From 8f43de671cf03ee20d5d652b6cd3ba575982a436 Mon Sep 17 00:00:00 2001 From: Yuren Hao Date: Mon, 6 Jul 2026 02:22:54 -0500 Subject: bp_lm twin control + env forensics probes + holo_a_track_avg (semi-convergence fix, ungated) BP+EMA 2x2 (local, healthy env): qknorm {1.9951, 1.9977}, no-qknorm {1.9728, 1.9732} -> parameterization-matched BP twin tops at ~1.97 vs EP 1.7888: the equilibrium computation's iteration/depth dividend = 0.18 CE from identical parameters. External anchor (tuned depth-1 BP, 1.7921): EP at parity. bp_lm on 107/2.3.1 gave 1.9823 ~= local -> plain backward exonerated on the pascal env; the 107 divergence (pair/floss/resreg dead by step 600) narrows to the EP reg/estimator loop. Single-step fingerprints all match (a/b/c/d) -> suspected intermittent kernel issue; discriminators in flight (torch-2.7 env probe + delay_hr2's step-2000 reg-on transition). Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn --- ep_run/holo_ep.py | 56 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) (limited to 'ep_run/holo_ep.py') diff --git a/ep_run/holo_ep.py b/ep_run/holo_ep.py index 5485bc7..d8d2be5 100644 --- a/ep_run/holo_ep.py +++ b/ep_run/holo_ep.py @@ -298,6 +298,62 @@ def holo_a_track_fast(blk, zs, xin, y, r, T2max, eps, K=10, exit_mult=5.0): return a_best.detach(), t_best +def holo_a_track_avg(blk, zs, xin, y, r, T2max, eps, K=10, exit_mult=5.0): + """track_fast + the semi-convergence fix (t2_probe 2026-07-05): the adjoint iteration on a + near-marginal operator SEMI-converges — error dips at a batch-dependent optimum then grows, and the + plain argmin-of-increment t_best gets fooled by rotating slow modes. Two changes: + (1) trend-aware stop: break after the increment rises on 2 consecutive checks past 2x inc_min + (instead of the blunt exit_mult=5 single-shot); + (2) plateau averaging: return the MEAN of the a_t whose increment <= 1.5x inc_min (the flat bottom + of the semi-convergence curve) — averages out the rotating error component around the optimum.""" + import torch.func as tf + B = zs.size(0) + Z = torch.cat([zs, zs], 0) + X2 = torch.cat([xin, xin], 0) + y2 = torch.cat([y, y], 0) + sg = torch.cat([torch.full((B, 1, 1), r, device=zs.device), torch.full((B, 1, 1), -r, device=zs.device)], 0) + fnc = lambda zz: blk.nc_force(zz) + a_prev = None + hist = [] # (inc, a_t) at each K-checkpoint + inc_min, rise = float('inf'), 0 + zs2a = torch.cat([zs, zs], 0) + kappa = getattr(blk, 'nbrake', 0.0) + for t in range(1, T2max + 1): + with torch.no_grad(): + zbar = 0.5 * (Z[:B] + Z[B:]) + f = rforce(blk, Z, X2) - sg * rgrad_ce(blk, Z, y2, denom=y.numel()) + if kappa > 0: + f = f - kappa * (Z - zs2a) + v0 = (Z[:B] - zbar).contiguous() + _, Jv0 = tf.jvp(fnc, (zbar,), (v0,)) + JTv0 = tf.vjp(fnc, zbar)[1](v0)[0] + corr0 = Jv0 - JTv0 + Z = Z + eps * (f - torch.cat([corr0, -corr0], 0)) + if t % K == 0 or t == T2max: + a_t = (Z[B:] - Z[:B]) / (2 * r) + if not torch.isfinite(a_t).all(): + break + if a_prev is not None: + inc = (a_t - a_prev).norm().item() + hist.append((inc, a_t)) + if inc < inc_min: + inc_min, rise = inc, 0 + elif inc > 2.0 * inc_min and t >= 3 * K: + rise += 1 # trend-aware: need 2 consecutive rising checks + if rise >= 2: + break + else: + rise = 0 + a_prev = a_t + if not hist: + return (a_prev if a_prev is not None else (Z[B:] - Z[:B]) / (2 * r)).detach(), T2max + flat = [a for inc, a in hist if inc <= 1.5 * inc_min] # the semi-convergence plateau + if not flat: + flat = [min(hist, key=lambda p: p[0])[1]] + a_avg = torch.stack(flat).mean(0) + return a_avg.detach(), len(hist) * K + + def holo_a_lockin(blk, zs, xin, y, r, P, ncyc, eps): """True oscillatory EP / lock-in estimator (Laborieux–Zenke taken literally) — the noisy-physics form: ONE trajectory, sinusoidal nudge beta(t)=r·sin(2πt/P), in-phase -- cgit v1.2.3