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(
30 description="An implementation of GPT with cache to solve a toy geometric reasoning task."
33 parser.add_argument("--log_filename", type=str, default="train.log")
35 parser.add_argument("--result_dir", type=str, default="results_default")
37 parser.add_argument("--seed", type=int, default=0)
39 parser.add_argument("--nb_epochs", type=int, default=25)
41 parser.add_argument("--nb_train_samples", type=int, default=200000)
43 parser.add_argument("--nb_test_samples", type=int, default=50000)
45 parser.add_argument("--batch_size", type=int, default=25)
47 parser.add_argument("--optim", type=str, default="adam")
49 parser.add_argument("--learning_rate", type=float, default=1e-3)
52 "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6"
55 parser.add_argument("--dim_model", type=int, default=512)
57 parser.add_argument("--dim_keys", type=int, default=64)
59 parser.add_argument("--dim_hidden", type=int, default=2048)
61 parser.add_argument("--nb_heads", type=int, default=8)
63 parser.add_argument("--nb_blocks", type=int, default=12)
65 parser.add_argument("--dropout", type=float, default=0.1)
67 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
69 parser.add_argument("--no_checkpoint", action="store_true", default=False)
71 parser.add_argument("--overwrite_results", 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):
131 for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
132 i = (ar_mask.sum(0) > 0).nonzero()
135 mygpt.BracketedSequence(input, 0, i.min())
136 ) # Needed to initialize the model's cache
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 ######################################################################
152 def batches(self, split="train"):
155 def vocabulary_size(self):
158 def produce_results(self, n_epoch, model):
162 ######################################################################
167 class TaskMaze(Task):
168 def map2seq(self, *m):
169 return torch.cat([x.flatten(1) for x in m], 1)
171 def seq2map(self, s):
172 s = s.reshape(s.size(0), -1, self.height, self.width)
173 return (s[:, k] for k in range(s.size(1)))
183 device=torch.device("cpu"),
185 self.batch_size = batch_size
190 mazes_train, paths_train = maze.create_maze_data(
195 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
197 mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
198 self.train_input = self.map2seq(mazes_train, paths_train)
199 self.nb_codes = self.train_input.max() + 1
201 mazes_test, paths_test = maze.create_maze_data(
206 progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
208 mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
209 self.test_input = self.map2seq(mazes_test, paths_test)
211 def batches(self, split="train", nb_to_use=-1):
212 assert split in {"train", "test"}
213 input = self.train_input if split == "train" else self.test_input
215 input = input[:nb_to_use]
216 for batch in tqdm.tqdm(
217 input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
221 def vocabulary_size(self):
224 def compute_error(self, model, split="train", nb_to_use=-1):
225 nb_total, nb_correct = 0, 0
226 for input in task.batches(split, nb_to_use):
227 result = input.clone()
228 ar_mask = result.new_zeros(result.size())
229 ar_mask[:, self.height * self.width :] = 1
230 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
231 mazes, paths = self.seq2map(result)
232 nb_correct += maze.path_correctness(mazes, paths).long().sum()
233 nb_total += mazes.size(0)
235 return nb_total, nb_correct
237 def produce_results(self, n_epoch, model):
238 with torch.autograd.no_grad():
242 train_nb_total, train_nb_correct = self.compute_error(
243 model, "train", nb_to_use=1000
246 f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
249 test_nb_total, test_nb_correct = self.compute_error(
250 model, "test", nb_to_use=1000
253 f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
256 input = self.test_input[:32]
257 result = input.clone()
258 ar_mask = result.new_zeros(result.size())
260 ar_mask[:, self.height * self.width :] = 1
261 masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
263 mazes, paths = self.seq2map(input)
264 _, predicted_paths = self.seq2map(result)
266 f"result_{n_epoch:04d}.png",
270 maze.path_correctness(mazes, predicted_paths),
276 ######################################################################
278 log_string(f"device {device}")
282 nb_train_samples=args.nb_train_samples,
283 nb_test_samples=args.nb_test_samples,
284 batch_size=args.batch_size,
285 height=args.maze_height,
286 width=args.maze_width,
287 nb_walls=args.maze_nb_walls,
292 vocabulary_size = task.vocabulary_size()
294 log_string(f"vocabulary_size {vocabulary_size}")
296 ##############################
299 vocabulary_size=vocabulary_size,
300 dim_model=args.dim_model,
301 dim_keys=args.dim_keys,
302 dim_hidden=args.dim_hidden,
303 nb_heads=args.nb_heads,
304 nb_blocks=args.nb_blocks,
306 dropout=args.dropout,
311 nb_parameters = sum(p.numel() for p in model.parameters())
312 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
314 ######################################################################
316 nb_epochs_finished = 0
318 if args.no_checkpoint:
319 log_string(f"not trying to load checkpoint.")
323 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
324 checkpoint = torch.load(checkpoint_name)
325 nb_epochs_finished = checkpoint["nb_epochs_finished"]
326 model.load_state_dict(checkpoint["model_state"])
327 torch.set_rng_state(checkpoint["rng_state"])
328 if torch.cuda.is_available():
329 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
331 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
333 except FileNotFoundError:
334 log_string("starting from scratch.")
337 log_string("error when loading the checkpoint.")
340 ######################################################################
342 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
345 for input in task.batches(split="train"):
346 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
347 token_probas = token_count / token_count.sum()
348 entropy = -torch.xlogy(token_probas, token_probas).sum()
349 train_set_perplexity = math.exp(entropy)
351 ##############################
353 if args.learning_rate_schedule == "cos":
354 learning_rate_schedule = {}
355 for n_epoch in range(args.nb_epochs):
356 u = n_epoch / args.nb_epochs * math.pi
357 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
362 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
366 learning_rate_schedule = {}
367 learning_rate = args.learning_rate
368 for n_epoch in range(args.nb_epochs):
370 learning_rate = u[n_epoch]
371 learning_rate_schedule[n_epoch] = learning_rate
373 log_string(f"learning_rate_schedule {learning_rate_schedule}")
375 ##############################
379 if nb_epochs_finished >= nb_epochs:
380 task.produce_results(nb_epochs_finished, model)
382 for n_epoch in range(nb_epochs_finished, nb_epochs):
384 learning_rate = learning_rate_schedule[n_epoch]
386 log_string(f"learning_rate {learning_rate}")
388 if args.optim == "sgd":
389 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
390 elif args.optim == "adam":
391 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
392 elif args.optim == "adamw":
393 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
395 raise ValueError(f"Unknown optimizer {args.optim}.")
399 nb_train_samples, acc_train_loss = 0, 0.0
401 for input in task.batches(split="train"):
402 input = input.to(device)
403 output = model(mygpt.BracketedSequence(input)).x
404 loss = F.cross_entropy(output.transpose(1, 2), input)
405 acc_train_loss += loss.item() * input.size(0)
406 nb_train_samples += input.size(0)
407 nb_samples_seen += input.size(0)
409 optimizer.zero_grad()
413 with torch.autograd.no_grad():
417 nb_test_samples, acc_test_loss = 0, 0.0
419 for input in task.batches(split="test"):
420 input = input.to(device)
422 # input, loss_masks, true_images = task.excise_last_image(input)
423 # input, loss_masks = task.add_true_image(input, true_images, loss_masks)
425 output = model(mygpt.BracketedSequence(input)).x
426 loss = F.cross_entropy(output.transpose(1, 2), input)
427 acc_test_loss += loss.item() * input.size(0)
428 nb_test_samples += input.size(0)
430 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
431 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
434 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
437 task.produce_results(n_epoch, model)
440 "nb_epochs_finished": n_epoch + 1,
441 "model_state": model.state_dict(),
442 "rng_state": torch.get_rng_state(),
445 if torch.cuda.is_available():
446 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
448 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
449 torch.save(checkpoint, checkpoint_name)
450 log_string(f"saved checkpoint {checkpoint_name}")
452 ######################################################################