| """No privileged outcomes in model construction; charged, selected-bit replay.""" |
| from __future__ import annotations |
|
|
| import time |
| import torch |
|
|
| from unionmind.decision_sufficiency.core import CONTEXTS, count_samples, encode, estimate, plan, push |
|
|
|
|
| class Timer: |
| def __init__(self, device): self.device = device |
| def __enter__(self): |
| torch.cuda.synchronize(self.device) |
| self.start = time.perf_counter() |
| return self |
| def __exit__(self, *args): |
| torch.cuda.synchronize(self.device) |
| self.seconds = time.perf_counter()-self.start |
|
|
|
|
| def construct(samples, model_name, method): |
| """Only labeled fitting samples enter the model/representation/planner.""" |
| counts = count_samples(samples[None], 6) |
| model = estimate(counts, model_name) |
| code, k = encode(model, method) |
| compact = model if method == 'identity' else push(model, code, k) |
| return model,code.to(torch.uint8),compact,plan(compact) |
|
|
|
|
| def select_samples(archive, regime, seed): |
| generator = torch.Generator(device=archive['source'].device).manual_seed(seed) |
| source = archive['source'][torch.randperm(len(archive['source']),device=archive['source'].device, |
| generator=generator)[:1024]] |
| adaptation = archive['adaptation'][torch.randperm(len(archive['adaptation']), |
| device=archive['source'].device,generator=generator)] |
| if regime == 'source_only': return source |
| if regime == 'target_64': return adaptation[:64] |
| if regime == 'target_256': |
| return None if len(adaptation) < 256 else adaptation[:256] |
| assert regime == 'source_plus_target_64' |
| return torch.cat((source,adaptation[:64])) |
|
|
|
|
| def replay(policy, code, current, future, budget, cost_index): |
| """Return final action, paid query count, and query-mask for each record. |
| |
| `future` is a retained offline payload. Only the bit selected by a paid |
| query is indexed. It is never passed to a model or decision table lookup. |
| """ |
| assert policy['shape'][0] == 1 |
| r = policy['shape'][1] |
| table = policy['choices'].reshape(3,1,r,len(policy['costs']),9)[:,0] |
| z = code[0,current].long() |
| context = torch.zeros_like(current) |
| action = torch.zeros_like(current) |
| count = torch.zeros_like(current) |
| seen = torch.zeros_like(current) |
| live = torch.ones_like(current,dtype=torch.bool) |
| child = torch.zeros((9,2,2),device=current.device,dtype=torch.long) |
| for t,c in enumerate(CONTEXTS): |
| for q in (0,1): |
| if c[q] == -1: |
| for o in (0,1): |
| child[t,q,o] = CONTEXTS.index(tuple(o if i == q else v for i,v in enumerate(c))) |
| for left in range(budget,-1,-1): |
| choice = table[left,z,cost_index,context] |
| querying = live & (choice >= 3) |
| q = (choice-3).clamp(0,1) |
| selected = querying.nonzero().flatten() |
| revealed = torch.zeros_like(current) |
| revealed[selected] = (future[selected] >> (1-q[selected])) & 1 |
| context = torch.where(querying,child[context,q,revealed],context) |
| action = torch.where(live & ~querying,choice,action) |
| count += querying.long() |
| seen |= torch.where(querying,1 << q,0) |
| live = querying |
| assert not bool(live.any()) |
| return action,count,seen |
|
|
