Revision 5: negative-result model card, report rev. 5 (PDF/MD/figure), CITATION.cff, iterations 020-021 code, logs, sealed tests and checkpoints
7420c83 verified | # CUERDAS-021 B: compact text with matched effort: few-shot and reparameterized soft prompt (~3.84M params, same steps/data/loss/selection). | |
| from c021 import * | |
| log = opener('resultsB.jsonl'); PRED = open(f'{OD}/predictionsB.jsonl', 'w'); SEEDS = [17] if SMOKE else [17, 29, 43] | |
| FMT = {'values': "{action} {target} {status} {window}", 'values_csv': "{action}, {target}, {status}, {window}", | |
| 'kv': "action: {action}; target: {target}; status: {status}; window: {window}.", 'template': R.TPL} | |
| def npos(fmt, ms): return sum(len(tok.encode(fmt.format(**dict(zip(FIELDS, m))), add_special_tokens=False)) for m in ms) / len(ms) + 15 | |
| def ev(sysname, rowfn, extra): | |
| res = {} | |
| for s in SETS: | |
| r = per_ex(rowfn, S[s], CAND); res[s] = r | |
| for i, c in enumerate(r): PRED.write(json.dumps({'sys': sysname, 'set': s, 'i': i, 'correct': bool(c)}) + '\n') | |
| PRED.flush(); log({'sys': sysname, **extra, **{s: sum(v) / len(v) for s, v in res.items()}}) | |
| for fn, fmt in FMT.items(): | |
| log({'positions_per_event': fn, **{s: round(npos(fmt, S[s]), 2) for s in SETS}}) | |
| # few-shot: 2 fixed training demos with all 4 answers (shared, cacheable prefix) | |
| demos = random.Random(0).sample(R.FIT, 2) | |
| for fn, fmt in FMT.items(): | |
| pre = ''.join(fmt.format(**dict(zip(FIELDS, d))) + ''.join(R.SUF_MAIN.format(field=FIELDS[j]) + ' ' + d[j] + '.' for j in range(4)) + '\n\n' for d in demos) | |
| P = len(tok.encode(pre, add_special_tokens=False)) | |
| ev(f'fewshot_{fn}', lambda mb, f=pre + fmt: text_rows(mb, R.SUF_MAIN, f), {'shared_prefix_positions': P}) | |
| class SoftPrompt(nn.Module): # prefix-tuning style reparameterization: P = base + MLP(base) | |
| def __init__(s, seed, n=8, h=3328): | |
| super().__init__(); torch.manual_seed(seed) | |
| s.base = nn.Parameter(Emb[torch.randint(1000, 20000, (n,))].clone()) | |
| s.mlp = nn.Sequential(nn.Linear(D, h), nn.Tanh(), nn.Linear(h, D)); nn.init.zeros_(s.mlp[-1].weight); nn.init.zeros_(s.mlp[-1].bias) | |
| def forward(s): return s.base + s.mlp(s.base) | |
| for fn in ('values', 'values_csv', 'kv'): | |
| fmt = FMT[fn] | |
| for sd in SEEDS: | |
| sp = SoftPrompt(sd); npar = sum(p.numel() for p in sp.parameters()); teach = load_teacher(sd) | |
| step, v = train(list(sp.parameters()), lambda mb, tpl: text_rows(mb, tpl, fmt, prefix=sp()), teach, sd, f'sp_{fn}_s{sd}', log, set_mode=lambda t: sp.train(t)) | |
| sp.eval().requires_grad_(False); torch.save(sp.state_dict(), f'{OD}/sp_{fn}_s{sd}.pt') | |
| with torch.no_grad(): pr = sp() | |
| ev(f'sp_{fn}_s{sd}', lambda mb: text_rows(mb, R.SUF_MAIN, fmt, prefix=pr), {'params': npar, 'selected_step': step, 'val': v, 'soft_prompt_positions': 8}) | |
| PRED.close() | |