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import torch, math, pickle
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=800
torch.manual_seed(1234); idx,y=L.get_batch('val',B,T)
idx=idx.to(dev) if hasattr(idx,'to') else idx
def measure_c(ckpt,c):
blk=EQBlock(512,16,256,256,s=1.0,c=c,attn_mode='thick'); blk.qknorm=True
ck=torch.load(ckpt,map_location=dev)
with torch.no_grad():
for p,w in zip(blk.allp,ck['allp']): p.copy_(w.to(dev))
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>1e2: break
win=[ress[i] for i in range(len(ress)) if 1e-6<ress[i]<1e-1] or ress[-200:]
rats=[win[i+1]/win[i] for i in range(len(win)-1) if win[i]>0]
rho=math.exp(sum(math.log(x) for x in rats)/len(rats)) if rats else float('nan')
return rho, ress[-1]
print("=== rho vs damping c — does more c pull the operator off the rho=1 threshold? ===")
print("(weights trained at c=1; this is eval-time c — a margin indicator, not the trained answer)")
for ck,lab in [('runs/redx_traj/s3200.pt','redx-s3200 (val2.74, marginal)'),('runs/bptt_final.pt','BPTT (1.83)')]:
for c in [1.0,1.5,2.0,3.0,4.0]:
try: rho,fr=measure_c(ck,c); print(f" {lab} c={c}: rho={rho:.4f} final_res={fr:.2e}")
except Exception as e: print(f" {lab} c={c}: ERR {repr(e)[:60]}")
print("=== DONE ===")
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