X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=quizz_machine.py;h=8dc23a5dc6dd17736a18a3dd1399c3835de74f7a;hb=239a52ec7face6fcd4515916e80813702fbdf49b;hp=c5870d093ad2a5ddafc537069dcf6e999afbfd4a;hpb=f9aee903b896ae73dafe0cce1dcc40d8a39accb0;p=culture.git diff --git a/quizz_machine.py b/quizz_machine.py index c5870d0..8dc23a5 100755 --- a/quizz_machine.py +++ b/quizz_machine.py @@ -12,47 +12,11 @@ import torch, torchvision from torch import nn from torch.nn import functional as F +import mygpt from mygpt import BracketedSequence ###################################################################### - -class Gang(nn.Module): - def __init__(self, models, nb_models_for_generation, mode="groupthink"): - super().__init__() - self.models = models - self.nb_models_for_generation = nb_models_for_generation - self.mode = mode - - def forward(self, bs): - # If first = 0, we are re-starting an auto-regressive process, - # that's the right moment to randomize who gonna do it - if bs.first == 0: - self.models_to_use = [ - self.models[k] - for k in torch.randperm(len(self.models))[ - : self.nb_models_for_generation - ] - ] - - all_the_logits = torch.cat( - [model(bs).x[None] for model in self.models_to_use], dim=0 - ) - - if self.mode == "groupthink": - y = all_the_logits.mean(dim=0) - elif self.mode == "groupwork": - m = torch.rand(all_the_logits.size(), device=all_the_logits.device) - m = (m.sort(dim=0).indices == 0).long() - y = (y * m).sum(dim=0) - else: - raise ValueError(f"Invalid mode {self.mode}") - - return BracketedSequence(y, bs.first, bs.nb) - - -###################################################################### - # ar_mask is a tensor with 0s and 1s, of same shape as input, with # 1s where tokens should be generated. The others are kept # unchanged. @@ -311,9 +275,7 @@ 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 +283,11 @@ 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 +313,12 @@ 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 +327,66 @@ 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, 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, + 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, + 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, @@ -413,68 +395,7 @@ class QuizzMachine: seq_logproba=seq_logproba, temperature=temperature, deterministic_synthesis=True, - # progress_bar_desc="sampling c_quizzes", 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() - - ###################################################################### - - def create_c_quizzes( - self, - nb, - model_for_generation, - models_for_validation, - min_ave_seq_logproba, - n_epoch, - result_dir, - ): - c_quizzes, ave_seq_logproba = self.generate_quizzes( - nb, model_for_generation, min_ave_seq_logproba - ) - - nb_correct = self.comput_correctness(c_quizzes, models_for_validation) - - return c_quizzes, nb_correct, ave_seq_logproba - - ###################################################################### - - def gang_create_c_quizzes( - self, - nb, - nb_models_for_generation, - models, - mode, - min_ave_seq_logproba, - 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, - )