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 import math, os, tqdm, warnings
10 import torch, torchvision
13 from torch.nn import functional as F
15 from mygpt import BracketedSequence
17 ######################################################################
20 def masked_inplace_autoregression(
26 deterministic_synthesis,
27 forbidden_tokens=None,
29 progress_bar_desc="autoregression",
30 device=torch.device("cpu"),
32 assert input.size() == ar_mask.size()
34 batches = zip(input.split(batch_size), ar_mask.split(batch_size))
36 if progress_bar_desc is not None:
40 desc=progress_bar_desc,
41 total=(input.size(0) + batch_size - 1) // batch_size,
44 with torch.autograd.no_grad():
50 for input, ar_mask in batches:
51 sum_logits += model.masked_inplace_autoregression(
54 temperature=temperature,
55 deterministic_synthesis=deterministic_synthesis,
56 forbidden_tokens=forbidden_tokens,
57 forced_biases=logit_biases,
65 ######################################################################
69 def batches(self, split="train", nb_to_use=-1, desc=None):
72 def vocabulary_size(self):
76 self, n_epoch, model, result_dir, logger, deterministic_synthesis
81 ######################################################################
87 def save_image(self, input, result_dir, filename, logger):
88 img = world.seq2img(input.to("cpu"), self.height, self.width)
89 image_name = os.path.join(result_dir, filename)
90 torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4)
91 logger(f"wrote {image_name}")
93 def make_ar_mask(self, input):
94 b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
95 return b.long()[None, :].expand_as(input)
104 device=torch.device("cpu"),
108 self.batch_size = batch_size
113 self.train_input = world.generate_seq(
114 nb_train_samples, height=self.height, width=self.width
117 self.test_input = world.generate_seq(
118 nb_test_samples, height=self.height, width=self.width
121 self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
123 self.train_quizzes = []
124 self.test_quizzes = []
126 if result_dir is not None:
128 self.train_input[:72], result_dir, f"world_train.png", logger
131 def batches(self, split="train", desc=None):
132 assert split in {"train", "test"}
134 input = self.train_input
135 quizzes = self.train_quizzes
137 input = self.test_input
138 quizzes = self.test_quizzes
141 quizzes = torch.cat(quizzes, dim=0)
142 if quizzes.size(0) > input.size(0) // 2:
143 i = torch.randperm(input.size(0))[: input.size(0) // 2]
146 i = torch.randperm(input.size(0))[: input.size(0) - quizzes.size(0)]
149 self.nb_batch_samples_world = input.size(0)
150 self.nb_batch_samples_quizzes = quizzes.size(0)
152 input = torch.cat([input, quizzes], dim=0)
154 self.nb_batch_samples_world = input.size(0)
155 self.nb_batch_samples_quizzes = 0
158 desc = f"epoch-{split}"
159 for batch in tqdm.tqdm(
160 input.split(self.batch_size), dynamic_ncols=True, desc=desc
164 def vocabulary_size(self):
168 self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
170 def compute_accuracy(input, logger=None):
172 ar_mask = self.make_ar_mask(input)
173 result = input.clone() * (1 - ar_mask)
175 masked_inplace_autoregression(
177 batch_size=self.batch_size,
181 deterministic_synthesis=deterministic_synthesis,
182 progress_bar_desc=None,
186 nb_total, nb_correct = (
188 (input == result).long().min(dim=1).values.sum(),
191 return nb_total, nb_correct
193 train_nb_total, train_nb_correct = compute_accuracy(self.train_input)
196 f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
199 test_nb_total, test_nb_correct = compute_accuracy(self.test_input, logger)
202 f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
205 main_test_accuracy = test_nb_correct / test_nb_total
206 logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
208 ##############################
210 input = self.test_input[:96]
211 ar_mask = self.make_ar_mask(input)
212 result = input.clone() * (1 - ar_mask)
214 masked_inplace_autoregression(
216 batch_size=self.batch_size,
220 deterministic_synthesis=deterministic_synthesis,
221 progress_bar_desc=None,
228 f"world_prediction_{n_epoch:04d}_{model.id:02d}.png",
232 return main_test_accuracy
234 def renew_samples(self, nb, for_train=True):
235 input = self.train_input if for_train else self.test_input
236 nb = min(nb, input.size(0))
237 input[:-nb] = input[nb:].clone()
238 input[-nb:] = world.generate_seq(nb, height=self.height, width=self.width).to(
242 def store_new_quizzes(self, new_quizzes, for_train=True):
244 self.train_quizzes.append(new_quizzes)
246 self.test_quizzes.append(new_quizzes)
248 def create_new_quizzes(
256 desired_average_logits=None,
258 ###############################################################
259 # Generate quizzes with model
261 quizzes = torch.empty(
262 nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
264 ar_mask = torch.full(quizzes.size(), 1, device=self.device)
266 sum_logits = masked_inplace_autoregression(
268 batch_size=self.batch_size,
272 deterministic_synthesis=False,
273 progress_bar_desc="creating quizzes",
277 average_logits = sum_logits / quizzes.numel()
279 if desired_average_logits is not None:
280 temperature = average_logits / desired_average_logits
281 masked_inplace_autoregression(
283 batch_size=self.batch_size,
286 temperature=temperature,
287 deterministic_synthesis=False,
288 progress_bar_desc="creating quizzes",
292 ###############################################################
293 # Create the reverse quizzes
295 l = self.height * self.width
296 direction = quizzes[:, l : l + 1]
297 direction = world.token_forward * (
298 direction == world.token_backward
299 ) + world.token_backward * (direction == world.token_forward)
300 reverse_quizzes = torch.cat(
301 [quizzes[:, l + 1 :], direction, quizzes[:, :l]], dim=1
304 ar_mask = self.make_ar_mask(quizzes)
306 ###############################################################
307 # Check how many of the other models can solve them in both
312 for m in other_models:
313 result = quizzes.clone()
315 masked_inplace_autoregression(
317 batch_size=self.batch_size,
321 deterministic_synthesis=True,
322 progress_bar_desc="solving quizzes",
326 correct = (quizzes == result).long().min(dim=-1).values
328 reverse_result = reverse_quizzes.clone()
330 masked_inplace_autoregression(
332 batch_size=self.batch_size,
333 input=reverse_result,
336 deterministic_synthesis=True,
337 progress_bar_desc="solving reversed quizzes",
342 (reverse_quizzes == reverse_result).long().min(dim=-1).values
345 nb_correct.append((correct * reverse_correct)[None, :])
347 nb_correct = torch.cat(nb_correct, dim=0)
349 filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat")
350 with open(filename, "w") as f:
354 return quizzes, nb_correct.sum(dim=0), average_logits