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(description="A maze shortest path solving with a GPT.")
31 parser.add_argument("--log_filename", type=str, default="train.log")
33 parser.add_argument("--result_dir", type=str, default="results_default")
35 parser.add_argument("--seed", type=int, default=0)
37 parser.add_argument("--nb_epochs", type=int, default=25)
39 parser.add_argument("--nb_train_samples", type=int, default=200000)
41 parser.add_argument("--nb_test_samples", type=int, default=50000)
43 parser.add_argument("--batch_size", type=int, default=25)
45 parser.add_argument("--optim", type=str, default="adam")
47 parser.add_argument("--learning_rate", type=float, default=1e-3)
50 "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6"
53 parser.add_argument("--dim_model", type=int, default=512)
55 parser.add_argument("--dim_keys", type=int, default=64)
57 parser.add_argument("--dim_hidden", type=int, default=2048)
59 parser.add_argument("--nb_heads", type=int, default=8)
61 parser.add_argument("--nb_blocks", type=int, default=12)
63 parser.add_argument("--dropout", type=float, default=0.1)
65 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
67 parser.add_argument("--random_regression_order", 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("--maze_height", type=int, default=13)
80 parser.add_argument("--maze_width", type=int, default=21)
82 parser.add_argument("--maze_nb_walls", type=int, default=15)
84 ##############################
87 parser.add_argument("--oneshot", action="store_true", default=False)
89 parser.add_argument("--oneshot_input", type=str, default="head")
91 parser.add_argument("--oneshot_output", type=str, default="trace")
93 ######################################################################
95 args = parser.parse_args()
98 os.mkdir(args.result_dir)
99 except FileExistsError:
100 if not args.overwrite_results:
101 print(f"result directory {args.result_dir} already exists")
104 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
107 # torch.backends.cudnn.deterministic = True
108 # torch.backends.cudnn.benchmark = False
109 # torch.use_deterministic_algorithms(True)
110 torch.manual_seed(args.seed)
111 if torch.cuda.is_available():
112 torch.cuda.manual_seed_all(args.seed)
114 ######################################################################
118 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
120 if log_file is not None:
121 log_file.write(t + s + "\n")
129 log_string(f"args.{n} {getattr(args, n)}")
131 ######################################################################
134 def generation_order(x, fixed_len):
135 if args.random_regression_order:
136 order = torch.rand(x.size(), device=x.device)
137 order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=x.device)
138 order = order.sort(1).indices
141 torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
146 def reorder(x, order, reverse=False): # x is NxTxD1x...xDk, order is NxT'
147 u = x.reshape(x.size()[:2] + (-1,))
148 order = order.unsqueeze(-1).expand(-1, -1, u.size(-1))
151 v.scatter_(1, order, u)
153 v = u.gather(1, order)
154 v = v.reshape(v.size()[:2] + x.size()[2:])
158 def shuffle(x, fixed_len):
159 order = generation_order(x, fixed_len)
160 return reorder(x, order), order
163 ######################################################################
165 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
166 # tokens that should be generated
169 def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
170 for input, ar_mask, order in zip(
171 input.split(batch_size), ar_mask.split(batch_size), order.split(batch_size)
173 i = (ar_mask.sum(0) > 0).nonzero()
175 # Needed to initialize the model's cache
176 model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
177 for s in range(i.min(), i.max() + 1):
178 output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
179 logits = output[:, s]
180 if args.deterministic_synthesis:
181 t_next = logits.argmax(1)
183 dist = torch.distributions.categorical.Categorical(logits=logits)
184 t_next = dist.sample()
185 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
188 ######################################################################
191 def compute_perplexity(model, task, fixed_len, split="train"):
192 with torch.autograd.no_grad():
196 nb_samples, acc_loss = 0, 0.0
198 for input in task.batches(split=split):
199 input = input.to(device)
200 x, order = shuffle(input, fixed_len)
201 x = model(mygpt.BracketedSequence(x), order=order).x
202 output = reorder(x, order, reverse=True)
203 loss = F.cross_entropy(output.transpose(1, 2), input)
204 acc_loss += loss.item() * input.size(0)
205 nb_samples += input.size(0)
209 return math.exp(min(100, acc_loss / nb_samples))
212 ######################################################################
215 def oneshot_policy_loss(mazes, output, policies, height, width):
216 masks = (mazes == maze.v_empty).unsqueeze(-1)
217 targets = policies.permute(0, 2, 1) * masks
218 output = output * masks
219 return -(output.log_softmax(-1) * targets).sum() / masks.sum()
222 def oneshot_trace_loss(mazes, output, policies, height, width):
223 masks = mazes == maze.v_empty
224 targets = maze.stationary_densities(
225 mazes.view(-1, height, width), policies.view(-1, 4, height, width)
227 targets = targets * masks
228 output = output.squeeze(-1) * masks
229 return (output - targets).abs().sum() / masks.sum()
232 def oneshot(gpt, task):
236 if args.oneshot_input == "head":
237 dim_in = args.dim_model
238 elif args.oneshot_input == "deep":
239 dim_in = args.dim_model * args.nb_blocks * 2
241 raise ValueError(f"{args.oneshot_input=}")
243 if args.oneshot_output == "policy":
245 compute_loss = oneshot_policy_loss
246 elif args.oneshot_output == "trace":
248 compute_loss = oneshot_trace_loss
250 raise ValueError(f"{args.oneshot_output=}")
252 model = nn.Sequential(
253 nn.Linear(dim_in, args.dim_model),
255 nn.Linear(args.dim_model, args.dim_model),
257 nn.Linear(args.dim_model, dim_out),
260 for n_epoch in range(args.nb_epochs):
261 learning_rate = learning_rate_schedule[n_epoch]
262 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
264 acc_train_loss, nb_train_samples = 0, 0
265 for mazes, policies in task.policy_batches(split="train"):
266 x, order = shuffle(mazes, task.height * task.width)
267 x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
268 output_gpt = reorder(x, order, reverse=True)
269 output = model(output_gpt)
271 loss = compute_loss(mazes, output, policies, task.height, task.width)
272 acc_train_loss += loss.item() * mazes.size(0)
273 nb_train_samples += mazes.size(0)
275 optimizer.zero_grad()
279 acc_test_loss, nb_test_samples = 0, 0
280 for mazes, policies in task.policy_batches(split="test"):
281 x, order = shuffle(mazes, task.height * task.width)
282 x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
283 output_gpt = reorder(x, order, reverse=True)
284 output = model(output_gpt)
285 loss = compute_loss(mazes, output, policies, task.height, task.width)
286 acc_test_loss += loss.item() * mazes.size(0)
287 nb_test_samples += mazes.size(0)
290 f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
293 # -------------------
294 mazes = task.test_input[:32, : task.height * task.width]
295 policies = task.test_policies[:32]
296 x, order = shuffle(mazes, task.height * task.width)
297 x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
298 output_gpt = reorder(x, order, reverse=True)
299 output = model(output_gpt)
300 if args.oneshot_output == "policy":
301 targets = policies.permute(0, 2, 1)
303 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
305 elif args.oneshot_output == "trace":
306 targets = maze.stationary_densities(
307 mazes.view(-1, task.height, task.width),
308 policies.view(-1, 4, task.height, task.width),
312 raise ValueError(f"{args.oneshot_output=}")
314 scores = scores.reshape(-1, task.height, task.width)
315 mazes = mazes.reshape(-1, task.height, task.width)
316 targets = targets.reshape(-1, task.height, task.width)
318 f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png"
321 os.path.join(args.result_dir, filename),
326 log_string(f"wrote {filename}")
328 # -------------------
333 ######################################################################
337 def batches(self, split="train", nb_to_use=-1, desc=None):
340 def vocabulary_size(self):
343 def produce_results(self, n_epoch, model):
347 ######################################################################
352 class TaskMaze(Task):
353 def map2seq(self, *m):
354 return torch.cat([x.flatten(1) for x in m], 1)
356 def seq2map(self, s):
357 s = s.reshape(s.size(0), -1, self.height, self.width)
358 return (s[:, k] for k in range(s.size(1)))
368 device=torch.device("cpu"),
370 self.batch_size = batch_size
375 train_mazes, train_paths, train_policies = maze.create_maze_data(
380 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
382 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
383 self.train_policies = train_policies.flatten(-2).to(device)
385 test_mazes, test_paths, test_policies = maze.create_maze_data(
390 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
392 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
393 self.test_policies = test_policies.flatten(-2).to(device)
395 self.nb_codes = self.train_input.max() + 1
397 def batches(self, split="train", nb_to_use=-1, desc=None):
398 assert split in {"train", "test"}
399 input = self.train_input if split == "train" else self.test_input
401 input = input[:nb_to_use]
403 desc = f"epoch-{split}"
404 for batch in tqdm.tqdm(
405 input.split(self.batch_size), dynamic_ncols=True, desc=desc
409 def policy_batches(self, split="train", nb_to_use=-1, desc=None):
410 assert split in {"train", "test"}
411 input = self.train_input if split == "train" else self.test_input
412 policies = self.train_policies if split == "train" else self.test_policies
413 input = input[:, : self.height * self.width]
414 policies = policies * (input != maze.v_wall)[:, None]
417 input = input[:nb_to_use]
418 policies = policies[:nb_to_use]
421 desc = f"epoch-{split}"
422 for batch in tqdm.tqdm(
423 zip(input.split(self.batch_size), policies.split(self.batch_size)),
429 def vocabulary_size(self):
432 def compute_error(self, model, split="train", nb_to_use=-1):
433 nb_total, nb_correct = 0, 0
434 for input in task.batches(split, nb_to_use):
435 result = input.clone()
436 ar_mask = result.new_zeros(result.size())
437 ar_mask[:, self.height * self.width :] = 1
438 result *= 1 - ar_mask
439 x, order = shuffle(result, self.height * self.width)
440 masked_inplace_autoregression(
441 model, self.batch_size, x, ar_mask, order=order
443 result = reorder(x, order, reverse=True)
444 mazes, paths = self.seq2map(result)
445 nb_correct += maze.path_correctness(mazes, paths).long().sum()
446 nb_total += mazes.size(0)
448 return nb_total, nb_correct
450 def produce_results(self, n_epoch, model):
451 with torch.autograd.no_grad():
455 train_nb_total, train_nb_correct = self.compute_error(
456 model, "train", nb_to_use=1000
459 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
462 test_nb_total, test_nb_correct = self.compute_error(
463 model, "test", nb_to_use=1000
466 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
469 input = self.test_input[:32]
470 result = input.clone()
471 ar_mask = result.new_zeros(result.size())
472 ar_mask[:, self.height * self.width :] = 1
473 result *= 1 - ar_mask
474 x, order = shuffle(result, self.height * self.width)
475 masked_inplace_autoregression(
476 model, self.batch_size, x, ar_mask, order=order
478 result = reorder(x, order, reverse=True)
480 mazes, paths = self.seq2map(input)
481 _, predicted_paths = self.seq2map(result)
482 filename = f"result_{n_epoch:04d}.png"
484 os.path.join(args.result_dir, filename),
487 predicted_paths=predicted_paths,
488 path_correct=maze.path_correctness(mazes, predicted_paths),
490 log_string(f"wrote {filename}")
495 ######################################################################
497 log_string(f"device {device}")
501 nb_train_samples=args.nb_train_samples,
502 nb_test_samples=args.nb_test_samples,
503 batch_size=args.batch_size,
504 height=args.maze_height,
505 width=args.maze_width,
506 nb_walls=args.maze_nb_walls,
511 vocabulary_size = task.vocabulary_size()
513 log_string(f"vocabulary_size {vocabulary_size}")
515 ##############################
518 vocabulary_size=vocabulary_size,
519 dim_model=args.dim_model,
520 dim_keys=args.dim_keys,
521 dim_hidden=args.dim_hidden,
522 nb_heads=args.nb_heads,
523 nb_blocks=args.nb_blocks,
525 dropout=args.dropout,
530 nb_parameters = sum(p.numel() for p in model.parameters())
531 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
533 ######################################################################
535 nb_epochs_finished = 0
537 if args.no_checkpoint:
538 log_string(f"not trying to load checkpoint.")
542 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
543 checkpoint = torch.load(checkpoint_name)
544 nb_epochs_finished = checkpoint["nb_epochs_finished"]
545 model.load_state_dict(checkpoint["model_state"])
546 torch.set_rng_state(checkpoint["rng_state"])
547 if torch.cuda.is_available():
548 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
550 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
552 except FileNotFoundError:
553 log_string("starting from scratch.")
556 log_string("error when loading the checkpoint.")
559 ######################################################################
562 for input in task.batches(split="train"):
563 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
564 token_probas = token_count / token_count.sum()
565 entropy = -torch.xlogy(token_probas, token_probas).sum()
566 train_set_perplexity = math.exp(entropy)
568 ##############################
570 if args.learning_rate_schedule == "cos":
571 learning_rate_schedule = {}
572 for n_epoch in range(args.nb_epochs):
573 u = n_epoch / args.nb_epochs * math.pi
574 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
579 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
583 learning_rate_schedule = {}
584 learning_rate = args.learning_rate
585 for n_epoch in range(args.nb_epochs):
587 learning_rate = u[n_epoch]
588 learning_rate_schedule[n_epoch] = learning_rate
590 log_string(f"learning_rate_schedule {learning_rate_schedule}")
592 ##############################
594 if nb_epochs_finished >= args.nb_epochs:
595 n_epoch = nb_epochs_finished
596 train_perplexity = compute_perplexity(
597 model, task, fixed_len=task.height * task.width, split="train"
599 test_perplexity = compute_perplexity(
600 model, task, fixed_len=task.height * task.width, split="test"
604 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
607 task.produce_results(n_epoch, model)
609 ##############################
611 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
612 learning_rate = learning_rate_schedule[n_epoch]
614 log_string(f"learning_rate {learning_rate}")
616 if args.optim == "sgd":
617 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
618 elif args.optim == "adam":
619 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
620 elif args.optim == "adamw":
621 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
623 raise ValueError(f"{args.optim=}")
627 nb_train_samples, acc_train_loss = 0, 0.0
629 for input in task.batches(split="train"):
630 input = input.to(device)
631 x, order = shuffle(input, task.height * task.width)
632 x = model(mygpt.BracketedSequence(x), order=order).x
633 output = reorder(x, order, reverse=True)
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)
638 optimizer.zero_grad()
642 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
643 test_perplexity = compute_perplexity(
644 model, task, fixed_len=task.height * task.width, split="test"
648 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
651 task.produce_results(n_epoch, model)
654 "nb_epochs_finished": n_epoch + 1,
655 "model_state": model.state_dict(),
656 "rng_state": torch.get_rng_state(),
659 if torch.cuda.is_available():
660 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
662 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
663 torch.save(checkpoint, checkpoint_name)
664 log_string(f"saved checkpoint {checkpoint_name}")
666 ######################################################################
671 ######################################################################