From b83947778e2c776f757a07d4719b7ce961d7ed55 Mon Sep 17 00:00:00 2001
From: Yuren Hao <yurenh2@illinois.edu>
Date: Fri, 3 Jul 2026 05:56:50 -0500
Subject: =?UTF-8?q?Initial=20commit:=20ept=20=E2=80=94=20backprop-free=20e?=
 =?UTF-8?q?quilibrium=20transformer=20(EP)?=
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Code (ep_run/), organized docs (docs/{method,campaign,hardware,outreach,paper}),
analysis scripts (scripts/), ONBOARDING.md entry point. Large data/checkpoints
git-ignored (share separately).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014FAPDWQ49M5Ye3NpTndTpn
---
 ep_run/knockout_s3200.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 ep_run/knockout_s3200.py

(limited to 'ep_run/knockout_s3200.py')

diff --git a/ep_run/knockout_s3200.py b/ep_run/knockout_s3200.py
new file mode 100644
index 0000000..1c05ca4
--- /dev/null
+++ b/ep_run/knockout_s3200.py
@@ -0,0 +1,27 @@
+import torch, pickle, math
+from pathlib import Path
+import lt_ep_train as L
+from lt_ep_train import EQBlock
+L.DD=Path('data/tinystories_bpe'); L.vocab=pickle.load(open(L.DD/'meta.pkl','rb'))['vocab_size']
+dev='cuda'; eps=0.1; B=8; T=256; N=3000
+torch.manual_seed(1234); idx,y=L.get_batch('val',B,T); idx=idx.to(dev) if hasattr(idx,'to') else idx
+ck=torch.load('runs/redx_traj/s3200.pt',map_location=dev)
+def relax_floor(alpha):
+    blk=EQBlock(512,16,256,256,s=1.0,c=1.0,attn_mode='thick'); blk.qknorm=True
+    with torch.no_grad():
+        for p,w in zip(blk.allp,ck['allp']): p.copy_(w.to(dev))
+        blk.WO.mul_(alpha)                      # scale attention OUTPUT contribution (alpha=0 -> no attention)
+        xin=blk.embed(idx).detach(); z=xin.clone(); ress=[]
+        for t in range(N):
+            z2=z+eps*blk.force(z,xin).detach()
+            r=(z2-z).norm().item()/(z.norm().item()+1e-9); ress.append(r); z=z2
+            if not math.isfinite(r) or r>1e3: return ('DIVERGED',t,r,0)
+    tail=ress[-800:]
+    return (ress[149] if len(ress)>149 else None, ress[-1], min(tail), max(tail))
+print("=== knockout: scale attention output (WO*alpha) on redx s3200, eval_relax 3000 steps ===")
+print("alpha=1 is the cycling operator; if cycle dies (res->0, monotone) as alpha falls -> attention asymmetry drives it")
+for a in [1.0, 0.7, 0.4, 0.2, 0.0]:
+    r150,rlast,tmin,tmax=relax_floor(a)
+    osc = (tmax-tmin)
+    print(f"  alpha={a}: res(150)={r150:.3e}  res(3000)={rlast:.3e}  tail[min={tmin:.2e},max={tmax:.2e}] osc={osc:.2e}  {'CYCLE' if osc>1e-3 and rlast>1e-3 else 'converged' if rlast<1e-3 else 'floored'}")
+print("=== DONE ===")
-- 
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