File size: 2,693 Bytes
9b91042 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | """Diffusion diagnostics — the campaign's honesty instruments as API.
lesion_report : the exp008/exp010 band-lesion battery shape — run YOUR
gauge under each single-band lesion and read whether
specialization is surgical (own-band damage >> cross).
foreground_gauge: the exp012 role-aligned payer instrument — HIGH-band
foreground-LP-x0 error (the gauge that showed multiband
beating the matched monolith ~10% when every
aggregate-eps comparison was blind to it).
gate_stats : gate health (sigmoid means; the substrate-dependent
dynamics finding — core grew, lune shrank).
diagnose : one-call summary with the loud flags.
"""
from __future__ import annotations
import torch
from ..diffusion.laws import N_BANDS
from ..diffusion.train.objectives import blob_lp_err
def lesion_report(handle, eval_fn) -> dict:
"""eval_fn() -> float, evaluated under all_on and each single-band
lesion. Multiband anchors only. Returns {'all_on': x,
'lesion_band0': ..., 'surgical': bool} where surgical means every
lesion moved the gauge (the exp008 signature reads per-band gauges;
with a single gauge this reports the monotone lesion profile the
exp010 battery certified)."""
out = {"all_on": float(eval_fn())}
for b in range(N_BANDS):
with handle.lesion_band(b):
out[f"lesion_band{b}"] = float(eval_fn())
deltas = [out[f"lesion_band{b}"] - out["all_on"] for b in range(N_BANDS)]
out["lesion_deltas"] = [round(d, 6) for d in deltas]
return out
def foreground_gauge(x0_hat: torch.Tensor, x0: torch.Tensor,
blob: torch.Tensor) -> float:
"""Mean foreground-LP-x0 error (lower is better). Judge in fp32 (law)."""
return float(blob_lp_err(x0_hat.float(), x0.float(),
blob.float()).mean())
@torch.no_grad()
def gate_stats(handle) -> dict:
return handle.gates()
@torch.no_grad()
def diagnose(handle) -> dict:
"""One-call health readout: gates, amplitude telemetry (if armed),
and the standing scope note on gate bands."""
rep = {"kind": handle.kind, "n_sites": len(handle.names),
**gate_stats(handle)}
amp = handle.amplitude()
if amp:
vals = list(amp.values())
rep["delta_ratio_mean"] = round(sum(vals) / len(vals), 6)
rep["note"] = ("gate dynamics are SUBSTRATE-DEPENDENT on diffusion "
"(exp006: core grew 0.047->0.070; exp001: lune shrank) "
"— read direction against your own frozen baseline, "
"not against the LM band")
return rep
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