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):
176 ######################################################################
180 def batches(self, split="train"):
183 def vocabulary_size(self):
186 def produce_results(self, n_epoch, model):
190 ######################################################################
195 class TaskMaze(Task):
196 def map2seq(self, *m):
197 return torch.cat([x.flatten(1) for x in m], 1)
199 def seq2map(self, s):
200 s = s.reshape(s.size(0), -1, self.height, self.width)
201 return (s[:, k] for k in range(s.size(1)))
211 device=torch.device("cpu"),
213 self.batch_size = batch_size
218 mazes_train, paths_train = maze.create_maze_data(
223 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
225 mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
226 self.train_input = self.map2seq(mazes_train, paths_train)
228 mazes_test, paths_test = maze.create_maze_data(
233 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
235 mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
236 self.test_input = self.map2seq(mazes_test, paths_test)
238 self.nb_codes = self.train_input.max() + 1
240 def batches(self, split="train", nb_to_use=-1):
241 assert split in {"train", "test"}
242 input = self.train_input if split == "train" else self.test_input
244 input = input[:nb_to_use]
245 for batch in tqdm.tqdm(
246 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
250 def vocabulary_size(self):
253 def compute_error(self, model, split="train", nb_to_use=-1):
254 nb_total, nb_correct = 0, 0
255 for input in task.batches(split, nb_to_use):
256 result = input.clone()
257 ar_mask = result.new_zeros(result.size())
258 ar_mask[:, self.height * self.width :] = 1
259 result *= 1 - ar_mask
260 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
261 mazes, paths = self.seq2map(result)
262 nb_correct += maze.path_correctness(mazes, paths).long().sum()
263 nb_total += mazes.size(0)
265 return nb_total, nb_correct
267 def produce_results(self, n_epoch, model):
268 with torch.autograd.no_grad():
272 train_nb_total, train_nb_correct = self.compute_error(
273 model, "train", nb_to_use=1000
276 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
279 test_nb_total, test_nb_correct = self.compute_error(
280 model, "test", nb_to_use=1000
283 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
286 input = self.test_input[:32]
287 result = input.clone()
288 ar_mask = result.new_zeros(result.size())
289 ar_mask[:, self.height * self.width :] = 1
290 result *= 1 - ar_mask
291 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
293 mazes, paths = self.seq2map(input)
294 _, predicted_paths = self.seq2map(result)
296 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
300 maze.path_correctness(mazes, predicted_paths),
306 ######################################################################
308 log_string(f"device {device}")
312 nb_train_samples=args.nb_train_samples,
313 nb_test_samples=args.nb_test_samples,
314 batch_size=args.batch_size,
315 height=args.maze_height,
316 width=args.maze_width,
317 nb_walls=args.maze_nb_walls,
322 vocabulary_size = task.vocabulary_size()
324 log_string(f"vocabulary_size {vocabulary_size}")
326 ##############################
329 vocabulary_size=vocabulary_size,
330 dim_model=args.dim_model,
331 dim_keys=args.dim_keys,
332 dim_hidden=args.dim_hidden,
333 nb_heads=args.nb_heads,
334 nb_blocks=args.nb_blocks,
336 dropout=args.dropout,
341 nb_parameters = sum(p.numel() for p in model.parameters())
342 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
344 ######################################################################
346 nb_epochs_finished = 0
348 if args.no_checkpoint:
349 log_string(f"not trying to load checkpoint.")
353 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
354 checkpoint = torch.load(checkpoint_name)
355 nb_epochs_finished = checkpoint["nb_epochs_finished"]
356 model.load_state_dict(checkpoint["model_state"])
357 torch.set_rng_state(checkpoint["rng_state"])
358 if torch.cuda.is_available():
359 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
361 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
363 except FileNotFoundError:
364 log_string("starting from scratch.")
367 log_string("error when loading the checkpoint.")
370 ######################################################################
372 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
375 for input in task.batches(split="train"):
376 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
377 token_probas = token_count / token_count.sum()
378 entropy = -torch.xlogy(token_probas, token_probas).sum()
379 train_set_perplexity = math.exp(entropy)
381 ##############################
383 if args.learning_rate_schedule == "cos":
384 learning_rate_schedule = {}
385 for n_epoch in range(args.nb_epochs):
386 u = n_epoch / args.nb_epochs * math.pi
387 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
392 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
396 learning_rate_schedule = {}
397 learning_rate = args.learning_rate
398 for n_epoch in range(args.nb_epochs):
400 learning_rate = u[n_epoch]
401 learning_rate_schedule[n_epoch] = learning_rate
403 log_string(f"learning_rate_schedule {learning_rate_schedule}")
405 ##############################
408 one_shot(model, task)
411 ##############################
413 if nb_epochs_finished >= nb_epochs:
414 n_epoch = nb_epochs_finished
415 train_perplexity = compute_perplexity(model, split="train")
416 test_perplexity = compute_perplexity(model, split="test")
419 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
422 task.produce_results(n_epoch, model)
426 ##############################
428 for n_epoch in range(nb_epochs_finished, nb_epochs):
429 learning_rate = learning_rate_schedule[n_epoch]
431 log_string(f"learning_rate {learning_rate}")
433 if args.optim == "sgd":
434 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
435 elif args.optim == "adam":
436 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
437 elif args.optim == "adamw":
438 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
440 raise ValueError(f"Unknown optimizer {args.optim}.")
444 nb_train_samples, acc_train_loss = 0, 0.0
446 for input in task.batches(split="train"):
447 input = input.to(device)
448 output = model(mygpt.BracketedSequence(input)).x
449 loss = F.cross_entropy(output.transpose(1, 2), input)
450 acc_train_loss += loss.item() * input.size(0)
451 nb_train_samples += input.size(0)
453 optimizer.zero_grad()
457 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
458 test_perplexity = compute_perplexity(model, split="test")
461 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
464 task.produce_results(n_epoch, model)
467 "nb_epochs_finished": n_epoch + 1,
468 "model_state": model.state_dict(),
469 "rng_state": torch.get_rng_state(),
472 if torch.cuda.is_available():
473 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
475 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
476 torch.save(checkpoint, checkpoint_name)
477 log_string(f"saved checkpoint {checkpoint_name}")
479 ######################################################################