| 1 | """Null-adapter baseline probe — per-kind calibration matrix (S02). |
| 2 | |
| 3 | Every numeric primitive reports its raw metric *and* a z-score against |
| 4 | a null-adapter distribution. This probe is the runtime engine that |
| 5 | establishes that distribution — for **every** numeric probe kind the |
| 6 | user has downstream in the suite, not just one. |
| 7 | |
| 8 | How it works: |
| 9 | |
| 10 | 1. The runner populates ``ctx.downstream_kinds`` with every probe kind |
| 11 | that appears after this one in the suite. |
| 12 | 2. For each target kind, we ask its probe class for a |
| 13 | :meth:`~dlm_sway.probes.base.Probe.calibrate_spec` — a small spec |
| 14 | suitable for null calibration. A probe that returns ``None`` opts |
| 15 | out (typically because its inputs can't be synthesized, e.g. |
| 16 | ``adapter_revert`` without an embedder, or ``adapter_ablation`` |
| 17 | which needs ``as_scaled_adapter`` that the proxy doesn't expose). |
| 18 | 3. For each calibrating kind × seed, we run the probe through a |
| 19 | :class:`~dlm_sway.probes._null_proxy.NullCalibrationBackendProxy` |
| 20 | which makes ``as_finetuned()`` yield ``as_null_adapter(seed)`` — |
| 21 | so the probe's own math is computing "what does my metric look |
| 22 | like when the fine-tune is structural noise?". |
| 23 | 4. We harvest each run's ``raw`` value, aggregate to ``(mean, std, n)`` |
| 24 | per kind, and publish under ``evidence["null_stats"]``. |
| 25 | 5. The runner threads ``null_stats`` into ``RunContext`` for every |
| 26 | subsequent probe, which then prefers the z-score path over the |
| 27 | fixed-threshold path (see :mod:`dlm_sway.probes._zscore`). |
| 28 | |
| 29 | Backends that don't implement |
| 30 | :class:`~dlm_sway.core.scoring.NullCalibratedBackend` cause this probe |
| 31 | to ``Verdict.SKIP``; every downstream probe falls back to fixed |
| 32 | thresholds and surfaces ``(no calibration)`` in the report. |
| 33 | """ |
| 34 | |
| 35 | from __future__ import annotations |
| 36 | |
| 37 | import math |
| 38 | import statistics |
| 39 | from typing import Any, Literal |
| 40 | |
| 41 | from pydantic import Field |
| 42 | |
| 43 | from dlm_sway.core.result import ProbeResult, Verdict, safe_finalize |
| 44 | from dlm_sway.core.scoring import NullCalibratedBackend |
| 45 | from dlm_sway.probes._null_cache import compute_key, load, save |
| 46 | from dlm_sway.probes._null_proxy import NullCalibrationBackendProxy |
| 47 | from dlm_sway.probes.base import Probe, ProbeSpec, RunContext, registry |
| 48 | |
| 49 | |
| 50 | class NullAdapterSpec(ProbeSpec): |
| 51 | """Spec for ``kind: null_adapter``. |
| 52 | |
| 53 | Place this probe **first** in the suite so its output populates |
| 54 | :attr:`RunContext.null_stats` before subsequent probes consult it. |
| 55 | """ |
| 56 | |
| 57 | kind: Literal["null_adapter"] = "null_adapter" |
| 58 | runs: int = Field(default=3, ge=1, le=10) |
| 59 | """Number of independent null adapters to evaluate. Three is the |
| 60 | smallest that yields a usable std; more is better but quickly |
| 61 | dominates suite runtime.""" |
| 62 | init_scale: float = 0.02 |
| 63 | """Stddev of the zero-mean Gaussian used to fill lora_A/lora_B.""" |
| 64 | seed_base: int = 1000 |
| 65 | """First seed; successive runs use ``seed_base + run_idx``.""" |
| 66 | calibrate_kinds: list[str] = Field(default_factory=list) |
| 67 | """Which probe kinds to calibrate. Empty = auto-populate from |
| 68 | ``ctx.downstream_kinds`` (the kinds that appear after this probe |
| 69 | in the suite). Set explicitly to force calibration of specific |
| 70 | kinds regardless of suite order.""" |
| 71 | cache: bool = True |
| 72 | """Read / write the on-disk calibration cache under |
| 73 | ``~/.dlm-sway/null-stats``. Keyed by backend identity + calibration |
| 74 | params. Disable to force a fresh calibration (e.g. when you suspect |
| 75 | the cached stats are stale).""" |
| 76 | |
| 77 | |
| 78 | class NullAdapterProbe(Probe): |
| 79 | """Populate ``ctx.null_stats`` with per-kind null distributions. |
| 80 | |
| 81 | The probe itself reports ``Verdict.PASS`` on success — its job is |
| 82 | calibration, not judgment. If the backend can't support null-view |
| 83 | substitution, reports ``Verdict.SKIP`` with a clear message; every |
| 84 | downstream numeric probe then falls back to fixed thresholds. |
| 85 | """ |
| 86 | |
| 87 | kind = "null_adapter" |
| 88 | spec_cls = NullAdapterSpec |
| 89 | category = "baseline" |
| 90 | |
| 91 | def run(self, spec: ProbeSpec, ctx: RunContext) -> ProbeResult: |
| 92 | assert isinstance(spec, NullAdapterSpec) |
| 93 | if not isinstance(ctx.backend, NullCalibratedBackend): |
| 94 | return ProbeResult( |
| 95 | name=spec.name, |
| 96 | kind=spec.kind, |
| 97 | verdict=Verdict.SKIP, |
| 98 | score=None, |
| 99 | message=( |
| 100 | "backend does not implement NullCalibratedBackend — " |
| 101 | "numeric probes will fall back to fixed thresholds" |
| 102 | ), |
| 103 | ) |
| 104 | |
| 105 | registered = registry() |
| 106 | |
| 107 | # Decide which kinds to calibrate. Explicit spec field wins; |
| 108 | # otherwise auto-populate from downstream_kinds. |
| 109 | target_kinds: list[str] = list(spec.calibrate_kinds) |
| 110 | if not target_kinds: |
| 111 | target_kinds = [k for k in ctx.downstream_kinds if k and k != spec.kind] |
| 112 | # De-dupe while preserving order; drop self and unregistered. |
| 113 | seen: set[str] = set() |
| 114 | filtered: list[str] = [] |
| 115 | for k in target_kinds: |
| 116 | if k == spec.kind or k in seen or k not in registered: |
| 117 | continue |
| 118 | seen.add(k) |
| 119 | filtered.append(k) |
| 120 | target_kinds = filtered |
| 121 | |
| 122 | # Cache lookup: backends can opt in by providing a |
| 123 | # ``cache_identity()`` method returning a stable string. The |
| 124 | # key incorporates both that identity and the calibration |
| 125 | # parameters that actually influence the output. |
| 126 | cache_key: str | None = None |
| 127 | if spec.cache: |
| 128 | backend_identity = _backend_identity(ctx.backend) |
| 129 | cache_key = compute_key( |
| 130 | backend_identity=backend_identity, |
| 131 | params={ |
| 132 | "runs": spec.runs, |
| 133 | "init_scale": spec.init_scale, |
| 134 | "seed_base": spec.seed_base, |
| 135 | "top_k": ctx.top_k, |
| 136 | "kinds": sorted(target_kinds), |
| 137 | }, |
| 138 | ) |
| 139 | cached = load(cache_key) |
| 140 | if cached is not None and "null_stats" in cached: |
| 141 | cached_evidence: dict[str, Any] = dict(cached) |
| 142 | cached_evidence.setdefault("skipped_kinds", []) |
| 143 | cached_evidence.setdefault("calibrated_kinds", list(cached["null_stats"].keys())) |
| 144 | cached_evidence["weight"] = spec.weight |
| 145 | cached_evidence["from_cache"] = True |
| 146 | return safe_finalize( |
| 147 | name=spec.name, |
| 148 | kind=spec.kind, |
| 149 | verdict=Verdict.PASS, |
| 150 | score=1.0, |
| 151 | evidence=cached_evidence, |
| 152 | message=( |
| 153 | f"null calibration: {len(cached['null_stats'])} kinds (loaded from cache)" |
| 154 | ), |
| 155 | ) |
| 156 | |
| 157 | per_kind_stats: dict[str, dict[str, float]] = {} |
| 158 | per_kind_samples: dict[str, list[float]] = {} |
| 159 | skipped_kinds: list[dict[str, str]] = [] |
| 160 | |
| 161 | for kind in target_kinds: |
| 162 | probe_cls = registered[kind] |
| 163 | try: |
| 164 | cal_spec = probe_cls.calibrate_spec(ctx) |
| 165 | except Exception as exc: # noqa: BLE001 — defensive |
| 166 | skipped_kinds.append({"kind": kind, "reason": f"calibrate_spec raised: {exc}"}) |
| 167 | continue |
| 168 | if cal_spec is None: |
| 169 | skipped_kinds.append( |
| 170 | { |
| 171 | "kind": kind, |
| 172 | "reason": "probe opted out (calibrate_spec returned None)", |
| 173 | } |
| 174 | ) |
| 175 | continue |
| 176 | |
| 177 | probe = probe_cls() |
| 178 | raws: list[float] = [] |
| 179 | errors: list[str] = [] |
| 180 | for run_idx in range(spec.runs): |
| 181 | seed = spec.seed_base + run_idx |
| 182 | proxy = NullCalibrationBackendProxy( |
| 183 | ctx.backend, seed=seed, init_scale=spec.init_scale |
| 184 | ) |
| 185 | cal_ctx = RunContext( |
| 186 | backend=proxy, |
| 187 | seed=seed, |
| 188 | top_k=ctx.top_k, |
| 189 | sections=ctx.sections, |
| 190 | doc_text=ctx.doc_text, |
| 191 | null_stats={}, # calibration uses fixed thresholds — no recursion |
| 192 | downstream_kinds=(), |
| 193 | ) |
| 194 | try: |
| 195 | cal_result = probe.run(cal_spec, cal_ctx) |
| 196 | except Exception as exc: # noqa: BLE001 |
| 197 | errors.append(f"seed={seed}: {type(exc).__name__}: {exc}") |
| 198 | continue |
| 199 | raw = cal_result.raw |
| 200 | if raw is not None and math.isfinite(raw): |
| 201 | raws.append(float(raw)) |
| 202 | elif cal_result.verdict == Verdict.ERROR: |
| 203 | errors.append(f"seed={seed}: probe ERROR — {cal_result.message}") |
| 204 | |
| 205 | if raws: |
| 206 | mean = statistics.fmean(raws) |
| 207 | std = statistics.pstdev(raws) if len(raws) > 1 else 0.0 |
| 208 | per_kind_stats[kind] = { |
| 209 | "mean": mean, |
| 210 | # C9: clamp the std floor so the downstream z-score |
| 211 | # path doesn't blow up when every seed produces |
| 212 | # identical raws. |
| 213 | "std": max(std, 1e-6), |
| 214 | "n": float(len(raws)), |
| 215 | } |
| 216 | per_kind_samples[kind] = raws |
| 217 | else: |
| 218 | reason = "no finite raws across all seeds" |
| 219 | if errors: |
| 220 | reason += f" ({errors[0]})" |
| 221 | skipped_kinds.append({"kind": kind, "reason": reason}) |
| 222 | |
| 223 | evidence: dict[str, Any] = { |
| 224 | "null_stats": per_kind_stats, |
| 225 | "per_kind_raw_samples": per_kind_samples, |
| 226 | "skipped_kinds": skipped_kinds, |
| 227 | "calibrated_kinds": list(per_kind_stats.keys()), |
| 228 | "runs": spec.runs, |
| 229 | "init_scale": spec.init_scale, |
| 230 | "seed_base": spec.seed_base, |
| 231 | "weight": spec.weight, |
| 232 | "from_cache": False, |
| 233 | } |
| 234 | |
| 235 | if cache_key is not None: |
| 236 | # Persist the stats dict only — the samples list can be |
| 237 | # large, and downstream consumers only need the aggregates. |
| 238 | save( |
| 239 | cache_key, |
| 240 | { |
| 241 | "null_stats": per_kind_stats, |
| 242 | "runs": spec.runs, |
| 243 | "init_scale": spec.init_scale, |
| 244 | "seed_base": spec.seed_base, |
| 245 | "calibrated_kinds": list(per_kind_stats.keys()), |
| 246 | }, |
| 247 | ) |
| 248 | |
| 249 | message = f"null calibration: {len(per_kind_stats)} kinds calibrated over {spec.runs} seeds" |
| 250 | if skipped_kinds: |
| 251 | message += f" ({len(skipped_kinds)} opted out)" |
| 252 | |
| 253 | return safe_finalize( |
| 254 | name=spec.name, |
| 255 | kind=spec.kind, |
| 256 | verdict=Verdict.PASS, |
| 257 | score=1.0, |
| 258 | evidence=evidence, |
| 259 | message=message, |
| 260 | ) |
| 261 | |
| 262 | |
| 263 | def _backend_identity(backend: Any) -> str | None: |
| 264 | """Ask the backend for a stable cache identity string, if it has one. |
| 265 | |
| 266 | Duck-typed: backends that can't uniquely identify themselves (the |
| 267 | dummy backend in tests, for example) simply don't provide this |
| 268 | method, and caching is skipped for them. |
| 269 | """ |
| 270 | fn = getattr(backend, "cache_identity", None) |
| 271 | if not callable(fn): |
| 272 | return None |
| 273 | try: |
| 274 | value = fn() |
| 275 | except Exception: # noqa: BLE001 — cache is best-effort |
| 276 | return None |
| 277 | return str(value) if value else None |
| 278 | |
| 279 | |
| 280 | def get_null_stats(ctx: RunContext, probe_kind: str) -> dict[str, float] | None: |
| 281 | """Look up null-adapter stats for ``probe_kind`` in the run context. |
| 282 | |
| 283 | Returns ``{"mean": …, "std": …, "n": …}`` when calibration ran for |
| 284 | this kind, else ``None``. Probes treat ``None`` as "fall back to |
| 285 | the fixed threshold from your spec" and surface ``(no calibration)`` |
| 286 | in the report. |
| 287 | """ |
| 288 | return ctx.null_stats.get(probe_kind) |