X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=quiz_machine.py;h=bc468d3d4cb3ce7d1aea7ef127773c4d494cdd32;hb=refs%2Fheads%2Fmaster;hp=45b2247f50519d382bc5e04bbab5612fb60fe698;hpb=719785dbea77989a54bf7592bb6919f2e8f3f6c5;p=culture.git

diff --git a/quiz_machine.py b/quiz_machine.py
deleted file mode 100755
index 45b2247..0000000
--- a/quiz_machine.py
+++ /dev/null
@@ -1,528 +0,0 @@
-#!/usr/bin/env python
-
-# Any copyright is dedicated to the Public Domain.
-# https://creativecommons.org/publicdomain/zero/1.0/
-
-# Written by Francois Fleuret <francois@fleuret.org>
-
-import math, os, tqdm, warnings
-
-import torch, torchvision
-
-from torch import nn
-from torch.nn import functional as F
-
-import mygpt
-from mygpt import BracketedSequence
-
-######################################################################
-
-# 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.
-
-
-def one_batch_masked_inplace_autoregression(
-    model,
-    input,
-    ar_mask,
-    seq_logproba,
-    temperature=1.0,
-    deterministic_synthesis=False,
-):
-    to_generate = (ar_mask.sum(0) > 0).nonzero()
-
-    if to_generate.min() > 0:
-        model(
-            BracketedSequence(input, 0, to_generate.min())
-        )  # Needed to initialize the model's cache
-    for s in range(to_generate.min(), to_generate.max() + 1):
-        output = model(BracketedSequence(input, s, 1)).x
-
-        logits = output[:, s]
-
-        logits = (logits / temperature).log_softmax(dim=-1)
-
-        if deterministic_synthesis:
-            t_next = logits.argmax(-1)
-        else:
-            dist = torch.distributions.categorical.Categorical(logits=logits)
-            t_next = dist.sample()
-
-        all_n = torch.arange(t_next.size(0))
-
-        seq_logproba += logits[all_n, t_next]
-
-        input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
-
-
-def masked_inplace_autoregression(
-    model,
-    batch_size,
-    input,
-    ar_mask,
-    seq_logproba,
-    temperature,
-    deterministic_synthesis,
-    forbidden_tokens=None,
-    logit_biases=None,
-    progress_bar_desc=None,
-    device=torch.device("cpu"),
-):
-    assert input.size() == ar_mask.size()
-
-    batches = zip(
-        input.split(batch_size),
-        ar_mask.split(batch_size),
-        seq_logproba.split(batch_size),
-    )
-
-    if progress_bar_desc is not None:
-        batches = tqdm.tqdm(
-            batches,
-            dynamic_ncols=True,
-            desc=progress_bar_desc,
-            total=(input.size(0) + batch_size - 1) // batch_size,
-        )
-
-    with torch.autograd.no_grad():
-        t = model.training
-        model.eval()
-
-        for input, ar_mask, seq_logproba in batches:
-            one_batch_masked_inplace_autoregression(
-                model=model,
-                input=input,
-                ar_mask=ar_mask,
-                seq_logproba=seq_logproba,
-                temperature=temperature,
-                deterministic_synthesis=deterministic_synthesis,
-            )
-
-        model.train(t)
-
-
-######################################################################
-
-
-class QuizMachine:
-    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 reverse_random_half_in_place(self, quizzes):
-        i = torch.rand(quizzes.size(0)) < 0.5
-        if i.any():
-            quizzes[i] = self.reverse_time(quizzes[i])
-
-    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):
-            a = [
-                torch.tensor([self.token_forward]),
-                prompt,
-                torch.tensor([self.token_forward]),
-                answer,
-            ]
-
-            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,
-        device=torch.device("cpu"),
-    ):
-        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.generate_token_sequences(nb_train_samples)
-        self.reverse_random_half_in_place(self.train_w_quizzes)
-        self.train_w_quizzes = self.train_w_quizzes.to(device)
-
-        self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
-        self.reverse_random_half_in_place(self.test_w_quizzes)
-        self.test_w_quizzes = self.test_w_quizzes.to(device)
-
-        self.train_c_quizzes = []
-        self.test_c_quizzes = []
-
-        if result_dir is not None:
-            self.save_quizzes(
-                result_dir,
-                "culture_w_quizzes",
-                self.train_w_quizzes[:72],
-            )
-
-    def save_quizzes(
-        self,
-        result_dir,
-        filename_prefix,
-        quizzes,
-        mistakes=None,
-    ):
-        quizzes = quizzes.clone()
-        n_forward = quizzes[quizzes[:, 0] == self.token_forward]
-        n_backward = quizzes[:, 0] == self.token_backward
-        backward = quizzes[n_backward]
-        assert n_forward.size(0) + backward.size(0) == quizzes.size(0)
-        quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
-
-        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":
-            w_quizzes = self.train_w_quizzes
-            c_quizzes = self.train_c_quizzes
-        else:
-            w_quizzes = self.test_w_quizzes
-            c_quizzes = self.test_c_quizzes
-
-        if len(c_quizzes) > 0:
-            c_quizzes = torch.cat(c_quizzes, dim=0)
-            if c_quizzes.size(0) > w_quizzes.size(0) // 2:
-                i = torch.randperm(c_quizzes.size(0))[: w_quizzes.size(0) // 2]
-                c_quizzes = c_quizzes[i]
-
-            i = torch.randperm(w_quizzes.size(0))[
-                : w_quizzes.size(0) - c_quizzes.size(0)
-            ]
-            w_quizzes = w_quizzes[i]
-
-            self.nb_batch_w_quizzes = w_quizzes.size(0)
-            self.nb_batch_c_quizzes = c_quizzes.size(0)
-
-            input = torch.cat([w_quizzes, c_quizzes], dim=0)
-        else:
-            input = w_quizzes
-            self.nb_batch_w_quizzes = w_quizzes.size(0)
-            self.nb_batch_c_quizzes = 0
-
-        # Shuffle
-        input = input[torch.randperm(input.size(0))]
-
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            yield batch
-
-    def vocabulary_size(self):
-        return self.nb_token_values
-
-    def produce_results(
-        self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
-    ):
-        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)
-
-            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,
-            )
-
-            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
-            )
-
-            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
-                ]
-                _, 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"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)"
-                )
-
-                self.logger(
-                    f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)"
-                )
-
-            return result, correct
-
-        compute_accuracy(self.train_w_quizzes[:nmax], log_prefix="train")
-
-        test_result, test_correct = compute_accuracy(
-            self.test_w_quizzes[:nmax], log_prefix="test"
-        )
-
-        main_test_accuracy = test_correct.sum() / test_correct.size(0)
-        self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
-
-        ##############################
-
-        self.save_quizzes(
-            result_dir,
-            f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
-            quizzes=test_result[:72],
-            mistakes=test_correct[:72] * 2 - 1,
-        )
-
-        return main_test_accuracy
-
-    def renew_w_quizzes(self, nb, for_train=True):
-        input = self.train_w_quizzes if for_train else self.test_w_quizzes
-        nb = min(nb, input.size(0))
-        input[:-nb] = input[nb:].clone()
-        fresh_w_quizzes = self.generate_token_sequences(nb)
-        self.reverse_random_half_in_place(fresh_w_quizzes)
-        input[-nb:] = fresh_w_quizzes.to(self.device)
-
-    def store_c_quizzes(self, new_c_quizzes, for_train=True):
-        if for_train:
-            self.train_c_quizzes.append(new_c_quizzes)
-        else:
-            self.test_c_quizzes.append(new_c_quizzes)
-
-    def compute_correctness(
-        self,
-        c_quizzes,
-        models_for_validation,
-        bidirectional_validation=False,
-        deterministic_validation=True,
-    ):
-        if bidirectional_validation:
-            backward_c_quizzes = self.forward_to_backward(c_quizzes)
-
-        seq_logproba = torch.zeros(
-            c_quizzes.size(0),
-            max([m.id for m in models_for_validation]) + 1,
-            device=self.device,
-        )
-
-        nb_correct = 0
-
-        seq_logproba[...] = 0.0
-
-        for model in models_for_validation:
-            result = c_quizzes.clone()
-
-            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[:, model.id],
-                temperature=1.0,
-                deterministic_synthesis=deterministic_validation,
-                # progress_bar_desc="solving c_quizzes",
-                device=self.device,
-            )
-
-            correct = (c_quizzes == result).long().min(dim=-1).values
-
-            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=backward_result,
-                    ar_mask=ar_mask,
-                    seq_logproba=seq_logproba[:, model.id],
-                    temperature=1.0,
-                    deterministic_synthesis=deterministic_validation,
-                    # progress_bar_desc="solving backward c_quizzes",
-                    device=self.device,
-                )
-
-                backward_correct = (
-                    (backward_c_quizzes == backward_result).long().min(dim=-1).values
-                )
-
-                correct *= backward_correct
-
-            # endif
-
-            nb_correct += correct
-
-        return nb_correct, seq_logproba
-
-    ###############################################################
-
-    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
-        )
-
-        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=self.make_ar_mask(c_quizzes, first=True),
-            seq_logproba=seq_logproba,
-            temperature=temperature,
-            deterministic_synthesis=False,
-            device=self.device,
-        )
-
-        # 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=self.make_ar_mask(c_quizzes),
-            seq_logproba=seq_logproba,
-            temperature=1 / temperature,
-            deterministic_synthesis=False,
-            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=self.make_ar_mask(c_quizzes),
-            seq_logproba=seq_logproba,
-            temperature=1 / temperature,
-            deterministic_synthesis=False,
-            device=self.device,
-        )
-
-        return c_quizzes