Add prospective active intervention experiment
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docs/active-intervention-v0-protocol-20260715.md
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# Active intervention + measurement v0 protocol
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Date: 2026-07-15 (Asia/Singapore)
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Status: **FROZEN BEFORE THE `chat_w20260313_1000` GPU RUN**.
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## Research question
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This experiment asks whether a tuner conditioned on direct engine-state
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trajectories can choose both a measurement horizon and a coupled configuration
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intervention with lower real-GPU cost than the same tuner using only external
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prefix outcomes.
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The contribution is not the controller, legality checks, telemetry collection,
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or the ridge model. The route remains open only if engine state changes an
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actual decision and reduces cost-to-near-oracle on unseen workloads.
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## Development result that motivates, but does not pass, the route
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The frozen trace-12 dataset contains 72 examples: six source decisions, four
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measurement checkpoints, and `noop/MNS/MBBT` actions. Features are direct
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continuous Layer-1 state summaries; cap-exclusive and bottleneck labels are
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excluded. Leave-one-repetition-out sequential replay uses the same model,
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candidate set, confidence rule, and checkpoint set for both modes.
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The external-outcome policy and telemetry policy both put all six decisions
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within 2% regret. Outcome-only selected a mean 262.5-second source measurement
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and cost 3.750 replay H20-hours across the six replayed decisions; telemetry
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selected 275 seconds and cost 3.833 H20-hours. Telemetry therefore increased
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the replay lower-bound cost by 2.22%, with no regret reduction. This is a
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negative result. It does not settle the question because the dataset has only
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two source regimes, one source is at the offered ceiling, and there is no joint
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MNS+MBBT action.
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Sanity: n=6 decisions; regret min=0, max=0.009412, distinct=3; source cutoff
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min=150s, max=300s, distinct=3 across the two policies; all costs are
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non-negative, regrets are in `[0,1]`, target results are not all identical, and
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the six decisions are complete exact-workload pairs.
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## Frozen prospective setup
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- Host: `dash0`, 8 NVIDIA H20 GPUs available; each TP4 server runs alone on
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GPUs 0-3. Co-location is prohibited for SLO verdicts.
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- Engine: patched vLLM `0.24.1.dev3+opprof` from
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`/home/admin/cpfs/wjh/vllm-opprof-phase3` in
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`/tmp/wjh/venvs/vllm-0.20.0-cu129`.
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- Model: `/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B`, BF16.
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- Workload: unseen `chat_w20260313_1000`; input 0-8192; output exactly 128;
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replay scale 0.5; 300-second arrival window.
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- Three disjoint repetitions: source rows are assigned by a deterministic
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SHA-256 modulo-3 partition before input filtering. Each repetition selects
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approximately 3300 requests, or 2.75 requests/s/GPU at TP4.
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- SLO: at least 95% pass; stepped TTFT 2/4/6 seconds; TPOT at most 50 ms.
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- Checkpoints: 75, 150, 225, and 300 seconds.
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- Full 2x2 surface:
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- source: `MNS=32, MBBT=4096`;
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- MNS action: `64,4096`;
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- MBBT action: `32,8192`;
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- joint action: `64,8192`;
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- `noop` retains the source.
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- Four config sessions are serialized. Each session uses a fresh server,
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warm-up, burn-in, and counter-rotated repetition order.
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- Expected campaign cost: 4.6-5.5 H20-hours; hard cap: 6.0 H20-hours;
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expected wall time: 75-100 minutes.
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The source is executed first. The frozen telemetry policy selects the next
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real config session; all remaining cells are then measured only to construct
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the exact finite-surface oracle. Oracle annotation after the selected action
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is reported separately from tuner cost.
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## Frozen policies
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Both policies fit the paired treatment effect
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```text
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target normalized SLO-goodput - source normalized SLO-goodput
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```
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from source config, full config delta, offered load, and external prefix
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outcomes. The telemetry policy additionally receives fixed direct Layer-1
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summaries and their interactions with `delta_log2(MNS)` and
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`delta_log2(MBBT)`. It does not receive a bottleneck label or a
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diagnosis-to-knob rule.
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At each checkpoint, jackknife models produce an effect distribution for
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`noop`, MNS, MBBT, and joint actions. Measurement stops at the earliest second
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consecutive checkpoint with the same confident best action; otherwise it uses
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the full 300 seconds. Confidence requires a predicted margin of at least 0.02
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and the best lower bound to exceed the second-best upper bound. If the final
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choice is not confident, the next run is the positive-UCB action, explicitly
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marked as a diagnostic intervention. The exact same rule is used for the
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outcome-only baseline.
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## Hypotheses and gates
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### H1: action value
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Engine state must change the selected intervention or its ranking and reduce
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real action regret. Prediction error or bottleneck-label accuracy is not a
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success metric.
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### H2: measurement value
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Engine state must select a shorter stable source measurement without increasing
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action regret. A shorter reconstructed prefix is only a trigger; it is not an
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actual GPU-cost claim until an early-terminated confirmation run measures
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startup, warm-up, drain, and cleanup.
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### H3: end-to-end cost
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Primary development metric is H20-hours to first reach a configuration within
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2% of the exact median-goodput oracle. The outcome-only and telemetry policies
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use the same measured config costs and differ only in source information.
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- At least 10% prospective replay cost reduction, telemetry regret at most 2%,
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and no outcome-only-to-telemetry harm triggers an actual early-stop
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confirmation.
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- At least 20% measured all-in H20-hour reduction is required for a contribution
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claim. This one task can only establish development feasibility; a paper
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claim additionally requires task-held-out replication.
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- Source median normalized goodput at or above 0.98 stops the surface before
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target runs because the workload has no material improvement headroom.
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- Any hash mismatch, missing/censored result, telemetry drop, non-monotonic
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phase, negative cost, ratio outside `[0,1]`, or all-identical config outcomes
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is a red flag and stops analysis.
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If the 10% trigger fails, this route is closed for the current engine-state
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representation. The experimental control plane is not retained as a fallback
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research contribution.
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