1418 lines
54 KiB
Plaintext
1418 lines
54 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3de606b8",
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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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"# 自然语言推断：微调BERT\n",
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":label:`sec_natural-language-inference-bert`\n",
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"\n",
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"在本章的前面几节中，我们已经为SNLI数据集（ :numref:`sec_natural-language-inference-and-dataset`）上的自然语言推断任务设计了一个基于注意力的结构（ :numref:`sec_natural-language-inference-attention`）。现在，我们通过微调BERT来重新审视这项任务。正如在 :numref:`sec_finetuning-bert`中讨论的那样，自然语言推断是一个序列级别的文本对分类问题，而微调BERT只需要一个额外的基于多层感知机的架构，如 :numref:`fig_nlp-map-nli-bert`中所示。\n",
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"\n",
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"\n",
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":label:`fig_nlp-map-nli-bert`\n",
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"\n",
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"本节将下载一个预训练好的小版本的BERT，然后对其进行微调，以便在SNLI数据集上进行自然语言推断。\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": "b8292939",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:03:01.182131Z",
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"iopub.status.busy": "2023-08-18T07:03:01.181284Z",
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"iopub.status.idle": "2023-08-18T07:03:04.072192Z",
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"shell.execute_reply": "2023-08-18T07:03:04.070948Z"
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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 json\n",
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"import multiprocessing\n",
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"import os\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"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c36b8eaa",
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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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"## [**加载预训练的BERT**]\n",
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"\n",
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"我们已经在 :numref:`sec_bert-dataset`和 :numref:`sec_bert-pretraining`WikiText-2数据集上预训练BERT（请注意，原始的BERT模型是在更大的语料库上预训练的）。正如在 :numref:`sec_bert-pretraining`中所讨论的，原始的BERT模型有数以亿计的参数。在下面，我们提供了两个版本的预训练的BERT：“bert.base”与原始的BERT基础模型一样大，需要大量的计算资源才能进行微调，而“bert.small”是一个小版本，以便于演示。\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": "e9c4aae6",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:03:04.081229Z",
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"iopub.status.busy": "2023-08-18T07:03:04.079078Z",
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"iopub.status.idle": "2023-08-18T07:03:04.087710Z",
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"shell.execute_reply": "2023-08-18T07:03:04.086601Z"
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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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"source": [
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"d2l.DATA_HUB['bert.base'] = (d2l.DATA_URL + 'bert.base.torch.zip',\n",
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" '225d66f04cae318b841a13d32af3acc165f253ac')\n",
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"d2l.DATA_HUB['bert.small'] = (d2l.DATA_URL + 'bert.small.torch.zip',\n",
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" 'c72329e68a732bef0452e4b96a1c341c8910f81f')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b5203402",
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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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"两个预训练好的BERT模型都包含一个定义词表的“vocab.json”文件和一个预训练参数的“pretrained.params”文件。我们实现了以下`load_pretrained_model`函数来[**加载预先训练好的BERT参数**]。\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": "d9d49c25",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:03:04.095593Z",
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"iopub.status.busy": "2023-08-18T07:03:04.093665Z",
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"iopub.status.idle": "2023-08-18T07:03:04.106137Z",
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"shell.execute_reply": "2023-08-18T07:03:04.104967Z"
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},
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"origin_pos": 10,
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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 load_pretrained_model(pretrained_model, num_hiddens, ffn_num_hiddens,\n",
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" num_heads, num_layers, dropout, max_len, devices):\n",
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" data_dir = d2l.download_extract(pretrained_model)\n",
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" # 定义空词表以加载预定义词表\n",
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" vocab = d2l.Vocab()\n",
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" vocab.idx_to_token = json.load(open(os.path.join(data_dir,\n",
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" 'vocab.json')))\n",
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" vocab.token_to_idx = {token: idx for idx, token in enumerate(\n",
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" vocab.idx_to_token)}\n",
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" bert = d2l.BERTModel(len(vocab), num_hiddens, norm_shape=[256],\n",
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" ffn_num_input=256, ffn_num_hiddens=ffn_num_hiddens,\n",
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" num_heads=4, num_layers=2, dropout=0.2,\n",
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" max_len=max_len, key_size=256, query_size=256,\n",
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" value_size=256, hid_in_features=256,\n",
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" mlm_in_features=256, nsp_in_features=256)\n",
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" # 加载预训练BERT参数\n",
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" bert.load_state_dict(torch.load(os.path.join(data_dir,\n",
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" 'pretrained.params')))\n",
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" return bert, vocab"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3ed9ec34",
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"metadata": {
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"origin_pos": 12
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},
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"source": [
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"为了便于在大多数机器上演示，我们将在本节中加载和微调经过预训练BERT的小版本（“bert.small”）。在练习中，我们将展示如何微调大得多的“bert.base”以显著提高测试精度。\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": "83fa73f8",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:03:04.114631Z",
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"iopub.status.busy": "2023-08-18T07:03:04.112010Z",
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"iopub.status.idle": "2023-08-18T07:03:08.335657Z",
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"shell.execute_reply": "2023-08-18T07:03:08.334563Z"
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},
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"origin_pos": 13,
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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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"Downloading ../data/bert.small.torch.zip from http://d2l-data.s3-accelerate.amazonaws.com/bert.small.torch.zip...\n"
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]
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}
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],
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"source": [
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"devices = d2l.try_all_gpus()\n",
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"bert, vocab = load_pretrained_model(\n",
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" 'bert.small', num_hiddens=256, ffn_num_hiddens=512, num_heads=4,\n",
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" num_layers=2, dropout=0.1, max_len=512, devices=devices)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2f4be336",
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"metadata": {
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"origin_pos": 15
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},
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"source": [
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"## [**微调BERT的数据集**]\n",
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"\n",
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"对于SNLI数据集的下游任务自然语言推断，我们定义了一个定制的数据集类`SNLIBERTDataset`。在每个样本中，前提和假设形成一对文本序列，并被打包成一个BERT输入序列，如 :numref:`fig_bert-two-seqs`所示。回想 :numref:`subsec_bert_input_rep`，片段索引用于区分BERT输入序列中的前提和假设。利用预定义的BERT输入序列的最大长度（`max_len`），持续移除输入文本对中较长文本的最后一个标记，直到满足`max_len`。为了加速生成用于微调BERT的SNLI数据集，我们使用4个工作进程并行生成训练或测试样本。\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": "3c6424fb",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:03:08.341547Z",
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"iopub.status.busy": "2023-08-18T07:03:08.340816Z",
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"iopub.status.idle": "2023-08-18T07:03:08.359529Z",
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"shell.execute_reply": "2023-08-18T07:03:08.358243Z"
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},
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"origin_pos": 17,
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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 SNLIBERTDataset(torch.utils.data.Dataset):\n",
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" def __init__(self, dataset, max_len, vocab=None):\n",
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" all_premise_hypothesis_tokens = [[\n",
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" p_tokens, h_tokens] for p_tokens, h_tokens in zip(\n",
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" *[d2l.tokenize([s.lower() for s in sentences])\n",
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" for sentences in dataset[:2]])]\n",
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"\n",
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" self.labels = torch.tensor(dataset[2])\n",
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" self.vocab = vocab\n",
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" self.max_len = max_len\n",
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" (self.all_token_ids, self.all_segments,\n",
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" self.valid_lens) = self._preprocess(all_premise_hypothesis_tokens)\n",
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" print('read ' + str(len(self.all_token_ids)) + ' examples')\n",
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"\n",
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" def _preprocess(self, all_premise_hypothesis_tokens):\n",
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" pool = multiprocessing.Pool(4) # 使用4个进程\n",
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" out = pool.map(self._mp_worker, all_premise_hypothesis_tokens)\n",
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" all_token_ids = [\n",
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" token_ids for token_ids, segments, valid_len in out]\n",
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" all_segments = [segments for token_ids, segments, valid_len in out]\n",
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" valid_lens = [valid_len for token_ids, segments, valid_len in out]\n",
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" return (torch.tensor(all_token_ids, dtype=torch.long),\n",
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" torch.tensor(all_segments, dtype=torch.long),\n",
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" torch.tensor(valid_lens))\n",
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"\n",
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" def _mp_worker(self, premise_hypothesis_tokens):\n",
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" p_tokens, h_tokens = premise_hypothesis_tokens\n",
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" self._truncate_pair_of_tokens(p_tokens, h_tokens)\n",
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" tokens, segments = d2l.get_tokens_and_segments(p_tokens, h_tokens)\n",
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" token_ids = self.vocab[tokens] + [self.vocab['<pad>']] \\\n",
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" * (self.max_len - len(tokens))\n",
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" segments = segments + [0] * (self.max_len - len(segments))\n",
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" valid_len = len(tokens)\n",
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" return token_ids, segments, valid_len\n",
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"\n",
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" def _truncate_pair_of_tokens(self, p_tokens, h_tokens):\n",
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" # 为BERT输入中的'<CLS>'、'<SEP>'和'<SEP>'词元保留位置\n",
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" while len(p_tokens) + len(h_tokens) > self.max_len - 3:\n",
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" if len(p_tokens) > len(h_tokens):\n",
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" p_tokens.pop()\n",
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" else:\n",
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" h_tokens.pop()\n",
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"\n",
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" def __getitem__(self, idx):\n",
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" return (self.all_token_ids[idx], self.all_segments[idx],\n",
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" self.valid_lens[idx]), self.labels[idx]\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.all_token_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": "1972357c",
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"metadata": {
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"origin_pos": 19
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},
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"source": [
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"下载完SNLI数据集后，我们通过实例化`SNLIBERTDataset`类来[**生成训练和测试样本**]。这些样本将在自然语言推断的训练和测试期间进行小批量读取。\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": "ba13f731",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:03:08.365140Z",
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"iopub.status.busy": "2023-08-18T07:03:08.364679Z",
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"iopub.status.idle": "2023-08-18T07:04:03.295593Z",
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"shell.execute_reply": "2023-08-18T07:04:03.294685Z"
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},
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"origin_pos": 21,
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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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"read 549367 examples\n"
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]
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},
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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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"read 9824 examples\n"
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]
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}
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],
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"source": [
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"# 如果出现显存不足错误，请减少“batch_size”。在原始的BERT模型中，max_len=512\n",
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"batch_size, max_len, num_workers = 512, 128, d2l.get_dataloader_workers()\n",
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"data_dir = d2l.download_extract('SNLI')\n",
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"train_set = SNLIBERTDataset(d2l.read_snli(data_dir, True), max_len, vocab)\n",
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"test_set = SNLIBERTDataset(d2l.read_snli(data_dir, False), max_len, vocab)\n",
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"train_iter = torch.utils.data.DataLoader(train_set, batch_size, shuffle=True,\n",
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" num_workers=num_workers)\n",
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"test_iter = torch.utils.data.DataLoader(test_set, batch_size,\n",
|
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" num_workers=num_workers)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6ae5ccc",
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"metadata": {
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"origin_pos": 23
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},
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"source": [
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"## 微调BERT\n",
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"\n",
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"如 :numref:`fig_bert-two-seqs`所示，用于自然语言推断的微调BERT只需要一个额外的多层感知机，该多层感知机由两个全连接层组成（请参见下面`BERTClassifier`类中的`self.hidden`和`self.output`）。[**这个多层感知机将特殊的“<cls>”词元**]的BERT表示进行了转换，该词元同时编码前提和假设的信息(**为自然语言推断的三个输出**)：蕴涵、矛盾和中性。\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": "8e26a41e",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:04:03.300395Z",
|
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"iopub.status.busy": "2023-08-18T07:04:03.299910Z",
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"iopub.status.idle": "2023-08-18T07:04:03.306353Z",
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"shell.execute_reply": "2023-08-18T07:04:03.305313Z"
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},
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"origin_pos": 25,
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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 BERTClassifier(nn.Module):\n",
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" def __init__(self, bert):\n",
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" super(BERTClassifier, self).__init__()\n",
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" self.encoder = bert.encoder\n",
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" self.hidden = bert.hidden\n",
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" self.output = nn.Linear(256, 3)\n",
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"\n",
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" def forward(self, inputs):\n",
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" tokens_X, segments_X, valid_lens_x = inputs\n",
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" encoded_X = self.encoder(tokens_X, segments_X, valid_lens_x)\n",
|
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" return self.output(self.hidden(encoded_X[:, 0, :]))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a8a1ecb",
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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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"在下文中，预训练的BERT模型`bert`被送到用于下游应用的`BERTClassifier`实例`net`中。在BERT微调的常见实现中，只有额外的多层感知机（`net.output`）的输出层的参数将从零开始学习。预训练BERT编码器（`net.encoder`）和额外的多层感知机的隐藏层（`net.hidden`）的所有参数都将进行微调。\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": "b814689b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:04:03.310896Z",
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"iopub.status.busy": "2023-08-18T07:04:03.310446Z",
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"iopub.status.idle": "2023-08-18T07:04:03.315332Z",
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"shell.execute_reply": "2023-08-18T07:04:03.314376Z"
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},
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"origin_pos": 29,
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"tab": [
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"net = BERTClassifier(bert)"
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"回想一下，在 :numref:`sec_bert`中，`MaskLM`类和`NextSentencePred`类在其使用的多层感知机中都有一些参数。这些参数是预训练BERT模型`bert`中参数的一部分，因此是`net`中的参数的一部分。然而，这些参数仅用于计算预训练过程中的遮蔽语言模型损失和下一句预测损失。这两个损失函数与微调下游应用无关，因此当BERT微调时，`MaskLM`和`NextSentencePred`中采用的多层感知机的参数不会更新（陈旧的，staled）。\n",
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"\n",
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"为了允许具有陈旧梯度的参数，标志`ignore_stale_grad=True`在`step`函数`d2l.train_batch_ch13`中被设置。我们通过该函数使用SNLI的训练集（`train_iter`）和测试集（`test_iter`）对`net`模型进行训练和评估。由于计算资源有限，[**训练**]和测试精度可以进一步提高：我们把对它的讨论留在练习中。\n"
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"loss 0.520, train acc 0.790, test acc 0.779\n",
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"10442.5 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]\n"
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}
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"source": [
|
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"lr, num_epochs = 1e-4, 5\n",
|
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"trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
|
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"loss = nn.CrossEntropyLoss(reduction='none')\n",
|
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"d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,\n",
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]
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},
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{
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"source": [
|
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"## 小结\n",
|
|
"\n",
|
|
"* 我们可以针对下游应用对预训练的BERT模型进行微调，例如在SNLI数据集上进行自然语言推断。\n",
|
|
"* 在微调过程中，BERT模型成为下游应用模型的一部分。仅与训练前损失相关的参数在微调期间不会更新。\n",
|
|
"\n",
|
|
"## 练习\n",
|
|
"\n",
|
|
"1. 如果您的计算资源允许，请微调一个更大的预训练BERT模型，该模型与原始的BERT基础模型一样大。修改`load_pretrained_model`函数中的参数设置：将“bert.small”替换为“bert.base”，将`num_hiddens=256`、`ffn_num_hiddens=512`、`num_heads=4`和`num_layers=2`的值分别增加到768、3072、12和12。通过增加微调迭代轮数（可能还会调优其他超参数），你可以获得高于0.86的测试精度吗？\n",
|
|
"1. 如何根据一对序列的长度比值截断它们？将此对截断方法与`SNLIBERTDataset`类中使用的方法进行比较。它们的利弊是什么？\n"
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]
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},
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{
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"id": "4a2eba9d",
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"metadata": {
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"origin_pos": 36,
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"tab": [
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"pytorch"
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]
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},
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"source": [
|
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"[Discussions](https://discuss.d2l.ai/t/5718)\n"
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]
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}
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],
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