X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=quizz_machine.py;h=62ae8ce94af2b09b909a8182ad3a4a0b4709a1c1;hb=455161a64dfc7a53d09ff1cd49f590ff9152cc37;hp=0d6d8f57cba918235f951333a64cb1a4c44133d2;hpb=d283cd3d46a6323fec4c6a0970ac71e553e4a486;p=culture.git diff --git a/quizz_machine.py b/quizz_machine.py index 0d6d8f5..62ae8ce 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) @@ -115,19 +105,94 @@ 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, answer] + a = [ + torch.tensor([self.token_forward]), + prompt, + torch.tensor([self.token_forward]), + answer, + ] else: - a = [torch.tensor([self.token_backward]), answer, prompt] + a = [ + torch.tensor([self.token_backward]), + answer, + torch.tensor([self.token_backward]), + prompt, + ] result.append(torch.cat(a, dim=0)[None, :]) @@ -138,6 +203,7 @@ class QuizzMachine: problem, nb_train_samples, nb_test_samples, + back_accuracy, batch_size, result_dir, logger, @@ -151,9 +217,12 @@ class QuizzMachine: 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.generate_token_sequences(nb_train_samples).to( device @@ -166,23 +235,23 @@ class QuizzMachine: if result_dir is not None: self.save_quizzes( - result_dir, "culture_w_quizzes", self.train_w_quizzes[:72] + result_dir, + "culture_w_quizzes", + self.train_w_quizzes[:72], + prediction=True, ) def save_quizzes(self, result_dir, filename_prefix, quizzes, prediction=False): - print(f"DEBUG {quizzes.size()=}") - l = (quizzes.size(1) - 1) // 2 - forward = (quizzes[:, 0] == self.token_forward).long() - backward = (quizzes[:, 0] == self.token_backward).long() - assert forward.equal(1 - backward) - first = quizzes[:, 1 : 1 + l] - second = quizzes[:, 1 + l : 1 + 2 * l] - prompts = forward[:, None] * first + backward[:, None] * second - answers = forward[:, None] * second + backward[:, None] * first + 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 prediction: - predicted_prompts = backward - predicted_answers = forward + predicted_prompts = ib + predicted_answers = torch.logical_not(ib) else: predicted_prompts = None predicted_answers = None @@ -190,8 +259,8 @@ class QuizzMachine: self.problem.save_quizzes( result_dir, filename_prefix, - prompts, - answers, + quizzes[:, 1 : 1 + self.prompt_len], + quizzes[:, 2 + self.prompt_len :], predicted_prompts, predicted_answers, ) @@ -242,7 +311,6 @@ class QuizzMachine: self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000 ): def compute_accuracy(input): - input = input[:nmax] ar_mask = self.make_ar_mask(input) result = input.clone() * (1 - ar_mask) seq_logproba = torch.empty(input.size(0), device=self.device) @@ -259,20 +327,61 @@ class QuizzMachine: device=self.device, ) - nb_total, nb_correct = ( - input.size(0), - (input == result).long().min(dim=1).values.sum(), - ) + if self.back_accuracy: + # If back_accuracy is True, we compute the accuracy on + # the backward quizzes not by counting how many time + # the real prompt A is equal to the reconstructed + # prompt A*, but how many time the answers B* computed + # from A* is equal to the correct answer. So we look + # for the accuracy of A->B*=B for the forward, but for + # the backward we look at B->A*->B*=B instead of B->A*=A + + n_forward = input[:, 0] == self.token_forward + nb_total = input[n_forward].size(0) + nb_correct = ( + (input[n_forward] == result[n_forward]) + .long() + .min(dim=1) + .values.sum() + .item() + ) + + n_backward = input[:, 0] == self.token_backward + back_input = self.reverse_time(result[n_backward]) + + if back_input.size(0) > 0: + back_input[:, 2 + self.prompt_len :] = input[ + n_backward, 1 : 1 + self.answer_len + ] + back_nb_total, back_nb_correct = compute_accuracy(back_input) + + self.logger( + f"accuracy {n_epoch=} {model.id=} {nb_correct} / {nb_total}" + ) + self.logger( + f"back_accuracy {n_epoch=} {model.id=} {back_nb_correct} / {back_nb_total}" + ) + + nb_total += back_nb_total + nb_correct += back_nb_correct + else: + self.logger( + f"accuracy {n_epoch=} {model.id=} {nb_correct} / {nb_total}" + ) + + else: + nb_total = input.size(0) + nb_correct = (input == result).long().min(dim=1).values.sum() return nb_total, nb_correct - train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes) + train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes[:nmax]) 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}%" ) - test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes) + test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes[:nmax]) 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}%" @@ -321,122 +430,114 @@ class QuizzMachine: else: self.test_c_quizzes.append(new_c_quizzes) - def reverse_time(self, c_quizzes): - l = (c_quizzes.size(1) - 1) // 2 - direction = c_quizzes[:, 0:1] - direction = self.token_forward * ( - direction == self.token_backward - ) + self.token_backward * (direction == self.token_forward) - - return torch.cat( - [direction, c_quizzes[:, l + 1 :], c_quizzes[:, 1 : l + 1]], dim=1 - ) - def compute_correctness( - self, c_quizzes, models_for_validation, both_directions=False + 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_directions: - 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): + 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_first = torch.zeros(c_quizzes.size(), device=self.device) - ar_mask_first[:, : ar_mask_first.size(1) // 2 + 1] = 1 - ar_mask_second = 1 - ar_mask_first - ar_mask_first[:, 0] = 0 - ar_mask_second[:, 0] = 0 - - seq_logproba = torch.empty(ar_mask_first.size(0), device=self.device) - - temperature = 10.0 + seq_logproba = torch.zeros(nb, device=self.device) # First, we generate the answer at high temperature 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_first, + ar_mask=self.make_ar_mask(c_quizzes, first=True), seq_logproba=seq_logproba, temperature=temperature, deterministic_synthesis=False, device=self.device, ) - ave_seq_logproba = seq_logproba.mean() - - # Then, we generate the prompt deterministically + # 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_second, + ar_mask=self.make_ar_mask(c_quizzes), seq_logproba=seq_logproba, - temperature=1.0, - deterministic_synthesis=True, + temperature=1 / temperature, + deterministic_synthesis=False, device=self.device, ) # Then we return the quizz, and re-generate the response, now - # deterministically + # at low temperature c_quizzes = self.reverse_time(c_quizzes) @@ -444,11 +545,11 @@ class QuizzMachine: model=model_for_generation, batch_size=self.batch_size, input=c_quizzes, - ar_mask=ar_mask_second, + 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, ) - return c_quizzes, seq_logproba.mean() + return c_quizzes