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
17 import sky, grids, quiz_machine
19 # world quizzes vs. culture quizzes
21 ######################################################################
23 if torch.cuda.is_available():
24 device = torch.device("cuda")
25 torch.backends.cuda.matmul.allow_tf32 = True
27 device = torch.device("cpu")
29 ######################################################################
31 parser = argparse.ArgumentParser(
32 formatter_class=argparse.ArgumentDefaultsHelpFormatter,
35 parser.add_argument("--log_filename", type=str, default="train.log")
37 parser.add_argument("--result_dir", type=str, default=None)
39 parser.add_argument("--seed", type=int, default=0)
41 parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
43 ########################################
45 parser.add_argument("--nb_epochs", type=int, default=10000)
47 parser.add_argument("--batch_size", type=int, default=None)
49 parser.add_argument("--physical_batch_size", type=int, default=None)
51 parser.add_argument("--nb_train_samples", type=int, default=None)
53 parser.add_argument("--nb_test_samples", type=int, default=None)
55 parser.add_argument("--learning_rate", type=float, default=5e-4)
57 ########################################
59 parser.add_argument("--model", type=str, default=None)
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 ########################################
75 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
77 parser.add_argument("--problem", type=str, default="grids")
79 parser.add_argument("--nb_threads", type=int, default=-1)
81 parser.add_argument("--nb_gpts", type=int, default=5)
83 parser.add_argument("--min_to_validate", type=int, default=None)
85 parser.add_argument("--max_to_validate", type=int, default=None)
87 parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
89 parser.add_argument("--generation_temperature", type=float, default=2.0)
91 parser.add_argument("--deterministic_validation", action="store_true", default=False)
93 parser.add_argument("--bidirectional_validation", action="store_true", default=False)
95 parser.add_argument("--dirty_debug", action="store_true", default=False)
97 ######################################################################
99 parser.add_argument("--sky_height", type=int, default=6)
101 parser.add_argument("--sky_width", type=int, default=8)
103 parser.add_argument("--sky_nb_birds", type=int, default=3)
105 parser.add_argument("--sky_nb_iterations", type=int, default=2)
107 parser.add_argument("--sky_speed", type=int, default=3)
109 ######################################################################
111 args = parser.parse_args()
113 if args.min_to_validate is None:
114 args.min_to_validate = args.nb_gpts - 1
116 if args.max_to_validate is None:
117 args.max_to_validate = args.nb_gpts - 1
119 if args.result_dir is None:
120 args.result_dir = f"results_culture"
122 ######################################################################
127 "nb_train_samples": 100000,
128 "nb_test_samples": 10000,
131 for k, v in default_args.items():
132 if getattr(args, k) is None:
135 ######################################################################
137 default_model_args = {
175 if args.model in default_model_args:
176 for k, v in default_model_args[args.model].items():
177 if getattr(args, k) is None:
180 raise ValueError(f"Unknown model {args.model}")
182 ######################################################################
185 os.mkdir(args.result_dir)
186 except FileExistsError:
187 print(f"result directory {args.result_dir} already exists")
190 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
193 # torch.backends.cudnn.deterministic = True
194 # torch.backends.cudnn.benchmark = False
195 # torch.use_deterministic_algorithms(True)
196 torch.manual_seed(args.seed)
197 if torch.cuda.is_available():
198 torch.cuda.manual_seed_all(args.seed)
200 ######################################################################
204 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
206 if log_file is not None:
207 log_file.write(t + s + "\n")
214 log_string(f"argv {' '.join(sys.argv)}")
217 log_string(f"args.{n} {getattr(args, n)}")
220 ######################################################################
223 args.nb_train_samples = 2500
224 args.nb_test_samples = 100
226 if args.physical_batch_size is None:
227 args.physical_batch_size = args.batch_size
229 assert args.batch_size % args.physical_batch_size == 0
231 assert args.nb_train_samples % args.batch_size == 0
232 assert args.nb_test_samples % args.batch_size == 0
234 if args.problem == "sky":
236 height=args.sky_height,
237 width=args.sky_width,
238 nb_birds=args.sky_nb_birds,
239 nb_iterations=args.sky_nb_iterations,
240 speed=args.sky_speed,
241 max_nb_cached_chunks=args.nb_train_samples // 100,
243 nb_threads=args.nb_threads,
245 back_accuracy = False
246 elif args.problem == "grids":
247 problem = grids.Grids(
248 max_nb_cached_chunks=args.nb_train_samples // 100,
250 nb_threads=args.nb_threads,
256 quiz_machine = quiz_machine.QuizMachine(
258 nb_train_samples=args.nb_train_samples,
259 nb_test_samples=args.nb_test_samples,
260 back_accuracy=back_accuracy,
261 batch_size=args.physical_batch_size,
262 result_dir=args.result_dir,
267 ######################################################################
269 log_string(f"device {device}")
271 vocabulary_size = quiz_machine.vocabulary_size()
273 log_string(f"vocabulary_size {vocabulary_size}")
275 ######################################################################
276 ##############################
279 def one_epoch(model, quiz_machine):
280 optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
284 nb_train_samples, acc_train_loss = 0, 0.0
286 for input in quiz_machine.batches(model, split="train"):
287 input = input.to(device)
289 if nb_train_samples % args.batch_size == 0:
290 optimizer.zero_grad()
292 output = model(mygpt.BracketedSequence(input)).x
293 loss = F.cross_entropy(output.transpose(1, 2), input)
294 acc_train_loss += loss.item() * input.size(0)
296 nb_train_samples += input.size(0)
300 if nb_train_samples % args.batch_size == 0:
303 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
305 log_string(f"train_perplexity {n_epoch} {train_perplexity}")
308 ######################################################################
311 def run_tests(model, quiz_machine, deterministic_synthesis):
312 with torch.autograd.no_grad():
315 nb_test_samples, acc_test_loss = 0, 0.0
316 nb_samples_accumulated = 0
318 for input in quiz_machine.batches(model, split="test"):
319 input = input.to(device)
321 bs = model(mygpt.BracketedSequence(input))
324 loss = F.cross_entropy(output.transpose(1, 2), input)
326 acc_test_loss += loss.item() * input.size(0)
328 nb_test_samples += input.size(0)
330 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
332 log_string(f"test_perplexity {n_epoch} {test_perplexity}")
334 model.main_test_accuracy = quiz_machine.produce_results(
337 result_dir=args.result_dir,
338 deterministic_synthesis=deterministic_synthesis,
342 ######################################################################
345 def standard_validity(logproba):
346 l = logproba.sort(dim=-1).values
347 return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.99))
348 # warnings.warn("TEST!!!", RuntimeWarning)
350 # return (l[:, 0] < math.log(0.99))
353 def valid_c_quizzes(recorded, criteria):
354 result = [q[criteria(lp)] for q, lp in recorded]
355 return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
358 ######################################################################
361 def create_c_quizzes(
367 quizzes_and_logproba_records = []
369 nb_to_create = nb_for_train + nb_for_test
371 # ------------------------------------------------------------
373 file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
375 with open(file_name, "w") as logp_file:
377 valid_c_quizzes(quizzes_and_logproba_records, standard_validity).size(0)
380 # Select a model at random to generate the new quizzes
382 model_for_generation = models[torch.randint(len(models), (1,))]
384 c_quizzes = quiz_machine.generate_quizzes(
386 model_for_generation=model_for_generation,
387 temperature=args.generation_temperature,
390 c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
392 if c_quizzes.size(0) > 0:
393 logproba = quiz_machine.logproba_solution(models, c_quizzes)
395 s = " ".join([str(x.item()) for x in l])
396 logp_file.write(s + "\n")
397 quizzes_and_logproba_records.append((c_quizzes, logproba))
399 nb_validated = valid_c_quizzes(
400 quizzes_and_logproba_records, standard_validity
404 f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
407 # store the new c_quizzes which have been validated
409 new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_records, standard_validity)
411 quiz_machine.reverse_random_half_in_place(new_c_quizzes)
413 quiz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
414 quiz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
416 # save a bunch of images to investigate what quizzes with a
417 # certain nb of correct predictions look like
419 q = new_c_quizzes[:72]
422 quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
425 ######################################################################
428 def create_c_quizzes_(
434 quizzes_and_nb_correct_records = []
436 nb_to_create = nb_for_train + nb_for_test
438 # ------------------------------------------------------------
440 standard_validity = lambda nb_correct: (nb_correct >= args.min_to_validate) & (
441 nb_correct <= args.max_to_validate
444 file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
446 with open(file_name, "w") as logp_file:
448 valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity).size(0)
451 # Select a model at random to generate the new quizzes
453 model_for_generation = models[torch.randint(len(models), (1,))]
455 c_quizzes = quiz_machine.generate_quizzes(
457 model_for_generation=model_for_generation,
458 temperature=args.generation_temperature,
461 # if args.prediction_correctness:
464 # logproba = quiz_machine.new(quiz_machine.size(0), len(models))
465 # for q,l in zip(quizzes.split(args.batch_size), logits.split(args.batch_size)):
466 # for model in models:
467 # l[...] = F.cross_entropy(model(q))
469 c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
471 if c_quizzes.size(0) > 0:
472 nb_correct, seq_logproba = quiz_machine.compute_correctness(
475 bidirectional_validation=args.bidirectional_validation,
476 deterministic_validation=args.deterministic_validation,
479 for n, l in zip(nb_correct, seq_logproba):
480 s = " ".join([str(x.item()) for x in l])
481 logp_file.write(f"{n} {s}\n")
484 nb_correct = torch.randint(
485 len(models) + 1, nb_correct.size(), device=c_quizzes.device
488 quizzes_and_nb_correct_records.append((c_quizzes, nb_correct))
490 nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
491 nv = " ".join([str(x.item()) for x in nv])
493 nb_validated = valid_c_quizzes(
494 quizzes_and_nb_correct_records, standard_validity
498 f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
501 # store the new c_quizzes which have been validated
503 new_c_quizzes = valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity)
505 quiz_machine.reverse_random_half_in_place(new_c_quizzes)
507 quiz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
508 quiz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
510 # save a bunch of images to investigate what quizzes with a
511 # certain nb of correct predictions look like
513 for n in range(len(models) + 1):
516 if n >= args.min_to_validate and n <= args.max_to_validate
521 quizzes_and_nb_correct_records, criteria=lambda nb_correct: nb_correct == n
524 quiz_machine.reverse_random_half_in_place(q)
527 quiz_machine.save_quizzes(
528 args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q
532 ######################################################################
536 for k in range(args.nb_gpts):
538 vocabulary_size=vocabulary_size,
539 dim_model=args.dim_model,
540 dim_keys=args.dim_keys,
541 dim_hidden=args.dim_hidden,
542 nb_heads=args.nb_heads,
543 nb_blocks=args.nb_blocks,
545 dropout=args.dropout,
548 model.main_test_accuracy = 0.0
551 model.train_w_quizzes = quiz_machine.generate_token_sequences(
552 args.nb_train_samples
554 quiz_machine.reverse_random_half_in_place(model.train_w_quizzes)
555 model.test_w_quizzes = quiz_machine.generate_token_sequences(
558 quiz_machine.reverse_random_half_in_place(model.test_w_quizzes)
563 nb_parameters = sum(p.numel() for p in models[0].parameters())
564 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
566 ######################################################################
568 # Compute the entropy of the training tokens
571 for input in quiz_machine.batches(models[0], split="train", desc="train-entropy"):
572 token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(
575 token_probas = token_count / token_count.sum()
576 entropy = -torch.xlogy(token_probas, token_probas).sum()
577 train_set_perplexity = math.exp(entropy)
579 ######################################################################
580 # A bit of paranoia never hurts
582 if args.max_percents_of_test_in_train >= 0:
584 def subsets_as_tuples(batches, cs):
586 for batch in batches:
588 s.add(tuple([v.item() for v in x]))
594 nb_test, nb_in_train = 0, 0
595 for test_subset in subsets_as_tuples(
596 quiz_machine.batches(models[0], split="test", desc="test-check"), 25000
599 for train_subset in subsets_as_tuples(
600 quiz_machine.batches(models[0], split="train", desc="train-check"), 25000
602 in_train.update(test_subset.intersection(train_subset))
603 nb_in_train += len(in_train)
604 nb_test += len(test_subset)
607 f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
611 nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
612 ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
614 ######################################################################
616 nb_new_c_quizzes_for_train = args.nb_train_samples // 50
617 nb_new_c_quizzes_for_test = args.nb_test_samples // 50
620 f"nb_new_c_quizzes_for_train {nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {nb_new_c_quizzes_for_test}"
623 ######################################################################
626 args.accuracy_to_make_c_quizzes = 0.0
628 nb_new_c_quizzes_for_train = 100
629 nb_new_c_quizzes_for_test = 10
631 ######################################################################
633 for n_epoch in range(args.nb_epochs):
634 log_string(f"--- epoch {n_epoch} ----------------------------------------")
636 cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
637 log_string(f"current_test_accuracies {cta}")
639 ##################################################
640 # Select, improve, and eval the worst model
642 weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
645 f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
648 one_epoch(weakest_model, quiz_machine)
651 f"train_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
654 run_tests(weakest_model, quiz_machine, deterministic_synthesis=False)
657 f"test_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
660 ##################################################
661 # Replace a fraction of the w_quizzes with fresh ones
664 f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes"
666 quiz_machine.renew_w_quizzes(model, args.nb_train_samples // args.nb_gpts)
668 ##################################################
669 # If all the models are good enough, generate new quizzes and
670 # re-compute the test errors
672 if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
676 nb_for_train=nb_new_c_quizzes_for_train,
677 nb_for_test=nb_new_c_quizzes_for_test,
681 run_tests(model, quiz_machine, deterministic_synthesis=False)
684 ######################################################################