"""THE AGGREGATE SPEED BENCH: full ep_step wall-time for every combination of the speed levers, on a
quiet local GPU, warm s2000 operator, B24 (production shape). Configs:
  base           : orig track, t2sel40, eager, manual attn      (the historical default)
  hf             : +holofast
  sd             : +sdpa (eager relax)
  hf+sd          : the exact-math pack at t2sel40
  hf+sd+t80      : the accuracy pack (cos 0.89->0.94)
  hf+sd+t80+avg  : + holoavg (trend/plateau estimator)
  cmp            : --compile alone (manual attn in graph)
  cmp(sdpa)+hf+t80+avg : FULL STACK (flash baked into compiled graph)
Reports median full-step time of 3 (after 1 warmup step each) + the step's res, as parity smoke."""
import time, torch
import lt_ep_train as L

torch.manual_seed(0)
blk = L.EQBlock(512, 16, 256, 256, c=1.0, attn_mode='thick'); blk.qknorm = True
ck = torch.load('runs/redx_traj/s2000.pt', map_location=L.dev)
with torch.no_grad():
    for p, w in zip(blk.allp, ck['allp']):
        p.copy_(w.to(L.dev))
blk.track = True
torch.manual_seed(42)
idx, y = L.get_batch('train', 24, 256)

CFG = [
    ('base',            dict(hf=False, sd=False, t2=40,  avg=False, cmp=False)),
    ('hf',              dict(hf=True,  sd=False, t2=40,  avg=False, cmp=False)),
    ('sd',              dict(hf=False, sd=True,  t2=40,  avg=False, cmp=False)),
    ('hf+sd',           dict(hf=True,  sd=True,  t2=40,  avg=False, cmp=False)),
    ('hf+sd+t80',       dict(hf=True,  sd=True,  t2=80,  avg=False, cmp=False)),
    ('hf+sd+t80+avg',   dict(hf=True,  sd=True,  t2=80,  avg=True,  cmp=False)),
    ('cmp',             dict(hf=False, sd=False, t2=40,  avg=False, cmp=True)),
    ('FULL(cmp_sdpa)',  dict(hf=True,  sd=True,  t2=80,  avg=True,  cmp=True)),
]

for name, c in CFG:
    blk.holofast, blk.sdpa, blk.holoavg = c['hf'], c['sd'], c['avg']
    blk._cstep = None
    if c['cmp']:
        _tf = blk.tforce_sdpa if c['sd'] else blk.tforce
        blk._cstep = torch.compile(lambda z, xin, _tf=_tf: z + 0.1 * _tf(z, xin))
    ts = []
    for rep in range(4):                                   # rep 0 = warmup (compile/JIT)
        torch.cuda.synchronize(); t = time.time()
        _, res = L.ep_step(blk, idx, y, 150, 20, 0.1, 0.02, jacreg=0.1, holo=2, hr=0.02,
                           t1max=300, res_est=1e-4, t2sel=c['t2'], corr_every=1, res_gate=0.0, resreg=0.2)
        torch.cuda.synchronize(); ts.append(time.time() - t)
    med = sorted(ts[1:])[1]
    print(f"{name:>16}: {med:6.2f}s/step  (res {res:.1e})", flush=True)
blk._cstep = None
print("AGG_BENCH_DONE", flush=True)