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("--world_height", type=int, default=13)
80 parser.add_argument("--world_width", type=int, default=21)
82 parser.add_argument("--world_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)
265 maze.save_image(f"result_{n_epoch:04d}.png", mazes, paths, predicted_paths)
270 ######################################################################
272 log_string(f"device {device}")
276 nb_train_samples=args.nb_train_samples,
277 nb_test_samples=args.nb_test_samples,
278 batch_size=args.batch_size,
279 height=args.world_height,
280 width=args.world_width,
281 nb_walls=args.world_nb_walls,
286 vocabulary_size = task.vocabulary_size()
288 log_string(f"vocabulary_size {vocabulary_size}")
290 ##############################
293 vocabulary_size=vocabulary_size,
294 dim_model=args.dim_model,
295 dim_keys=args.dim_keys,
296 dim_hidden=args.dim_hidden,
297 nb_heads=args.nb_heads,
298 nb_blocks=args.nb_blocks,
300 dropout=args.dropout,
305 nb_parameters = sum(p.numel() for p in model.parameters())
306 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
308 ######################################################################
310 nb_epochs_finished = 0
312 if args.no_checkpoint:
313 log_string(f"not trying to load checkpoint.")
317 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
318 checkpoint = torch.load(checkpoint_name)
319 nb_epochs_finished = checkpoint["nb_epochs_finished"]
320 model.load_state_dict(checkpoint["model_state"])
321 torch.set_rng_state(checkpoint["rng_state"])
322 if torch.cuda.is_available():
323 torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
325 log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
327 except FileNotFoundError:
328 log_string("starting from scratch.")
331 log_string("error when loading the checkpoint.")
334 ######################################################################
336 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
339 for input in task.batches(split="train"):
340 token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
341 token_probas = token_count / token_count.sum()
342 entropy = -torch.xlogy(token_probas, token_probas).sum()
343 train_set_perplexity = math.exp(entropy)
345 ##############################
347 if args.learning_rate_schedule == "cos":
348 learning_rate_schedule = {}
349 for n_epoch in range(args.nb_epochs):
350 u = n_epoch / args.nb_epochs * math.pi
351 learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
356 tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
360 learning_rate_schedule = {}
361 learning_rate = args.learning_rate
362 for n_epoch in range(args.nb_epochs):
364 learning_rate = u[n_epoch]
365 learning_rate_schedule[n_epoch] = learning_rate
367 log_string(f"learning_rate_schedule {learning_rate_schedule}")
369 ##############################
373 if nb_epochs_finished >= nb_epochs:
374 task.produce_results(nb_epochs_finished, model)
376 for n_epoch in range(nb_epochs_finished, nb_epochs):
378 learning_rate = learning_rate_schedule[n_epoch]
380 log_string(f"learning_rate {learning_rate}")
382 if args.optim == "sgd":
383 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
384 elif args.optim == "adam":
385 optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
386 elif args.optim == "adamw":
387 optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
389 raise ValueError(f"Unknown optimizer {args.optim}.")
393 nb_train_samples, acc_train_loss = 0, 0.0
395 for input in task.batches(split="train"):
396 input = input.to(device)
397 output = model(mygpt.BracketedSequence(input)).x
398 loss = F.cross_entropy(output.transpose(1, 2), input)
399 acc_train_loss += loss.item() * input.size(0)
400 nb_train_samples += input.size(0)
401 nb_samples_seen += input.size(0)
403 optimizer.zero_grad()
407 with torch.autograd.no_grad():
411 nb_test_samples, acc_test_loss = 0, 0.0
413 for input in task.batches(split="test"):
414 input = input.to(device)
416 # input, loss_masks, true_images = task.excise_last_image(input)
417 # input, loss_masks = task.add_true_image(input, true_images, loss_masks)
419 output = model(mygpt.BracketedSequence(input)).x
420 loss = F.cross_entropy(output.transpose(1, 2), input)
421 acc_test_loss += loss.item() * input.size(0)
422 nb_test_samples += input.size(0)
424 train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
425 test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
428 f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
431 task.produce_results(n_epoch, model)
434 "nb_epochs_finished": n_epoch + 1,
435 "model_state": model.state_dict(),
436 "rng_state": torch.get_rng_state(),
439 if torch.cuda.is_available():
440 checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
442 checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
443 torch.save(checkpoint, checkpoint_name)
444 log_string(f"saved checkpoint {checkpoint_name}")
446 ######################################################################