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
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="sandbox, picoclvr, mnist, maze, snake, stack, expr, rpl, world",
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("--nb_epochs", type=int, default=None)
47 parser.add_argument("--batch_size", type=int, default=None)
49 parser.add_argument("--nb_train_samples", type=int, default=None)
51 parser.add_argument("--nb_test_samples", type=int, default=None)
53 parser.add_argument("--optim", type=str, default="adam")
55 parser.add_argument("--learning_rate", type=float, default=1e-4)
57 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
59 parser.add_argument("--model", type=str, default="37M")
61 parser.add_argument("--dim_model", type=int, default=None)
63 parser.add_argument("--dim_keys", type=int, default=None)
65 parser.add_argument("--dim_hidden", type=int, default=None)
67 parser.add_argument("--nb_heads", type=int, default=None)
69 parser.add_argument("--nb_blocks", type=int, default=None)
71 parser.add_argument("--dropout", type=float, default=0.1)
73 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
75 parser.add_argument("--no_checkpoint", action="store_true", default=False)
77 parser.add_argument("--overwrite_results", action="store_true", default=False)
79 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
81 ##############################
84 parser.add_argument("--rpl_nb_starting_values", type=int, default=5)
86 parser.add_argument("--rpl_max_input", type=int, default=9)
88 parser.add_argument("--rpl_prog_len", type=int, default=10)
90 parser.add_argument("--rpl_nb_runs", type=int, default=8)
92 parser.add_argument("--rpl_no_prog", action="store_true", default=False)
94 ##############################
97 parser.add_argument("--sandbox_level", type=int, default=0)
99 parser.add_argument("--sandbox_levels_nb_items", type=int, default=25)
101 parser.add_argument("--sandbox_levels_len_source", type=int, default=6)
103 parser.add_argument("--sandbox_levels_len_result", type=int, default=8)
105 ##############################
108 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
110 parser.add_argument("--picoclvr_height", type=int, default=12)
112 parser.add_argument("--picoclvr_width", type=int, default=16)
114 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
116 ##############################
119 parser.add_argument("--maze_height", type=int, default=23)
121 parser.add_argument("--maze_width", type=int, default=39)
123 parser.add_argument("--maze_nb_walls", type=int, default=45)
125 ##############################
128 parser.add_argument("--snake_height", type=int, default=6)
130 parser.add_argument("--snake_width", type=int, default=8)
132 parser.add_argument("--snake_nb_colors", type=int, default=5)
134 parser.add_argument("--snake_length", type=int, default=200)
136 ##############################
139 parser.add_argument("--stack_nb_steps", type=int, default=100)
141 parser.add_argument("--stack_nb_stacks", type=int, default=3)
143 parser.add_argument("--stack_nb_digits", type=int, default=3)
145 parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
147 ##############################
150 parser.add_argument("--expr_nb_variables", type=int, default=5)
152 parser.add_argument("--expr_sequence_length", type=int, default=40)
154 parser.add_argument("--expr_operand_max", type=int, default=9)
156 parser.add_argument("--expr_result_max", type=int, default=99)
158 parser.add_argument("--expr_input_file", type=str, default=None)
160 ##############################
163 parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
165 ######################################################################
167 args = parser.parse_args()
169 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
171 if args.result_dir is None:
172 args.result_dir = f"results_{args.task}"
174 ######################################################################
176 default_task_args = {
180 "nb_train_samples": 100000,
181 "nb_test_samples": 10000,
186 "nb_train_samples": 250000,
187 "nb_test_samples": 10000,
192 "nb_train_samples": 250000,
193 "nb_test_samples": 10000,
198 "nb_train_samples": 250000,
199 "nb_test_samples": 10000,
204 "nb_train_samples": 250000,
205 "nb_test_samples": 10000,
210 "nb_train_samples": 100000,
211 "nb_test_samples": 1000,
216 "nb_train_samples": 1000000,
217 "nb_test_samples": 10000,
222 "nb_train_samples": 100000,
223 "nb_test_samples": 10000,
228 "nb_train_samples": 25000,
229 "nb_test_samples": 1000,
233 if args.task in default_task_args:
234 for k, v in default_task_args[args.task].items():
235 if getattr(args, k) is None:
238 ######################################################################
240 default_model_args = {
271 if args.model in default_model_args:
272 for k, v in default_model_args[args.model].items():
273 if getattr(args, k) is None:
276 raise ValueError(f"Unknown model {args.model}")
278 ######################################################################
281 os.mkdir(args.result_dir)
282 except FileExistsError:
283 if not args.overwrite_results:
284 print(f"result directory {args.result_dir} already exists")
287 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
290 # torch.backends.cudnn.deterministic = True
291 # torch.backends.cudnn.benchmark = False
292 # torch.use_deterministic_algorithms(True)
293 torch.manual_seed(args.seed)
294 if torch.cuda.is_available():
295 torch.cuda.manual_seed_all(args.seed)
297 ######################################################################
301 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
303 if log_file is not None:
304 log_file.write(t + s + "\n")
312 log_string(f"args.{n} {getattr(args, n)}")
315 ######################################################################
318 def picoclvr_pruner_horizontal_green(p):
319 return not ("green" in p and ("left" in p or "right" in p))
322 picoclvr_pruner_train = (
323 picoclvr_pruner_horizontal_green
324 if args.picocvlr_prune_properties in {"train+eval"}
328 picoclvr_pruner_eval = (
329 (lambda p: not picoclvr_pruner_horizontal_green(p))
330 if args.picocvlr_prune_properties in {"train+eval", "eval"}
334 ######################################################################
336 if args.task == "sandbox":
337 if args.sandbox_level == 0:
338 problem = problems.ProblemLevel0(
339 nb_sentences=args.sandbox_levels_nb_items,
340 len_prompt=args.sandbox_levels_len_source,
341 len_result=args.sandbox_levels_len_result,
343 elif args.sandbox_level == 1:
344 problem = problems.ProblemLevel1(
345 nb_operators=args.sandbox_levels_nb_items,
346 len_source=args.sandbox_levels_len_source,
347 len_result=args.sandbox_levels_len_result,
349 elif args.sandbox_level == 2:
350 problem = problems.ProblemLevel2(
351 len_source=args.sandbox_levels_len_source,
352 len_result=args.sandbox_levels_len_result,
355 raise ValueError(f"Unknown sandbox level {args.sandbox_level}")
357 task = tasks.SandBox(
359 # problems.ProblemAddition(zero_padded=False, inverted_result=False),
360 problems.ProblemLenId(len_max=args.sandbox_levels_len_source),
361 nb_train_samples=args.nb_train_samples,
362 nb_test_samples=args.nb_test_samples,
363 batch_size=args.batch_size,
368 elif args.task == "picoclvr":
369 task = tasks.PicoCLVR(
370 nb_train_samples=args.nb_train_samples,
371 nb_test_samples=args.nb_test_samples,
372 batch_size=args.batch_size,
373 height=args.picoclvr_height,
374 width=args.picoclvr_width,
375 nb_colors=args.picoclvr_nb_colors,
378 pruner_train=picoclvr_pruner_train,
379 pruner_eval=picoclvr_pruner_eval,
382 elif args.task == "mnist":
384 nb_train_samples=args.nb_train_samples,
385 nb_test_samples=args.nb_test_samples,
386 batch_size=args.batch_size,
390 elif args.task == "maze":
392 nb_train_samples=args.nb_train_samples,
393 nb_test_samples=args.nb_test_samples,
394 batch_size=args.batch_size,
395 height=args.maze_height,
396 width=args.maze_width,
397 nb_walls=args.maze_nb_walls,
401 elif args.task == "snake":
403 nb_train_samples=args.nb_train_samples,
404 nb_test_samples=args.nb_test_samples,
405 batch_size=args.batch_size,
406 height=args.snake_height,
407 width=args.snake_width,
408 nb_colors=args.snake_nb_colors,
409 length=args.snake_length,
410 prompt_length=args.snake_length // 2,
414 elif args.task == "stack":
416 nb_train_samples=args.nb_train_samples,
417 nb_test_samples=args.nb_test_samples,
418 batch_size=args.batch_size,
420 nb_steps=args.stack_nb_steps,
421 nb_stacks=args.stack_nb_stacks,
422 nb_digits=args.stack_nb_digits,
423 fraction_values_for_train=args.stack_fraction_values_for_train,
427 elif args.task == "expr":
429 nb_train_samples=args.nb_train_samples,
430 nb_test_samples=args.nb_test_samples,
431 nb_variables=args.expr_nb_variables,
432 sequence_length=args.expr_sequence_length,
433 operand_max=args.expr_operand_max,
434 result_max=args.expr_result_max,
435 batch_size=args.batch_size,
439 elif args.task == "rpl":
441 nb_train_samples=args.nb_train_samples,
442 nb_test_samples=args.nb_test_samples,
443 batch_size=args.batch_size,
444 nb_starting_values=args.rpl_nb_starting_values,
445 max_input=args.rpl_max_input,
446 prog_len=args.rpl_prog_len,
447 nb_runs=args.rpl_nb_runs,
448 no_prog=args.rpl_no_prog,
453 elif args.task == "world":
455 nb_train_samples=args.nb_train_samples,
456 nb_test_samples=args.nb_test_samples,
457 batch_size=args.batch_size,
458 vqae_nb_epochs=args.world_vqae_nb_epochs,
464 raise ValueError(f"Unknown task {args.task}")
466 ######################################################################
468 log_string(f"device {device}")
470 vocabulary_size = task.vocabulary_size()
472 log_string(f"vocabulary_size {vocabulary_size}")
474 ##############################
477 vocabulary_size=vocabulary_size,
478 dim_model=args.dim_model,
479 dim_keys=args.dim_keys,
480 dim_hidden=args.dim_hidden,
481 nb_heads=args.nb_heads,
482 nb_blocks=args.nb_blocks,
484 dropout=args.dropout,
489 nb_parameters = sum(p.numel() for p in model.parameters())
490 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
492 ######################################################################
494 nb_epochs_finished = 0
496 if args.no_checkpoint:
497 log_string(f"not trying to load checkpoint.")
501 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
502 checkpoint = torch.load(checkpoint_name)
503 nb_epochs_finished = checkpoint["nb_epochs_finished"]
504 model.load_state_dict(checkpoint["model_state"])
505 torch.set_rng_state(checkpoint["rng_state"])
506 if torch.cuda.is_available():
507 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
509 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
511 except FileNotFoundError:
512 log_string("starting from scratch.")
515 log_string("error when loading the checkpoint.")
518 ######################################################################
520 if args.task == "expr" and args.expr_input_file is not None:
521 task.produce_results(
522 n_epoch=nb_epochs_finished,
524 result_dir=args.result_dir,
526 deterministic_synthesis=args.deterministic_synthesis,
527 input_file=args.expr_input_file,
532 ######################################################################
534 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
536 # Compute the entropy of the training tokens
539 for input in task.batches(split="train"):
540 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
541 token_probas = token_count / token_count.sum()
542 entropy = -torch.xlogy(token_probas, token_probas).sum()
543 train_set_perplexity = math.exp(entropy)
545 ######################################################################
546 # A bit of paranoia never hurts
549 def subsets_as_tuples(batches, cs):
551 for batch in batches:
553 s.add(tuple([v.item() for v in x]))
560 nb_test, nb_in_train = 0, 0
561 for test_subset in subsets_as_tuples(task.batches(split="test"), 25000):
563 for train_subset in subsets_as_tuples(task.batches(split="train"), 25000):
564 in_train.update(test_subset.intersection(train_subset))
565 nb_in_train += len(in_train)
566 nb_test += len(test_subset)
569 f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
573 nb_in_train <= nb_test // 100
574 ), "More than 1% of test samples are in the train set"
576 ##############################
578 if args.learning_rate_schedule == "cos":
579 learning_rate_schedule = {}
580 for n_epoch in range(args.nb_epochs):
581 u = n_epoch / args.nb_epochs * math.pi
582 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
587 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
591 learning_rate_schedule = {}
592 learning_rate = args.learning_rate
593 for n_epoch in range(args.nb_epochs):
595 learning_rate = u[n_epoch]
596 learning_rate_schedule[n_epoch] = learning_rate
598 log_string(f"learning_rate_schedule {learning_rate_schedule}")
600 ##############################
604 if nb_epochs_finished >= nb_epochs:
605 task.produce_results(
606 n_epoch=nb_epochs_finished,
608 result_dir=args.result_dir,
610 deterministic_synthesis=args.deterministic_synthesis,
613 for n_epoch in range(nb_epochs_finished, nb_epochs):
614 learning_rate = learning_rate_schedule[n_epoch]
616 log_string(f"learning_rate {learning_rate}")
618 if args.optim == "sgd":
619 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
620 elif args.optim == "adam":
621 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
622 elif args.optim == "adamw":
623 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
625 raise ValueError(f"Unknown optimizer {args.optim}.")
629 nb_train_samples, acc_train_loss = 0, 0.0
631 for input in task.batches(split="train"):
632 input = input.to(device)
633 output = model(mygpt.BracketedSequence(input)).x
634 loss = F.cross_entropy(output.transpose(1, 2), input)
635 acc_train_loss += loss.item() * input.size(0)
636 nb_train_samples += input.size(0)
637 nb_samples_seen += input.size(0)
639 optimizer.zero_grad()
643 with torch.autograd.no_grad():
646 nb_test_samples, acc_test_loss = 0, 0.0
648 for input in task.batches(split="test"):
649 input = input.to(device)
651 output = model(mygpt.BracketedSequence(input)).x
652 loss = F.cross_entropy(output.transpose(1, 2), input)
653 acc_test_loss += loss.item() * input.size(0)
654 nb_test_samples += input.size(0)
656 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
657 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
660 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
663 task.produce_results(
666 result_dir=args.result_dir,
668 deterministic_synthesis=args.deterministic_synthesis,
672 "nb_epochs_finished": n_epoch + 1,
673 "model_state": model.state_dict(),
674 "rng_state": torch.get_rng_state(),
677 if torch.cuda.is_available():
678 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
680 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
681 torch.save(checkpoint, checkpoint_name)
682 log_string(f"saved checkpoint {checkpoint_name}")
684 ######################################################################