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
|
"""Disambiguate the parity gap: real bug vs FD-amplified fp noise.
(a) single-eval exactness: at a synthetic two-phase state Z=[zs+d, zs-d], compare the full doubled-batch
correction (Jv-JTv) against the halved+mirrored one. Exact math => allclose at fp level.
(b) estimator noise floor: perturb zs by 1e-6 relative and rerun the ORIGINAL holo_a_track — if a_best
moves by ~the same 0.4 rel, the parity gap is the estimator's intrinsic FD sensitivity, not a bug."""
import torch, torch.func as tf
import lt_ep_train as L
from holo_ep import holo_a_track
torch.manual_seed(0)
blk = L.EQBlock(512, 16, 256, 256, c=1.0, attn_mode='thick'); blk.qknorm = True
ck = torch.load('runs/redx_traj/s2000.pt', map_location=L.dev)
with torch.no_grad():
for p, w in zip(blk.allp, ck['allp']):
p.copy_(w.to(L.dev))
torch.manual_seed(42)
idx, y = L.get_batch('train', 24, 256)
xin = blk.embed(idx).detach()
zs = L.relax(blk, xin.clone(), xin, 150, 0.1)
B = zs.size(0)
fnc = lambda zz: blk.nc_force(zz)
# (a) single-eval exactness
torch.manual_seed(7)
d = 0.02 * torch.randn_like(zs)
Z = torch.cat([zs + d, zs - d], 0)
zbar = 0.5 * (Z[:B] + Z[B:])
zb2 = torch.cat([zbar, zbar], 0)
v = (Z - zb2).contiguous()
with torch.no_grad():
_, Jv = tf.jvp(fnc, (zb2,), (v,))
JTv = tf.vjp(fnc, zb2)[1](v)[0]
corr_full = Jv - JTv
v0 = (Z[:B] - zbar).contiguous()
_, Jv0 = tf.jvp(fnc, (zbar,), (v0,))
JTv0 = tf.vjp(fnc, zbar)[1](v0)[0]
corr_half = torch.cat([Jv0 - JTv0, -(Jv0 - JTv0)], 0)
rel_a = ((corr_full - corr_half).norm() / (corr_full.norm() + 1e-12)).item()
print(f"(a) single-eval: rel={rel_a:.2e} ({'EXACT — gap is fp/FD noise' if rel_a < 1e-4 else 'REAL BUG'})", flush=True)
# (b) estimator noise floor of the ORIGINAL
r, T2, eps = 0.02, 40, 0.1
a_ref, _ = holo_a_track(blk, zs, xin, y, r, T2, eps)
zs_p = zs + 1e-6 * zs.norm() / (zs.numel() ** 0.5) * torch.randn_like(zs)
a_prt, _ = holo_a_track(blk, zs_p, xin, y, r, T2, eps)
rel_b = ((a_prt - a_ref).norm() / (a_ref.norm() + 1e-12)).item()
cos_b = torch.nn.functional.cosine_similarity(a_ref.flatten(), a_prt.flatten(), dim=0).item()
print(f"(b) orig-vs-orig under 1e-6 state noise: rel={rel_b:.2e} cos={cos_b:.6f}", flush=True)
print("HOLO_FAST_PROBE_DONE", flush=True)
|
