mirror of
https://github.com/datawhalechina/llms-from-scratch-cn.git
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363 lines
12 KiB
Plaintext
363 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4be7fb0b",
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"metadata": {},
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"source": [
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"# 4.5 在transfomer模块中连接注意力层和线性层"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8adbb40c",
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"metadata": {},
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"source": [
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"在本节中，我们将实现transfomer模块，这是GPT和其他大型语言模型（LLM）架构的基本构建块。\n",
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"该模块在 1.24 亿参数的 GPT-2 架构中重复了十几次，结合了我们之前介绍过的几个概念：多头注意力、层归一化、dropout、前馈层和 GELU 激活函数，如图 4.13 所示。\n",
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"在下一节中，我们将把这个ransfomer模块连接到 GPT 架构的其余部分。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d49496c5",
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"metadata": {},
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"source": [
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"**图 4.13 transfomer模块的图示。\n",
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"图的底部显示了输入的token，这些标记已经被嵌入到768维的向量中。\n",
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"每一行对应一个token的向量表示。\n",
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"transfomer模块的输出是与输入具有相同维度的向量，然后可以将其馈送到 LLM 中的后续层。**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41853c9f",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "5bda05b2",
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"metadata": {},
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"source": [
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"如图 4.13 所示，transfomer模块结合了多个组件，包括第 3 章中的屏蔽多头注意力模块和我们在第 4.3 节中实现的前馈模块。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "996e660c",
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"metadata": {},
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"source": [
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"当transfomer模块处理输入序列时，序列中的每个元素（例如，单词或子词标记）都由固定大小的向量表示（在图 4.13 的情况下，为 768 维）。\n",
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"transfomer模块内部的操作，包括多头注意力和前馈层，都旨在以保持其维度的方式转换这些向量。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f9ea84b",
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"metadata": {},
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"source": [
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"多头注意力模块中的自注意力机制的思想是，它能够识别并分析输入序列中各元素之间的关系。\n",
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"与此同时，前馈网络在每个位置独立地修改数据。\n",
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"这种组合不仅可以更细致地理解和处理输入，还可以增强模型处理复杂数据模式的整体能力。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "09ad2b03",
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"metadata": {},
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"source": [
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"在下面代码中，我们可以按照如下方法创建 Transformer模块："
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b878325",
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"metadata": {},
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"source": [
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"### 代码示例4.6 GPT 的transfomer模块组件"
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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": 1,
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"id": "f57d53a1",
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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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"import torch.nn as nn\n",
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"from torch.nn import LayerNorm"
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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": 2,
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"id": "d8a3199b",
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"metadata": {},
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"outputs": [],
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"source": [
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"class TransformerBlock(nn.Module):\n",
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" def __init__(self, cfg):\n",
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" super().__init__()\n",
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" self.att = MultiHeadAttention(\n",
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" d_in=cfg[\"emb_dim\"],\n",
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" d_out=cfg[\"emb_dim\"],\n",
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" context_length=cfg[\"context_length\"],\n",
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" num_heads=cfg[\"n_heads\"],\n",
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" dropout=cfg[\"drop_rate\"],\n",
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" qkv_bias=cfg[\"qkv_bias\"])\n",
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"\n",
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" self.ff = FeedForward(cfg)\n",
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" self.norm1 = LayerNorm(cfg[\"emb_dim\"])\n",
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" self.norm2 = LayerNorm(cfg[\"emb_dim\"])\n",
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" self.drop_resid = nn.Dropout(cfg[\"drop_rate\"])\n",
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" \n",
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" def forward(self, x):\n",
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" #A\n",
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" shortcut = x\n",
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" x = self.norm1(x)\n",
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" x = self.att(x)\n",
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" x = self.drop_resid(x)\n",
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" x = x + shortcut # Add the original input back\n",
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" \n",
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" shortcut = x #B\n",
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" x = self.norm2(x)\n",
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" x = self.ff(x)\n",
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" x = self.drop_resid(x)\n",
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" x = x + shortcut #C\n",
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" return x"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4d1f6344",
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"metadata": {},
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"source": [
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"上述给出的代码在 PyTorch 中定义了一个 TransformerBlock 类，包括一个多头注意力机制（MultiHeadAttention）和一个前馈网络（FeedForward），这两者都是根据提供的配置字典（例如 GPT_CONFIG_124M）来配置的。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "73c84673",
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"metadata": {},
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"source": [
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"在这两个组件之前应用层归一化 (LayerNorm)，并在它们之后应用 dropout，以正则化模型并防止过度拟合。\n",
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"这也称为 Pre-LayerNorm。\n",
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"在较旧的架构中，例如原始的transfomer模块，层归一化是在自注意力和前馈网络之后应用的，被称为 Post-LayerNorm，这通常会导致较差的训练动态。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "238742e6",
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"metadata": {},
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"source": [
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"该类还实现了前向传递，其中每个组件后面都有一个快捷连接，该连接将块的输入添加到其输出。\n",
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"一关键特性有助于训练过程中梯度在网络中的流动，并如4.4节所解释的那样，改善了深度模型的学习效果。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "64d75abd",
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"metadata": {},
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"source": [
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"使用我们之前定义的 GPT_CONFIG_124M 字典，让我们实例化一个transfomer模块并为其提供一些示例数据。"
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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": 3,
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"id": "b4592fed",
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"metadata": {},
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"outputs": [],
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"source": [
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"class FeedForward(nn.Module):\n",
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" def __init__(self, cfg):\n",
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" super().__init__()\n",
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" self.linear1 = nn.Linear(cfg[\"emb_dim\"], cfg[\"emb_dim\"] * 4)\n",
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" self.relu = nn.ReLU()\n",
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" self.linear2 = nn.Linear(cfg[\"emb_dim\"] * 4, cfg[\"emb_dim\"])\n",
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" self.dropout = nn.Dropout(cfg[\"drop_rate\"])\n",
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"\n",
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" def forward(self, x):\n",
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" x = self.relu(self.linear1(x))\n",
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" x = self.dropout(x)\n",
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" x = self.linear2(x)\n",
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" return x\n",
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" \n",
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"class MultiHeadAttention(nn.Module):\n",
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" def __init__(self, d_in, d_out,\n",
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" context_length, dropout, num_heads, qkv_bias=False):\n",
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" super().__init__()\n",
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" assert d_out % num_heads == 0, \"d_out must be divisible by num_heads\"\n",
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" self.d_out = d_out\n",
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" self.num_heads = num_heads\n",
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" self.head_dim = d_out // num_heads #A\n",
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" self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
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" self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
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" self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)\n",
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" self.out_proj = nn.Linear(d_out, d_out) #B\n",
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" self.dropout = nn.Dropout(dropout)\n",
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" self.register_buffer(\n",
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" 'mask',\n",
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" torch.triu(torch.ones(context_length, context_length), diagonal=1)\n",
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" )\n",
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" def forward(self, x):\n",
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" b, num_tokens, d_in = x.shape\n",
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" keys = self.W_key(x) #C\n",
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" queries = self.W_query(x) #C\n",
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" values = self.W_value(x) #C\n",
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" \n",
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" keys = keys.view(b, num_tokens, self.num_heads, self.head_dim) #D\n",
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" values = values.view(b, num_tokens, self.num_heads, self.head_dim) #D\n",
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" queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)#D\n",
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"\n",
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" keys = keys.transpose(1, 2) #E\n",
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" queries = queries.transpose(1, 2) #E\n",
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" values = values.transpose(1, 2) #E\n",
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"\n",
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" attn_scores = queries @ keys.transpose(2, 3) #F\n",
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" mask_bool = self.mask.bool()[:num_tokens, :num_tokens] #G\n",
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"\n",
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" attn_scores.masked_fill_(mask_bool, -torch.inf) #H\n",
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"\n",
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" attn_weights = torch.softmax(\n",
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" attn_scores / keys.shape[-1]**0.5, dim=-1)\n",
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" attn_weights = self.dropout(attn_weights)\n",
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"\n",
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" context_vec = (attn_weights @ values).transpose(1, 2) #I\n",
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" #J\n",
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" context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)\n",
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" context_vec = self.out_proj(context_vec) #K\n",
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" return context_vec\n",
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"\n",
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"GPT_CONFIG_124M = {\n",
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" \"vocab_size\": 50257, # Vocabulary size\n",
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" \"context_length\": 1024, # Context length\n",
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" \"emb_dim\": 768, # Embedding dimension\n",
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" \"n_heads\": 12, # Number of attention heads\n",
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" \"n_layers\": 12, # Number of layers\n",
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" \"drop_rate\": 0.1, # Dropout rate\n",
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" \"qkv_bias\": False # Query-Key-Value bias\n",
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"}"
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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": 4,
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"id": "c0bc169c",
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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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"Input shape: torch.Size([2, 4, 768])\n",
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"Output shape: torch.Size([2, 4, 768])\n"
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]
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}
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],
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"source": [
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"torch.manual_seed(123)\n",
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"x = torch.rand(2, 4, 768) #A\n",
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"block = TransformerBlock(GPT_CONFIG_124M)\n",
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"output = block(x)\n",
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"print(\"Input shape:\", x.shape)\n",
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"print(\"Output shape:\", output.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "55d9528c",
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"metadata": {},
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"source": [
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"输出如下所示:"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4988db16",
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"metadata": {},
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"source": [
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"Input shape: torch.Size([2, 4, 768]) \\\n",
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"Output shape: torch.Size([2, 4, 768])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4c115f72",
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"metadata": {},
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"source": [
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"从代码输出中我们可以看到，transfomer模块在其输出中保持了输入的维度，这表明transfomer架构在整个网络中处理数据序列时不改变其形状。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9bd353ef",
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"metadata": {},
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"source": [
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"在整个transfomer模块架构中保持形状并不是偶然的，而是其设计的一个关键方面。\n",
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"这种设计使其能够有效应用于广泛的序列到序列任务，其中每个输出向量直接对应于输入向量，从而保持一对一的关系。\n",
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"然而，正如我们在第 3 章中学到的那样，输出是一个上下文向量，它封装了整个输入序列的信息。\n",
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"这意味着，虽然序列的物理维度（长度和特征大小）在通过transfomer模块时保持不变，但每个输出向量的内容被重新编码以整合来自整个输入序列的上下文信息。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd1279d7",
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"metadata": {},
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"source": [
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"在本节中实现的transfomer模块让我们拥有了所有构建块，正如图4.14所示，这些构建块是在下一节中实现GPT架构所需要的。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e82d8a5",
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"metadata": {},
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"source": [
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"**图 4.14 展示了迄今为止我们在本章中实现的不同概念的心智模型。**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "21776f36",
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"metadata": {},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"id": "7379f576",
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"metadata": {},
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"source": [
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"如图 4.14 所示，transfomer模块结合了层归一化、前馈网络（包括 GELU 激活）和快捷连接，我们在本章早些时候已经介绍过。\n",
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"如我们将在即将到来的章节中看到的，这个transfomer模块将构成我们将要实现的GPT架构的主要组成部分。"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python (cell)",
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"language": "python",
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"name": "cell"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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