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("--no_checkpoint", action="store_true", default=False)
69 parser.add_argument("--overwrite_results", action="store_true", default=False)
71 parser.add_argument("--one_shot", 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 parser.add_argument("--oneshot_mode", type=str, default="head")
86 ######################################################################
88 args = parser.parse_args()
91 os.mkdir(args.result_dir)
92 except FileExistsError:
93 if not args.overwrite_results:
94 print(f"result directory {args.result_dir} already exists")
97 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
100 # torch.backends.cudnn.deterministic = True
101 # torch.backends.cudnn.benchmark = False
102 # torch.use_deterministic_algorithms(True)
103 torch.manual_seed(args.seed)
104 if torch.cuda.is_available():
105 torch.cuda.manual_seed_all(args.seed)
107 ######################################################################
111 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
113 if log_file is not None:
114 log_file.write(t + s + "\n")
122 log_string(f"args.{n} {getattr(args, n)}")
124 ######################################################################
127 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
128 # tokens that should be generated
131 def masked_inplace_autoregression(model, batch_size, input, ar_mask):
132 for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
133 i = (ar_mask.sum(0) > 0).nonzero()
135 # Needed to initialize the model's cache
136 model(mygpt.BracketedSequence(input, 0, i.min()))
137 for s in range(i.min(), i.max() + 1):
138 output = model(mygpt.BracketedSequence(input, s, 1)).x
139 logits = output[:, s]
140 if args.deterministic_synthesis:
141 t_next = logits.argmax(1)
143 dist = torch.distributions.categorical.Categorical(logits=logits)
144 t_next = dist.sample()
145 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
148 ######################################################################
151 def compute_perplexity(model, split="train"):
152 with torch.autograd.no_grad():
156 nb_samples, acc_loss = 0, 0.0
158 for input in task.batches(split=split):
159 input = input.to(device)
161 output = model(mygpt.BracketedSequence(input)).x
162 loss = F.cross_entropy(output.transpose(1, 2), input)
163 acc_loss += loss.item() * input.size(0)
164 nb_samples += input.size(0)
168 return math.exp(min(100, acc_loss / nb_samples))
171 ######################################################################
174 def one_shot(gpt, task):
177 dim_in = args.dim_model * (args.nb_blocks * 2 if args.oneshot_mode == "deep" else 1)
178 model = nn.Sequential(
179 nn.Linear(dim_in, args.dim_model),
181 nn.Linear(args.dim_model, args.dim_model),
183 nn.Linear(args.dim_model, 4),
186 for n_epoch in range(args.nb_epochs):
187 learning_rate = learning_rate_schedule[n_epoch]
188 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
190 acc_train_loss, nb_train_samples = 0, 0
191 for input, policies in task.policy_batches(split="train"):
193 # print(f'{input.size()=} {policies.size()=}')
194 # s = maze.stationary_densities(
197 mask = input.unsqueeze(-1) == maze.v_empty
198 output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
199 output = model(output_gpt)
200 targets = policies.permute(0, 2, 1) * mask
201 output = output * mask
202 # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
203 loss = -(output.log_softmax(-1) * targets).sum() / mask.sum()
204 acc_train_loss += loss.item() * input.size(0)
205 nb_train_samples += input.size(0)
207 optimizer.zero_grad()
211 acc_test_loss, nb_test_samples = 0, 0
212 for input, policies in task.policy_batches(split="test"):
213 mask = input.unsqueeze(-1) == maze.v_empty
214 output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
215 output = model(output_gpt)
216 targets = policies.permute(0, 2, 1) * mask
217 output = output * mask
218 # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
219 loss = -(output.log_softmax(-1) * targets).sum() / mask.sum()
220 acc_test_loss += loss.item() * input.size(0)
221 nb_test_samples += input.size(0)
224 f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
227 # -------------------
228 input = task.test_input[:32, : task.height * task.width]
229 targets = task.test_policies[:32].permute(0, 2, 1)
230 output_gpt = gpt(mygpt.BracketedSequence(input), mode=args.oneshot_mode).x
231 output = model(output_gpt)
232 # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
233 # losses = losses * mask
234 # losses = losses / losses.max()
235 # losses = (output.softmax(-1) - targets).abs().max(-1).values
236 # losses = (losses >= 0.05).float()
238 (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
240 losses = losses.reshape(-1, args.maze_height, args.maze_width)
241 input = input.reshape(-1, args.maze_height, args.maze_width)
244 args.result_dir, f"oneshot_{args.oneshot_mode}_{n_epoch:04d}.png"
249 # -------------------
254 ######################################################################
258 def batches(self, split="train"):
261 def vocabulary_size(self):
264 def produce_results(self, n_epoch, model):
268 ######################################################################
273 class TaskMaze(Task):
274 def map2seq(self, *m):
275 return torch.cat([x.flatten(1) for x in m], 1)
277 def seq2map(self, s):
278 s = s.reshape(s.size(0), -1, self.height, self.width)
279 return (s[:, k] for k in range(s.size(1)))
289 device=torch.device("cpu"),
291 self.batch_size = batch_size
296 train_mazes, train_paths, train_policies = maze.create_maze_data(
301 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
303 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
304 self.train_policies = train_policies.flatten(-2).to(device)
306 test_mazes, test_paths, test_policies = maze.create_maze_data(
311 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
313 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
314 self.test_policies = test_policies.flatten(-2).to(device)
316 self.nb_codes = self.train_input.max() + 1
318 def batches(self, split="train", nb_to_use=-1):
319 assert split in {"train", "test"}
320 input = self.train_input if split == "train" else self.test_input
322 input = input[:nb_to_use]
323 for batch in tqdm.tqdm(
324 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
328 def policy_batches(self, split="train", nb_to_use=-1):
329 assert split in {"train", "test"}
330 input = self.train_input if split == "train" else self.test_input
331 policies = self.train_policies if split == "train" else self.test_policies
332 input = input[:, : self.height * self.width]
333 policies = policies * (input != maze.v_wall)[:, None]
336 input = input[:nb_to_use]
337 policies = policies[:nb_to_use]
339 for batch in tqdm.tqdm(
340 zip(input.split(self.batch_size), policies.split(self.batch_size)),
342 desc=f"epoch-{split}",
346 def vocabulary_size(self):
349 def compute_error(self, model, split="train", nb_to_use=-1):
350 nb_total, nb_correct = 0, 0
351 for input in task.batches(split, nb_to_use):
352 result = input.clone()
353 ar_mask = result.new_zeros(result.size())
354 ar_mask[:, self.height * self.width :] = 1
355 result *= 1 - ar_mask
356 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
357 mazes, paths = self.seq2map(result)
358 nb_correct += maze.path_correctness(mazes, paths).long().sum()
359 nb_total += mazes.size(0)
361 return nb_total, nb_correct
363 def produce_results(self, n_epoch, model):
364 with torch.autograd.no_grad():
368 train_nb_total, train_nb_correct = self.compute_error(
369 model, "train", nb_to_use=1000
372 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
375 test_nb_total, test_nb_correct = self.compute_error(
376 model, "test", nb_to_use=1000
379 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
382 input = self.test_input[:32]
383 result = input.clone()
384 ar_mask = result.new_zeros(result.size())
385 ar_mask[:, self.height * self.width :] = 1
386 result *= 1 - ar_mask
387 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
389 mazes, paths = self.seq2map(input)
390 _, predicted_paths = self.seq2map(result)
392 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
395 predicted_paths=predicted_paths,
396 path_correct=maze.path_correctness(mazes, predicted_paths),
402 ######################################################################
404 log_string(f"device {device}")
408 nb_train_samples=args.nb_train_samples,
409 nb_test_samples=args.nb_test_samples,
410 batch_size=args.batch_size,
411 height=args.maze_height,
412 width=args.maze_width,
413 nb_walls=args.maze_nb_walls,
418 vocabulary_size = task.vocabulary_size()
420 log_string(f"vocabulary_size {vocabulary_size}")
422 ##############################
425 vocabulary_size=vocabulary_size,
426 dim_model=args.dim_model,
427 dim_keys=args.dim_keys,
428 dim_hidden=args.dim_hidden,
429 nb_heads=args.nb_heads,
430 nb_blocks=args.nb_blocks,
432 dropout=args.dropout,
437 nb_parameters = sum(p.numel() for p in model.parameters())
438 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
440 ######################################################################
442 nb_epochs_finished = 0
444 if args.no_checkpoint:
445 log_string(f"not trying to load checkpoint.")
449 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
450 checkpoint = torch.load(checkpoint_name)
451 nb_epochs_finished = checkpoint["nb_epochs_finished"]
452 model.load_state_dict(checkpoint["model_state"])
453 torch.set_rng_state(checkpoint["rng_state"])
454 if torch.cuda.is_available():
455 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
457 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
459 except FileNotFoundError:
460 log_string("starting from scratch.")
463 log_string("error when loading the checkpoint.")
466 ######################################################################
469 for input in task.batches(split="train"):
470 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
471 token_probas = token_count / token_count.sum()
472 entropy = -torch.xlogy(token_probas, token_probas).sum()
473 train_set_perplexity = math.exp(entropy)
475 ##############################
477 if args.learning_rate_schedule == "cos":
478 learning_rate_schedule = {}
479 for n_epoch in range(args.nb_epochs):
480 u = n_epoch / args.nb_epochs * math.pi
481 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
486 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
490 learning_rate_schedule = {}
491 learning_rate = args.learning_rate
492 for n_epoch in range(args.nb_epochs):
494 learning_rate = u[n_epoch]
495 learning_rate_schedule[n_epoch] = learning_rate
497 log_string(f"learning_rate_schedule {learning_rate_schedule}")
499 ##############################
502 one_shot(model, task)
505 ##############################
507 if nb_epochs_finished >= args.nb_epochs:
508 n_epoch = nb_epochs_finished
509 train_perplexity = compute_perplexity(model, split="train")
510 test_perplexity = compute_perplexity(model, split="test")
513 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
516 task.produce_results(n_epoch, model)
520 ##############################
522 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
523 learning_rate = learning_rate_schedule[n_epoch]
525 log_string(f"learning_rate {learning_rate}")
527 if args.optim == "sgd":
528 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
529 elif args.optim == "adam":
530 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
531 elif args.optim == "adamw":
532 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
534 raise ValueError(f"Unknown optimizer {args.optim}.")
538 nb_train_samples, acc_train_loss = 0, 0.0
540 for input in task.batches(split="train"):
541 input = input.to(device)
542 output = model(mygpt.BracketedSequence(input)).x
543 loss = F.cross_entropy(output.transpose(1, 2), input)
544 acc_train_loss += loss.item() * input.size(0)
545 nb_train_samples += input.size(0)
547 optimizer.zero_grad()
551 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
552 test_perplexity = compute_perplexity(model, split="test")
555 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
558 task.produce_results(n_epoch, model)
561 "nb_epochs_finished": n_epoch + 1,
562 "model_state": model.state_dict(),
563 "rng_state": torch.get_rng_state(),
566 if torch.cuda.is_available():
567 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
569 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
570 torch.save(checkpoint, checkpoint_name)
571 log_string(f"saved checkpoint {checkpoint_name}")
573 ######################################################################