1265 lines
42 KiB
Plaintext
1265 lines
42 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "01f456ba",
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"metadata": {
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"origin_pos": 0
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},
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"source": [
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"# 预训练word2vec\n",
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":label:`sec_word2vec_pretraining`\n",
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"\n",
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"我们继续实现 :numref:`sec_word2vec`中定义的跳元语法模型。然后，我们将在PTB数据集上使用负采样预训练word2vec。首先，让我们通过调用`d2l.load_data_ptb`函数来获得该数据集的数据迭代器和词表，该函数在 :numref:`sec_word2vec_data`中进行了描述。\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": 1,
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"id": "0aeafaa5",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:10.079752Z",
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"iopub.status.busy": "2023-08-18T07:16:10.079196Z",
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"iopub.status.idle": "2023-08-18T07:16:27.336361Z",
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"shell.execute_reply": "2023-08-18T07:16:27.335336Z"
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},
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import math\n",
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"import torch\n",
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"from torch import nn\n",
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"from d2l import torch as d2l\n",
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"\n",
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"batch_size, max_window_size, num_noise_words = 512, 5, 5\n",
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"data_iter, vocab = d2l.load_data_ptb(batch_size, max_window_size,\n",
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" num_noise_words)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d0cf231a",
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"metadata": {
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"origin_pos": 4
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},
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"source": [
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"## 跳元模型\n",
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"\n",
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"我们通过嵌入层和批量矩阵乘法实现了跳元模型。首先，让我们回顾一下嵌入层是如何工作的。\n",
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"\n",
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"### 嵌入层\n",
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"\n",
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"如 :numref:`sec_seq2seq`中所述，嵌入层将词元的索引映射到其特征向量。该层的权重是一个矩阵，其行数等于字典大小（`input_dim`），列数等于每个标记的向量维数（`output_dim`）。在词嵌入模型训练之后，这个权重就是我们所需要的。\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": 2,
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"id": "773b7192",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.340860Z",
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"iopub.status.busy": "2023-08-18T07:16:27.340156Z",
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"iopub.status.idle": "2023-08-18T07:16:27.365326Z",
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"shell.execute_reply": "2023-08-18T07:16:27.364347Z"
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},
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"origin_pos": 6,
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"tab": [
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"pytorch"
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]
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},
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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 embedding_weight (torch.Size([20, 4]), dtype=torch.float32)\n"
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]
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}
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],
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"source": [
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"embed = nn.Embedding(num_embeddings=20, embedding_dim=4)\n",
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"print(f'Parameter embedding_weight ({embed.weight.shape}, '\n",
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" f'dtype={embed.weight.dtype})')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "80720da7",
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"metadata": {
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"origin_pos": 8
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},
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"source": [
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"嵌入层的输入是词元（词）的索引。对于任何词元索引$i$，其向量表示可以从嵌入层中的权重矩阵的第$i$行获得。由于向量维度（`output_dim`）被设置为4，因此当小批量词元索引的形状为（2，3）时，嵌入层返回具有形状（2，3，4）的向量。\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": 3,
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"id": "1fa70f42",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.368945Z",
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"iopub.status.busy": "2023-08-18T07:16:27.368367Z",
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"iopub.status.idle": "2023-08-18T07:16:27.377116Z",
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"shell.execute_reply": "2023-08-18T07:16:27.376157Z"
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},
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"origin_pos": 9,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[[-1.4754, -0.3612, -0.4246, 0.5805],\n",
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" [-0.3160, 0.8830, 0.5328, 0.2179],\n",
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" [-0.0378, -0.5559, 1.4525, 0.6230]],\n",
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"\n",
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" [[ 0.0829, -1.0549, 0.6381, 0.7886],\n",
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" [-0.3862, -0.1291, 0.4160, -0.6710],\n",
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" [-0.4056, 0.0370, -0.6308, -0.2865]]], grad_fn=<EmbeddingBackward0>)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
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"embed(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": "4b441409",
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"metadata": {
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"origin_pos": 10
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},
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"source": [
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"### 定义前向传播\n",
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"\n",
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"在前向传播中，跳元语法模型的输入包括形状为（批量大小，1）的中心词索引`center`和形状为（批量大小，`max_len`）的上下文与噪声词索引`contexts_and_negatives`，其中`max_len`在 :numref:`subsec_word2vec-minibatch-loading`中定义。这两个变量首先通过嵌入层从词元索引转换成向量，然后它们的批量矩阵相乘（在 :numref:`subsec_batch_dot`中描述）返回形状为（批量大小，1，`max_len`）的输出。输出中的每个元素是中心词向量和上下文或噪声词向量的点积。\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": 4,
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"id": "cd4cc024",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.380875Z",
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"iopub.status.busy": "2023-08-18T07:16:27.380348Z",
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"iopub.status.idle": "2023-08-18T07:16:27.385076Z",
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"shell.execute_reply": "2023-08-18T07:16:27.384084Z"
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},
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"origin_pos": 12,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"def skip_gram(center, contexts_and_negatives, embed_v, embed_u):\n",
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" v = embed_v(center)\n",
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" u = embed_u(contexts_and_negatives)\n",
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" pred = torch.bmm(v, u.permute(0, 2, 1))\n",
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" return pred"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b5dc971",
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"metadata": {
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"origin_pos": 14
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},
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"source": [
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"让我们为一些样例输入打印此`skip_gram`函数的输出形状。\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": 5,
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"id": "1747bbfa",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.388992Z",
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"iopub.status.busy": "2023-08-18T07:16:27.388308Z",
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"iopub.status.idle": "2023-08-18T07:16:27.396357Z",
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"shell.execute_reply": "2023-08-18T07:16:27.395365Z"
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},
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"origin_pos": 16,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([2, 1, 4])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"skip_gram(torch.ones((2, 1), dtype=torch.long),\n",
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" torch.ones((2, 4), dtype=torch.long), embed, embed).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": "dde6d712",
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"metadata": {
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"origin_pos": 18
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},
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"source": [
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"## 训练\n",
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"\n",
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"在训练带负采样的跳元模型之前，我们先定义它的损失函数。\n",
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"\n",
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"### 二元交叉熵损失\n",
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"\n",
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"根据 :numref:`subsec_negative-sampling`中负采样损失函数的定义，我们将使用二元交叉熵损失。\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": 6,
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"id": "5ec8d9c3",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.400654Z",
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"iopub.status.busy": "2023-08-18T07:16:27.400159Z",
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"iopub.status.idle": "2023-08-18T07:16:27.405793Z",
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"shell.execute_reply": "2023-08-18T07:16:27.404774Z"
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},
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"origin_pos": 20,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"class SigmoidBCELoss(nn.Module):\n",
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" # 带掩码的二元交叉熵损失\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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"\n",
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" def forward(self, inputs, target, mask=None):\n",
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" out = nn.functional.binary_cross_entropy_with_logits(\n",
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" inputs, target, weight=mask, reduction=\"none\")\n",
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" return out.mean(dim=1)\n",
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"\n",
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"loss = SigmoidBCELoss()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9dd7eefc",
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"metadata": {
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"origin_pos": 22
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},
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"source": [
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"回想一下我们在 :numref:`subsec_word2vec-minibatch-loading`中对掩码变量和标签变量的描述。下面计算给定变量的二进制交叉熵损失。\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": 7,
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"id": "0e0fcee0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.410664Z",
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"iopub.status.busy": "2023-08-18T07:16:27.409825Z",
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"iopub.status.idle": "2023-08-18T07:16:27.423445Z",
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"shell.execute_reply": "2023-08-18T07:16:27.422478Z"
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},
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"origin_pos": 23,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([0.9352, 1.8462])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pred = torch.tensor([[1.1, -2.2, 3.3, -4.4]] * 2)\n",
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"label = torch.tensor([[1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0]])\n",
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"mask = torch.tensor([[1, 1, 1, 1], [1, 1, 0, 0]])\n",
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"loss(pred, label, mask) * mask.shape[1] / mask.sum(axis=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": "4bcfe374",
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"metadata": {
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"origin_pos": 25
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},
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"source": [
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"下面显示了如何使用二元交叉熵损失中的Sigmoid激活函数（以较低效率的方式）计算上述结果。我们可以将这两个输出视为两个规范化的损失，在非掩码预测上进行平均。\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": 8,
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"id": "b79ec9c9",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:16:27.427864Z",
|
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"iopub.status.busy": "2023-08-18T07:16:27.427357Z",
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"iopub.status.idle": "2023-08-18T07:16:27.432489Z",
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"shell.execute_reply": "2023-08-18T07:16:27.431711Z"
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},
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"origin_pos": 26,
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"tab": [
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"pytorch"
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]
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},
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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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"0.9352\n",
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"1.8462\n"
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]
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}
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],
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"source": [
|
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"def sigmd(x):\n",
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" return -math.log(1 / (1 + math.exp(-x)))\n",
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"\n",
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"print(f'{(sigmd(1.1) + sigmd(2.2) + sigmd(-3.3) + sigmd(4.4)) / 4:.4f}')\n",
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"print(f'{(sigmd(-1.1) + sigmd(-2.2)) / 2:.4f}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0cab2e83",
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"metadata": {
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"origin_pos": 27
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},
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"source": [
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"### 初始化模型参数\n",
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"\n",
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"我们定义了两个嵌入层，将词表中的所有单词分别作为中心词和上下文词使用。字向量维度`embed_size`被设置为100。\n"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "8a373a4d",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-08-18T07:16:27.436933Z",
|
|
"iopub.status.busy": "2023-08-18T07:16:27.436455Z",
|
|
"iopub.status.idle": "2023-08-18T07:16:27.461291Z",
|
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"shell.execute_reply": "2023-08-18T07:16:27.460112Z"
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},
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"origin_pos": 29,
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"tab": [
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"pytorch"
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]
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},
|
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"outputs": [],
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"source": [
|
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"embed_size = 100\n",
|
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"net = nn.Sequential(nn.Embedding(num_embeddings=len(vocab),\n",
|
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" embedding_dim=embed_size),\n",
|
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" nn.Embedding(num_embeddings=len(vocab),\n",
|
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" embedding_dim=embed_size))"
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]
|
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},
|
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{
|
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"cell_type": "markdown",
|
|
"id": "7d9e7090",
|
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"metadata": {
|
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"origin_pos": 30
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},
|
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"source": [
|
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"### 定义训练阶段代码\n",
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"\n",
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"训练阶段代码实现定义如下。由于填充的存在，损失函数的计算与以前的训练函数略有不同。\n"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "763d58ba",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-08-18T07:16:27.466398Z",
|
|
"iopub.status.busy": "2023-08-18T07:16:27.466107Z",
|
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"iopub.status.idle": "2023-08-18T07:16:27.478915Z",
|
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"shell.execute_reply": "2023-08-18T07:16:27.477777Z"
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},
|
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"origin_pos": 32,
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"tab": [
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"pytorch"
|
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]
|
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},
|
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"outputs": [],
|
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"source": [
|
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"def train(net, data_iter, lr, num_epochs, device=d2l.try_gpu()):\n",
|
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" def init_weights(m):\n",
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" if type(m) == nn.Embedding:\n",
|
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" nn.init.xavier_uniform_(m.weight)\n",
|
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" net.apply(init_weights)\n",
|
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" net = net.to(device)\n",
|
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" optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
|
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" animator = d2l.Animator(xlabel='epoch', ylabel='loss',\n",
|
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" xlim=[1, num_epochs])\n",
|
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" # 规范化的损失之和，规范化的损失数\n",
|
|
" metric = d2l.Accumulator(2)\n",
|
|
" for epoch in range(num_epochs):\n",
|
|
" timer, num_batches = d2l.Timer(), len(data_iter)\n",
|
|
" for i, batch in enumerate(data_iter):\n",
|
|
" optimizer.zero_grad()\n",
|
|
" center, context_negative, mask, label = [\n",
|
|
" data.to(device) for data in batch]\n",
|
|
"\n",
|
|
" pred = skip_gram(center, context_negative, net[0], net[1])\n",
|
|
" l = (loss(pred.reshape(label.shape).float(), label.float(), mask)\n",
|
|
" / mask.sum(axis=1) * mask.shape[1])\n",
|
|
" l.sum().backward()\n",
|
|
" optimizer.step()\n",
|
|
" metric.add(l.sum(), l.numel())\n",
|
|
" if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:\n",
|
|
" animator.add(epoch + (i + 1) / num_batches,\n",
|
|
" (metric[0] / metric[1],))\n",
|
|
" print(f'loss {metric[0] / metric[1]:.3f}, '\n",
|
|
" f'{metric[1] / timer.stop():.1f} tokens/sec on {str(device)}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ee280a14",
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|
"metadata": {
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"origin_pos": 34
|
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},
|
|
"source": [
|
|
"现在，我们可以使用负采样来训练跳元模型。\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "c10e8868",
|
|
"metadata": {
|
|
"execution": {
|
|
"iopub.execute_input": "2023-08-18T07:16:27.484286Z",
|
|
"iopub.status.busy": "2023-08-18T07:16:27.483558Z",
|
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"iopub.status.idle": "2023-08-18T07:16:54.164551Z",
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"shell.execute_reply": "2023-08-18T07:16:54.163546Z"
|
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},
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"origin_pos": 35,
|
|
"tab": [
|
|
"pytorch"
|
|
]
|
|
},
|
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"outputs": [
|
|
{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
|
"text": [
|
|
"loss 0.410, 377799.5 tokens/sec on cuda:0\n"
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]
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},
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"source": [
|
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"## 应用词嵌入\n",
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|
":label:`subsec_apply-word-embed`\n",
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|
"\n",
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"在训练word2vec模型之后，我们可以使用训练好模型中词向量的余弦相似度来从词表中找到与输入单词语义最相似的单词。\n"
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|
|
"iopub.status.idle": "2023-08-18T07:16:54.176483Z",
|
|
"shell.execute_reply": "2023-08-18T07:16:54.175544Z"
|
|
},
|
|
"origin_pos": 38,
|
|
"tab": [
|
|
"pytorch"
|
|
]
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"cosine sim=0.773: microprocessor\n",
|
|
"cosine sim=0.589: hitachi\n",
|
|
"cosine sim=0.582: computers\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def get_similar_tokens(query_token, k, embed):\n",
|
|
" W = embed.weight.data\n",
|
|
" x = W[vocab[query_token]]\n",
|
|
" # 计算余弦相似性。增加1e-9以获得数值稳定性\n",
|
|
" cos = torch.mv(W, x) / torch.sqrt(torch.sum(W * W, dim=1) *\n",
|
|
" torch.sum(x * x) + 1e-9)\n",
|
|
" topk = torch.topk(cos, k=k+1)[1].cpu().numpy().astype('int32')\n",
|
|
" for i in topk[1:]: # 删除输入词\n",
|
|
" print(f'cosine sim={float(cos[i]):.3f}: {vocab.to_tokens(i)}')\n",
|
|
"\n",
|
|
"get_similar_tokens('chip', 3, net[0])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1b343f9c",
|
|
"metadata": {
|
|
"origin_pos": 40
|
|
},
|
|
"source": [
|
|
"## 小结\n",
|
|
"\n",
|
|
"* 我们可以使用嵌入层和二元交叉熵损失来训练带负采样的跳元模型。\n",
|
|
"* 词嵌入的应用包括基于词向量的余弦相似度为给定词找到语义相似的词。\n",
|
|
"\n",
|
|
"## 练习\n",
|
|
"\n",
|
|
"1. 使用训练好的模型，找出其他输入词在语义上相似的词。您能通过调优超参数来改进结果吗？\n",
|
|
"1. 当训练语料库很大时，在更新模型参数时，我们经常对当前小批量的*中心词*进行上下文词和噪声词的采样。换言之，同一中心词在不同的训练迭代轮数可以有不同的上下文词或噪声词。这种方法的好处是什么？尝试实现这种训练方法。\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "99139c06",
|
|
"metadata": {
|
|
"origin_pos": 42,
|
|
"tab": [
|
|
"pytorch"
|
|
]
|
|
},
|
|
"source": [
|
|
"[Discussions](https://discuss.d2l.ai/t/5740)\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"language_info": {
|
|
"name": "python"
|
|
},
|
|
"required_libs": []
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
} |