From: François Fleuret Date: Sat, 13 Jul 2024 10:57:08 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=4abb8abab468b782f4481083b26399f293542c7f;p=culture.git Update. --- diff --git a/main.py b/main.py index 9599cf3..957fd85 100755 --- a/main.py +++ b/main.py @@ -370,77 +370,94 @@ def standard_validity(logproba): ) -def valid_c_quizzes(recorded, criteria): - result = [q[criteria(lp)] for q, lp in recorded] - return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([]) +def valid_quizzes_and_logprobas(recorded, criteria): + validated_quizzes, validated_logprobas = [], [] + for q, lp in recorded: + validated_indices = criteria(lp) + validated_quizzes.append(q[validated_indices]) + validated_logprobas.append(lp[validated_indices]) + + if len(validated_quizzes) > 0: + return torch.cat(validated_quizzes, dim=0), torch.cat( + validated_logprobas, dim=0 + ) + else: + return None, None ###################################################################### -def create_c_quizzes( - models, - quiz_machine, - nb_for_train=1000, - nb_for_test=100, -): - quizzes_and_logproba_records = [] - +def create_c_quizzes(models, quiz_machine, nb_for_train=1000, nb_for_test=100): nb_to_create = nb_for_train + nb_for_test - # ------------------------------------------------------------ + recorded_quizzes_logprobas = [] - file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat") + nb_validated = 0 - with open(file_name, "w") as logp_file: - while ( - valid_c_quizzes(quizzes_and_logproba_records, standard_validity).size(0) - < nb_to_create - ): - # Select a model at random to generate the new quizzes + while nb_validated < nb_to_create: + model_for_generation = models[torch.randint(len(models), (1,))] - model_for_generation = models[torch.randint(len(models), (1,))] + c_quizzes = quiz_machine.generate_quizzes( + nb_to_create, + model_for_generation=model_for_generation, + temperature=args.generation_temperature, + ) - c_quizzes = quiz_machine.generate_quizzes( - nb_to_create, - model_for_generation=model_for_generation, - temperature=args.generation_temperature, - ) + c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)] - c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)] + if c_quizzes.size(0) > 0: + logproba = quiz_machine.logproba_of_solutions(models, c_quizzes) + recorded_quizzes_logprobas.append((c_quizzes, logproba)) - if c_quizzes.size(0) > 0: - logproba = quiz_machine.logproba_of_solutions(models, c_quizzes) - for l in logproba: - s = " ".join([str(x.item()) for x in l]) - logp_file.write(s + "\n") - quizzes_and_logproba_records.append((c_quizzes, logproba)) + validated_quizzes, validated_logprobas = valid_quizzes_and_logprobas( + recorded_quizzes_logprobas, standard_validity + ) - nb_validated = valid_c_quizzes( - quizzes_and_logproba_records, standard_validity - ).size(0) + if validated_quizzes is not None: + nb_validated = validated_quizzes.size(0) - log_string( - f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}" - ) + log_string( + f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}" + ) # store the new c_quizzes which have been validated - new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_records, standard_validity) + quiz_machine.reverse_random_half_in_place(validated_quizzes) + quiz_machine.store_c_quizzes(validated_quizzes[:nb_for_train], for_train=True) + quiz_machine.store_c_quizzes( + validated_quizzes[nb_for_train:nb_to_create], for_train=False + ) + + ###################################################################### + # save the log probas + + file_name = os.path.join( + args.result_dir, f"culture_c_quiz_all_{n_epoch:04d}_logp.dat" + ) - quiz_machine.reverse_random_half_in_place(new_c_quizzes) + with open(file_name, "w") as logp_file: + for _, ll in recorded_quizzes_logprobas: + for l in ll: + s = " ".join([str(x.item()) for x in l]) + logp_file.write(s + "\n") - quiz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True) - quiz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False) + ###################################################################### + # save images with their logprobas - # save images + vq = validated_quizzes[:72] + vl = validated_logprobas[:72] - q = new_c_quizzes[:72] + if vq.size(0) > 0: + prefix = f"culture_c_quiz_{n_epoch:04d}" - if q.size(0) > 0: - quiz_machine.save_quiz_illustrations( - args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q - ) + file_name = os.path.join(args.result_dir, prefix + "_logp.dat") + with open(file_name, "w") as logp_file: + for l in vl: + s = " ".join([str(x.item()) for x in l]) + logp_file.write(s + "\n") + + quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq) ###################################################################### @@ -478,9 +495,9 @@ if args.resume: filename = f"gpt_{model.id:03d}.pth" try: - model.load_state_dict( - torch.load(os.path.join(args.result_dir, filename)) - ) + d = torch.load(os.path.join(args.result_dir, filename)) + model.load_state_dict(d[0]) + model.main_test_accuracy = d[1] log_string(f"successfully loaded {filename}") except FileNotFoundError: log_string(f"cannot find {filename}") @@ -604,19 +621,17 @@ for n_epoch in range(args.nb_epochs): for t in threads: t.join() + # Save the models to disk + for model in weakest_models: filename = f"gpt_{model.id:03d}.pth" - torch.save(model.state_dict(), os.path.join(args.result_dir, filename)) + torch.save( + (model.state_dict(), model.main_test_accuracy), + os.path.join(args.result_dir, filename), + ) log_string(f"wrote {filename}") - ################################################## - # Replace a fraction of the w_quizzes with fresh ones - - log_string( - f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes" - ) - - # Renew entirely the train set + # Renew the training samples for model in weakest_models: quiz_machine.renew_w_quizzes(model, args.nb_train_samples) @@ -633,6 +648,8 @@ for n_epoch in range(args.nb_epochs): nb_for_test=nb_new_c_quizzes_for_test, ) - quiz_machine.save_c_quizzes(os.path.join(args.result_dir, "c_quizzes.pth")) + filename = "c_quizzes.pth" + quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename)) + log_string(f"wrote {filename}") ###################################################################### diff --git a/quiz_machine.py b/quiz_machine.py index eab41dc..ef766c4 100755 --- a/quiz_machine.py +++ b/quiz_machine.py @@ -5,7 +5,7 @@ # Written by Francois Fleuret -import math, os, tqdm, warnings +import math, os, tqdm, warnings, sys import torch, torchvision @@ -17,6 +17,36 @@ from mygpt import BracketedSequence import threading +###################################################################### +# if output is log(P(X=y)) and target is Y, returns -log P(X=Y) + H(X +# | X != Y) + + +# output is NxCxT and target is NxT +def confusion(output, target, reduction="mean"): + N, C, T = output.shape + output = output.permute(0, 2, 1).reshape(-1, C) + target = target.flatten() + all_t = torch.arange(N * T, device=output.device) + output = output.log_softmax(dim=-1) + result = -output[all_t, target] + + output[all_t, target] = float("-inf") + output = output.log_softmax(dim=-1) + e = output.exp() + output[all_t, target] = 0 + result = result - (output * e).sum(-1) + + if reduction == "none": + return result.reshape(N, T) + elif reduction == "mean": + return result.reshape(N, T).mean() + elif reduction == "sum": + return result.reshape(N, T).sum() + else: + raise ValueError(f"unknown reduction '{reduction}'.") + + ###################################################################### # ar_mask is a tensor with 0s and 1s, of same shape as input, with @@ -189,6 +219,9 @@ class QuizMachine: def generate_token_sequences(self, nb): prompts, answers = self.problem.generate_prompts_and_answers(nb) + print(f"DEBUG {prompts.size()=} {answers.size()=}") + sys.stdout.flush() + if self.prompt_len is None: self.prompt_len = prompts.size(1)