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[Перевод] С новым годом: GPT в 500 строках на SQL

В минувшем году все только и говорили об ИИ и о том, как он может всё за вас сделать.

Мне нравится, когда кто-то или что-то работает за меня. Поэтому решил: попрошу-ка ChatGPT написать за меня новогодний пост:

"Эй, ChatGPT. А ты можешь реализовать большую языковую модель на SQL?"
"Нет, SQL не подходит для реализации больших языковых моделей. Язык SQL предназначен для выполнения запросов к данным, хранящимся в РСУБД и для управления этими запросами. Для реализации большой языковой модели требуются сложные алгоритмы, обширные датасеты, а зачастую – и фреймворки для глубокого обучения. SQL всеми этими возможностями не обладает."

Что ж, лишний раз убеждаешься, что, если хочешь что-то сделать хорошо – сделай это сам.

Давайте же воодушевимся этим оптимистическим планом и реализуем большую языковую модель на языке SQL.

Теория

Готовя этот пост, я вдохновлялся чудесной статьёй GPT in 60 Lines of NumPy, которую написал Джей Моди. В этой статье объяснено внутреннее устройство модели GPT, причём, это сделано гораздо лучше, чем могло бы получиться у меня. Поэтому для порядка вкратце напомню контекст.

Что представляет собой генеративная большая языковая модель с технической точки зрения?

Генеративная LLM – это функция. Она принимает на ввод текстовую последовательность символов (в терминологии ИИ она называется «промпт») и возвращает массив строк и чисел. Вот какова сигнатура этой функции:

llm(prompt: str) -> list[tuple[str, float]]

Это детерминированная функция. У неё под капотом выполняется серьёзная математика, но вся эта математика жёстко вшита в систему. Если вы будете многократно сообщать этой функции один и тот же ввод, то всякий раз будете получать от неё один и тот же вывод.

Тем, кому доводилось пользоваться ChatGPT и подобными продуктами, это может показаться удивительным, ведь система может давать разные ответы на один и тот же вопрос. Да, всё верно. Чуть ниже объясню, как это устроено.

Что за значения возвращает эта функция?

Нечто подобное:

	llm("I wish you a happy New")
 
0       (' Year', 0.967553)
1       (' Years', 0.018199688)
2       (' year', 0.003573329)
3       (' York', 0.003114716)
4       (' New', 0.0009022804)
…
50252   (' carbohyd', 2.3950911e-15)
50253   (' volunte', 2.2590102e-15)
50254   ('pmwiki', 1.369229e-15)
50255   (' proport', 1.1198108e-15)
50256   (' cumbers', 7.568147e-17)

Она возвращает массив кортежей. Каждый кортеж состоит из слова (скорее, строки) и числа. Число — это вероятность, с которой данное слово может оказаться промпте на следующей позиции. Модель «думает», что за фразой «I wish you a happy New…» («Поздравляю тебя с Новым…») с вероятностью 96,7% последует строка «Year» (…годом) – и так далее.

Слово «думает» выше поставлено в кавычках, так как, естественно, модель ни о чём не думает. Она механически возвращает массивы слов и чисел в соответствии с некоторой жёстко прописанной внутренней логикой.

Если эта система такая топорная и детерминированная, как же ей удаётся генерировать разные тексты?

Большие языковые модели используются в текстовых приложениях (чатботы, генераторы контента, кодинг-ассистенты, т.д.). Эти приложения раз за разом вызывают модель и выбирают подсказанное ею слово (с некоторой степенью произвольности). Последнее предложенное слово добавляется к промпту, и модель вызывается снова. Этот цикл продолжается до тех пор, пока не будет сгенерировано достаточное количество слов.

Накопленная таким образом последовательность слов выглядит как текст на естественном языке, с полноценной грамматикой, синтаксисом и даже мнимыми признаками интеллекта и логики. В данном отношении языковые модели подобны марковским цепям, работающим по такому же принципу.

На внутреннем уровне большая языковая модель устроена так, что каждое последующее предлагаемое слово естественным образом продолжало промпт, с учётом имеющейся грамматики, семантики и тональности. Оснастить функцию такой логикой стало возможно благодаря целой серии научных прорывов (и каторжной работе программистов). Удалось разработать целое семейство алгоритмов под названием GPT или «генеративные предобученные трансформеры».

Что такое "генеративный предобученный трансформер"

«Генеративный» означает, что алгоритм генерирует текст (рекурсивно достраивая продолжения к промпту как было показано выше).

«Трансформер» означает, что алгоритм использует конкретный тип нейронной сети. Сеть-трансформер была разработана компанией Google и описана в этой статье.

Термин «предобученный» требует небольшого исторического экскурса. Изначально считалось, что способность модели достраивать текст – это всего лишь предпосылка для получения более глубоких логических выводов в рамках специализированных задач (нужно улавливать логическую связь между фразами), классификации (например, угадать, сколько звёзд у отеля, проанализировав отзывы постояльцев), машинного перевода и т.д. Считалось, что две эти части алгоритма требуется обучать отдельно, и языковая часть позиционировалась как предобучение, подготовка к последующему обучению решать реальную задачу.

Как сформулировано в исходной статье по GPT:

Продемонстрировано, что возможно добиться больших успехов при решении таких задач путём генеративного предобучения языковой модели на разнородном корпусе неразмеченного текста с последующей тонкой настройкой различных параметров для различных слоёв сети (discriminative fine-tuning) в контексте каждой конкретной задачи.

Такие представления сохранялись до тех пор, пока не стало понятно, что для достаточно большой модели второй шаг зачастую излишен. Модель-трансформер, обученная исключительно генерации текстов, оказалась в состоянии без дополнительного обучения следовать человеческим инструкциям, содержащимся в этих текстах. Таким образом, не требовалось никакой дополнительной тонкой настройки.

Итак, с этими вопросами мы разобрались. Теперь давайте сосредоточимся на реализации.

Генерация

Вот что происходит, если мы пытаемся сгенерировать текст на основе промпта при помощи GPT2:

def generate(prompt: str) -> str:
  # Преобразует строку в список токенов.
  tokens = tokenize(prompt) # tokenize(prompt: str) -> list[int]
 
  while True:
 
    # Выполняет алгоритм.
    # Возвращает вероятностные значения для токенов: список из 50257 чисел с плавающей точкой, в сумме дающих 1.
    candidates = gpt2(tokens) # gpt2(tokens: list[int]) -> list[float]
 
    # Выбирает следующий токен из списка предложенных 
    next_token = select_next_token(candidates)
    # select_next_token(candidates: list[float]) -> int
 
    # Прикрепляет его к списку токенов
    tokens.append(next_token)
 
    # Решаем, хотим ли мы прекратить генерацию.
    # Здесь может быть счётчик токенов, задержка, стоп-слово или что-то ещё.
    if should_stop_generating():
      break
 
  # Преобразует список токенов в строку
  completion = detokenize(tokens) # detokenize(tokens: list[int]) -> str
  return completion

Давайте реализуем все эти элементы один за другим на языке SQL.

Токенизатор

Прежде, чем текст можно будет скормить нейронке, его требуется преобразовать в список чисел. Конечно, нас это не удивляет: именно такие задачи решаются при помощи текстовых кодировок вроде Unicode. Но обычный Unicode не слишком хорошо работает с нейронными сетями.

В сущности, нейронные сети построены на обширном перемножении матриц, и вся прогностическая сила нейронных сетей заключена в коэффициентах этих матриц. Некоторые из таких матриц содержат по строке на каждое из возможных значений в «алфавите», в других предусмотрено по строке на «символ».

В этом контексте смысл терминов «алфавит» и «символ» требуется пояснить. В Unicode длина «алфавита» составляет 149186 символов (именно столько отдельных точек Unicode существует на момент подготовки этой статьи), а в качестве «символа» может выступать, например: ﷽ (да, это одиночная точка Unicode, номер 65021, она соответствует целой фразе на арабском языке, особенно важной для мусульман). Отмечу, что эту фразу можно было бы записать и обычной арабской вязью. Таким образом, одному тексту может соответствовать несколько вариантов кодировки.

Давайте разберём эту ситуацию на примере слова "PostgreSQL". Если бы мы попытались закодировать её (преобразовать в массив чисел) при помощи Unicode, то у нас получилось бы 10 чисел, которые могли бы находиться в диапазоне от 1 до 149186. Таким образом, в нашей нейронной сети требовалось бы хранить матрицу из 149186 строк и совершить ряд вычислений над 10 строками из этой матрицы. Некоторые из этих строк (соответствующие буквам латиницы) использовались бы особенно часто, и в них было бы заключено много информации. Другие символы, например, смайлик какашки и таинственные символы из мёртвых языков едва ли вообще хоть раз использовались, но и на их хранение требуется место.

Естественно, мы хотим, чтобы оба эти числа, и длина «алфавита», и количество «символов» оставались как можно меньше. В идеале «символы» нашего алфавита должны быть распределены равномерно, а ещё мы по-прежнему хотим, чтобы наша кодировка не уступала по мощности Unicode.

Интуитивно понятно, что это можно сделать, присвоив уникальные номера тем последовательностям слов, которые часто встречаются в обрабатываемых нами текстах. В Unicode вышеупомянутая религиозная арабская фраза может быть закодирована либо как один кодовый символ, либо буква за буквой. Поскольку мы выкатываем нашу собственную кодировку, точно так же мы можем поступать с теми словами и фразами, которые важны в рамках нашей модели (например, они часто встречаются в текстах).

Например, можно было бы выделить конкретные числа для строк "Post", "greSQL" и "ing". Таким образом, оба слова "PostgreSQL" и "Posting" в нашем представлении будут иметь длину по 2. Конечно же, мы стремимся закреплять отдельные кодовые символы для более конкретных последовательностей и отдельных байт. Даже натыкаясь на тарабарщину или на иноязычный текст, такую информацию всё равно удастся закодировать, хотя, на это и потребуется больше времени.

В GPT2 используется вариант алгоритма под названием кодирование диадами (Byte Pair Encoding), предназначенный специально для этой цели. Токенизатор GPT2 использует словарь из 50257 кодовых точек (в терминологии ИИ — «токенов»), которые соответствуют различным последовательностям байт в UTF-8 (плюс отдельный токен, означающий «конец текста»).

Этот словарь мы собрали путём статистического анализа, выполненного таким образом:

1. Начинаем с простой кодировки из 256 токенов: по токену за байт.

2. Берём большой корпус текстов (предпочтительно именно тот, на котором будет обучаться модель).

3. Кодируем его.

4. Вычисляем, какая пара токенов встречается чаще всего. Пусть это будет 0x20 0x74 (пробел, за которым следует строчная "t").

5. Присваиваем следующее доступное значение (257) этой паре байт.

6. Повторяем шаги 3-5, теперь обращая внимание на последовательности байт. Если последовательность байт можно закодировать сложным токеном, то используем сложный токен. При явных неоднозначностях (например, "abc" может в одном случае быть закодировано как "a" + "bc" или как "ab" + "c"), то пользуйтесь той, номер которой меньше (это означает, что она была добавлена раньше и, следовательно, встречается чаще). Это нужно делать рекурсивно, пока все последовательности, которые можно сложить в один токен, будут сложены в один токен.

7. Выполним такое «складывание» 50000 раз подряд.

Число 50000 выбрано разработчиками более или менее произвольно. В других моделях количество токенов также держится в подобном диапазоне (от 30k до 100k).

На каждой итерации данного алгоритма в словарь будет добавляться новый токен, получаемый конкатенацией двух предыдущих. В конечном итоге получим 50256 токенов. Добавляем токен с фиксированным номером, означающий «конец текста» - и всё готово.

В версии алгоритма BTE для GPT2 есть ещё один уровень кодировки: в словаре токенов эти токены отображаются на строки, а не на массивы байт. Отображение с байт на строковые символы определяется в этой функции. Тот словарь, который она производит, находится в таблице encoder.

Рассмотрим, как реализовать токенизатор на SQL.

Данный токенизатор – неотъемлемая часть GPT2, и словарь токенов можно скачать с сайта OpenAI, равно как и оставшуюся часть модели. Нам понадобится импортировать её в таблицу tokenizer. В самом конце этого поста вы найдёте ссылку на репозиторий с кодом. Этот код автоматизирует заполнение таблиц базы данных, необходимых для работы с моделью.

В рамках рекурсивного подхода CTE разбиваем слово на токены (начиная с единичных байт) и объединяем наилучшие смежные пары, пока больше материала для объединения не останется. Само объединение происходит по принципу вложенного рекурсивного CTE.

Для примера я выбрал слово «Mississippilessly». Каждая запись в наборе результатов соответствует наилучшей найденной к данному моменту паре, которую можно «сложить», а также прогресс в обработке запроса.

WITH    RECURSIVE
        bpe AS
        (
        SELECT  (n + 1)::BIGINT AS position, character, TRUE AS continue, 1 AS step,
                NULL::INT AS token, NULL::TEXT AS combined
        FROM    CONVERT_TO('Mississippilessly', 'UTF-8') AS bytes
        CROSS JOIN LATERAL
                GENERATE_SERIES(0, LENGTH(bytes) - 1) AS n
        JOIN    encoder
        ON      byte = GET_BYTE(bytes, n)
        UNION ALL
        (
        WITH    RECURSIVE
                base AS
                (
                SELECT  *
                FROM    bpe
                WHERE   continue
                ),
                bn AS
                (
                SELECT  ROW_NUMBER() OVER (ORDER BY position) AS position,
                        continue,
                        character,
                        character || LEAD(character) OVER (ORDER BY position) AS cluster
                FROM    base
                ),
                top_rank AS
                (
                SELECT  tokenizer.*
                FROM    bn
                CROSS JOIN LATERAL
                        (
                        SELECT  *
                        FROM    tokenizer
                        WHERE   tokenizer.cluster = bn.cluster
                        LIMIT   1
                        ) tokenizer
                ORDER BY
                        token
                LIMIT   1
                ),
                breaks AS
                (
                SELECT  0::BIGINT AS position, 1 AS length
                UNION ALL
                SELECT  bn.position,
                        CASE WHEN token IS NULL THEN 1 ELSE 2 END
                FROM    breaks
                JOIN    bn
                ON      bn.position = breaks.position + length
                LEFT JOIN
                        top_rank
                USING   (cluster)
                )
        SELECT  position, character, token IS NOT NULL,
                (SELECT step + 1 FROM base LIMIT 1), token, top_rank.cluster
        FROM    breaks
        LEFT JOIN
                top_rank
        ON      1 = 1
        CROSS JOIN LATERAL
                (
                SELECT  STRING_AGG(character, '' ORDER BY position) AS character
                FROM    bn
                WHERE   bn.position >= breaks.position
                        AND bn.position < breaks.position + length
                ) bn
        WHERE   position > 0
        )
        )
SELECT  step, MAX(token) AS token, MAX(combined) AS combined, ARRAY_AGG(character ORDER BY position)
FROM    bpe
WHERE   continue
GROUP BY
        step
ORDER BY
        step
9afef708330d99752ee4da59fe3fceaf.png

На каждом шаге алгоритм BPE находит пару токенов, наиболее подходящую для слияния, и объединяет их (здесь в выводе вы видите такую слитую пару и ее ранг). При помощи такой процедуры размер пространства токенов можно сократить с 150k, присущих Unicode, до 50k, а количество токенов в данном конкретном слове с 17 до 5. В обоих случаях это отличное улучшение.

Когда приходится работать с множеством слов, токенизатор сначала разбивает текст на отдельные слова при помощи этого регулярного выражения и объединяет токены в каждом слове отдельно. К сожалению, при работе с PostgreSQL свойства символов Unicode не поддерживаются на уровне регулярных выражений, поэтому мне пришлось немного отладить систему (возможно, в процессе я угробил нормальную поддержку Unicode). Вот как результат выглядит в SQL:

WITH    input AS
        (
        SELECT  'PostgreSQL is great' AS prompt
        ),
        clusters AS
        (
        SELECT  part_position, bpe.*
        FROM    input
        CROSS JOIN LATERAL
                REGEXP_MATCHES(prompt, '''s|''t|''re|''ve|''m|''ll|''d| ?\w+| ?\d+| ?[^\s\w\d]+|\s+(?!\S)|\s+', 'g') WITH ORDINALITY AS rm (part, part_position)
        CROSS JOIN LATERAL
                (
                WITH    RECURSIVE
                        bpe AS
                        (
                        SELECT  (n + 1)::BIGINT AS position, character, TRUE AS continue
                        FROM    CONVERT_TO(part[1], 'UTF-8') AS bytes
                        CROSS JOIN LATERAL
                                GENERATE_SERIES(0, LENGTH(bytes) - 1) AS n
                        JOIN    encoder
                        ON      byte = GET_BYTE(bytes, n)
                        UNION ALL
                        (
                        WITH    RECURSIVE
                                base AS
                                (
                                SELECT  *
                                FROM    bpe
                                WHERE   continue
                                ),
                                bn AS
                                (
                                SELECT  ROW_NUMBER() OVER (ORDER BY position) AS position,
                                        continue,
                                        character,
                                        character || LEAD(character) OVER (ORDER BY position) AS cluster
                                FROM    base
                                ),
                                top_rank AS
                                (
                                SELECT  tokenizer.*
                                FROM    bn
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  *
                                        FROM    tokenizer
                                        WHERE   tokenizer.cluster = bn.cluster
                                        LIMIT   1
                                        ) tokenizer
                                ORDER BY
                                        token
                                LIMIT   1
                                ),
                                breaks AS
                                (
                                SELECT  0::BIGINT AS position, 1 AS length
                                UNION ALL
                                SELECT  bn.position,
                                        CASE WHEN token IS NULL THEN 1 ELSE 2 END
                                FROM    breaks
                                JOIN    bn
                                ON      bn.position = breaks.position + length
                                LEFT JOIN
                                        top_rank
                                USING   (cluster)
                                )
                        SELECT  position, character, token IS NOT NULL
                        FROM    breaks
                        LEFT JOIN
                                top_rank
                        ON      1 = 1
                        CROSS JOIN LATERAL
                                (
                                SELECT  STRING_AGG(character, '' ORDER BY position) AS character
                                FROM    bn
                                WHERE   bn.position >= breaks.position
                                        AND bn.position < breaks.position + length
                                ) bn
                        WHERE   position > 0
                        )
                        )
                SELECT  position, character AS cluster
                FROM    bpe
                WHERE   NOT continue
                ) bpe
        ),
        tokens AS
        (
        SELECT  token, cluster
        FROM    clusters
        JOIN    tokenizer
        USING   (cluster)
        )
SELECT  *
FROM    tokens
05c9a2cc7365cf2c4798ffb6c884c433.png

Странный символ Ġ соответствует пробелу.

Этот запрос токенизирует промпт и преобразует его в массив чисел. Вот промпт и готов к долгому пути сквозь слои модели.

Векторные представления

Токены - это представления единиц естественного языка (в целом, примерно 0,75 слова на токен), поэтому любая модель, претендующая на успешное автозавершение текстов, должна каким-то образом кодировать отношения между этими единицами. Даже в изоляции части речи обладают наборами ортогональных свойств.

Возьмём, к примеру, слово «subpoena» (повестка) – оказывается, в токенизаторе GPT2 на него отведён целый токен. С точки зрения английского языка – это существительное? Да, бесспорно. А глагол? Да, и глаголом может быть. А прилагательным? Уже не очень, но, в принципе, контекст придумать можно. Это канцелярит? Чёрт возьми, ещё какой. И так далее.

Все эти свойства ортогональны, то есть, не зависят друг от друга. Слово может быть официозным существительным, но не глаголом и не прилагательным. В английском возможны любые сочетания этих признаков.

Сущности с ортогональными свойствами лучше всего кодируются в виде векторных представлений. Пусть у нас будет не единственное свойство (например, номер), а много. При желании мы можем крутить ими как захотим. Например, фраза «В судебном решении, которое процитировал адвокат, фигурирует…» с высокой вероятностью продолжается каким-то существительным, которое, не менее вероятно, стилистически относится к канцеляриту. Нас совершенно не интересует, есть ли у этого слова маргинальная семантика «глагол», либо «прилагательное», либо «цветок».

В математике отображение сравнительно узких значений на более широкие пространства (например, ID токенов на векторы) называется векторным представлением. Именно этим мы здесь и занимаемся.

Как решить, какие именно свойства окажутся представлены в этих векторах? А мы не будем. Мы просто предоставим достаточно векторного пространства для каждого токена и понадеемся, что модель на этапе обучения получит достаточно информации, чтобы заполнить все эти измерения осмысленным содержимым. В GPT2 под векторы отводится 768 измерений. Заранее (и, на самом деле, даже в ретроспективе) не известно, какое свойство слова будет закодировано, например, в измерении 247. Определённо, что-то закодировано там будет, но что именно – сказать сложно.

Какие свойства каждого из токенов мы внедрим в векторное пространство? Любые, которые помогут нам спрогнозировать, каков будет следующий токен.

ID токена? Естественно. Разные токены означают разные вещи.

Позиция токена в тексте? Да, пожалуйста. «Сине-фиолетовый» и «фиолетово-синий» — не одно и то же.

Взаимные отношения токенов? Естественно! Это, пожалуй, наиважнейшая часть задачи, и блок внимания (Attention) в архитектуре трансформера как раз впервые позволил решить эту часть задачи правильно.

Токены и позиции легко поддаются векторному представлению. Допустим, есть фраза «PostgreSQL is great», о которой заранее известно, что она отображается на четыре токена: [6307, 47701, 318, 1049].

Среди прочих параметров GPT2 есть две матрицы, называющиеся WTE (векторное представление лексических токенов) и WPE (векторное представление позиций слов). Как понятно из названия, в первом хранятся векторные представления токенов, а во втором – векторные представления их позиций. Конкретные значения, которыми окажутся заполнены эти представления, набираются в результате обучения модели, в данном случае GPT2. Насколько известно, в таблицах баз данных wte и wpe есть константы. Размер

WTE равен 50257×768, а размер WPE равен 1024×768. Второе означает, что в GPT2 можно использовать промпт, состоящий не более чем из 1024 токенов. Если записать в промпт больше токенов, мы просто не сможем вытянуть для токенов сверх 1024 их позиционные представления. Это архитектурный аспект (в терминологии ИИ – «гиперпараметр») модели, устанавливаемый на этапе её проектирования, и обучением его изменить невозможно. Когда говорят о «контекстном окне» большой языковой модели, имеют в виду именно это число.

Токен 6307 стоит у нас на месте 0, токен 47701 на месте 1, токен 318 на месте 2, а токен 1049 на месте 3. Для каждого из этих токенов и их позиций у нас по два вектора: один из WTE и один из WPE. Их требуется сложить. В результате получатся четыре вектора, которые послужат вводом для следующей части алгоритма: так работает нейронная сеть прямого распространения, в которой действует механизм внимания.

Для работы с SQL мы воспользуемся pgvector, это расширение PostgreSQL.

Небольшая оговорка: код для этого поста я пишу на обычном SQL, иногда вставляю чистые SQL-функции в качестве вспомогательных элементов. В рамках этого поста осуществить такое не составит труда, равно как определить векторные операции над массивами. Это делается за счёт небольшого снижения производительности (и уже было реализовано в версии 1 и работало, пусть и медленно). С распространением ИИ и ростом влияния векторных баз данных pgvector или эквивалентный механизм определённо укоренится в ядре PostgreSQL в течение двух-трёх релизов. Я просто решил оседлать волну, которая принесёт меня в будущее.

Вот как это делается в SQL:

WITH    embeddings AS
        (
        SELECT  place, values
        FROM    UNNEST(ARRAY[6307, 47701, 318, 1049]) WITH ORDINALITY AS tokens (token, ordinality)
        CROSS JOIN LATERAL
                (
                SELECT  ordinality - 1 AS place
                ) o
        CROSS JOIN LATERAL
                (
                SELECT  wte.values + wpe.values AS values
                FROM    wte
                CROSS JOIN
                        wpe
                WHERE   wte.token = tokens.token
                        AND wpe.place = o.place
                ) embedding
        )
SELECT  place, (values::REAL[])[0:5]
FROM    embeddings
2eb33d41dc02c972da3f448c339b4366.png

(Чтобы вывод был короче, в этом запросе показаны только первые 5 измерений для каждого вектора).

Внимание

Та часть трансформерной архитектуры, благодаря которой она действительно феерит, называется «механизм внутреннего внимания» (self-attention). Он был впервые описан в 2017 году в статье «Attention is all you need» под авторством Васмани и др. Пожалуй, это наиизвестнейшая статья об искусственном интеллекте, и с тех пор её название превратилось в болванку (клише для именования других статей).

Итак, вероятно, к настоящему моменту у нас уже есть несколько векторов, в которых закодированы определённые синтаксические и семантические свойства слов, содержащихся в нашем промпте. Нам каким-то образом нужно донести эти свойства до последнего вектора. Сейчас будет небольшой спойлер: именно в последнем векторе будет храниться представление того слова, которым продолжится фраза.

Во фразе вида a «I looked at the violet and saw that it was not the usual …» (я взглянул на лиловое пятно и заметил, что это не обычный…), на месте многоточия должно быть что-то, увиденное вами (об этом свидетельствует слово «saw»), обладающее свойством «лиловости» и при этом «необычное» (сочетаем токены «не» и «обычный», тем самым меняя знак измерения «обычность» на противоположный). Можно привести такую реалистичную аналогию: человек читает книгу на иностранном языке, о котором имеет некоторое представление, но свободно им не владеет. В таком случае человеку придётся аккуратно сплетать каждое слово со следующим, и, если он не обратит внимания на ключевую часть фразы, то всю фразу поймёт неправильно.

Чтобы обеспечить такой перенос значения от одного токена к другому, требуется разрешить векторам всех токенов влиять друг на друга. Так, возвращаясь к английскому: если мы хотим наполнить слово «it» конкретной семантикой, то какая часть семантики должна поступить от предыдущих векторов в промпте, а какая – остаться от самого слова «it»?

В данной модели эта задача решается при помощи 12 наборов матриц, именуемых Q (запрос), K (ключ) и V (значение). В каждой из них по 64 столбца. Их мы получаем из векторных представлений через 768×2304-мерное линейное преобразование c_attn, веса и смещения которого хранятся в таблицах c_attn_w и c_attn_b.

Результатом c_attn является матрица со n_token строк и 2304 столбцами (3×12×64). Она состоит из 12 Q-матриц, 12 K-матриц и 12 V-матриц, упорядоченных горизонтально именно в таком порядке.

Каждый набор, состоящий из Q, K и V, называется «головой». С их помощью выполняется шаг под названием «многоголовое условное внутреннее внимание», на котором вычисляется функция внимания.

Она вычисляется по этой формуле:

b7a3f0ee8816d3c95951491a32765034.png

где softmax – это функция нормализации весов. Она определяется так:

f63cb8d1f794504cc4a9512b4ba3ef4b.png

M - это матрица-константа, именуемая «условной маской». Она определяется так:

6663f6f6cf9689a78b9b50a827fe4b53.png

Функция Softmax обнуляет все отрицательные бесконечности.

Почему необходимо использовать маски?

В промпте из предыдущего примера было 4 токена, и модель первым делом вычисляла 4 векторных представления для 4 этих токенов. По мере дальнейшей работы модели над этими векторами будет выполнено много вычислений, но в основном они пойдут независимо и параллельно. Изменения в одном векторе не затронут другие векторы, как если бы иных векторов не существовало. Векторы влияют друг на друга только в пределах блока внутреннего внимания.

Как только модель справится с математикой, претенденты на роль следующего токена будут выбираться исключительно исходя из последнего векторного представления. Вся информация должна направляться именно к этому последнему вектору, а не прочь от него. Временные значения последнего вектора не должны влиять на временные значения предыдущих векторов, использовавшихся в ходе прямого распространения по данной модели.

Именно поэтому мы «маскируем» позднейшие векторы, чтобы они не влияли на более ранние через этот конкретный канал. В этом и заключается условность «многоголового условного внутреннего внимания».

Почему матрицы называются "запрос", "ключ" и "значение"?

Честно говоря, даже не уверен, что это хорошая аналогия. Но, тем не менее, попытаюсь объяснить, какова логика таких названий.

Как правило, в машинном обучении при вычислениях не должны использоваться циклы переменной длины или ветвление инструкций. Все задачи должны решаться компоновкой простых аналитических функций (сложение, умножение, возведение в степень, логарифмы и тригонометрия). Так обеспечивается обратное распространение для эффективной реализации которого требуются такие технологии, как автоматическое дифференцирование.

Математическая модель хранилища ключей и значений заключена в таком выражении:

de4db9304ce9fe7850fa07f4aa82a4db.png

Но это не гладкая дифференцируемая функция, и с обратным распространением она работать не будет. Чтобы формула работала, необходимо получить гладкую функцию, которая была бы близка к v, когда k близка к q, и близка к 0 в иных случаях.

Для этой цели отлично подходит Гауссово распределение («колоколообразная кривая») "bell curve"), отмасштабированная к v с ожиданием k и достаточно небольшим стандартным отклонением:

cb7ce72dab8bcafebcd14cf861b908a2.png

где σ - произвольный параметр, определяющий, насколько «остра» колоколообразная кривая.

Если взять в векторном пространстве с достаточно большим количеством измерений фиксированный вектор K и несколько векторов Q, которые случайным и равномерным образом отклоняются от K в каждом из измерений, то их скалярные произведения сложатся именно в колоколообразную кривую. Соответственно, в векторном пространстве «дифференцируемое хранилище ключей и значений» можно смоделировать при помощи выражения

673f867f6aacaf8ba018b96d5399f9e2.png

того самого, которым мы пользовались в нашей функции внимания.

Опять же, эта аналогия немного натянута. Лучше не слишком зацикливаться на концептуальной составляющей внимания, то есть, на потоке операций, хеш-таблицах и пр. Можете считать, что они просто вдохновили меня на описанный математический фокус, который я догадался протестировать – и на практике он оказался весьма хорош.

Проиллюстрирую этот шаг:

WITH    embeddings AS
        (
        SELECT  place, values
        FROM    UNNEST(ARRAY[6307, 47701, 318, 1049]) WITH ORDINALITY AS tokens (token, ordinality)
        CROSS JOIN LATERAL
                (
                SELECT  ordinality - 1 AS place
                ) o
        CROSS JOIN LATERAL
                (
                SELECT  wte.values + wpe.values AS values
                FROM    wte
                CROSS JOIN
                        wpe
                WHERE   wte.token = tokens.token
                        AND wpe.place = o.place
                ) embedding
        ),
        c_attn_w AS
        (
        SELECT  *
        FROM    c_attn_w
        WHERE   block = 0
        ),
        c_attn_b AS
        (
        SELECT  *
        FROM    c_attn_b
        WHERE   block = 0
        ),
        ln_1_g AS
        (
        SELECT  *
        FROM    ln_1_g
        WHERE   block = 0
        ),
        ln_1_b AS
        (
        SELECT  *
        FROM    ln_1_b
        WHERE   block = 0
        ),
        mha_norm AS
        (
        SELECT  place, mm.values + c_attn_b.values AS values
        FROM    (
                SELECT  place, ARRAY_AGG(INNER_PRODUCT(c_attn_w.values, layer_norm.values) ORDER BY y)::VECTOR(2304) AS values
                FROM    (
                        SELECT  place, agg.values * ln_1_g.values + ln_1_b.values AS values
                        FROM    (
                                SELECT  place, norm.values
                                FROM    embeddings
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  AVG(value) AS mean,
                                                VAR_POP(value) AS variance
                                        FROM    UNNEST(values::REAL[]) value
                                        ) agg
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                        ) norm
                                ) agg
                        CROSS JOIN
                                ln_1_b
                        CROSS JOIN
                                ln_1_g
                        ) layer_norm
                CROSS JOIN
                        c_attn_w
                GROUP BY
                        place
                ) mm
        CROSS JOIN
                c_attn_b
        ),
        head AS
        (
        SELECT  place,
                (values::REAL[])[1:64]::VECTOR(64) AS q,
                (values::REAL[])[1 + 768:64 + 768]::VECTOR(64) AS k,
                (values::REAL[])[1 + 1536:64 + 1536]::VECTOR(64) AS v
        FROM    mha_norm
        ),
        sm_input AS
        (
        SELECT  h1.place AS x, h2.place AS y, INNER_PRODUCT(h1.q, h2.k) / 8 + CASE WHEN h2.place > h1.place THEN -1E10 ELSE 0 END AS value
        FROM    head h1
        CROSS JOIN
                head h2
        ),
        sm_diff AS
        (
        SELECT  x, y, value - MAX(value) OVER (PARTITION BY x) AS diff
        FROM    sm_input
        ),
        sm_exp AS
        (
        SELECT  x, y, CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
        FROM    sm_diff
        ),
        softmax AS
        (
        SELECT  x, y AS place, e / SUM(e) OVER (PARTITION BY x) AS value
        FROM    sm_exp
        ),
        attention AS
        (
        SELECT  place, (ARRAY_AGG(value ORDER BY ordinality))[:3] AS values
        FROM    (
                SELECT  x AS place, SUM(ARRAY_FILL(softmax.value, ARRAY[64])::VECTOR(64) * head.v) AS values
                FROM    softmax
                JOIN    head
                USING   (place)
                GROUP BY
                        x
                ) q
        CROSS JOIN LATERAL
                UNNEST(values::REAL[]) WITH ORDINALITY v (value, ordinality)
        GROUP BY
                place
        )
SELECT  place,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((q::REAL[])[:3]) AS n) AS q,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((k::REAL[])[:3]) AS n) AS k,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((v::REAL[])[:3]) AS n) AS v,
        matrix,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((values::REAL[])[:3]) AS n) AS attention
FROM    head
JOIN    attention
USING   (place)
JOIN    (
        SELECT  x AS place, STRING_AGG(CASE WHEN value > 0 THEN TO_CHAR(value, '0.00') ELSE '    0' END, ' ' ORDER BY place) AS matrix
        FROM    softmax
        GROUP BY
                x
        ) softmax_grouped
USING   (place)
6f968ec80257c3b21a63e633a5c9d13f.png

Вот что мы сделали:

1. Прежде, чем вычислять функцию внимания, мы нормализовали векторы при помощи линейного преобразования R’ = RГ1 + B1. Матрица Г1 и вектор B1называются, соответственно, «масштабирование» и «сдвиг». Это параметры, изученные моделью, которые хранятся в таблицах ln_1_g и ln_1_b

2. Мы показываем только первую голову первого слоя, используемого в этом алгоритме. После того, как умножим векторы на изученные коэффициенты из c_attn_w и c_attn_b («вес» и «смещение»), порежем полученные в результате 2304-мерные векторы. Возьмём 64-мерные векторы, начинающиеся в позициях 0, 768 и 1536. Они соответствуют векторам Q, K и V первой головы.

3. EXP в PostgreSQL плохо работает с очень малыми числами, и именно поэтому мы прибегаем к нулю, если EXP получает аргумент менее -745,13.

4. Для каждого вектора мы показываем только первые три элемента. Марицу внимания показываем полностью.

Итак, значение первого вектора копируется в вывод как есть (так мы поступаем с первым значением на каждом слое алгоритма). Это означает, что, когда модель уже обучена, результирующее векторное представление первого токена будет определяться только значением первого токена. В принципе, на этапе рекурсивного вывода, пока токены только добавляются в промпт, по сравнению с предыдущей итерацией изменится лишь самое последнее векторное представление в выводе. Именно поэтому применяется условная маска.

Заглянем немного вперёд: блок внимания – это единственное место во всём алгоритме, где токены могут влиять друг на друга на этапе прямого прохода. Поскольку на данном этапе мы не позволяем позднейшим токенам влиять на более раннее (и отключили такую возможность), все вычисления, проделанные над более ранними токенами, можно повторно использовать на разных итерациях прямого прохода по модели.

Напомню, модель при работе добавляет токены в промпт. Если исходный (токенизированный) промпт – это «Post greSQL Ġis Ġgreat», а следующий, например, будет «Post greSQL Ġis Ġgreat Ġfor», то все результаты вычислений, проделанных над первыми четырьмя токенами, можно будет повторно использовать и в новом промпте. Они никогда не изменятся, независимо от того, что ещё будет прикреплено к этому промпту.

В статье Джея Муди, которую я привёл для примера, этот аспект не задействуется (равно как и в моей статье – для простоты), но в оригинальной реализации GPT2 он задействован.

Закончив работу со всеми головами, получаем 12 матриц. Каждая из них будет по 64 столбца в ширину и по n_tokens строк в высоту. Чтобы вновь отобразить её на размерность векторных представлений (768), нам просто нужно составить эти матрицы горизонтально.

Последний этап обработки многоголового внимания связан с проецированием значений путём линейного преобразования в том же самом измерении. Соответствующие веса и смещения хранятся в таблицах c_proj_w и c_proj_b.

Ниже приведён полный код, описывающий реализацию многоголового внимания в первом слое:

WITH    embeddings AS
        (
        SELECT  place, values
        FROM    UNNEST(ARRAY[6307, 47701, 318, 1049]) WITH ORDINALITY AS tokens (token, ordinality)
        CROSS JOIN LATERAL
                (
                SELECT  ordinality - 1 AS place
                ) o
        CROSS JOIN LATERAL
                (
                SELECT  wte.values + wpe.values AS values
                FROM    wte
                CROSS JOIN
                        wpe
                WHERE   wte.token = tokens.token
                        AND wpe.place = o.place
                ) embedding
        ),
        c_proj_w AS
        (
        SELECT  *
        FROM    c_proj_w
        WHERE   block = 0
        ),
        c_proj_b AS
        (
        SELECT  *
        FROM    c_proj_b
        WHERE   block = 0
        ),
        mlp_c_fc_w AS
        (
        SELECT  *
        FROM    mlp_c_fc_w
        WHERE   block = 0
        ),
        mlp_c_fc_b AS
        (
        SELECT  *
        FROM    mlp_c_fc_b
        WHERE   block = 0
        ),
        mlp_c_proj_w AS
        (
        SELECT  *
        FROM    mlp_c_proj_w
        WHERE   block = 0
        ),
        mlp_c_proj_b AS
        (
        SELECT  *
        FROM    mlp_c_proj_b
        WHERE   block = 0
        ),
        c_attn_w AS
        (
        SELECT  *
        FROM    c_attn_w
        WHERE   block = 0
        ),
        c_attn_b AS
        (
        SELECT  *
        FROM    c_attn_b
        WHERE   block = 0
        ),
        ln_1_g AS
        (
        SELECT  *
        FROM    ln_1_g
        WHERE   block = 0
        ),
        ln_1_b AS
        (
        SELECT  *
        FROM    ln_1_b
        WHERE   block = 0
        ),
        mha_norm AS
        (
        SELECT  place, mm.values + c_attn_b.values AS values
        FROM    (
                SELECT  place, ARRAY_AGG(INNER_PRODUCT(c_attn_w.values, layer_norm.values) ORDER BY y)::VECTOR(2304) AS values
                FROM    (
                        SELECT  place, agg.values * ln_1_g.values + ln_1_b.values AS values
                        FROM    (
                                SELECT  place, norm.values
                                FROM    embeddings
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  AVG(value) AS mean,
                                                VAR_POP(value) AS variance
                                        FROM    UNNEST(values::REAL[]) value
                                        ) agg
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                        ) norm
                                ) agg
                        CROSS JOIN
                                ln_1_b
                        CROSS JOIN
                                ln_1_g
                        ) layer_norm
                CROSS JOIN
                        c_attn_w
                GROUP BY
                        place
                ) mm
        CROSS JOIN
                c_attn_b
        ),
        heads AS
        (
        SELECT  place, head,
                (values::REAL[])[(head * 64 + 1):(head * 64 + 64)]::VECTOR(64) AS q,
                (values::REAL[])[(head * 64 + 1 + 768):(head * 64 + 64 + 768)]::VECTOR(64) AS k,
                (values::REAL[])[(head * 64 + 1 + 1536):(head * 64 + 64 + 1536)]::VECTOR(64) AS v
        FROM    mha_norm
        CROSS JOIN
                GENERATE_SERIES(0, 11) head
        ),
        sm_input AS
        (
        SELECT  head, h1.place AS x, h2.place AS y, INNER_PRODUCT(h1.q, h2.k) / 8 + CASE WHEN h2.place > h1.place THEN -1E10 ELSE 0 END AS value
        FROM    heads h1
        JOIN    heads h2
        USING   (head)
        ),
        sm_diff AS
        (
        SELECT  head, x, y, value - MAX(value) OVER (PARTITION BY head, x) AS diff
        FROM    sm_input
        ),
        sm_exp AS
        (
        SELECT  head, x, y, CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
        FROM    sm_diff
        ),
        softmax AS
        (
        SELECT  head, x, y AS place, e / SUM(e) OVER (PARTITION BY head, x) AS value
        FROM    sm_exp
        ),
        attention AS
        (
        SELECT  place, ARRAY_AGG(value ORDER BY head * 64 + ordinality)::VECTOR(768) AS values
        FROM    (
                SELECT  head, x AS place, SUM(ARRAY_FILL(softmax.value, ARRAY[64])::VECTOR(64) * heads.v) AS values
                FROM    softmax
                JOIN    heads
                USING   (head, place)
                GROUP BY
                        head, x
                ) q
        CROSS JOIN LATERAL
                UNNEST(values::REAL[]) WITH ORDINALITY v (value, ordinality)
        GROUP BY
                place
        ),
        mha AS
        (
        SELECT  place, w.values + c_proj_b.values AS values
        FROM    (
                SELECT  attention.place, ARRAY_AGG(INNER_PRODUCT(attention.values, c_proj_w.values) ORDER BY c_proj_w.place)::VECTOR(768) AS values
                FROM    attention
                CROSS JOIN
                        c_proj_w
                GROUP BY
                        attention.place
                ) w
        CROSS JOIN
                c_proj_b
        )
SELECT  place,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((values::REAL[])[:10]) AS n) AS q
FROM    mha
d5d6733a783e738538d2d95ec2114fca.png

Прежде чем результаты многоголового внимания будут переданы на следующий шаг, к ним нужно добавить оригинальные входные данные. Этот фокус был описан ещё в исходной статье по трансформерам. Предполагается, что он должен помочь с затухающими и взрывными градиентами.

Это распространённая проблема при обучении: иногда градиенты параметров оказываются слишком малы или слишком велики. Если менять их при очередной итерации обучения, это также очень слабо влияет на функцию потерь (поэтому модель сходится очень медленно). Бывает и обратный эффект: даже минимальное изменение отбрасывает функцию потерь слишком далеко от локального минимума, тем самым обесценивая всю работу по обучению.

Прямое распространение

Именно в этом и заключается работа глубоких нейронных сетей. Большая часть параметров модели на самом деле используется именно на этом этапе.

Здесь мы имеем дело с многослойным перцептроном, у которого три слоя (768, 3072, 768), а в качестве функции активации используем линейную единицу гауссовской ошибки (GELU):

1582eb4081148863170cf26deff796b0.png

Как показывает практика, эта функция даёт очень хорошие результаты при работе с глубокими нейронными сетями. Её можно аналитически аппроксимировать вот так:

4e7d6916854cc78e19fab9621165d0de.png

Изученные параметры линейного преобразования для связей в слое называются c_fc (768 → 3072) и c_proj (3072 → 768). Значения с первого слоя сначала нормализуются при помощи коэффициентов, содержащихся в изученном параметре ln_2. По завершении этапа прямого распространения его ввод вновь добавляется к выводу. Этот элемент также входит в самый первый вариант дизайна трансформеров. Весь этап прямого распространения выглядит так:

А вот как это делается на SQL:

WITH    embeddings AS
        (
        SELECT  place, values
        FROM    UNNEST(ARRAY[6307, 47701, 318, 1049]) WITH ORDINALITY AS tokens (token, ordinality)
        CROSS JOIN LATERAL
                (
                SELECT  ordinality - 1 AS place
                ) o
        CROSS JOIN LATERAL
                (
                SELECT  wte.values + wpe.values AS values
                FROM    wte
                CROSS JOIN
                        wpe
                WHERE   wte.token = tokens.token
                        AND wpe.place = o.place
                ) embedding
        ),
        c_proj_w AS
        (
        SELECT  *
        FROM    c_proj_w
        WHERE   block = 0
        ),
        c_proj_b AS
        (
        SELECT  *
        FROM    c_proj_b
        WHERE   block = 0
        ),
        mlp_c_fc_w AS
        (
        SELECT  *
        FROM    mlp_c_fc_w
        WHERE   block = 0
        ),
        mlp_c_fc_b AS
        (
        SELECT  *
        FROM    mlp_c_fc_b
        WHERE   block = 0
        ),
        mlp_c_proj_w AS
        (
        SELECT  *
        FROM    mlp_c_proj_w
        WHERE   block = 0
        ),
        mlp_c_proj_b AS
        (
        SELECT  *
        FROM    mlp_c_proj_b
        WHERE   block = 0
        ),
        c_attn_w AS
        (
        SELECT  *
        FROM    c_attn_w
        WHERE   block = 0
        ),
        c_attn_b AS
        (
        SELECT  *
        FROM    c_attn_b
        WHERE   block = 0
        ),
        ln_1_g AS
        (
        SELECT  *
        FROM    ln_1_g
        WHERE   block = 0
        ),
        ln_1_b AS
        (
        SELECT  *
        FROM    ln_1_b
        WHERE   block = 0
        ),
        ln_2_b AS
        (
        SELECT  *
        FROM    ln_2_b
        WHERE   block = 0
        ),
        ln_2_g AS
        (
        SELECT  *
        FROM    ln_2_g
        WHERE   block = 0
        ),
        mha_norm AS
        (
        SELECT  place, mm.values + c_attn_b.values AS values
        FROM    (
                SELECT  place, ARRAY_AGG(INNER_PRODUCT(c_attn_w.values, layer_norm.values) ORDER BY y)::VECTOR(2304) AS values
                FROM    (
                        SELECT  place, agg.values * ln_1_g.values + ln_1_b.values AS values
                        FROM    (
                                SELECT  place, norm.values
                                FROM    embeddings
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  AVG(value) AS mean,
                                                VAR_POP(value) AS variance
                                        FROM    UNNEST(values::REAL[]) value
                                        ) agg
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                        ) norm
                                ) agg
                        CROSS JOIN
                                ln_1_b
                        CROSS JOIN
                                ln_1_g
                        ) layer_norm
                CROSS JOIN
                        c_attn_w
                GROUP BY
                        place
                ) mm
        CROSS JOIN
                c_attn_b
        ),
        heads AS
        (
        SELECT  place, head,
                (values::REAL[])[(head * 64 + 1):(head * 64 + 64)]::VECTOR(64) AS q,
                (values::REAL[])[(head * 64 + 1 + 768):(head * 64 + 64 + 768)]::VECTOR(64) AS k,
                (values::REAL[])[(head * 64 + 1 + 1536):(head * 64 + 64 + 1536)]::VECTOR(64) AS v
        FROM    mha_norm
        CROSS JOIN
                GENERATE_SERIES(0, 11) head
        ),
        sm_input AS
        (
        SELECT  head, h1.place AS x, h2.place AS y, INNER_PRODUCT(h1.q, h2.k) / 8 + CASE WHEN h2.place > h1.place THEN -1E10 ELSE 0 END AS value
        FROM    heads h1
        JOIN    heads h2
        USING   (head)
        ),
        sm_diff AS
        (
        SELECT  head, x, y, value - MAX(value) OVER (PARTITION BY head, x) AS diff
        FROM    sm_input
        ),
        sm_exp AS
        (
        SELECT  head, x, y, CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
        FROM    sm_diff
        ),
        softmax AS
        (
        SELECT  head, x, y AS place, e / SUM(e) OVER (PARTITION BY head, x) AS value
        FROM    sm_exp
        ),
        attention AS
        (
        SELECT  place, ARRAY_AGG(value ORDER BY head * 64 + ordinality)::VECTOR(768) AS values
        FROM    (
                SELECT  head, x AS place, SUM(ARRAY_FILL(softmax.value, ARRAY[64])::VECTOR(64) * heads.v) AS values
                FROM    softmax
                JOIN    heads
                USING   (head, place)
                GROUP BY
                        head, x
                ) q
        CROSS JOIN LATERAL
                UNNEST(values::REAL[]) WITH ORDINALITY v (value, ordinality)
        GROUP BY
                place
        ),
        mha AS
        (
        SELECT  place, w.values + c_proj_b.values + embeddings.values AS values
        FROM    (
                SELECT  attention.place, ARRAY_AGG(INNER_PRODUCT(attention.values, c_proj_w.values) ORDER BY c_proj_w.place)::VECTOR(768) AS values
                FROM    attention
                CROSS JOIN
                        c_proj_w
                GROUP BY
                        attention.place
                ) w
        CROSS JOIN
                c_proj_b
        JOIN    embeddings
        USING   (place)
        ),
        ffn_norm AS
        (
        SELECT  place, agg.values * ln_2_g.values + ln_2_b.values AS values
        FROM    (
                SELECT  place, norm.values
                FROM    mha
                CROSS JOIN LATERAL
                        (
                        SELECT  AVG(value) AS mean,
                                VAR_POP(value) AS variance
                        FROM    UNNEST(values::REAL[]) value
                        ) agg
                CROSS JOIN LATERAL
                        (
                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                        ) norm
                ) agg
        CROSS JOIN
                ln_2_b
        CROSS JOIN
                ln_2_g
        ),
        ffn_a AS
        (
        SELECT  gelu.place, gelu.values
        FROM    (
                SELECT  place, w.values + mlp_c_fc_b.values AS values
                FROM    (
                        SELECT  ffn_norm.place, ARRAY_AGG(INNER_PRODUCT(ffn_norm.values, mlp_c_fc_w.values) ORDER BY mlp_c_fc_w.place)::VECTOR(3072) AS values
                        FROM    ffn_norm
                        CROSS JOIN
                                mlp_c_fc_w
                        GROUP BY
                                ffn_norm.place
                        ) w
                CROSS JOIN
                        mlp_c_fc_b
                ) v
        CROSS JOIN LATERAL
                (
                SELECT  place, ARRAY_AGG(0.5 * value * (1 + TANH(0.797884560802 * (value + 0.044715 * value*value*value))) ORDER BY ordinality)::VECTOR(3072) AS values
                FROM    UNNEST(values::REAL[]) WITH ORDINALITY n (value, ordinality)
                GROUP BY
                        place
                ) gelu
        ),
        ffn AS
        (
        SELECT  place, w.values + mlp_c_proj_b.values + mha.values AS values
        FROM    (
                SELECT  ffn_a.place, ARRAY_AGG(INNER_PRODUCT(ffn_a.values, mlp_c_proj_w.values) ORDER BY mlp_c_proj_w.place)::VECTOR(768) AS values
                FROM    ffn_a
                CROSS JOIN
                        mlp_c_proj_w
                GROUP BY
                        ffn_a.place
                ) w
        CROSS JOIN
                mlp_c_proj_b
        JOIN    mha
        USING   (place)
        )
SELECT  place,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((values::REAL[])[:10]) AS n) AS q
FROM    ffn
9080f54c68ca45f66b84cda8eba312be.png

Именно этот вывод получаем из первого блока GPT2.

Блоки

Операции, рассмотренные на предыдущих этапах, повторяются в каждом из слоёв (именуемых «блоками»). Блоки выстраиваются в виде конвейера, так, что вывод из предыдущего блока сразу подаётся на ввод следующему. У каждого из блоков – свой набор изученных параметров.

В SQL потребуется соединить блоки при помощи рекурсивного CTE.

Как только финальный блок выдаст значения, результат потребуется нормализовать при помощи изученного параметра ln_f.

Вот как в итоге будет выглядеть модель:

7699aba465e680cfde43e9ff6ec6a2b2.png

А вот как результат выглядит на SQL:

WITH    RECURSIVE
        initial AS
        (
        SELECT  ARRAY[6307, 47701, 318, 1049] AS input
        ),
        hparams AS
        (
        SELECT  12 AS n_block
        ),
        embeddings AS
        (
        SELECT  place, values
        FROM    initial
        CROSS JOIN
                hparams
        CROSS JOIN LATERAL
                UNNEST(input) WITH ORDINALITY AS tokens (token, ordinality)
        CROSS JOIN LATERAL
                (
                SELECT  ordinality - 1 AS place
                ) o
        CROSS JOIN LATERAL
                (
                SELECT  wte.values + wpe.values AS values
                FROM    wte
                CROSS JOIN
                        wpe
                WHERE   wte.token = tokens.token
                        AND wpe.place = o.place
                ) embedding
        ),
        transform AS
        (
        SELECT  0 AS block, place, values
        FROM    embeddings
        UNION ALL
        (
        WITH    previous AS
                (
                SELECT  *
                FROM    transform
                )
        SELECT  block + 1 AS block, transformed_layer.*
        FROM    hparams
        CROSS JOIN LATERAL
                (
                SELECT  block
                FROM    previous
                WHERE   block < 12
                LIMIT   1
                ) q
        CROSS JOIN LATERAL
                (
                WITH    ln_2_b AS
                        (
                        SELECT  *
                        FROM    ln_2_b
                        WHERE   block = q.block
                        ),
                        ln_2_g AS
                        (
                        SELECT  *
                        FROM    ln_2_g
                        WHERE   block = q.block
                        ),
                        c_proj_w AS
                        (
                        SELECT  *
                        FROM    c_proj_w
                        WHERE   block = q.block
                        ),
                        c_proj_b AS
                        (
                        SELECT  *
                        FROM    c_proj_b
                        WHERE   block = q.block
                        ),
                        mlp_c_fc_w AS
                        (
                        SELECT  *
                        FROM    mlp_c_fc_w
                        WHERE   block = q.block
                        ),
                        mlp_c_fc_b AS
                        (
                        SELECT  *
                        FROM    mlp_c_fc_b
                        WHERE   block = q.block
                        ),
                        mlp_c_proj_w AS
                        (
                        SELECT  *
                        FROM    mlp_c_proj_w
                        WHERE   block = q.block
                        ),
                        mlp_c_proj_b AS
                        (
                        SELECT  *
                        FROM    mlp_c_proj_b
                        WHERE   block = q.block
                        ),
                        c_attn_w AS
                        (
                        SELECT  *
                        FROM    c_attn_w
                        WHERE   block = q.block
                        ),
                        c_attn_b AS
                        (
                        SELECT  *
                        FROM    c_attn_b
                        WHERE   block = q.block
                        ),
                        ln_1_g AS
                        (
                        SELECT  *
                        FROM    ln_1_g
                        WHERE   block = q.block
                        ),
                        ln_1_b AS
                        (
                        SELECT  *
                        FROM    ln_1_b
                        WHERE   block = q.block
                        ),
                        mha_norm AS
                        (
                        SELECT  place, mm.values + c_attn_b.values AS values
                        FROM    (
                                SELECT  place, ARRAY_AGG(INNER_PRODUCT(c_attn_w.values, layer_norm.values) ORDER BY y)::VECTOR(2304) AS values
                                FROM    (
                                        SELECT  place, agg.values * ln_1_g.values + ln_1_b.values AS values
                                        FROM    (
                                                SELECT  place, norm.values
                                                FROM    previous
                                                CROSS JOIN LATERAL
                                                        (
                                                        SELECT  AVG(value) AS mean,
                                                                VAR_POP(value) AS variance
                                                        FROM    UNNEST(values::REAL[]) value
                                                        ) agg
                                                CROSS JOIN LATERAL
                                                        (
                                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                                        ) norm
                                                ) agg
                                        CROSS JOIN
                                                ln_1_b
                                        CROSS JOIN
                                                ln_1_g
                                        ) layer_norm
                                CROSS JOIN
                                        c_attn_w
                                GROUP BY
                                        place
                                ) mm
                        CROSS JOIN
                                c_attn_b
                        ),
                        heads AS
                        (
                        SELECT  place, head,
                                (values::REAL[])[(head * 64 + 1):(head * 64 + 64)]::VECTOR(64) AS q,
                                (values::REAL[])[(head * 64 + 1 + 768):(head * 64 + 64 + 768)]::VECTOR(64) AS k,
                                (values::REAL[])[(head * 64 + 1 + 1536):(head * 64 + 64 + 1536)]::VECTOR(64) AS v
                        FROM    mha_norm
                        CROSS JOIN
                                GENERATE_SERIES(0, 11) head
                        ),
                        sm_input AS
                        (
                        SELECT  head, h1.place AS x, h2.place AS y, INNER_PRODUCT(h1.q, h2.k) / 8 + CASE WHEN h2.place > h1.place THEN -1E10 ELSE 0 END AS value
                        FROM    heads h1
                        JOIN    heads h2
                        USING   (head)
                        ),
                        sm_diff AS
                        (
                        SELECT  head, x, y, value - MAX(value) OVER (PARTITION BY head, x) AS diff
                        FROM    sm_input
                        ),
                        sm_exp AS
                        (
                        SELECT  head, x, y, CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
                        FROM    sm_diff
                        ),
                        softmax AS
                        (
                        SELECT  head, x, y AS place, e / SUM(e) OVER (PARTITION BY head, x) AS value
                        FROM    sm_exp
                        ),
                        attention AS
                        (
                        SELECT  place, ARRAY_AGG(value ORDER BY head * 64 + ordinality)::VECTOR(768) AS values
                        FROM    (
                                SELECT  head, x AS place, SUM(ARRAY_FILL(softmax.value, ARRAY[64])::VECTOR(64) * heads.v) AS values
                                FROM    softmax
                                JOIN    heads
                                USING   (head, place)
                                GROUP BY
                                        head, x
                                ) q
                        CROSS JOIN LATERAL
                                UNNEST(values::REAL[]) WITH ORDINALITY v (value, ordinality)
                        GROUP BY
                                place
                        ),
                        mha AS
                        (
                        SELECT  place, w.values + c_proj_b.values + previous.values AS values
                        FROM    (
                                SELECT  attention.place, ARRAY_AGG(INNER_PRODUCT(attention.values, c_proj_w.values) ORDER BY c_proj_w.place)::VECTOR(768) AS values
                                FROM    attention
                                CROSS JOIN
                                        c_proj_w
                                GROUP BY
                                        attention.place
                                ) w
                        CROSS JOIN
                                c_proj_b
                        JOIN    previous
                        USING   (place)
                        ),
                        ffn_norm AS
                        (
                        SELECT  place, agg.values * ln_2_g.values + ln_2_b.values AS values
                        FROM    (
                                SELECT  place, norm.values
                                FROM    mha
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  AVG(value) AS mean,
                                                VAR_POP(value) AS variance
                                        FROM    UNNEST(values::REAL[]) value
                                        ) agg
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                        ) norm
                                ) agg
                        CROSS JOIN
                                ln_2_b
                        CROSS JOIN
                                ln_2_g
                        ),
                        ffn_a AS
                        (
                        SELECT  gelu.place, gelu.values
                        FROM    (
                                SELECT  place, w.values + mlp_c_fc_b.values AS values
                                FROM    (
                                        SELECT  ffn_norm.place, ARRAY_AGG(INNER_PRODUCT(ffn_norm.values, mlp_c_fc_w.values) ORDER BY mlp_c_fc_w.place)::VECTOR(3072) AS values
                                        FROM    ffn_norm
                                        CROSS JOIN
                                                mlp_c_fc_w
                                        GROUP BY
                                                ffn_norm.place
                                        ) w
                                CROSS JOIN
                                        mlp_c_fc_b
                                ) v
                        CROSS JOIN LATERAL
                                (
                                SELECT  place, ARRAY_AGG(0.5 * value * (1 + TANH(0.797884560802 * (value + 0.044715 * value*value*value))) ORDER BY ordinality)::VECTOR(3072) AS values
                                FROM    UNNEST(values::REAL[]) WITH ORDINALITY n (value, ordinality)
                                GROUP BY
                                        place
                                ) gelu
                        ),
                        ffn AS
                        (
                        SELECT  place, w.values + mlp_c_proj_b.values + mha.values AS values
                        FROM    (
                                SELECT  ffn_a.place, ARRAY_AGG(INNER_PRODUCT(ffn_a.values, mlp_c_proj_w.values) ORDER BY mlp_c_proj_w.place)::VECTOR(768) AS values
                                FROM    ffn_a
                                CROSS JOIN
                                        mlp_c_proj_w
                                GROUP BY
                                        ffn_a.place
                                ) w
                        CROSS JOIN
                                mlp_c_proj_b
                        JOIN    mha
                        USING   (place)
                        )
                SELECT  *
                FROM    ffn
                ) transformed_layer
        )
        ),
        block_output AS
        (
        SELECT  *
        FROM    hparams
        JOIN    transform
        ON      transform.block = n_block
        ),
        ln_f AS
        (
        SELECT  place, norm.values * ln_f_g.values + ln_f_b.values AS values
        FROM    block_output
        CROSS JOIN LATERAL
                (
                SELECT  AVG(value) AS mean,
                        VAR_POP(value) AS variance
                FROM    UNNEST(values::REAL[]) AS n(value)
                ) agg
        CROSS JOIN LATERAL
                (
                SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n (value, ordinality)
                ) norm
        CROSS JOIN
                ln_f_b
        CROSS JOIN
                ln_f_g
        )
SELECT  place,
        (SELECT STRING_AGG(TO_CHAR(n, 'S0.000'), ' ') || ' …' FROM UNNEST((values::REAL[])[:10]) AS n) AS q
FROM    ln_f
378876fdfdedf326e8861f0f96c15bc7.png

Это вывод модели.

Четвёртый вектор – это и есть фактическое представление следующего токена, предсказанного моделью. Нам просто нужно отобразить его обратно на токены.

Токены

У нас уже есть векторное представление (768-мерное), в котором, согласно модели, заключены семантика и грамматика наиболее вероятного продолжения промпта. Теперь нам нужно отобразить его обратно на токен.

Одна из первых операций, совершаемых моделью – это отображение токенов на их векторные представления. Эта операция делается при помощи матрицы wpe размером 50257×768. Той же самой матрицей нам потребуется воспользоваться, чтобы отобразить векторное представление обратно на токен.

Проблема в том, что строго обратное отображение выполнить невозможно: (вероятно) векторное представление не будет совпадать ни с одной из строк в матрице. Поэтому нам придётся найти токен, который «ближе всего» к векторному представлению.

Поскольку (как мы надеемся), в измерениях векторных представлений заключены некоторые семантические и грамматические аспекты токена, необходимо сопоставить их как можно точнее. Один из способов консолидировать «степень близости» каждого из измерений – вычислить скалярное произведение двух векторных представлений. Чем выше будет это значение, тем ближе токен к прогнозу.

Для этого умножим векторное представление на матрицу wte. В результате получим матрицу из одного столбца и 50257 строк. Каждое значение в этом результате будет произведением спрогнозированного вектора и векторного представления токена. Чем выше это число, тем вероятнее, что токен станет продолжением промпта.

Чтобы выбрать следующий токен, нужно просто преобразовать значения сходства в значения вероятности. Для этого нам пригодится старая добрая функция softmax (та самая, при помощи которой мы уже нормализовали веса внимания).

Почему стоит использовать softmax для работы с вероятностями?

Softmax – это удобное свойство аксиомы выбора Льюса. Оно таково: относительная вероятность двух вариантов не зависит от наличия прочих вероятностных вариантов. Если вариант A влвое вероятнее варианта B, то это соотношение не зависит от наличия или отсутствия других вариантов (хотя, конечно, абсолютные значения при этом могут измениться).

Вектор скалярных произведений («логит» в терминологии ИИ) содержит произвольные значения, которые могут не укладываться в какую-либо шкалу. Если балл A выше, чем балл B, то известно, что A вероятнее – а больше ничего не известно. Можно подкорректировать входные значения softmax так, как нам угодно, главное, соблюдать их порядок (более крупные значения остаются более крупными).

Распространённый подход, позволяющий этого добиться – нормализация значений путём вычитания наибольшего значения из каждого значения в множестве (так что наибольшее значение становится равным 0, а все остальные – отрицательным числам). Затем берём фиксированное количество наивысших значений (скажем, топ-5 или топ-10). Наконец, перед передачей всех значений softmax умножаем их на константу.

Количество наивысших значений, с которым мы работаем, обычно называется top_n, а множитель-константа (вернее, обратное ему число) называется «температурой» (Т). Чем выше температура, тем более гладкое распределение вероятностей у нас получится, и тем выше вероятность, что на место продолжения будет выбран не первый попавшийся токен.

Формула для расчёта вероятностей токенов

a6a685d634b1bb26d07ca1cb2cdda672.png

где scores — это множество из наивысших top_n баллов.

Почему этот показатель называется "температурой"

Функция softmax также называется распределение Больцмана. Она широко используется в физике. Среди прочего, на ней основана барометрическая формула, по которой рассчитывается изменение плотности воздуха в зависимости от высоты.

Как известно, тёплый воздух поднимается вверх и распределяется на сравнительно большой высоте. В горячем воздухе сравнительно высока вероятность, что молекула ль частых столкновений с другими молекулами заберётся на такую высоту, куда в ином случае бы не попала. В отличие от ситуации с более холодными температурами, плотность воздуха увеличивается на больших высотах и падает на уровне моря.

Сравните, как воздух движется при разных температурах:

e109ce6f14a9d797ac4ced0f00151a86.gif792fe2af01b49345c3ac0a6c8e08502a.gif

\Иллюстрация Доминика Форда, Bouncing Balls and the Boltzmann Distribution

Аналогично, в рассматриваемом нами примере, чем больше «температура», тем выше вероятность, что будет выбран не самый очевидный токен (разумеется, за счёт наиболее очевидных). Логический вывод становится менее предсказуемым и более «творческим».

Давайте выразим всё это на SQL. У нас был промпт «PostgreSQL is great». Вот первые 5 токенов, которые, в соответствии с этой моделью, являются наиболее вероятными продолжениями этой фразы. Показаны их вероятности при разной температуре:

WITH    RECURSIVE
        initial AS
        (
        SELECT  ARRAY[6307, 47701, 318, 1049] AS input
        ),
        hparams AS
        (
        SELECT  12 AS n_block,
                5 AS top_n,
                ARRAY_LENGTH(input, 1) AS n_seq
        FROM    initial
        ),
        embeddings AS
        (
        SELECT  place, values
        FROM    initial
        CROSS JOIN
                hparams
        CROSS JOIN LATERAL
                UNNEST(input) WITH ORDINALITY AS tokens (token, ordinality)
        CROSS JOIN LATERAL
                (
                SELECT  ordinality - 1 AS place
                ) o
        CROSS JOIN LATERAL
                (
                SELECT  wte.values + wpe.values AS values
                FROM    wte
                CROSS JOIN
                        wpe
                WHERE   wte.token = tokens.token
                        AND wpe.place = o.place
                ) embedding
        ),
        transform AS
        (
        SELECT  0 AS block, place, values
        FROM    embeddings
        UNION ALL
        (
        WITH    previous AS
                (
                SELECT  *
                FROM    transform
                )
        SELECT  block + 1 AS block, transformed_layer.*
        FROM    hparams
        CROSS JOIN LATERAL
                (
                SELECT  block
                FROM    previous
                WHERE   block < 12
                LIMIT   1
                ) q
        CROSS JOIN LATERAL
                (
                WITH    ln_2_b AS
                        (
                        SELECT  *
                        FROM    ln_2_b
                        WHERE   block = q.block
                        ),
                        ln_2_g AS
                        (
                        SELECT  *
                        FROM    ln_2_g
                        WHERE   block = q.block
                        ),
                        c_proj_w AS
                        (
                        SELECT  *
                        FROM    c_proj_w
                        WHERE   block = q.block
                        ),
                        c_proj_b AS
                        (
                        SELECT  *
                        FROM    c_proj_b
                        WHERE   block = q.block
                        ),
                        mlp_c_fc_w AS
                        (
                        SELECT  *
                        FROM    mlp_c_fc_w
                        WHERE   block = q.block
                        ),
                        mlp_c_fc_b AS
                        (
                        SELECT  *
                        FROM    mlp_c_fc_b
                        WHERE   block = q.block
                        ),
                        mlp_c_proj_w AS
                        (
                        SELECT  *
                        FROM    mlp_c_proj_w
                        WHERE   block = q.block
                        ),
                        mlp_c_proj_b AS
                        (
                        SELECT  *
                        FROM    mlp_c_proj_b
                        WHERE   block = q.block
                        ),
                        c_attn_w AS
                        (
                        SELECT  *
                        FROM    c_attn_w
                        WHERE   block = q.block
                        ),
                        c_attn_b AS
                        (
                        SELECT  *
                        FROM    c_attn_b
                        WHERE   block = q.block
                        ),
                        ln_1_g AS
                        (
                        SELECT  *
                        FROM    ln_1_g
                        WHERE   block = q.block
                        ),
                        ln_1_b AS
                        (
                        SELECT  *
                        FROM    ln_1_b
                        WHERE   block = q.block
                        ),
                        mha_norm AS
                        (
                        SELECT  place, mm.values + c_attn_b.values AS values
                        FROM    (
                                SELECT  place, ARRAY_AGG(INNER_PRODUCT(c_attn_w.values, layer_norm.values) ORDER BY y)::VECTOR(2304) AS values
                                FROM    (
                                        SELECT  place, agg.values * ln_1_g.values + ln_1_b.values AS values
                                        FROM    (
                                                SELECT  place, norm.values
                                                FROM    previous
                                                CROSS JOIN LATERAL
                                                        (
                                                        SELECT  AVG(value) AS mean,
                                                                VAR_POP(value) AS variance
                                                        FROM    UNNEST(values::REAL[]) value
                                                        ) agg
                                                CROSS JOIN LATERAL
                                                        (
                                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                                        ) norm
                                                ) agg
                                        CROSS JOIN
                                                ln_1_b
                                        CROSS JOIN
                                                ln_1_g
                                        ) layer_norm
                                CROSS JOIN
                                        c_attn_w
                                GROUP BY
                                        place
                                ) mm
                        CROSS JOIN
                                c_attn_b
                        ),
                        heads AS
                        (
                        SELECT  place, head,
                                (values::REAL[])[(head * 64 + 1):(head * 64 + 64)]::VECTOR(64) AS q,
                                (values::REAL[])[(head * 64 + 1 + 768):(head * 64 + 64 + 768)]::VECTOR(64) AS k,
                                (values::REAL[])[(head * 64 + 1 + 1536):(head * 64 + 64 + 1536)]::VECTOR(64) AS v
                        FROM    mha_norm
                        CROSS JOIN
                                GENERATE_SERIES(0, 11) head
                        ),
                        sm_input AS
                        (
                        SELECT  head, h1.place AS x, h2.place AS y, INNER_PRODUCT(h1.q, h2.k) / 8 + CASE WHEN h2.place > h1.place THEN -1E10 ELSE 0 END AS value
                        FROM    heads h1
                        JOIN    heads h2
                        USING   (head)
                        ),
                        sm_diff AS
                        (
                        SELECT  head, x, y, value - MAX(value) OVER (PARTITION BY head, x) AS diff
                        FROM    sm_input
                        ),
                        sm_exp AS
                        (
                        SELECT  head, x, y, CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
                        FROM    sm_diff
                        ),
                        softmax AS
                        (
                        SELECT  head, x, y AS place, e / SUM(e) OVER (PARTITION BY head, x) AS value
                        FROM    sm_exp
                        ),
                        attention AS
                        (
                        SELECT  place, ARRAY_AGG(value ORDER BY head * 64 + ordinality)::VECTOR(768) AS values
                        FROM    (
                                SELECT  head, x AS place, SUM(ARRAY_FILL(softmax.value, ARRAY[64])::VECTOR(64) * heads.v) AS values
                                FROM    softmax
                                JOIN    heads
                                USING   (head, place)
                                GROUP BY
                                        head, x
                                ) q
                        CROSS JOIN LATERAL
                                UNNEST(values::REAL[]) WITH ORDINALITY v (value, ordinality)
                        GROUP BY
                                place
                        ),
                        mha AS
                        (
                        SELECT  place, w.values + c_proj_b.values + previous.values AS values
                        FROM    (
                                SELECT  attention.place, ARRAY_AGG(INNER_PRODUCT(attention.values, c_proj_w.values) ORDER BY c_proj_w.place)::VECTOR(768) AS values
                                FROM    attention
                                CROSS JOIN
                                        c_proj_w
                                GROUP BY
                                        attention.place
                                ) w
                        CROSS JOIN
                                c_proj_b
                        JOIN    previous
                        USING   (place)
                        ),
                        ffn_norm AS
                        (
                        SELECT  place, agg.values * ln_2_g.values + ln_2_b.values AS values
                        FROM    (
                                SELECT  place, norm.values
                                FROM    mha
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  AVG(value) AS mean,
                                                VAR_POP(value) AS variance
                                        FROM    UNNEST(values::REAL[]) value
                                        ) agg
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                        ) norm
                                ) agg
                        CROSS JOIN
                                ln_2_b
                        CROSS JOIN
                                ln_2_g
                        ),
                        ffn_a AS
                        (
                        SELECT  gelu.place, gelu.values
                        FROM    (
                                SELECT  place, w.values + mlp_c_fc_b.values AS values
                                FROM    (
                                        SELECT  ffn_norm.place, ARRAY_AGG(INNER_PRODUCT(ffn_norm.values, mlp_c_fc_w.values) ORDER BY mlp_c_fc_w.place)::VECTOR(3072) AS values
                                        FROM    ffn_norm
                                        CROSS JOIN
                                                mlp_c_fc_w
                                        GROUP BY
                                                ffn_norm.place
                                        ) w
                                CROSS JOIN
                                        mlp_c_fc_b
                                ) v
                        CROSS JOIN LATERAL
                                (
                                SELECT  place, ARRAY_AGG(0.5 * value * (1 + TANH(0.797884560802 * (value + 0.044715 * value*value*value))) ORDER BY ordinality)::VECTOR(3072) AS values
                                FROM    UNNEST(values::REAL[]) WITH ORDINALITY n (value, ordinality)
                                GROUP BY
                                        place
                                ) gelu
                        ),
                        ffn AS
                        (
                        SELECT  place, w.values + mlp_c_proj_b.values + mha.values AS values
                        FROM    (
                                SELECT  ffn_a.place, ARRAY_AGG(INNER_PRODUCT(ffn_a.values, mlp_c_proj_w.values) ORDER BY mlp_c_proj_w.place)::VECTOR(768) AS values
                                FROM    ffn_a
                                CROSS JOIN
                                        mlp_c_proj_w
                                GROUP BY
                                        ffn_a.place
                                ) w
                        CROSS JOIN
                                mlp_c_proj_b
                        JOIN    mha
                        USING   (place)
                        )
                SELECT  *
                FROM    ffn
                ) transformed_layer
        )
        ),
        block_output AS
        (
        SELECT  *
        FROM    hparams
        JOIN    transform
        ON      transform.block = n_block
        ),
        ln_f AS
        (
        SELECT  place, norm.values * ln_f_g.values + ln_f_b.values AS values
        FROM    block_output
        CROSS JOIN LATERAL
                (
                SELECT  AVG(value) AS mean,
                        VAR_POP(value) AS variance
                FROM    UNNEST(values::REAL[]) AS n(value)
                ) agg
        CROSS JOIN LATERAL
                (
                SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n (value, ordinality)
                ) norm
        CROSS JOIN
                ln_f_b
        CROSS JOIN
                ln_f_g
        ),
        logits AS
        (
        SELECT  logits.*
        FROM    hparams
        CROSS JOIN LATERAL
                (
                SELECT  token, INNER_PRODUCT(ln_f.values, wte.values) AS value
                FROM    ln_f
                CROSS JOIN
                        wte
                WHERE   ln_f.place = n_seq - 1
                ORDER BY
                        value DESC
                LIMIT   (top_n)
                ) logits
        ),
        temperatures (temperature) AS
        (
        VALUES
        (0.5),
        (1),
        (2)
        ),
        tokens AS
        (
        SELECT  token, value, softmax, temperature
        FROM    temperatures
        CROSS JOIN LATERAL
                (
                SELECT  *, (e / SUM(e) OVER ()) AS softmax
                FROM    (
                        SELECT  *,
                                (value - MAX(value) OVER ()) / temperature AS diff
                        FROM    logits
                        ) exp_x
                CROSS JOIN LATERAL
                        (
                        SELECT  CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
                        ) exp
                ) q
        )
SELECT  token,
        cluster,
        TO_CHAR(t1.value, 'S00.000') AS score,
        TO_CHAR(t1.softmax, '0.00') AS "temperature = 0.5",
        TO_CHAR(t2.softmax, '0.00') AS "temperature = 1",
        TO_CHAR(t3.softmax, '0.00') AS "temperature = 2"
FROM    (
        SELECT  *
        FROM    tokens
        WHERE   temperature = 0.5
        ) t1
JOIN    (
        SELECT  *
        FROM    tokens
        WHERE   temperature = 1
        ) t2
USING   (token)
JOIN    (
        SELECT  *
        FROM    tokens
        WHERE   temperature = 2
        ) t3
USING   (token)
JOIN    tokenizer
USING   (token)
39b00c06e909aab6901bceb4228137b2.png

Логический вывод

Вот мы и готовы перейти к реальному логическому выводу: запустить модель, выбрать токен в соответствии с полученной вероятностью, добавить токен к промпту и повторять этот процесс до тех пор, пока не будет сгенерировано достаточное количество токенов.

Как было показано выше, сама большая языковая модель является детерминированной. Это просто последовательные перемножения матриц и другие математические операции, производимые над заранее определёнными константами. Пока промпт и такие гиперпараметры как температура и топ-N остаются неизменными, вывод также остаётся неизменным.

Единственный недетерминированный процесс в данном случае – это выбор токена. В нём заложена (переменная) степень случайности. Вот почему чатботы, основанные на GPT, могут давать разные ответы на один и тот же промпт.

Воспользуемся в качестве промпта фразой «Happy New Year! I wish» и прикажем модели сгенерировать 10 новых токенов для этого промпта. Температуру установим в 2, а top_n в 5.

У меня на компьютере на этот запрос уходит 2 минуты и 44 секунды. Вот такой вывод получается:

SELECT SETSEED(0.20231231);
 
WITH    RECURSIVE
        input AS
        (
        SELECT  'Happy New Year! I wish you' AS prompt,
                10 AS threshold,
                2 AS temperature,
                1 AS top_n
        ),
        clusters AS
        (
        SELECT  part_position, bpe.*
        FROM    input
        CROSS JOIN LATERAL
                REGEXP_MATCHES(prompt, '''s|''t|''re|''ve|''m|''ll|''d| ?\w+| ?\d+| ?[^\s\w\d]+|\s+(?!\S)|\s+', 'g') WITH ORDINALITY AS rm (part, part_position)
        CROSS JOIN LATERAL
                (
                WITH    RECURSIVE
                        bpe AS
                        (
                        SELECT  (n + 1)::BIGINT AS position, character, TRUE AS continue
                        FROM    CONVERT_TO(part[1], 'UTF-8') AS bytes
                        CROSS JOIN LATERAL
                                GENERATE_SERIES(0, LENGTH(bytes) - 1) AS n
                        JOIN    encoder
                        ON      byte = GET_BYTE(bytes, n)
                        UNION ALL
                        (
                        WITH    RECURSIVE
                                base AS
                                (
                                SELECT  *
                                FROM    bpe
                                WHERE   continue
                                ),
                                bn AS
                                (
                                SELECT  ROW_NUMBER() OVER (ORDER BY position) AS position,
                                        continue,
                                        character,
                                        character || LEAD(character) OVER (ORDER BY position) AS cluster
                                FROM    base
                                ),
                                top_rank AS
                                (
                                SELECT  tokenizer.*
                                FROM    bn
                                CROSS JOIN LATERAL
                                        (
                                        SELECT  *
                                        FROM    tokenizer
                                        WHERE   tokenizer.cluster = bn.cluster
                                        LIMIT   1
                                        ) tokenizer
                                ORDER BY
                                        token
                                LIMIT   1
                                ),
                                breaks AS
                                (
                                SELECT  0::BIGINT AS position, 1 AS length
                                UNION ALL
                                SELECT  bn.position,
                                        CASE WHEN token IS NULL THEN 1 ELSE 2 END
                                FROM    breaks
                                JOIN    bn
                                ON      bn.position = breaks.position + length
                                LEFT JOIN
                                        top_rank
                                USING   (cluster)
                                )
                        SELECT  position, character, token IS NOT NULL
                        FROM    breaks
                        LEFT JOIN
                                top_rank
                        ON      1 = 1
                        CROSS JOIN LATERAL
                                (
                                SELECT  STRING_AGG(character, '' ORDER BY position) AS character
                                FROM    bn
                                WHERE   bn.position >= breaks.position
                                        AND bn.position < breaks.position + length
                                ) bn
                        WHERE   position > 0
                        )
                        )
                SELECT  position, character AS cluster
                FROM    bpe
                WHERE   NOT continue
                ) bpe
        ),
        tokens AS
        (
        SELECT  ARRAY_AGG(token ORDER BY part_position, position) AS input
        FROM    clusters
        JOIN    tokenizer
        USING   (cluster)
        ),
        gpt AS
        (
        SELECT  input, ARRAY_LENGTH(input, 1) AS original_length
        FROM    tokens
        UNION ALL
        SELECT  input || next_token.token, original_length
        FROM    gpt
        CROSS JOIN
                input
        CROSS JOIN LATERAL
                (
                WITH    RECURSIVE
                        hparams AS
                        (
                        SELECT  ARRAY_LENGTH(input, 1) AS n_seq,
                                12 AS n_block
                        ),
                        embeddings AS
                        (
                        SELECT  place, values
                        FROM    hparams
                        CROSS JOIN LATERAL
                                UNNEST(input) WITH ORDINALITY AS tokens (token, ordinality)
                        CROSS JOIN LATERAL
                                (
                                SELECT  ordinality - 1 AS place
                                ) o
                        CROSS JOIN LATERAL
                                (
                                SELECT  wte.values + wpe.values AS values
                                FROM    wte
                                CROSS JOIN
                                        wpe
                                WHERE   wte.token = tokens.token
                                        AND wpe.place = o.place
                                ) embedding
                        ),
                        transform AS
                        (
                        SELECT  0 AS block, place, values
                        FROM    embeddings
                        UNION ALL
                        (
                        WITH    previous AS
                                (
                                SELECT  *
                                FROM    transform
                                )
                        SELECT  block + 1 AS block, transformed_layer.*
                        FROM    hparams
                        CROSS JOIN LATERAL
                                (
                                SELECT  block
                                FROM    previous
                                WHERE   block < 12
                                LIMIT   1
                                ) q
                        CROSS JOIN LATERAL
                                (
                                WITH    ln_2_b AS
                                        (
                                        SELECT  *
                                        FROM    ln_2_b
                                        WHERE   block = q.block
                                        ),
                                        ln_2_g AS
                                        (
                                        SELECT  *
                                        FROM    ln_2_g
                                        WHERE   block = q.block
                                        ),
                                        c_proj_w AS
                                        (
                                        SELECT  *
                                        FROM    c_proj_w
                                        WHERE   block = q.block
                                        ),
                                        c_proj_b AS
                                        (
                                        SELECT  *
                                        FROM    c_proj_b
                                        WHERE   block = q.block
                                        ),
                                        mlp_c_fc_w AS
                                        (
                                        SELECT  *
                                        FROM    mlp_c_fc_w
                                        WHERE   block = q.block
                                        ),
                                        mlp_c_fc_b AS
                                        (
                                        SELECT  *
                                        FROM    mlp_c_fc_b
                                        WHERE   block = q.block
                                        ),
                                        mlp_c_proj_w AS
                                        (
                                        SELECT  *
                                        FROM    mlp_c_proj_w
                                        WHERE   block = q.block
                                        ),
                                        mlp_c_proj_b AS
                                        (
                                        SELECT  *
                                        FROM    mlp_c_proj_b
                                        WHERE   block = q.block
                                        ),
                                        c_attn_w AS
                                        (
                                        SELECT  *
                                        FROM    c_attn_w
                                        WHERE   block = q.block
                                        ),
                                        c_attn_b AS
                                        (
                                        SELECT  *
                                        FROM    c_attn_b
                                        WHERE   block = q.block
                                        ),
                                        ln_1_g AS
                                        (
                                        SELECT  *
                                        FROM    ln_1_g
                                        WHERE   block = q.block
                                        ),
                                        ln_1_b AS
                                        (
                                        SELECT  *
                                        FROM    ln_1_b
                                        WHERE   block = q.block
                                        ),
                                        mha_norm AS
                                        (
                                        SELECT  place, mm.values + c_attn_b.values AS values
                                        FROM    (
                                                SELECT  place, ARRAY_AGG(INNER_PRODUCT(c_attn_w.values, layer_norm.values) ORDER BY y)::VECTOR(2304) AS values
                                                FROM    (
                                                        SELECT  place, agg.values * ln_1_g.values + ln_1_b.values AS values
                                                        FROM    (
                                                                SELECT  place, norm.values
                                                                FROM    previous
                                                                CROSS JOIN LATERAL
                                                                        (
                                                                        SELECT  AVG(value) AS mean,
                                                                                VAR_POP(value) AS variance
                                                                        FROM    UNNEST(values::REAL[]) value
                                                                        ) agg
                                                                CROSS JOIN LATERAL
                                                                        (
                                                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                                                        ) norm
                                                                ) agg
                                                        CROSS JOIN
                                                                ln_1_b
                                                        CROSS JOIN
                                                                ln_1_g
                                                        ) layer_norm
                                                CROSS JOIN
                                                        c_attn_w
                                                GROUP BY
                                                        place
                                                ) mm
                                        CROSS JOIN
                                                c_attn_b
                                        ),
                                        heads AS
                                        (
                                        SELECT  place, head,
                                                (values::REAL[])[(head * 64 + 1):(head * 64 + 64)]::VECTOR(64) AS q,
                                                (values::REAL[])[(head * 64 + 1 + 768):(head * 64 + 64 + 768)]::VECTOR(64) AS k,
                                                (values::REAL[])[(head * 64 + 1 + 1536):(head * 64 + 64 + 1536)]::VECTOR(64) AS v
                                        FROM    mha_norm
                                        CROSS JOIN
                                                GENERATE_SERIES(0, 11) head
                                        ),
                                        sm_input AS
                                        (
                                        SELECT  head, h1.place AS x, h2.place AS y, INNER_PRODUCT(h1.q, h2.k) / 8 + CASE WHEN h2.place > h1.place THEN -1E10 ELSE 0 END AS value
                                        FROM    heads h1
                                        JOIN    heads h2
                                        USING   (head)
                                        ),
                                        sm_diff AS
                                        (
                                        SELECT  head, x, y, value - MAX(value) OVER (PARTITION BY head, x) AS diff
                                        FROM    sm_input
                                        ),
                                        sm_exp AS
                                        (
                                        SELECT  head, x, y, CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
                                        FROM    sm_diff
                                        ),
                                        softmax AS
                                        (
                                        SELECT  head, x, y AS place, e / SUM(e) OVER (PARTITION BY head, x) AS value
                                        FROM    sm_exp
                                        ),
                                        attention AS
                                        (
                                        SELECT  place, ARRAY_AGG(value ORDER BY head * 64 + ordinality)::VECTOR(768) AS values
                                        FROM    (
                                                SELECT  head, x AS place, SUM(ARRAY_FILL(softmax.value, ARRAY[64])::VECTOR(64) * heads.v) AS values
                                                FROM    softmax
                                                JOIN    heads
                                                USING   (head, place)
                                                GROUP BY
                                                        head, x
                                                ) q
                                        CROSS JOIN LATERAL
                                                UNNEST(values::REAL[]) WITH ORDINALITY v (value, ordinality)
                                        GROUP BY
                                                place
                                        ),
                                        mha AS
                                        (
                                        SELECT  place, w.values + c_proj_b.values + previous.values AS values
                                        FROM    (
                                                SELECT  attention.place, ARRAY_AGG(INNER_PRODUCT(attention.values, c_proj_w.values) ORDER BY c_proj_w.place)::VECTOR(768) AS values
                                                FROM    attention
                                                CROSS JOIN
                                                        c_proj_w
                                                GROUP BY
                                                        attention.place
                                                ) w
                                        CROSS JOIN
                                                c_proj_b
                                        JOIN    previous
                                        USING   (place)
                                        ),
                                        ffn_norm AS
                                        (
                                        SELECT  place, agg.values * ln_2_g.values + ln_2_b.values AS values
                                        FROM    (
                                                SELECT  place, norm.values
                                                FROM    mha
                                                CROSS JOIN LATERAL
                                                        (
                                                        SELECT  AVG(value) AS mean,
                                                                VAR_POP(value) AS variance
                                                        FROM    UNNEST(values::REAL[]) value
                                                        ) agg
                                                CROSS JOIN LATERAL
                                                        (
                                                        SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                                        FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n(value, ordinality)
                                                        ) norm
                                                ) agg
                                        CROSS JOIN
                                                ln_2_b
                                        CROSS JOIN
                                                ln_2_g
                                        ),
                                        ffn_a AS
                                        (
                                        SELECT  gelu.place, gelu.values
                                        FROM    (
                                                SELECT  place, w.values + mlp_c_fc_b.values AS values
                                                FROM    (
                                                        SELECT  ffn_norm.place, ARRAY_AGG(INNER_PRODUCT(ffn_norm.values, mlp_c_fc_w.values) ORDER BY mlp_c_fc_w.place)::VECTOR(3072) AS values
                                                        FROM    ffn_norm
                                                        CROSS JOIN
                                                                mlp_c_fc_w
                                                        GROUP BY
                                                                ffn_norm.place
                                                        ) w
                                                CROSS JOIN
                                                        mlp_c_fc_b
                                                ) v
                                        CROSS JOIN LATERAL
                                                (
                                                SELECT  place, ARRAY_AGG(0.5 * value * (1 + TANH(0.797884560802 * (value + 0.044715 * value*value*value))) ORDER BY ordinality)::VECTOR(3072) AS values
                                                FROM    UNNEST(values::REAL[]) WITH ORDINALITY n (value, ordinality)
                                                GROUP BY
                                                        place
                                                ) gelu
                                        ),
                                        ffn AS
                                        (
                                        SELECT  place, w.values + mlp_c_proj_b.values + mha.values AS values
                                        FROM    (
                                                SELECT  ffn_a.place, ARRAY_AGG(INNER_PRODUCT(ffn_a.values, mlp_c_proj_w.values) ORDER BY mlp_c_proj_w.place)::VECTOR(768) AS values
                                                FROM    ffn_a
                                                CROSS JOIN
                                                        mlp_c_proj_w
                                                GROUP BY
                                                        ffn_a.place
                                                ) w
                                        CROSS JOIN
                                                mlp_c_proj_b
                                        JOIN    mha
                                        USING   (place)
                                        )
                                SELECT  *
                                FROM    ffn
                                ) transformed_layer
                        )
                        ),
                        block_output AS
                        (
                        SELECT  *
                        FROM    hparams
                        JOIN    transform
                        ON      transform.block = n_block
                        ),
                        ln_f AS
                        (
                        SELECT  place, norm.values * ln_f_g.values + ln_f_b.values AS values
                        FROM    block_output
                        CROSS JOIN LATERAL
                                (
                                SELECT  AVG(value) AS mean,
                                        VAR_POP(value) AS variance
                                FROM    UNNEST(values::REAL[]) AS n(value)
                                ) agg
                        CROSS JOIN LATERAL
                                (
                                SELECT  ARRAY_AGG((value - mean) / SQRT(variance + 1E-5) ORDER BY ordinality)::VECTOR(768) AS values
                                FROM    UNNEST(values::REAL[]) WITH ORDINALITY AS n (value, ordinality)
                                ) norm
                        CROSS JOIN
                                ln_f_b
                        CROSS JOIN
                                ln_f_g
                        ),
                        logits AS
                        (
                        SELECT  token, INNER_PRODUCT(ln_f.values, wte.values) AS value
                        FROM    hparams
                        JOIN    ln_f
                        ON      ln_f.place = n_seq - 1
                        CROSS JOIN
                                wte
                        ORDER BY
                                value DESC
                        LIMIT   (top_n)
                        ),
                        tokens AS
                        (
                        SELECT  token,
                                high - softmax AS low,
                                high
                        FROM    (
                                SELECT  *,
                                        SUM(softmax) OVER (ORDER BY softmax) AS high
                                FROM    (
                                        SELECT  *, (e / SUM(e) OVER ()) AS softmax
                                        FROM    (
                                                SELECT  *,
                                                        (value - MAX(value) OVER ()) / temperature AS diff
                                                FROM    logits
                                                ) exp_x
                                        CROSS JOIN LATERAL
                                                (
                                                SELECT  CASE WHEN diff < -745.13 THEN 0 ELSE EXP(diff) END AS e
                                                ) exp
                                        ) q
                                ) q
                        ),
                        next_token AS
                        (
                        SELECT  *
                        FROM    (
                                SELECT  RANDOM() AS rnd
                                ) r
                        CROSS JOIN LATERAL
                                (
                                SELECT  *
                                FROM    tokens
                                WHERE   rnd >= low
                                        AND rnd < high
                                ) nt
                        )
                SELECT  *
                FROM    next_token
                ) next_token
        WHERE   ARRAY_LENGTH(input, 1) < original_length + threshold
                AND next_token.token <> 50256
        ),
        output AS
        (
        SELECT  CONVERT_FROM(STRING_AGG(SET_BYTE('\x00', 0, byte), '' ORDER BY position), 'UTF8') AS response
        FROM    (
                SELECT  STRING_AGG(cluster, '' ORDER BY ordinality) AS response
                FROM    input
                JOIN    gpt
                ON      ARRAY_LENGTH(input, 1) = original_length + threshold
                CROSS JOIN LATERAL
                        UNNEST(input) WITH ORDINALITY n (token, ordinality)
                JOIN    tokenizer
                USING   (token)
                ) q
        CROSS JOIN LATERAL
                STRING_TO_TABLE(response, NULL) WITH ORDINALITY n (character, position)
        JOIN    encoder
        USING   (character)
        )
SELECT  *
FROM    output
1a00f841de73f9bb1baf6ce719e8ae7d.png

ИИ со своей работой справился отлично. Я искренне желаю вам всего самого лучшего в наступившем году!

Все запросы и код для установки выложены в следующем репозитории на GitHub: quassnoi/explain-extended-2024.

Источник

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  • 09.07.26 19:06 Toivo Walli

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  • 13.07.26 23:49 [email protected]

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  • 15.07.26 11:53 Sarah Green

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  • 16.07.26 21:38 patricialovick86

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  • 16.07.26 21:38 patricialovick86

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  • 18.07.26 23:51 bernalzenaida

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  • 19.07.26 04:10 Fraddy Pual

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  • 19.07.26 19:53 kimberlyhebertt6877

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  • 19.07.26 19:53 kimberlyhebertt6877

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  • 19.07.26 19:54 kimberlyhebertt6877

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  • 30.07.26 00:04 Ahmed

    A really cool analysis, thank you. I was especially struck by how strictly the order is defined: data processing and formatting come first, with color only at the very end. This really saves you from the typical mistake of "first a pretty palette, then we figure out what the chart is for." And the palette validator with OKLCH + colorblindness check is absolutely fantastic; you almost never see that in regular tools. By the way, while reading about rank-trajectory and the logarithmic scale, I immediately remembered how convenient it is to analyze dynamics on charts in ExpertOption—I've been trading there for quite some time now. When the data is well visualized, decisions are noticeably easier and more relaxed. I also liked the point about "no more than eight colors" and the dual-axis ban. Strict restrictions sometimes actually produce better results than complete freedom. I'll try this approach myself.

  • 30.07.26 17:27 wendytaylor015

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  • 30.07.26 17:27 wendytaylor015

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  • 31.07.26 16:21 rssllhrnsb

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TEXT : (tatianasorina06 at G.Ma IL ..c 0 m ) Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.

  • 01.08.26 15:05 keithwilson9899

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  • 01.08.26 15:05 keithwilson9899

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  • 03.08.26 20:05 Philip

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  • 03.08.26 20:06 Philip

    I was a victim of a crypto theft involving a Pink Drainer, which resulted in the theft of my Wrapped Bitcoin (WBTC) from my Polygon network. The experience was incredibly frustrating and distressing, as I had no idea how to recover my funds. Unfortunately, by the time I noticed the theft, the funds had already been drained to an address that I had no control over, making it seem like an irreversible situation.The attack happened when I clicked on what appeared to be a legitimate link. I didn’t realize at the time that it was a phishing attempt designed to siphon off my private keys and access my wallet. The Pink Drainer, a type of malicious script used by attackers, is specifically designed to exploit such vulnerabilities in crypto wallets. The moment I realized that my WBTC had been drained from my Polygon network. I felt completely helpless, as I didn’t have direct access to the thief's address, and there was no way to reverse the transaction on my own at that point, I started searching for ways to recover my funds, but most resources only offered generic advice that wasn’t practical in this particular case. I quickly realized that if I wanted to have any hope of getting my assets back, I would need professional assistance. After some research, I came across a reliable and trusted recovery team called Aspen Recovery Experts. Their expertise in cryptocurrency recovery, especially in cases like mine, seemed promising.I decided to reach out to Aspen Recovery Experts, and I’m incredibly grateful that I did. Their team of crypto recovery experts was able to help me trace the stolen funds and identify the path the funds took after they left my wallet. Using advanced tools and techniques, they were able to track the transactions on the blockchain, helping me understand where my WBTC had been sent. More importantly, they worked tirelessly to assist me in contacting the necessary parties and even interfaced with blockchain analysts to help facilitate the recovery process.Thanks to their efforts, I was able to successfully recover my stolen funds. The entire process took some time, but the Aspen Recovery Experts dedicated team provided regular updates and kept me informed throughout the process, which gave me a sense of hope and relief during an otherwise stressful time. If you’re ever in a similar situation, I highly recommend reaching out to a trusted recovery team like Aspen Recovery Expertshhh . Their professionalism, knowledge, and expertise were critical in helping me recover my funds and regain control of my crypto assets. Whatsapp : +1 747 231 9036 Telegram : @ prohackerspy Email : [email protected]

  • 04.08.26 11:05 Kisnoles

    Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.

  • 04.08.26 12:37 kimberlyhebertt6877

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  • 04.08.26 12:37 kimberlyhebertt6877

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  • 06.08.26 08:11 ROMMYHENDERSON344

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  • 06.08.26 13:56 lydiassmith567

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  • 06.08.26 13:56 lydiassmith567

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  • 11.08.26 03:43 raymont0714

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  • 12.08.26 16:37 rssllhrnsb

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  • 16.08.26 01:44 Matt Kegan

    CapitalNode Analytics. They help to investigate and recover stolen digital assets from fake trading platforms. Great firm i must say.

  • 18.08.26 04:59 marcushenderson624

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  • 18.08.26 04:59 marcushenderson624

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  • 19.08.26 14:50 BAYER7043

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  • 20.08.26 11:32 michaeldavenport218

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  • 20.08.26 11:32 michaeldavenport218

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  • 23.08.26 20:02 leslieyee

    Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.

  • 25.08.26 13:44 lydiassmith567

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  • 25.08.26 13:44 lydiassmith567

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  • 30.08.26 15:37 [email protected]

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  • 30.08.26 15:37 [email protected]

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  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 11:16 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 01.09.26 17:34 Garry42

    Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 02.09.26 03:43 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 04.09.26 21:51 Kovengray

    Recovering your lost investment funds as the case might be, is not what you can do alone, you’d require the service of a trained recovery specialist. A recovery specialist is a person or a group of people who are well equipped to work around the brokerage network. They have vast knowledge about the whole network and have the right software and private keys to follow any transaction. I was ripped off trading online to an investment broker, good thing I got every penny back through the help of Gavin ray he’s a genius Contact : Gavinray78 at gmail com or WhatsApp +1 352 322 2096 It is also important to be patient and really calm during the process.

  • 05.09.26 21:38 [email protected]

    I invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place ‎Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 21:31 lisawerth897

    My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684

  • 09.09.26 23:22 Fraddy Pual

    Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 11.09.26 03:23 kimberlyhebertt6877

    I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684

  • 15.09.26 03:35 elioduncan

    I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.09.26 15:45 lydiassmith567

    HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 05.10.26 21:26 [email protected]

    I Invested 45,000  Euro, and later aggreviated to 198,000 Euro.  I requested  to place Withdrawal of my funds to be paid to my Bank account. But nothing happened I was subjected to pay more fee until I can get my funds on my account, i got in touch with Theodore ryan here on this platform who had helped a lot of people, I followed all instructions and he legally got back my withheld funds, I got threatened by the company that if I don’t pay they will get my account frozen, all thanks to Theodore ryan and I highly recommend him to anyone dealing with an unregulated broker company… contact him on his Gmail - theodoreryan318@  gmail .  com

  • 06.10.26 06:13 melissathomason573

    Professional Crypto Recovery Testimonial After losing a significant amount of cryptocurrency through a scam on Twitter, I was unsure whether my Bitcoin funds could ever be recovered. The experience was extremely stressful, particularly because I encountered several individuals and services claiming they could help recover my assets, only to discover that some were fraudulent themselves. A friend eventually referred me to CAPITAL CRYPTO RECOVER. Their team handled my case professionally, discreetly, and efficiently. With their assistance, I successfully recovered approximately $130,000 worth of USDT in just over a week. My experience taught me the importance of being extremely cautious when seeking cryptocurrency recovery services, as scammers often target victims a second time. I am grateful to CAPITAL CRYPTO RECOVER for the assistance I received and wanted to share my experience with others who may be facing a similar situation. If you’ve been compromised, don’t lose hope—and be careful of fraudsters posing as saviors. CAPITAL CRYPTO RECOVER are real professionals in crypto recovery. I am a living proof of their effectiveness, you can reach them through Phone (Call/WhatsApp): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 06.10.26 06:13 melissathomason573

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  • 06.10.26 17:44 Darrell1717

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