X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=quizz_machine.py;h=6f7492de907f7257888a46a93dad6f37bbdda964;hb=6b4e192557e03528ffd10364123de454aa9c9f08;hp=c5870d093ad2a5ddafc537069dcf6e999afbfd4a;hpb=f9aee903b896ae73dafe0cce1dcc40d8a39accb0;p=culture.git diff --git a/quizz_machine.py b/quizz_machine.py index c5870d0..6f7492d 100755 --- a/quizz_machine.py +++ b/quizz_machine.py @@ -12,6 +12,7 @@ import torch, torchvision from torch import nn from torch.nn import functional as F +import mygpt from mygpt import BracketedSequence ###################################################################### @@ -20,7 +21,7 @@ from mygpt import BracketedSequence class Gang(nn.Module): def __init__(self, models, nb_models_for_generation, mode="groupthink"): super().__init__() - self.models = models + self.models = nn.ModuleList(models) self.nb_models_for_generation = nb_models_for_generation self.mode = mode @@ -311,9 +312,7 @@ class QuizzMachine: else: self.test_c_quizzes.append(new_c_quizzes) - def comput_correctness(self, c_quizzes, models_for_validation): - # Create the reverse quizzes - + def reverse_time(self, c_quizzes): token_forward, token_backward = self.problem.direction_tokens() l = (c_quizzes.size(1) - 1) // 2 @@ -321,9 +320,11 @@ class QuizzMachine: direction = self.problem.token_forward * ( direction == self.problem.token_backward ) + self.problem.token_backward * (direction == self.problem.token_forward) - reverse_c_quizzes = torch.cat( - [c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1 - ) + + return torch.cat([c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1) + + def comput_correctness(self, c_quizzes, models_for_validation): + reversed_c_quizzes = self.reverse_time(c_quizzes) ar_mask = self.make_ar_mask(c_quizzes) seq_logproba = torch.empty(ar_mask.size(0), device=self.device) @@ -349,12 +350,12 @@ class QuizzMachine: correct = (c_quizzes == result).long().min(dim=-1).values - reverse_result = reverse_c_quizzes.clone() + reversed_result = reversed_c_quizzes.clone() masked_inplace_autoregression( model=model, batch_size=self.batch_size, - input=reverse_result, + input=reversed_result, ar_mask=ar_mask, seq_logproba=seq_logproba, temperature=1.0, @@ -363,48 +364,70 @@ class QuizzMachine: device=self.device, ) - reverse_correct = ( - (reverse_c_quizzes == reverse_result).long().min(dim=-1).values + reversed_correct = ( + (reversed_c_quizzes == reversed_result).long().min(dim=-1).values ) - nb_correct += correct * reverse_correct + nb_correct += correct * reversed_correct return nb_correct ############################################################### - def generate_quizzes(self, nb, model_for_generation, min_ave_seq_logproba): + def generate_quizzes( + self, nb, model_for_generation, min_ave_seq_logproba, reverse_cleanup=False + ): c_quizzes = torch.empty( nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64 ) ar_mask_prompt = torch.zeros(c_quizzes.size(), device=self.device) - ar_mask_prompt[:, ar_mask_prompt.size(1) // 2 + 1] = 1 + ar_mask_prompt[:, : ar_mask_prompt.size(1) // 2 + 1] = 1 ar_mask_solve = 1 - ar_mask_prompt - seq_logproba = torch.empty(ar_mask.size(0), device=self.device) + seq_logproba = torch.empty(ar_mask_prompt.size(0), device=self.device) - # bracketing of the temperature to get the target logproba + if reverse_cleanup: + warnings.warn("very high temperature with reversed cleanup", RuntimeWarning) + temperature = 10.0 + else: + temperature = 1.0 - temperature = 1 - d_temperature = 1 / 3 + # warnings.warn("noise injection", RuntimeWarning) + # noise_std = torch.rand(1).item() + # self.logger(f"{noise_std=}") - while True: - seq_logproba[...] = 0 + # mygpt.set_noise_injection(model_for_generation, noise_std) - masked_inplace_autoregression( - model=model_for_generation, - batch_size=self.batch_size, - input=c_quizzes, - ar_mask=ar_mask_prompt, - seq_logproba=seq_logproba, - temperature=temperature, - deterministic_synthesis=False, - # progress_bar_desc="sampling c_quizzes", - device=self.device, - ) + masked_inplace_autoregression( + model=model_for_generation, + batch_size=self.batch_size, + input=c_quizzes, + ar_mask=ar_mask_prompt, + seq_logproba=seq_logproba, + temperature=temperature, + deterministic_synthesis=False, + # progress_bar_desc="sampling c_quizzes", + device=self.device, + ) - ave_seq_logproba = seq_logproba.mean() + # mygpt.set_noise_injection(model_for_generation, 0.0) + ave_seq_logproba = seq_logproba.mean() + + masked_inplace_autoregression( + model=model_for_generation, + batch_size=self.batch_size, + input=c_quizzes, + ar_mask=ar_mask_solve, + seq_logproba=seq_logproba, + temperature=temperature, + deterministic_synthesis=True, + # progress_bar_desc="sampling c_quizzes", + device=self.device, + ) + + if reverse_cleanup: + c_quizzes = self.reverse_time(c_quizzes) masked_inplace_autoregression( model=model_for_generation, batch_size=self.batch_size, @@ -417,24 +440,6 @@ class QuizzMachine: device=self.device, ) - # If we do not have target logprobs, get out now - if min_ave_seq_logproba is None: - break - - # Oh man that's ugly - if ave_seq_logproba < min_ave_seq_logproba: - if d_temperature > 0: - d_temperature *= -1 / 3 - temperature += d_temperature - elif ave_seq_logproba > min_ave_seq_logproba * 0.99: - if d_temperature < 0: - d_temperature *= -1 / 3 - temperature += d_temperature - else: - break - - self.logger(f"changing temperature to {temperature}") - return c_quizzes, seq_logproba.mean() ###################################################################### @@ -445,11 +450,15 @@ class QuizzMachine: model_for_generation, models_for_validation, min_ave_seq_logproba, + reverse_cleanup, n_epoch, result_dir, ): c_quizzes, ave_seq_logproba = self.generate_quizzes( - nb, model_for_generation, min_ave_seq_logproba + nb, + model_for_generation=model_for_generation, + min_ave_seq_logproba=min_ave_seq_logproba, + reverse_cleanup=reverse_cleanup, ) nb_correct = self.comput_correctness(c_quizzes, models_for_validation) @@ -465,16 +474,18 @@ class QuizzMachine: models, mode, min_ave_seq_logproba, + reverse_cleanup, n_epoch, result_dir, ): model_for_generation = Gang(models, nb_models_for_generation, mode) models_for_validation = models return self.create_c_quizzes( - nb, - model_for_generation, - models_for_validation, - min_ave_seq_logproba, - n_epoch, - result_dir, + nb=nb, + model_for_generation=model_for_generation, + models_for_validation=models_for_validation, + min_ave_seq_logproba=min_ave_seq_logproba, + reverse_cleanup=reverse_cleanup, + n_epoch=n_epoch, + result_dir=result_dir, )