import torch, time, math
import lt_ep_train as LT
torch.manual_seed(0)
blk=LT.EQBlock(512,16,256,256,c=1.0,attn_mode='thick'); blk.qknorm=True; blk.track=True; blk.navg=1; blk.li_avg=0
ck=torch.load('runs/ep_resreg_warm.pt',map_location='cuda')
with torch.no_grad():
    for p,s in zip(blk.allp,ck['allp']): p.copy_(s.to('cuda'))
idx,y=LT.get_batch('train',8,256)
base=dict(T1=150,T2=20,eps=0.1,beta=0.02,jacreg=0.1,holo=2,hr=0.2,t2sel=80,t1max=150,res_est=1e-4,resreg=0.2)
g32,_=LT.ep_step(blk,idx,y,**base)
def cos(ga):
    n=da=db=0.0
    for p in blk.block:
        a=ga.get(id(p)); b=g32.get(id(p))
        if a is None or b is None: continue
        a=a.float(); b=b.float(); n+=float((a*b).sum()); da+=float((a*a).sum()); db+=float((b*b).sum())
    return round(n/(math.sqrt(da*db)+1e-20),4)
def T(fn,reps=2):
    fn(); torch.cuda.synchronize(); t0=time.time()
    for _ in range(reps): fn()
    torch.cuda.synchronize(); return round((time.time()-t0)/reps*1000)
fp32ms=T(lambda: LT.ep_step(blk,idx,y,**base)); print("fp32:",fp32ms,"ms",flush=True)
def trial(name,**ac):
    try:
        def run():
            with torch.autocast('cuda',dtype=torch.bfloat16,**ac): return LT.ep_step(blk,idx,y,**base)[0]
        g=run(); print(f"{name:22s} OK  cos={cos(g)}  ms={T(run)}  (fp32={fp32ms})",flush=True)
    except Exception as e: print(f"{name:22s} FAIL: {type(e).__name__}: {str(e)[:80]}",flush=True)
trial("cache_enabled=False", cache_enabled=False)
print("DONE",flush=True)