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import torch, glob, re, 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) # FIXED batch across all ckpts
idx, y = L.get_batch('val', B, T)
idx = idx.to(dev) if hasattr(idx,'to') else idx
def measure(ckpt):
blk = EQBlock(512,16,256,256, s=1.0, c=1.0, 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], len(ress)
valmap={}
for l in open('runs/ep_redx.log'):
if l.startswith('step'):
m=re.search(r'step\s+(\d+)/.*val CE ([\d.]+)', l)
if m: valmap[int(m.group(1))]=m.group(2)
print("=== BIFURCATION PROBE — free-phase contraction ratio rho (fixed batch, 800 relax steps) ===")
print("rho<1 contractive | rho->1 marginal | rho>=1 divergent ; final_res = floor reached")
r,fr,n = measure('runs/bptt_final.pt'); print(f"REF BPTT(val~1.83): rho={r:.4f} final_res={fr:.2e} steps={n}")
print("redx approach to blowup:")
for ck in sorted(glob.glob('runs/redx_traj/s*.pt'), key=lambda p:int(re.search(r's(\d+)',p).group(1))):
step=int(re.search(r's(\d+)',ck).group(1))
try: r,fr,n = measure(ck); print(f" step {step:5d} (val {valmap.get(step,'?')}): rho={r:.4f} final_res={fr:.2e} steps={n}")
except Exception as e: print(f" step {step:5d}: ERR {repr(e)[:80]}")
print("=== DONE ===")
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