3 # Any copyright is dedicated to the Public Domain.
4 # https://creativecommons.org/publicdomain/zero/1.0/
6 # Written by Francois Fleuret <francois@fleuret.org>
8 import math, sys, argparse, time, tqdm, os, datetime
10 import torch, torchvision
12 from torch.nn import functional as F
15 import mygpt, tasks, problems
17 ######################################################################
19 if torch.cuda.is_available():
20 device = torch.device("cuda")
21 torch.backends.cuda.matmul.allow_tf32 = True
23 device = torch.device("cpu")
25 ######################################################################
27 parser = argparse.ArgumentParser(
28 description="An implementation of GPT with cache.",
29 formatter_class=argparse.ArgumentDefaultsHelpFormatter,
36 help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp",
39 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
41 parser.add_argument("--result_dir", type=str, default=None)
43 parser.add_argument("--seed", type=int, default=0)
45 parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
47 ########################################
49 parser.add_argument("--nb_epochs", type=int, default=25)
51 parser.add_argument("--batch_size", type=int, default=None)
53 parser.add_argument("--nb_train_samples", type=int, default=None)
55 parser.add_argument("--nb_test_samples", type=int, default=None)
57 parser.add_argument("--optim", type=str, default="adam")
59 parser.add_argument("--learning_rate", type=float, default=1e-4)
61 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
63 ########################################
65 parser.add_argument("--model", type=str, default=None)
67 parser.add_argument("--dim_model", type=int, default=None)
69 parser.add_argument("--dim_keys", type=int, default=None)
71 parser.add_argument("--dim_hidden", type=int, default=None)
73 parser.add_argument("--nb_heads", type=int, default=None)
75 parser.add_argument("--nb_blocks", type=int, default=None)
77 parser.add_argument("--dropout", type=float, default=0.1)
79 ########################################
81 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
83 parser.add_argument("--no_checkpoint", action="store_true", default=False)
85 parser.add_argument("--overwrite_results", action="store_true", default=False)
87 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
89 ##############################
92 parser.add_argument("--filetask_file", type=str, default=None)
94 ##############################
97 parser.add_argument("--rpl_nb_starting_values", type=int, default=3)
99 parser.add_argument("--rpl_max_input", type=int, default=9)
101 parser.add_argument("--rpl_prog_len", type=int, default=8)
103 parser.add_argument("--rpl_nb_runs", type=int, default=5)
105 parser.add_argument("--rpl_no_prog", action="store_true", default=False)
107 ##############################
110 parser.add_argument("--grid_size", type=int, default=6)
112 parser.add_argument("--grid_fraction_play", type=float, default=0)
114 ##############################
117 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
119 parser.add_argument("--picoclvr_height", type=int, default=12)
121 parser.add_argument("--picoclvr_width", type=int, default=16)
123 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
125 ##############################
128 parser.add_argument("--maze_height", type=int, default=13)
130 parser.add_argument("--maze_width", type=int, default=21)
132 parser.add_argument("--maze_nb_walls", type=int, default=15)
134 ##############################
137 parser.add_argument("--snake_height", type=int, default=9)
139 parser.add_argument("--snake_width", type=int, default=12)
141 parser.add_argument("--snake_nb_colors", type=int, default=5)
143 parser.add_argument("--snake_length", type=int, default=200)
145 ##############################
148 parser.add_argument("--stack_nb_steps", type=int, default=100)
150 parser.add_argument("--stack_nb_stacks", type=int, default=3)
152 parser.add_argument("--stack_nb_digits", type=int, default=3)
154 parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
156 ##############################
159 parser.add_argument("--expr_nb_variables", type=int, default=5)
161 parser.add_argument("--expr_sequence_length", type=int, default=40)
163 parser.add_argument("--expr_operand_max", type=int, default=9)
165 parser.add_argument("--expr_result_max", type=int, default=99)
167 parser.add_argument("--expr_input_file", type=str, default=None)
169 ##############################
172 parser.add_argument("--mixing_hard", action="store_true", default=False)
174 parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
176 ######################################################################
178 args = parser.parse_args()
180 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
182 if args.result_dir is None:
183 args.result_dir = f"results_{args.task}"
185 ######################################################################
187 default_task_args = {
191 "nb_train_samples": 250000,
192 "nb_test_samples": 10000,
197 "nb_train_samples": 250000,
198 "nb_test_samples": 10000,
203 "nb_train_samples": 50000,
204 "nb_test_samples": 10000,
209 "nb_train_samples": 2500000,
210 "nb_test_samples": 10000,
215 "nb_train_samples": 250000,
216 "nb_test_samples": 10000,
221 "nb_train_samples": 100000,
222 "nb_test_samples": 1000,
227 "nb_train_samples": 1000000,
228 "nb_test_samples": 10000,
233 "nb_train_samples": 50000,
234 "nb_test_samples": 10000,
239 "nb_train_samples": 100000,
240 "nb_test_samples": 10000,
245 "nb_train_samples": 250000,
246 "nb_test_samples": 10000,
251 "nb_train_samples": 2500000,
252 "nb_test_samples": 10000,
257 "nb_train_samples": 250000,
258 "nb_test_samples": 10000,
263 "nb_train_samples": 100000,
264 "nb_test_samples": 1000,
269 "nb_train_samples": 50000,
270 "nb_test_samples": 10000,
275 "nb_train_samples": 25000,
276 "nb_test_samples": 1000,
281 "nb_train_samples": 250000,
282 "nb_test_samples": 10000,
287 "nb_train_samples": 60000,
288 "nb_test_samples": 10000,
292 if args.task in default_task_args:
293 for k, v in default_task_args[args.task].items():
294 if getattr(args, k) is None:
297 ######################################################################
299 default_model_args = {
337 if args.model in default_model_args:
338 for k, v in default_model_args[args.model].items():
339 if getattr(args, k) is None:
342 raise ValueError(f"Unknown model {args.model}")
344 ######################################################################
347 os.mkdir(args.result_dir)
348 except FileExistsError:
349 if not args.overwrite_results:
350 print(f"result directory {args.result_dir} already exists")
353 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
356 # torch.backends.cudnn.deterministic = True
357 # torch.backends.cudnn.benchmark = False
358 # torch.use_deterministic_algorithms(True)
359 torch.manual_seed(args.seed)
360 if torch.cuda.is_available():
361 torch.cuda.manual_seed_all(args.seed)
363 ######################################################################
367 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
369 if log_file is not None:
370 log_file.write(t + s + "\n")
377 log_string(f"argv {' '.join(sys.argv)}")
380 log_string(f"args.{n} {getattr(args, n)}")
383 ######################################################################
386 def picoclvr_pruner_horizontal_green(p):
387 return not ("green" in p and ("left" in p or "right" in p))
390 picoclvr_pruner_train = (
391 picoclvr_pruner_horizontal_green
392 if args.picocvlr_prune_properties in {"train+eval"}
396 picoclvr_pruner_eval = (
397 (lambda p: not picoclvr_pruner_horizontal_green(p))
398 if args.picocvlr_prune_properties in {"train+eval", "eval"}
402 ######################################################################
404 if args.task == "file":
406 args.filetask_file is not None
407 ), "You have to specify the task file with --filetask_file <filename>"
408 task = tasks.TaskFromFile(
410 nb_train_samples=args.nb_train_samples,
411 nb_test_samples=args.nb_test_samples,
412 batch_size=args.batch_size,
415 args.max_percents_of_test_in_train = 0
417 elif args.task == "byheart":
418 task = tasks.SandBox(
419 problem=problems.ProblemByHeart(),
420 nb_train_samples=args.nb_train_samples,
421 nb_test_samples=args.nb_test_samples,
422 batch_size=args.batch_size,
426 args.max_percents_of_test_in_train = -1
428 elif args.task == "learnop":
429 task = tasks.SandBox(
430 problem=problems.ProblemLearnOperator(),
431 nb_train_samples=args.nb_train_samples,
432 nb_test_samples=args.nb_test_samples,
433 batch_size=args.batch_size,
439 elif args.task == "guessop":
440 task = tasks.SandBox(
441 problem=problems.ProblemGuessOperator(),
442 nb_train_samples=args.nb_train_samples,
443 nb_test_samples=args.nb_test_samples,
444 batch_size=args.batch_size,
450 elif args.task == "twotargets":
451 task = tasks.SandBox(
452 problem=problems.ProblemTwoTargets(),
453 nb_train_samples=args.nb_train_samples,
454 nb_test_samples=args.nb_test_samples,
455 batch_size=args.batch_size,
460 elif args.task == "memory":
461 task = tasks.SandBox(
462 problem=problems.ProblemMemory(),
463 nb_train_samples=args.nb_train_samples,
464 nb_test_samples=args.nb_test_samples,
465 batch_size=args.batch_size,
470 elif args.task == "mixing":
471 task = tasks.SandBox(
472 problem=problems.ProblemMixing(
473 hard=args.mixing_hard, random_start=not args.mixing_deterministic_start
475 nb_train_samples=args.nb_train_samples,
476 nb_test_samples=args.nb_test_samples,
477 batch_size=args.batch_size,
482 elif args.task == "addition":
483 task = tasks.SandBox(
484 problem=problems.ProblemAddition(),
485 nb_train_samples=args.nb_train_samples,
486 nb_test_samples=args.nb_test_samples,
487 batch_size=args.batch_size,
492 elif args.task == "picoclvr":
493 task = tasks.PicoCLVR(
494 nb_train_samples=args.nb_train_samples,
495 nb_test_samples=args.nb_test_samples,
496 batch_size=args.batch_size,
497 height=args.picoclvr_height,
498 width=args.picoclvr_width,
499 nb_colors=args.picoclvr_nb_colors,
502 pruner_train=picoclvr_pruner_train,
503 pruner_eval=picoclvr_pruner_eval,
506 elif args.task == "mnist":
508 nb_train_samples=args.nb_train_samples,
509 nb_test_samples=args.nb_test_samples,
510 batch_size=args.batch_size,
514 elif args.task == "maze":
516 nb_train_samples=args.nb_train_samples,
517 nb_test_samples=args.nb_test_samples,
518 batch_size=args.batch_size,
519 height=args.maze_height,
520 width=args.maze_width,
521 nb_walls=args.maze_nb_walls,
525 elif args.task == "snake":
527 nb_train_samples=args.nb_train_samples,
528 nb_test_samples=args.nb_test_samples,
529 batch_size=args.batch_size,
530 height=args.snake_height,
531 width=args.snake_width,
532 nb_colors=args.snake_nb_colors,
533 length=args.snake_length,
534 prompt_length=args.snake_length // 2,
538 elif args.task == "stack":
540 nb_train_samples=args.nb_train_samples,
541 nb_test_samples=args.nb_test_samples,
542 batch_size=args.batch_size,
544 nb_steps=args.stack_nb_steps,
545 nb_stacks=args.stack_nb_stacks,
546 nb_digits=args.stack_nb_digits,
547 fraction_values_for_train=args.stack_fraction_values_for_train,
551 elif args.task == "expr":
553 nb_train_samples=args.nb_train_samples,
554 nb_test_samples=args.nb_test_samples,
555 nb_variables=args.expr_nb_variables,
556 sequence_length=args.expr_sequence_length,
557 operand_max=args.expr_operand_max,
558 result_max=args.expr_result_max,
559 batch_size=args.batch_size,
563 elif args.task == "rpl":
565 nb_train_samples=args.nb_train_samples,
566 nb_test_samples=args.nb_test_samples,
567 batch_size=args.batch_size,
568 nb_starting_values=args.rpl_nb_starting_values,
569 max_input=args.rpl_max_input,
570 prog_len=args.rpl_prog_len,
571 nb_runs=args.rpl_nb_runs,
572 no_prog=args.rpl_no_prog,
577 elif args.task == "grid":
579 nb_train_samples=args.nb_train_samples,
580 nb_test_samples=args.nb_test_samples,
581 batch_size=args.batch_size,
583 fraction_play=args.grid_fraction_play,
588 elif args.task == "qmlp":
590 nb_train_samples=args.nb_train_samples,
591 nb_test_samples=args.nb_test_samples,
592 batch_size=args.batch_size,
593 result_dir=args.result_dir,
599 raise ValueError(f"Unknown task {args.task}")
601 ######################################################################
603 log_string(f"device {device}")
605 vocabulary_size = task.vocabulary_size()
607 log_string(f"vocabulary_size {vocabulary_size}")
609 ##############################
612 vocabulary_size=vocabulary_size,
613 dim_model=args.dim_model,
614 dim_keys=args.dim_keys,
615 dim_hidden=args.dim_hidden,
616 nb_heads=args.nb_heads,
617 nb_blocks=args.nb_blocks,
619 dropout=args.dropout,
624 nb_parameters = sum(p.numel() for p in model.parameters())
625 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
627 ######################################################################
629 nb_epochs_finished = 0
631 if args.no_checkpoint:
632 log_string(f"not trying to load checkpoint.")
636 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
637 checkpoint = torch.load(checkpoint_name)
638 nb_epochs_finished = checkpoint["nb_epochs_finished"]
639 model.load_state_dict(checkpoint["model_state"])
640 torch.set_rng_state(checkpoint["rng_state"])
641 if torch.cuda.is_available():
642 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
644 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
646 except FileNotFoundError:
647 log_string("starting from scratch.")
650 log_string("error when loading the checkpoint.")
653 ######################################################################
655 if args.task == "expr" and args.expr_input_file is not None:
656 task.produce_results(
657 n_epoch=nb_epochs_finished,
659 result_dir=args.result_dir,
661 deterministic_synthesis=args.deterministic_synthesis,
662 input_file=args.expr_input_file,
667 ######################################################################
669 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
671 # Compute the entropy of the training tokens
674 for input in task.batches(split="train"):
675 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
676 token_probas = token_count / token_count.sum()
677 entropy = -torch.xlogy(token_probas, token_probas).sum()
678 train_set_perplexity = math.exp(entropy)
680 ######################################################################
681 # A bit of paranoia never hurts
683 if args.max_percents_of_test_in_train >= 0:
685 def subsets_as_tuples(batches, cs):
687 for batch in batches:
689 s.add(tuple([v.item() for v in x]))
695 nb_test, nb_in_train = 0, 0
696 for test_subset in subsets_as_tuples(task.batches(split="test"), 25000):
698 for train_subset in subsets_as_tuples(task.batches(split="train"), 25000):
699 in_train.update(test_subset.intersection(train_subset))
700 nb_in_train += len(in_train)
701 nb_test += len(test_subset)
704 f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
708 nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
709 ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
711 ##############################
713 if args.learning_rate_schedule == "cos":
714 learning_rate_schedule = {}
715 for n_epoch in range(args.nb_epochs):
716 u = n_epoch / args.nb_epochs * math.pi
717 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
722 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
726 learning_rate_schedule = {}
727 learning_rate = args.learning_rate
728 for n_epoch in range(args.nb_epochs):
730 learning_rate = u[n_epoch]
731 learning_rate_schedule[n_epoch] = learning_rate
733 log_string(f"learning_rate_schedule {learning_rate_schedule}")
735 ##############################
739 if nb_epochs_finished >= nb_epochs:
740 task.produce_results(
741 n_epoch=nb_epochs_finished,
743 result_dir=args.result_dir,
745 deterministic_synthesis=args.deterministic_synthesis,
748 time_pred_result = None
750 for n_epoch in range(nb_epochs_finished, nb_epochs):
751 learning_rate = learning_rate_schedule[n_epoch]
753 log_string(f"learning_rate {learning_rate}")
755 if args.optim == "sgd":
756 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
757 elif args.optim == "adam":
758 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
759 elif args.optim == "adamw":
760 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
762 raise ValueError(f"Unknown optimizer {args.optim}.")
766 nb_train_samples, acc_train_loss = 0, 0.0
768 for input in task.batches(split="train"):
769 input = input.to(device)
770 output = model(mygpt.BracketedSequence(input)).x
771 loss = F.cross_entropy(output.transpose(1, 2), input)
772 acc_train_loss += loss.item() * input.size(0)
773 nb_train_samples += input.size(0)
774 nb_samples_seen += input.size(0)
776 optimizer.zero_grad()
780 with torch.autograd.no_grad():
783 nb_test_samples, acc_test_loss = 0, 0.0
785 for input in task.batches(split="test"):
786 input = input.to(device)
788 output = model(mygpt.BracketedSequence(input)).x
789 loss = F.cross_entropy(output.transpose(1, 2), input)
790 acc_test_loss += loss.item() * input.size(0)
791 nb_test_samples += input.size(0)
793 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
794 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
797 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
800 task.produce_results(
803 result_dir=args.result_dir,
805 deterministic_synthesis=args.deterministic_synthesis,
808 time_current_result = datetime.datetime.now()
809 if time_pred_result is not None:
811 f"next_result {time_current_result + (time_current_result - time_pred_result)}"
813 time_pred_result = time_current_result
816 "nb_epochs_finished": n_epoch + 1,
817 "model_state": model.state_dict(),
818 "rng_state": torch.get_rng_state(),
821 if torch.cuda.is_available():
822 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
824 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
825 torch.save(checkpoint, checkpoint_name)
826 log_string(f"saved checkpoint {checkpoint_name}")
828 ######################################################################