X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=quizz_machine.py;h=92b579980ec1a10aec128f8a91927f303417163c;hb=d3d4ce7bb2b799f4bf81a936987e3a8938514af8;hp=198d279f3d8f9da8d0b602e622d3a8f690b907fd;hpb=240870f5535bac35a08c552108d032854a8e2c38;p=culture.git diff --git a/quizz_machine.py b/quizz_machine.py index 198d279..92b5799 100755 --- a/quizz_machine.py +++ b/quizz_machine.py @@ -29,8 +29,6 @@ def one_batch_masked_inplace_autoregression( seq_logproba, temperature=1.0, deterministic_synthesis=False, - forbidden_tokens=None, - forced_biases=None, ): to_generate = (ar_mask.sum(0) > 0).nonzero() @@ -45,12 +43,6 @@ def one_batch_masked_inplace_autoregression( logits = (logits / temperature).log_softmax(dim=-1) - if forbidden_tokens is not None: - logits = logits.masked_fill(forbidden_tokens, float("-inf")) - - if forced_biases is not None: - logits = logits + forced_biases[None, :] - if deterministic_synthesis: t_next = logits.argmax(-1) else: @@ -104,8 +96,6 @@ def masked_inplace_autoregression( seq_logproba=seq_logproba, temperature=temperature, deterministic_synthesis=deterministic_synthesis, - forbidden_tokens=forbidden_tokens, - forced_biases=logit_biases, ) model.train(t) @@ -115,15 +105,105 @@ def masked_inplace_autoregression( class QuizzMachine: - 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) + def indices_forward_and_backward(self, quizzes): + i_forward = quizzes[:, 0] == self.token_forward + j_forward = quizzes[:, 1 + self.prompt_len] == self.token_forward + i_backward = quizzes[:, 0] == self.token_backward + j_backward = quizzes[:, 1 + self.answer_len] == self.token_backward + assert torch.logical_or( + torch.logical_and(i_forward, j_forward), + torch.logical_and(i_backward, j_backward), + ).all() + return i_forward, i_backward + + def reverse_time(self, quizzes): + i_forward, i_backward = self.indices_forward_and_backward(quizzes) + + forward_to_backward = torch.cat( + [ + quizzes[:, 0:1], + quizzes[:, 2 + self.prompt_len : 2 + self.prompt_len + self.answer_len], + quizzes[:, 1 + self.prompt_len : 1 + self.prompt_len + 1], + quizzes[:, 1 : 1 + self.prompt_len], + ], + dim=1, + ) + + forward_to_backward[:, 0] = self.token_backward + forward_to_backward[:, 1 + self.answer_len] = self.token_backward + + backward_to_forward = torch.cat( + [ + quizzes[:, 0:1], + quizzes[:, 2 + self.answer_len :], + quizzes[:, 1 + self.answer_len : 2 + self.answer_len], + quizzes[:, 1 : 1 + self.answer_len], + ], + dim=1, + ) + + backward_to_forward[:, 0] = self.token_forward + backward_to_forward[:, 1 + self.prompt_len] = self.token_forward + + m = i_forward.long()[:, None] + + return m * forward_to_backward + (1 - m) * backward_to_forward + + def make_ar_mask(self, quizzes, first=False): + i_forward, i_backward = self.indices_forward_and_backward(quizzes) + + t = torch.arange(quizzes.size(1), device=quizzes.device) + + if first: + m_forward = (t >= 1).long() * (t < 1 + self.prompt_len).long() + m_backward = (t >= 1).long() * (t < 1 + self.answer_len).long() + else: + m_forward = (t >= 2 + self.prompt_len).long() + m_backward = (t >= 2 + self.answer_len).long() + + m = i_forward.long()[:, None] + + return m * m_forward + (1 - m) * m_backward + + def generate_token_sequences(self, nb): + prompts, answers = self.problem.generate_prompts_and_answers(nb) + + if self.prompt_len is None: + self.prompt_len = prompts.size(1) + + if self.answer_len is None: + self.answer_len = answers.size(1) + + assert prompts.size(1) == self.prompt_len and answers.size(1) == self.answer_len + + result = [] + + for prompt, answer in zip(prompts, answers): + if torch.rand(1) < 0.5: + a = [ + torch.tensor([self.token_forward]), + prompt, + torch.tensor([self.token_forward]), + answer, + ] + else: + a = [ + torch.tensor([self.token_backward]), + answer, + torch.tensor([self.token_backward]), + prompt, + ] + + result.append(torch.cat(a, dim=0)[None, :]) + + return torch.cat(result, dim=0) def __init__( self, problem, nb_train_samples, nb_test_samples, + back_accuracy, batch_size, result_dir, logger, @@ -131,28 +211,75 @@ class QuizzMachine: ): super().__init__() + v = problem.nb_token_values() + self.token_forward = v + self.token_backward = v + 1 + self.nb_token_values = v + 2 + self.problem = problem + self.back_accuracy = back_accuracy self.batch_size = batch_size self.device = device self.logger = logger + self.prompt_len = None + self.answer_len = None - self.train_w_quizzes = self.problem.generate_token_sequences( - nb_train_samples - ).to(device) - self.test_w_quizzes = self.problem.generate_token_sequences(nb_test_samples).to( + self.train_w_quizzes = self.generate_token_sequences(nb_train_samples).to( device ) - self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1 + self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device) self.train_c_quizzes = [] self.test_c_quizzes = [] if result_dir is not None: - self.problem.save_quizzes( - self.train_w_quizzes[:72], result_dir, "culture_w_quizzes" + self.save_quizzes( + result_dir, + "culture_w_quizzes", + self.train_w_quizzes[:72], + n_backward=self.train_w_quizzes[:72, 0] == self.token_backward, ) + def save_quizzes( + self, + result_dir, + filename_prefix, + quizzes, + n_backward=None, + mistakes=None, + ): + quizzes = quizzes.clone() + forward = quizzes[quizzes[:, 0] == self.token_forward] + ib = quizzes[:, 0] == self.token_backward + backward = quizzes[ib] + assert forward.size(0) + backward.size(0) == quizzes.size(0) + quizzes[ib] = self.reverse_time(quizzes[ib]) + + if n_backward is None: + predicted_prompts = None + predicted_answers = None + else: + predicted_prompts = n_backward.long() + predicted_answers = 1 - predicted_prompts + if mistakes is not None: + # 0/-1/+1 ~ not-to-predict / predicted wrong / predicted correct + predicted_prompts *= mistakes + predicted_answers *= mistakes + else: + # 0/2 ~ not-to-predict / to predict + predicted_prompts *= 2 + predicted_answers *= 2 + + self.problem.save_quizzes( + result_dir, + filename_prefix, + quizzes[:, 1 : 1 + self.prompt_len], + quizzes[:, 2 + self.prompt_len :], + predicted_prompts, + predicted_answers, + ) + def batches(self, split="train", desc=None): assert split in {"train", "test"} if split == "train": @@ -193,13 +320,12 @@ class QuizzMachine: yield batch def vocabulary_size(self): - return self.nb_codes + return self.nb_token_values def produce_results( self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000 ): - def compute_accuracy(input): - input = input[:nmax] + def compute_accuracy(input, log_prefix=None): ar_mask = self.make_ar_mask(input) result = input.clone() * (1 - ar_mask) seq_logproba = torch.empty(input.size(0), device=self.device) @@ -216,49 +342,56 @@ class QuizzMachine: device=self.device, ) - nb_total, nb_correct = ( - input.size(0), - (input == result).long().min(dim=1).values.sum(), + correct = torch.empty(input.size(0), dtype=torch.int64, device=input.device) + + n_forward = input[:, 0] == self.token_forward + n_backward = input[:, 0] == self.token_backward + + correct[n_forward] = ( + (input[n_forward] == result[n_forward]).long().min(dim=1).values ) - return nb_total, nb_correct + if self.back_accuracy and n_backward.any(): + # accuracy of B->A*->B*=B instead of B->A*=A + back_input = self.reverse_time(result[n_backward]) + back_input[:, 2 + self.prompt_len :] = input[ + n_backward, 1 : 1 + self.answer_len + ] + result[n_backward], correct[n_backward] = compute_accuracy(back_input) + + if log_prefix is not None: + forward_nb_correct = correct[n_forward].sum() + forward_nb_total = correct[n_forward].size(0) + backward_nb_correct = correct[n_backward].sum() + backward_nb_total = correct[n_backward].size(0) + + self.logger( + f"forward_accuracy {log_prefix} {n_epoch} {model.id=} {forward_nb_correct} / {forward_nb_total}" + ) - train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes) + self.logger( + f"backward_accuracy {log_prefix} {n_epoch} {model.id=} {backward_nb_correct} / {backward_nb_total}" + ) - self.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}%" - ) + return result, correct - test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes) + compute_accuracy(self.train_w_quizzes[:nmax], log_prefix="train") - self.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}%" + test_result, test_correct = compute_accuracy( + self.test_w_quizzes[:nmax], log_prefix="test" ) - main_test_accuracy = test_nb_correct / test_nb_total + main_test_accuracy = test_correct.sum() / test_correct.size(0) self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}") ############################## - input = self.test_w_quizzes[:96] - ar_mask = self.make_ar_mask(input) - result = input.clone() * (1 - ar_mask) - seq_logproba = torch.empty(input.size(0), device=self.device) - - masked_inplace_autoregression( - model=model, - batch_size=self.batch_size, - input=result, - ar_mask=ar_mask, - seq_logproba=seq_logproba, - temperature=1.0, - deterministic_synthesis=deterministic_synthesis, - progress_bar_desc=None, - device=self.device, - ) - - self.problem.save_quizzes( - result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}" + self.save_quizzes( + result_dir, + f"culture_prediction_{n_epoch:04d}_{model.id:02d}", + quizzes=test_result[:72], + n_backward=self.test_w_quizzes[:72, 0] == self.token_backward, + mistakes=test_correct[:72] * 2 - 1, ) return main_test_accuracy @@ -267,7 +400,7 @@ class QuizzMachine: 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:] = self.problem.generate_token_sequences(nb).to(self.device) + input[-nb:] = self.generate_token_sequences(nb).to(self.device) def store_c_quizzes(self, new_c_quizzes, for_train=True): if for_train: @@ -275,146 +408,126 @@ class QuizzMachine: else: self.test_c_quizzes.append(new_c_quizzes) - def reverse_time(self, c_quizzes): - token_forward, token_backward = self.problem.direction_tokens() - - l = (c_quizzes.size(1) - 1) // 2 - direction = c_quizzes[:, l : l + 1] - direction = self.problem.token_forward * ( - direction == self.problem.token_backward - ) + self.problem.token_backward * (direction == self.problem.token_forward) - - return torch.cat([c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1) - def compute_correctness( - self, c_quizzes, models_for_validation, both_direction=True + self, + c_quizzes, + models_for_validation, + bidirectional_validation=False, + deterministic_validation=True, ): - 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) + if bidirectional_validation: + backward_c_quizzes = self.forward_to_backward(c_quizzes) - # Check how many of models can solve the quizzes in both directions + seq_logproba = torch.zeros( + c_quizzes.size(0), + max([m.id for m in models_for_validation]) + 1, + device=self.device, + ) nb_correct = 0 for model in models_for_validation: result = c_quizzes.clone() + seq_logproba[...] = 0.0 + + ar_mask = self.make_ar_mask(result) + masked_inplace_autoregression( model=model, batch_size=self.batch_size, input=result, ar_mask=ar_mask, - seq_logproba=seq_logproba, + seq_logproba=seq_logproba[:, model.id], temperature=1.0, - deterministic_synthesis=True, + deterministic_synthesis=deterministic_validation, # progress_bar_desc="solving c_quizzes", device=self.device, ) correct = (c_quizzes == result).long().min(dim=-1).values - if both_direction: - reversed_result = reversed_c_quizzes.clone() + if bidirectional_validation: + backward_result = backward_c_quizzes.clone() + + ar_mask = self.make_ar_mask(backward_result) masked_inplace_autoregression( model=model, batch_size=self.batch_size, - input=reversed_result, + input=backward_result, ar_mask=ar_mask, - seq_logproba=seq_logproba, + seq_logproba=seq_logproba[:, model.id], temperature=1.0, - deterministic_synthesis=True, - # progress_bar_desc="solving reversed c_quizzes", + deterministic_synthesis=deterministic_validation, + # progress_bar_desc="solving backward c_quizzes", device=self.device, ) - reversed_correct = ( - (reversed_c_quizzes == reversed_result).long().min(dim=-1).values + backward_correct = ( + (backward_c_quizzes == backward_result).long().min(dim=-1).values ) - correct *= reversed_correct + correct *= backward_correct # endif nb_correct += correct - return nb_correct + return nb_correct, seq_logproba ############################################################### - def generate_quizzes(self, nb, model_for_generation, reverse_cleanup=False): + def generate_quizzes(self, nb, model_for_generation, temperature=1.0): 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_solve = 1 - ar_mask_prompt - seq_logproba = torch.empty(ar_mask_prompt.size(0), device=self.device) - - if reverse_cleanup: - warnings.warn("very high temperature with reversed cleanup", RuntimeWarning) - temperature = 10.0 - else: - temperature = 1.0 + seq_logproba = torch.zeros(nb, device=self.device) - # warnings.warn("noise injection", RuntimeWarning) - # noise_std = torch.rand(1).item() - # self.logger(f"{noise_std=}") + # First, we generate the answer at high temperature - # mygpt.set_noise_injection(model_for_generation, noise_std) + c_quizzes[:, 0] = self.token_backward + c_quizzes[:, 1 + self.answer_len] = self.token_backward masked_inplace_autoregression( model=model_for_generation, batch_size=self.batch_size, input=c_quizzes, - ar_mask=ar_mask_prompt, + ar_mask=self.make_ar_mask(c_quizzes, first=True), seq_logproba=seq_logproba, temperature=temperature, deterministic_synthesis=False, device=self.device, ) - # mygpt.set_noise_injection(model_for_generation, 0.0) - - ave_seq_logproba = seq_logproba.mean() + # Then, we generate the prompt at low temperature masked_inplace_autoregression( model=model_for_generation, batch_size=self.batch_size, input=c_quizzes, - ar_mask=ar_mask_solve, + ar_mask=self.make_ar_mask(c_quizzes), seq_logproba=seq_logproba, - temperature=temperature, - deterministic_synthesis=True, + temperature=1 / temperature, + deterministic_synthesis=False, 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, - input=c_quizzes, - ar_mask=ar_mask_solve, - seq_logproba=seq_logproba, - temperature=temperature, - deterministic_synthesis=True, - device=self.device, - ) + # Then we return the quizz, and re-generate the response, now + # at low temperature - c_quizzes = self.reverse_time(c_quizzes) - 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, - ) + c_quizzes = self.reverse_time(c_quizzes) + + masked_inplace_autoregression( + model=model_for_generation, + batch_size=self.batch_size, + input=c_quizzes, + ar_mask=self.make_ar_mask(c_quizzes), + seq_logproba=seq_logproba, + temperature=1 / temperature, + deterministic_synthesis=False, + device=self.device, + ) - return c_quizzes, seq_logproba.mean() + return c_quizzes