X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=43f7d5318ad6abb3ce90ffb87fa1d65cf92d8eef;hb=15192743a5dee8d88650319d64610f1603d21472;hp=8680ba14e719852dd1f0cefe4bb3674195edf3bc;hpb=5c751aa1bbfbcf42654f4626f81905acfa946c15;p=culture.git diff --git a/tasks.py b/tasks.py index 8680ba1..43f7d53 100755 --- a/tasks.py +++ b/tasks.py @@ -14,9 +14,6 @@ from torch.nn import functional as F from mygpt import BracketedSequence -# from graph import save_attention_image -save_attention_image = None - ###################################################################### @@ -25,6 +22,8 @@ def masked_inplace_autoregression( batch_size, input, ar_mask, + summed_logits, + temperature, deterministic_synthesis, forbidden_tokens=None, logit_biases=None, @@ -49,11 +48,13 @@ def masked_inplace_autoregression( for input, ar_mask in batches: model.masked_inplace_autoregression( - input, - ar_mask, - deterministic_synthesis, - forbidden_tokens, - logit_biases, + input=input, + ar_mask=ar_mask, + summed_logits=summed_logits, + temperature=temperature, + deterministic_synthesis=deterministic_synthesis, + forbidden_tokens=forbidden_tokens, + forced_biases=logit_biases, ) model.train(t) @@ -82,11 +83,14 @@ import world class World(Task): def save_image(self, input, result_dir, filename, logger): - img = world.sample2img(input.to("cpu"), self.height, self.width) + img = world.seq2img(input.to("cpu"), self.height, self.width) image_name = os.path.join(result_dir, filename) - torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2) + torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=6, padding=4) logger(f"wrote {image_name}") + def save_quizzes(self, input, result_dir, filename_prefix, logger): + self.save_image(input, result_dir, filename_prefix + ".png", logger) + def make_ar_mask(self, input): b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2 return b.long()[None, :].expand_as(input) @@ -107,49 +111,55 @@ class World(Task): self.height = 6 self.width = 8 - self.train_input = world.generate( + self.train_w_quizzes = world.generate_seq( nb_train_samples, height=self.height, width=self.width ).to(device) - self.test_input = world.generate( + self.test_w_quizzes = world.generate_seq( nb_test_samples, height=self.height, width=self.width ).to(device) - self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 + self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1 - self.train_quizzes = [] - self.test_quizzes = [] + self.train_c_quizzes = [] + self.test_c_quizzes = [] if result_dir is not None: - self.save_image( - self.train_input[:96], result_dir, f"world_train.png", logger + self.save_quizzes( + self.train_w_quizzes[:72], result_dir, f"culture_w_quizzes", logger ) def batches(self, split="train", desc=None): assert split in {"train", "test"} if split == "train": - input = self.train_input - quizzes = self.train_quizzes + w_quizzes = self.train_w_quizzes + c_quizzes = self.train_c_quizzes else: - input = self.test_input - quizzes = self.test_quizzes + w_quizzes = self.test_w_quizzes + c_quizzes = self.test_c_quizzes - if len(quizzes) > 0: - quizzes = torch.cat(quizzes, dim=0) - if quizzes.size(0) > input.size(0) // 2: - i = torch.randperm(input.size(0))[: input.size(0) // 2] - quizzes = quizzes[i] + if len(c_quizzes) > 0: + c_quizzes = torch.cat(c_quizzes, dim=0) + if c_quizzes.size(0) > w_quizzes.size(0) // 2: + i = torch.randperm(w_quizzes.size(0))[: w_quizzes.size(0) // 2] + c_quizzes = c_quizzes[i] - i = torch.randperm(input.size(0))[: input.size(0) - quizzes.size(0)] - input = input[i] + i = torch.randperm(w_quizzes.size(0))[ + : w_quizzes.size(0) - c_quizzes.size(0) + ] + w_quizzes = w_quizzes[i] - self.nb_batch_samples_world = input.size(0) - self.nb_batch_samples_quizzes = quizzes.size(0) + self.nb_batch_w_quizzes = w_quizzes.size(0) + self.nb_batch_c_quizzes = c_quizzes.size(0) - input = torch.cat([input, quizzes], dim=0) + input = torch.cat([w_quizzes, c_quizzes], dim=0) else: - self.nb_batch_samples_world = input.size(0) - self.nb_batch_samples_quizzes = 0 + input = w_quizzes + self.nb_batch_w_quizzes = w_quizzes.size(0) + self.nb_batch_c_quizzes = 0 + + # Shuffle + input = input[torch.randperm(input.size(0))] if desc is None: desc = f"epoch-{split}" @@ -170,11 +180,13 @@ class World(Task): result = input.clone() * (1 - ar_mask) masked_inplace_autoregression( - model, - self.batch_size, - result, - ar_mask, - deterministic_synthesis, + model=model, + batch_size=self.batch_size, + input=result, + ar_mask=ar_mask, + summed_logits=None, + temperature=1.0, + deterministic_synthesis=deterministic_synthesis, progress_bar_desc=None, device=self.device, ) @@ -186,13 +198,13 @@ class World(Task): return nb_total, nb_correct - train_nb_total, train_nb_correct = compute_accuracy(self.train_input) + train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes) logger( 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}%" ) - test_nb_total, test_nb_correct = compute_accuracy(self.test_input, logger) + test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes, logger) logger( 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}%" @@ -203,36 +215,46 @@ class World(Task): ############################## - input = self.test_input[:96] + input = self.test_w_quizzes[:96] ar_mask = self.make_ar_mask(input) result = input.clone() * (1 - ar_mask) masked_inplace_autoregression( - model, - self.batch_size, - result, - ar_mask, - deterministic_synthesis, + model=model, + batch_size=self.batch_size, + input=result, + ar_mask=ar_mask, + summed_logits=None, + temperature=1.0, + deterministic_synthesis=deterministic_synthesis, progress_bar_desc=None, device=self.device, ) - self.save_image( - result[:96], + self.save_quizzes( + result[:72], result_dir, - f"world_prediction_{n_epoch:04d}_{model.id:02d}.png", + f"culture_prediction_{n_epoch:04d}_{model.id:02d}", logger, ) return main_test_accuracy - def store_new_quizzes(self, new_quizzes, for_train=True): + def renew_w_quizzes(self, nb, for_train=True): + input = self.train_w_quizzes if for_train else self.test_w_quizzes + nb = min(nb, input.size(0)) + input[:-nb] = input[nb:].clone() + input[-nb:] = world.generate_seq(nb, height=self.height, width=self.width).to( + self.device + ) + + def store_c_quizzes(self, new_c_quizzes, for_train=True): if for_train: - self.train_quizzes.append(new_quizzes) + self.train_c_quizzes.append(new_c_quizzes) else: - self.test_quizzes.append(new_quizzes) + self.test_c_quizzes.append(new_c_quizzes) - def create_new_quizzes( + def create_c_quizzes( self, n_epoch, result_dir, @@ -240,62 +262,119 @@ class World(Task): nb, model, other_models, + desired_average_logits=None, ): - new_quizzes = torch.empty( + ############################################################### + # Generate quizzes with model + + c_quizzes = torch.empty( nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64 ) - ar_mask = torch.full(new_quizzes.size(), 1, device=self.device) - masked_inplace_autoregression( - model, - self.batch_size, - new_quizzes, - ar_mask, - deterministic_synthesis=False, - progress_bar_desc="new quizzes", - device=self.device, + ar_mask = torch.full(c_quizzes.size(), 1, device=self.device) + summed_logits = torch.empty(nb, device=self.device) + + temperature = 1 + d_temperature = 1 + + while True: + summed_logits[...] = 0 + + masked_inplace_autoregression( + model=model, + batch_size=self.batch_size, + input=c_quizzes, + ar_mask=ar_mask, + summed_logits=summed_logits, + temperature=temperature, + deterministic_synthesis=False, + progress_bar_desc="sampling c_quizzes", + device=self.device, + ) + + average_logits = summed_logits.mean() + + logger(f"{average_logits=} {desired_average_logits=}") + + if desired_average_logits is None: + break + + # Oh man that's ugly + if average_logits < desired_average_logits * 1.1: + if d_temperature > 0: + d_temperature *= -0.5 + temperature += d_temperature + elif average_logits > desired_average_logits: + if d_temperature < 0: + d_temperature *= -0.5 + temperature += d_temperature + else: + break + + logger(f"chaging temperature to {temperature}") + + ############################################################### + # Create the reverse quizzes + + l = self.height * self.width + direction = c_quizzes[:, l : l + 1] + direction = world.token_forward * ( + direction == world.token_backward + ) + world.token_backward * (direction == world.token_forward) + reverse_c_quizzes = torch.cat( + [c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1 ) - ar_mask = self.make_ar_mask(new_quizzes) + ar_mask = self.make_ar_mask(c_quizzes) + + ############################################################### + # Check how many of the other models can solve them in both + # directions - nb_correct = 0 + nb_correct = [] for m in other_models: - result = new_quizzes.clone() + result = c_quizzes.clone() masked_inplace_autoregression( - m, - self.batch_size, - result, - ar_mask, + model=m, + batch_size=self.batch_size, + input=result, + ar_mask=ar_mask, + summed_logits=None, + temperature=1.0, deterministic_synthesis=True, - progress_bar_desc="solving quizzes", + progress_bar_desc="solving c_quizzes", device=self.device, ) - l = self.height * self.width - direction = new_quizzes[:, l : l + 1] - direction = world.token_forward * ( - direction == world.token_backward - ) + world.token_backward * (direction == world.token_forward) - inverted_quizzes = torch.cat( - [new_quizzes[:, l + 1 :], direction, new_quizzes[:, :l]], dim=1 - ) + correct = (c_quizzes == result).long().min(dim=-1).values - inverted_result = inverted_quizzes.clone() + reverse_result = reverse_c_quizzes.clone() masked_inplace_autoregression( - m, - self.batch_size, - inverted_result, - ar_mask, + model=m, + batch_size=self.batch_size, + input=reverse_result, + ar_mask=ar_mask, + summed_logits=None, + temperature=1.0, deterministic_synthesis=True, - progress_bar_desc="solving reverse quizzes", + progress_bar_desc="solving reversed c_quizzes", device=self.device, ) - nb_correct += (new_quizzes == result).long().min(dim=-1).values * ( - inverted_quizzes == inverted_result - ).long().min(dim=-1).values + reverse_correct = ( + (reverse_c_quizzes == reverse_result).long().min(dim=-1).values + ) + + nb_correct.append((correct * reverse_correct)[None, :]) + + nb_correct = torch.cat(nb_correct, dim=0).sum(dim=0) + + # filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat") + # with open(filename, "w") as f: + # for k in nb_correct: + # f.write(f"{k}\n") - return new_quizzes, nb_correct + return c_quizzes, nb_correct, summed_logits.mean()