X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=6b46fa0f23b68ca2c88466be37b60c069758bce6;hb=e0ab20005e2578edff27d4246c6904cf1047ed22;hp=d63398c1246158e4e8b7519366a7a4bd95ccd08d;hpb=d283cd3d46a6323fec4c6a0970ac71e553e4a486;p=culture.git diff --git a/main.py b/main.py index d63398c..6b46fa0 100755 --- a/main.py +++ b/main.py @@ -13,7 +13,7 @@ from torch.nn import functional as F import ffutils import mygpt -import sky, wireworld, quizz_machine +import sky, grids, quiz_machine # world quizzes vs. culture quizzes @@ -57,7 +57,7 @@ parser.add_argument("--nb_train_samples", type=int, default=None) parser.add_argument("--nb_test_samples", type=int, default=None) -parser.add_argument("--learning_rate", type=float, default=1e-3) +parser.add_argument("--learning_rate", type=float, default=5e-4) ######################################## @@ -79,20 +79,26 @@ parser.add_argument("--dropout", type=float, default=0.1) parser.add_argument("--deterministic_synthesis", action="store_true", default=False) -parser.add_argument("--both_directions", action="store_true", default=False) - -parser.add_argument("--problem", type=str, default="sky") +parser.add_argument("--problem", type=str, default="grids") parser.add_argument("--nb_gpts", type=int, default=5) -parser.add_argument("--min_to_validate", type=int, default=4) +parser.add_argument("--min_to_validate", type=int, default=None) -parser.add_argument("--max_to_validate", type=int, default=4) +parser.add_argument("--max_to_validate", type=int, default=None) parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975) +parser.add_argument("--generation_temperature", type=float, default=2.0) + +parser.add_argument("--deterministic_validation", action="store_true", default=False) + +parser.add_argument("--bidirectional_validation", action="store_true", default=False) + parser.add_argument("--dirty_debug", action="store_true", default=False) +###################################################################### + parser.add_argument("--sky_height", type=int, default=6) parser.add_argument("--sky_width", type=int, default=8) @@ -107,6 +113,12 @@ parser.add_argument("--sky_speed", type=int, default=3) args = parser.parse_args() +if args.min_to_validate is None: + args.min_to_validate = args.nb_gpts - 1 + +if args.max_to_validate is None: + args.max_to_validate = args.nb_gpts - 1 + if args.result_dir is None: args.result_dir = f"results_culture" @@ -114,6 +126,7 @@ if args.result_dir is None: if args.dirty_debug: args.accuracy_to_make_c_quizzes = 0.0 + args.nb_gpts = 2 nb_new_c_quizzes_for_train = 100 nb_new_c_quizzes_for_test = 10 @@ -237,15 +250,18 @@ if args.problem == "sky": nb_iterations=args.sky_nb_iterations, speed=args.sky_speed, ) -elif args.problem == "wireworld": - problem = wireworld.Wireworld(height=8, width=10, nb_iterations=2, speed=5) + back_accuracy = False +elif args.problem == "grids": + problem = grids.Grids(device=device) + back_accuracy = True else: raise ValueError -quizz_machine = quizz_machine.QuizzMachine( +quiz_machine = quiz_machine.QuizMachine( problem=problem, nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, + back_accuracy=back_accuracy, batch_size=args.physical_batch_size, result_dir=args.result_dir, logger=log_string, @@ -256,7 +272,7 @@ quizz_machine = quizz_machine.QuizzMachine( log_string(f"device {device}") -vocabulary_size = quizz_machine.vocabulary_size() +vocabulary_size = quiz_machine.vocabulary_size() log_string(f"vocabulary_size {vocabulary_size}") @@ -265,8 +281,8 @@ log_string(f"vocabulary_size {vocabulary_size}") # Compute the entropy of the training tokens token_count = 0 -for input in quizz_machine.batches(split="train", desc="train-entropy"): - token_count += F.one_hot(input, num_classes=quizz_machine.vocabulary_size()).sum( +for input in quiz_machine.batches(split="train", desc="train-entropy"): + token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum( (0, 1) ) token_probas = token_count / token_count.sum() @@ -290,11 +306,11 @@ if args.max_percents_of_test_in_train >= 0: nb_test, nb_in_train = 0, 0 for test_subset in subsets_as_tuples( - quizz_machine.batches(split="test", desc="test-check"), 25000 + quiz_machine.batches(split="test", desc="test-check"), 25000 ): in_train = set() for train_subset in subsets_as_tuples( - quizz_machine.batches(split="train", desc="train-check"), 25000 + quiz_machine.batches(split="train", desc="train-check"), 25000 ): in_train.update(test_subset.intersection(train_subset)) nb_in_train += len(in_train) @@ -311,14 +327,14 @@ if args.max_percents_of_test_in_train >= 0: ############################## -def one_epoch(model, quizz_machine): +def one_epoch(model, quiz_machine): optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate) model.train() nb_train_samples, acc_train_loss = 0, 0.0 - for input in quizz_machine.batches(split="train"): + for input in quiz_machine.batches(split="train"): input = input.to(device) if nb_train_samples % args.batch_size == 0: @@ -343,14 +359,14 @@ def one_epoch(model, quizz_machine): ###################################################################### -def run_tests(model, quizz_machine, deterministic_synthesis): +def run_tests(model, quiz_machine, deterministic_synthesis): with torch.autograd.no_grad(): model.eval() nb_test_samples, acc_test_loss = 0, 0.0 nb_samples_accumulated = 0 - for input in quizz_machine.batches(split="test"): + for input in quiz_machine.batches(split="test"): input = input.to(device) bs = model(mygpt.BracketedSequence(input)) @@ -362,17 +378,17 @@ def run_tests(model, quizz_machine, deterministic_synthesis): nb_test_samples += input.size(0) - model.main_test_accuracy = quizz_machine.produce_results( + test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples)) + + log_string(f"test_perplexity {n_epoch} {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, ) - test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples)) - - log_string(f"test_perplexity {n_epoch} {test_perplexity}") - ###################################################################### @@ -387,11 +403,11 @@ def valid_c_quizzes(recorded, criteria): def create_c_quizzes( models, - quizz_machine, + quiz_machine, nb_for_train=1000, nb_for_test=100, ): - recorded = [] + quizzes_and_nb_correct_records = [] nb_to_create = nb_for_train + nb_for_test @@ -401,40 +417,60 @@ def create_c_quizzes( nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate ) - while valid_c_quizzes(recorded, standard_validity).size(0) < nb_to_create: - model_for_generation = models[torch.randint(len(models), (1,))] + file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat") - c_quizzes, ave_seq_logproba = quizz_machine.generate_quizzes( - nb_to_create, - model_for_generation=model_for_generation, - ) + with open(file_name, "w") as logp_file: + while ( + valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity).size(0) + < nb_to_create + ): + # Select a model at random to generate the new quizzes - nb_correct = quizz_machine.compute_correctness( - c_quizzes, models, both_directions=args.both_directions - ) + model_for_generation = models[torch.randint(len(models), (1,))] - if args.dirty_debug: - nb_correct = torch.randint( - len(models) + 1, nb_correct.size(), device=c_quizzes.device + c_quizzes = quiz_machine.generate_quizzes( + nb_to_create, + model_for_generation=model_for_generation, + temperature=args.generation_temperature, ) - recorded.append((c_quizzes, nb_correct)) + nb_correct, seq_logproba = quiz_machine.compute_correctness( + c_quizzes, + models, + bidirectional_validation=args.bidirectional_validation, + deterministic_validation=args.deterministic_validation, + ) - nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0) - nv = " ".join([str(x.item()) for x in nv]) + for n, l in zip(nb_correct, seq_logproba): + s = " ".join([str(x.item()) for x in l]) + logp_file.write(f"{n} {s}\n") - nb_validated = valid_c_quizzes(recorded, standard_validity).size(0) + if args.dirty_debug: + nb_correct = torch.randint( + len(models) + 1, nb_correct.size(), device=c_quizzes.device + ) - log_string( - f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}" - ) + quizzes_and_nb_correct_records.append((c_quizzes, nb_correct)) + + nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0) + nv = " ".join([str(x.item()) for x in nv]) + + nb_validated = valid_c_quizzes( + quizzes_and_nb_correct_records, standard_validity + ).size(0) + + log_string( + f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}" + ) # store the new c_quizzes which have been validated - new_c_quizzes = valid_c_quizzes(recorded, standard_validity) + new_c_quizzes = valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity) - quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True) - quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False) + 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 @@ -446,10 +482,14 @@ def create_c_quizzes( else "" ) - q = valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72] + q = valid_c_quizzes( + quizzes_and_nb_correct_records, criteria=lambda nb_correct: nb_correct == n + )[:72] + + quiz_machine.reverse_random_half_in_place(q) if q.size(0) > 0: - quizz_machine.save_quizzes( + quiz_machine.save_quizzes( args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q ) @@ -484,6 +524,9 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") for n_epoch in range(args.nb_epochs): log_string(f"--- epoch {n_epoch} ----------------------------------------") + cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models]) + log_string(f"current_test_accuracies {cta}") + # Select, improve, and eval the worst model weakest_model = min(models, key=lambda m: float(m.main_test_accuracy)) @@ -492,24 +535,21 @@ for n_epoch in range(args.nb_epochs): f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}" ) - one_epoch(weakest_model, quizz_machine) + one_epoch(weakest_model, quiz_machine) log_string( - f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" + f"train_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}" ) - run_tests(weakest_model, quizz_machine, deterministic_synthesis=False) + run_tests(weakest_model, quiz_machine, deterministic_synthesis=False) log_string( - f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" + f"test_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}" ) - cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models]) - log_string(f"current_test_accuracies {cta}") - # Replace a fraction of the w_quizzes with fresh ones - quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts) + quiz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts) # If all the models are good enough, generate new quizzes and # re-compute the test errors @@ -517,13 +557,13 @@ for n_epoch in range(args.nb_epochs): if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes: create_c_quizzes( models, - quizz_machine, + quiz_machine, nb_for_train=nb_new_c_quizzes_for_train, nb_for_test=nb_new_c_quizzes_for_test, ) for model in models: - run_tests(model, quizz_machine, deterministic_synthesis=False) + run_tests(model, quiz_machine, deterministic_synthesis=False) ######################################################################