X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=d412e6c60f6ac119ac9a706ddf27c88120be5b36;hb=c9c018e4c19ce92892d7652082fb90719d57441c;hp=d92c4a58b35b759a923bcc0f2ec3f30b634da4c1;hpb=0388ce599d0c60f1e3de4f796d60a3577081d22f;p=culture.git diff --git a/main.py b/main.py index d92c4a5..d412e6c 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, wireworld, 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="twotargets", - 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=50) +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="10: 2e-5,30: 4e-6") +parser.add_argument("--learning_rate", type=float, default=1e-3) ######################################## @@ -82,251 +79,58 @@ parser.add_argument("--dropout", type=float, default=0.1) parser.add_argument("--deterministic_synthesis", action="store_true", default=False) -parser.add_argument("--no_checkpoint", action="store_true", default=False) - -parser.add_argument("--resume", action="store_true", default=False) - -parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth") - -############################## -# 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("--grid_fraction_play", type=float, default=0) - -############################## -# picoclvr options - -parser.add_argument("--picoclvr_nb_colors", type=int, default=5) - -parser.add_argument("--picoclvr_height", type=int, default=12) - -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("--snake_height", type=int, default=9) - -parser.add_argument("--snake_width", type=int, default=12) - -parser.add_argument("--snake_nb_colors", type=int, default=5) - -parser.add_argument("--snake_length", type=int, default=200) - -############################## -# 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("--expr_nb_variables", type=int, default=5) +parser.add_argument("--reverse_cleanup", action="store_true", default=True) -parser.add_argument("--expr_sequence_length", type=int, default=40) +parser.add_argument("--validation_forward_only", action="store_true", default=False) -parser.add_argument("--expr_operand_max", type=int, default=9) +parser.add_argument("--problem", type=str, default="sky") -parser.add_argument("--expr_result_max", type=int, default=99) +parser.add_argument("--nb_gpts", type=int, default=5) -parser.add_argument("--expr_input_file", type=str, default=None) +parser.add_argument("--min_to_validate", type=int, default=4) -############################## -# Mixing - -parser.add_argument("--mixing_hard", action="store_true", default=False) +parser.add_argument("--max_to_validate", type=int, default=4) -parser.add_argument("--mixing_deterministic_start", action="store_true", default=False) +parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975) -############################## -# greed options +parser.add_argument("--dirty_debug", action="store_true", default=False) -parser.add_argument("--greed_height", type=int, default=5) +parser.add_argument("--sky_height", type=int, default=6) -parser.add_argument("--greed_width", type=int, default=7) +parser.add_argument("--sky_width", type=int, default=8) -parser.add_argument("--greed_T", type=int, default=25) +parser.add_argument("--sky_nb_birds", type=int, default=3) -parser.add_argument("--greed_nb_walls", type=int, default=5) +parser.add_argument("--sky_nb_iterations", type=int, default=2) -parser.add_argument("--greed_nb_coins", type=int, default=2) +parser.add_argument("--sky_speed", type=int, default=3) ###################################################################### 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}" + args.result_dir = f"results_culture" ###################################################################### -default_task_args = { - "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, - }, - "world": { - "model": "37M", - "batch_size": 25, - "nb_train_samples": 50000, - "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, - }, +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_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) ###################################################################### @@ -380,9 +184,8 @@ else: try: os.mkdir(args.result_dir) except FileExistsError: - if not args.resume: - print(f"result directory {args.result_dir} already exists") - exit(1) + print(f"result directory {args.result_dir} already exists") + exit(1) log_file = open(os.path.join(args.result_dir, args.log_filename), "a") @@ -416,24 +219,9 @@ 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 @@ -443,302 +231,46 @@ 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, - 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, +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 == "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, - ) - +elif args.problem == "wireworld": + problem = wireworld.Wireworld(height=8, width=10, nb_iterations=2, speed=5) 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, + 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}") -############################## - -model = mygpt.MyGPT( - vocabulary_size=vocabulary_size, - dim_model=args.dim_model, - dim_keys=args.dim_keys, - dim_hidden=args.dim_hidden, - nb_heads=args.nb_heads, - nb_blocks=args.nb_blocks, - causal=True, - dropout=args.dropout, -) - -model.to(device) - -nb_parameters = sum(p.numel() for p in model.parameters()) -log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") - -###################################################################### - -nb_epochs_finished = 0 - -if args.no_checkpoint: - log_string(f"not trying to load checkpoint.") - -else: - try: - checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name) - checkpoint = torch.load(checkpoint_name) - nb_epochs_finished = checkpoint["nb_epochs_finished"] - model.load_state_dict(checkpoint["model_state"]) - torch.set_rng_state(checkpoint["rng_state"]) - if torch.cuda.is_available(): - torch.cuda.set_rng_state(checkpoint["cuda_rng_state"]) - - log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.") - - except FileNotFoundError: - log_string("starting from scratch.") - - except: - log_string("error when loading the checkpoint.") - exit(1) - -###################################################################### - -if args.task == "expr" and args.expr_input_file is not None: - task.produce_results( - n_epoch=nb_epochs_finished, - model=model, - result_dir=args.result_dir, - logger=log_string, - deterministic_synthesis=args.deterministic_synthesis, - input_file=args.expr_input_file, - ) - - exit(0) - ###################################################################### # 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) @@ -760,11 +292,11 @@ 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) @@ -780,61 +312,15 @@ 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: - u = { - int(k): float(v) - for k, v in [ - tuple(x.split(":")) for x in args.learning_rate_schedule.split(",") - ] - } - - 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}") -############################## - -if nb_epochs_finished >= args.nb_epochs: - task.produce_results( - n_epoch=nb_epochs_finished, - model=model, - result_dir=args.result_dir, - logger=log_string, - deterministic_synthesis=args.deterministic_synthesis, - ) - -time_pred_result = None - -###################################################################### - - -def one_epoch(model, task, learning_rate): - log_string(f"learning_rate {learning_rate}") - - if args.optim == "sgd": - optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) - elif args.optim == "adam": - optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) - elif args.optim == "adamw": - optimizer = torch.optim.AdamW(model.parameters(), lr=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: @@ -853,20 +339,20 @@ def one_epoch(model, task, learning_rate): train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples)) - log_string(f"train)perplexity {n_epoch} {train_perplexity}") + log_string(f"train_perplexity {n_epoch} {train_perplexity}") ###################################################################### -def run_tests(model, task): +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)) @@ -878,45 +364,168 @@ def run_tests(model, task): nb_test_samples += input.size(0) - task.produce_results( + model.main_test_accuracy = quizz_machine.produce_results( n_epoch=n_epoch, model=model, result_dir=args.result_dir, - logger=log_string, - deterministic_synthesis=args.deterministic_synthesis, + 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}") + + log_string(f"test_perplexity {n_epoch} {test_perplexity}") ###################################################################### -for n_epoch in range(nb_epochs_finished, args.nb_epochs): - learning_rate = learning_rate_schedule[n_epoch] - one_epoch(model, task, learning_rate) +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([]) - run_tests(model, task) - time_current_result = datetime.datetime.now() - if time_pred_result is not None: +###################################################################### + + +def create_c_quizzes( + models, + quizz_machine, + nb_for_train=1000, + nb_for_test=100, +): + recorded = [] + + nb_to_create = 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 + ) + + while valid_c_quizzes(recorded, standard_validity).size(0) < nb_to_create: + model_for_generation = models[torch.randint(len(models), (1,))] + + c_quizzes, ave_seq_logproba = quizz_machine.generate_quizzes( + nb_to_create, + model_for_generation=model_for_generation, + reverse_cleanup=args.reverse_cleanup, + ) + + nb_correct = quizz_machine.compute_correctness( + c_quizzes, models, both_directions=not args.validation_forward_only + ) + + 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"next_result {time_current_result + (time_current_result - time_pred_result)}" + f"keep c_quizzes kept {nv} nb_accumulated {nb_validated} / {nb_to_create}" ) - time_pred_result = time_current_result - checkpoint = { - "nb_epochs_finished": n_epoch + 1, - "model_state": model.state_dict(), - "rng_state": torch.get_rng_state(), - } + # store the new c_quizzes which have been validated - if torch.cuda.is_available(): - checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state() + 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.problem.save_quizzes( + q, + args.result_dir, + f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", + ) + + +###################################################################### + +models = [] + +for k in range(args.nb_gpts): + model = mygpt.MyGPT( + vocabulary_size=vocabulary_size, + dim_model=args.dim_model, + dim_keys=args.dim_keys, + dim_hidden=args.dim_hidden, + nb_heads=args.nb_heads, + nb_blocks=args.nb_blocks, + causal=True, + dropout=args.dropout, + ).to(device) + + model.main_test_accuracy = 0.0 + model.id = k + + models.append(model) + + +nb_parameters = sum(p.numel() for p in models[0].parameters()) +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} ----------------------------------------") + + weakest_model = min(models, key=lambda m: float(m.main_test_accuracy)) + + log_string( + f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}" + ) + + # improve it + one_epoch(weakest_model, quizz_machine) + + log_string( + f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}" + ) + + # test it + run_tests(weakest_model, quizz_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}" + ) + + 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) + + 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, + ) + + # We update everyone + for model in models: + run_tests(model, quizz_machine, deterministic_synthesis=False) - checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name) - torch.save(checkpoint, checkpoint_name) - log_string(f"saved checkpoint {checkpoint_name}") ######################################################################