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 nb_rank_error(output, targets):
173 output = output.reshape(-1, output.size(-1))
174 targets = targets.reshape(-1, targets.size(-1))
175 i = outputs.argmax(1)
176 # out=input.gather out[i][j]=input[i][index[i][j]]
177 # u[k]=targets[k][i[k]]
178 return output[targets.argmax(1)]
181 def one_shot(gpt, task):
184 model = nn.Linear(args.dim_model, 4).to(device)
186 for n_epoch in range(args.nb_epochs):
187 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
189 acc_train_loss, nb_train_samples = 0, 0
190 for input, targets in task.policy_batches(split="train"):
191 output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
192 output = model(output_gpt)
193 loss = -(output.log_softmax(-1) * targets).sum(-1).mean()
194 acc_train_loss += loss.item() * input.size(0)
195 nb_train_samples += input.size(0)
197 optimizer.zero_grad()
201 acc_test_loss, nb_test_samples = 0, 0
202 for input, targets in task.policy_batches(split="test"):
203 output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
204 output = model(output_gpt)
205 loss = -(output.log_softmax(-1) * targets).sum(-1).mean()
206 acc_test_loss += loss.item() * input.size(0)
207 nb_test_samples += input.size(0)
210 f"{n_epoch=} {acc_train_loss/nb_train_samples=} {acc_test_loss/nb_test_samples=}"
216 ######################################################################
220 def batches(self, split="train"):
223 def vocabulary_size(self):
226 def produce_results(self, n_epoch, model):
230 ######################################################################
235 class TaskMaze(Task):
236 def map2seq(self, *m):
237 return torch.cat([x.flatten(1) for x in m], 1)
239 def seq2map(self, s):
240 s = s.reshape(s.size(0), -1, self.height, self.width)
241 return (s[:, k] for k in range(s.size(1)))
251 device=torch.device("cpu"),
253 self.batch_size = batch_size
258 train_mazes, train_paths, train_policies = maze.create_maze_data(
263 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
265 self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
266 self.train_policies = train_policies.flatten(-2).permute(0, 2, 1).to(device)
268 test_mazes, test_paths, test_policies = maze.create_maze_data(
273 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
275 self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
276 self.test_policies = test_policies.flatten(-2).permute(0, 2, 1).to(device)
278 self.nb_codes = self.train_input.max() + 1
280 def batches(self, split="train", nb_to_use=-1):
281 assert split in {"train", "test"}
282 input = self.train_input if split == "train" else self.test_input
284 input = input[:nb_to_use]
285 for batch in tqdm.tqdm(
286 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
290 def policy_batches(self, split="train", nb_to_use=-1):
291 assert split in {"train", "test"}
292 input = self.train_input if split == "train" else self.test_input
293 targets = self.train_policies if split == "train" else self.test_policies
294 input = input[:, : self.height * self.width]
295 targets = targets * (input != maze.v_wall)[:, :, None]
298 input = input[:nb_to_use]
299 targets = targets[:nb_to_use]
301 for batch in tqdm.tqdm(
302 zip(input.split(self.batch_size), targets.split(self.batch_size)),
304 desc=f"epoch-{split}",
308 def vocabulary_size(self):
311 def compute_error(self, model, split="train", nb_to_use=-1):
312 nb_total, nb_correct = 0, 0
313 for input in task.batches(split, nb_to_use):
314 result = input.clone()
315 ar_mask = result.new_zeros(result.size())
316 ar_mask[:, self.height * self.width :] = 1
317 result *= 1 - ar_mask
318 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
319 mazes, paths = self.seq2map(result)
320 nb_correct += maze.path_correctness(mazes, paths).long().sum()
321 nb_total += mazes.size(0)
323 return nb_total, nb_correct
325 def produce_results(self, n_epoch, model):
326 with torch.autograd.no_grad():
330 train_nb_total, train_nb_correct = self.compute_error(
331 model, "train", nb_to_use=1000
334 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
337 test_nb_total, test_nb_correct = self.compute_error(
338 model, "test", nb_to_use=1000
341 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
344 input = self.test_input[:32]
345 result = input.clone()
346 ar_mask = result.new_zeros(result.size())
347 ar_mask[:, self.height * self.width :] = 1
348 result *= 1 - ar_mask
349 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
351 mazes, paths = self.seq2map(input)
352 _, predicted_paths = self.seq2map(result)
354 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
358 maze.path_correctness(mazes, predicted_paths),
364 ######################################################################
366 log_string(f"device {device}")
370 nb_train_samples=args.nb_train_samples,
371 nb_test_samples=args.nb_test_samples,
372 batch_size=args.batch_size,
373 height=args.maze_height,
374 width=args.maze_width,
375 nb_walls=args.maze_nb_walls,
380 vocabulary_size = task.vocabulary_size()
382 log_string(f"vocabulary_size {vocabulary_size}")
384 ##############################
387 vocabulary_size=vocabulary_size,
388 dim_model=args.dim_model,
389 dim_keys=args.dim_keys,
390 dim_hidden=args.dim_hidden,
391 nb_heads=args.nb_heads,
392 nb_blocks=args.nb_blocks,
394 dropout=args.dropout,
399 nb_parameters = sum(p.numel() for p in model.parameters())
400 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
402 ######################################################################
404 nb_epochs_finished = 0
406 if args.no_checkpoint:
407 log_string(f"not trying to load checkpoint.")
411 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
412 checkpoint = torch.load(checkpoint_name)
413 nb_epochs_finished = checkpoint["nb_epochs_finished"]
414 model.load_state_dict(checkpoint["model_state"])
415 torch.set_rng_state(checkpoint["rng_state"])
416 if torch.cuda.is_available():
417 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
419 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
421 except FileNotFoundError:
422 log_string("starting from scratch.")
425 log_string("error when loading the checkpoint.")
428 ######################################################################
431 for input in task.batches(split="train"):
432 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
433 token_probas = token_count / token_count.sum()
434 entropy = -torch.xlogy(token_probas, token_probas).sum()
435 train_set_perplexity = math.exp(entropy)
437 ##############################
439 if args.learning_rate_schedule == "cos":
440 learning_rate_schedule = {}
441 for n_epoch in range(args.nb_epochs):
442 u = n_epoch / args.nb_epochs * math.pi
443 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
448 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
452 learning_rate_schedule = {}
453 learning_rate = args.learning_rate
454 for n_epoch in range(args.nb_epochs):
456 learning_rate = u[n_epoch]
457 learning_rate_schedule[n_epoch] = learning_rate
459 log_string(f"learning_rate_schedule {learning_rate_schedule}")
461 ##############################
464 one_shot(model, task)
467 ##############################
469 if nb_epochs_finished >= args.nb_epochs:
470 n_epoch = nb_epochs_finished
471 train_perplexity = compute_perplexity(model, split="train")
472 test_perplexity = compute_perplexity(model, split="test")
475 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
478 task.produce_results(n_epoch, model)
482 ##############################
484 for n_epoch in range(nb_epochs_finished, args.nb_epochs):
485 learning_rate = learning_rate_schedule[n_epoch]
487 log_string(f"learning_rate {learning_rate}")
489 if args.optim == "sgd":
490 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
491 elif args.optim == "adam":
492 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
493 elif args.optim == "adamw":
494 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
496 raise ValueError(f"Unknown optimizer {args.optim}.")
500 nb_train_samples, acc_train_loss = 0, 0.0
502 for input in task.batches(split="train"):
503 input = input.to(device)
504 output = model(mygpt.BracketedSequence(input)).x
505 loss = F.cross_entropy(output.transpose(1, 2), input)
506 acc_train_loss += loss.item() * input.size(0)
507 nb_train_samples += input.size(0)
509 optimizer.zero_grad()
513 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
514 test_perplexity = compute_perplexity(model, split="test")
517 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
520 task.produce_results(n_epoch, model)
523 "nb_epochs_finished": n_epoch + 1,
524 "model_state": model.state_dict(),
525 "rng_state": torch.get_rng_state(),
528 if torch.cuda.is_available():
529 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
531 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
532 torch.save(checkpoint, checkpoint_name)
533 log_string(f"saved checkpoint {checkpoint_name}")
535 ######################################################################