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 # torch.backends.cuda.matmul.allow_tf23
9 # torch.autocast(torch.bfloat16)
11 import math, sys, argparse, time, tqdm, os
13 import torch, torchvision
15 from torch.nn import functional as F
17 import mygpt, tensorstack
19 ######################################################################
21 if torch.cuda.is_available():
22 device = torch.device("cuda")
23 torch.backends.cuda.matmul.allow_tf32 = True
25 device = torch.device("cpu")
27 ######################################################################
29 parser = argparse.ArgumentParser(
30 description="An implementation of GPT with cache.",
31 formatter_class=argparse.ArgumentDefaultsHelpFormatter,
35 "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake"
38 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
40 parser.add_argument("--result_dir", type=str, default="results_default")
42 parser.add_argument("--seed", type=int, default=0)
44 parser.add_argument("--nb_epochs", type=int, default=None)
46 parser.add_argument("--batch_size", type=int, default=None)
48 parser.add_argument("--nb_train_samples", type=int, default=250000)
50 parser.add_argument("--nb_test_samples", type=int, default=10000)
52 parser.add_argument("--optim", type=str, default="adam")
54 parser.add_argument("--learning_rate", type=float, default=1e-4)
56 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
58 parser.add_argument("--dim_model", type=int, default=512)
60 parser.add_argument("--dim_keys", type=int, default=64)
62 parser.add_argument("--dim_hidden", type=int, default=2048)
64 parser.add_argument("--nb_heads", type=int, default=8)
66 parser.add_argument("--nb_blocks", type=int, default=12)
68 parser.add_argument("--dropout", type=float, default=0.1)
70 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
72 parser.add_argument("--no_checkpoint", action="store_true", default=False)
74 parser.add_argument("--overwrite_results", action="store_true", default=False)
76 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
78 ##############################
81 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
83 parser.add_argument("--picoclvr_height", type=int, default=12)
85 parser.add_argument("--picoclvr_width", type=int, default=16)
87 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
89 ##############################
92 parser.add_argument("--maze_height", type=int, default=13)
94 parser.add_argument("--maze_width", type=int, default=21)
96 parser.add_argument("--maze_nb_walls", type=int, default=15)
98 ##############################
101 parser.add_argument("--snake_height", type=int, default=6)
103 parser.add_argument("--snake_width", type=int, default=8)
105 parser.add_argument("--snake_nb_colors", type=int, default=5)
107 parser.add_argument("--snake_length", type=int, default=200)
109 ######################################################################
111 args = parser.parse_args()
113 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
116 os.mkdir(args.result_dir)
117 except FileExistsError:
118 if not args.overwrite_results:
119 print(f"result directory {args.result_dir} already exists")
122 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
125 # torch.backends.cudnn.deterministic = True
126 # torch.backends.cudnn.benchmark = False
127 # torch.use_deterministic_algorithms(True)
128 torch.manual_seed(args.seed)
129 if torch.cuda.is_available():
130 torch.cuda.manual_seed_all(args.seed)
132 ######################################################################
153 if args.task in default_args:
154 for k, v in default_args[args.task].items():
155 if getattr(args, k) is None:
158 ######################################################################
162 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
164 if log_file is not None:
165 log_file.write(t + s + "\n")
173 log_string(f"args.{n} {getattr(args, n)}")
175 ######################################################################
178 # ra_mask is boolean, with 1s on the values to generate
181 def masked_inplace_autoregression(
186 forbidden_tokens=None,
187 progress_bar_desc="autoregression",
188 device=torch.device("cpu"),
190 batches = zip(input.split(batch_size), ar_mask.split(batch_size))
191 if progress_bar_desc is not None:
195 desc=progress_bar_desc,
196 total=input.size(0) // batch_size,
198 for input, ar_mask in batches:
199 i = (ar_mask.sum(0) > 0).nonzero()
202 mygpt.BracketedSequence(input, 0, i.min())
203 ) # Needed to initialize the model's cache
204 for s in range(i.min(), i.max() + 1):
205 output = model(mygpt.BracketedSequence(input, s, 1)).x
206 logits = output[:, s]
207 if forbidden_tokens is not None:
208 logits = logits.masked_fill(forbidden_tokens, float("-inf"))
209 if args.deterministic_synthesis:
210 t_next = logits.argmax(1)
212 dist = torch.distributions.categorical.Categorical(logits=logits)
213 t_next = dist.sample()
214 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
217 ######################################################################
221 def batches(self, split="train"):
224 def vocabulary_size(self):
227 def produce_results(self, n_epoch, model):
231 ######################################################################
236 class TaskPicoCLVR(Task):
237 # Make a tensor from a list of strings
238 def tensorize(self, descr):
239 token_descr = [s.strip().split(" ") for s in descr]
240 l = max([len(s) for s in token_descr])
241 token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
242 id_descr = [[self.token2id[u] for u in s] for s in token_descr]
243 return torch.tensor(id_descr, device=self.device)
245 # Make a list of strings from a tensor
246 def detensorize(self, x):
247 return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
249 # trim all the tensors in the tuple z to remove as much token from
250 # left and right in the first tensor. If z is a tuple, all its
251 # elements are trimed according to the triming for the first
252 def trim(self, z, token="<nul>"):
253 n = self.token2id[token]
256 i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
257 a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
258 return tuple([t[:, a:b] for t in z])
260 i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
261 a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
264 ######################
265 # Not the cleanest part of the code
267 # Extract the last image of each sequence, from the last <img>
268 # included, and set to <nul> all the tokens from the beginning of
269 # that image to the end
270 def excise_last_image(self, input):
271 t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
272 nb_img_tokens = self.height * self.width + 1
274 input = input.clone()
275 t = (input == t_img).long()
276 tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
277 i = (t * tail_masks).nonzero(as_tuple=True)
280 i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
282 images = self.trim(input[j])
284 loss_masks = 1 - tail_masks
285 input, loss_masks = self.trim((input, loss_masks))
286 return input, loss_masks, images
288 def add_true_image(self, input, images, loss_masks):
289 t_nul = self.token2id["<nul>"]
290 nb_img_tokens = self.height * self.width + 1
291 input = F.pad(input, (0, nb_img_tokens), value=t_nul)
292 loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
293 t = (input == t_nul).long()
294 i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
297 i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
301 input, loss_masks = self.trim((input, loss_masks))
302 return input, loss_masks
304 def add_generated_image(self, input, loss_masks, model):
305 t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
306 nb_img_tokens = self.height * self.width + 1
308 input = F.pad(input, (0, nb_img_tokens), value=t_nul)
309 loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
310 t = (input == t_nul).long()
311 i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
318 + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
320 ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
323 torch.arange(self.vocabulary_size(), device=input.device) == t_nul
325 with torch.autograd.no_grad():
328 masked_inplace_autoregression(
334 progress_bar_desc=None,
339 input, loss_masks = self.trim((input, loss_masks))
341 return input, loss_masks
343 ######################
353 device=torch.device("cpu"),
357 def generate_descr(nb, cache_suffix, pruner):
358 return picoclvr.generate(
368 self.batch_size = batch_size
370 self.pruner_train = pruner_train
371 self.pruner_eval = pruner_eval
374 "nb_train_samples": nb_train_samples,
375 "nb_test_samples": nb_test_samples,
378 "nb_colors": nb_colors,
379 "batch_size": batch_size,
380 "rng_state": list(torch.get_rng_state()),
384 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
386 self.train_descr = generate_descr(
387 nb_train_samples, "train", pruner=self.pruner_train
389 self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
391 # Build the tokenizer
392 tokens = {"<nul>", "<img>"}
393 for d in [self.train_descr, self.test_descr]:
395 for t in s.strip().split(" "):
397 # make this set a sorted list to get the same tensors given
399 tokens = list(tokens)
401 self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
402 self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
404 # Tokenize the train and test sets
405 self.train_input = self.tensorize(self.train_descr)
406 self.test_input = self.tensorize(self.test_descr)
408 def batches(self, split="train"):
409 assert split in {"train", "test"}
410 input = self.train_input if split == "train" else self.test_input
411 for batch in tqdm.tqdm(
412 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
414 yield self.trim(batch)
416 def vocabulary_size(self):
417 return len(self.token2id)
419 def compute_missing_properties(self, n_epoch, model, pruner=None):
420 acc_nb_requested_properties = []
421 acc_nb_missing_properties = []
424 for input in tqdm.tqdm(
425 self.test_input.split(self.batch_size),
427 desc=f"test-properties",
429 tape, loss_masks, _ = self.excise_last_image(input)
430 tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
431 result_descr = self.detensorize(tape)
432 np = picoclvr.nb_properties(
438 nb_requested_properties, _, nb_missing_properties = zip(*np)
439 acc_nb_requested_properties += nb_requested_properties
440 acc_nb_missing_properties += nb_missing_properties
441 acc_nb_results += len(result_descr)
443 nb_requested_properties = sum(acc_nb_requested_properties)
444 nb_missing_properties = sum(acc_nb_missing_properties)
446 prefix = "" if pruner is None else "pruned_"
447 log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
449 f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
452 f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
455 ######################################################################
457 def produce_results(self, n_epoch, model):
458 self.compute_missing_properties(n_epoch, model)
460 if self.pruner_eval is not None:
461 self.compute_missing_properties(n_epoch, model, self.pruner_eval)
463 nb_tokens_to_generate = self.height * self.width + 3
468 for primer_descr in [
469 "red above green <sep> green top <sep> blue right of red",
470 "there is red <sep> there is yellow <sep> there is blue",
471 "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
472 "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
474 primer += [primer_descr] * nb_per_primer
476 tape = self.tensorize(primer)
477 loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
478 tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
479 result_descr = self.detensorize(tape)
481 np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
483 acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
484 acc_nb_results = len(result_descr)
486 nb_requested_properties = sum(acc_nb_requested_properties)
487 nb_missing_properties = sum(acc_nb_missing_properties)
490 log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
492 f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
495 f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
498 img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
502 img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
506 torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
512 image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png")
513 torchvision.utils.save_image(
514 img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
516 log_string(f"wrote {image_name}")
519 ######################################################################
522 class TaskMNIST(Task):
523 def __init__(self, batch_size, device=torch.device("cpu")):
525 self.batch_size = batch_size
527 def batches(self, split="train"):
528 assert split in {"train", "test"}
529 data_set = torchvision.datasets.MNIST(
530 root="./data", train=(split == "train"), download=True
532 data_input = data_set.data.view(-1, 28 * 28).long()
533 if args.nb_train_samples is not None:
534 data_input = data_input[: args.nb_train_samples]
535 for batch in tqdm.tqdm(
536 data_input.split(self.batch_size), desc=f"epoch-{split}"
540 def vocabulary_size(self):
543 def produce_results(self, n_epoch, model):
544 results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
545 ar_mask = torch.full_like(results, 1)
546 masked_inplace_autoregression(
547 model, self.batch_size, results, ar_mask, device=self.device
549 image_name = os.path.join(args.result_dir, f"mnist_result_{n_epoch:04d}.png")
550 torchvision.utils.save_image(
551 1 - results.reshape(-1, 1, 28, 28) / 255.0,
556 log_string(f"wrote {image_name}")
559 ######################################################################
564 class TaskMaze(Task):
565 def map2seq(self, *m):
566 return torch.cat([x.flatten(1) for x in m], 1)
568 def seq2map(self, s):
569 s = s.reshape(s.size(0), -1, self.height, self.width)
570 return (s[:, k] for k in range(s.size(1)))
580 device=torch.device("cpu"),
582 self.batch_size = batch_size
587 train_mazes, train_paths, _ = maze.create_maze_data(
592 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
594 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
596 test_mazes, test_paths, _ = maze.create_maze_data(
601 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
603 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
605 self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
607 def batches(self, split="train", nb_to_use=-1, desc=None):
608 assert split in {"train", "test"}
609 input = self.train_input if split == "train" else self.test_input
611 input = input[:nb_to_use]
613 desc = f"epoch-{split}"
614 for batch in tqdm.tqdm(
615 input.split(self.batch_size), dynamic_ncols=True, desc=desc
619 def vocabulary_size(self):
622 def compute_error(self, model, split="train", nb_to_use=-1):
623 nb_total, nb_correct = 0, 0
625 self.width * self.height,
626 self.width * self.height,
630 for input in tqdm.tqdm(
631 task.batches(split, nb_to_use),
635 result = input.clone()
636 ar_mask = result.new_zeros(result.size())
637 ar_mask[:, self.height * self.width :] = 1
638 result *= 1 - ar_mask
639 masked_inplace_autoregression(
644 progress_bar_desc=None,
647 mazes, paths = self.seq2map(result)
648 path_correctness = maze.path_correctness(mazes, paths)
649 nb_correct += path_correctness.long().sum()
650 nb_total += mazes.size(0)
652 optimal_path_lengths = (
653 (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
655 predicted_path_lengths = (
656 (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
658 optimal_path_lengths = optimal_path_lengths[path_correctness]
659 predicted_path_lengths = predicted_path_lengths[path_correctness]
660 count[optimal_path_lengths, predicted_path_lengths] += 1
666 : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
669 return nb_total, nb_correct, count
671 def produce_results(self, n_epoch, model):
672 with torch.autograd.no_grad():
676 train_nb_total, train_nb_correct, count = self.compute_error(
677 model, "train", nb_to_use=1000
680 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
683 test_nb_total, test_nb_correct, count = self.compute_error(
684 model, "test", nb_to_use=1000
687 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
690 if count is not None:
691 proportion_optimal = count.diagonal().sum().float() / count.sum()
692 log_string(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
694 os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
696 for i in range(count.size(0)):
697 for j in range(count.size(1)):
698 eol = " " if j < count.size(1) - 1 else "\n"
699 f.write(f"{count[i,j]}{eol}")
701 input = self.test_input[:48]
702 result = input.clone()
703 ar_mask = result.new_zeros(result.size())
704 ar_mask[:, self.height * self.width :] = 1
705 result *= 1 - ar_mask
706 masked_inplace_autoregression(
707 model, self.batch_size, result, ar_mask, device=self.device
710 mazes, paths = self.seq2map(input)
711 _, predicted_paths = self.seq2map(result)
713 filename = os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.png")
718 predicted_paths=predicted_paths,
719 path_correct=maze.path_correctness(mazes, predicted_paths),
720 path_optimal=maze.path_optimality(paths, predicted_paths),
722 log_string(f"wrote {filename}")
727 ######################################################################
733 class TaskSnake(Task):
744 device=torch.device("cpu"),
746 self.batch_size = batch_size
750 self.prompt_length = prompt_length
752 self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
761 self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
771 self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
773 def batches(self, split="train", nb_to_use=-1, desc=None):
774 assert split in {"train", "test"}
775 input = self.train_input if split == "train" else self.test_input
777 input = input[:nb_to_use]
779 desc = f"epoch-{split}"
780 for batch in tqdm.tqdm(
781 input.split(self.batch_size), dynamic_ncols=True, desc=desc
785 def vocabulary_size(self):
788 def produce_results(self, n_epoch, model):
789 with torch.autograd.no_grad():
793 def compute_nb_correct(input, prior_visits):
794 result = input.clone()
795 i = torch.arange(result.size(1), device=result.device)[None, :]
797 torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
801 result *= 1 - ar_mask
803 # snake.solver(result,ar_mask)
805 masked_inplace_autoregression(
806 model, self.batch_size, result, ar_mask, device=self.device
809 nb_total = ((prior_visits > 0) * ar_mask).sum()
812 (result == input).long() * (prior_visits > 0) * ar_mask
815 # nb_total = result.size(0)
816 # nb_correct = ((result - input).abs().sum(1) == 0).sum()
818 return nb_total, nb_correct
820 # train_nb_total, train_nb_correct = compute_nb_correct(
821 # self.train_input, self.train_prior_visits
825 # f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
828 test_nb_total, test_nb_correct = compute_nb_correct(
829 self.test_input[:1000], self.test_prior_visits[:1000]
833 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
839 ######################################################################
842 def picoclvr_pruner_horizontal_green(p):
843 return not ("green" in p and ("left" in p or "right" in p))
846 picoclvr_pruner_train = (
847 picoclvr_pruner_horizontal_green
848 if args.picocvlr_prune_properties in {"train+eval"}
852 picoclvr_pruner_eval = (
853 (lambda p: not picoclvr_pruner_horizontal_green(p))
854 if args.picocvlr_prune_properties in {"train+eval", "eval"}
858 ######################################################################
860 if args.task == "picoclvr":
862 nb_train_samples=args.nb_train_samples,
863 nb_test_samples=args.nb_test_samples,
864 batch_size=args.batch_size,
865 height=args.picoclvr_height,
866 width=args.picoclvr_width,
867 nb_colors=args.picoclvr_nb_colors,
869 pruner_train=picoclvr_pruner_train,
870 pruner_eval=picoclvr_pruner_eval,
873 elif args.task == "mnist":
875 batch_size=args.batch_size,
879 elif args.task == "maze":
881 nb_train_samples=args.nb_train_samples,
882 nb_test_samples=args.nb_test_samples,
883 batch_size=args.batch_size,
884 height=args.maze_height,
885 width=args.maze_width,
886 nb_walls=args.maze_nb_walls,
890 elif args.task == "snake":
892 nb_train_samples=args.nb_train_samples,
893 nb_test_samples=args.nb_test_samples,
894 batch_size=args.batch_size,
895 height=args.snake_height,
896 width=args.snake_width,
897 nb_colors=args.snake_nb_colors,
898 length=args.snake_length,
899 prompt_length=args.snake_length // 2,
904 raise ValueError(f"Unknown task {args.task}")
906 ######################################################################
908 log_string(f"device {device}")
910 vocabulary_size = task.vocabulary_size()
912 log_string(f"vocabulary_size {vocabulary_size}")
914 ##############################
917 vocabulary_size=vocabulary_size,
918 dim_model=args.dim_model,
919 dim_keys=args.dim_keys,
920 dim_hidden=args.dim_hidden,
921 nb_heads=args.nb_heads,
922 nb_blocks=args.nb_blocks,
924 dropout=args.dropout,
929 nb_parameters = sum(p.numel() for p in model.parameters())
930 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
932 ######################################################################
934 nb_epochs_finished = 0
936 if args.no_checkpoint:
937 log_string(f"not trying to load checkpoint.")
941 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
942 checkpoint = torch.load(checkpoint_name)
943 nb_epochs_finished = checkpoint["nb_epochs_finished"]
944 model.load_state_dict(checkpoint["model_state"])
945 torch.set_rng_state(checkpoint["rng_state"])
946 if torch.cuda.is_available():
947 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
949 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
951 except FileNotFoundError:
952 log_string("starting from scratch.")
955 log_string("error when loading the checkpoint.")
958 ######################################################################
960 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
963 for input in task.batches(split="train"):
964 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
965 token_probas = token_count / token_count.sum()
966 entropy = -torch.xlogy(token_probas, token_probas).sum()
967 train_set_perplexity = math.exp(entropy)
969 ##############################
971 if args.learning_rate_schedule == "cos":
972 learning_rate_schedule = {}
973 for n_epoch in range(args.nb_epochs):
974 u = n_epoch / args.nb_epochs * math.pi
975 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
980 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
984 learning_rate_schedule = {}
985 learning_rate = args.learning_rate
986 for n_epoch in range(args.nb_epochs):
988 learning_rate = u[n_epoch]
989 learning_rate_schedule[n_epoch] = learning_rate
991 log_string(f"learning_rate_schedule {learning_rate_schedule}")
993 ##############################
997 if nb_epochs_finished >= nb_epochs:
998 task.produce_results(nb_epochs_finished, model)
1000 for n_epoch in range(nb_epochs_finished, nb_epochs):
1001 learning_rate = learning_rate_schedule[n_epoch]
1003 log_string(f"learning_rate {learning_rate}")
1005 if args.optim == "sgd":
1006 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
1007 elif args.optim == "adam":
1008 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
1009 elif args.optim == "adamw":
1010 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
1012 raise ValueError(f"Unknown optimizer {args.optim}.")
1016 nb_train_samples, acc_train_loss = 0, 0.0
1018 for input in task.batches(split="train"):
1019 input = input.to(device)
1020 output = model(mygpt.BracketedSequence(input)).x
1021 loss = F.cross_entropy(output.transpose(1, 2), input)
1022 acc_train_loss += loss.item() * input.size(0)
1023 nb_train_samples += input.size(0)
1024 nb_samples_seen += input.size(0)
1026 optimizer.zero_grad()
1030 with torch.autograd.no_grad():
1033 nb_test_samples, acc_test_loss = 0, 0.0
1035 for input in task.batches(split="test"):
1036 input = input.to(device)
1038 output = model(mygpt.BracketedSequence(input)).x
1039 loss = F.cross_entropy(output.transpose(1, 2), input)
1040 acc_test_loss += loss.item() * input.size(0)
1041 nb_test_samples += input.size(0)
1043 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
1044 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
1047 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
1050 task.produce_results(n_epoch, model)
1053 "nb_epochs_finished": n_epoch + 1,
1054 "model_state": model.state_dict(),
1055 "rng_state": torch.get_rng_state(),
1058 if torch.cuda.is_available():
1059 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
1061 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
1062 torch.save(checkpoint, checkpoint_name)
1063 log_string(f"saved checkpoint {checkpoint_name}")
1065 ######################################################################