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, warnings
10 import torch, torchvision
12 from torch.nn import functional as F
15 import mygpt, tasks, problems
17 ######################################################################
22 if x in {"1", "true", "yes"}:
24 elif x in {"0", "false", "no"}:
30 parser = argparse.ArgumentParser(
31 description="An implementation of GPT with cache.",
32 formatter_class=argparse.ArgumentDefaultsHelpFormatter,
39 help="byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp",
42 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
44 parser.add_argument("--result_dir", type=str, default=None)
46 parser.add_argument("--seed", type=int, default=0)
48 parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
50 parser.add_argument("--force_cpu", type=str2bool, default=False)
52 ########################################
54 parser.add_argument("--nb_epochs", type=int, default=50)
56 parser.add_argument("--batch_size", type=int, default=None)
58 parser.add_argument("--nb_train_samples", type=int, default=None)
60 parser.add_argument("--nb_test_samples", type=int, default=None)
62 parser.add_argument("--optim", type=str, default="adam")
64 ########################################
66 parser.add_argument("--nb_warmup_iter", type=int, default=100)
68 parser.add_argument("--nb_decay_iter", type=int, default=5000)
70 parser.add_argument("--learning_rate", type=float, default=6e-4)
72 parser.add_argument("--min_learning_rate", type=float, default=6e-5)
76 parser.add_argument("--legacy_lr_schedule", type=str2bool, default=True)
78 parser.add_argument("--legacy_large_lr", type=float, default=1e-4)
80 parser.add_argument("--legacy_small_lr", type=float, default=2e-5)
82 parser.add_argument("--legacy_nb_epoch_large_lr", type=float, default=10)
84 ########################################
86 parser.add_argument("--model", type=str, default=None)
88 parser.add_argument("--attention", type=str, default=None)
90 parser.add_argument("--dim_model", type=int, default=None)
92 parser.add_argument("--dim_keys", type=int, default=None)
94 parser.add_argument("--dim_hidden", type=int, default=None)
96 parser.add_argument("--nb_heads", type=int, default=None)
98 parser.add_argument("--nb_lines", type=int, default=None)
100 parser.add_argument("--caterpillar_height", type=int, default=None)
102 parser.add_argument("--gate_dropout_proba", type=float, default=0.0)
104 parser.add_argument("--gate_dropout_sync", type=bool, default=False)
106 parser.add_argument("--rho_inner_loss", type=float, default=0.0)
108 parser.add_argument("--nb_blocks", type=int, default=None)
110 parser.add_argument("--dropout", type=float, default=0.1)
112 ########################################
114 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
116 parser.add_argument("--no_checkpoint", action="store_true", default=False)
118 parser.add_argument("--continue_training", action="store_true", default=False)
120 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
122 ##############################
125 parser.add_argument("--rpl_nb_starting_values", type=int, default=3)
127 parser.add_argument("--rpl_max_input", type=int, default=9)
129 parser.add_argument("--rpl_prog_len", type=int, default=8)
131 parser.add_argument("--rpl_nb_runs", type=int, default=5)
133 parser.add_argument("--rpl_no_prog", action="store_true", default=False)
135 ##############################
138 parser.add_argument("--grid_size", type=int, default=6)
140 parser.add_argument("--grid_nb_colors", type=int, default=6)
142 parser.add_argument("--grid_nb_shapes", type=int, default=6)
144 ##############################
147 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
149 parser.add_argument("--picoclvr_height", type=int, default=12)
151 parser.add_argument("--picoclvr_width", type=int, default=16)
153 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
155 ##############################
158 parser.add_argument("--maze_height", type=int, default=13)
160 parser.add_argument("--maze_width", type=int, default=21)
162 parser.add_argument("--maze_nb_walls", type=int, default=15)
164 ##############################
167 parser.add_argument("--snake_height", type=int, default=9)
169 parser.add_argument("--snake_width", type=int, default=12)
171 parser.add_argument("--snake_nb_colors", type=int, default=5)
173 parser.add_argument("--snake_length", type=int, default=200)
175 ##############################
178 parser.add_argument("--stack_nb_steps", type=int, default=100)
180 parser.add_argument("--stack_nb_stacks", type=int, default=3)
182 parser.add_argument("--stack_nb_digits", type=int, default=3)
184 parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
186 ##############################
189 parser.add_argument("--expr_nb_variables", type=int, default=5)
191 parser.add_argument("--expr_sequence_length", type=int, default=40)
193 parser.add_argument("--expr_operand_max", type=int, default=9)
195 parser.add_argument("--expr_result_max", type=int, default=99)
197 parser.add_argument("--expr_input_file", type=str, default=None)
199 ##############################
202 parser.add_argument("--memory_len_total", type=int, default=32)
204 ##############################
207 parser.add_argument("--mixing_hard", action="store_true", default=False)
209 parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
211 ######################################################################
213 # args = parser.parse_args()
215 args, sup_args = parser.parse_known_args()
217 sup_args = dict([x.removeprefix("--").split("=") for x in sup_args])
219 if args.result_dir is None:
220 args.result_dir = f"results_{args.task}_{args.model}"
222 ######################################################################
224 if not args.force_cpu and torch.cuda.is_available():
225 device = torch.device("cuda")
226 torch.backends.cuda.matmul.allow_tf32 = True
228 device = torch.device("cpu")
230 ######################################################################
232 default_task_args = {
236 "nb_train_samples": 250000,
237 "nb_test_samples": 10000,
242 "nb_train_samples": 50000,
243 "nb_test_samples": 10000,
248 "nb_train_samples": 2500000,
249 "nb_test_samples": 10000,
254 "nb_train_samples": 250000,
255 "nb_test_samples": 10000,
260 "nb_train_samples": 100000,
261 "nb_test_samples": 1000,
266 "nb_train_samples": 1000000,
267 "nb_test_samples": 10000,
272 "nb_train_samples": 50000,
273 "nb_test_samples": 10000,
278 "nb_train_samples": 100000,
279 "nb_test_samples": 10000,
284 "nb_train_samples": 250000,
285 "nb_test_samples": 10000,
290 "nb_train_samples": 2500000,
291 "nb_test_samples": 10000,
296 "nb_train_samples": 250000,
297 "nb_test_samples": 10000,
302 "nb_train_samples": 100000,
303 "nb_test_samples": 1000,
308 "nb_train_samples": 50000,
309 "nb_test_samples": 10000,
314 "nb_train_samples": 25000,
315 "nb_test_samples": 10000,
320 "nb_train_samples": 250000,
321 "nb_test_samples": 10000,
326 "nb_train_samples": 60000,
327 "nb_test_samples": 10000,
331 if args.task in default_task_args:
332 for k, v in default_task_args[args.task].items():
333 if getattr(args, k) is None:
336 ######################################################################
338 default_model_args = {
348 "attention": "caterpillar",
354 "caterpillar_height": 4,
366 "attention": "caterpillar",
372 "caterpillar_height": 4,
384 "attention": "caterpillar",
390 "caterpillar_height": 32,
402 "attention": "caterpillar",
419 "attention": "caterpillar",
429 if args.model in default_model_args:
430 for k, v in default_model_args[args.model].items():
431 if getattr(args, k) is None:
434 raise ValueError(f"Unknown model {args.model}")
436 ######################################################################
439 os.mkdir(args.result_dir)
440 except FileExistsError:
441 if not args.continue_training:
442 print(f"result directory {args.result_dir} already exists")
445 loss_file = open(os.path.join(args.result_dir, "loss.dat"), "a")
447 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
450 # torch.backends.cudnn.deterministic = True
451 # torch.backends.cudnn.benchmark = False
452 # torch.use_deterministic_algorithms(True)
453 torch.manual_seed(args.seed)
454 if torch.cuda.is_available():
455 torch.cuda.manual_seed_all(args.seed)
457 ######################################################################
461 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
463 if log_file is not None:
464 log_file.write(t + s + "\n")
471 with os.popen("sha256sum *.py") as f:
473 log_string(f"sha256sum {l.strip()}")
475 now = time.strftime("%Y%m%d-%H%M%S", time.localtime())
476 os.system(f"tar zcvf {args.result_dir}/src-{now}.tgz *.py *.sh")
478 log_string(f"argv {' '.join(sys.argv)}")
481 log_string(f"args.{n} {getattr(args, n)}")
483 for k, v in sup_args.items():
484 log_string(f'sup_args["{k}"] "{v}"')
487 ######################################################################
490 def get_lr(n_epoch, it):
491 if args.legacy_lr_schedule:
492 # my crude scheduling to compare to previous baseline, added
495 if it < args.nb_warmup_iter:
496 return args.legacy_large_lr * it / args.nb_warmup_iter
497 elif n_epoch < args.legacy_nb_epoch_large_lr:
498 return args.legacy_large_lr
500 return args.legacy_small_lr
504 # 1) linear warmup for warmup_iter steps
505 if it < args.nb_warmup_iter:
506 return args.learning_rate * it / args.nb_warmup_iter
507 # 2) if it > nb_decay_iter, return min learning rate
508 if it > args.nb_decay_iter:
509 return args.min_learning_rate
510 # 3) in between, use cosine decay down to min learning rate
511 decay_ratio = (it - args.nb_warmup_iter) / (
512 args.nb_decay_iter - args.nb_warmup_iter
514 coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1
515 return args.min_learning_rate + coeff * (
516 args.learning_rate - args.min_learning_rate
520 ######################################################################
523 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
526 def picoclvr_pruner_horizontal_green(p):
527 return not ("green" in p and ("left" in p or "right" in p))
530 picoclvr_pruner_train = (
531 picoclvr_pruner_horizontal_green
532 if args.picocvlr_prune_properties in {"train+eval"}
536 picoclvr_pruner_eval = (
537 (lambda p: not picoclvr_pruner_horizontal_green(p))
538 if args.picocvlr_prune_properties in {"train+eval", "eval"}
542 ######################################################################
546 if args.task == "byheart":
547 task = tasks.SandBox(
548 problem=problems.ProblemByHeart(),
549 nb_train_samples=args.nb_train_samples,
550 nb_test_samples=args.nb_test_samples,
551 batch_size=args.batch_size,
555 args.max_percents_of_test_in_train = -1
557 elif args.task == "learnop":
558 task = tasks.SandBox(
559 problem=problems.ProblemLearnOperator(),
560 nb_train_samples=args.nb_train_samples,
561 nb_test_samples=args.nb_test_samples,
562 batch_size=args.batch_size,
568 elif args.task == "guessop":
569 task = tasks.SandBox(
570 problem=problems.ProblemGuessOperator(),
571 nb_train_samples=args.nb_train_samples,
572 nb_test_samples=args.nb_test_samples,
573 batch_size=args.batch_size,
579 elif args.task == "twotargets":
580 task = tasks.SandBox(
581 problem=problems.ProblemTwoTargets(),
582 nb_train_samples=args.nb_train_samples,
583 nb_test_samples=args.nb_test_samples,
584 batch_size=args.batch_size,
589 elif args.task == "memory":
590 task = tasks.SandBox(
591 problem=problems.ProblemMemory(len_total=args.memory_len_total),
592 nb_train_samples=args.nb_train_samples,
593 nb_test_samples=args.nb_test_samples,
594 batch_size=args.batch_size,
599 elif args.task == "mixing":
600 task = tasks.SandBox(
601 problem=problems.ProblemMixing(
602 hard=args.mixing_hard, random_start=not args.mixing_deterministic_start
604 nb_train_samples=args.nb_train_samples,
605 nb_test_samples=args.nb_test_samples,
606 batch_size=args.batch_size,
611 elif args.task == "addition":
612 task = tasks.SandBox(
613 problem=problems.ProblemAddition(),
614 nb_train_samples=args.nb_train_samples,
615 nb_test_samples=args.nb_test_samples,
616 batch_size=args.batch_size,
621 elif args.task == "picoclvr":
622 task = tasks.PicoCLVR(
623 nb_train_samples=args.nb_train_samples,
624 nb_test_samples=args.nb_test_samples,
625 batch_size=args.batch_size,
626 height=args.picoclvr_height,
627 width=args.picoclvr_width,
628 nb_colors=args.picoclvr_nb_colors,
631 pruner_train=picoclvr_pruner_train,
632 pruner_eval=picoclvr_pruner_eval,
635 elif args.task == "mnist":
637 nb_train_samples=args.nb_train_samples,
638 nb_test_samples=args.nb_test_samples,
639 batch_size=args.batch_size,
643 elif args.task == "maze":
645 nb_train_samples=args.nb_train_samples,
646 nb_test_samples=args.nb_test_samples,
647 batch_size=args.batch_size,
648 height=args.maze_height,
649 width=args.maze_width,
650 nb_walls=args.maze_nb_walls,
654 elif args.task == "snake":
656 nb_train_samples=args.nb_train_samples,
657 nb_test_samples=args.nb_test_samples,
658 batch_size=args.batch_size,
659 height=args.snake_height,
660 width=args.snake_width,
661 nb_colors=args.snake_nb_colors,
662 length=args.snake_length,
663 prompt_length=args.snake_length // 2,
667 elif args.task == "stack":
669 nb_train_samples=args.nb_train_samples,
670 nb_test_samples=args.nb_test_samples,
671 batch_size=args.batch_size,
673 nb_steps=args.stack_nb_steps,
674 nb_stacks=args.stack_nb_stacks,
675 nb_digits=args.stack_nb_digits,
676 fraction_values_for_train=args.stack_fraction_values_for_train,
680 elif args.task == "expr":
682 nb_train_samples=args.nb_train_samples,
683 nb_test_samples=args.nb_test_samples,
684 nb_variables=args.expr_nb_variables,
685 sequence_length=args.expr_sequence_length,
686 operand_max=args.expr_operand_max,
687 result_max=args.expr_result_max,
688 batch_size=args.batch_size,
692 elif args.task == "rpl":
694 nb_train_samples=args.nb_train_samples,
695 nb_test_samples=args.nb_test_samples,
696 batch_size=args.batch_size,
697 nb_starting_values=args.rpl_nb_starting_values,
698 max_input=args.rpl_max_input,
699 prog_len=args.rpl_prog_len,
700 nb_runs=args.rpl_nb_runs,
701 no_prog=args.rpl_no_prog,
706 elif args.task == "grid":
708 nb_train_samples=args.nb_train_samples,
709 nb_test_samples=args.nb_test_samples,
710 batch_size=args.batch_size,
712 nb_shapes=args.grid_nb_shapes,
713 nb_colors=args.grid_nb_colors,
718 elif args.task == "qmlp":
720 nb_train_samples=args.nb_train_samples,
721 nb_test_samples=args.nb_test_samples,
722 batch_size=args.batch_size,
723 result_dir=args.result_dir,
729 raise ValueError(f"Unknown task {args.task}")
731 ######################################################################
733 log_string(f"device {device}")
735 vocabulary_size = task.vocabulary_size()
737 log_string(f"vocabulary_size {vocabulary_size}")
739 ##############################
742 vocabulary_size=vocabulary_size,
743 dim_model=args.dim_model,
744 dim_keys=args.dim_keys,
745 dim_hidden=args.dim_hidden,
746 nb_heads=args.nb_heads,
747 nb_lines=args.nb_lines,
748 caterpillar_height=args.caterpillar_height,
749 nb_blocks=args.nb_blocks,
751 dropout=args.dropout,
752 attention_layer=args.attention,
759 nb_parameters = sum(p.numel() for p in model.parameters())
760 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
762 ######################################################################
764 nb_epochs_finished = 0
766 if args.no_checkpoint:
767 log_string(f"not trying to load checkpoint.")
771 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
772 checkpoint = torch.load(checkpoint_name)
773 nb_epochs_finished = checkpoint["nb_epochs_finished"]
774 model.load_state_dict(checkpoint["model_state"])
775 torch.set_rng_state(checkpoint["rng_state"])
776 if torch.cuda.is_available():
777 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
779 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
781 except FileNotFoundError:
782 log_string("starting from scratch.")
785 log_string("error when loading the checkpoint.")
788 ######################################################################
790 if args.task == "expr" and args.expr_input_file is not None:
791 task.produce_results(
792 n_epoch=nb_epochs_finished,
794 result_dir=args.result_dir,
796 deterministic_synthesis=args.deterministic_synthesis,
797 input_file=args.expr_input_file,
802 ######################################################################
804 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
806 # Compute the entropy of the training tokens
809 for input in task.batches(split="train"):
810 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
811 token_probas = token_count / token_count.sum()
812 entropy = -torch.xlogy(token_probas, token_probas).sum()
813 train_set_perplexity = math.exp(entropy)
815 ######################################################################
816 # A bit of paranoia never hurts
818 if args.max_percents_of_test_in_train >= 0:
820 def subsets_as_tuples(batches, cs):
822 for batch in batches:
824 s.add(tuple([v.item() for v in x]))
830 nb_test, nb_in_train = 0, 0
831 for test_subset in subsets_as_tuples(task.batches(split="test"), 25000):
833 for train_subset in subsets_as_tuples(task.batches(split="train"), 25000):
834 in_train.update(test_subset.intersection(train_subset))
835 nb_in_train += len(in_train)
836 nb_test += len(test_subset)
839 f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
843 nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
844 ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
846 ##############################
848 if "calibrate" in sup_args:
849 for input in task.batches(split="train", desc="calibrate"):
850 input = input.to(device)
851 output = model(mygpt.BracketedSequence(input)).x
853 for n, m in model.named_modules():
856 if isinstance(x, mygpt.Calibrator):
857 print(f"####### ${n} | ${a} ########################")
858 mean, std = x.moments()
859 print("mean\n", mean, "\n")
860 print("std\n", std, "\n")
861 print(f"############################################\n\n")
865 ##############################
869 if nb_epochs_finished >= nb_epochs:
870 task.produce_results(
871 n_epoch=nb_epochs_finished,
873 result_dir=args.result_dir,
875 deterministic_synthesis=args.deterministic_synthesis,
878 time_pred_result = datetime.datetime.now()
884 for n_epoch in range(nb_epochs_finished, nb_epochs):
885 if args.optim == "sgd":
886 optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate)
887 elif args.optim == "adam":
888 optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
889 elif args.optim == "adamw":
890 optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate)
892 raise ValueError(f"Unknown optimizer {args.optim}.")
896 nb_train_samples, acc_train_loss, acc_train_inner_loss = 0, 0.0, 0.0
898 for input in task.batches(split="train"):
899 model.reset_inner_loss()
900 input = input.to(device)
902 output = model(mygpt.BracketedSequence(input)).x
903 loss = F.cross_entropy(output.transpose(1, 2), input)
904 inner_loss = model.get_inner_loss()
906 acc_train_loss += loss.item() * input.size(0)
907 acc_train_inner_loss += inner_loss.item() * input.size(0)
909 nb_train_samples += input.size(0)
910 nb_samples_seen += input.size(0)
912 total_loss = loss + (
913 args.rho_inner_loss * inner_loss if args.rho_inner_loss > 0 else 0.0
917 lr = get_lr(n_epoch, it)
918 for param_group in optimizer.param_groups:
919 param_group["lr"] = lr
921 # log_string(f"learning_rate {lr}")
923 optimizer.zero_grad()
924 total_loss.backward()
927 grad_norm = sum([p.grad.pow(2).sum() for p in model.parameters()]).sqrt()
929 loss_file.write(f"{n_epoch} {n_batch} {loss.item()} {grad_norm.item()}\n")
933 with torch.autograd.no_grad():
936 nb_test_samples, acc_test_loss = 0, 0.0
938 for input in task.batches(split="test"):
939 input = input.to(device)
941 output = model(mygpt.BracketedSequence(input)).x
942 loss = F.cross_entropy(output.transpose(1, 2), input)
943 acc_test_loss += loss.item() * input.size(0)
944 nb_test_samples += input.size(0)
947 f"loss {n_epoch} train_loss {acc_train_loss/nb_train_samples} train_inner_loss {acc_train_inner_loss/nb_train_samples} test_prediction {acc_test_loss/nb_test_samples}"
950 task.produce_results(
953 result_dir=args.result_dir,
955 deterministic_synthesis=args.deterministic_synthesis,
958 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
959 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
962 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
965 time_current_result = datetime.datetime.now()
967 f"next_result {time_current_result + (time_current_result - time_pred_result)}"
969 time_pred_result = time_current_result
972 "nb_epochs_finished": n_epoch + 1,
973 "model_state": model.state_dict(),
974 "rng_state": torch.get_rng_state(),
977 if torch.cuda.is_available():
978 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
980 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
981 torch.save(checkpoint, checkpoint_name)
982 log_string(f"saved checkpoint {checkpoint_name}")
984 ######################################################################