alephllm-mini-beatrix-training / mini-beatrix-3 /code /amoe-lora /src /amoe /diagnostics /diffusion.py
mini-beatrix-3/code: the training code pinned to the commits that train the run (alephllm 0.10.5 @e8b7440, amoe-lora 0.2.10 @f3db5d4) + the pod's mission files, 2026-09-27
b8b19f6 verified | """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()) | |
| def gate_stats(handle) -> dict: | |
| return handle.gates() | |
| 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 | |