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Translated_Book/ch02/2.7 构建词符嵌入.ipynb
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Translated_Book/ch02/2.7 构建词符嵌入.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2cd2fcda-2fda-4aa8-8bc8-de1e496f9db1",
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"metadata": {},
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"source": [
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"## 2.7 构建词符嵌入"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a301068-6ab2-44ff-a915-1ba11688274f",
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"metadata": {},
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"source": [
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"- 我们准备用于大语言模型（LLM）的数据已经差不多就绪了\n",
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"- 接下来，我们要做的最后一步是使用嵌入层将 token 嵌入到连续的向量表示中。token本身不可计算，需要将其映射到一个连续向量空间，才可以进行后续运算，这个映射的结果就是该token对应的embedding\n",
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"- 通常，这些用来转换词符的嵌入层是大语言模型（LLM）的一部分，并且在模型训练的过程中会不断调整和优化。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e85089aa-8671-4e5f-a2b3-ef252004ee4c",
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"metadata": {},
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"source": [
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"<img src=\"https://github.com/datawhalechina/llms-from-scratch-cn/blob/main/Translated_Book/img/fig-2-15.jpg?raw=true\" width=\"400px\">"
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]
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},
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{
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"cell_type": "markdown",
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"id": "44e014ca-1fc5-4b90-b6fa-c2097bb92c0b",
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"metadata": {},
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"source": [
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"- 假设我们在分词后有以下四个输入示例，对应的输入ID分别是5、1、3和2："
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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": "15a6304c-9474-4470-b85d-3991a49fa653",
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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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"input_ids = torch.tensor([2, 3, 5, 1])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14da6344-2c71-4837-858d-dd120005ba05",
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"metadata": {},
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"source": [
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"- 为了简化问题，假设我们有一个只包含6个单词的小型词汇表，我们想要创建大小为3的嵌入。"
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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": "93cb2cee-9aa6-4bb8-8977-c65661d16eda",
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"metadata": {},
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"outputs": [],
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"source": [
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"vocab_size = 6\n",
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"output_dim = 3\n",
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"\n",
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"torch.manual_seed(123)\n",
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"embedding_layer = torch.nn.Embedding(vocab_size, output_dim)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ff241f6-78eb-4e4a-a55f-5b2b6196d5b0",
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"metadata": {},
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"source": [
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"- 这将会生成一个6x3的权重矩阵："
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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": "a686eb61-e737-4351-8f1c-222913d47468",
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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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"Parameter containing:\n",
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"tensor([[ 0.3374, -0.1778, -0.1690],\n",
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" [ 0.9178, 1.5810, 1.3010],\n",
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" [ 1.2753, -0.2010, -0.1606],\n",
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" [-0.4015, 0.9666, -1.1481],\n",
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" [-1.1589, 0.3255, -0.6315],\n",
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" [-2.8400, -0.7849, -1.4096]], requires_grad=True)\n"
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]
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}
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],
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"source": [
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"print(embedding_layer.weight)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5e54d5f1",
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"metadata": {},
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"source": [
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"- 由于嵌入层只是独热编码和矩阵乘法方法的一种更高效的实现，它可以被视为一个可以通过反向传播进行优化的神经网络层。\n",
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"- 对于那些熟悉独热编码的人来说，上述嵌入层的方法本质上只是实现独热编码后进行矩阵乘法的一种更高效的手段，这种方法在全连接层中使用，其详细说明可以在补充代码[./embedding_vs_matmul](https://github.com/datawhalechina/llms-from-scratch-cn/tree/main/ch02/03_bonus_embedding-vs-matmul)中找到。\n",
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"- 因为嵌入层只是独热编码和矩阵乘法方法的一种更高效的实现，所以它可以被视为一个可以通过反向传播算法进行优化的神经网络层。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b0d58c3-83c0-4205-aca2-9c48b19fd4a7",
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"metadata": {},
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"source": [
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"- 要将ID为3的词符转换为一个3维向量，我们执行以下步骤："
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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": "e43600ba-f287-4746-8ddf-d0f71a9023ca",
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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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"tensor([[-0.4015, 0.9666, -1.1481]], grad_fn=<EmbeddingBackward0>)\n"
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]
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}
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],
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"source": [
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"print(embedding_layer(torch.tensor([3])))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a7bbf625-4f36-491d-87b4-3969efb784b0",
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"metadata": {},
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"source": [
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"- 注意，上述内容是`embedding_layer`权重矩阵中的第4行。\n",
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"- 为了嵌入上面所有的四个`input_ids`值，我们执行以下操作："
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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": 5,
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"id": "50280ead-0363-44c8-8c35-bb885d92c8b7",
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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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"tensor([[ 1.2753, -0.2010, -0.1606],\n",
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" [-0.4015, 0.9666, -1.1481],\n",
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" [-2.8400, -0.7849, -1.4096],\n",
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" [ 0.9178, 1.5810, 1.3010]], grad_fn=<EmbeddingBackward0>)\n"
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]
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}
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],
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"source": [
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"print(embedding_layer(input_ids))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "be97ced4-bd13-42b7-866a-4d699a17e155",
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"metadata": {},
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"source": [
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"- 嵌入层本质上是一种查找操作："
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]
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},
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{
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"cell_type": "markdown",
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"id": "f33c2741-bf1b-4c60-b7fd-61409d556646",
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"metadata": {},
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"source": [
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"<img src=\"https://github.com/datawhalechina/llms-from-scratch-cn/blob/main/Translated_Book/img/fig-2-16.jpg?raw=true\" width=\"500px\">"
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]
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},
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{
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"cell_type": "markdown",
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"id": "08218d9f-aa1a-4afb-a105-72ff96a54e73",
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"metadata": {},
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"source": [
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"- **您可能对比较嵌入层与常规线性层的附加内容感兴趣：[../03_bonus_embedding-vs-matmul](https://github.com/datawhalechina/llms-from-scratch-cn/tree/main/ch02/03_bonus_embedding-vs-matmul)**"
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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 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.9.13"
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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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