| 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 collections.abc import Mapping |
| 40 | from typing import Any, Literal |
| 41 | |
| 42 | from pydantic import Field |
| 43 | |
| 44 | from dlm_sway.core.result import ProbeResult, Verdict, safe_finalize |
| 45 | from dlm_sway.core.scoring import NullCalibratedBackend |
| 46 | from dlm_sway.probes._null_cache import compute_key, load, save |
| 47 | from dlm_sway.probes._null_proxy import NullCalibrationBackendProxy |
| 48 | from dlm_sway.probes.base import Probe, ProbeSpec, RunContext, registry |
| 49 | |
| 50 | |
| 51 | class NullAdapterSpec(ProbeSpec): |
| 52 | """Spec for ``kind: null_adapter``. |
| 53 | |
| 54 | Place this probe **first** in the suite so its output populates |
| 55 | :attr:`RunContext.null_stats` before subsequent probes consult it. |
| 56 | """ |
| 57 | |
| 58 | kind: Literal["null_adapter"] = "null_adapter" |
| 59 | runs: int = Field(default=3, ge=1, le=10) |
| 60 | """Number of independent null adapters to evaluate. Three is the |
| 61 | smallest that yields a usable std; more is better but quickly |
| 62 | dominates suite runtime.""" |
| 63 | init_scale: float = 0.02 |
| 64 | """Stddev of the zero-mean Gaussian used to fill lora_A/lora_B.""" |
| 65 | seed_base: int = 1000 |
| 66 | """First seed; successive runs use ``seed_base + run_idx``.""" |
| 67 | calibrate_kinds: list[str] = Field(default_factory=list) |
| 68 | """Which probe kinds to calibrate. Empty = auto-populate from |
| 69 | ``ctx.downstream_kinds`` (the kinds that appear after this probe |
| 70 | in the suite). Set explicitly to force calibration of specific |
| 71 | kinds regardless of suite order.""" |
| 72 | cache: bool = True |
| 73 | """Read / write the on-disk calibration cache under |
| 74 | ``~/.dlm-sway/null-stats``. Keyed by backend identity + calibration |
| 75 | params. Disable to force a fresh calibration (e.g. when you suspect |
| 76 | the cached stats are stale).""" |
| 77 | rank_multipliers: list[float] = Field(default_factory=lambda: [1.0]) |
| 78 | """Rank multipliers at which to calibrate. Each value scales the |
| 79 | null-adapter noise std by ``sqrt(multiplier)`` — mathematically |
| 80 | equivalent to rank-scaling the LoRA output variance. Default |
| 81 | ``[1.0]`` preserves pre-S10 single-rank behavior byte-for-byte. |
| 82 | |
| 83 | Three-point profiles like ``[0.5, 1.0, 2.0]`` let users read |
| 84 | "how rank-saturated is my adapter?" off the report: |
| 85 | |
| 86 | - A healthy adapter's z-score is stable across multipliers. |
| 87 | - An adapter that's barely above noise at its own rank but |
| 88 | solidly above noise at ``0.5x`` is rank-saturated — a smaller |
| 89 | rank would have yielded a sharper signal. |
| 90 | |
| 91 | Per-rank stats land in ``evidence["null_stats_by_rank"]`` keyed |
| 92 | by ``f"rank_{mult:.2f}"``; the 1.0x group (when present) also |
| 93 | lands under ``evidence["null_stats"]`` for back-compat with |
| 94 | probes that consume a single calibration level. |
| 95 | """ |
| 96 | |
| 97 | |
| 98 | class NullAdapterProbe(Probe): |
| 99 | """Populate ``ctx.null_stats`` with per-kind null distributions. |
| 100 | |
| 101 | The probe itself reports ``Verdict.PASS`` on success — its job is |
| 102 | calibration, not judgment. If the backend can't support null-view |
| 103 | substitution, reports ``Verdict.SKIP`` with a clear message; every |
| 104 | downstream numeric probe then falls back to fixed thresholds. |
| 105 | """ |
| 106 | |
| 107 | kind = "null_adapter" |
| 108 | spec_cls = NullAdapterSpec |
| 109 | category = "baseline" |
| 110 | |
| 111 | def run(self, spec: ProbeSpec, ctx: RunContext) -> ProbeResult: |
| 112 | assert isinstance(spec, NullAdapterSpec) |
| 113 | if not isinstance(ctx.backend, NullCalibratedBackend): |
| 114 | return ProbeResult( |
| 115 | name=spec.name, |
| 116 | kind=spec.kind, |
| 117 | verdict=Verdict.SKIP, |
| 118 | score=None, |
| 119 | message=( |
| 120 | "backend does not implement NullCalibratedBackend — " |
| 121 | "numeric probes will fall back to fixed thresholds" |
| 122 | ), |
| 123 | ) |
| 124 | |
| 125 | registered = registry() |
| 126 | |
| 127 | # Decide which kinds to calibrate. Explicit spec field wins; |
| 128 | # otherwise auto-populate from downstream_kinds. |
| 129 | target_kinds: list[str] = list(spec.calibrate_kinds) |
| 130 | if not target_kinds: |
| 131 | target_kinds = [k for k in ctx.downstream_kinds if k and k != spec.kind] |
| 132 | # De-dupe while preserving order; drop self and unregistered. |
| 133 | seen: set[str] = set() |
| 134 | filtered: list[str] = [] |
| 135 | for k in target_kinds: |
| 136 | if k == spec.kind or k in seen or k not in registered: |
| 137 | continue |
| 138 | seen.add(k) |
| 139 | filtered.append(k) |
| 140 | target_kinds = filtered |
| 141 | |
| 142 | # Validate rank multipliers up front; empty list is nonsensical. |
| 143 | rank_multipliers = list(spec.rank_multipliers) or [1.0] |
| 144 | for mult in rank_multipliers: |
| 145 | if mult <= 0.0 or not math.isfinite(mult): |
| 146 | return ProbeResult( |
| 147 | name=spec.name, |
| 148 | kind=spec.kind, |
| 149 | verdict=Verdict.ERROR, |
| 150 | score=None, |
| 151 | message=f"rank_multipliers must be positive and finite; got {mult!r}", |
| 152 | ) |
| 153 | |
| 154 | # Cache lookup: backends can opt in by providing a |
| 155 | # ``cache_identity()`` method returning a stable string. The |
| 156 | # key incorporates both that identity and the calibration |
| 157 | # parameters that actually influence the output — including |
| 158 | # the sorted rank-multiplier tuple so multi-rank caches don't |
| 159 | # collide with single-rank. |
| 160 | cache_key: str | None = None |
| 161 | if spec.cache: |
| 162 | backend_identity = _backend_identity(ctx.backend) |
| 163 | cache_key = compute_key( |
| 164 | backend_identity=backend_identity, |
| 165 | params={ |
| 166 | "runs": spec.runs, |
| 167 | "init_scale": spec.init_scale, |
| 168 | "seed_base": spec.seed_base, |
| 169 | "top_k": ctx.top_k, |
| 170 | "kinds": sorted(target_kinds), |
| 171 | "rank_multipliers": sorted(rank_multipliers), |
| 172 | }, |
| 173 | ) |
| 174 | cached = load(cache_key) |
| 175 | if cached is not None and "null_stats_by_rank" in cached: |
| 176 | return _pass_from_cache(spec, cached) |
| 177 | # Pre-S10 cache entries only have ``null_stats`` (implicit |
| 178 | # single-rank). Promote them into the new shape so repeated |
| 179 | # runs benefit from the existing cache. |
| 180 | # |
| 181 | # DC3 (Audit 02) — this branch is legacy compatibility and |
| 182 | # will be removed in the next minor version bump. After S10 |
| 183 | # shipped, every newly-written cache carries |
| 184 | # ``null_stats_by_rank``; promoted entries get rewritten on |
| 185 | # the first full recalibration. We keep this shim for one |
| 186 | # release cycle to avoid stranding users whose cache files |
| 187 | # predate S10. |
| 188 | if cached is not None and "null_stats" in cached: |
| 189 | promoted = dict(cached) |
| 190 | promoted["null_stats_by_rank"] = { |
| 191 | _rank_key(1.0): cached["null_stats"], |
| 192 | } |
| 193 | return _pass_from_cache(spec, promoted) |
| 194 | |
| 195 | null_stats_by_rank: dict[str, dict[str, dict[str, float]]] = {} |
| 196 | per_rank_skipped: dict[str, list[dict[str, str]]] = {} |
| 197 | per_rank_samples: dict[str, dict[str, list[float]]] = {} |
| 198 | |
| 199 | for mult in rank_multipliers: |
| 200 | rkey = _rank_key(mult) |
| 201 | per_kind_stats, samples, skipped = _calibrate_at_rank( |
| 202 | ctx=ctx, |
| 203 | spec=spec, |
| 204 | target_kinds=target_kinds, |
| 205 | registered=registered, |
| 206 | rank_scale=mult, |
| 207 | ) |
| 208 | null_stats_by_rank[rkey] = per_kind_stats |
| 209 | per_rank_samples[rkey] = samples |
| 210 | per_rank_skipped[rkey] = skipped |
| 211 | |
| 212 | # Back-compat surface: ``null_stats`` is the 1.0x group when |
| 213 | # present, else the first multiplier's stats (so older probes |
| 214 | # that only read the single-rank dict still get *something*). |
| 215 | primary_rkey = _rank_key(1.0) |
| 216 | if primary_rkey in null_stats_by_rank: |
| 217 | primary_stats = null_stats_by_rank[primary_rkey] |
| 218 | primary_skipped = per_rank_skipped[primary_rkey] |
| 219 | primary_samples = per_rank_samples[primary_rkey] |
| 220 | else: |
| 221 | first_rkey = _rank_key(rank_multipliers[0]) |
| 222 | primary_stats = null_stats_by_rank[first_rkey] |
| 223 | primary_skipped = per_rank_skipped[first_rkey] |
| 224 | primary_samples = per_rank_samples[first_rkey] |
| 225 | |
| 226 | evidence: dict[str, Any] = { |
| 227 | "null_stats": primary_stats, |
| 228 | "null_stats_by_rank": null_stats_by_rank, |
| 229 | "per_kind_raw_samples": primary_samples, |
| 230 | "skipped_kinds": primary_skipped, |
| 231 | "calibrated_kinds": list(primary_stats.keys()), |
| 232 | "runs": spec.runs, |
| 233 | "init_scale": spec.init_scale, |
| 234 | "seed_base": spec.seed_base, |
| 235 | "rank_multipliers": rank_multipliers, |
| 236 | "weight": spec.weight, |
| 237 | "from_cache": False, |
| 238 | } |
| 239 | |
| 240 | if cache_key is not None: |
| 241 | save( |
| 242 | cache_key, |
| 243 | { |
| 244 | "null_stats": primary_stats, |
| 245 | "null_stats_by_rank": null_stats_by_rank, |
| 246 | "runs": spec.runs, |
| 247 | "init_scale": spec.init_scale, |
| 248 | "seed_base": spec.seed_base, |
| 249 | "rank_multipliers": rank_multipliers, |
| 250 | "calibrated_kinds": list(primary_stats.keys()), |
| 251 | }, |
| 252 | ) |
| 253 | |
| 254 | if len(rank_multipliers) == 1: |
| 255 | message = ( |
| 256 | f"null calibration: {len(primary_stats)} kinds calibrated over {spec.runs} seeds" |
| 257 | ) |
| 258 | else: |
| 259 | mults_str = ", ".join(f"{m:g}x" for m in rank_multipliers) |
| 260 | message = ( |
| 261 | f"null calibration: {len(primary_stats)} kinds × " |
| 262 | f"{len(rank_multipliers)} ranks [{mults_str}] over {spec.runs} seeds" |
| 263 | ) |
| 264 | if primary_skipped: |
| 265 | message += f" ({len(primary_skipped)} opted out)" |
| 266 | |
| 267 | return safe_finalize( |
| 268 | name=spec.name, |
| 269 | kind=spec.kind, |
| 270 | verdict=Verdict.PASS, |
| 271 | score=1.0, |
| 272 | evidence=evidence, |
| 273 | message=message, |
| 274 | ) |
| 275 | |
| 276 | |
| 277 | def _rank_key(mult: float) -> str: |
| 278 | """Canonical string key for a rank multiplier. Stable across runs.""" |
| 279 | return f"rank_{mult:.2f}" |
| 280 | |
| 281 | |
| 282 | def _calibrate_at_rank( |
| 283 | *, |
| 284 | ctx: RunContext, |
| 285 | spec: NullAdapterSpec, |
| 286 | target_kinds: list[str], |
| 287 | registered: dict[str, type[Probe]], |
| 288 | rank_scale: float, |
| 289 | ) -> tuple[dict[str, dict[str, float]], dict[str, list[float]], list[dict[str, str]]]: |
| 290 | """Run the full kind × seed calibration matrix at one rank multiplier. |
| 291 | |
| 292 | Returns ``(per_kind_stats, per_kind_samples, skipped)``. |
| 293 | """ |
| 294 | per_kind_stats: dict[str, dict[str, float]] = {} |
| 295 | per_kind_samples: dict[str, list[float]] = {} |
| 296 | skipped: list[dict[str, str]] = [] |
| 297 | |
| 298 | for kind in target_kinds: |
| 299 | probe_cls = registered[kind] |
| 300 | try: |
| 301 | cal_spec = probe_cls.calibrate_spec(ctx) |
| 302 | except Exception as exc: # noqa: BLE001 — defensive |
| 303 | skipped.append({"kind": kind, "reason": f"calibrate_spec raised: {exc}"}) |
| 304 | continue |
| 305 | if cal_spec is None: |
| 306 | skipped.append( |
| 307 | {"kind": kind, "reason": "probe opted out (calibrate_spec returned None)"} |
| 308 | ) |
| 309 | continue |
| 310 | |
| 311 | probe = probe_cls() |
| 312 | raws: list[float] = [] |
| 313 | errors: list[str] = [] |
| 314 | for run_idx in range(spec.runs): |
| 315 | seed = spec.seed_base + run_idx |
| 316 | proxy = NullCalibrationBackendProxy( |
| 317 | ctx.backend, # type: ignore[arg-type] |
| 318 | seed=seed, |
| 319 | init_scale=spec.init_scale, |
| 320 | rank_scale=rank_scale, |
| 321 | ) |
| 322 | cal_ctx = RunContext( |
| 323 | backend=proxy, |
| 324 | seed=seed, |
| 325 | top_k=ctx.top_k, |
| 326 | sections=ctx.sections, |
| 327 | doc_text=ctx.doc_text, |
| 328 | null_stats={}, # calibration uses fixed thresholds — no recursion |
| 329 | downstream_kinds=(), |
| 330 | ) |
| 331 | try: |
| 332 | cal_result = probe.run(cal_spec, cal_ctx) |
| 333 | except Exception as exc: # noqa: BLE001 |
| 334 | errors.append(f"seed={seed}: {type(exc).__name__}: {exc}") |
| 335 | continue |
| 336 | raw = cal_result.raw |
| 337 | if raw is not None and math.isfinite(raw): |
| 338 | raws.append(float(raw)) |
| 339 | elif cal_result.verdict == Verdict.ERROR: |
| 340 | errors.append(f"seed={seed}: probe ERROR — {cal_result.message}") |
| 341 | |
| 342 | if raws: |
| 343 | mean = statistics.fmean(raws) |
| 344 | raw_std = statistics.pstdev(raws) if len(raws) > 1 else 0.0 |
| 345 | # F02 (Audit 03) — detect the degenerate case (``runs: 1`` |
| 346 | # or every seed producing the *exact* same raw) as a first- |
| 347 | # class property of the stats dict. The previous code hid |
| 348 | # this via ``max(std, 1e-6)`` which collided with |
| 349 | # :data:``_zscore.MIN_STD`` and let the z-score path fire |
| 350 | # on a std that had been synthetically lifted from ``0.0`` |
| 351 | # — the path that produced the ``+290,766σ`` observation in |
| 352 | # the audit. A multi-seed run with genuinely small variance |
| 353 | # (e.g. 5e-7 on a low-noise dummy) is NOT degenerate; we |
| 354 | # keep the 1e-6 floor for that case so valid-but-tight |
| 355 | # calibrations still z-score. ``z_score`` inspects both the |
| 356 | # ``degenerate`` flag and the ``std < MIN_STD`` threshold. |
| 357 | degenerate = len(raws) <= 1 or raw_std == 0.0 |
| 358 | per_kind_stats[kind] = { |
| 359 | "mean": mean, |
| 360 | "std": max(raw_std, 1e-6), |
| 361 | "n": float(len(raws)), |
| 362 | "degenerate": 1.0 if degenerate else 0.0, |
| 363 | } |
| 364 | per_kind_samples[kind] = raws |
| 365 | else: |
| 366 | reason = "no finite raws across all seeds" |
| 367 | if errors: |
| 368 | reason += f" ({errors[0]})" |
| 369 | skipped.append({"kind": kind, "reason": reason}) |
| 370 | |
| 371 | return per_kind_stats, per_kind_samples, skipped |
| 372 | |
| 373 | |
| 374 | def _pass_from_cache(spec: NullAdapterSpec, cached: dict[str, Any]) -> ProbeResult: |
| 375 | """Rebuild a PASS result from a cache-loaded evidence dict.""" |
| 376 | stats_by_rank: dict[str, dict[str, dict[str, float]]] = dict( |
| 377 | cached.get("null_stats_by_rank") or {} |
| 378 | ) |
| 379 | # Prefer the explicit 1.0x group; fall back to the legacy ``null_stats``. |
| 380 | primary_stats = stats_by_rank.get(_rank_key(1.0), cached.get("null_stats", {})) |
| 381 | evidence: dict[str, Any] = dict(cached) |
| 382 | evidence["null_stats"] = primary_stats |
| 383 | evidence["null_stats_by_rank"] = stats_by_rank |
| 384 | evidence.setdefault("skipped_kinds", []) |
| 385 | evidence.setdefault("calibrated_kinds", list(primary_stats.keys())) |
| 386 | evidence["weight"] = spec.weight |
| 387 | evidence["from_cache"] = True |
| 388 | n_kinds = len(primary_stats) |
| 389 | n_ranks = len(stats_by_rank) |
| 390 | message = ( |
| 391 | f"null calibration: {n_kinds} kinds (loaded from cache)" |
| 392 | if n_ranks <= 1 |
| 393 | else f"null calibration: {n_kinds} kinds × {n_ranks} ranks (loaded from cache)" |
| 394 | ) |
| 395 | return safe_finalize( |
| 396 | name=spec.name, |
| 397 | kind=spec.kind, |
| 398 | verdict=Verdict.PASS, |
| 399 | score=1.0, |
| 400 | evidence=evidence, |
| 401 | message=message, |
| 402 | ) |
| 403 | |
| 404 | |
| 405 | def _backend_identity(backend: Any) -> str | None: |
| 406 | """Ask the backend for a stable cache identity string, if it has one. |
| 407 | |
| 408 | Duck-typed: backends that can't uniquely identify themselves (the |
| 409 | dummy backend in tests, for example) simply don't provide this |
| 410 | method, and caching is skipped for them. |
| 411 | """ |
| 412 | fn = getattr(backend, "cache_identity", None) |
| 413 | if not callable(fn): |
| 414 | return None |
| 415 | try: |
| 416 | value = fn() |
| 417 | except Exception: # noqa: BLE001 — cache is best-effort |
| 418 | return None |
| 419 | return str(value) if value else None |
| 420 | |
| 421 | |
| 422 | def get_null_stats(ctx: RunContext, probe_kind: str) -> Mapping[str, float] | None: |
| 423 | """Look up null-adapter stats for ``probe_kind`` in the run context. |
| 424 | |
| 425 | Returns ``{"mean": …, "std": …, "n": …}`` when calibration ran for |
| 426 | this kind, else ``None``. Probes treat ``None`` as "fall back to |
| 427 | the fixed threshold from your spec" and surface ``(no calibration)`` |
| 428 | in the report. |
| 429 | """ |
| 430 | return ctx.null_stats.get(probe_kind) |
| 431 | |
| 432 | |
| 433 | def get_null_stats_by_rank( |
| 434 | ctx: RunContext, probe_kind: str |
| 435 | ) -> Mapping[str, Mapping[str, float]] | None: |
| 436 | """Look up per-rank null-adapter stats for ``probe_kind``. |
| 437 | |
| 438 | Returns ``{rank_key: {"mean": …, "std": …, "n": …}}`` across every |
| 439 | rank multiplier the ``null_adapter`` probe calibrated. ``None`` when |
| 440 | no multi-rank calibration ran (pre-S10 behavior, or S02's single- |
| 441 | rank default). |
| 442 | """ |
| 443 | by_rank = ctx.null_stats_by_rank |
| 444 | if not by_rank: |
| 445 | return None |
| 446 | out: dict[str, Mapping[str, float]] = {} |
| 447 | for rkey, kind_map in by_rank.items(): |
| 448 | if probe_kind in kind_map: |
| 449 | out[rkey] = kind_map[probe_kind] |
| 450 | return out or None |