-
-def log_string(s):
- t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
-
- if log_file is not None:
- log_file.write(t + s + "\n")
- log_file.flush()
-
- print(t + s)
- sys.stdout.flush()
-
-
-######################################################################
-
-
-def run_tests(model, quiz_machine, deterministic_synthesis, local_device=None):
- if local_device is None:
- local_device = device
-
- with torch.autograd.no_grad():
- model.eval().to(local_device)
-
- nb_test_samples, acc_test_loss = 0, 0.0
- nb_samples_accumulated = 0
-
- for input in quiz_machine.batches(model, split="test"):
- input = input.to(local_device)
-
- bs = model(mygpt.BracketedSequence(input))
- output = bs.x
-
- loss = F.cross_entropy(output.transpose(1, 2), input)
-
- acc_test_loss += loss.item() * input.size(0)
-
- nb_test_samples += input.size(0)
-
- test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
-
- log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
-
- model.main_test_accuracy = quiz_machine.produce_results(
- n_epoch=n_epoch,
- model=model,
- result_dir=args.result_dir,
- deterministic_synthesis=deterministic_synthesis,
- )
-
-
-def one_epoch(model, quiz_machine, local_device=None):
- if local_device is None:
- local_device = device
-
- optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-
- model.to(local_device).train()
-
- nb_train_samples, acc_train_loss = 0, 0.0
-
- for input in quiz_machine.batches(model, split="train"):
- input = input.to(local_device)
-
- if nb_train_samples % args.batch_size == 0:
- optimizer.zero_grad()
-
- output = model(mygpt.BracketedSequence(input)).x
- loss = F.cross_entropy(output.transpose(1, 2), input)
- acc_train_loss += loss.item() * input.size(0)
-
- nb_train_samples += input.size(0)
-
- loss.backward()
-
- if nb_train_samples % args.batch_size == 0:
- optimizer.step()
-
- train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
-
- log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
-
- run_tests(model, quiz_machine, deterministic_synthesis=False)
-
-
-######################################################################
-
-
-def standard_validity(logproba):
- l = logproba.sort(dim=-1).values
- return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.99))
- # warnings.warn("TEST!!!", RuntimeWarning)
- # print(l.exp())
- # return (l[:, 0] < math.log(0.99))
-
-
-def valid_c_quizzes(recorded, criteria):
- result = [q[criteria(lp)] for q, lp in recorded]
- return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
-
-
-######################################################################
-
-
-def create_c_quizzes(
- models,
- quiz_machine,
- nb_for_train=1000,
- nb_for_test=100,
-):
- quizzes_and_logproba_records = []
-
- nb_to_create = nb_for_train + nb_for_test
-
- # ------------------------------------------------------------
-
- file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
-
- with open(file_name, "w") as logp_file:
- while (
- valid_c_quizzes(quizzes_and_logproba_records, standard_validity).size(0)
- < nb_to_create
- ):
- # Select a model at random to generate the new quizzes
-
- model_for_generation = models[torch.randint(len(models), (1,))]
-
- c_quizzes = quiz_machine.generate_quizzes(
- nb_to_create,
- model_for_generation=model_for_generation,
- temperature=args.generation_temperature,
- )
-
- c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
-
- if c_quizzes.size(0) > 0:
- logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
- for l in logproba:
- s = " ".join([str(x.item()) for x in l])
- logp_file.write(s + "\n")
- quizzes_and_logproba_records.append((c_quizzes, logproba))
-
- nb_validated = valid_c_quizzes(
- quizzes_and_logproba_records, standard_validity
- ).size(0)
-
- log_string(
- f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
- )
-
- # store the new c_quizzes which have been validated
-
- new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_records, standard_validity)
-
- quiz_machine.reverse_random_half_in_place(new_c_quizzes)
-
- quiz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
- quiz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
-
- # save a bunch of images to investigate what quizzes with a
- # certain nb of correct predictions look like
-
- q = new_c_quizzes[:72]
-
- if q.size(0) > 0:
- quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
-
-
-######################################################################
-