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, itertools, 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 to solve a toy geometric reasoning task."
33 parser.add_argument("--task", type=str, default="picoclvr")
35 parser.add_argument("--log_filename", type=str, default="train.log")
37 parser.add_argument("--result_dir", type=str, default="results_default")
39 parser.add_argument("--seed", type=int, default=0)
41 parser.add_argument("--nb_epochs", type=int, default=25)
43 parser.add_argument("--batch_size", type=int, default=25)
45 parser.add_argument("--nb_train_samples", type=int, default=250000)
47 parser.add_argument("--nb_test_samples", type=int, default=10000)
49 parser.add_argument("--optim", type=str, default="adam")
51 parser.add_argument("--learning_rate", type=float, default=1e-4)
53 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
55 parser.add_argument("--dim_model", type=int, default=512)
57 parser.add_argument("--dim_keys", type=int, default=64)
59 parser.add_argument("--dim_hidden", type=int, default=2048)
61 parser.add_argument("--nb_heads", type=int, default=8)
63 parser.add_argument("--nb_blocks", type=int, default=12)
65 parser.add_argument("--dropout", type=float, default=0.1)
67 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
69 parser.add_argument("--no_checkpoint", action="store_true", default=False)
71 parser.add_argument("--overwrite_results", action="store_true", default=False)
73 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
75 ##############################
78 parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
80 parser.add_argument("--picoclvr_height", type=int, default=12)
82 parser.add_argument("--picoclvr_width", type=int, default=16)
84 parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
86 ##############################
89 parser.add_argument("--maze_height", type=int, default=13)
91 parser.add_argument("--maze_width", type=int, default=21)
93 parser.add_argument("--maze_nb_walls", type=int, default=15)
95 ######################################################################
97 args = parser.parse_args()
99 assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
102 os.mkdir(args.result_dir)
103 except FileExistsError:
104 if not args.overwrite_results:
105 print(f"result directory {args.result_dir} already exists")
108 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
111 # torch.backends.cudnn.deterministic = True
112 # torch.backends.cudnn.benchmark = False
113 # torch.use_deterministic_algorithms(True)
114 torch.manual_seed(args.seed)
115 if torch.cuda.is_available():
116 torch.cuda.manual_seed_all(args.seed)
118 ######################################################################
122 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
124 if log_file is not None:
125 log_file.write(t + s + "\n")
133 log_string(f"args.{n} {getattr(args, n)}")
135 ######################################################################
138 def masked_inplace_autoregression(
139 model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu")
141 for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
142 i = (ar_mask.sum(0) > 0).nonzero()
145 mygpt.BracketedSequence(input, 0, i.min())
146 ) # Needed to initialize the model's cache
147 for s in range(i.min(), i.max() + 1):
148 output = model(mygpt.BracketedSequence(input, s, 1)).x
149 logits = output[:, s]
150 if forbidden_tokens is not None:
151 logits = logits.masked_fill(forbidden_tokens, float("-inf"))
152 if args.deterministic_synthesis:
153 t_next = logits.argmax(1)
155 dist = torch.distributions.categorical.Categorical(logits=logits)
156 t_next = dist.sample()
157 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
160 ######################################################################
164 def batches(self, split="train"):
167 def vocabulary_size(self):
170 def produce_results(self, n_epoch, model):
174 ######################################################################
179 class TaskPicoCLVR(Task):
180 # Make a tensor from a list of strings
181 def tensorize(self, descr):
182 token_descr = [s.strip().split(" ") for s in descr]
183 l = max([len(s) for s in token_descr])
184 token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
185 id_descr = [[self.token2id[u] for u in s] for s in token_descr]
186 return torch.tensor(id_descr, device=self.device)
188 # Make a list of strings from a tensor
189 def detensorize(self, x):
190 return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
192 # trim all the tensors in the tuple z to remove as much token from
193 # left and right in the first tensor. If z is a tuple, all its
194 # elements are trimed according to the triming for the first
195 def trim(self, z, token="<nul>"):
196 n = self.token2id[token]
199 i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
200 a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
201 return tuple([t[:, a:b] for t in z])
203 i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
204 a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
207 ######################
208 # Not the cleanest part of the code
210 # Extract the last image of each sequence, from the last <img>
211 # included, and set to <nul> all the tokens from the beginning of
212 # that image to the end
213 def excise_last_image(self, input):
214 t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
215 nb_img_tokens = self.height * self.width + 1
217 input = input.clone()
218 t = (input == t_img).long()
219 tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
220 i = (t * tail_masks).nonzero(as_tuple=True)
223 i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
225 images = self.trim(input[j])
227 loss_masks = 1 - tail_masks
228 input, loss_masks = self.trim((input, loss_masks))
229 return input, loss_masks, images
231 def add_true_image(self, input, images, loss_masks):
232 t_nul = self.token2id["<nul>"]
233 nb_img_tokens = self.height * self.width + 1
234 input = F.pad(input, (0, nb_img_tokens), value=t_nul)
235 loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
236 t = (input == t_nul).long()
237 i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
240 i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
244 input, loss_masks = self.trim((input, loss_masks))
245 return input, loss_masks
247 def add_generated_image(self, input, loss_masks, model):
248 t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
249 nb_img_tokens = self.height * self.width + 1
251 input = F.pad(input, (0, nb_img_tokens), value=t_nul)
252 loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
253 t = (input == t_nul).long()
254 i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
261 + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
263 ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
266 torch.arange(self.vocabulary_size(), device=input.device) == t_nul
268 with torch.autograd.no_grad():
271 masked_inplace_autoregression(
281 input, loss_masks = self.trim((input, loss_masks))
283 return input, loss_masks
285 ######################
295 device=torch.device("cpu"),
299 def generate_descr(nb, cache_suffix, pruner):
300 return picoclvr.generate(
310 self.batch_size = batch_size
312 self.pruner_train = pruner_train
313 self.pruner_eval = pruner_eval
316 "nb_train_samples": nb_train_samples,
317 "nb_test_samples": nb_test_samples,
320 "nb_colors": nb_colors,
321 "batch_size": batch_size,
322 "rng_state": list(torch.get_rng_state()),
326 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
328 self.train_descr = generate_descr(
329 nb_train_samples, "train", pruner=self.pruner_train
331 self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
333 # Build the tokenizer
334 tokens = {"<nul>", "<img>"}
335 for d in [self.train_descr, self.test_descr]:
337 for t in s.strip().split(" "):
339 # make this set a sorted list to get the same tensors given
341 tokens = list(tokens)
343 self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
344 self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
346 # Tokenize the train and test sets
347 self.train_input = self.tensorize(self.train_descr)
348 self.test_input = self.tensorize(self.test_descr)
350 def batches(self, split="train"):
351 assert split in {"train", "test"}
352 input = self.train_input if split == "train" else self.test_input
353 for batch in tqdm.tqdm(
354 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
356 yield self.trim(batch)
358 def vocabulary_size(self):
359 return len(self.token2id)
361 def compute_missing_properties(self, n_epoch, model, pruner=None):
362 acc_nb_requested_properties = []
363 acc_nb_missing_properties = []
366 for input in tqdm.tqdm(
367 self.test_input.split(self.batch_size),
369 desc=f"test-properties",
371 tape, loss_masks, _ = self.excise_last_image(input)
372 tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
373 result_descr = self.detensorize(tape)
374 np = picoclvr.nb_properties(
380 nb_requested_properties, _, nb_missing_properties = zip(*np)
381 acc_nb_requested_properties += nb_requested_properties
382 acc_nb_missing_properties += nb_missing_properties
383 acc_nb_results += len(result_descr)
385 nb_requested_properties = sum(acc_nb_requested_properties)
386 nb_missing_properties = sum(acc_nb_missing_properties)
388 prefix = "" if pruner is None else "pruned_"
389 log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
391 f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
394 f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
397 ######################################################################
399 def produce_results(self, n_epoch, model):
400 self.compute_missing_properties(n_epoch, model)
402 if self.pruner_eval is not None:
403 self.compute_missing_properties(n_epoch, model, self.pruner_eval)
405 nb_tokens_to_generate = self.height * self.width + 3
410 for primer_descr in [
411 "red above green <sep> green top <sep> blue right of red",
412 "there is red <sep> there is yellow <sep> there is blue",
413 "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
414 "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
416 primer += [primer_descr] * nb_per_primer
418 tape = self.tensorize(primer)
419 loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
420 tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
421 result_descr = self.detensorize(tape)
423 np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
425 acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
426 acc_nb_results = len(result_descr)
428 nb_requested_properties = sum(acc_nb_requested_properties)
429 nb_missing_properties = sum(acc_nb_missing_properties)
432 log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
434 f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
437 f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
440 img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
444 img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
448 torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
454 image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png")
455 torchvision.utils.save_image(
456 img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0
458 log_string(f"wrote {image_name}")
461 ######################################################################
464 class TaskMNIST(Task):
465 def __init__(self, batch_size, device=torch.device("cpu")):
467 self.batch_size = batch_size
469 def batches(self, split="train"):
470 assert split in {"train", "test"}
471 data_set = torchvision.datasets.MNIST(
472 root="./data", train=(split == "train"), download=True
474 data_input = data_set.data.view(-1, 28 * 28).long()
475 if args.nb_train_samples is not None:
476 data_input = data_input[: args.nb_train_samples]
477 for batch in tqdm.tqdm(
478 data_input.split(self.batch_size), desc=f"epoch-{split}"
482 def vocabulary_size(self):
485 def produce_results(self, n_epoch, model):
486 results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
487 ar_mask = torch.full_like(results, 1)
488 masked_inplace_autoregression(
489 model, self.batch_size, results, ar_mask, device=self.device
491 image_name = os.path.join(args.result_dir, f"result_mnist_{n_epoch:04d}.png")
492 torchvision.utils.save_image(
493 1 - results.reshape(-1, 1, 28, 28) / 255.0,
498 log_string(f"wrote {image_name}")
501 ######################################################################
506 class TaskMaze(Task):
507 def map2seq(self, *m):
508 return torch.cat([x.flatten(1) for x in m], 1)
510 def seq2map(self, s):
511 s = s.reshape(s.size(0), -1, self.height, self.width)
512 return (s[:, k] for k in range(s.size(1)))
522 device=torch.device("cpu"),
524 self.batch_size = batch_size
529 train_mazes, train_paths, _ = maze.create_maze_data(
534 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
536 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
538 test_mazes, test_paths, _ = maze.create_maze_data(
543 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
545 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
547 self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
549 def batches(self, split="train", nb_to_use=-1, desc=None):
550 assert split in {"train", "test"}
551 input = self.train_input if split == "train" else self.test_input
553 input = input[:nb_to_use]
555 desc = f"epoch-{split}"
556 for batch in tqdm.tqdm(
557 input.split(self.batch_size), dynamic_ncols=True, desc=desc
561 def vocabulary_size(self):
564 def compute_error(self, model, split="train", nb_to_use=-1):
565 nb_total, nb_correct = 0, 0
566 for input in task.batches(split, nb_to_use):
567 result = input.clone()
568 ar_mask = result.new_zeros(result.size())
569 ar_mask[:, self.height * self.width :] = 1
570 result *= 1 - ar_mask
571 masked_inplace_autoregression(
572 model, self.batch_size, result, ar_mask, device=self.device
574 mazes, paths = self.seq2map(result)
575 nb_correct += maze.path_correctness(mazes, paths).long().sum()
576 nb_total += mazes.size(0)
578 return nb_total, nb_correct
580 def produce_results(self, n_epoch, model):
581 with torch.autograd.no_grad():
585 train_nb_total, train_nb_correct = self.compute_error(
586 model, "train", nb_to_use=1000
589 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
592 test_nb_total, test_nb_correct = self.compute_error(
593 model, "test", nb_to_use=1000
596 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
599 input = self.test_input[:48]
600 result = input.clone()
601 ar_mask = result.new_zeros(result.size())
602 ar_mask[:, self.height * self.width :] = 1
603 result *= 1 - ar_mask
604 masked_inplace_autoregression(
605 model, self.batch_size, result, ar_mask, device=self.device
608 mazes, paths = self.seq2map(input)
609 _, predicted_paths = self.seq2map(result)
611 filename = os.path.join(args.result_dir, f"result_{n_epoch:04d}.png")
616 predicted_paths=predicted_paths,
617 path_correct=maze.path_correctness(mazes, predicted_paths),
619 log_string(f"wrote {filename}")
624 ######################################################################
626 class TaskSnake(Task):
635 device=torch.device("cpu"),
637 self.batch_size = batch_size
645 self.nb_codes = max(self.train_input.max(), self.train_input.max()) + 1
647 def batches(self, split="train", nb_to_use=-1, desc=None):
648 assert split in {"train", "test"}
649 input = self.train_input if split == "train" else self.test_input
651 input = input[:nb_to_use]
653 desc = f"epoch-{split}"
654 for batch in tqdm.tqdm(
655 input.split(self.batch_size), dynamic_ncols=True, desc=desc
660 ######################################################################
663 def picoclvr_pruner_horizontal_green(p):
664 return not ("green" in p and ("left" in p or "right" in p))
667 picoclvr_pruner_train = (
668 picoclvr_pruner_horizontal_green
669 if args.picocvlr_prune_properties in {"train+eval"}
673 picoclvr_pruner_eval = (
674 (lambda p: not picoclvr_pruner_horizontal_green(p))
675 if args.picocvlr_prune_properties in {"train+eval", "eval"}
679 ######################################################################
681 if args.task == "picoclvr":
683 nb_train_samples=args.nb_train_samples,
684 nb_test_samples=args.nb_test_samples,
685 batch_size=args.batch_size,
686 height=args.picoclvr_height,
687 width=args.picoclvr_width,
688 nb_colors=args.picoclvr_nb_colors,
690 pruner_train=picoclvr_pruner_train,
691 pruner_eval=picoclvr_pruner_eval,
694 elif args.task == "mnist":
696 batch_size=args.batch_size,
700 elif args.task == "maze":
702 nb_train_samples=args.nb_train_samples,
703 nb_test_samples=args.nb_test_samples,
704 batch_size=args.batch_size,
705 height=args.maze_height,
706 width=args.maze_width,
707 nb_walls=args.maze_nb_walls,
712 raise ValueError(f"Unknown task {args.task}")
714 ######################################################################
716 log_string(f"device {device}")
718 vocabulary_size = task.vocabulary_size()
720 log_string(f"vocabulary_size {vocabulary_size}")
722 ##############################
725 vocabulary_size=vocabulary_size,
726 dim_model=args.dim_model,
727 dim_keys=args.dim_keys,
728 dim_hidden=args.dim_hidden,
729 nb_heads=args.nb_heads,
730 nb_blocks=args.nb_blocks,
732 dropout=args.dropout,
737 nb_parameters = sum(p.numel() for p in model.parameters())
738 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
740 ######################################################################
742 nb_epochs_finished = 0
744 if args.no_checkpoint:
745 log_string(f"not trying to load checkpoint.")
749 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
750 checkpoint = torch.load(checkpoint_name)
751 nb_epochs_finished = checkpoint["nb_epochs_finished"]
752 model.load_state_dict(checkpoint["model_state"])
753 torch.set_rng_state(checkpoint["rng_state"])
754 if torch.cuda.is_available():
755 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
757 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
759 except FileNotFoundError:
760 log_string("starting from scratch.")
763 log_string("error when loading the checkpoint.")
766 ######################################################################
768 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
771 for input in task.batches(split="train"):
772 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
773 token_probas = token_count / token_count.sum()
774 entropy = -torch.xlogy(token_probas, token_probas).sum()
775 train_set_perplexity = math.exp(entropy)
777 ##############################
779 if args.learning_rate_schedule == "cos":
780 learning_rate_schedule = {}
781 for n_epoch in range(args.nb_epochs):
782 u = n_epoch / args.nb_epochs * math.pi
783 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
788 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
792 learning_rate_schedule = {}
793 learning_rate = args.learning_rate
794 for n_epoch in range(args.nb_epochs):
796 learning_rate = u[n_epoch]
797 learning_rate_schedule[n_epoch] = learning_rate
799 log_string(f"learning_rate_schedule {learning_rate_schedule}")
801 ##############################
805 if nb_epochs_finished >= nb_epochs:
806 task.produce_results(nb_epochs_finished, model)
808 for n_epoch in range(nb_epochs_finished, nb_epochs):
809 learning_rate = learning_rate_schedule[n_epoch]
811 log_string(f"learning_rate {learning_rate}")
813 if args.optim == "sgd":
814 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
815 elif args.optim == "adam":
816 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
817 elif args.optim == "adamw":
818 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
820 raise ValueError(f"Unknown optimizer {args.optim}.")
824 nb_train_samples, acc_train_loss = 0, 0.0
826 for input in task.batches(split="train"):
827 input = input.to(device)
828 output = model(mygpt.BracketedSequence(input)).x
829 loss = F.cross_entropy(output.transpose(1, 2), input)
830 acc_train_loss += loss.item() * input.size(0)
831 nb_train_samples += input.size(0)
832 nb_samples_seen += input.size(0)
834 optimizer.zero_grad()
838 with torch.autograd.no_grad():
841 nb_test_samples, acc_test_loss = 0, 0.0
843 for input in task.batches(split="test"):
844 input = input.to(device)
846 # input, loss_masks, true_images = task.excise_last_image(input)
847 # input, loss_masks = task.add_true_image(input, true_images, loss_masks)
849 output = model(mygpt.BracketedSequence(input)).x
850 loss = F.cross_entropy(output.transpose(1, 2), input)
851 acc_test_loss += loss.item() * input.size(0)
852 nb_test_samples += input.size(0)
854 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
855 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
858 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
861 task.produce_results(n_epoch, model)
864 "nb_epochs_finished": n_epoch + 1,
865 "model_state": model.state_dict(),
866 "rng_state": torch.get_rng_state(),
869 if torch.cuda.is_available():
870 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
872 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
873 torch.save(checkpoint, checkpoint_name)
874 log_string(f"saved checkpoint {checkpoint_name}")
876 ######################################################################