Spica Optimization Goals

An OptimizationGoal (the goal: block of a SmartSearchConfig) declares what “better” means plus the SLA constraint. It is pinned, never searched. It picks one OptimizationTarget and — for pareto — the list of scalar objectives whose frontier to trace. The target also derives the Dynamo Planner’s optimization_target (OptimizationTarget.planner_optimization_target; see search-space.md).

The whole goal: block is optional: it defaults to a throughput goal with no SLA (OptimizationGoal() → target = throughput). OptimizationGoal itself is extra="forbid", so unknown goal keys are rejected.

The goal drives two things in score.py: the objective read from each replay trace_report (objective_value), and step 3 of scoring — rank (scalar) or pareto_front (multi-objective).

Targets

Every OptimizationTarget and the exact report metric it reads (score.objective_value):

e2e_latency is the only minimized target: OptimizationTarget.maximize returns False for it (and raises for pareto, which has no single direction). score_report negates minimized targets so higher is always better internally; for a Pareto goal the raw (unsigned) value is kept and _dominates applies each objective’s own direction. Missing-key defaults differ by direction: a maximized target reads 0.0 when its key is absent, but e2e_latency defaults to +inf — so a report missing mean_e2e_latency_ms scores worst-possible (-inf after negation) rather than best.

The *_per_user metric is already a rate (mean of per-token-gap 1000/itl), so it gets no GPU/time normalization — it is the InferenceX x-axis (tok/s/user).

avg_gpu — the per-GPU divisor

The two *_per_gpu targets divide a tok/s rate by the time-averaged provisioned GPU count (score._avg_gpu):

avg_gpu = gpu_hours / e2e_hours        # e2e_hours = duration_ms / 3_600_000

This is the integral of provisioned GPUs over the run divided by its duration:

• static deployment — avg_gpu collapses to the fixed GPU count (gpu_hours = gpu_count * e2e_hours).
• planner-scaled run — avg_gpu averages provisioned GPUs over startup + serve + drain.

Dividing the rate by gpu_hours directly would be wrong: the rate already has time divided out. _avg_gpu returns 0.0 when gpu_hours or duration_ms is <= 0 (missing report keys default to 0.0, so the guard covers both missing and non-positive values), and the *_per_gpu targets then return 0.0 (divide-by-zero guard).

SLA requirement rule

Only the goodput targets need an SLA — their metric (goodput_output_throughput_tok_s) counts only SLA-satisfying requests (the replay bridge’s per-request goodput SLA). _SLA_TARGETS = {goodput, goodput_per_gpu}.

OptimizationGoal._validate_goal computes the effective objective set — {target} for a scalar goal, or the resolved pareto_objectives for a Pareto goal — and requires an SLA iff that set intersects _SLA_TARGETS. So an SLA is mandatory when:

• target is goodput or goodput_per_gpu, or
• target is pareto and its objectives include one of those.

A satisfying SLA is e2e_ms, or both ttft_ms and itl_ms. Note SLA is not gated during feasibility (is_feasible checks only the GPU budget) — it lives entirely inside the goodput metric, so an aggregate latency gate would double-count it.

SLATarget shape (ms, each > 0, extra="forbid"):

Pareto

pareto is the one multi-objective target. Instead of a scalar score it optimizes the tradeoff between the scalar targets in pareto_objectives.

• objectives — OptimizationGoal.pareto_objectives (list[OptimizationTarget] | None). resolved_pareto_objectives returns it, or the default pair _DEFAULT_PARETO_OBJECTIVES = (throughput_per_gpu, throughput_per_user) only when it is unset (None) — the InferenceX tok/s/gpu (y) vs tok/s/user (x) frontier. An explicit list is kept as-is so the validator can reject it: >= 2 entries, distinct, no pareto among them, and pareto_objectives is only legal under a pareto target.

• Vizier study — the sweep declares one metric per objective (each with its own MAXIMIZE/MINIMIZE goal): run_smart_search passes sampler_objectives = [(t.value, t.maximize) for t in resolved_pareto_objectives] to the VizierBranchSampler, which appends a vz.MetricInformation per objective (sampler.py). The Vizier algorithm is "DEFAULT" for every study (scalar or Pareto); what changes is the number of metrics. With >= 2 metrics DEFAULT (GP-UCB-PE) behaves multi-objective — optimizing the Pareto tradeoff via hypervolume scalarization — and each observe reports every objective’s raw value in one measurement. (Single-objective goals declare the sampler’s default single maximized "objective" metric, pre-signed by the caller.)

• front — score.pareto_front returns the non-dominated subset. _dominates(a, b) is true iff a is at least as good as b on every objective (in that objective’s own maximize direction) and strictly better on at least one. The front is sorted by the last objective ascending — the x-axis — so the list traces the frontier left-to-right (e.g. low→high per-user throughput). run_smart_search returns this front for a Pareto goal, and rank (best score, ties → fewer GPUs) for every scalar goal.

• swept load dimension — workload.concurrency is always one fixed in-flight cap. A Pareto workload may instead set kv_load_ratio: [min, max], which Vizier models as a continuous parameter. Each ratio is converted to an absolute concurrency from that candidate’s decode/agg KV capacity, so the model compares equivalent load pressure across different replica and parallel configurations. If a synthetic Pareto workload omits all load fields, the range defaults to [0.0, 1.0]; see traffic.md.

Per-objective raw values are stored on Candidate.objectives (keyed by OptimizationTarget value, e.g. {"throughput_per_gpu": .., "throughput_per_user": ..}) by make_candidate; Candidate.score carries the first objective’s value as a headline number only (not used for Pareto ranking). objectives is None for a scalar goal.