X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=43241dd79a96da7d7a1f902f8be95541d0ab56c7;hb=d3d4ce7bb2b799f4bf81a936987e3a8938514af8;hp=11d712a0d6ece85c5c4a3e37022f85d83870b6c7;hpb=8adf0586ee5aeb9fbdf81b78c7ff4b484a9b82ab;p=culture.git diff --git a/main.py b/main.py index 11d712a..43241dd 100755 --- a/main.py +++ b/main.py @@ -12,7 +12,15 @@ from torch import nn from torch.nn import functional as F import ffutils -import mygpt, tasks, problems +import mygpt +import sky, reasoning, quizz_machine + +# world quizzes vs. culture quizzes + +###################################################################### + +nb_new_c_quizzes_for_train = 1000 +nb_new_c_quizzes_for_test = 100 ###################################################################### @@ -29,14 +37,7 @@ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) -parser.add_argument( - "--task", - type=str, - default="world", - help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, greed", -) - -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) @@ -46,7 +47,7 @@ parser.add_argument("--max_percents_of_test_in_train", type=int, default=1) ######################################## -parser.add_argument("--nb_epochs", type=int, default=100) +parser.add_argument("--nb_epochs", type=int, default=10000) parser.add_argument("--batch_size", type=int, default=None) @@ -56,11 +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("--optim", type=str, default="adam") - -parser.add_argument("--learning_rate", type=float, default=1e-4) - -parser.add_argument("--learning_rate_schedule", type=str, default=None) +parser.add_argument("--learning_rate", type=float, default=1e-3) ######################################## @@ -82,245 +79,68 @@ parser.add_argument("--dropout", type=float, default=0.1) parser.add_argument("--deterministic_synthesis", action="store_true", default=False) -############################## -# filetask - -parser.add_argument("--filetask_train_file", type=str, default=None) - -parser.add_argument("--filetask_test_file", type=str, default=None) - -############################## -# rpl options - -parser.add_argument("--rpl_nb_starting_values", type=int, default=3) - -parser.add_argument("--rpl_max_input", type=int, default=9) - -parser.add_argument("--rpl_prog_len", type=int, default=8) - -parser.add_argument("--rpl_nb_runs", type=int, default=5) - -parser.add_argument("--rpl_no_prog", action="store_true", default=False) - -############################## -# grid options - -parser.add_argument("--grid_size", type=int, default=6) +parser.add_argument("--problem", type=str, default="sky") -parser.add_argument("--grid_fraction_play", type=float, default=0) +parser.add_argument("--nb_gpts", type=int, default=5) -############################## -# picoclvr options - -parser.add_argument("--picoclvr_nb_colors", type=int, default=5) - -parser.add_argument("--picoclvr_height", type=int, default=12) +parser.add_argument("--min_to_validate", type=int, default=None) -parser.add_argument("--picoclvr_width", type=int, default=16) - -parser.add_argument("--picocvlr_prune_properties", type=str, default="none") - -############################## -# Maze options - -parser.add_argument("--maze_height", type=int, default=13) - -parser.add_argument("--maze_width", type=int, default=21) - -parser.add_argument("--maze_nb_walls", type=int, default=15) - -############################## -# Snake options +parser.add_argument("--max_to_validate", type=int, default=None) -parser.add_argument("--snake_height", type=int, default=9) +parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975) -parser.add_argument("--snake_width", type=int, default=12) +parser.add_argument("--generation_temperature", type=float, default=2.0) -parser.add_argument("--snake_nb_colors", type=int, default=5) +parser.add_argument("--deterministic_validation", action="store_true", default=False) -parser.add_argument("--snake_length", type=int, default=200) +parser.add_argument("--bidirectional_validation", action="store_true", default=False) -############################## -# ByHeart options - -parser.add_argument("--byheart_separation", type=int, default=1) - -############################## -# Stack options - -parser.add_argument("--stack_nb_steps", type=int, default=100) - -parser.add_argument("--stack_nb_stacks", type=int, default=3) - -parser.add_argument("--stack_nb_digits", type=int, default=3) - -parser.add_argument("--stack_fraction_values_for_train", type=float, default=None) - -############################## -# Expr options +parser.add_argument("--dirty_debug", action="store_true", default=False) -parser.add_argument("--expr_nb_variables", type=int, default=5) - -parser.add_argument("--expr_sequence_length", type=int, default=40) - -parser.add_argument("--expr_operand_max", type=int, default=9) - -parser.add_argument("--expr_result_max", type=int, default=99) +###################################################################### -parser.add_argument("--expr_input_file", type=str, default=None) +parser.add_argument("--sky_height", type=int, default=6) -############################## -# Mixing +parser.add_argument("--sky_width", type=int, default=8) -parser.add_argument("--mixing_hard", action="store_true", default=False) +parser.add_argument("--sky_nb_birds", type=int, default=3) -parser.add_argument("--mixing_deterministic_start", action="store_true", default=False) +parser.add_argument("--sky_nb_iterations", type=int, default=2) -############################## -# greed options +parser.add_argument("--sky_speed", type=int, default=3) -parser.add_argument("--greed_height", type=int, default=5) +###################################################################### -parser.add_argument("--greed_width", type=int, default=7) +args = parser.parse_args() -parser.add_argument("--greed_T", type=int, default=25) +if args.min_to_validate is None: + args.min_to_validate = args.nb_gpts - 1 -parser.add_argument("--greed_nb_walls", type=int, default=5) +if args.max_to_validate is None: + args.max_to_validate = args.nb_gpts - 1 -parser.add_argument("--greed_nb_coins", type=int, default=2) +if args.result_dir is None: + args.result_dir = f"results_culture" ###################################################################### -args = parser.parse_args() - -assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"} - -if args.result_dir is None: - args.result_dir = f"results_{args.task}" +if args.dirty_debug: + args.accuracy_to_make_c_quizzes = 0.0 + nb_new_c_quizzes_for_train = 100 + nb_new_c_quizzes_for_test = 10 ###################################################################### -default_task_args = { - "world": { - "model": "37M", - "batch_size": 100, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "file": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "addition": { - "model": "352M", - "batch_size": 25, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "byheart": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 50000, - "nb_test_samples": 10000, - }, - "expr": { - "model": "352M", - "batch_size": 25, - "nb_train_samples": 2500000, - "nb_test_samples": 10000, - }, - "grid": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "qmlp": { - "model": "37M", - "batch_size": 10, - "nb_train_samples": 100000, - "nb_test_samples": 1000, - }, - "guessop": { - "model": "352M", - "batch_size": 25, - "nb_train_samples": 1000000, - "nb_test_samples": 10000, - }, - "learnop": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 50000, - "nb_test_samples": 10000, - }, - "maze": { - "model": "37M", - "batch_size": 5, - "nb_train_samples": 100000, - "nb_test_samples": 10000, - }, - "picoclvr": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "rpl": { - "model": "352M", - "batch_size": 5, - "nb_train_samples": 2500000, - "nb_test_samples": 10000, - }, - "snake": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "stack": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 100000, - "nb_test_samples": 1000, - }, - "twotargets": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 50000, - "nb_test_samples": 10000, - }, - "memory": { - "model": "37M", - "batch_size": 100, - "nb_train_samples": 25000, - "nb_test_samples": 1000, - }, - "mixing": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 250000, - "nb_test_samples": 10000, - }, - "mnist": { - "model": "37M", - "batch_size": 10, - "nb_train_samples": 60000, - "nb_test_samples": 10000, - }, - "greed": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 25000, - "nb_test_samples": 10000, - }, +default_args = { + "model": "37M", + "batch_size": 100, + "nb_train_samples": 100000, + "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) ###################################################################### @@ -409,24 +229,9 @@ for n in vars(args): ###################################################################### - -def picoclvr_pruner_horizontal_green(p): - return not ("green" in p and ("left" in p or "right" in p)) - - -picoclvr_pruner_train = ( - picoclvr_pruner_horizontal_green - if args.picocvlr_prune_properties in {"train+eval"} - else None -) - -picoclvr_pruner_eval = ( - (lambda p: not picoclvr_pruner_horizontal_green(p)) - if args.picocvlr_prune_properties in {"train+eval", "eval"} - else None -) - -###################################################################### +if args.dirty_debug: + args.nb_train_samples = 2500 + args.nb_test_samples = 100 if args.physical_batch_size is None: args.physical_batch_size = args.batch_size @@ -436,235 +241,37 @@ 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, +if args.problem == "sky": + problem = sky.Sky( + height=args.sky_height, + width=args.sky_width, + nb_birds=args.sky_nb_birds, + nb_iterations=args.sky_nb_iterations, + speed=args.sky_speed, ) - -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, - ) - + back_accuracy = False +elif args.problem == "reasoning": + problem = reasoning.Reasoning(device=device) + back_accuracy = True else: - raise ValueError(f"Unknown task {args.task}") + raise ValueError + +quizz_machine = quizz_machine.QuizzMachine( + 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, + 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}") @@ -673,8 +280,10 @@ 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) @@ -696,11 +305,11 @@ 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) @@ -716,49 +325,15 @@ if args.max_percents_of_test_in_train >= 0: ############################## -if args.learning_rate_schedule == "cos": - learning_rate_schedule = {} - for n_epoch in range(args.nb_epochs): - u = n_epoch / args.nb_epochs * math.pi - learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u)) -else: - if args.learning_rate_schedule is not None: - u = { - int(k): float(v) - for k, v in [ - tuple(x.split(":")) for x in args.learning_rate_schedule.split(",") - ] - } - else: - u = {} - - learning_rate_schedule = {} - learning_rate = args.learning_rate - for n_epoch in range(args.nb_epochs): - if n_epoch in u: - learning_rate = u[n_epoch] - learning_rate_schedule[n_epoch] = learning_rate - -log_string(f"learning_rate_schedule {learning_rate_schedule}") -###################################################################### - - -def one_epoch(model, task): - if args.optim == "sgd": - optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate) - elif args.optim == "adam": - optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate) - elif args.optim == "adamw": - optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate) - else: - raise ValueError(f"Unknown optimizer {args.optim}.") +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: @@ -783,14 +358,14 @@ 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)) @@ -802,65 +377,115 @@ def run_tests(model, task, deterministic_synthesis): nb_test_samples += input.size(0) - main_test_accuracy = task.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 = quizz_machine.produce_results( n_epoch=n_epoch, model=model, result_dir=args.result_dir, - logger=log_string, 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}") +###################################################################### - model.main_test_accuracy = main_test_accuracy + +def valid_c_quizzes(recorded, criteria): + result = [q[criteria(c)] for q, c in recorded] + return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([]) ###################################################################### -def create_quizzes( - model, - other_models, - task, +def create_c_quizzes( + models, + quizz_machine, nb_for_train=1000, nb_for_test=100, ): - kept = [] + recorded = [] - while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test: - new_quizzes, nb_correct = task.create_new_quizzes( - n_epoch=n_epoch, - result_dir=args.result_dir, - logger=log_string, - nb=4 * (nb_for_train + nb_for_test), - model=model, - other_models=other_models, - ) + nb_to_create = nb_for_train + nb_for_test - 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] + standard_validity = lambda nb_correct: torch.logical_and( + nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate + ) - task.store_new_quizzes(new_quizzes[:nb_for_train], for_train=True) - task.store_new_quizzes(new_quizzes[nb_for_train:], for_train=False) + 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(recorded, standard_validity).size(0) < nb_to_create: + # Select a model at random to generate the new quizzes - task.save_image( - new_quizzes[:96], - args.result_dir, - f"world_new_{n_epoch:04d}.png", - log_string, - ) + model_for_generation = models[torch.randint(len(models), (1,))] + + c_quizzes = quizz_machine.generate_quizzes( + nb_to_create, + model_for_generation=model_for_generation, + temperature=args.generation_temperature, + ) + + nb_correct, seq_logproba = quizz_machine.compute_correctness( + c_quizzes, + models, + bidirectional_validation=args.bidirectional_validation, + deterministic_validation=args.deterministic_validation, + ) + + 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") + + if args.dirty_debug: + nb_correct = torch.randint( + len(models) + 1, nb_correct.size(), device=c_quizzes.device + ) + + recorded.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(recorded, 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) + + 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) + + # save a bunch of images to investigate what quizzes with a + # certain nb of correct predictions look like + + for n in range(len(models) + 1): + s = ( + "_validated" + if n >= args.min_to_validate and n <= args.max_to_validate + else "" + ) + + q = valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72] + + if q.size(0) > 0: + quizz_machine.save_quizzes( + args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q + ) ###################################################################### models = [] -for k in range(5): +for k in range(args.nb_gpts): model = mygpt.MyGPT( vocabulary_size=vocabulary_size, dim_model=args.dim_model, @@ -883,36 +508,49 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") ###################################################################### -accuracy_to_make_quizzes = 0.975 - for n_epoch in range(args.nb_epochs): - models.sort(key=lambda model: model.main_test_accuracy) + 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)) - model = models[0] + log_string( + f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}" + ) + + one_epoch(weakest_model, quizz_machine) log_string( - f"training model {model.id} main_test_accuracy {model.main_test_accuracy}" + f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" ) - one_epoch(model, task) + run_tests(weakest_model, quizz_machine, deterministic_synthesis=False) log_string( - f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}" + f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" ) - run_tests(model, task, deterministic_synthesis=False) + # Replace a fraction of the w_quizzes with fresh ones - if model.main_test_accuracy >= accuracy_to_make_quizzes: - other_models = models.copy() - other_models.remove(model) + quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts) - create_quizzes( - model, - other_models, - task, - nb_for_train=1000, - nb_for_test=100, + # If all the models are good enough, generate new quizzes and + # re-compute the test errors + + if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes: + create_c_quizzes( + models, + quizz_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) + ######################################################################