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("--checkpoint_name", type=str, default="checkpoint.pth")
73 ##############################
76 parser.add_argument("--maze_height", type=int, default=13)
78 parser.add_argument("--maze_width", type=int, default=21)
80 parser.add_argument("--maze_nb_walls", type=int, default=15)
82 ######################################################################
84 args = parser.parse_args()
87 os.mkdir(args.result_dir)
88 except FileExistsError:
89 if not args.overwrite_results:
90 print(f"result directory {args.result_dir} already exists")
93 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
96 # torch.backends.cudnn.deterministic = True
97 # torch.backends.cudnn.benchmark = False
98 # torch.use_deterministic_algorithms(True)
99 torch.manual_seed(args.seed)
100 if torch.cuda.is_available():
101 torch.cuda.manual_seed_all(args.seed)
103 ######################################################################
107 t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
109 if log_file is not None:
110 log_file.write(t + s + "\n")
118 log_string(f"args.{n} {getattr(args, n)}")
120 ######################################################################
123 # ar_mask is a Boolean matrix of same shape as input, with 1s on the
124 # tokens that should be generated
127 def masked_inplace_autoregression(model, batch_size, input, ar_mask):
129 for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
130 i = (ar_mask.sum(0) > 0).nonzero()
132 # Needed to initialize the model's cache
133 model(mygpt.BracketedSequence(input, 0, i.min()))
134 for s in range(i.min(), i.max() + 1):
135 output = model(mygpt.BracketedSequence(input, s, 1)).x
136 logits = output[:, s]
137 if args.deterministic_synthesis:
138 t_next = logits.argmax(1)
140 dist = torch.distributions.categorical.Categorical(logits=logits)
141 t_next = dist.sample()
142 input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
145 ######################################################################
149 def batches(self, split="train"):
152 def vocabulary_size(self):
155 def produce_results(self, n_epoch, model):
159 ######################################################################
164 class TaskMaze(Task):
165 def map2seq(self, *m):
166 return torch.cat([x.flatten(1) for x in m], 1)
168 def seq2map(self, s):
169 s = s.reshape(s.size(0), -1, self.height, self.width)
170 return (s[:, k] for k in range(s.size(1)))
180 device=torch.device("cpu"),
182 self.batch_size = batch_size
187 mazes_train, paths_train = maze.create_maze_data(
192 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
194 mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
195 self.train_input = self.map2seq(mazes_train, paths_train)
197 mazes_test, paths_test = maze.create_maze_data(
202 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
204 mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
205 self.test_input = self.map2seq(mazes_test, paths_test)
207 self.nb_codes = self.train_input.max() + 1
209 def batches(self, split="train", nb_to_use=-1):
210 assert split in {"train", "test"}
211 input = self.train_input if split == "train" else self.test_input
213 input = input[:nb_to_use]
214 for batch in tqdm.tqdm(
215 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
219 def vocabulary_size(self):
222 def compute_error(self, model, split="train", nb_to_use=-1):
223 nb_total, nb_correct = 0, 0
224 for input in task.batches(split, nb_to_use):
225 result = input.clone()
226 ar_mask = result.new_zeros(result.size())
227 ar_mask[:, self.height * self.width :] = 1
228 result *= 1 - ar_mask
229 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
230 mazes, paths = self.seq2map(result)
231 nb_correct += maze.path_correctness(mazes, paths).long().sum()
232 nb_total += mazes.size(0)
234 return nb_total, nb_correct
236 def produce_results(self, n_epoch, model):
237 with torch.autograd.no_grad():
241 train_nb_total, train_nb_correct = self.compute_error(
242 model, "train", nb_to_use=1000
245 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
248 test_nb_total, test_nb_correct = self.compute_error(
249 model, "test", nb_to_use=1000
252 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
255 input = self.test_input[:32]
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)
262 mazes, paths = self.seq2map(input)
263 _, predicted_paths = self.seq2map(result)
265 os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
269 maze.path_correctness(mazes, predicted_paths),
275 ######################################################################
277 log_string(f"device {device}")
281 nb_train_samples=args.nb_train_samples,
282 nb_test_samples=args.nb_test_samples,
283 batch_size=args.batch_size,
284 height=args.maze_height,
285 width=args.maze_width,
286 nb_walls=args.maze_nb_walls,
291 vocabulary_size = task.vocabulary_size()
293 log_string(f"vocabulary_size {vocabulary_size}")
295 ##############################
298 vocabulary_size=vocabulary_size,
299 dim_model=args.dim_model,
300 dim_keys=args.dim_keys,
301 dim_hidden=args.dim_hidden,
302 nb_heads=args.nb_heads,
303 nb_blocks=args.nb_blocks,
305 dropout=args.dropout,
310 nb_parameters = sum(p.numel() for p in model.parameters())
311 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
313 ######################################################################
315 nb_epochs_finished = 0
317 if args.no_checkpoint:
318 log_string(f"not trying to load checkpoint.")
322 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
323 checkpoint = torch.load(checkpoint_name)
324 nb_epochs_finished = checkpoint["nb_epochs_finished"]
325 model.load_state_dict(checkpoint["model_state"])
326 torch.set_rng_state(checkpoint["rng_state"])
327 if torch.cuda.is_available():
328 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
330 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
332 except FileNotFoundError:
333 log_string("starting from scratch.")
336 log_string("error when loading the checkpoint.")
339 ######################################################################
341 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
344 for input in task.batches(split="train"):
345 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
346 token_probas = token_count / token_count.sum()
347 entropy = -torch.xlogy(token_probas, token_probas).sum()
348 train_set_perplexity = math.exp(entropy)
350 ##############################
352 if args.learning_rate_schedule == "cos":
353 learning_rate_schedule = {}
354 for n_epoch in range(args.nb_epochs):
355 u = n_epoch / args.nb_epochs * math.pi
356 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
361 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
365 learning_rate_schedule = {}
366 learning_rate = args.learning_rate
367 for n_epoch in range(args.nb_epochs):
369 learning_rate = u[n_epoch]
370 learning_rate_schedule[n_epoch] = learning_rate
372 log_string(f"learning_rate_schedule {learning_rate_schedule}")
374 ##############################
378 if nb_epochs_finished >= nb_epochs:
379 task.produce_results(nb_epochs_finished, model)
381 for n_epoch in range(nb_epochs_finished, nb_epochs):
383 learning_rate = learning_rate_schedule[n_epoch]
385 log_string(f"learning_rate {learning_rate}")
387 if args.optim == "sgd":
388 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
389 elif args.optim == "adam":
390 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
391 elif args.optim == "adamw":
392 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
394 raise ValueError(f"Unknown optimizer {args.optim}.")
398 nb_train_samples, acc_train_loss = 0, 0.0
400 for input in task.batches(split="train"):
401 input = input.to(device)
402 output = model(mygpt.BracketedSequence(input)).x
403 loss = F.cross_entropy(output.transpose(1, 2), input)
404 acc_train_loss += loss.item() * input.size(0)
405 nb_train_samples += input.size(0)
406 nb_samples_seen += input.size(0)
408 optimizer.zero_grad()
412 with torch.autograd.no_grad():
416 nb_test_samples, acc_test_loss = 0, 0.0
418 for input in task.batches(split="test"):
419 input = input.to(device)
421 output = model(mygpt.BracketedSequence(input)).x
422 loss = F.cross_entropy(output.transpose(1, 2), input)
423 acc_test_loss += loss.item() * input.size(0)
424 nb_test_samples += input.size(0)
426 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
427 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
430 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
433 task.produce_results(n_epoch, model)
436 "nb_epochs_finished": n_epoch + 1,
437 "model_state": model.state_dict(),
438 "rng_state": torch.get_rng_state(),
441 if torch.cuda.is_available():
442 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
444 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
445 torch.save(checkpoint, checkpoint_name)
446 log_string(f"saved checkpoint {checkpoint_name}")
448 ######################################################################