enabled with profile-driven planner
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The no-harness min-prompt baseline is already available and only needs to be reused for comparison unless the setup changes.
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## Experiment Started
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Started on `dash0` (`11.73.2.172`) at commit `17e9681`.
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- tmux session: `qwen27b-profileplanner-harness-20260512`
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- spec: `.aituner-tight/specs/dash0-qwen27b-chat-0-8k-ttft4s-tpot25-gpu3skip-12iter-harness-profileplanner-20260512.json`
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- study id: `dash0-qwen27b-chat-0-8k-ttft4s-tpot25-gpu3skip-12iter-harness-profileplanner-20260512`
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- log: `.aituner-tight/logs/qwen27b-profileplanner-harness-20260512.log`
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- status at launch check: `trial-0001` baseline is running under AITuner; no manual intervention in the tuning loop.
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