X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=b88847ef75e58bd8aca48df3a706319c768f1087;hb=504f61114d90b57e1d0faf55a298756da2c8fbfa;hp=2d5b14891aa00487794868054a2630a6fead6e33;hpb=9f787901b2c7591a323f843ab973fe6abcf6b8ce;p=culture.git diff --git a/main.py b/main.py index 2d5b148..b88847e 100755 --- a/main.py +++ b/main.py @@ -13,13 +13,12 @@ from torch.nn import functional as F import ffutils import mygpt -import sky, quizz_machine +import sky, wireworld, quizz_machine # world quizzes vs. culture quizzes ###################################################################### -accuracy_to_make_c_quizzes = 0.975 nb_new_c_quizzes_for_train = 1000 nb_new_c_quizzes_for_test = 100 @@ -38,7 +37,7 @@ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) -parser.add_argument("--log_filename", type=str, default="train.log", help=" ") +parser.add_argument("--log_filename", type=str, default="train.log") parser.add_argument("--result_dir", type=str, default=None) @@ -58,7 +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-4) +parser.add_argument("--learning_rate", type=float, default=1e-3) ######################################## @@ -80,8 +79,20 @@ parser.add_argument("--dropout", type=float, default=0.1) parser.add_argument("--deterministic_synthesis", action="store_true", default=False) +parser.add_argument("--problem", type=str, default="sky") + parser.add_argument("--nb_gpts", type=int, default=5) +parser.add_argument("--nb_models_for_generation", type=int, default=1) + +parser.add_argument("--generation_mode", type=str, default="groupthink") + +parser.add_argument("--min_to_validate", type=int, default=4) + +parser.add_argument("--max_to_validate", type=int, default=4) + +parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975) + parser.add_argument("--dirty_debug", action="store_true", default=False) ###################################################################### @@ -94,7 +105,7 @@ if args.result_dir is None: ###################################################################### if args.dirty_debug: - accuracy_to_make_c_quizzes = 0.0 + args.accuracy_to_make_c_quizzes = 0.0 nb_new_c_quizzes_for_train = 100 nb_new_c_quizzes_for_test = 10 @@ -103,7 +114,7 @@ if args.dirty_debug: default_args = { "model": "37M", "batch_size": 100, - "nb_train_samples": 250000, + "nb_train_samples": 100000, "nb_test_samples": 10000, } @@ -210,8 +221,15 @@ else: assert args.nb_train_samples % args.batch_size == 0 assert args.nb_test_samples % args.batch_size == 0 +if args.problem=="sky": + problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2, speed=2), +elif args.problem="wireworld": + problem=wireworld.Wireworld(height=10, width=15, nb_iterations=4) +else: + raise ValueError + quizz_machine = quizz_machine.QuizzMachine( - sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2), + problem=problem, nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.physical_batch_size, @@ -334,7 +352,6 @@ def run_tests(model, quizz_machine, deterministic_synthesis): n_epoch=n_epoch, model=model, result_dir=args.result_dir, - logger=log_string, deterministic_synthesis=deterministic_synthesis, ) @@ -355,55 +372,90 @@ def create_c_quizzes( nb_for_test=100, min_ave_seq_logproba=None, ): - kept = [] + # We will store the generated quizzes for each number of + # correct prediction + recorded = dict([(n, []) for n in range(len(models) + 1)]) + model_indexes = [] sum_logits, sum_nb_c_quizzes = 0, 0 - while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test: - nb_to_generate = nb_for_train + nb_for_test + def nb_generated(): + return sum([sum([x.size(0) for x in recorded[n]]) for n in recorded.keys()]) - if len(model_indexes) == 0: - model_indexes = [i.item() for i in torch.randperm(len(models))] - - model = models[model_indexes.pop()] + def nb_validated(): + return sum( + [ + sum([x.size(0) for x in recorded[n]]) + for n in range(args.min_to_validate, args.max_to_validate + 1) + ] + ) - new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes( - nb=nb_to_generate, - model_for_generation=model, - models_for_validation=models, + nb_to_create = nb_for_train + nb_for_test + + while nb_validated() < nb_to_create: + ( + new_c_quizzes, + nb_correct, + ave_seq_logproba, + ) = quizz_machine.gang_create_c_quizzes( + nb=nb_to_create, + nb_models_for_generation=args.nb_models_for_generation, + models=models, + mode=args.generation_mode, min_ave_seq_logproba=min_ave_seq_logproba, n_epoch=n_epoch, result_dir=args.result_dir, - logger=log_string, ) sum_logits += new_c_quizzes.size(0) * ave_seq_logproba sum_nb_c_quizzes += new_c_quizzes.size(0) - to_keep = new_c_quizzes[nb_correct == len(models) - 1] - if args.dirty_debug: - to_keep = new_c_quizzes[ - torch.randint(3, (new_c_quizzes.size(0),), device=new_c_quizzes.device) - == 0 - ] + nb_correct = torch.randint( + len(models) + 1, nb_correct.size(), device=new_c_quizzes.device + ) - kept.append(to_keep) + for n in range(nb_correct.max() + 1): + recorded[n].append(new_c_quizzes[nb_correct == n].clone()) log_string( - f"keep c_quizzes {to_keep.size(0)}/{new_c_quizzes.size(0)} ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%) total {sum([ x.size(0) for x in kept])}/{nb_to_generate}" + f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}" ) - new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test] + # concatenate and shuffle + for n in recorded.keys(): + if len(recorded[n]) > 0: + q = torch.cat(recorded[n], dim=0) + q = q[torch.randperm(q.size(0), device=q.device)] + recorded[n] = q + else: + del recorded[n] + + new_c_quizzes = torch.cat( + [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)], + dim=0, + ) + + new_c_quizzes = new_c_quizzes[ + torch.randperm(new_c_quizzes.size(0), device=new_c_quizzes.device)[ + : nb_for_train + nb_for_test + ] + ] 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) - quizz_machine.problem.save_quizzes( - new_c_quizzes[:72], - args.result_dir, - f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}", - ) + for n in recorded.keys(): + s = ( + "_validated" + if n >= args.min_to_validate and n <= args.max_to_validate + else "" + ) + quizz_machine.problem.save_quizzes( + recorded[n][:72], + args.result_dir, + f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", + ) return sum_logits / sum_nb_c_quizzes @@ -442,7 +494,8 @@ for n_epoch in range(args.nb_epochs): a = [(model.id, float(model.main_test_accuracy)) for model in models] a.sort(key=lambda p: p[0]) - log_string(f"current accuracies {a}") + s = " ".join([f"{p[1]*100:.02f}%" for p in a]) + log_string(f"current accuracies {s}") # select the model with lowest accuracy models.sort(key=lambda model: model.main_test_accuracy) @@ -468,7 +521,7 @@ for n_epoch in range(args.nb_epochs): f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" ) - if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes: + if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes: ave_seq_logproba = create_c_quizzes( models, quizz_machine, @@ -478,12 +531,12 @@ for n_epoch in range(args.nb_epochs): ) # We keep the first average logits as a reference - if min_ave_seq_logproba is None: - min_ave_seq_logproba = ave_seq_logproba - else: - log_string( - f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}" - ) + # if min_ave_seq_logproba is None: + # min_ave_seq_logproba = ave_seq_logproba + # else: + # log_string( + # f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}" + # ) # We update everyone for model in models: