diff --git a/experiments/llm_only/configs/gemma_generate.json b/experiments/llm_only/configs/gemma_generate.json new file mode 100644 index 0000000..bb6c9d1 --- /dev/null +++ b/experiments/llm_only/configs/gemma_generate.json @@ -0,0 +1,19 @@ +{ + "bpe_tokenizer": "checkpoints/bpe_tokenizer.json", + "test_prompts": [ + "Open weights", + "The Llama model is", + "Efficient transformers" + ], + "model_config_path": "checkpoints/gemma-bpe/config.json", + "model_weights": "checkpoints/gemma-bpe/model.pt", + "generation": { + "max_new_tokens": 40, + "temperature": 0.8, + "do_sample": true, + "top_k": null, + "top_p": null + }, + "log_path": "checkpoints/gemma_only_generation_logs.json" + } + \ No newline at end of file diff --git a/experiments/llm_only/configs/gemma_train.json b/experiments/llm_only/configs/gemma_train.json new file mode 100644 index 0000000..b3e4dfd --- /dev/null +++ b/experiments/llm_only/configs/gemma_train.json @@ -0,0 +1,28 @@ +{ + "bpe_tokenizer": "checkpoints/bpe_tokenizer.json", + "bpe_vocab_size": 1000, + "bpe_special_tokens": ["", "", "", ""], + "test_prompts": ["Open source AI", "What is Llama?"], + "model_config": { + "vocab_size": null, + "embed_dim": 256, + "num_q_heads": 4, + "num_kv_heads": 2, + "head_size": 64, + "num_layers": 4, + "max_position_embeddings": 512, + "num_experts": 8, + "top_k_experts": 2, + "window_size": 16, + "dropout": 0.1 + }, + "model_weights": "checkpoints/gemma-bpe/model.pt", + "model_config_path": "checkpoints/gemma-bpe/config.json", + "training": { + "learning_rate": 0.0003, + "batch_size": 2, + "num_epochs": 3, + "warmup_steps": 50 + }, + "log_path": "checkpoints/gemma_only_training_logs.json" + } \ No newline at end of file diff --git a/experiments/llm_only/run_llm_experiment.py b/experiments/llm_only/run_llm_experiment.py index 105315e..dc8ec95 100644 --- a/experiments/llm_only/run_llm_experiment.py +++ b/experiments/llm_only/run_llm_experiment.py @@ -48,6 +48,9 @@ def load_model_class(model_name): elif model_name.lower() == 'mixtral': from llm.models.mixtral import Mixtral return Mixtral + elif model_name.lower() == 'gemma': + from llm.models.gemma import Gemma + return Gemma else: raise ValueError(f"Модель '{model_name}' не поддерживается.") diff --git a/llm/src/llm/models/gemma/__init__.py b/llm/src/llm/models/gemma/__init__.py new file mode 100644 index 0000000..7fc481f --- /dev/null +++ b/llm/src/llm/models/gemma/__init__.py @@ -0,0 +1,3 @@ +from .gemma import Gemma + +__all__ = ["Gemma"] diff --git a/llm/src/llm/models/gemma/gemma.py b/llm/src/llm/models/gemma/gemma.py new file mode 100644 index 0000000..c6dbd51 --- /dev/null +++ b/llm/src/llm/models/gemma/gemma.py @@ -0,0 +1,452 @@ +import torch +import math +from torch import nn +from torch import Tensor +import torch.nn.functional as F +from math import sqrt +from llm.core.base_model import BaseModel + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self._eps = eps + self._w = nn.Parameter(torch.ones(dim)) + + def forward(self, x: torch.Tensor): # [batch_size × seq_len × emb_size] + rms = (x.pow(2).mean(-1, keepdim=True) + self._eps) ** 0.5 + norm_x = x / rms + return self._w * norm_x + +class TokenEmbeddings(nn.Module): + def __init__(self, vocab_size: int, emb_size: int): + super().__init__() + self._embedding = nn.Embedding( + num_embeddings=vocab_size, + embedding_dim=emb_size + ) + + def forward(self, x: Tensor) -> Tensor: + return self._embedding(x) + + @property + def num_embeddings(self) -> int: + return self._embedding.num_embeddings + + @property + def embedding_dim(self) -> int: + return self._embedding.embedding_dim + + +class GELU(nn.Module): + def __init__(self): + super().__init__() + self.sqrt_2_over_pi = torch.sqrt(torch.tensor(2.0) / math.pi) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return 0.5 * x * (1 + torch.tanh( + self.sqrt_2_over_pi * (x + 0.044715 * torch.pow(x, 3)) + )) + +class GeGLU(nn.Module): + def __init__(self, emb_size: int, dropout: float = 0.1): + super().__init__() + + self._gate = nn.Linear(emb_size, 4 * emb_size) + self._up = nn.Linear(emb_size, 4 * emb_size) + self._down = nn.Linear(4 * emb_size, emb_size) + self._activation = GELU() + self._dropout = nn.Dropout(dropout) + + def forward(self, x: torch.Tensor): # [batch_size × seq_len × emb_size]. + gate_out = self._gate(x) # [batch, seq, 4*emb] + activation_out = self._activation(gate_out) # [batch, seq, 4*emb] + up_out = self._up(x) # [batch, seq, 4*emb] + out = up_out * activation_out # поэлементное! + out = self._down(out) # [batch, seq, emb] + return self._dropout(out) + + +import torch +from torch import nn +from typing import Optional + + +class RoPE(nn.Module): + + def __init__(self, head_size: int, max_seq_len: int, base: int = 10_000): + super().__init__() + assert head_size % 2 == 0, "head_size должен быть четным" + + # Вычисление частот: θ_i = base^(-2i/d) для i ∈ [0, d/2-1] + freqs = 1.0 / (base ** (2 * torch.arange(head_size // 2).float() / head_size)) + + # Позиции от 0 до max_seq_len-1 + positions = torch.arange(max_seq_len).float() + + # Внешнее произведение: m * θ_i для всех позиций и частот + freq_matrix = positions.unsqueeze(1) * freqs.unsqueeze(0) + + # Предвычисление матриц косинусов и синусов + self.register_buffer("cos_matrix", torch.cos(freq_matrix)) + self.register_buffer("sin_matrix", torch.sin(freq_matrix)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: # [batch_size × seq_len × head_size] [batch_size × num_heads × seq_len × head_size] + batch_size, num_heads, seq_len, head_size = x.shape + + # Берем нужную часть матриц и приводим к типу x + cos = self.cos_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2] + sin = self.sin_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2] + + # Явное изменение формы для broadcasting + cos = cos.reshape(1, 1, seq_len, head_size // 2) + sin = sin.reshape(1, 1, seq_len, head_size // 2) + + # Разделяем на четные и нечетные компоненты по ПОСЛЕДНЕМУ измерению + x_even = x[..., 0::2] # [batch_size, num_heads, seq_len, head_size//2] + x_odd = x[..., 1::2] # [batch_size, num_heads, seq_len, head_size//2] + + # Применяем поворот: q' = q * cos(mθ) + rotate(q) * sin(mθ) + x_rotated_even = x_even * cos - x_odd * sin + x_rotated_odd = x_even * sin + x_odd * cos + + # Объединяем обратно в исходную размерность + x_rotated = torch.stack([x_rotated_even, x_rotated_odd], dim=-1) + x_rotated = x_rotated.flatten(-2) # [batch_size, seq_len, head_size] + + return x_rotated + +import torch +from torch import nn +import torch.nn.functional as F + +class MultiQueryAttention(nn.Module): + def __init__( + self, + num_q_heads: int, + emb_size: int, + head_size: int, + max_seq_len: int, + rope: RoPE = None, + dropout: float = 0.1, + ): + super().__init__() + self._num_q_heads = num_q_heads + self._head_size = head_size + self._max_seq_len = max_seq_len + self._rope = rope + + self._q = nn.Linear(emb_size, num_q_heads * head_size) + self._k = nn.Linear(emb_size, head_size) + self._v = nn.Linear(emb_size, head_size) + + # Создание causal маски + mask = torch.tril(torch.ones(max_seq_len, max_seq_len)) + self.register_buffer( + "_tril_mask", mask.bool() if hasattr(torch, "bool") else mask.byte() + ) + + self._layer = nn.Linear(num_q_heads * head_size, emb_size) + self._dropout = nn.Dropout(dropout) + + def forward( + self, + x: torch.Tensor, + mask: torch.Tensor = None, + use_cache: bool = True, + cache: list = None, + ): + batch_size, seq_len, emb_size = x.shape + if seq_len > self._max_seq_len: + raise ValueError( + f"Длина последовательности {seq_len} превышает максимум {self._max_seq_len}" + ) + + # Пропустите тензор x через матрицы Wq, Wk , Wv, чтобы получить матрицы запроса, ключа и значения. + k = self._k(x) # [B, T, hs] + q = self._q(x) # [B, T, hs] + v = self._v(x) # [B, T, hs] + + # Шаг 2: Изменение формы для multi-head + # [batch_size, seq_len, num_heads * head_size] + # -> [batch_size, seq_len, num_heads, head_size] + q = q.reshape(batch_size, seq_len, self._num_q_heads, self._head_size) + k = k.reshape(batch_size, seq_len, 1, self._head_size) + v = v.reshape(batch_size, seq_len, 1, self._head_size) + + # 3. Transpose: [B, T, H, hs] -> [B, H, T, hs] + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + + # Пропустите матрицы запроса и ключа через экземпляр rope, чтобы выполнить поворот. + if self._rope is not None: + # Применяем RoPE к Q и K (НЕ к V!) + q = self._rope(q) # [B, T, hs] + k = self._rope(k) # [B, T, hs] + + + # Если cache пришел, то объединяем кэш и одну строку из ключа и значения. Это будут новые key и value для последующих вычислений. + # 5. Кэширование (для autoregressive generation) + if cache is not None: + k_cache, v_cache = cache + k = torch.cat([k_cache, k], dim=2) # Concat по seq_len (dim=2) + v = torch.cat([v_cache, v], dim=2) + + + # Перемножим матрицы запроса и ключа (транспонированную), чтобы вычислить матрицу внимания. + # И разделить все значения в матрице внимания на корень из head_size. + scores = q @ k.transpose(-2, -1) / (self._head_size ** 0.5) + + # Если cache пришел, то маску не накладываем. Иначе наложите на матрицу внимания треугольную маску, созданную при инициализации. Все скрытые значения должны быть приведены к минус бесконечности: float('-inf'). + if cache is None: + scores = scores.masked_fill( + ~self._tril_mask[:seq_len, :seq_len], float("-inf") + ) + + # Применить к матрице внимания (построчно) функцию Softmax. + weights = F.softmax(scores, dim=-1) + + # Перемножим матрицу внимания и матрицу значения. + x_out = weights @ v # [B, T, hs] + + + # Измените форму тензора на batch_size × seq_len × num_heads*head_size. + # Transpose обратно и concatenate heads + x_out = x_out.transpose(1, 2) # [B, T_q, H, hs] + x_out = x_out.contiguous() # Важно для reshape! + concatenated_attention = x_out.reshape(batch_size, seq_len, self._num_q_heads * self._head_size) + + + # Пропустите получившийся тензор через последний линейный слой. + # 3. Проецируем в пространство эмбеддингов + projected_output = self._layer(concatenated_attention) + + + # 4. Применяем dropout для регуляризации + final_output = self._dropout(projected_output) + + if use_cache is True: + return (final_output, (k, v)) + else: + return (final_output, None) + + +class Decoder(nn.Module): + def __init__(self, + num_q_heads: int, + emb_size: int, + head_size: int, + max_seq_len: int, + rope: RoPE, + dropout: float = 0.1 + ): + super().__init__() + self._heads = MultiQueryAttention( + num_q_heads=num_q_heads, + emb_size=emb_size, + head_size=head_size, + max_seq_len=max_seq_len, + rope=rope, + dropout=dropout + ) + self._ff = GeGLU(emb_size=emb_size, dropout=dropout) + self._norm1 = RMSNorm(emb_size) + self._norm2 = RMSNorm(emb_size) + + def forward(self, x: torch.Tensor, mask: torch.Tensor = None, use_cache: bool = True, cache: list = None) -> torch.Tensor: + norm1_out = self._norm1(x) + attention, kv_caches = self._heads(norm1_out, mask, use_cache=use_cache, cache=cache) + out = attention + x + + norm2_out = self._norm2(out) + ffn_out = self._ff(norm2_out) + + if use_cache is True: + return (ffn_out + out, kv_caches) + else: + return (ffn_out + out, None) + + + +from torch import nn +import torch +import torch.nn.functional as F + +class Gemma(BaseModel): + def __init__(self, config): + super().__init__(config) + + self._max_seq_len = config["max_position_embeddings"] + + # Инициализация слоев + self._token_embeddings = TokenEmbeddings( + vocab_size=config["vocab_size"], + emb_size=config["embed_dim"] + ) + self._position_embeddings = RoPE( + head_size=config["embed_dim"] // config["num_q_heads"], + max_seq_len=config["max_position_embeddings"] + ) + #self._position_embeddings = PositionalEmbeddings( + # max_seq_len=max_seq_len, + # emb_size=emb_size + #) + self._dropout = nn.Dropout(config["dropout"]) + self._decoders = nn.ModuleList([Decoder( + num_q_heads=config["num_q_heads"], + emb_size=config["embed_dim"], + head_size=config["embed_dim"] // config["num_q_heads"], + max_seq_len=config["max_position_embeddings"], + rope=self._position_embeddings, + dropout=config["dropout"] + ) for _ in range(config["num_layers"])]) + self._norm = RMSNorm(config["embed_dim"]) + self._linear = nn.Linear(config["embed_dim"], config["vocab_size"]) + + def forward(self, x: torch.Tensor, use_cache: bool = True, cache: list = None) -> tuple: + # Проверка длины последовательности (только при отсутствии кэша) + if cache is None and x.size(1) > self._max_seq_len: + raise ValueError(f"Длина последовательности {x.size(1)} превышает максимальную {self.max_seq_len}") + + # Эмбеддинги токенов и позиций + tok_out = self._token_embeddings(x) # [batch, seq_len, emb_size] + #pos_out = self._position_embeddings(x) # [batch, seq_len, emb_size] + + # Комбинирование + out = self._dropout(tok_out) # [batch, seq_len, emb_size] + + # Стек декодеров с передачей кэша + new_cache = [] + for i, decoder in enumerate(self._decoders): + decoder_cache = cache[i] if cache is not None else None + decoder_result = decoder(out, use_cache=use_cache, cache=decoder_cache) + + # Извлекаем результат из кортежа + if use_cache: + out, decoder_new_cache = decoder_result + new_cache.append(decoder_new_cache) + else: + out = decoder_result[0] + + out = self._norm(out) + logits = self._linear(out) + + # Возвращаем результат с учетом use_cache + if use_cache: + return (logits, new_cache) + else: + return (logits, None) + + def generate(self, + x: torch.Tensor, + max_new_tokens: int, + do_sample: bool, + temperature: float = 1.0, + top_k: int = None, + top_p: float = None, + use_cache: bool = True + ) -> torch.Tensor: + + cache = None + + for _ in range(max_new_tokens): + if use_cache and cache is not None: + # Используем кэш - передаем только последний токен + x_input = x[:, -1:] # [batch_size, 1] + else: + # Первая итерация или кэш отключен - передаем всю последовательность + x_input = x + + # Прямой проход с кэшем + logits, new_cache = self.forward(x_input, use_cache=use_cache, cache=cache) + + # Обновляем кэш для следующей итерации + if use_cache: + cache = new_cache + + last_logits = logits[:, -1, :] # [batch_size, vocab_size] + + # Масштабируем логиты температурой + if temperature > 0: + logits_scaled = last_logits / temperature + else: + logits_scaled = last_logits + + if do_sample == True and top_k != None: + _, topk_indices = torch.topk(logits_scaled, top_k, dim=-1) + + # # Заменим все НЕ top-k логиты на -inf + masked_logits = logits_scaled.clone() + vocab_size = logits_scaled.size(-1) + + # создаём маску: 1, если токен НЕ в topk_indices + mask = torch.ones_like(logits_scaled, dtype=torch.bool if hasattr(torch, "bool") else torch.uint8) + mask.scatter_(1, topk_indices, False if hasattr(torch, "bool") else 0) # 0 там, где top-k индексы + masked_logits[mask.bool() if hasattr(torch, "bool") else mask.byte()] = float('-inf') + + logits_scaled = masked_logits + + if do_sample == True and top_p != None: + # 1. Применим softmax, чтобы получить вероятности: + probs = F.softmax(logits_scaled, dim=-1) # [B, vocab_size] + # 2. Отсортируем токены по убыванию вероятностей: + sorted_probs, sorted_indices = torch.sort(probs, descending=True, dim=-1) + # 3. Посчитаем кумулятивную сумму вероятностей: + cum_probs = torch.cumsum(sorted_probs, dim=-1) # [B, vocab_size] + # 4. Определим маску: оставить токены, пока сумма < top_p + sorted_mask = (cum_probs <= top_p).bool() if hasattr(torch, "bool") else (cum_probs <= top_p).byte() # [B, vocab_size] + # Гарантируем, что хотя бы первый токен останется + sorted_mask[:, 0] = True if hasattr(torch, "bool") else 1 + # 5. Преобразуем маску обратно в оригинальный порядок: + # Создаём полную маску из 0 + mask = torch.zeros_like(probs, dtype=torch.bool if hasattr(torch, "bool") else torch.uint8) + # Устанавливаем 1 в местах нужных токенов + mask.scatter_(dim=1, index=sorted_indices, src=sorted_mask) + # 6. Зануляем логиты токенов вне топ-p: + logits_scaled[~mask] = float('-inf') + + # 4. Применяем Softmax + probs = F.softmax(logits_scaled, dim=-1) # [batch_size, vocab_size] + + + if do_sample == True: + # 5. Если do_sample равен True, то отбираем токен случайно с помощью torch.multinomial + next_token = torch.multinomial(probs, num_samples=1) # [batch_size, 1] + else: + # 5. Если do_sample равен False, то выбираем токен с максимальной вероятностью + next_token = torch.argmax(probs, dim=-1, keepdim=True) # [batch_size, 1] + + # 6. Добавляем его к последовательности + x = torch.cat([x, next_token], dim=1) # [batch_size, seq_len+1] + return x + + def save(self, path): + torch.save({ + 'model_state_dict': self.state_dict(), + 'vocab_size': self._vocab_size, + 'max_seq_len': self._max_seq_len, + 'emb_size': self._emb_size, + 'num_heads': self._num_heads, + 'head_size': self._head_size, + 'num_layers': self._num_layers + }, path) + + @classmethod + def load(cls, path, device): + checkpoint = torch.load(path, map_location=device) + model = cls( + vocab_size=checkpoint['vocab_size'], + max_seq_len=checkpoint['max_seq_len'], + emb_size=checkpoint['emb_size'], + num_heads=checkpoint['num_heads'], + head_size=checkpoint['head_size'], + num_layers=checkpoint['num_layers'] + ) + model.load_state_dict(checkpoint['model_state_dict']) + model.to(device) + return model + + @property + def max_seq_len(self) -> int: + return self._max_seq_len \ No newline at end of file diff --git a/llm/tests/models/test_gemma.py b/llm/tests/models/test_gemma.py new file mode 100644 index 0000000..6305bbb --- /dev/null +++ b/llm/tests/models/test_gemma.py @@ -0,0 +1,56 @@ +# llm/tests/models/test_gemma.py + +import torch +import pytest +from llm.models.gemma.gemma import Gemma + +@pytest.fixture +def config(): + return { + "vocab_size": 100, + "embed_dim": 32, + "num_q_heads": 4, + "num_layers": 2, + "max_position_embeddings": 16, + "dropout": 0.0, + } + +@pytest.fixture +def model(config): + return Gemma(config) + +def test_forward_basic(model): + x = torch.randint(0, 100, (2, 8)) + logits, cache = model(x) + assert logits.shape == (2, 8, 100) + assert isinstance(cache, list) + assert len(cache) == model._decoders.__len__() + +def test_forward_with_cache(model): + x = torch.randint(0, 100, (2, 4)) + logits, cache = model(x, use_cache=True) + # Второй проход с cache и одним новым токеном + x2 = torch.randint(0, 100, (2, 1)) + logits2, cache2 = model(x2, use_cache=True, cache=cache) + assert logits2.shape == (2, 1, 100) + assert isinstance(cache2, list) + +def test_generate_and_shape(model): + x = torch.randint(0, 100, (1, 5)) + result = model.generate(x, max_new_tokens=3, do_sample=False) + assert result.shape == (1, 8) + +def test_forward_sequence_too_long(model, config): + x = torch.randint(0, 100, (1, config["max_position_embeddings"] + 1)) + with pytest.raises(ValueError): + model(x) + +def test_generate_with_sampling_topk(model): + x = torch.randint(0, 100, (1, 3)) + out = model.generate(x, max_new_tokens=2, do_sample=True, top_k=5) + assert out.shape == (1, 5) + +def test_generate_with_sampling_topp(model): + x = torch.randint(0, 100, (1, 3)) + out = model.generate(x, max_new_tokens=2, do_sample=True, top_p=0.8) + assert out.shape == (1, 5) diff --git a/notebooks/gemma.ipynb b/notebooks/gemma.ipynb new file mode 100644 index 0000000..20c22ce --- /dev/null +++ b/notebooks/gemma.ipynb @@ -0,0 +1,1344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1636810a", + "metadata": {}, + "source": [ + "# Gemma\n", + "\n", + "

\n", + " \"arch\"\n", + "

\n", + "\n", + "Gemma 1 вышла в феврале 2024 года.\n", + "\n", + "По архитектуре модель больше всего похожа на Llama'у. Содержит уже знакомые нам RoPE и RMSNorm. Но есть и новинки:\n", + "\n", + "* **Multi-Query Attention (MQA)** — крайне экономный вариант механизма внимания.\n", + "* **GeGLU** — гибридная функция активации. Почти клон SwiGLU :)\n", + "\n", + "Обе довольно легкие для внедрения, по сравнению с прошлыми новинками :)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cea30169", + "metadata": {}, + "source": [ + "# Multi-Query Attention\n", + "\n", + "По своей сути, Multi-Query Attention (MQA) — это частный случай Grouped Query Attention (GQA), который мы реализовали в уроке про Mistral.\n", + "\n", + "

\n", + " \"mqa\"\n", + "

\n", + "\n", + "В GQA на каждую голову приходится один вектор запроса (query). При этом каждый вектор ключа (key) и значения (value) обслуживает ( n )-голов.\n", + "Так вот, в MQA на все головы (в одном блоке декодера) приходится всего по одному вектору ключа (key) и одному вектору значения (value). Это такая радикальная форма экономии :)\n" + ] + }, + { + "cell_type": "markdown", + "id": "539550fe", + "metadata": {}, + "source": [ + "**Multi-Query Attention (разработка)**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "6be61c63", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from typing import Optional\n", + "\n", + "\n", + "class RoPE(nn.Module):\n", + "\n", + " def __init__(self, head_size: int, max_seq_len: int, base: int = 10_000):\n", + " super().__init__()\n", + " assert head_size % 2 == 0, \"head_size должен быть четным\"\n", + "\n", + " # Вычисление частот: θ_i = base^(-2i/d) для i ∈ [0, d/2-1]\n", + " freqs = 1.0 / (base ** (2 * torch.arange(head_size // 2).float() / head_size))\n", + "\n", + " # Позиции от 0 до max_seq_len-1\n", + " positions = torch.arange(max_seq_len).float()\n", + "\n", + " # Внешнее произведение: m * θ_i для всех позиций и частот\n", + " freq_matrix = positions.unsqueeze(1) * freqs.unsqueeze(0)\n", + "\n", + " # Предвычисление матриц косинусов и синусов\n", + " self.register_buffer(\"cos_matrix\", torch.cos(freq_matrix))\n", + " self.register_buffer(\"sin_matrix\", torch.sin(freq_matrix))\n", + "\n", + " def forward(self, x: torch.Tensor) -> torch.Tensor: # [batch_size × seq_len × head_size] [batch_size × num_heads × seq_len × head_size]\n", + " batch_size, num_heads, seq_len, head_size = x.shape\n", + "\n", + " # Берем нужную часть матриц и приводим к типу x\n", + " cos = self.cos_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2]\n", + " sin = self.sin_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2]\n", + "\n", + " # Явное изменение формы для broadcasting\n", + " cos = cos.reshape(1, 1, seq_len, head_size // 2)\n", + " sin = sin.reshape(1, 1, seq_len, head_size // 2)\n", + "\n", + " # Разделяем на четные и нечетные компоненты по ПОСЛЕДНЕМУ измерению\n", + " x_even = x[..., 0::2] # [batch_size, num_heads, seq_len, head_size//2]\n", + " x_odd = x[..., 1::2] # [batch_size, num_heads, seq_len, head_size//2]\n", + "\n", + " # Применяем поворот: q' = q * cos(mθ) + rotate(q) * sin(mθ)\n", + " x_rotated_even = x_even * cos - x_odd * sin\n", + " x_rotated_odd = x_even * sin + x_odd * cos\n", + "\n", + " # Объединяем обратно в исходную размерность\n", + " x_rotated = torch.stack([x_rotated_even, x_rotated_odd], dim=-1)\n", + " x_rotated = x_rotated.flatten(-2) # [batch_size, seq_len, head_size]\n", + "\n", + " return x_rotated" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "811921b1", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "\n", + "class MultiQueryAttention(nn.Module):\n", + " def __init__(\n", + " self,\n", + " num_q_heads: int,\n", + " emb_size: int,\n", + " head_size: int,\n", + " max_seq_len: int,\n", + " rope: RoPE = None,\n", + " dropout: float = 0.1,\n", + " ):\n", + " super().__init__()\n", + " self._num_q_heads = num_q_heads\n", + " self._head_size = head_size\n", + " self._max_seq_len = max_seq_len\n", + " self._rope = rope\n", + " \n", + " self._q = nn.Linear(emb_size, num_q_heads * head_size)\n", + " self._k = nn.Linear(emb_size, head_size)\n", + " self._v = nn.Linear(emb_size, head_size)\n", + "\n", + " # Создание causal маски\n", + " mask = torch.tril(torch.ones(max_seq_len, max_seq_len))\n", + " self.register_buffer(\n", + " \"_tril_mask\", mask.bool() if hasattr(torch, \"bool\") else mask.byte()\n", + " )\n", + " \n", + " self._layer = nn.Linear(num_q_heads * head_size, emb_size)\n", + " self._dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(\n", + " self,\n", + " x: torch.Tensor,\n", + " mask: torch.Tensor = None,\n", + " use_cache: bool = True,\n", + " cache: list = None,\n", + " ):\n", + " batch_size, seq_len, emb_size = x.shape\n", + " if seq_len > self._max_seq_len:\n", + " raise ValueError(\n", + " f\"Длина последовательности {seq_len} превышает максимум {self._max_seq_len}\"\n", + " )\n", + "\n", + " # Пропустите тензор x через матрицы Wq, Wk , Wv, чтобы получить матрицы запроса, ключа и значения.\n", + " k = self._k(x) # [B, T, hs]\n", + " q = self._q(x) # [B, T, hs]\n", + " v = self._v(x) # [B, T, hs]\n", + "\n", + " # Шаг 2: Изменение формы для multi-head\n", + " # [batch_size, seq_len, num_heads * head_size] \n", + " # -> [batch_size, seq_len, num_heads, head_size]\n", + " q = q.reshape(batch_size, seq_len, self._num_q_heads, self._head_size)\n", + " k = k.reshape(batch_size, seq_len, 1, self._head_size)\n", + " v = v.reshape(batch_size, seq_len, 1, self._head_size)\n", + "\n", + " # 3. Transpose: [B, T, H, hs] -> [B, H, T, hs]\n", + " q = q.transpose(1, 2)\n", + " k = k.transpose(1, 2)\n", + " v = v.transpose(1, 2)\n", + "\n", + " # Пропустите матрицы запроса и ключа через экземпляр rope, чтобы выполнить поворот.\n", + " if self._rope is not None:\n", + " # Применяем RoPE к Q и K (НЕ к V!)\n", + " q = self._rope(q) # [B, T, hs]\n", + " k = self._rope(k) # [B, T, hs]\n", + "\n", + "\n", + " # Если cache пришел, то объединяем кэш и одну строку из ключа и значения. Это будут новые key и value для последующих вычислений.\n", + " # 5. Кэширование (для autoregressive generation)\n", + " if cache is not None:\n", + " k_cache, v_cache = cache\n", + " k = torch.cat([k_cache, k], dim=2) # Concat по seq_len (dim=2)\n", + " v = torch.cat([v_cache, v], dim=2)\n", + "\n", + "\n", + " # Перемножим матрицы запроса и ключа (транспонированную), чтобы вычислить матрицу внимания.\n", + " # И разделить все значения в матрице внимания на корень из head_size.\n", + " scores = q @ k.transpose(-2, -1) / (self._head_size ** 0.5)\n", + "\n", + " # Если cache пришел, то маску не накладываем. Иначе наложите на матрицу внимания треугольную маску, созданную при инициализации. Все скрытые значения должны быть приведены к минус бесконечности: float('-inf').\n", + " if cache is None:\n", + " scores = scores.masked_fill(\n", + " ~self._tril_mask[:seq_len, :seq_len], float(\"-inf\")\n", + " )\n", + "\n", + " # Применить к матрице внимания (построчно) функцию Softmax.\n", + " weights = F.softmax(scores, dim=-1)\n", + "\n", + " # Перемножим матрицу внимания и матрицу значения.\n", + " x_out = weights @ v # [B, T, hs]\n", + "\n", + "\n", + " # Измените форму тензора на batch_size × seq_len × num_heads*head_size.\n", + " # Transpose обратно и concatenate heads\n", + " x_out = x_out.transpose(1, 2) # [B, T_q, H, hs]\n", + " x_out = x_out.contiguous() # Важно для reshape!\n", + " concatenated_attention = x_out.reshape(batch_size, seq_len, self._num_q_heads * self._head_size)\n", + "\n", + "\n", + " # Пропустите получившийся тензор через последний линейный слой.\n", + " # 3. Проецируем в пространство эмбеддингов\n", + " projected_output = self._layer(concatenated_attention)\n", + "\n", + "\n", + " # 4. Применяем dropout для регуляризации\n", + " final_output = self._dropout(projected_output)\n", + "\n", + " if use_cache is True:\n", + " return (final_output, (k, v))\n", + " else:\n", + " return (final_output, None)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "97771d9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Test 1 - Output shape: torch.Size([2, 10, 512])\n", + "✅ Test 2 - First output shape: torch.Size([2, 5, 512])\n", + "✅ Test 2 - Second output shape: torch.Size([2, 1, 512])\n", + "\n", + "✅ Все тесты пройдены!\n" + ] + } + ], + "source": [ + "\n", + "# Параметры\n", + "batch_size = 2\n", + "seq_len = 10\n", + "emb_size = 512\n", + "num_q_heads = 8\n", + "head_size = 64\n", + "max_seq_len = 512\n", + "\n", + " # Создание модели\n", + "rope = RoPE(head_size=head_size, max_seq_len=max_seq_len)\n", + "mha = MultiQueryAttention(\n", + " num_q_heads=num_q_heads,\n", + " emb_size=emb_size,\n", + " head_size=head_size,\n", + " max_seq_len=max_seq_len,\n", + " rope=rope,\n", + " dropout=0.1,\n", + ")\n", + "\n", + " # Тест 1: Обычный forward pass\n", + "x = torch.randn(batch_size, seq_len, emb_size)\n", + "output, cache = mha(x, use_cache=False)\n", + "print(f\"✅ Test 1 - Output shape: {output.shape}\") # [2, 10, 512]\n", + "assert output.shape == (batch_size, seq_len, emb_size)\n", + "\n", + " # Тест 2: С кэшированием\n", + "x1 = torch.randn(batch_size, 5, emb_size)\n", + "output1, cache1 = mha(x1, use_cache=True)\n", + "print(f\"✅ Test 2 - First output shape: {output1.shape}\") # [2, 5, 512]\n", + "\n", + "x2 = torch.randn(batch_size, 1, emb_size)\n", + "output2, cache2 = mha(x2, use_cache=True, cache=cache1)\n", + "print(f\"✅ Test 2 - Second output shape: {output2.shape}\") # [2, 1, 512]\n", + "\n", + "print(\"\\n✅ Все тесты пройдены!\")" + ] + }, + { + "cell_type": "markdown", + "id": "b5875022", + "metadata": {}, + "source": [ + "Вот конвертированный Markdown для твоего HTML:\n", + "\n", + "---\n", + "\n", + "# GeGLU\n", + "\n", + "

\n", + " \"geglu\"\n", + "

\n", + "\n", + "GeGLU — это гибридная функция активации.\n", + "По сути, это та же **SwiGLU**, которую мы реализовали в **Llama**, просто у неё в качестве базовой функции вместо **SiLU** (как в Llama) используется **GELU** (как в GPT-2).\n" + ] + }, + { + "cell_type": "markdown", + "id": "a3345832", + "metadata": {}, + "source": [ + "**GeGLU (разработка)**" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "82f52110", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "\n", + "class GELU(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.sqrt_2_over_pi = torch.sqrt(torch.tensor(2.0) / math.pi)\n", + " \n", + " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", + " return 0.5 * x * (1 + torch.tanh(\n", + " self.sqrt_2_over_pi * (x + 0.044715 * torch.pow(x, 3))\n", + " ))\n", + "\n", + "class GeGLU(nn.Module):\n", + " def __init__(self, emb_size: int, dropout: float = 0.1):\n", + " super().__init__()\n", + "\n", + " self._gate = nn.Linear(emb_size, 4 * emb_size)\n", + " self._up = nn.Linear(emb_size, 4 * emb_size)\n", + " self._down = nn.Linear(4 * emb_size, emb_size)\n", + " self._activation = GELU()\n", + " self._dropout = nn.Dropout(dropout)\n", + "\n", + " def forward(self, x: torch.Tensor): # [batch_size × seq_len × emb_size].\n", + " gate_out = self._gate(x) # [batch, seq, 4*emb]\n", + " activation_out = self._activation(gate_out) # [batch, seq, 4*emb]\n", + " up_out = self._up(x) # [batch, seq, 4*emb]\n", + " out = up_out * activation_out # поэлементное!\n", + " out = self._down(out) # [batch, seq, emb]\n", + " return self._dropout(out)\n" + ] + }, + { + "cell_type": "markdown", + "id": "db378855", + "metadata": {}, + "source": [ + "# Full Model" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "568437e8", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch import Tensor\n", + "import torch.nn.functional as F\n", + "from math import sqrt\n", + "\n", + "\n", + " \n", + "class RMSNorm(nn.Module):\n", + " def __init__(self, dim: int, eps: float = 1e-6):\n", + " super().__init__()\n", + " self._eps = eps\n", + " self._w = nn.Parameter(torch.ones(dim))\n", + " \n", + " def forward(self, x: torch.Tensor): # [batch_size × seq_len × emb_size]\n", + " rms = (x.pow(2).mean(-1, keepdim=True) + self._eps) ** 0.5\n", + " norm_x = x / rms\n", + " return self._w * norm_x\n", + "\n", + "class TokenEmbeddings(nn.Module):\n", + " def __init__(self, vocab_size: int, emb_size: int):\n", + " super().__init__()\n", + " self._embedding = nn.Embedding(\n", + " num_embeddings=vocab_size,\n", + " embedding_dim=emb_size\n", + " )\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " return self._embedding(x)\n", + "\n", + " @property\n", + " def num_embeddings(self) -> int:\n", + " return self._embedding.num_embeddings\n", + "\n", + " @property\n", + " def embedding_dim(self) -> int:\n", + " return self._embedding.embedding_dim\n", + "\n", + "\n", + "\n", + "import torch\n", + "from torch import nn\n", + "from typing import Optional\n", + "\n", + "\n", + "class RoPE(nn.Module):\n", + "\n", + " def __init__(self, head_size: int, max_seq_len: int, base: int = 10_000):\n", + " super().__init__()\n", + " assert head_size % 2 == 0, \"head_size должен быть четным\"\n", + "\n", + " # Вычисление частот: θ_i = base^(-2i/d) для i ∈ [0, d/2-1]\n", + " freqs = 1.0 / (base ** (2 * torch.arange(head_size // 2).float() / head_size))\n", + "\n", + " # Позиции от 0 до max_seq_len-1\n", + " positions = torch.arange(max_seq_len).float()\n", + "\n", + " # Внешнее произведение: m * θ_i для всех позиций и частот\n", + " freq_matrix = positions.unsqueeze(1) * freqs.unsqueeze(0)\n", + "\n", + " # Предвычисление матриц косинусов и синусов\n", + " self.register_buffer(\"cos_matrix\", torch.cos(freq_matrix))\n", + " self.register_buffer(\"sin_matrix\", torch.sin(freq_matrix))\n", + "\n", + " def forward(self, x: torch.Tensor) -> torch.Tensor: # [batch_size × seq_len × head_size] [batch_size × num_heads × seq_len × head_size]\n", + " batch_size, num_heads, seq_len, head_size = x.shape\n", + "\n", + " # Берем нужную часть матриц и приводим к типу x\n", + " cos = self.cos_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2]\n", + " sin = self.sin_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2]\n", + "\n", + " # Явное изменение формы для broadcasting\n", + " cos = cos.reshape(1, 1, seq_len, head_size // 2)\n", + " sin = sin.reshape(1, 1, seq_len, head_size // 2)\n", + "\n", + " # Разделяем на четные и нечетные компоненты по ПОСЛЕДНЕМУ измерению\n", + " x_even = x[..., 0::2] # [batch_size, num_heads, seq_len, head_size//2]\n", + " x_odd = x[..., 1::2] # [batch_size, num_heads, seq_len, head_size//2]\n", + "\n", + " # Применяем поворот: q' = q * cos(mθ) + rotate(q) * sin(mθ)\n", + " x_rotated_even = x_even * cos - x_odd * sin\n", + " x_rotated_odd = x_even * sin + x_odd * cos\n", + "\n", + " # Объединяем обратно в исходную размерность\n", + " x_rotated = torch.stack([x_rotated_even, x_rotated_odd], dim=-1)\n", + " x_rotated = x_rotated.flatten(-2) # [batch_size, seq_len, head_size]\n", + "\n", + " return x_rotated\n", + "\n", + "\n", + "class Decoder(nn.Module):\n", + " def __init__(self, \n", + " num_q_heads: int,\n", + " emb_size: int,\n", + " head_size: int,\n", + " max_seq_len: int,\n", + " rope: RoPE,\n", + " dropout: float = 0.1\n", + " ):\n", + " super().__init__()\n", + " self._heads = MultiQueryAttention(\n", + " num_q_heads=num_q_heads, \n", + " emb_size=emb_size, \n", + " head_size=head_size, \n", + " max_seq_len=max_seq_len,\n", + " rope=rope,\n", + " dropout=dropout\n", + " )\n", + " self._ff = GeGLU(emb_size=emb_size, dropout=dropout)\n", + " self._norm1 = RMSNorm(emb_size)\n", + " self._norm2 = RMSNorm(emb_size)\n", + "\n", + " def forward(self, x: torch.Tensor, mask: torch.Tensor = None, use_cache: bool = True, cache: list = None) -> torch.Tensor:\n", + " norm1_out = self._norm1(x)\n", + " attention, kv_caches = self._heads(norm1_out, mask, use_cache=use_cache, cache=cache)\n", + " out = attention + x\n", + " \n", + " norm2_out = self._norm2(out)\n", + " ffn_out = self._ff(norm2_out)\n", + "\n", + " if use_cache is True:\n", + " return (ffn_out + out, kv_caches)\n", + " else:\n", + " return (ffn_out + out, None)\n", + "\n", + "\n", + "\n", + "from torch import nn\n", + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "class Gemma(nn.Module):\n", + " def __init__(self,\n", + " vocab_size: int,\n", + " max_seq_len: int,\n", + " emb_size: int,\n", + " num_q_heads: int,\n", + " head_size: int,\n", + " num_layers: int,\n", + " dropout: float = 0.1,\n", + " device: str = 'cpu'\n", + " ):\n", + " super().__init__()\n", + " self._vocab_size = vocab_size\n", + " self._max_seq_len = max_seq_len\n", + " self._emb_size = emb_size\n", + " self._num_q_heads = num_q_heads\n", + " self._head_size = head_size\n", + " self._num_layers = num_layers\n", + " self._dropout = dropout\n", + " self._device = device\n", + " \n", + " self.validation_loss = None\n", + "\n", + " # Инициализация слоев\n", + " self._token_embeddings = TokenEmbeddings(\n", + " vocab_size=vocab_size, \n", + " emb_size=emb_size\n", + " )\n", + " self._position_embeddings = RoPE(\n", + " head_size=head_size,\n", + " max_seq_len=max_seq_len\n", + " )\n", + " #self._position_embeddings = PositionalEmbeddings(\n", + " # max_seq_len=max_seq_len, \n", + " # emb_size=emb_size\n", + " #)\n", + " self._dropout = nn.Dropout(dropout)\n", + " self._decoders = nn.ModuleList([Decoder(\n", + " num_q_heads=num_q_heads,\n", + " emb_size=emb_size,\n", + " head_size=head_size,\n", + " max_seq_len=max_seq_len,\n", + " rope=self._position_embeddings,\n", + " dropout=dropout \n", + " ) for _ in range(num_layers)])\n", + " self._norm = RMSNorm(emb_size)\n", + " self._linear = nn.Linear(emb_size, vocab_size)\n", + "\n", + " def forward(self, x: torch.Tensor, use_cache: bool = True, cache: list = None) -> tuple:\n", + " # Проверка длины последовательности (только при отсутствии кэша)\n", + " if cache is None and x.size(1) > self._max_seq_len:\n", + " raise ValueError(f\"Длина последовательности {x.size(1)} превышает максимальную {self.max_seq_len}\")\n", + " \n", + " # Эмбеддинги токенов и позиций\n", + " tok_out = self._token_embeddings(x) # [batch, seq_len, emb_size]\n", + " #pos_out = self._position_embeddings(x) # [batch, seq_len, emb_size]\n", + " \n", + " # Комбинирование\n", + " out = self._dropout(tok_out) # [batch, seq_len, emb_size]\n", + " \n", + " # Стек декодеров с передачей кэша\n", + " new_cache = []\n", + " for i, decoder in enumerate(self._decoders):\n", + " decoder_cache = cache[i] if cache is not None else None\n", + " decoder_result = decoder(out, use_cache=use_cache, cache=decoder_cache)\n", + "\n", + " # Извлекаем результат из кортежа\n", + " if use_cache:\n", + " out, decoder_new_cache = decoder_result\n", + " new_cache.append(decoder_new_cache)\n", + " else:\n", + " out = decoder_result[0]\n", + "\n", + " out = self._norm(out)\n", + " logits = self._linear(out)\n", + " \n", + " # Возвращаем результат с учетом use_cache\n", + " if use_cache:\n", + " return (logits, new_cache)\n", + " else:\n", + " return (logits, None)\n", + "\n", + " def generate(self,\n", + " x: torch.Tensor, \n", + " max_new_tokens: int, \n", + " do_sample: bool,\n", + " temperature: float = 1.0,\n", + " top_k: int = None,\n", + " top_p: float = None,\n", + " use_cache: bool = True\n", + " ) -> torch.Tensor:\n", + " cache = None\n", + "\n", + " for _ in range(max_new_tokens):\n", + " if use_cache and cache is not None:\n", + " # Используем кэш - передаем только последний токен\n", + " x_input = x[:, -1:] # [batch_size, 1]\n", + " else:\n", + " # Первая итерация или кэш отключен - передаем всю последовательность\n", + " x_input = x\n", + " \n", + " # Прямой проход с кэшем\n", + " logits, new_cache = self.forward(x_input, use_cache=use_cache, cache=cache)\n", + " \n", + " # Обновляем кэш для следующей итерации\n", + " if use_cache:\n", + " cache = new_cache\n", + "\n", + " last_logits = logits[:, -1, :] # [batch_size, vocab_size]\n", + "\n", + " # Масштабируем логиты температурой\n", + " if temperature > 0:\n", + " logits_scaled = last_logits / temperature\n", + " else:\n", + " logits_scaled = last_logits\n", + "\n", + " if do_sample == True and top_k != None:\n", + " _, topk_indices = torch.topk(logits_scaled, top_k, dim=-1)\n", + "\n", + " # # Заменим все НЕ top-k логиты на -inf\n", + " masked_logits = logits_scaled.clone()\n", + " vocab_size = logits_scaled.size(-1)\n", + "\n", + " # создаём маску: 1, если токен НЕ в topk_indices\n", + " mask = torch.ones_like(logits_scaled, dtype=torch.uint8)\n", + " mask.scatter_(1, topk_indices, 0) # 0 там, где top-k индексы\n", + " masked_logits[mask.byte()] = float('-inf')\n", + "\n", + " logits_scaled = masked_logits\n", + "\n", + " if do_sample == True and top_p != None:\n", + " # 1. Применим softmax, чтобы получить вероятности:\n", + " probs = F.softmax(logits_scaled, dim=-1) # [B, vocab_size]\n", + " # 2. Отсортируем токены по убыванию вероятностей:\n", + " sorted_probs, sorted_indices = torch.sort(probs, descending=True, dim=-1)\n", + " # 3. Посчитаем кумулятивную сумму вероятностей:\n", + " cum_probs = torch.cumsum(sorted_probs, dim=-1) # [B, vocab_size]\n", + " # 4. Определим маску: оставить токены, пока сумма < top_p\n", + " sorted_mask = (cum_probs <= top_p).byte() # [B, vocab_size]\n", + " # Гарантируем, что хотя бы первый токен останется\n", + " sorted_mask[:, 0] = 1\n", + " # 5. Преобразуем маску обратно в оригинальный порядок:\n", + " # Создаём полную маску из 0\n", + " mask = torch.zeros_like(probs, dtype=torch.uint8)\n", + " # Устанавливаем 1 в местах нужных токенов\n", + " mask.scatter_(dim=1, index=sorted_indices, src=sorted_mask)\n", + " # 6. Зануляем логиты токенов вне топ-p:\n", + " logits_scaled[~mask] = float('-inf')\n", + "\n", + " # 4. Применяем Softmax\n", + " probs = F.softmax(logits_scaled, dim=-1) # [batch_size, vocab_size]\n", + "\n", + "\n", + " if do_sample == True:\n", + " # 5. Если do_sample равен True, то отбираем токен случайно с помощью torch.multinomial\n", + " next_token = torch.multinomial(probs, num_samples=1) # [batch_size, 1]\n", + " else:\n", + " # 5. Если do_sample равен False, то выбираем токен с максимальной вероятностью\n", + " next_token = torch.argmax(probs, dim=-1, keepdim=True) # [batch_size, 1]\n", + " \n", + " # 6. Добавляем его к последовательности\n", + " x = torch.cat([x, next_token], dim=1) # [batch_size, seq_len+1]\n", + " return x\n", + "\n", + " def save(self, path):\n", + " torch.save({\n", + " 'model_state_dict': self.state_dict(),\n", + " 'vocab_size': self._vocab_size,\n", + " 'max_seq_len': self._max_seq_len,\n", + " 'emb_size': self._emb_size,\n", + " 'num_heads': self._num_heads,\n", + " 'head_size': self._head_size,\n", + " 'num_layers': self._num_layers\n", + " }, path)\n", + "\n", + " @classmethod\n", + " def load(cls, path, device):\n", + " checkpoint = torch.load(path, map_location=device)\n", + " model = cls(\n", + " vocab_size=checkpoint['vocab_size'],\n", + " max_seq_len=checkpoint['max_seq_len'],\n", + " emb_size=checkpoint['emb_size'],\n", + " num_heads=checkpoint['num_heads'],\n", + " head_size=checkpoint['head_size'],\n", + " num_layers=checkpoint['num_layers']\n", + " )\n", + " model.load_state_dict(checkpoint['model_state_dict'])\n", + " model.to(device)\n", + " return model\n", + "\n", + " @property\n", + " def max_seq_len(self) -> int:\n", + " return self._max_seq_len\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "42746fea", + "metadata": {}, + "source": [ + "## 2. Обучение Gemma\n", + "\n", + "Gemma обучается в два этапа:\n", + "\n", + "- 1️⃣ **Предобучение (Unsupervised Pretraining)** \n", + "- 2️⃣ **Дообучение (Supervised Fine-Tuning)**" + ] + }, + { + "cell_type": "markdown", + "id": "f6b0234d", + "metadata": {}, + "source": [ + "\n", + "\n", + "### 5.1 Предобучение\n", + "\n", + "На первом этапе модель обучается без разметки: она получает большой корпус текстов и учится **предсказывать следующий токен** по предыдущим.\n", + "\n", + "Функция потерь:\n", + "$$\n", + "L = - \\sum_{t=1}^{T} \\log P(x_t | x_1, x_2, ..., x_{t-1})\n", + "$$\n", + "\n", + "Таким образом, модель учится строить вероятностную модель языка, \"угадывая\" продолжение текста.\n" + ] + }, + { + "cell_type": "markdown", + "id": "82c94641", + "metadata": {}, + "source": [ + "Во время **предобучения** Mistral учится **предсказывать следующий токен** (language modeling task). \n", + "Формально: \n", + "$$ \n", + "P(x_t ,|, x_1, x_2, \\dots, x_{t-1}) \n", + "$$ \n", + "То есть, если на вход подаётся предложение `\"I love deep\"`, модель должна предсказать `\"learning\"`.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b064fadc", + "metadata": {}, + "source": [ + "### ✅ 5.1.1 Подготовка данных\n", + "\n", + "Создадим **датасет** на основе BPE-токенизатора:" + ] + }, + { + "cell_type": "markdown", + "id": "f1516a37", + "metadata": {}, + "source": [ + "**BPE Tokenizator**" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "8a5a975a", + "metadata": {}, + "outputs": [], + "source": [ + "class BPE:\n", + " def __init__(self, vocab_size: int):\n", + " self.vocab_size = vocab_size\n", + " self.id2token = {}\n", + " self.token2id = {}\n", + "\n", + " def fit(self, text: str):\n", + " # 1. Получаем уникальные токены (символы)\n", + " unique_tokens = sorted(set(text))\n", + " tokens = unique_tokens.copy()\n", + "\n", + " # 2. Разбиваем текст на токены-символы\n", + " sequence = list(text)\n", + "\n", + " # 3. Объединяем токены до достижения нужного размера словаря\n", + " while len(tokens) < self.vocab_size:\n", + " #print(f'len={len(tokens)} < {self.vocab_size}')\n", + " # Считаем частоты пар\n", + " pair_freq = {}\n", + " for i in range(len(sequence) - 1):\n", + " pair = (sequence[i], sequence[i + 1])\n", + " #print(f'pair = {pair}')\n", + " if pair not in pair_freq:\n", + " pair_freq[pair] = 0\n", + " pair_freq[pair] += 1\n", + "\n", + "\n", + " #print(f'pair_freq = {pair_freq}') \n", + " if not pair_freq:\n", + " break # нет пар — выходим\n", + "\n", + " #for x in pair_freq.items():\n", + " # self.debug(x, sequence)\n", + "\n", + " # Находим самую частую пару (в случае равенства — та, что встретилась первой)\n", + " most_frequent_pair = max(pair_freq.items(), key=lambda x: (x[1], -self._pair_first_index(sequence, x[0])))[0]\n", + " #print(most_frequent_pair)\n", + " # Создаем новый токен\n", + " new_token = most_frequent_pair[0] + most_frequent_pair[1]\n", + " #print(f\"new token={new_token}\")\n", + " tokens.append(new_token)\n", + " #print(f\"tokens={tokens}\")\n", + "\n", + " i = 0\n", + " new_sequence = []\n", + "\n", + " while i < len(sequence):\n", + " if i < len(sequence) - 1 and (sequence[i], sequence[i + 1]) == most_frequent_pair:\n", + " new_sequence.append(new_token)\n", + " i += 2 # пропускаем два символа — заменённую пару\n", + " else:\n", + " new_sequence.append(sequence[i])\n", + " i += 1\n", + " sequence = new_sequence\n", + " #break\n", + " \n", + " # 4. Создаем словари\n", + " self.vocab = tokens.copy()\n", + " self.token2id = dict(zip(tokens, range(self.vocab_size)))\n", + " self.id2token = dict(zip(range(self.vocab_size), tokens))\n", + "\n", + " def _pair_first_index(self, sequence, pair):\n", + " for i in range(len(sequence) - 1):\n", + " if (sequence[i], sequence[i + 1]) == pair:\n", + " return i\n", + " return float('inf') # если пара не найдена (в теории не должно случиться)\n", + "\n", + "\n", + " def encode(self, text: str):\n", + " # 1. Разбиваем текст на токены-символы\n", + " sequence = list(text)\n", + " # 2. Инициализация пустого списка токенов\n", + " tokens = []\n", + " # 3. Установить i = 0\n", + " i = 0\n", + " while i < len(text):\n", + " # 3.1 Найти все токены в словаре, начинающиеся с text[i]\n", + " start_char = text[i]\n", + " result = [token for token in self.vocab if token.startswith(start_char)]\n", + " # 3.2 Выбрать самый длинный подходящий токен\n", + " find_token = self._find_max_matching_token(text[i:], result)\n", + " if find_token is None:\n", + " # Обработка неизвестного символа\n", + " tokens.append(text[i]) # Добавляем сам символ как токен\n", + " i += 1\n", + " else:\n", + " # 3.3 Добавить токен в результат\n", + " tokens.append(find_token)\n", + " # 3.4 Увеличить i на длину токена\n", + " i += len(find_token)\n", + "\n", + " # 4. Заменить токены на их ID\n", + " return self._tokens_to_ids(tokens)\n", + "\n", + " def _find_max_matching_token(self, text: str, tokens: list):\n", + " \"\"\"Находит самый длинный токен из списка, с которого начинается текст\"\"\"\n", + " matching = [token for token in tokens if text.startswith(token)]\n", + " return max(matching, key=len) if matching else None\n", + "\n", + " def _tokens_to_ids(self, tokens):\n", + " \"\"\"Конвертирует список токенов в их ID с обработкой неизвестных токенов\"\"\"\n", + " ids = []\n", + " for token in tokens:\n", + " if token in self.token2id:\n", + " ids.append(self.token2id[token])\n", + " else:\n", + " ids.append(0) # Специальное значение\n", + " return ids\n", + "\n", + "\n", + " def decode(self, ids: list) -> str:\n", + " return ''.join(self._ids_to_tokens(ids))\n", + "\n", + " def _ids_to_tokens(self, ids: list) -> list:\n", + " \"\"\"Конвертирует список Ids в их tokens\"\"\"\n", + " tokens = []\n", + " for id in ids:\n", + " if id in self.id2token:\n", + " tokens.append(self.id2token[id])\n", + " else:\n", + " tokens.append('') # Специальное значение\n", + " return tokens\n", + "\n", + "\n", + " def save(self, filename):\n", + " with open(filename, 'wb') as f:\n", + " dill.dump(self, f)\n", + " print(f\"Объект сохранён в {filename}\")\n", + "\n", + "\n", + " @classmethod\n", + " def load(cls, filename):\n", + " with open(filename, 'rb') as f:\n", + " obj = dill.load(f)\n", + " \n", + " print(f\"Объект загружен из {filename}\")\n", + " return obj" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "1927f6d2", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "from torch.utils.data import Dataset, DataLoader\n", + "\n", + "class GPTDataset(Dataset):\n", + " def __init__(self, text: str, bpe: BPE, block_size: int):\n", + " self.bpe = bpe\n", + " self.block_size = block_size\n", + " self.data = bpe.encode(text)\n", + " \n", + " def __len__(self):\n", + " return len(self.data) - self.block_size\n", + "\n", + " def __getitem__(self, idx):\n", + " x = torch.tensor(self.data[idx:idx+self.block_size], dtype=torch.long)\n", + " y = torch.tensor(self.data[idx+1:idx+self.block_size+1], dtype=torch.long)\n", + " return x, y" + ] + }, + { + "cell_type": "markdown", + "id": "a14d0c68", + "metadata": {}, + "source": [ + "### ✅ 5.1.2 Цикл обучения\n", + "\n", + "Для обучения создадим функцию:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7c5c57b0", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn.functional as F\n", + "from torch import optim\n", + "\n", + "def train_gemma(model, dataset, epochs=5, batch_size=32, lr=3e-4, device='cpu'):\n", + " dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", + " optimizer = optim.AdamW(model.parameters(), lr=lr)\n", + "\n", + " model.to(device)\n", + " model.train()\n", + "\n", + " for epoch in range(epochs):\n", + " total_loss = 0\n", + " for x, y in dataloader:\n", + " x, y = x.to(device), y.to(device)\n", + "\n", + " # Прямой проход\n", + " logits, _ = model(x, use_cache=False) # [B, T, vocab_size]\n", + "\n", + " # Перестроим выход под CrossEntropy\n", + " loss = F.cross_entropy(logits.view(-1, logits.size(-1)), y.view(-1))\n", + "\n", + " # Обратное распространение\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " total_loss += loss.item()\n", + "\n", + " avg_loss = total_loss / len(dataloader)\n", + " print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}\")\n", + "\n", + " return model" + ] + }, + { + "cell_type": "markdown", + "id": "f96dea80", + "metadata": {}, + "source": [ + "### ✅ 5.1.3 Пример запуска\n", + "\n", + "\n", + "**🧠 Конфигурация Gemma Mini**\n", + "\n", + "\n", + "| Параметр | Значение | Описание |\n", + "| --------------- | -------- | --------------------------------------------- |\n", + "| **vocab_size** | `50257` | Размер словаря (BPE токенизатор OpenAI) |\n", + "| **max_seq_len** | `512` | Максимальная длина входной последовательности |\n", + "| **emb_size** | `256` | Размер эмбеддингов (векторное пространство) |\n", + "| **num_heads** | `4` | Количество голов в multi-head attention |\n", + "| **head_size** | `64` | Размерность одной головы внимания (768 / 12) |\n", + "| **num_layers** | `4` | Количество блоков (декодеров) |\n", + "| **dropout** | `0.1` | Вероятность дропаута |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "cda62fc2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset length: 20\n", + "Epoch 1/100, Loss: 3.8178\n", + "Epoch 2/100, Loss: 1.5683\n", + "Epoch 3/100, Loss: 0.6454\n", + "Epoch 4/100, Loss: 0.3353\n", + "Epoch 5/100, Loss: 0.2306\n", + "Epoch 6/100, Loss: 0.1581\n", + "Epoch 7/100, Loss: 0.1253\n", + "Epoch 8/100, Loss: 0.1063\n", + "Epoch 9/100, Loss: 0.0923\n", + "Epoch 10/100, Loss: 0.0909\n", + "Epoch 11/100, Loss: 0.0761\n", + "Epoch 12/100, Loss: 0.0932\n", + "Epoch 13/100, Loss: 0.0775\n", + "Epoch 14/100, Loss: 0.0797\n", + "Epoch 15/100, Loss: 0.0623\n", + "Epoch 16/100, Loss: 0.0795\n", + "Epoch 17/100, Loss: 0.0703\n", + "Epoch 18/100, Loss: 0.0581\n", + "Epoch 19/100, Loss: 0.0613\n", + "Epoch 20/100, Loss: 0.0660\n", + "Epoch 21/100, Loss: 0.0731\n", + "Epoch 22/100, Loss: 0.0644\n", + "Epoch 23/100, Loss: 0.0602\n", + "Epoch 24/100, Loss: 0.0557\n", + "Epoch 25/100, Loss: 0.0595\n", + "Epoch 26/100, Loss: 0.0688\n", + "Epoch 27/100, Loss: 0.0545\n", + "Epoch 28/100, Loss: 0.0561\n", + "Epoch 29/100, Loss: 0.0581\n", + "Epoch 30/100, Loss: 0.0627\n", + "Epoch 31/100, Loss: 0.0555\n", + "Epoch 32/100, Loss: 0.0538\n", + "Epoch 33/100, Loss: 0.0531\n", + "Epoch 34/100, Loss: 0.0535\n", + "Epoch 35/100, Loss: 0.0474\n", + "Epoch 36/100, Loss: 0.0516\n", + "Epoch 37/100, Loss: 0.0540\n", + "Epoch 38/100, Loss: 0.0533\n", + "Epoch 39/100, Loss: 0.0519\n", + "Epoch 40/100, Loss: 0.0606\n", + "Epoch 41/100, Loss: 0.0489\n", + "Epoch 42/100, Loss: 0.0513\n", + "Epoch 43/100, Loss: 0.0563\n", + "Epoch 44/100, Loss: 0.0522\n", + "Epoch 45/100, Loss: 0.0512\n", + "Epoch 46/100, Loss: 0.0490\n", + "Epoch 47/100, Loss: 0.0469\n", + "Epoch 48/100, Loss: 0.0500\n", + "Epoch 49/100, Loss: 0.0497\n", + "Epoch 50/100, Loss: 0.0532\n", + "Epoch 51/100, Loss: 0.0557\n", + "Epoch 52/100, Loss: 0.0480\n", + "Epoch 53/100, Loss: 0.0593\n", + "Epoch 54/100, Loss: 0.0498\n", + "Epoch 55/100, Loss: 0.0476\n", + "Epoch 56/100, Loss: 0.0496\n", + "Epoch 57/100, Loss: 0.0445\n", + "Epoch 58/100, Loss: 0.0494\n", + "Epoch 59/100, Loss: 0.0572\n", + "Epoch 60/100, Loss: 0.0490\n", + "Epoch 61/100, Loss: 0.0580\n", + "Epoch 62/100, Loss: 0.0499\n", + "Epoch 63/100, Loss: 0.0501\n", + "Epoch 64/100, Loss: 0.0538\n", + "Epoch 65/100, Loss: 0.0484\n", + "Epoch 66/100, Loss: 0.0520\n", + "Epoch 67/100, Loss: 0.0527\n", + "Epoch 68/100, Loss: 0.0501\n", + "Epoch 69/100, Loss: 0.0506\n", + "Epoch 70/100, Loss: 0.0480\n", + "Epoch 71/100, Loss: 0.0470\n", + "Epoch 72/100, Loss: 0.0498\n", + "Epoch 73/100, Loss: 0.0484\n", + "Epoch 74/100, Loss: 0.0435\n", + "Epoch 75/100, Loss: 0.0456\n", + "Epoch 76/100, Loss: 0.0480\n", + "Epoch 77/100, Loss: 0.0477\n", + "Epoch 78/100, Loss: 0.0494\n", + "Epoch 79/100, Loss: 0.0490\n", + "Epoch 80/100, Loss: 0.0474\n", + "Epoch 81/100, Loss: 0.0462\n", + "Epoch 82/100, Loss: 0.0432\n", + "Epoch 83/100, Loss: 0.0447\n", + "Epoch 84/100, Loss: 0.0482\n", + "Epoch 85/100, Loss: 0.0493\n", + "Epoch 86/100, Loss: 0.0452\n", + "Epoch 87/100, Loss: 0.0417\n", + "Epoch 88/100, Loss: 0.0489\n", + "Epoch 89/100, Loss: 0.0487\n", + "Epoch 90/100, Loss: 0.0486\n", + "Epoch 91/100, Loss: 0.0451\n", + "Epoch 92/100, Loss: 0.0443\n", + "Epoch 93/100, Loss: 0.0442\n", + "Epoch 94/100, Loss: 0.0486\n", + "Epoch 95/100, Loss: 0.0464\n", + "Epoch 96/100, Loss: 0.0429\n", + "Epoch 97/100, Loss: 0.0461\n", + "Epoch 98/100, Loss: 0.0496\n", + "Epoch 99/100, Loss: 0.0476\n", + "Epoch 100/100, Loss: 0.0441\n" + ] + }, + { + "data": { + "text/plain": [ + "Gemma(\n", + " (_token_embeddings): TokenEmbeddings(\n", + " (_embedding): Embedding(100, 256)\n", + " )\n", + " (_position_embeddings): RoPE()\n", + " (_dropout): Dropout(p=0.1, inplace=False)\n", + " (_decoders): ModuleList(\n", + " (0-3): 4 x Decoder(\n", + " (_heads): MultiQueryAttention(\n", + " (_rope): RoPE()\n", + " (_q): Linear(in_features=256, out_features=256, bias=True)\n", + " (_k): Linear(in_features=256, out_features=64, bias=True)\n", + " (_v): Linear(in_features=256, out_features=64, bias=True)\n", + " (_layer): Linear(in_features=256, out_features=256, bias=True)\n", + " (_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (_ff): GeGLU(\n", + " (_gate): Linear(in_features=256, out_features=1024, bias=True)\n", + " (_up): Linear(in_features=256, out_features=1024, bias=True)\n", + " (_down): Linear(in_features=1024, out_features=256, bias=True)\n", + " (_activation): GELU()\n", + " (_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (_norm1): RMSNorm()\n", + " (_norm2): RMSNorm()\n", + " )\n", + " )\n", + " (_norm): RMSNorm()\n", + " (_linear): Linear(in_features=256, out_features=100, bias=True)\n", + ")" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1. Исходный текст\n", + "text = \"Deep learning is amazing. Transformers changed the world. Attention is all you need. GPT models revolutionized NLP.\"\n", + "\n", + "# 2. Обучаем токенизатор\n", + "bpe = BPE(vocab_size=100)\n", + "bpe.fit(text)\n", + "\n", + "# 3. Создаем датасет\n", + "dataset = GPTDataset(text, bpe, block_size=8)\n", + "print(f\"Dataset length: {len(dataset)}\")\n", + "\n", + "# 4. Инициализируем модель\n", + "model = Gemma(\n", + " vocab_size=len(bpe.vocab), # размер словаря BPE\n", + " max_seq_len=512, # GPT-2 использует контекст в 512 токена\n", + " emb_size=256, # размер эмбеддингов\n", + " num_q_heads=4, # количество голов внимания\n", + " head_size=64, # размер каждой головы (256 / 4)\n", + " num_layers=4, # количество блоков Transformer\n", + " dropout=0.1 # стандартный dropout GPT-2\n", + ")\n", + "\n", + "# 5. Обучаем\n", + "train_gemma(model, dataset, epochs=100, batch_size=4)" + ] + }, + { + "cell_type": "markdown", + "id": "f5a37671", + "metadata": {}, + "source": [ + "\n", + "---\n", + "\n", + "### 5.2 Дообучение\n", + "\n", + "После предобучения Gemma уже знает структуру и грамматику языка. \n", + "На втором этапе она дообучается на конкретных задачах (например, классификация, QA) с помощью размеченных данных.\n", + "\n", + "Технически это почти то же обучение, только:\n", + "\n", + "- Загружаем модель с уже обученными весами.\n", + "- Используем новые данные.\n", + "- Можно уменьшить скорость обучения.\n", + "- Иногда замораживают часть слоёв (например, эмбеддинги).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "d062af63", + "metadata": {}, + "outputs": [], + "source": [ + "def fine_tune_gemma(model, dataset, epochs=3, batch_size=16, lr=1e-5, device='cpu', freeze_embeddings=True):\n", + " if freeze_embeddings:\n", + " for param in model._token_embeddings.parameters():\n", + " param.requires_grad = False\n", + " for param in model._position_embeddings.parameters():\n", + " param.requires_grad = False\n", + "\n", + " dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", + " optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=lr)\n", + "\n", + " model.to(device)\n", + " model.train()\n", + "\n", + " for epoch in range(epochs):\n", + " total_loss = 0\n", + " for x, y in dataloader:\n", + " x, y = x.to(device), y.to(device)\n", + " logits, _ = model(x, use_cache=False)\n", + " loss = F.cross_entropy(logits.view(-1, logits.size(-1)), y.view(-1))\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + " total_loss += loss.item()\n", + " print(f\"Fine-tune Epoch {epoch+1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "064dd678", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine-tune Epoch 1/10, Loss: 4.9095\n", + "Fine-tune Epoch 2/10, Loss: 2.8684\n", + "Fine-tune Epoch 3/10, Loss: 1.7589\n", + "Fine-tune Epoch 4/10, Loss: 1.3044\n", + "Fine-tune Epoch 5/10, Loss: 1.0614\n", + "Fine-tune Epoch 6/10, Loss: 0.8326\n", + "Fine-tune Epoch 7/10, Loss: 0.6908\n", + "Fine-tune Epoch 8/10, Loss: 0.5926\n", + "Fine-tune Epoch 9/10, Loss: 0.5082\n", + "Fine-tune Epoch 10/10, Loss: 0.4758\n" + ] + } + ], + "source": [ + "# Например, мы хотим дообучить модель на стиле коротких технических фраз\n", + "fine_tune_text = \"\"\"\n", + "Transformers revolutionize NLP.\n", + "Deep learning enables self-attention.\n", + "GPT generates text autoregressively.\n", + "\"\"\"\n", + "\n", + "dataset = GPTDataset(fine_tune_text, bpe, block_size=8)\n", + "\n", + "\n", + "# Запуск дообучения\n", + "fine_tune_gemma(model, dataset, epochs=10, batch_size=4, lr=1e-4)" + ] + }, + { + "cell_type": "markdown", + "id": "a496ddae", + "metadata": {}, + "source": [ + "## 📝 6. Генерация текста после обучения" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "645f777c", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_text(model, bpe, prompt: str, max_new_tokens=20, device='cpu'):\n", + " model.eval()\n", + " ids = torch.tensor([bpe.encode(prompt)], dtype=torch.long).to(device)\n", + " out = model.generate(ids, max_new_tokens=max_new_tokens, do_sample=True)\n", + " text = bpe.decode(out[0].tolist())\n", + " return text" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "14778ecd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deep learningenena lf lenenssssf \n" + ] + } + ], + "source": [ + "print(generate_text(model, bpe, \"Deep learning\", max_new_tokens=20))" + ] + }, + { + "cell_type": "markdown", + "id": "1b70d909", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}