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 ######################################################################
86 args = parser.parse_args()
89 os.mkdir(args.result_dir)
90 except FileExistsError:
91 if not args.overwrite_results:
92 print(f"result directory {args.result_dir} already exists")
95 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
98 # torch.backends.cudnn.deterministic = True
99 # torch.backends.cudnn.benchmark = False
100 # torch.use_deterministic_algorithms(True)
101 torch.manual_seed(args.seed)
102 if torch.cuda.is_available():
103 torch.cuda.manual_seed_all(args.seed)
105 ######################################################################
109 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
111 if log_file is not None:
112 log_file.write(t + s + "\n")
120 log_string(f"args.{n} {getattr(args, n)}")
122 ######################################################################
125 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
126 # tokens that should be generated
129 def masked_inplace_autoregression(model, batch_size, input, ar_mask):
130 for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
131 i = (ar_mask.sum(0) > 0).nonzero()
133 # Needed to initialize the model's cache
134 model(mygpt.BracketedSequence(input, 0, i.min()))
135 for s in range(i.min(), i.max() + 1):
136 output = model(mygpt.BracketedSequence(input, s, 1)).x
137 logits = output[:, s]
138 if args.deterministic_synthesis:
139 t_next = logits.argmax(1)
141 dist = torch.distributions.categorical.Categorical(logits=logits)
142 t_next = dist.sample()
143 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
146 ######################################################################
149 def compute_perplexity(model, split="train"):
150 with torch.autograd.no_grad():
154 nb_samples, acc_loss = 0, 0.0
156 for input in task.batches(split=split):
157 input = input.to(device)
159 output = model(mygpt.BracketedSequence(input)).x
160 loss = F.cross_entropy(output.transpose(1, 2), input)
161 acc_loss += loss.item() * input.size(0)
162 nb_samples += input.size(0)
166 return math.exp(min(100, acc_loss / nb_samples))
169 ######################################################################
172 def one_shot(gpt, task):
175 model = nn.Sequential(
176 nn.Linear(args.dim_model, args.dim_model),
178 nn.Linear(args.dim_model, args.dim_model),
180 nn.Linear(args.dim_model, 4),
183 for n_epoch in range(args.nb_epochs):
184 learning_rate = learning_rate_schedule[n_epoch]
185 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
187 acc_train_loss, nb_train_samples = 0, 0
188 for input, targets in task.policy_batches(split="train"):
189 output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
190 output = model(output_gpt)
191 targets = targets * (input.unsqueeze(-1) == maze.v_empty)
192 output = output * (input.unsqueeze(-1) == maze.v_empty)
194 -(output.log_softmax(-1) * targets).sum()
195 / (input == maze.v_empty).sum()
196 + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
198 acc_train_loss += loss.item() * input.size(0)
199 nb_train_samples += input.size(0)
201 optimizer.zero_grad()
205 acc_test_loss, nb_test_samples = 0, 0
206 for input, targets in task.policy_batches(split="test"):
207 output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
208 output = model(output_gpt)
209 targets = targets * (input.unsqueeze(-1) == maze.v_empty)
210 output = output * (input.unsqueeze(-1) == maze.v_empty)
212 -(output.log_softmax(-1) * targets).sum()
213 / (input == maze.v_empty).sum()
214 + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
216 acc_test_loss += loss.item() * input.size(0)
217 nb_test_samples += input.size(0)
220 f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
223 # -------------------
224 input = task.test_input[:32, : task.height * task.width]
225 targets = task.test_policies[:32]
226 output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
227 output = model(output_gpt)
228 losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
229 losses = losses * (input == maze.v_empty)
230 losses = losses / losses.max()
231 losses = losses.reshape(-1, args.maze_height, args.maze_width)
232 input = input.reshape(-1, args.maze_height, args.maze_width)
234 os.path.join(args.result_dir, f"oneshot_{n_epoch:04d}.png"),
238 # -------------------
243 ######################################################################
247 def batches(self, split="train"):
250 def vocabulary_size(self):
253 def produce_results(self, n_epoch, model):
257 ######################################################################
262 class TaskMaze(Task):
263 def map2seq(self, *m):
264 return torch.cat([x.flatten(1) for x in m], 1)
266 def seq2map(self, s):
267 s = s.reshape(s.size(0), -1, self.height, self.width)
268 return (s[:, k] for k in range(s.size(1)))
278 device=torch.device("cpu"),
280 self.batch_size = batch_size
285 train_mazes, train_paths, train_policies = maze.create_maze_data(
290 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
292 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
293 self.train_policies = train_policies.flatten(-2).permute(0, 2, 1).to(device)
295 test_mazes, test_paths, test_policies = maze.create_maze_data(
300 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
302 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
303 self.test_policies = test_policies.flatten(-2).permute(0, 2, 1).to(device)
305 self.nb_codes = self.train_input.max() + 1
307 def batches(self, split="train", nb_to_use=-1):
308 assert split in {"train", "test"}
309 input = self.train_input if split == "train" else self.test_input
311 input = input[:nb_to_use]
312 for batch in tqdm.tqdm(
313 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
317 def policy_batches(self, split="train", nb_to_use=-1):
318 assert split in {"train", "test"}
319 input = self.train_input if split == "train" else self.test_input
320 targets = self.train_policies if split == "train" else self.test_policies
321 input = input[:, : self.height * self.width]
322 targets = targets * (input != maze.v_wall)[:, :, None]
325 input = input[:nb_to_use]
326 targets = targets[:nb_to_use]
328 for batch in tqdm.tqdm(
329 zip(input.split(self.batch_size), targets.split(self.batch_size)),
331 desc=f"epoch-{split}",
335 def vocabulary_size(self):
338 def compute_error(self, model, split="train", nb_to_use=-1):
339 nb_total, nb_correct = 0, 0
340 for input in task.batches(split, nb_to_use):
341 result = input.clone()
342 ar_mask = result.new_zeros(result.size())
343 ar_mask[:, self.height * self.width :] = 1
344 result *= 1 - ar_mask
345 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
346 mazes, paths = self.seq2map(result)
347 nb_correct += maze.path_correctness(mazes, paths).long().sum()
348 nb_total += mazes.size(0)
350 return nb_total, nb_correct
352 def produce_results(self, n_epoch, model):
353 with torch.autograd.no_grad():
357 train_nb_total, train_nb_correct = self.compute_error(
358 model, "train", nb_to_use=1000
361 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
364 test_nb_total, test_nb_correct = self.compute_error(
365 model, "test", nb_to_use=1000
368 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
371 input = self.test_input[:32]
372 result = input.clone()
373 ar_mask = result.new_zeros(result.size())
374 ar_mask[:, self.height * self.width :] = 1
375 result *= 1 - ar_mask
376 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
378 mazes, paths = self.seq2map(input)
379 _, predicted_paths = self.seq2map(result)
381 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
384 predicted_paths=predicted_paths,
385 path_correct=maze.path_correctness(mazes, predicted_paths),
391 ######################################################################
393 log_string(f"device {device}")
397 nb_train_samples=args.nb_train_samples,
398 nb_test_samples=args.nb_test_samples,
399 batch_size=args.batch_size,
400 height=args.maze_height,
401 width=args.maze_width,
402 nb_walls=args.maze_nb_walls,
407 vocabulary_size = task.vocabulary_size()
409 log_string(f"vocabulary_size {vocabulary_size}")
411 ##############################
414 vocabulary_size=vocabulary_size,
415 dim_model=args.dim_model,
416 dim_keys=args.dim_keys,
417 dim_hidden=args.dim_hidden,
418 nb_heads=args.nb_heads,
419 nb_blocks=args.nb_blocks,
421 dropout=args.dropout,
426 nb_parameters = sum(p.numel() for p in model.parameters())
427 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
429 ######################################################################
431 nb_epochs_finished = 0
433 if args.no_checkpoint:
434 log_string(f"not trying to load checkpoint.")
438 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
439 checkpoint = torch.load(checkpoint_name)
440 nb_epochs_finished = checkpoint["nb_epochs_finished"]
441 model.load_state_dict(checkpoint["model_state"])
442 torch.set_rng_state(checkpoint["rng_state"])
443 if torch.cuda.is_available():
444 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
446 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
448 except FileNotFoundError:
449 log_string("starting from scratch.")
452 log_string("error when loading the checkpoint.")
455 ######################################################################
458 for input in task.batches(split="train"):
459 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
460 token_probas = token_count / token_count.sum()
461 entropy = -torch.xlogy(token_probas, token_probas).sum()
462 train_set_perplexity = math.exp(entropy)
464 ##############################
466 if args.learning_rate_schedule == "cos":
467 learning_rate_schedule = {}
468 for n_epoch in range(args.nb_epochs):
469 u = n_epoch / args.nb_epochs * math.pi
470 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
475 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
479 learning_rate_schedule = {}
480 learning_rate = args.learning_rate
481 for n_epoch in range(args.nb_epochs):
483 learning_rate = u[n_epoch]
484 learning_rate_schedule[n_epoch] = learning_rate
486 log_string(f"learning_rate_schedule {learning_rate_schedule}")
488 ##############################
491 one_shot(model, task)
494 ##############################
496 if nb_epochs_finished >= args.nb_epochs:
497 n_epoch = nb_epochs_finished
498 train_perplexity = compute_perplexity(model, split="train")
499 test_perplexity = compute_perplexity(model, split="test")
502 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
505 task.produce_results(n_epoch, model)
509 ##############################
511 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
512 learning_rate = learning_rate_schedule[n_epoch]
514 log_string(f"learning_rate {learning_rate}")
516 if args.optim == "sgd":
517 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
518 elif args.optim == "adam":
519 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
520 elif args.optim == "adamw":
521 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
523 raise ValueError(f"Unknown optimizer {args.optim}.")
527 nb_train_samples, acc_train_loss = 0, 0.0
529 for input in task.batches(split="train"):
530 input = input.to(device)
531 output = model(mygpt.BracketedSequence(input)).x
532 loss = F.cross_entropy(output.transpose(1, 2), input)
533 acc_train_loss += loss.item() * input.size(0)
534 nb_train_samples += input.size(0)
536 optimizer.zero_grad()
540 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
541 test_perplexity = compute_perplexity(model, split="test")
544 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
547 task.produce_results(n_epoch, model)
550 "nb_epochs_finished": n_epoch + 1,
551 "model_state": model.state_dict(),
552 "rng_state": torch.get_rng_state(),
555 if torch.cuda.is_available():
556 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
558 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
559 torch.save(checkpoint, checkpoint_name)
560 log_string(f"saved checkpoint {checkpoint_name}")
562 ######################################################################