cuerdas / 017 /run017.py

Cuerdas 013-018

5851d4e verified 13 days ago

20 kB

# CUERDAS-015: transferencia (valores/consultas nuevas), vocabulario aleatorizado, K=1 vs K=4. CPU.
import json,random,time,re,sys,statistics as st,itertools
import torch,torch.nn as nn,torch.nn.functional as F
from transformers import AutoModelForCausalLM,AutoTokenizer,AutoConfig
torch.set_num_threads(8)
import os
SRC=os.environ.get('SRC','/workspace/cuerdas014/x/cuerdas014'); MP=os.environ.get('MP','/workspace/cuerdas014/x/models/smollm2-135m-instruct'); OD=os.environ.get('OD','/workspace/cuerdas015')
DEV='cuda' if torch.cuda.is_available() else 'cpu'
tok=AutoTokenizer.from_pretrained(MP); lm=AutoModelForCausalLM.from_pretrained(MP,torch_dtype=torch.float32).eval().requires_grad_(False).to(DEV)
if DEV=='cuda': torch.set_default_device('cuda')
Emb=lm.get_input_embeddings().weight; D=Emb.shape[1]
meta=json.load(open(f'{SRC}/data/expanded.json')); V=meta['vocabulary']; FIELDS=['action','target','status','window']
rows=meta['rows']; SPL=meta['splits']
TPL="The action is {action}. The target is {target}. The status is {status}. The window is {window}."
SUF_MAIN="\nAccording to the record, the {field} is"
SUF_TRAIN=[SUF_MAIN,"\nThe {field} is","\nIn this record, the {field} is","\nQuestion: what is the {field}? Answer:"]
SUF_HELD={"recorded":"\nThe recorded {field} is","value":"\nThe {field} value is","past":"\nThe {field} was"}
def t1(w): return tok.encode(' '+w,add_special_tokens=False)
# ---- vocabularios nuevos (nunca en entrenamiento) ----
NEW={'action':['inspect','repair','cancel','approve','archive','scan','print','send'],
     'target':['sensor','battery','printer','server','truck','door','screen','cable'],
     'status':['broken','stable','late','empty','full','warm','missing','urgent'],
     'window':['night','evening','weekend','noon','spring','summer','winter','midnight']}
PHRASE={'action':['shut down','back up','sign off','log out'],'target':['air filter','fuel tank','water heater','hard drive'],
        'status':['out of stock','on hold','under review','half full'],'window':['next week','last night','this month','later today']}
# limpiar: primer token único dentro de (vocab original ∪ nuevos ∪ frases) por campo
for j,f in enumerate(FIELDS):
    used={t1(w)[0] for w in V[j]}; keep=[];keepp=[]
    for w in NEW[f]:
        if len(t1(w))==1 and t1(w)[0] not in used and w not in V[j]: keep.append(w); used.add(t1(w)[0])
    for w in PHRASE[f]:
        if len(t1(w))>1 and t1(w)[0] not in used: keepp.append(w); used.add(t1(w)[0])
    NEW[f]=keep; PHRASE[f]=keepp
BANNED={t1(w)[0] for f in FIELDS for w in NEW[f]+PHRASE[f]}|{t1(w)[0] for vs in V for w in vs}
# pool de palabras aleatorias para entrenamiento (meta-aprendizaje de binding)
POOL=[]
for t,i in tok.get_vocab().items():
    if t.startswith('Ġ') and re.fullmatch(r'[a-z]{3,9}',t[1:]) and i not in BANNED: POOL.append(t[1:])
random.Random(0).shuffle(POOL); POOL_VAL=POOL[3000:3200]; POOL=POOL[:3000]
# ---- splits: los de 014 + test FRESCO nuevo del universo no usado ----
used_vals={tuple(r['values']) for r in rows}
fit=[tuple(rows[i]['values']) for i in SPL['fit']]; seen_at={v[:2] for v in fit}; seen_sw={v[2:] for v in fit}
rng=random.Random(15015); uni=[v for v in itertools.product(*[range(len(x)) for x in V]) if v not in used_vals]; rng.shuffle(uni)
fresh_id=[v for v in uni if v[:2] in seen_at and v[2:] in seen_sw][:200]; fresh_ood=[v for v in uni if not(v[:2] in seen_at and v[2:] in seen_sw)][:400]
def W(v): return [V[j][v[j]] for j in range(4)]
FIT=[W(v) for v in fit]; VAL=[W(tuple(rows[i]['values'])) for i in SPL['validation']]
TEST014={'id':[W(tuple(rows[i]['values'])) for i in SPL['id_test']],'ood':[W(tuple(rows[i]['values'])) for i in SPL['ood_test']]}
FRESH={'id':[W(v) for v in fresh_id],'ood':[W(v) for v in fresh_ood]}
r2=random.Random(77)
def novel_set(nnew,n=96,phr=False):  # nnew campos con valor nuevo (los demás del vocab), tomados de FRESH
    out=[]
    for k in range(n):
        base=list(FRESH['ood'][k%len(FRESH['ood'])]); js=r2.sample(range(4),nnew)
        for j in js: base[j]=r2.choice(PHRASE[FIELDS[j]] if phr else NEW[FIELDS[j]])
        out.append(base)
    return out
TRANSFER={'1_new':novel_set(1),'2_new':novel_set(2),'4_new':novel_set(4),'1_phrase':novel_set(1,phr=True)}
CAND=[sorted({t1(w)[0] for w in V[j]+NEW[f]+PHRASE[f]}) for j,f in enumerate(FIELDS)]  # opciones válidas: abiertas (vocab+nuevos)
CAND=[torch.tensor(c) for c in CAND]
def lex(w): return Emb[t1(w)].mean(0)
def gold_idx(j,w): return int((CAND[j]==t1(w)[0]).nonzero()[0])
# ---- codecs ----
class Binder(nn.Module):  # K=4: ranura por campo, binding de cualquier palabra a un rol
    def __init__(s,seed):
        super().__init__(); torch.manual_seed(seed); s.K=4
        anc=torch.stack([Emb[t1(f)[0]] for f in FIELDS]); s.roles=nn.Parameter(anc.clone())
        s.net=nn.Sequential(nn.LayerNorm(D),nn.Linear(D,128),nn.GELU(),nn.Linear(128,D)); nn.init.normal_(s.net[-1].weight,std=.002); nn.init.zeros_(s.net[-1].bias)
        s.register_buffer('sc',Emb.std().clone())
    def forward(s,L): c=L+s.roles[None]; return c+s.sc*s.net(c)        # L: [B,4,D]
class Pack1(nn.Module):  # K=1: empaqueta los 4 campos en UNA posición
    def __init__(s,seed):
        super().__init__(); torch.manual_seed(seed); s.K=1
        s.roles=nn.Parameter(torch.randn(4,D)*.02)
        s.net=nn.Sequential(nn.LayerNorm(4*D),nn.Linear(4*D,512),nn.GELU(),nn.Linear(512,D)); s.register_buffer('sc',Emb.std().clone())
        s.mix=nn.Parameter(torch.full((4,),.25))
    def forward(s,L):
        c=L+s.roles[None]; base=(s.mix[None,:,None]*c).sum(1)
        return (base+s.sc*s.net(c.flatten(1)))[:,None]
class Their014(nn.Module):  # su RoleAdapter, con léxico extensible (mismo cálculo que su forward)
    def __init__(s,seed):
        super().__init__(); ck=torch.load(f'{SRC}/checkpoints/expanded_role_ce_k4_seed{seed}/best.pt',map_location=DEV,weights_only=False)['adapter_state']
        s.K=4; s.roles=nn.Parameter(ck['roles']); s.sc=ck['scale']
        s.net=nn.Sequential(nn.LayerNorm(D),nn.Linear(D,128),nn.GELU(),nn.Linear(128,D)); s.net.load_state_dict({k[4:]:v for k,v in ck.items() if k.startswith('net.')})
    def forward(s,L): c=L+s.roles[None]; return c+s.sc*s.net(c)
# ---- lectura por el LLM congelado ----
def sufids(tpl,j): return tok.encode(tpl.format(field=FIELDS[j]),add_special_tokens=False)
def run_rows(embs,tails):  # listas de tensores [L,D] -> logits última posición (padding a la derecha)
    L=max(e.shape[0] for e in embs); B=len(embs)
    x=torch.zeros(B,L,D); m=torch.zeros(B,L,dtype=torch.long)
    for i,e in enumerate(embs): x[i,:e.shape[0]]=e; m[i,:e.shape[0]]=1
    h=lm.model(inputs_embeds=x,attention_mask=m).last_hidden_state[torch.arange(B),m.sum(1)-1]
    return lm.lm_head(h)
def codec_logits(cod,msgs,tpl,js=range(4)):
    S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs]))
    embs=[];meta_=[]
    for i,m in enumerate(msgs):
        for j in js: embs.append(torch.cat([S[i],Emb[sufids(tpl,j)]])); meta_.append((i,j))
    return run_rows(embs,None),meta_
def text_logits(msgs,tpl):
    embs=[];meta_=[]
    for i,m in enumerate(msgs):
        for j in range(4): embs.append(Emb[tok.encode(TPL.format(**dict(zip(FIELDS,m)))+tpl.format(field=FIELDS[j]),add_special_tokens=False)]); meta_.append((i,j))
    return run_rows(embs,None),meta_
@torch.no_grad()
def acc(route,msgs,tpl=SUF_MAIN,cod=None,bs=24):
    ok=[]
    for b in range(0,len(msgs),bs):
        mb=msgs[b:b+bs]; lg,mt=(text_logits(mb,tpl) if route=='text' else codec_logits(cod,mb,tpl))
        good=torch.ones(len(mb),dtype=torch.bool)
        for r,(i,j) in enumerate(mt): good[i]&=int(lg[r][CAND[j]].argmax())==gold_idx(j,mb[i][j])
        ok+=good.tolist()
    return sum(ok)/len(ok)
@torch.no_grad()
def nolm(cod,msgs):  # anti-lookup: decodificar valor desde las ranuras por coseno, sin LLM
    S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs])); ok=0
    for i,m in enumerate(msgs):
        g=True
        for j in range(4):
            cands=F.normalize(Emb[CAND[j]],dim=-1); s=S[i,j] if S.shape[1]==4 else S[i].mean(0)
            g&=int((F.normalize(s,dim=-1)@cands.T).argmax())==gold_idx(j,m[j])
        ok+=g
    return ok/len(msgs)
def train(cod,steps,seed,p_rand=.5,p_phr=.15,bs=12):
    g=random.Random(seed); opt=torch.optim.AdamW(cod.parameters(),lr=2e-3,weight_decay=1e-3)
    for s in range(steps):
        msgs=[]
        for _ in range(bs):
            m=list(g.choice(FIT))
            for j in range(4):
                u=g.random()
                if u<p_phr: m[j]=g.choice(POOL)+' '+g.choice(POOL)
                elif u<p_phr+p_rand: m[j]=g.choice(POOL)
            msgs.append(m)
        tpl=g.choice(SUF_TRAIN); lg,mt=codec_logits(cod,msgs,tpl)
        gold=torch.tensor([t1(msgs[i][j])[0] for i,j in mt]); loss=F.cross_entropy(lg,gold)  # CE sobre TODO el vocabulario: copiar la palabra ligada al rol
        opt.zero_grad(); loss.backward(); opt.step()
    return cod.eval()
OUT=OD+'/results017.jsonl'
def log(d): open(OUT,'a').write(json.dumps(d)+"\n"); print(json.dumps(d),flush=True)
def evaluate_all(name,cod=None,quick=False):
    route='text' if cod is None else 'codec'; r={'name':name}
    r['val']=acc(route,VAL[:96],cod=cod)
    if not quick:
        for k,v in FRESH.items(): r['fresh_'+k]=acc(route,v,cod=cod)
        for k,v in TRANSFER.items(): r['transfer_'+k]=acc(route,v,cod=cod)
        sub=FRESH['id'][:48]+FRESH['ood'][:48]
        for k,t in SUF_HELD.items(): r['query_'+k]=acc(route,sub,t,cod=cod)
        if cod is not None: r['noLLM_fresh_ood']=nolm(cod,FRESH['ood'][:200]); r['noLLM_transfer_4new']=nolm(cod,TRANSFER['4_new'])
    log(r); return r

def _branch(cache):
    if hasattr(cache,'to_legacy_cache'): cache=cache.to_legacy_cache()
    return tuple((k.repeat_interleave(4,0),v.repeat_interleave(4,0)) for k,v in cache)
@torch.no_grad()
def one_event(route,m,cod,dev):
    tails=[sufids(SUF_MAIN,j) for j in range(4)]; sn=0
    while all(t[sn]==tails[0][sn] for t in tails): sn+=1
    if route=='text':
        pre=Emb[tok.encode(TPL.format(**dict(zip(FIELDS,m))),add_special_tokens=False)+tails[0][:sn]][None]
    else:
        pre=torch.cat([cod(torch.stack([lex(w) for w in m])[None].to(dev))[0],Emb[tails[0][:sn]]])[None]
    o=lm.model(inputs_embeds=pre,use_cache=True)
    h=lm.model(input_ids=torch.tensor([t[sn:] for t in tails],device=dev),past_key_values=_branch(o.past_key_values),use_cache=True).last_hidden_state[:,-1]
    out=[int(lm.lm_head(h)[j][CAND[j].to(dev)].argmax()) for j in range(4)]
    return pre.shape[1]+4*len(tails[0][sn:])
def latency(cod,dev,n=40,reps=3):
    sync=(lambda: torch.cuda.synchronize()) if dev!='cpu' else (lambda:None)
    ev=FRESH['ood'][:n]; T={'text':[],'codec':[]}; P={}
    for m in ev[:5]: one_event('text',m,cod,dev); one_event('codec',m,cod,dev)
    rr=random.Random(3)
    for _ in range(reps):
        for m in ev:
            for r in rr.sample(['text','codec'],2):
                sync(); t0=time.perf_counter(); P[r]=one_event(r,m,cod,dev); sync(); T[r].append(1e3*(time.perf_counter()-t0))
    q=lambda x:sorted(x)[int(.9*len(x))]
    return {k:dict(median_ms=st.median(v),p90_ms=q(v),positions=P[k]) for k,v in T.items()}

class PackK(nn.Module):  # superposición aprendida: ranura_k = sum_j a_kj (e_j + U_j V_j^T e_j + rol_j) + MLP(concat)
    def __init__(s,K,seed,r=64):
        super().__init__(); torch.manual_seed(seed); s.K=K
        s.roles=nn.Parameter(torch.randn(4,D)*.02); s.U=nn.Parameter(torch.randn(4,D,r)*.01); s.V=nn.Parameter(torch.randn(4,D,r)*.01)
        s.a=nn.Parameter(torch.full((K,4),1.0/4*K if K<4 else 1.0))
        s.net=nn.Sequential(nn.LayerNorm(4*D),nn.Linear(4*D,1024),nn.GELU(),nn.Linear(1024,K*D)); nn.init.normal_(s.net[-1].weight,std=.002); nn.init.zeros_(s.net[-1].bias)
        s.register_buffer('sc',Emb.std().clone())
    def forward(s,L):
        c=L+torch.einsum('bjd,jdr,jer->bje',L,s.V,s.U)+s.roles[None]
        base=torch.einsum('kj,bjd->bkd',s.a,c)
        return base+s.sc*s.net(c.flatten(1)).view(-1,s.K,D)
def train_kd(cod,steps,seed,p_rand=.3,bs=12,lr=1e-3):
    g=random.Random(seed); opt=torch.optim.AdamW(cod.parameters(),lr=lr,weight_decay=1e-3)
    sch=torch.optim.lr_scheduler.OneCycleLR(opt,max_lr=lr,total_steps=steps,pct_start=.05)
    for s_ in range(steps):
        msgs=[]
        for _ in range(bs):
            m=list(g.choice(FIT))
            for j in range(4):
                if g.random()<p_rand: m[j]=g.choice(POOL)
            msgs.append(m)
        tpl=g.choice(SUF_TRAIN)
        S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs]))
        embs=[];hs=[];tg=[]
        for i,m in enumerate(msgs):
            for j in range(4): embs.append(torch.cat([S[i],Emb[sufids(tpl,j)]])); tg.append(t1(m[j])[0])
        with torch.no_grad():
            te=[Emb[tok.encode(TPL.format(**dict(zip(FIELDS,m)))+tpl.format(field=FIELDS[j]),add_special_tokens=False)] for m in msgs for j in range(4)]
            Lt=max(e.shape[0] for e in te); x=torch.zeros(len(te),Lt,D); mk=torch.zeros(len(te),Lt,dtype=torch.long)
            for i,e in enumerate(te): x[i,:e.shape[0]]=e; mk[i,:e.shape[0]]=1
            ht=lm.model(inputs_embeds=x,attention_mask=mk).last_hidden_state[torch.arange(len(te)),mk.sum(1)-1]; lt=lm.lm_head(ht)
        L=max(e.shape[0] for e in embs); x=torch.zeros(len(embs),L,D); mk=torch.zeros(len(embs),L,dtype=torch.long)
        for i,e in enumerate(embs): x[i,:e.shape[0]]=e; mk[i,:e.shape[0]]=1
        hsd=lm.model(inputs_embeds=x,attention_mask=mk).last_hidden_state[torch.arange(len(embs)),mk.sum(1)-1]; ls=lm.lm_head(hsd)
        loss=F.cross_entropy(ls,torch.tensor(tg))+F.kl_div(F.log_softmax(ls,-1),F.log_softmax(lt,-1),log_target=True,reduction='batchmean')+.5*(1-F.cosine_similarity(hsd,ht,-1).mean())
        opt.zero_grad(); loss.backward(); torch.nn.utils.clip_grad_norm_(cod.parameters(),1.0); opt.step(); sch.step()
        if s_%500==0: print(json.dumps({'step':s_,'loss':float(loss)}),flush=True)
    return cod.eval()
@torch.no_grad()
def probe(cod):  # anti-lookup fuerte: sonda lineal (ridge, sin LLM) entrenada en slots de FIT, evaluada en FRESH ood y 4_new
    def slots(ms): return cod(torch.stack([torch.stack([lex(w) for w in m]) for m in ms])).flatten(1)
    Xtr=torch.cat([slots(FIT[i:i+100]) for i in range(0,len(FIT),100)]); res={}
    for name,ms in (('fresh_ood',FRESH['ood']),('transfer_4_new',TRANSFER['4_new'])):
        Xte=slots(ms); ok=torch.ones(len(ms),dtype=torch.bool,device='cpu')
        for j in range(4):  # regresión al embedding del valor, decodificación por coseno contra opciones (abierta a palabras nuevas)
            Y=torch.stack([lex(m[j]) for m in FIT]); A=torch.linalg.solve(Xtr.T@Xtr+10*torch.eye(Xtr.shape[1]),Xtr.T@Y)
            P=F.normalize(Xte@A,dim=-1)@F.normalize(Emb[CAND[j]],dim=-1).T
            ok&=P.argmax(1).cpu()==torch.tensor([gold_idx(j,m[j]) for m in ms],device='cpu')
        res['probe_'+name]=float(ok.float().mean())
    return res

rv=random.Random(99)
VAL_T=[[rv.choice(POOL_VAL) for j in range(4)] for _ in range(64)]   # validación de transferencia: palabras nunca usadas en entrenamiento ni en test
CAND_FULL=None
@torch.no_grad()
def acc_open(cod,msgs,bs=16):  # exactitud sobre TODO el vocabulario (palabras de validación no están en CAND)
    ok=[]
    for b in range(0,len(msgs),bs):
        mb=msgs[b:b+bs]; lg,mt=codec_logits(cod,mb,SUF_MAIN); good=torch.ones(len(mb),dtype=torch.bool)
        for r,(i,j) in enumerate(mt): good[i]&=int(lg[r].argmax())==t1(mb[i][j])[0]
        ok+=good.tolist()
    return sum(ok)/len(ok)
def readout_h(cod,msgs,tpl):
    S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs])); embs=[]
    for i,m in enumerate(msgs):
        for j in range(4): embs.append(torch.cat([S[i],Emb[sufids(tpl,j)]]))
    L=max(e.shape[0] for e in embs); x=torch.zeros(len(embs),L,D); mk=torch.zeros(len(embs),L,dtype=torch.long)
    for i,e in enumerate(embs): x[i,:e.shape[0]]=e; mk[i,:e.shape[0]]=1
    h=lm.model(inputs_embeds=x,attention_mask=mk).last_hidden_state[torch.arange(len(embs)),mk.sum(1)-1]; return h,lm.lm_head(h)
def distill(stu,teach,steps,seed,tagname,bs=12,lr=1e-3,every=250):
    g=random.Random(seed); opt=torch.optim.AdamW(stu.parameters(),lr=lr,weight_decay=1e-3)
    sch=torch.optim.lr_scheduler.OneCycleLR(opt,max_lr=lr,total_steps=steps,pct_start=.05); best=(-1,None,0)
    for s_ in range(1,steps+1):
        msgs=[]
        for _ in range(bs):
            m=list(g.choice(FIT))
            for j in range(4):
                u=g.random()
                if u<.1: m[j]=g.choice(POOL)+' '+g.choice(POOL)
                elif u<.5: m[j]=g.choice(POOL)
            msgs.append(m)
        tpl=g.choice(SUF_TRAIN)
        with torch.no_grad(): ht,lt=readout_h(teach,msgs,tpl)
        hs,ls=readout_h(stu,msgs,tpl); tg=torch.tensor([t1(m[j])[0] for m in msgs for j in range(4)])
        loss=F.cross_entropy(ls,tg)+F.kl_div(F.log_softmax(ls,-1),F.log_softmax(lt,-1),log_target=True,reduction='batchmean')+.5*(1-F.cosine_similarity(hs,ht,-1).mean())
        opt.zero_grad(); loss.backward(); torch.nn.utils.clip_grad_norm_(stu.parameters(),1.0); opt.step(); sch.step()
        if s_%every==0:
            stu.eval(); v1=acc('codec',VAL[:96],cod=stu); v2=acc_open(stu,VAL_T); stu.train()
            sc=v1+v2; log({'run':tagname,'step':s_,'val_contract':v1,'val_transfer_open':v2})
            if sc>best[0]: best=(sc,{k:v.detach().clone() for k,v in stu.state_dict().items()},s_)
    stu.load_state_dict(best[1]); stu.eval(); return stu,best[2]
def grow_down(src,K):  # inicializar K pequeño desde K mayor (curriculum)
    dst=PackK(K,0); sd=src.state_dict(); dd=dst.state_dict()
    for k in ('roles','U','V','net.0.weight','net.0.bias','net.1.weight','net.1.bias','sc'): dd[k]=sd[k].clone()
    Ks=src.K; dd['a']=sd['a'].view(K,Ks//K,4).sum(1) if Ks%K==0 else sd['a'].mean(0,keepdim=True).expand(K,4).clone()
    W=sd['net.3.weight'].view(Ks,D,-1); b=sd['net.3.bias'].view(Ks,D)
    dd['net.3.weight']=W.view(K,Ks//K,D,-1).sum(1).reshape(K*D,-1); dd['net.3.bias']=b.view(K,Ks//K,D).sum(1).reshape(-1)
    dst.load_state_dict(dd); return dst
@torch.no_grad()
def batched_latency(cod,bsz,reps=5):
    sync=(lambda: torch.cuda.synchronize()) if DEV=='cuda' else (lambda:None)
    ms=FRESH['ood'][:bsz]; res={}
    for route in ('text','codec'):
        for _ in range(2): (text_logits(ms,SUF_MAIN) if route=='text' else codec_logits(cod,ms,SUF_MAIN))
        T=[]
        for _ in range(reps):
            sync(); t0=time.perf_counter(); (text_logits(ms,SUF_MAIN) if route=='text' else codec_logits(cod,ms,SUF_MAIN)); sync(); T.append(1e3*(time.perf_counter()-t0))
        res[route]=dict(median_ms=st.median(T),max_ms=max(T))
    return res
if __name__=='__main__':
    INP=os.environ.get('INP',''); seeds=[int(x) for x in os.environ.get('SEEDS','17,29,43').split(',')]
    for sd in seeds:
        t0=time.time(); teach=Binder(sd); teach.load_state_dict(torch.load(f'{INP}/binder4_1000_s{sd}.pt',map_location=DEV)); teach.eval().requires_grad_(False)
        k2=PackK(2,sd); k2,b2=distill(k2,teach,int(os.environ.get('S2',1000)),sd,f'k2_s{sd}'); torch.save(k2.state_dict(),f'{OD}/k2_s{sd}.pt')
        r=evaluate_all(f'k2_s{sd}',k2); log({'name':r['name'],'best_step':b2,'val_transfer_open':acc_open(k2,VAL_T),**probe(k2)})
        k1=grow_down(k2,1); k1,b1=distill(k1,teach,int(os.environ.get('S1',1500)),sd,f'k1_s{sd}'); torch.save(k1.state_dict(),f'{OD}/k1_s{sd}.pt')
        r=evaluate_all(f'k1_s{sd}',k1); log({'name':r['name'],'best_step':b1,'val_transfer_open':acc_open(k1,VAL_T),'train_s':round(time.time()-t0),**probe(k1)})
        if sd==seeds[0]:
            for nm,c in (('k2',k2),('k1',k1)):
                log({'latency1':nm,'gpu':torch.cuda.get_device_name() if DEV=='cuda' else 'cpu',**latency(c,DEV)})
                for bz in (16,64): log({'latency_batch':nm,'batch':bz,'gpu':torch.cuda.get_device_name() if DEV=='cuda' else 'cpu',**batched_latency(c,bz)})

	# CUERDAS-015: transferencia (valores/consultas nuevas), vocabulario aleatorizado, K=1 vs K=4. CPU.
	import json,random,time,re,sys,statistics as st,itertools
	import torch,torch.nn as nn,torch.nn.functional as F
	from transformers import AutoModelForCausalLM,AutoTokenizer,AutoConfig
	torch.set_num_threads(8)
	import os
	SRC=os.environ.get('SRC','/workspace/cuerdas014/x/cuerdas014'); MP=os.environ.get('MP','/workspace/cuerdas014/x/models/smollm2-135m-instruct'); OD=os.environ.get('OD','/workspace/cuerdas015')
	DEV='cuda' if torch.cuda.is_available() else 'cpu'
	tok=AutoTokenizer.from_pretrained(MP); lm=AutoModelForCausalLM.from_pretrained(MP,torch_dtype=torch.float32).eval().requires_grad_(False).to(DEV)
	if DEV=='cuda': torch.set_default_device('cuda')
	Emb=lm.get_input_embeddings().weight; D=Emb.shape[1]
	meta=json.load(open(f'{SRC}/data/expanded.json')); V=meta['vocabulary']; FIELDS=['action','target','status','window']
	rows=meta['rows']; SPL=meta['splits']
	TPL="The action is {action}. The target is {target}. The status is {status}. The window is {window}."
	SUF_MAIN="\nAccording to the record, the {field} is"
	SUF_TRAIN=[SUF_MAIN,"\nThe {field} is","\nIn this record, the {field} is","\nQuestion: what is the {field}? Answer:"]
	SUF_HELD={"recorded":"\nThe recorded {field} is","value":"\nThe {field} value is","past":"\nThe {field} was"}
	def t1(w): return tok.encode(' '+w,add_special_tokens=False)
	# ---- vocabularios nuevos (nunca en entrenamiento) ----
	NEW={'action':['inspect','repair','cancel','approve','archive','scan','print','send'],
	'target':['sensor','battery','printer','server','truck','door','screen','cable'],
	'status':['broken','stable','late','empty','full','warm','missing','urgent'],
	'window':['night','evening','weekend','noon','spring','summer','winter','midnight']}
	PHRASE={'action':['shut down','back up','sign off','log out'],'target':['air filter','fuel tank','water heater','hard drive'],
	'status':['out of stock','on hold','under review','half full'],'window':['next week','last night','this month','later today']}
	# limpiar: primer token único dentro de (vocab original ∪ nuevos ∪ frases) por campo
	for j,f in enumerate(FIELDS):
	used={t1(w)[0] for w in V[j]}; keep=[];keepp=[]
	for w in NEW[f]:
	if len(t1(w))==1 and t1(w)[0] not in used and w not in V[j]: keep.append(w); used.add(t1(w)[0])
	for w in PHRASE[f]:
	if len(t1(w))>1 and t1(w)[0] not in used: keepp.append(w); used.add(t1(w)[0])
	NEW[f]=keep; PHRASE[f]=keepp
	BANNED={t1(w)[0] for f in FIELDS for w in NEW[f]+PHRASE[f]}\|{t1(w)[0] for vs in V for w in vs}
	# pool de palabras aleatorias para entrenamiento (meta-aprendizaje de binding)
	POOL=[]
	for t,i in tok.get_vocab().items():
	if t.startswith('Ġ') and re.fullmatch(r'[a-z]{3,9}',t[1:]) and i not in BANNED: POOL.append(t[1:])
	random.Random(0).shuffle(POOL); POOL_VAL=POOL[3000:3200]; POOL=POOL[:3000]
	# ---- splits: los de 014 + test FRESCO nuevo del universo no usado ----
	used_vals={tuple(r['values']) for r in rows}
	fit=[tuple(rows[i]['values']) for i in SPL['fit']]; seen_at={v[:2] for v in fit}; seen_sw={v[2:] for v in fit}
	rng=random.Random(15015); uni=[v for v in itertools.product(*[range(len(x)) for x in V]) if v not in used_vals]; rng.shuffle(uni)
	fresh_id=[v for v in uni if v[:2] in seen_at and v[2:] in seen_sw][:200]; fresh_ood=[v for v in uni if not(v[:2] in seen_at and v[2:] in seen_sw)][:400]
	def W(v): return [V[j][v[j]] for j in range(4)]
	FIT=[W(v) for v in fit]; VAL=[W(tuple(rows[i]['values'])) for i in SPL['validation']]
	TEST014={'id':[W(tuple(rows[i]['values'])) for i in SPL['id_test']],'ood':[W(tuple(rows[i]['values'])) for i in SPL['ood_test']]}
	FRESH={'id':[W(v) for v in fresh_id],'ood':[W(v) for v in fresh_ood]}
	r2=random.Random(77)
	def novel_set(nnew,n=96,phr=False): # nnew campos con valor nuevo (los demás del vocab), tomados de FRESH
	out=[]
	for k in range(n):
	base=list(FRESH['ood'][k%len(FRESH['ood'])]); js=r2.sample(range(4),nnew)
	for j in js: base[j]=r2.choice(PHRASE[FIELDS[j]] if phr else NEW[FIELDS[j]])
	out.append(base)
	return out
	TRANSFER={'1_new':novel_set(1),'2_new':novel_set(2),'4_new':novel_set(4),'1_phrase':novel_set(1,phr=True)}
	CAND=[sorted({t1(w)[0] for w in V[j]+NEW[f]+PHRASE[f]}) for j,f in enumerate(FIELDS)] # opciones válidas: abiertas (vocab+nuevos)
	CAND=[torch.tensor(c) for c in CAND]
	def lex(w): return Emb[t1(w)].mean(0)
	def gold_idx(j,w): return int((CAND[j]==t1(w)[0]).nonzero()[0])
	# ---- codecs ----
	class Binder(nn.Module): # K=4: ranura por campo, binding de cualquier palabra a un rol
	def __init__(s,seed):
	super().__init__(); torch.manual_seed(seed); s.K=4
	anc=torch.stack([Emb[t1(f)[0]] for f in FIELDS]); s.roles=nn.Parameter(anc.clone())
	s.net=nn.Sequential(nn.LayerNorm(D),nn.Linear(D,128),nn.GELU(),nn.Linear(128,D)); nn.init.normal_(s.net[-1].weight,std=.002); nn.init.zeros_(s.net[-1].bias)
	s.register_buffer('sc',Emb.std().clone())
	def forward(s,L): c=L+s.roles[None]; return c+s.sc*s.net(c) # L: [B,4,D]
	class Pack1(nn.Module): # K=1: empaqueta los 4 campos en UNA posición
	def __init__(s,seed):
	super().__init__(); torch.manual_seed(seed); s.K=1
	s.roles=nn.Parameter(torch.randn(4,D)*.02)
	s.net=nn.Sequential(nn.LayerNorm(4D),nn.Linear(4D,512),nn.GELU(),nn.Linear(512,D)); s.register_buffer('sc',Emb.std().clone())
	s.mix=nn.Parameter(torch.full((4,),.25))
	def forward(s,L):
	c=L+s.roles[None]; base=(s.mix[None,:,None]*c).sum(1)
	return (base+s.sc*s.net(c.flatten(1)))[:,None]
	class Their014(nn.Module): # su RoleAdapter, con léxico extensible (mismo cálculo que su forward)
	def __init__(s,seed):
	super().__init__(); ck=torch.load(f'{SRC}/checkpoints/expanded_role_ce_k4_seed{seed}/best.pt',map_location=DEV,weights_only=False)['adapter_state']
	s.K=4; s.roles=nn.Parameter(ck['roles']); s.sc=ck['scale']
	s.net=nn.Sequential(nn.LayerNorm(D),nn.Linear(D,128),nn.GELU(),nn.Linear(128,D)); s.net.load_state_dict({k[4:]:v for k,v in ck.items() if k.startswith('net.')})
	def forward(s,L): c=L+s.roles[None]; return c+s.sc*s.net(c)
	# ---- lectura por el LLM congelado ----
	def sufids(tpl,j): return tok.encode(tpl.format(field=FIELDS[j]),add_special_tokens=False)
	def run_rows(embs,tails): # listas de tensores [L,D] -> logits última posición (padding a la derecha)
	L=max(e.shape[0] for e in embs); B=len(embs)
	x=torch.zeros(B,L,D); m=torch.zeros(B,L,dtype=torch.long)
	for i,e in enumerate(embs): x[i,:e.shape[0]]=e; m[i,:e.shape[0]]=1
	h=lm.model(inputs_embeds=x,attention_mask=m).last_hidden_state[torch.arange(B),m.sum(1)-1]
	return lm.lm_head(h)
	def codec_logits(cod,msgs,tpl,js=range(4)):
	S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs]))
	embs=[];meta_=[]
	for i,m in enumerate(msgs):
	for j in js: embs.append(torch.cat([S[i],Emb[sufids(tpl,j)]])); meta_.append((i,j))
	return run_rows(embs,None),meta_
	def text_logits(msgs,tpl):
	embs=[];meta_=[]
	for i,m in enumerate(msgs):
	for j in range(4): embs.append(Emb[tok.encode(TPL.format(**dict(zip(FIELDS,m)))+tpl.format(field=FIELDS[j]),add_special_tokens=False)]); meta_.append((i,j))
	return run_rows(embs,None),meta_
	@torch.no_grad()
	def acc(route,msgs,tpl=SUF_MAIN,cod=None,bs=24):
	ok=[]
	for b in range(0,len(msgs),bs):
	mb=msgs[b:b+bs]; lg,mt=(text_logits(mb,tpl) if route=='text' else codec_logits(cod,mb,tpl))
	good=torch.ones(len(mb),dtype=torch.bool)
	for r,(i,j) in enumerate(mt): good[i]&=int(lg[r][CAND[j]].argmax())==gold_idx(j,mb[i][j])
	ok+=good.tolist()
	return sum(ok)/len(ok)
	@torch.no_grad()
	def nolm(cod,msgs): # anti-lookup: decodificar valor desde las ranuras por coseno, sin LLM
	S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs])); ok=0
	for i,m in enumerate(msgs):
	g=True
	for j in range(4):
	cands=F.normalize(Emb[CAND[j]],dim=-1); s=S[i,j] if S.shape[1]==4 else S[i].mean(0)
	g&=int((F.normalize(s,dim=-1)@cands.T).argmax())==gold_idx(j,m[j])
	ok+=g
	return ok/len(msgs)
	def train(cod,steps,seed,p_rand=.5,p_phr=.15,bs=12):
	g=random.Random(seed); opt=torch.optim.AdamW(cod.parameters(),lr=2e-3,weight_decay=1e-3)
	for s in range(steps):
	msgs=[]
	for _ in range(bs):
	m=list(g.choice(FIT))
	for j in range(4):
	u=g.random()
	if u<p_phr: m[j]=g.choice(POOL)+' '+g.choice(POOL)
	elif u<p_phr+p_rand: m[j]=g.choice(POOL)
	msgs.append(m)
	tpl=g.choice(SUF_TRAIN); lg,mt=codec_logits(cod,msgs,tpl)
	gold=torch.tensor([t1(msgs[i][j])[0] for i,j in mt]); loss=F.cross_entropy(lg,gold) # CE sobre TODO el vocabulario: copiar la palabra ligada al rol
	opt.zero_grad(); loss.backward(); opt.step()
	return cod.eval()
	OUT=OD+'/results017.jsonl'
	def log(d): open(OUT,'a').write(json.dumps(d)+"\n"); print(json.dumps(d),flush=True)
	def evaluate_all(name,cod=None,quick=False):
	route='text' if cod is None else 'codec'; r={'name':name}
	r['val']=acc(route,VAL[:96],cod=cod)
	if not quick:
	for k,v in FRESH.items(): r['fresh_'+k]=acc(route,v,cod=cod)
	for k,v in TRANSFER.items(): r['transfer_'+k]=acc(route,v,cod=cod)
	sub=FRESH['id'][:48]+FRESH['ood'][:48]
	for k,t in SUF_HELD.items(): r['query_'+k]=acc(route,sub,t,cod=cod)
	if cod is not None: r['noLLM_fresh_ood']=nolm(cod,FRESH['ood'][:200]); r['noLLM_transfer_4new']=nolm(cod,TRANSFER['4_new'])
	log(r); return r

	def _branch(cache):
	if hasattr(cache,'to_legacy_cache'): cache=cache.to_legacy_cache()
	return tuple((k.repeat_interleave(4,0),v.repeat_interleave(4,0)) for k,v in cache)
	@torch.no_grad()
	def one_event(route,m,cod,dev):
	tails=[sufids(SUF_MAIN,j) for j in range(4)]; sn=0
	while all(t[sn]==tails[0][sn] for t in tails): sn+=1
	if route=='text':
	pre=Emb[tok.encode(TPL.format(**dict(zip(FIELDS,m))),add_special_tokens=False)+tails[0][:sn]][None]
	else:
	pre=torch.cat([cod(torch.stack([lex(w) for w in m])[None].to(dev))[0],Emb[tails[0][:sn]]])[None]
	o=lm.model(inputs_embeds=pre,use_cache=True)
	h=lm.model(input_ids=torch.tensor([t[sn:] for t in tails],device=dev),past_key_values=_branch(o.past_key_values),use_cache=True).last_hidden_state[:,-1]
	out=[int(lm.lm_head(h)[j][CAND[j].to(dev)].argmax()) for j in range(4)]
	return pre.shape[1]+4*len(tails[0][sn:])
	def latency(cod,dev,n=40,reps=3):
	sync=(lambda: torch.cuda.synchronize()) if dev!='cpu' else (lambda:None)
	ev=FRESH['ood'][:n]; T={'text':[],'codec':[]}; P={}
	for m in ev[:5]: one_event('text',m,cod,dev); one_event('codec',m,cod,dev)
	rr=random.Random(3)
	for _ in range(reps):
	for m in ev:
	for r in rr.sample(['text','codec'],2):
	sync(); t0=time.perf_counter(); P[r]=one_event(r,m,cod,dev); sync(); T[r].append(1e3*(time.perf_counter()-t0))
	q=lambda x:sorted(x)[int(.9*len(x))]
	return {k:dict(median_ms=st.median(v),p90_ms=q(v),positions=P[k]) for k,v in T.items()}

	class PackK(nn.Module): # superposición aprendida: ranura_k = sum_j a_kj (e_j + U_j V_j^T e_j + rol_j) + MLP(concat)
	def __init__(s,K,seed,r=64):
	super().__init__(); torch.manual_seed(seed); s.K=K
	s.roles=nn.Parameter(torch.randn(4,D).02); s.U=nn.Parameter(torch.randn(4,D,r).01); s.V=nn.Parameter(torch.randn(4,D,r)*.01)
	s.a=nn.Parameter(torch.full((K,4),1.0/4*K if K<4 else 1.0))
	s.net=nn.Sequential(nn.LayerNorm(4D),nn.Linear(4D,1024),nn.GELU(),nn.Linear(1024,K*D)); nn.init.normal_(s.net[-1].weight,std=.002); nn.init.zeros_(s.net[-1].bias)
	s.register_buffer('sc',Emb.std().clone())
	def forward(s,L):
	c=L+torch.einsum('bjd,jdr,jer->bje',L,s.V,s.U)+s.roles[None]
	base=torch.einsum('kj,bjd->bkd',s.a,c)
	return base+s.sc*s.net(c.flatten(1)).view(-1,s.K,D)
	def train_kd(cod,steps,seed,p_rand=.3,bs=12,lr=1e-3):
	g=random.Random(seed); opt=torch.optim.AdamW(cod.parameters(),lr=lr,weight_decay=1e-3)
	sch=torch.optim.lr_scheduler.OneCycleLR(opt,max_lr=lr,total_steps=steps,pct_start=.05)
	for s_ in range(steps):
	msgs=[]
	for _ in range(bs):
	m=list(g.choice(FIT))
	for j in range(4):
	if g.random()<p_rand: m[j]=g.choice(POOL)
	msgs.append(m)
	tpl=g.choice(SUF_TRAIN)
	S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs]))
	embs=[];hs=[];tg=[]
	for i,m in enumerate(msgs):
	for j in range(4): embs.append(torch.cat([S[i],Emb[sufids(tpl,j)]])); tg.append(t1(m[j])[0])
	with torch.no_grad():
	te=[Emb[tok.encode(TPL.format(**dict(zip(FIELDS,m)))+tpl.format(field=FIELDS[j]),add_special_tokens=False)] for m in msgs for j in range(4)]
	Lt=max(e.shape[0] for e in te); x=torch.zeros(len(te),Lt,D); mk=torch.zeros(len(te),Lt,dtype=torch.long)
	for i,e in enumerate(te): x[i,:e.shape[0]]=e; mk[i,:e.shape[0]]=1
	ht=lm.model(inputs_embeds=x,attention_mask=mk).last_hidden_state[torch.arange(len(te)),mk.sum(1)-1]; lt=lm.lm_head(ht)
	L=max(e.shape[0] for e in embs); x=torch.zeros(len(embs),L,D); mk=torch.zeros(len(embs),L,dtype=torch.long)
	for i,e in enumerate(embs): x[i,:e.shape[0]]=e; mk[i,:e.shape[0]]=1
	hsd=lm.model(inputs_embeds=x,attention_mask=mk).last_hidden_state[torch.arange(len(embs)),mk.sum(1)-1]; ls=lm.lm_head(hsd)
	loss=F.cross_entropy(ls,torch.tensor(tg))+F.kl_div(F.log_softmax(ls,-1),F.log_softmax(lt,-1),log_target=True,reduction='batchmean')+.5*(1-F.cosine_similarity(hsd,ht,-1).mean())
	opt.zero_grad(); loss.backward(); torch.nn.utils.clip_grad_norm_(cod.parameters(),1.0); opt.step(); sch.step()
	if s_%500==0: print(json.dumps({'step':s_,'loss':float(loss)}),flush=True)
	return cod.eval()
	@torch.no_grad()
	def probe(cod): # anti-lookup fuerte: sonda lineal (ridge, sin LLM) entrenada en slots de FIT, evaluada en FRESH ood y 4_new
	def slots(ms): return cod(torch.stack([torch.stack([lex(w) for w in m]) for m in ms])).flatten(1)
	Xtr=torch.cat([slots(FIT[i:i+100]) for i in range(0,len(FIT),100)]); res={}
	for name,ms in (('fresh_ood',FRESH['ood']),('transfer_4_new',TRANSFER['4_new'])):
	Xte=slots(ms); ok=torch.ones(len(ms),dtype=torch.bool,device='cpu')
	for j in range(4): # regresión al embedding del valor, decodificación por coseno contra opciones (abierta a palabras nuevas)
	Y=torch.stack([lex(m[j]) for m in FIT]); A=torch.linalg.solve(Xtr.T@Xtr+10*torch.eye(Xtr.shape[1]),Xtr.T@Y)
	P=F.normalize(Xte@A,dim=-1)@F.normalize(Emb[CAND[j]],dim=-1).T
	ok&=P.argmax(1).cpu()==torch.tensor([gold_idx(j,m[j]) for m in ms],device='cpu')
	res['probe_'+name]=float(ok.float().mean())
	return res

	rv=random.Random(99)
	VAL_T=[[rv.choice(POOL_VAL) for j in range(4)] for _ in range(64)] # validación de transferencia: palabras nunca usadas en entrenamiento ni en test
	CAND_FULL=None
	@torch.no_grad()
	def acc_open(cod,msgs,bs=16): # exactitud sobre TODO el vocabulario (palabras de validación no están en CAND)
	ok=[]
	for b in range(0,len(msgs),bs):
	mb=msgs[b:b+bs]; lg,mt=codec_logits(cod,mb,SUF_MAIN); good=torch.ones(len(mb),dtype=torch.bool)
	for r,(i,j) in enumerate(mt): good[i]&=int(lg[r].argmax())==t1(mb[i][j])[0]
	ok+=good.tolist()
	return sum(ok)/len(ok)
	def readout_h(cod,msgs,tpl):
	S=cod(torch.stack([torch.stack([lex(w) for w in m]) for m in msgs])); embs=[]
	for i,m in enumerate(msgs):
	for j in range(4): embs.append(torch.cat([S[i],Emb[sufids(tpl,j)]]))
	L=max(e.shape[0] for e in embs); x=torch.zeros(len(embs),L,D); mk=torch.zeros(len(embs),L,dtype=torch.long)
	for i,e in enumerate(embs): x[i,:e.shape[0]]=e; mk[i,:e.shape[0]]=1
	h=lm.model(inputs_embeds=x,attention_mask=mk).last_hidden_state[torch.arange(len(embs)),mk.sum(1)-1]; return h,lm.lm_head(h)
	def distill(stu,teach,steps,seed,tagname,bs=12,lr=1e-3,every=250):
	g=random.Random(seed); opt=torch.optim.AdamW(stu.parameters(),lr=lr,weight_decay=1e-3)
	sch=torch.optim.lr_scheduler.OneCycleLR(opt,max_lr=lr,total_steps=steps,pct_start=.05); best=(-1,None,0)
	for s_ in range(1,steps+1):
	msgs=[]
	for _ in range(bs):
	m=list(g.choice(FIT))
	for j in range(4):
	u=g.random()
	if u<.1: m[j]=g.choice(POOL)+' '+g.choice(POOL)
	elif u<.5: m[j]=g.choice(POOL)
	msgs.append(m)
	tpl=g.choice(SUF_TRAIN)
	with torch.no_grad(): ht,lt=readout_h(teach,msgs,tpl)
	hs,ls=readout_h(stu,msgs,tpl); tg=torch.tensor([t1(m[j])[0] for m in msgs for j in range(4)])
	loss=F.cross_entropy(ls,tg)+F.kl_div(F.log_softmax(ls,-1),F.log_softmax(lt,-1),log_target=True,reduction='batchmean')+.5*(1-F.cosine_similarity(hs,ht,-1).mean())
	opt.zero_grad(); loss.backward(); torch.nn.utils.clip_grad_norm_(stu.parameters(),1.0); opt.step(); sch.step()
	if s_%every==0:
	stu.eval(); v1=acc('codec',VAL[:96],cod=stu); v2=acc_open(stu,VAL_T); stu.train()
	sc=v1+v2; log({'run':tagname,'step':s_,'val_contract':v1,'val_transfer_open':v2})
	if sc>best[0]: best=(sc,{k:v.detach().clone() for k,v in stu.state_dict().items()},s_)
	stu.load_state_dict(best[1]); stu.eval(); return stu,best[2]
	def grow_down(src,K): # inicializar K pequeño desde K mayor (curriculum)
	dst=PackK(K,0); sd=src.state_dict(); dd=dst.state_dict()
	for k in ('roles','U','V','net.0.weight','net.0.bias','net.1.weight','net.1.bias','sc'): dd[k]=sd[k].clone()
	Ks=src.K; dd['a']=sd['a'].view(K,Ks//K,4).sum(1) if Ks%K==0 else sd['a'].mean(0,keepdim=True).expand(K,4).clone()
	W=sd['net.3.weight'].view(Ks,D,-1); b=sd['net.3.bias'].view(Ks,D)
	dd['net.3.weight']=W.view(K,Ks//K,D,-1).sum(1).reshape(K*D,-1); dd['net.3.bias']=b.view(K,Ks//K,D).sum(1).reshape(-1)
	dst.load_state_dict(dd); return dst
	@torch.no_grad()
	def batched_latency(cod,bsz,reps=5):
	sync=(lambda: torch.cuda.synchronize()) if DEV=='cuda' else (lambda:None)
	ms=FRESH['ood'][:bsz]; res={}
	for route in ('text','codec'):
	for _ in range(2): (text_logits(ms,SUF_MAIN) if route=='text' else codec_logits(cod,ms,SUF_MAIN))
	T=[]
	for _ in range(reps):
	sync(); t0=time.perf_counter(); (text_logits(ms,SUF_MAIN) if route=='text' else codec_logits(cod,ms,SUF_MAIN)); sync(); T.append(1e3*(time.perf_counter()-t0))
	res[route]=dict(median_ms=st.median(T),max_ms=max(T))
	return res
	if __name__=='__main__':
	INP=os.environ.get('INP',''); seeds=[int(x) for x in os.environ.get('SEEDS','17,29,43').split(',')]
	for sd in seeds:
	t0=time.time(); teach=Binder(sd); teach.load_state_dict(torch.load(f'{INP}/binder4_1000_s{sd}.pt',map_location=DEV)); teach.eval().requires_grad_(False)
	k2=PackK(2,sd); k2,b2=distill(k2,teach,int(os.environ.get('S2',1000)),sd,f'k2_s{sd}'); torch.save(k2.state_dict(),f'{OD}/k2_s{sd}.pt')
	r=evaluate_all(f'k2_s{sd}',k2); log({'name':r['name'],'best_step':b2,'val_transfer_open':acc_open(k2,VAL_T),**probe(k2)})
	k1=grow_down(k2,1); k1,b1=distill(k1,teach,int(os.environ.get('S1',1500)),sd,f'k1_s{sd}'); torch.save(k1.state_dict(),f'{OD}/k1_s{sd}.pt')
	r=evaluate_all(f'k1_s{sd}',k1); log({'name':r['name'],'best_step':b1,'val_transfer_open':acc_open(k1,VAL_T),'train_s':round(time.time()-t0),**probe(k1)})
	if sd==seeds[0]:
	for nm,c in (('k2',k2),('k1',k1)):
	log({'latency1':nm,'gpu':torch.cuda.get_device_name() if DEV=='cuda' else 'cpu',**latency(c,DEV)})
	for bz in (16,64): log({'latency_batch':nm,'batch':bz,'gpu':torch.cuda.get_device_name() if DEV=='cuda' else 'cpu',**batched_latency(c,bz)})