X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=main.py;h=524715a33bf152a7b364d43d0b3d44c17bfe727a;hb=e2c3b8046c3fddef8aacb74cf5f848d42044897e;hp=e0588224f07ecafde8105b00c2b004e0b195e249;hpb=5c751aa1bbfbcf42654f4626f81905acfa946c15;p=culture.git diff --git a/main.py b/main.py index e058822..524715a 100755 --- a/main.py +++ b/main.py @@ -12,7 +12,16 @@ from torch import nn from torch.nn import functional as F import ffutils -import mygpt, tasks, problems +import mygpt +import sky, 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 ###################################################################### @@ -29,8 +38,6 @@ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) -parser.add_argument("--task", type=str, default="world", help="world") - parser.add_argument("--log_filename", type=str, default="train.log", help=" ") parser.add_argument("--result_dir", type=str, default=None) @@ -75,30 +82,34 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa parser.add_argument("--nb_gpts", type=int, default=5) -parser.add_argument("--check", action="store_true", default=False) +parser.add_argument("--dirty_debug", action="store_true", default=False) ###################################################################### args = parser.parse_args() if args.result_dir is None: - args.result_dir = f"results_{args.task}" + args.result_dir = f"results_culture" ###################################################################### -default_task_args = { - "world": { - "model": "37M", - "batch_size": 100, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, +if args.dirty_debug: + accuracy_to_make_c_quizzes = 0.0 + nb_new_c_quizzes_for_train = 100 + nb_new_c_quizzes_for_test = 10 + +###################################################################### + +default_args = { + "model": "37M", + "batch_size": 100, + "nb_train_samples": 250000, + "nb_test_samples": 10000, } -if args.task in default_task_args: - for k, v in default_task_args[args.task].items(): - if getattr(args, k) is None: - setattr(args, k, v) +for k, v in default_args.items(): + if getattr(args, k) is None: + setattr(args, k, v) ###################################################################### @@ -187,8 +198,8 @@ for n in vars(args): ###################################################################### -if args.check: - args.nb_train_samples = 500 +if args.dirty_debug: + args.nb_train_samples = 2500 args.nb_test_samples = 100 if args.physical_batch_size is None: @@ -199,235 +210,21 @@ else: assert args.nb_train_samples % args.batch_size == 0 assert args.nb_test_samples % args.batch_size == 0 -if args.task == "file": - assert ( - args.filetask_train_file is not None and args.filetask_test_file is not None - ), "You have to specify the task train and test files" - task = tasks.TaskFromFile( - args.filetask_train_file, - args.filetask_test_file, - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - shuffle=True, - device=device, - ) - args.max_percents_of_test_in_train = 0 - -elif args.task == "byheart": - task = tasks.SandBox( - problem=problems.ProblemByHeart(separation=args.byheart_separation), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - args.max_percents_of_test_in_train = -1 - -elif args.task == "world": - task = tasks.World( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - result_dir=args.result_dir, - logger=log_string, - device=device, - ) - args.max_percents_of_test_in_train = -1 - -elif args.task == "learnop": - task = tasks.SandBox( - problem=problems.ProblemLearnOperator(), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - - -elif args.task == "guessop": - task = tasks.SandBox( - problem=problems.ProblemGuessOperator(), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - - -elif args.task == "twotargets": - task = tasks.SandBox( - problem=problems.ProblemTwoTargets(), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - -elif args.task == "memory": - task = tasks.SandBox( - problem=problems.ProblemMemory(), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - -elif args.task == "mixing": - task = tasks.SandBox( - problem=problems.ProblemMixing( - hard=args.mixing_hard, random_start=not args.mixing_deterministic_start - ), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - -elif args.task == "addition": - task = tasks.SandBox( - problem=problems.ProblemAddition(), - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - device=device, - ) - -elif args.task == "picoclvr": - task = tasks.PicoCLVR( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - height=args.picoclvr_height, - width=args.picoclvr_width, - nb_colors=args.picoclvr_nb_colors, - logger=log_string, - device=device, - pruner_train=picoclvr_pruner_train, - pruner_eval=picoclvr_pruner_eval, - ) - -elif args.task == "mnist": - task = tasks.MNIST( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - device=device, - ) - -elif args.task == "maze": - task = tasks.Maze( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - height=args.maze_height, - width=args.maze_width, - nb_walls=args.maze_nb_walls, - device="cpu", - ) - -elif args.task == "snake": - task = tasks.Snake( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - height=args.snake_height, - width=args.snake_width, - nb_colors=args.snake_nb_colors, - length=args.snake_length, - prompt_length=args.snake_length // 2, - device=device, - ) - -elif args.task == "stack": - task = tasks.Stack( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - logger=log_string, - nb_steps=args.stack_nb_steps, - nb_stacks=args.stack_nb_stacks, - nb_digits=args.stack_nb_digits, - fraction_values_for_train=args.stack_fraction_values_for_train, - device=device, - ) - -elif args.task == "expr": - task = tasks.Expr( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - nb_variables=args.expr_nb_variables, - sequence_length=args.expr_sequence_length, - operand_max=args.expr_operand_max, - result_max=args.expr_result_max, - batch_size=args.physical_batch_size, - device=device, - ) - -elif args.task == "rpl": - task = tasks.RPL( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - nb_starting_values=args.rpl_nb_starting_values, - max_input=args.rpl_max_input, - prog_len=args.rpl_prog_len, - nb_runs=args.rpl_nb_runs, - no_prog=args.rpl_no_prog, - logger=log_string, - device=device, - ) - -elif args.task == "grid": - task = tasks.Grid( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - size=args.grid_size, - fraction_play=args.grid_fraction_play, - logger=log_string, - device=device, - ) - -elif args.task == "qmlp": - task = tasks.QMLP( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - result_dir=args.result_dir, - logger=log_string, - device=device, - ) - -elif args.task == "greed": - task = tasks.Greed( - nb_train_samples=args.nb_train_samples, - nb_test_samples=args.nb_test_samples, - batch_size=args.physical_batch_size, - height=args.greed_height, - width=args.greed_width, - T=args.greed_T, - nb_walls=args.greed_nb_walls, - nb_coins=args.greed_nb_coins, - logger=log_string, - device=device, - ) - -else: - raise ValueError(f"Unknown task {args.task}") +quizz_machine = quizz_machine.QuizzMachine( + sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2), + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.physical_batch_size, + result_dir=args.result_dir, + logger=log_string, + device=device, +) ###################################################################### log_string(f"device {device}") -vocabulary_size = task.vocabulary_size() +vocabulary_size = quizz_machine.vocabulary_size() log_string(f"vocabulary_size {vocabulary_size}") @@ -436,8 +233,10 @@ log_string(f"vocabulary_size {vocabulary_size}") # Compute the entropy of the training tokens token_count = 0 -for input in task.batches(split="train", desc="train-entropy"): - token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1)) +for input in quizz_machine.batches(split="train", desc="train-entropy"): + token_count += F.one_hot(input, num_classes=quizz_machine.vocabulary_size()).sum( + (0, 1) + ) token_probas = token_count / token_count.sum() entropy = -torch.xlogy(token_probas, token_probas).sum() train_set_perplexity = math.exp(entropy) @@ -459,11 +258,11 @@ if args.max_percents_of_test_in_train >= 0: nb_test, nb_in_train = 0, 0 for test_subset in subsets_as_tuples( - task.batches(split="test", desc="test-check"), 25000 + quizz_machine.batches(split="test", desc="test-check"), 25000 ): in_train = set() for train_subset in subsets_as_tuples( - task.batches(split="train", desc="train-check"), 25000 + quizz_machine.batches(split="train", desc="train-check"), 25000 ): in_train.update(test_subset.intersection(train_subset)) nb_in_train += len(in_train) @@ -480,14 +279,14 @@ if args.max_percents_of_test_in_train >= 0: ############################## -def one_epoch(model, task): +def one_epoch(model, quizz_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 task.batches(split="train"): + for input in quizz_machine.batches(split="train"): input = input.to(device) if nb_train_samples % args.batch_size == 0: @@ -512,14 +311,14 @@ def one_epoch(model, task): ###################################################################### -def run_tests(model, task, deterministic_synthesis): +def run_tests(model, quizz_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 task.batches(split="test"): + for input in quizz_machine.batches(split="test"): input = input.to(device) bs = model(mygpt.BracketedSequence(input)) @@ -531,7 +330,7 @@ def run_tests(model, task, deterministic_synthesis): nb_test_samples += input.size(0) - main_test_accuracy = task.produce_results( + main_test_accuracy = quizz_machine.produce_results( n_epoch=n_epoch, model=model, result_dir=args.result_dir, @@ -549,41 +348,59 @@ def run_tests(model, task, deterministic_synthesis): ###################################################################### -def create_quizzes( +def create_c_quizzes( model, other_models, - task, + quizz_machine, nb_for_train=1000, nb_for_test=100, + min_ave_seq_logproba=None, ): kept = [] + sum_logits, sum_nb_c_quizzes = 0, 0 + while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test: - new_quizzes, nb_correct = task.create_new_quizzes( + nb_to_generate = 4 * (nb_for_train + nb_for_test) + + new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes( n_epoch=n_epoch, result_dir=args.result_dir, logger=log_string, - nb=4 * (nb_for_train + nb_for_test), + nb=nb_to_generate, model=model, other_models=other_models, + min_ave_seq_logproba=min_ave_seq_logproba, + ) + + 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(other_models) - 1] + + if args.dirty_debug: + to_keep = new_c_quizzes + + log_string( + f"keep {to_keep.size(0)}/{new_c_quizzes.size(0)} c_quizzes ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%)" ) - to_keep = new_quizzes[nb_correct == len(other_models) - 1] - log_string(f"keep {to_keep.size(0)} quizzes") kept.append(to_keep) - new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test] + new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test] - task.store_new_quizzes(new_quizzes[:nb_for_train], for_train=True) - task.store_new_quizzes(new_quizzes[nb_for_train:], for_train=False) + 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) - task.save_image( - new_quizzes[:96], + quizz_machine.problem.save_quizzes( + new_c_quizzes[:72], args.result_dir, - f"world_quiz_{n_epoch:04d}_{model.id:02d}.png", + f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}", log_string, ) + return sum_logits / sum_nb_c_quizzes + ###################################################################### @@ -612,16 +429,15 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") ###################################################################### -accuracy_to_make_quizzes = 0.975 -nb_new_quizzes_for_train = 1000 -nb_new_quizzes_for_test = 100 - -if args.check: - accuracy_to_make_quizzes = 0.0 - nb_new_quizzes_for_train = 10 - nb_new_quizzes_for_test = 10 +min_ave_seq_logproba = None for n_epoch in range(args.nb_epochs): + log_string(f"--- epoch {n_epoch} ----------------------------------------") + + 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}") + # select the model with lowest accuracy models.sort(key=lambda model: model.main_test_accuracy) model = models[0] @@ -631,26 +447,45 @@ for n_epoch in range(args.nb_epochs): ) # improve it - one_epoch(model, task) + one_epoch(model, quizz_machine) + + quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts) log_string( - f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}" + f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" ) # test it - run_tests(model, task, deterministic_synthesis=False) + run_tests(model, quizz_machine, deterministic_synthesis=False) - if model.main_test_accuracy >= accuracy_to_make_quizzes: + log_string( + 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: other_models = models.copy() other_models.remove(model) - create_quizzes( + ave_seq_logproba = create_c_quizzes( model, other_models, - task, - nb_for_train=nb_new_quizzes_for_train, - nb_for_test=nb_new_quizzes_for_test, + quizz_machine, + nb_for_train=nb_new_c_quizzes_for_train, + nb_for_test=nb_new_c_quizzes_for_test, + min_ave_seq_logproba=min_ave_seq_logproba, ) + # 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}" + ) + + # We update everyone + for model in models: + run_tests(model, quizz_machine, deterministic_synthesis=False) + ######################################################################