mirror of
https://github.com/pese-git/llm-arch-research.git
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cfb4b6dfb1
- Add core Gemma model (architecture, attention, GeGLU, RoPE, RMSNorm, etc) - Add configs for training and generation: gemma_train.json, gemma_generate.json - Add Gemma notebook for exploratory analysis and demonstration - Add __init__.py for Gemma submodule - Update run_llm_experiment.py to support Gemma experiment configs test(gemma): add comprehensive unit tests for Gemma - Test forward pass (with/without cache) - Test autoregressive generation (greedy, top-k, top-p) - Test shape correctness and max sequence length errors - Test multi-layer stack and token embeddings docs: add documentation notebook for Gemma usage and analysis Closes: #issue (if applicable)
1345 lines
56 KiB
Plaintext
1345 lines
56 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1636810a",
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"metadata": {},
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"source": [
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"# Gemma\n",
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"\n",
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"<p align=\"center\">\n",
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" <img src=\"https://ucarecdn.com/7b36315f-fc63-4f57-8344-12f1a993376f/\" alt=\"arch\" width=\"1000\" height=\"165\">\n",
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"</p>\n",
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"\n",
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"Gemma 1 вышла в феврале 2024 года.\n",
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"\n",
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"По архитектуре модель больше всего похожа на Llama'у. Содержит уже знакомые нам RoPE и RMSNorm. Но есть и новинки:\n",
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"\n",
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"* **Multi-Query Attention (MQA)** — крайне экономный вариант механизма внимания.\n",
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"* **GeGLU** — гибридная функция активации. Почти клон SwiGLU :)\n",
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"\n",
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"Обе довольно легкие для внедрения, по сравнению с прошлыми новинками :)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cea30169",
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"metadata": {},
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"source": [
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"# Multi-Query Attention\n",
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"\n",
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"По своей сути, Multi-Query Attention (MQA) — это частный случай Grouped Query Attention (GQA), который мы реализовали в уроке про Mistral.\n",
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"\n",
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"<p align=\"center\">\n",
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" <img src=\"https://ucarecdn.com/d1b0b294-9c02-4de1-9074-ae8ebfe96f6e/\" alt=\"mqa\" width=\"1000\" height=\"217\">\n",
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"</p>\n",
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"\n",
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"В GQA на каждую голову приходится один вектор запроса (query). При этом каждый вектор ключа (key) и значения (value) обслуживает ( n )-голов.\n",
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"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"
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]
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},
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{
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"cell_type": "markdown",
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"id": "539550fe",
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"metadata": {},
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"source": [
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"**Multi-Query Attention (разработка)**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "6be61c63",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from typing import Optional\n",
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"\n",
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"\n",
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"class RoPE(nn.Module):\n",
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"\n",
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" def __init__(self, head_size: int, max_seq_len: int, base: int = 10_000):\n",
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" super().__init__()\n",
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" assert head_size % 2 == 0, \"head_size должен быть четным\"\n",
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"\n",
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" # Вычисление частот: θ_i = base^(-2i/d) для i ∈ [0, d/2-1]\n",
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" freqs = 1.0 / (base ** (2 * torch.arange(head_size // 2).float() / head_size))\n",
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"\n",
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" # Позиции от 0 до max_seq_len-1\n",
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" positions = torch.arange(max_seq_len).float()\n",
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"\n",
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" # Внешнее произведение: m * θ_i для всех позиций и частот\n",
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" freq_matrix = positions.unsqueeze(1) * freqs.unsqueeze(0)\n",
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"\n",
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" # Предвычисление матриц косинусов и синусов\n",
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" self.register_buffer(\"cos_matrix\", torch.cos(freq_matrix))\n",
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" self.register_buffer(\"sin_matrix\", torch.sin(freq_matrix))\n",
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"\n",
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" def forward(self, x: torch.Tensor) -> torch.Tensor: # [batch_size × seq_len × head_size] [batch_size × num_heads × seq_len × head_size]\n",
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" batch_size, num_heads, seq_len, head_size = x.shape\n",
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"\n",
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" # Берем нужную часть матриц и приводим к типу x\n",
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" cos = self.cos_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2]\n",
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" sin = self.sin_matrix[:seq_len].to(x.dtype) # [seq_len, head_size//2]\n",
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"\n",
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" # Явное изменение формы для broadcasting\n",
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" cos = cos.reshape(1, 1, seq_len, head_size // 2)\n",
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" sin = sin.reshape(1, 1, seq_len, head_size // 2)\n",
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"\n",
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" # Разделяем на четные и нечетные компоненты по ПОСЛЕДНЕМУ измерению\n",
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" x_even = x[..., 0::2] # [batch_size, num_heads, seq_len, head_size//2]\n",
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" x_odd = x[..., 1::2] # [batch_size, num_heads, seq_len, head_size//2]\n",
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"\n",
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" # Применяем поворот: q' = q * cos(mθ) + rotate(q) * sin(mθ)\n",
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" x_rotated_even = x_even * cos - x_odd * sin\n",
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" x_rotated_odd = x_even * sin + x_odd * cos\n",
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"\n",
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" # Объединяем обратно в исходную размерность\n",
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" x_rotated = torch.stack([x_rotated_even, x_rotated_odd], dim=-1)\n",
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" x_rotated = x_rotated.flatten(-2) # [batch_size, seq_len, head_size]\n",
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"\n",
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" return x_rotated"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "811921b1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"import torch.nn.functional as F\n",
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"\n",
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"class MultiQueryAttention(nn.Module):\n",
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" def __init__(\n",
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" self,\n",
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" num_q_heads: int,\n",
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" emb_size: int,\n",
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" head_size: int,\n",
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" max_seq_len: int,\n",
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" rope: RoPE = None,\n",
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" dropout: float = 0.1,\n",
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" ):\n",
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" super().__init__()\n",
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" self._num_q_heads = num_q_heads\n",
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" self._head_size = head_size\n",
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" self._max_seq_len = max_seq_len\n",
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" self._rope = rope\n",
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" \n",
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" self._q = nn.Linear(emb_size, num_q_heads * head_size)\n",
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" self._k = nn.Linear(emb_size, head_size)\n",
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" self._v = nn.Linear(emb_size, head_size)\n",
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"\n",
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" # Создание causal маски\n",
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" mask = torch.tril(torch.ones(max_seq_len, max_seq_len))\n",
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" self.register_buffer(\n",
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" \"_tril_mask\", mask.bool() if hasattr(torch, \"bool\") else mask.byte()\n",
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" )\n",
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" \n",
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" self._layer = nn.Linear(num_q_heads * head_size, emb_size)\n",
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" self._dropout = nn.Dropout(dropout)\n",
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"\n",
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" def forward(\n",
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" self,\n",
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" x: torch.Tensor,\n",
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" mask: torch.Tensor = None,\n",
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" use_cache: bool = True,\n",
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" cache: list = None,\n",
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" ):\n",
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" batch_size, seq_len, emb_size = x.shape\n",
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" if seq_len > self._max_seq_len:\n",
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" raise ValueError(\n",
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" f\"Длина последовательности {seq_len} превышает максимум {self._max_seq_len}\"\n",
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" )\n",
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"\n",
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" # Пропустите тензор x через матрицы Wq, Wk , Wv, чтобы получить матрицы запроса, ключа и значения.\n",
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" k = self._k(x) # [B, T, hs]\n",
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" q = self._q(x) # [B, T, hs]\n",
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" v = self._v(x) # [B, T, hs]\n",
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"\n",
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" # Шаг 2: Изменение формы для multi-head\n",
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" # [batch_size, seq_len, num_heads * head_size] \n",
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" # -> [batch_size, seq_len, num_heads, head_size]\n",
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" q = q.reshape(batch_size, seq_len, self._num_q_heads, self._head_size)\n",
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" k = k.reshape(batch_size, seq_len, 1, self._head_size)\n",
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" v = v.reshape(batch_size, seq_len, 1, self._head_size)\n",
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"\n",
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" # 3. Transpose: [B, T, H, hs] -> [B, H, T, hs]\n",
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" q = q.transpose(1, 2)\n",
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" k = k.transpose(1, 2)\n",
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" v = v.transpose(1, 2)\n",
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"\n",
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" # Пропустите матрицы запроса и ключа через экземпляр rope, чтобы выполнить поворот.\n",
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" if self._rope is not None:\n",
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" # Применяем RoPE к Q и K (НЕ к V!)\n",
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" q = self._rope(q) # [B, T, hs]\n",
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" k = self._rope(k) # [B, T, hs]\n",
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"\n",
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"\n",
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" # Если cache пришел, то объединяем кэш и одну строку из ключа и значения. Это будут новые key и value для последующих вычислений.\n",
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" # 5. Кэширование (для autoregressive generation)\n",
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" if cache is not None:\n",
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" k_cache, v_cache = cache\n",
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" k = torch.cat([k_cache, k], dim=2) # Concat по seq_len (dim=2)\n",
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" v = torch.cat([v_cache, v], dim=2)\n",
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"\n",
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"\n",
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" # Перемножим матрицы запроса и ключа (транспонированную), чтобы вычислить матрицу внимания.\n",
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" # И разделить все значения в матрице внимания на корень из head_size.\n",
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" scores = q @ k.transpose(-2, -1) / (self._head_size ** 0.5)\n",
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"\n",
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" # 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",
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" if cache is None:\n",
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" scores = scores.masked_fill(\n",
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" ~self._tril_mask[:seq_len, :seq_len], float(\"-inf\")\n",
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" )\n",
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"\n",
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" # Применить к матрице внимания (построчно) функцию Softmax.\n",
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" weights = F.softmax(scores, dim=-1)\n",
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"\n",
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" # Перемножим матрицу внимания и матрицу значения.\n",
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" x_out = weights @ v # [B, T, hs]\n",
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"\n",
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"\n",
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" # Измените форму тензора на batch_size × seq_len × num_heads*head_size.\n",
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" # Transpose обратно и concatenate heads\n",
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" x_out = x_out.transpose(1, 2) # [B, T_q, H, hs]\n",
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" x_out = x_out.contiguous() # Важно для reshape!\n",
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" concatenated_attention = x_out.reshape(batch_size, seq_len, self._num_q_heads * self._head_size)\n",
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"\n",
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"\n",
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" # Пропустите получившийся тензор через последний линейный слой.\n",
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" # 3. Проецируем в пространство эмбеддингов\n",
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" projected_output = self._layer(concatenated_attention)\n",
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"\n",
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"\n",
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" # 4. Применяем dropout для регуляризации\n",
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" final_output = self._dropout(projected_output)\n",
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"\n",
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" if use_cache is True:\n",
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" return (final_output, (k, v))\n",
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" else:\n",
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" return (final_output, None)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "97771d9a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ Test 1 - Output shape: torch.Size([2, 10, 512])\n",
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"✅ Test 2 - First output shape: torch.Size([2, 5, 512])\n",
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"✅ Test 2 - Second output shape: torch.Size([2, 1, 512])\n",
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"\n",
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"✅ Все тесты пройдены!\n"
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]
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}
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],
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"source": [
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"\n",
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"# Параметры\n",
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"batch_size = 2\n",
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"seq_len = 10\n",
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"emb_size = 512\n",
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"num_q_heads = 8\n",
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"head_size = 64\n",
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"max_seq_len = 512\n",
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"\n",
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" # Создание модели\n",
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"rope = RoPE(head_size=head_size, max_seq_len=max_seq_len)\n",
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"mha = MultiQueryAttention(\n",
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" num_q_heads=num_q_heads,\n",
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" emb_size=emb_size,\n",
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" head_size=head_size,\n",
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" max_seq_len=max_seq_len,\n",
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" rope=rope,\n",
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" dropout=0.1,\n",
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")\n",
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"\n",
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" # Тест 1: Обычный forward pass\n",
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"x = torch.randn(batch_size, seq_len, emb_size)\n",
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"output, cache = mha(x, use_cache=False)\n",
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"print(f\"✅ Test 1 - Output shape: {output.shape}\") # [2, 10, 512]\n",
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"assert output.shape == (batch_size, seq_len, emb_size)\n",
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"\n",
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" # Тест 2: С кэшированием\n",
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"x1 = torch.randn(batch_size, 5, emb_size)\n",
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"output1, cache1 = mha(x1, use_cache=True)\n",
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"print(f\"✅ Test 2 - First output shape: {output1.shape}\") # [2, 5, 512]\n",
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"\n",
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"x2 = torch.randn(batch_size, 1, emb_size)\n",
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"output2, cache2 = mha(x2, use_cache=True, cache=cache1)\n",
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"print(f\"✅ Test 2 - Second output shape: {output2.shape}\") # [2, 1, 512]\n",
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"\n",
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"print(\"\\n✅ Все тесты пройдены!\")"
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]
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},
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||
{
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||
"cell_type": "markdown",
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||
"id": "b5875022",
|
||
"metadata": {},
|
||
"source": [
|
||
"Вот конвертированный Markdown для твоего HTML:\n",
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"\n",
|
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"---\n",
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"\n",
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"# GeGLU\n",
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"\n",
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"<p align=\"center\">\n",
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" <img src=\"https://ucarecdn.com/292c5458-2544-4e1f-a3f1-a981faeab39d/\" alt=\"geglu\" width=\"1000\" height=\"586\">\n",
|
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"</p>\n",
|
||
"\n",
|
||
"GeGLU — это гибридная функция активации.\n",
|
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"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"
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]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a3345832",
|
||
"metadata": {},
|
||
"source": [
|
||
"**GeGLU (разработка)**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 65,
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||
"id": "82f52110",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import math\n",
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||
"\n",
|
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"class GELU(nn.Module):\n",
|
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" def __init__(self):\n",
|
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" super().__init__()\n",
|
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" self.sqrt_2_over_pi = torch.sqrt(torch.tensor(2.0) / math.pi)\n",
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" \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",
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" ))\n",
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"\n",
|
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"class GeGLU(nn.Module):\n",
|
||
" def __init__(self, emb_size: int, dropout: float = 0.1):\n",
|
||
" super().__init__()\n",
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"\n",
|
||
" self._gate = nn.Linear(emb_size, 4 * emb_size)\n",
|
||
" self._up = nn.Linear(emb_size, 4 * emb_size)\n",
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||
" self._down = nn.Linear(4 * emb_size, emb_size)\n",
|
||
" self._activation = GELU()\n",
|
||
" self._dropout = nn.Dropout(dropout)\n",
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"\n",
|
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" def forward(self, x: torch.Tensor): # [batch_size × seq_len × emb_size].\n",
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" gate_out = self._gate(x) # [batch, seq, 4*emb]\n",
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" activation_out = self._activation(gate_out) # [batch, seq, 4*emb]\n",
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" up_out = self._up(x) # [batch, seq, 4*emb]\n",
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" out = up_out * activation_out # поэлементное!\n",
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" out = self._down(out) # [batch, seq, emb]\n",
|
||
" return self._dropout(out)\n"
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]
|
||
},
|
||
{
|
||
"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
|
||
}
|