holofast: exact halved-jvp AEP track — 1.55x nudged phase, gradient-gate passed

holo_a_track computed the doubled-batch jvp/vjp on [v0; -v0] at a shared
anchor zbar — exact antisymmetric redundancy (phase deviations from the
common mode are exact negatives). holo_a_track_fast computes at batch B and
mirrors: single-eval parity 6e-7 (exact); trajectory-level 45% divergence
SHARED with the original's own FD noise floor (1e-6 state noise -> 49%
self-divergence — the 2r=0.04 finite difference amplifies fp noise; training
averages it via pema/momentum). Ship gate: cos(EP,BPTT) orig vs fast
indistinguishable (0.907/0.912, 0.853/0.853, 0.918/0.918). Timing 6.43->4.16s
on the T2=40 nudged phase (contended GPU, relative). --holofast flag,
default off; queued ablation arms deliberately stay on orig for fidelity.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn

1 files changed, 33 insertions, 0 deletions

diff --git a/ep_run/holo_fast_parity.py b/ep_run/holo_fast_parity.py
new file mode 100644
index 0000000..d2df0da
--- /dev/null
+++ b/ep_run/holo_fast_parity.py
@@ -0,0 +1,33 @@
+"""Parity + timing for holo_a_track_fast (exact halved-jvp restructure) vs holo_a_track.
+Same ckpt (s2000), same batch, same T2max: a_best must match to fp noise, t_best exactly;
+timing over 3 reps each (GPU contended — relative ratio is the signal)."""
+import time, torch
+import lt_ep_train as L
+from holo_ep import holo_a_track, holo_a_track_fast
+
+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)
+
+r, T2, eps = 0.02, 40, 0.1
+a0, t0 = holo_a_track(blk, zs, xin, y, r, T2, eps)          # warmup + reference
+a1, t1 = holo_a_track_fast(blk, zs, xin, y, r, T2, eps)
+rel = ((a1 - a0).norm() / (a0.norm() + 1e-12)).item()
+cos = torch.nn.functional.cosine_similarity(a0.flatten(), a1.flatten(), dim=0).item()
+print(f"parity: rel_diff={rel:.2e} cos={cos:.8f} t_best {t0} vs {t1}", flush=True)
+
+for name, fn in (('orig', holo_a_track), ('fast', holo_a_track_fast)):
+    ts = []
+    for _ in range(3):
+        torch.cuda.synchronize(); t = time.time()
+        fn(blk, zs, xin, y, r, T2, eps)
+        torch.cuda.synchronize(); ts.append(time.time() - t)
+    print(f"{name}: {min(ts):.3f}s (best of 3)", flush=True)
+print("HOLO_FAST_PARITY_DONE", flush=True)