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import torch, 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'; B=8; T=256
torch.manual_seed(1234); idx,y=L.get_batch('val',B,T); idx=idx.to(dev) if hasattr(idx,'to') else idx
CK={'s3200':torch.load('runs/redx_traj/s3200.pt',map_location=dev),
's2000':torch.load('runs/redx_traj/s2000.pt',map_location=dev)}
def mkblk(name):
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[name]['allp']): p.copy_(w.to(dev))
return blk
def fg(blk,z,xin):
f=blk.force(z,xin).detach(); return f, f.norm().item()/(z.norm().item()+1e-9)
# corrected controller: shrink on OVERSHOOT (g rose), grow otherwise
def run(name, e0=0.05, emin=0.003, emax=0.1, up=1.05, down=0.7, tol=1.0, N=8000):
blk=mkblk(name)
with torch.no_grad():
xin=blk.embed(idx).detach(); z=xin.clone(); eps=e0; prev=None; gs=[]; eh=[]
for t in range(N):
f,g=fg(blk,z,xin); gs.append(g); eh.append(eps)
if prev is not None:
if g > prev*tol: eps=max(emin, eps*down) # residual climbed -> eps too big
else: eps=min(emax, eps*up) # contracting -> grow for speed
prev=g; z=z+eps*f
tail=gs[-500:]
return dict(gmin=min(tail), gmean=sum(tail)/len(tail), avg_eps=sum(eh)/len(eh), final_eps=eh[-1])
print("=== corrected adaptive-eps (shrink on OVERSHOOT) — calibrate on stiff + smooth ===")
print("target: s3200 converges (g~0.09) at avg_eps>0.005 (faster than naive); s2000 stays eps~0.1")
for name in ('s3200','s2000'):
print(f"-- {name} --")
for tag,kw in [("A up1.05 dn0.7", dict(up=1.05,down=0.7)),
("B up1.1 dn0.5", dict(up=1.1,down=0.5)),
("C up1.03 dn0.8 tol1.02", dict(up=1.03,down=0.8,tol=1.02))]:
r=run(name,**kw)
print(f" {tag}: g_tail[min={r['gmin']:.4f} mean={r['gmean']:.4f}] avg_eps={r['avg_eps']:.4f} final_eps={r['final_eps']:.4f}")
print("=== DONE ===")
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