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=order.device)
138 return order.sort(1).indices
141 torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
145 def shuffle(x, order, reorder=False):
147 order = order.unsqueeze(-1).expand(-1, -1, x.size(-1))
150 y.scatter_(1, order, x)
153 return x.gather(1, order)
156 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
157 # tokens that should be generated
160 def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
161 for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
162 i = (ar_mask.sum(0) > 0).nonzero()
164 # Needed to initialize the model's cache
165 model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
166 for s in range(i.min(), i.max() + 1):
167 output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
168 logits = output[:, s]
169 if args.deterministic_synthesis:
170 t_next = logits.argmax(1)
172 dist = torch.distributions.categorical.Categorical(logits=logits)
173 t_next = dist.sample()
174 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
177 ######################################################################
180 def compute_perplexity(model, split="train"):
181 with torch.autograd.no_grad():
185 nb_samples, acc_loss = 0, 0.0
187 for input in task.batches(split=split):
188 input = input.to(device)
189 order = generation_order(input, task.height * task.width)
190 input = shuffle(input, order)
191 output = model(mygpt.BracketedSequence(input), order=order).x
192 loss = F.cross_entropy(output.transpose(1, 2), input)
193 acc_loss += loss.item() * input.size(0)
194 nb_samples += input.size(0)
198 return math.exp(min(100, acc_loss / nb_samples))
201 ######################################################################
204 def oneshot_policy_loss(mazes, output, policies, height, width):
205 masks = (mazes == maze.v_empty).unsqueeze(-1)
206 targets = policies.permute(0, 2, 1) * masks
207 output = output * masks
208 return -(output.log_softmax(-1) * targets).sum() / masks.sum()
211 def oneshot_trace_loss(mazes, output, policies, height, width):
212 masks = mazes == maze.v_empty
213 targets = maze.stationary_densities(
214 mazes.view(-1, height, width), policies.view(-1, 4, height, width)
216 targets = targets * masks
217 output = output.squeeze(-1) * masks
218 return (output - targets).abs().sum() / masks.sum()
221 def oneshot(gpt, task):
225 if args.oneshot_input == "head":
226 dim_in = args.dim_model
227 elif args.oneshot_input == "deep":
228 dim_in = args.dim_model * args.nb_blocks * 2
230 raise ValueError(f"{args.oneshot_input=}")
232 if args.oneshot_output == "policy":
234 compute_loss = oneshot_policy_loss
235 elif args.oneshot_output == "trace":
237 compute_loss = oneshot_trace_loss
239 raise ValueError(f"{args.oneshot_output=}")
241 model = nn.Sequential(
242 nn.Linear(dim_in, args.dim_model),
244 nn.Linear(args.dim_model, args.dim_model),
246 nn.Linear(args.dim_model, dim_out),
249 for n_epoch in range(args.nb_epochs):
250 learning_rate = learning_rate_schedule[n_epoch]
251 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
253 acc_train_loss, nb_train_samples = 0, 0
254 for mazes, policies in task.policy_batches(split="train"):
255 order = generation_order(mazes, task.height * task.width)
256 x = shuffle(mazes, order)
257 x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
258 output_gpt = shuffle(x, order, reorder=True)
259 output = model(output_gpt)
261 loss = compute_loss(mazes, output, policies, task.height, task.width)
262 acc_train_loss += loss.item() * mazes.size(0)
263 nb_train_samples += mazes.size(0)
265 optimizer.zero_grad()
269 acc_test_loss, nb_test_samples = 0, 0
270 for mazes, policies in task.policy_batches(split="test"):
271 order = generation_order(mazes, task.height * task.width)
272 x = shuffle(mazes, order)
273 x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
274 output_gpt = shuffle(x, order, reorder=True)
275 output = model(output_gpt)
276 loss = compute_loss(mazes, output, policies, task.height, task.width)
277 acc_test_loss += loss.item() * mazes.size(0)
278 nb_test_samples += mazes.size(0)
281 f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
284 # -------------------
285 mazes = task.test_input[:32, : task.height * task.width]
286 policies = task.test_policies[:32]
287 order = generation_order(mazes, task.height * task.width)
288 x = shuffle(mazes, order)
289 x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
290 output_gpt = shuffle(x, order, reorder=True)
291 output = model(output_gpt)
292 if args.oneshot_output == "policy":
293 targets = policies.permute(0, 2, 1)
295 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
297 elif args.oneshot_output == "trace":
298 targets = maze.stationary_densities(
299 mazes.view(-1, task.height, task.width),
300 policies.view(-1, 4, task.height, task.width),
304 raise ValueError(f"{args.oneshot_output=}")
306 scores = scores.reshape(-1, task.height, task.width)
307 mazes = mazes.reshape(-1, task.height, task.width)
308 targets = targets.reshape(-1, task.height, task.width)
312 f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png",
318 # -------------------
323 ######################################################################
327 def batches(self, split="train", nb_to_use=-1, desc=None):
330 def vocabulary_size(self):
333 def produce_results(self, n_epoch, model):
337 ######################################################################
342 class TaskMaze(Task):
343 def map2seq(self, *m):
344 return torch.cat([x.flatten(1) for x in m], 1)
346 def seq2map(self, s):
347 s = s.reshape(s.size(0), -1, self.height, self.width)
348 return (s[:, k] for k in range(s.size(1)))
358 device=torch.device("cpu"),
360 self.batch_size = batch_size
365 train_mazes, train_paths, train_policies = maze.create_maze_data(
370 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
372 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
373 self.train_policies = train_policies.flatten(-2).to(device)
375 test_mazes, test_paths, test_policies = maze.create_maze_data(
380 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
382 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
383 self.test_policies = test_policies.flatten(-2).to(device)
385 self.nb_codes = self.train_input.max() + 1
387 def batches(self, split="train", nb_to_use=-1, desc=None):
388 assert split in {"train", "test"}
389 input = self.train_input if split == "train" else self.test_input
391 input = input[:nb_to_use]
393 desc = f"epoch-{split}"
394 for batch in tqdm.tqdm(
395 input.split(self.batch_size), dynamic_ncols=True, desc=desc
399 def policy_batches(self, split="train", nb_to_use=-1, desc=None):
400 assert split in {"train", "test"}
401 input = self.train_input if split == "train" else self.test_input
402 policies = self.train_policies if split == "train" else self.test_policies
403 input = input[:, : self.height * self.width]
404 policies = policies * (input != maze.v_wall)[:, None]
407 input = input[:nb_to_use]
408 policies = policies[:nb_to_use]
411 desc = f"epoch-{split}"
412 for batch in tqdm.tqdm(
413 zip(input.split(self.batch_size), policies.split(self.batch_size)),
419 def vocabulary_size(self):
422 def compute_error(self, model, split="train", nb_to_use=-1):
423 nb_total, nb_correct = 0, 0
424 for input in task.batches(split, nb_to_use):
425 result = input.clone()
426 ar_mask = result.new_zeros(result.size())
427 ar_mask[:, self.height * self.width :] = 1
428 result *= 1 - ar_mask
429 order = generation_order(result, self.height * self.width)
430 masked_inplace_autoregression(
431 model, self.batch_size, result, ar_mask, order=order
433 result = shuffle(result, order, reorder=True)
434 mazes, paths = self.seq2map(result)
435 nb_correct += maze.path_correctness(mazes, paths).long().sum()
436 nb_total += mazes.size(0)
438 return nb_total, nb_correct
440 def produce_results(self, n_epoch, model):
441 with torch.autograd.no_grad():
445 train_nb_total, train_nb_correct = self.compute_error(
446 model, "train", nb_to_use=1000
449 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
452 test_nb_total, test_nb_correct = self.compute_error(
453 model, "test", nb_to_use=1000
456 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
459 input = self.test_input[:32]
460 result = input.clone()
461 ar_mask = result.new_zeros(result.size())
462 ar_mask[:, self.height * self.width :] = 1
463 result *= 1 - ar_mask
464 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
466 mazes, paths = self.seq2map(input)
467 _, predicted_paths = self.seq2map(result)
469 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
472 predicted_paths=predicted_paths,
473 path_correct=maze.path_correctness(mazes, predicted_paths),
479 ######################################################################
481 log_string(f"device {device}")
485 nb_train_samples=args.nb_train_samples,
486 nb_test_samples=args.nb_test_samples,
487 batch_size=args.batch_size,
488 height=args.maze_height,
489 width=args.maze_width,
490 nb_walls=args.maze_nb_walls,
495 vocabulary_size = task.vocabulary_size()
497 log_string(f"vocabulary_size {vocabulary_size}")
499 ##############################
502 vocabulary_size=vocabulary_size,
503 dim_model=args.dim_model,
504 dim_keys=args.dim_keys,
505 dim_hidden=args.dim_hidden,
506 nb_heads=args.nb_heads,
507 nb_blocks=args.nb_blocks,
509 dropout=args.dropout,
514 nb_parameters = sum(p.numel() for p in model.parameters())
515 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
517 ######################################################################
519 nb_epochs_finished = 0
521 if args.no_checkpoint:
522 log_string(f"not trying to load checkpoint.")
526 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
527 checkpoint = torch.load(checkpoint_name)
528 nb_epochs_finished = checkpoint["nb_epochs_finished"]
529 model.load_state_dict(checkpoint["model_state"])
530 torch.set_rng_state(checkpoint["rng_state"])
531 if torch.cuda.is_available():
532 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
534 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
536 except FileNotFoundError:
537 log_string("starting from scratch.")
540 log_string("error when loading the checkpoint.")
543 ######################################################################
546 for input in task.batches(split="train"):
547 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
548 token_probas = token_count / token_count.sum()
549 entropy = -torch.xlogy(token_probas, token_probas).sum()
550 train_set_perplexity = math.exp(entropy)
552 ##############################
554 if args.learning_rate_schedule == "cos":
555 learning_rate_schedule = {}
556 for n_epoch in range(args.nb_epochs):
557 u = n_epoch / args.nb_epochs * math.pi
558 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
563 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
567 learning_rate_schedule = {}
568 learning_rate = args.learning_rate
569 for n_epoch in range(args.nb_epochs):
571 learning_rate = u[n_epoch]
572 learning_rate_schedule[n_epoch] = learning_rate
574 log_string(f"learning_rate_schedule {learning_rate_schedule}")
576 ##############################
578 if nb_epochs_finished >= args.nb_epochs:
579 n_epoch = nb_epochs_finished
580 train_perplexity = compute_perplexity(model, split="train")
581 test_perplexity = compute_perplexity(model, split="test")
584 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
587 task.produce_results(n_epoch, model)
589 ##############################
591 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
592 learning_rate = learning_rate_schedule[n_epoch]
594 log_string(f"learning_rate {learning_rate}")
596 if args.optim == "sgd":
597 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
598 elif args.optim == "adam":
599 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
600 elif args.optim == "adamw":
601 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
603 raise ValueError(f"{args.optim=}")
607 nb_train_samples, acc_train_loss = 0, 0.0
609 for input in task.batches(split="train"):
610 input = input.to(device)
611 order = generation_order(input, task.height * task.width)
612 input = shuffle(input, order)
613 output = model(mygpt.BracketedSequence(input), order=order).x
614 loss = F.cross_entropy(output.transpose(1, 2), input)
615 acc_train_loss += loss.item() * input.size(0)
616 nb_train_samples += input.size(0)
618 optimizer.zero_grad()
622 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
623 test_perplexity = compute_perplexity(model, split="test")
626 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
629 task.produce_results(n_epoch, model)
632 "nb_epochs_finished": n_epoch + 1,
633 "model_state": model.state_dict(),
634 "rng_state": torch.get_rng_state(),
637 if torch.cuda.is_available():
638 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
640 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
641 torch.save(checkpoint, checkpoint_name)
642 log_string(f"saved checkpoint {checkpoint_name}")
644 ######################################################################
649 ######################################################################