Demos/21a. 微调 LLM：实现抽取式问答.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xvSGDbExff_I"
   },
   "source": [
    "# 微调 LLM：实现抽取式问答\n",
    "\n",
    "> 指导文章：[22a. 微调 LLM：实现抽取式问答](../Guide/22a.%20微调%20LLM：实现抽取式问答.md) | 作业文章：[22b. 作业 - Bert 微调抽取式问答](../Guide/22b.%20作业%20-%20Bert%20微调抽取式问答.md)\n",
    "\n",
    "**在线链接**：[Kaggle](https://www.kaggle.com/code/aidemos/21a-llm) | [Colab](https://colab.research.google.com/drive/1jgdoO7fKk7Tsn2yi28ytsDQ8VXdthnIm?usp=sharing)\n",
    "\n",
    "## 前言\n",
    "\n",
    "**预训练 + 微调**是一个非常主流的范式，适用于各种下游任务，如文本分类、命名实体识别、机器翻译等。在这篇文章中，我们将以**抽取式问答任务**为例，再次尝试微调预训练模型。\n",
    "\n",
    "首先，了解什么是**抽取式问答**：根据「给定的问题」和「**包含**答案的文本」，从中**抽取**出对应的答案片段，**不需要生成新的词语**。\n",
    "\n",
    "**举例说明**：\n",
    "\n",
    "- **文本**：`BERT 是由 Google 提出的预训练语言模型，它在多个 NLP 任务上取得了 SOTA 的成绩。`\n",
    "- **问题**：`谁提出了 BERT？`\n",
    "- **答案**：`Google`\n",
    "\n",
    "> 如果去掉“抽取式”的限定，广义上的“问答”更接近于**生成式问答（Generative Question Answering）**，即答案并非固定的文本片段，模型基于理解进行**生成**，最终的答案不拘泥于特定的文本。\n",
    ">\n",
    "> **举例说明**：\n",
    ">\n",
    "> - **文本**：同上。\n",
    "> - **问题**：同上。\n",
    "> - **答案**：`BERT 是由 Google 提出的预训练语言模型。具体来说，它是由 Jacob Devlin 等研究人员在 2018 年的论文《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》中首次介绍的。BERT 在多个 NLP 任务上取得了 SOTA（State-of-the-Art）的成绩，推动了自然语言处理领域的快速发展。`（该答案由 GPT-4o 生成）\n",
    "\n",
    "#### Q: 模型怎么完成抽取式问答任务？输出是什么？\n",
    "\n",
    "停下来思考一下，是直接生成答案对应的词或句子吗？\n",
    "\n",
    "**不是**，输出的是**答案在文本中的起始和结束位置**。通过下图进行理解：\n",
    "\n",
    "> ![Extractive-QA-model](../Guide/assets/Extractive-QA-model.png)\n",
    "\n",
    "模型的最终输出为两个向量：起始位置得分向量 $\\mathbf{s} \\in \\mathbb{R}^N$ 和结束位置得分向量 $\\mathbf{e} \\in \\mathbb{R}^N$，其中 $N$ 是输入序列的长度。\n",
    "\n",
    "对于每个位置 $i$，模型计算其作为答案起始位置和结束位置的得分：\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "s_i &= \\mathbf{w}_{\\text{start}} \\mathbf{h}_i + b_{\\text{start}} \\\\\n",
    "e_i &= \\mathbf{w}_{\\text{end}} \\mathbf{h}_i + b_{\\text{end}}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "其中, $\\mathbf{h}_i \\in \\mathbb{R}^H$ 是编码器在位置 $i$ 的隐藏状态输出 ($\\mathbf{h}$ 就是 BERT 模型的最终输出), $H$ 是隐藏层的维度。$\\mathbf{w}_{\\text{start}} \\in \\mathbb{R}^H$ 和 $\\mathbf{w}_{\\text{end}} \\in \\mathbb{R}^H$ 是权重向量（对应于 `nn.Linear(H, 1)`，这里写成了常见的数学形式，了解线性层代码的同学可以当做 $\\mathbf{h}\\mathbf{w}^\\top$）, $b_{\\text{start}}$ 和 $b_{\\text{end}}$ 是偏置项。\n",
    "\n",
    "然后，对得分向量进行 softmax 操作，得到每个位置作为起始和结束位置的概率分布：\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "P_{\\text{start}}(i) &= \\frac{e^{s_i}}{\\sum_{j=1}^{N} e^{s_j}} \\\\\n",
    "P_{\\text{end}}(i) &= \\frac{e^{e_i}}{\\sum_{j=1}^{N} e^{e_j}}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "在推理时，选择具有最高概率的起始位置 $\\hat{s}$ 和结束位置 $\\hat{e}$。为了保证答案的合理性，通常要求 $\\hat{s} \\leq \\hat{e}$，并且答案的长度不超过预设的最大长度 $L_{\\text{max}}$。此时的行为称为后处理（Postprocessing），根据实际需求进行。\n",
    "\n",
    "最终，答案就是输入序列中从位置 $\\hat{s}$ 到 $\\hat{e}$ 的片段，即：\n",
    "\n",
    "$$\n",
    "\\text{Answer} = \\text{Input}[\\hat{s}:\\hat{e}]\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NYAHsHNbzdKm"
   },
   "source": [
    "## 前置准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 下载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YPrc4Eie9Yo5",
    "outputId": "1640c875-58d1-4288-e382-e9fa9db39b9f"
   },
   "outputs": [],
   "source": [
    "# 通过以下命令下载数据集\n",
    "!wget https://github.com/Hoper-J/HUNG-YI_LEE_Machine-Learning_Homework/raw/refs/heads/master/HW07/ml2023spring-hw7.zip\n",
    "!unzip ml2023spring-hw7.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TevOvhC03m0h"
   },
   "source": [
    "### 安装库 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tbxWFX_jpDom",
    "outputId": "138488c2-96fa-4c41-d450-9d0a1b74f36c",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install transformers\n",
    "!pip install accelerate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8dKM4yCh4LI_"
   },
   "source": [
    "### 导入库\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WOTHHtWJoahe"
   },
   "outputs": [],
   "source": [
    "# ========== 标准库模块 ==========\n",
    "import json\n",
    "import random\n",
    "\n",
    "# ========== 第三方库 ==========\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "# ========== 深度学习相关库 ==========\n",
    "import torch\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "\n",
    "# Transformers (Hugging Face)\n",
    "from transformers import (\n",
    "    AdamW,\n",
    "    AutoTokenizer,\n",
    "    AutoModelForQuestionAnswering,\n",
    "    get_linear_schedule_with_warmup\n",
    ")\n",
    "\n",
    "# 加速库\n",
    "from accelerate import Accelerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置设备和随机数种子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "# For Mac M1, M2...\n",
    "# device = \"mps\" if torch.backends.mps.is_available() else (\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "def same_seeds(seed):\n",
    "    \"\"\"\n",
    "    设置随机种子以确保结果的可复现性。\n",
    "\n",
    "    参数：\n",
    "    - seed (int): 要设置的随机种子值。\n",
    "    \"\"\"\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed(seed)\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2YgXHuVLp_6j"
   },
   "source": [
    "## 加载模型和分词器\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220,
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     ]
    },
    "id": "xyBCYGjAp3ym",
    "outputId": "81f25c6d-cd7f-4be1-e085-f8a54bd1c27d",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = AutoModelForQuestionAnswering.from_pretrained(\"bert-base-chinese\").to(device)\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-chinese\")\n",
    "\n",
    "# 预训练模型也可以换成其他的\n",
    "# model = AutoModelForQuestionAnswering.from_pretrained(\"luhua/chinese_pretrain_mrc_macbert_large\").to(device)\n",
    "# tokenizer = AutoTokenizer.from_pretrained(\"luhua/chinese_pretrain_mrc_macbert_large\")\n",
    "\n",
    "# 你可以忽略警告消息（它弹出是因为新的 QA 预测头是随机初始化的）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q: 什么是 `AutoModelForQuestionAnswering`？\n",
    "\n",
    "`AutoModelForQuestionAnswering` 是 Hugging Face 提供的自动模型加载类，除了加载指定模型的预训练权重之外，**它会在模型的顶层添加一个用于问答任务的输出层**，用于预测答案的起始位置和结束位置。如果模型本身已经包含用于问答任务的输出层，则会加载相应的权重；如果没有，则会在顶层添加一个**新的输出层**，随机初始化权重（这时候会有警告信息）。\n",
    "\n",
    "不妨打印看看这个输出层到底是什么："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.qa_outputs)\n",
    "# print(model)  # 如果感兴趣，也可以打印整个模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Td-GTmk5OW4"
   },
   "source": [
    "## 数据部分\n",
    "\n",
    "> 两个繁体中文阅读理解数据集：[DRCD](https://github.com/DRCKnowledgeTeam/DRCD) 和 [ODSQA](https://github.com/Chia-Hsuan-Lee/ODSQA)。\n",
    "\n",
    "- **训练集（DRCD + DRCD-backtrans）**：包含 15,329 个段落和 26,918 个问题。一个段落可能对应多个问题。\n",
    "- **开发集（DRCD + DRCD-backtrans）**：包含 1,255 个段落和 2,863 个问题。用于验证。\n",
    "- **测试集（DRCD + ODSQA）**：包含 1,606 个段落和 3,504 个问题。测试集的段落没有提供答案，需要模型进行预测。\n",
    "\n",
    "所有数据集的格式相同：\n",
    "\n",
    "- `id`：问题编号\n",
    "- `paragraph_id`：段落编号\n",
    "- `question_text`：问题文本\n",
    "- `answer_text`：答案文本\n",
    "- `answer_start`：答案在段落中的起始字符位置\n",
    "- `answer_end`：答案在段落中的结束字符位置\n",
    "\n",
    "![数据集格式](../Guide/assets/4215768313590de87aab01adcad78c90.png)\n",
    "\n",
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NvX7hlepogvu"
   },
   "outputs": [],
   "source": [
    "def read_data(file):\n",
    "    with open(file, 'r', encoding=\"utf-8\") as reader:\n",
    "        data = json.load(reader)\n",
    "    return data[\"questions\"], data[\"paragraphs\"]\n",
    "\n",
    "train_questions, train_paragraphs = read_data(\"hw7_train.json\")\n",
    "dev_questions, dev_paragraphs = read_data(\"hw7_dev.json\")\n",
    "test_questions, test_paragraphs = read_data(\"hw7_test.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fm0rpTHq0e4N"
   },
   "source": [
    "### 分词处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rTZ6B70Hoxie",
    "outputId": "db13805e-02a9-4cbe-e6d2-66c0e3860c2b",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 分别对问题和段落进行分词\n",
    "# 设置 add_special_tokens=False，因为在自定义数据集 QA_Dataset 的 __getitem__ 中会手动添加特殊标记\n",
    "\n",
    "train_questions_tokenized = tokenizer(\n",
    "    [q[\"question_text\"] for q in train_questions], add_special_tokens=False\n",
    ")\n",
    "dev_questions_tokenized = tokenizer(\n",
    "    [q[\"question_text\"] for q in dev_questions], add_special_tokens=False\n",
    ")\n",
    "test_questions_tokenized = tokenizer(\n",
    "    [q[\"question_text\"] for q in test_questions], add_special_tokens=False\n",
    ")\n",
    "\n",
    "train_paragraphs_tokenized = tokenizer(train_paragraphs, add_special_tokens=False)\n",
    "dev_paragraphs_tokenized = tokenizer(dev_paragraphs, add_special_tokens=False)\n",
    "test_paragraphs_tokenized = tokenizer(test_paragraphs, add_special_tokens=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ws8c8_4d5UCI"
   },
   "source": [
    "### 自定义数据集处理\n",
    "\n",
    "以下代码定义了一个 `QA_Dataset` 类，用于处理问答数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Xjooag-Swnuh"
   },
   "outputs": [],
   "source": [
    "class QA_Dataset(Dataset):\n",
    "    \"\"\"\n",
    "    自定义的问答数据集类，用于处理问答任务的数据。\n",
    "\n",
    "    参数：\n",
    "    - split (str): 数据集的类型，'train'、'dev' 或 'test'。\n",
    "    - questions (list): 问题列表，每个元素是一个字典，包含问题的详细信息。\n",
    "    - tokenized_questions (BatchEncoding): 分词后的问题，由 tokenizer 生成。\n",
    "    - tokenized_paragraphs (BatchEncoding): 分词后的段落列表，由 tokenizer 生成。\n",
    "    \n",
    "    属性（即 __init_() 中的 self.xxx）：\n",
    "    - max_question_len (int): 问题的最大长度（以分词后的 token 数计）。\n",
    "    - max_paragraph_len (int): 段落的最大长度（以分词后的 token 数计）。\n",
    "    - doc_stride (int): 段落窗口滑动步长。\n",
    "    - max_seq_len (int): 输入序列的最大长度。\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, split, questions, tokenized_questions, tokenized_paragraphs):\n",
    "        self.split = split\n",
    "        self.questions = questions\n",
    "        self.tokenized_questions = tokenized_questions\n",
    "        self.tokenized_paragraphs = tokenized_paragraphs\n",
    "        self.max_question_len = 60\n",
    "        self.max_paragraph_len = 150\n",
    "\n",
    "        # 设置段落窗口滑动步长为段落最大长度的 10%\n",
    "        self.doc_stride = int(self.max_paragraph_len * 0.1)\n",
    "\n",
    "        # 输入序列长度 = [CLS] + question + [SEP] + paragraph + [SEP]\n",
    "        self.max_seq_len = 1 + self.max_question_len + 1 + self.max_paragraph_len + 1\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回数据集中样本的数量。\n",
    "\n",
    "        返回：\n",
    "        - (int): 数据集的长度。\n",
    "        \"\"\"\n",
    "        return len(self.questions)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        \"\"\"\n",
    "        获取数据集中指定索引的样本。\n",
    "\n",
    "        参数：\n",
    "        - idx (int): 样本的索引。\n",
    "\n",
    "        返回：\n",
    "        - 对于训练集，返回一个输入张量和对应的答案位置：\n",
    "          (input_ids, token_type_ids, attention_mask, answer_start_token, answer_end_token)\n",
    "        - 对于验证和测试集，返回包含多个窗口的输入张量列表：\n",
    "          (input_ids_list, token_type_ids_list, attention_mask_list)\n",
    "        \"\"\"\n",
    "        question = self.questions[idx]\n",
    "        tokenized_question = self.tokenized_questions[idx]\n",
    "        tokenized_paragraph = self.tokenized_paragraphs[question[\"paragraph_id\"]]\n",
    "\n",
    "        ##### 预处理 #####\n",
    "        if self.split == \"train\":\n",
    "            # 将答案在段落文本中的起始/结束位置转换为在分词后段落中的起始/结束位置\n",
    "            answer_start_token = tokenized_paragraph.char_to_token(question[\"answer_start\"])\n",
    "            answer_end_token = tokenized_paragraph.char_to_token(question[\"answer_end\"])\n",
    "\n",
    "            # 防止模型学习到「答案总是位于中间的位置」，加入随机偏移\n",
    "            mid = (answer_start_token + answer_end_token) // 2\n",
    "            max_offset = self.max_paragraph_len // 2   # 最大偏移量为段落长度的1/2，这是可调的\n",
    "            random_offset = np.random.randint(-max_offset, max_offset)  # 在 [-max_offset, +max_offset] 范围内随机选择偏移量\n",
    "            paragraph_start = max(0, min(mid + random_offset - self.max_paragraph_len // 2, len(tokenized_paragraph) - self.max_paragraph_len))\n",
    "            paragraph_end = paragraph_start + self.max_paragraph_len\n",
    "            \n",
    "            # 切片问题/段落，并添加特殊标记（101：CLS，102：SEP）\n",
    "            input_ids_question = [101] + tokenized_question.ids[:self.max_question_len] + [102]\n",
    "            # ... = [tokenizer.cls_token_id] + tokenized_question.ids[: self.max_question_len] + [tokenizer.sep_token_id]\n",
    "            input_ids_paragraph = tokenized_paragraph.ids[paragraph_start : paragraph_end] + [102]\n",
    "            # ... = ... + [tokenizer.sep_token_id]\n",
    "\n",
    "            # 将答案在分词后段落中的起始/结束位置转换为窗口中的起始/结束位置\n",
    "            answer_start_token += len(input_ids_question) - paragraph_start\n",
    "            answer_end_token += len(input_ids_question) - paragraph_start\n",
    "\n",
    "            # 填充序列，生成模型的输入\n",
    "            input_ids, token_type_ids, attention_mask = self.padding(input_ids_question, input_ids_paragraph)\n",
    "            \n",
    "            return torch.tensor(input_ids), torch.tensor(token_type_ids), torch.tensor(attention_mask), answer_start_token, answer_end_token\n",
    "\n",
    "        else:\n",
    "            # 验证集和测试集的处理\n",
    "            input_ids_list, token_type_ids_list, attention_mask_list = [], [], []\n",
    "\n",
    "            # 段落被分割成多个窗口，每个窗口的起始位置由步长 \"doc_stride\" 分隔\n",
    "            for i in range(0, len(tokenized_paragraph), self.doc_stride):\n",
    "                # 切片问题/段落并添加特殊标记（101：CLS，102：SEP）\n",
    "                input_ids_question = [101] + tokenized_question.ids[:self.max_question_len] + [102]\n",
    "                # ... = [tokenizer.cls_token_id] + tokenized_question.ids[: self.max_question_len] + [tokenizer.sep_token_id]\n",
    "                input_ids_paragraph = tokenized_paragraph.ids[i : i + self.max_paragraph_len] + [102]\n",
    "                # ... = ... + [tokenizer.sep_token_id]\n",
    "\n",
    "                # 填充序列，生成模型的输入\n",
    "                input_ids, token_type_ids, attention_mask = self.padding(input_ids_question, input_ids_paragraph)\n",
    "\n",
    "                input_ids_list.append(input_ids)\n",
    "                token_type_ids_list.append(token_type_ids)\n",
    "                attention_mask_list.append(attention_mask)\n",
    "\n",
    "            return torch.tensor(input_ids_list), torch.tensor(token_type_ids_list), torch.tensor(attention_mask_list)\n",
    "\n",
    "    def padding(self, input_ids_question, input_ids_paragraph):\n",
    "        \"\"\"\n",
    "        对输入的序列进行填充，生成统一长度的模型输入。\n",
    "\n",
    "        参数：\n",
    "        - input_ids_question (list): 问题部分的输入 ID 列表。\n",
    "        - input_ids_paragraph (list): 段落部分的输入 ID 列表。\n",
    "\n",
    "        返回：\n",
    "        - input_ids (list): 填充后的输入 ID 列表。\n",
    "        - token_type_ids (list): 区分问题和段落的标记列表。\n",
    "        - attention_mask (list): 注意力掩码列表，指示哪些位置是有效的输入。\n",
    "        \"\"\"\n",
    "        # 计算需要填充的长度\n",
    "        padding_len = self.max_seq_len - len(input_ids_question) - len(input_ids_paragraph)\n",
    "        # 填充输入序列\n",
    "        input_ids = input_ids_question + input_ids_paragraph + [0] * padding_len\n",
    "        # 构造区分问题和段落的 token_type_ids\n",
    "        token_type_ids = [0] * len(input_ids_question) + [1] * len(input_ids_paragraph) + [0] * padding_len\n",
    "        # 构造注意力掩码，有效位置为 1，填充位置为 0\n",
    "        attention_mask = [1] * (len(input_ids_question) + len(input_ids_paragraph)) + [0] * padding_len\n",
    "\n",
    "        return input_ids, token_type_ids, attention_mask\n",
    "\n",
    "train_set = QA_Dataset(\"train\", train_questions, train_questions_tokenized, train_paragraphs_tokenized)\n",
    "dev_set = QA_Dataset(\"dev\", dev_questions, dev_questions_tokenized, dev_paragraphs_tokenized)\n",
    "test_set = QA_Dataset(\"test\", test_questions, test_questions_tokenized, test_paragraphs_tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**解释**：\n",
    "\n",
    "- **训练集处理**：定位答案的起始和结束位置，将包含答案的段落部分截取为一个窗口（引入随机偏移，防止模型过拟合于答案总在中间的位置）。然后将问题和段落合并为一个输入序列，并进行填充。\n",
    "\n",
    "- **验证/测试集处理**：将段落分成多个窗口，每个窗口之间的步长由 `self.doc_stride` 决定，然后将每个窗口作为模型的输入。验证和测试时不需要答案位置，因此只需生成多个窗口作为输入。\n",
    "\n",
    "  - `self.doc_stride` 通过控制窗口之间的滑动步长。\n",
    "\n",
    "  - **训练阶段**不需要使用 `doc_stride`，因为训练时我们已经知道答案的位置，可以直接截取包含答案的窗口。但在**验证和测试**阶段，由于模型并不知道答案的位置，`doc_stride` 保证每个窗口之间有足够的重叠（overlap），减少遗漏答案。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5_H1kqhR8CdM"
   },
   "source": [
    "## 评估函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SqeA3PLPxOHu"
   },
   "outputs": [],
   "source": [
    "def evaluate(data, output):\n",
    "    \"\"\"\n",
    "    对模型的输出进行后处理，获取预测的答案文本。\n",
    "\n",
    "    参数：\n",
    "    - data (tuple): 包含输入数据的元组，(input_ids, token_type_ids, attention_mask)。\n",
    "    - output (transformers.modeling_outputs.QuestionAnsweringModelOutput): 模型的输出结果。\n",
    "\n",
    "    返回：\n",
    "    - answer (str): 模型预测的答案文本。\n",
    "    \"\"\"\n",
    "    answer = ''\n",
    "    max_prob = float('-inf')\n",
    "    num_of_windows = data[0].shape[1]\n",
    "\n",
    "    for k in range(num_of_windows):\n",
    "        # 通过选择最可能的起始位置/结束位置来获得答案\n",
    "        start_prob, start_index = torch.max(output.start_logits[k], dim=0)\n",
    "        end_prob, end_index = torch.max(output.end_logits[k], dim=0)\n",
    "\n",
    "        # 确保起始位置索引小于或等于结束位置索引，避免选择错误的起始和结束位置对\n",
    "        if start_index <= end_index:\n",
    "            # 答案的概率计算为 start_prob 和 end_prob 的和\n",
    "            prob = start_prob + end_prob\n",
    "\n",
    "            # 如果当前窗口的答案具有更高的概率，则更新结果\n",
    "            if prob > max_prob:\n",
    "                max_prob = prob\n",
    "                # 将标记转换为字符（例如，[1920, 7032] --> \"大 金\"）\n",
    "                answer = tokenizer.decode(data[0][0][k][start_index : end_index + 1])\n",
    "        else:\n",
    "            # 如果起始位置索引 > 结束位置索引，则跳过此对（可能是错误情况）\n",
    "            continue\n",
    "    # 移除答案中的空格（例如，\"大 金\" --> \"大金\"）\n",
    "    return answer.replace(' ','')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练部分\n",
    "\n",
    "### 设置超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 超参数\n",
    "num_epoch = 1  # 训练的轮数\n",
    "validation = True  # 是否在每个 epoch 结束后进行验证\n",
    "logging_step = 100  # 每隔多少步打印一次训练日志\n",
    "learning_rate = 1e-5  # 学习率\n",
    "train_batch_size = 8  # 训练时的批次大小\n",
    "\n",
    "# 优化器\n",
    "optimizer = AdamW(model.parameters(), lr=learning_rate)\n",
    "\n",
    "# 数据加载器\n",
    "# 注意：不要更改 dev_loader / test_loader 的批次大小！\n",
    "# 虽然批次大小=1，但它实际上是由同一对 QA 的多个窗口组成的批次\n",
    "train_loader = DataLoader(train_set, batch_size=train_batch_size, shuffle=True, pin_memory=True)\n",
    "dev_loader = DataLoader(dev_set, batch_size=1, shuffle=False, pin_memory=True)\n",
    "test_loader = DataLoader(test_set, batch_size=1, shuffle=False, pin_memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习率调度器\n",
    "\n",
    "#### 带有 Warmup 的线性衰减\n",
    "\n",
    "使用调度器一般可以加速模型的收敛速度，不同的 Warmup 比例对学习率的影响：\n",
    "![不同 Warmup 比例的学习率曲线](../Guide/assets/20240920020716.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 总训练步数\n",
    "total_steps = len(train_loader) * num_epoch\n",
    "num_warmup_steps = int(0.2 * total_steps)  # TODO: 调整 warmup 步数的比率\n",
    "\n",
    "# [Hugging Face] 应用带有 warmup 的线性学习率衰减\n",
    "scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps\n",
    ")\n",
    "\n",
    "# # [PyTorch] 替代方法：应用不带 warmup 的线性学习率衰减\n",
    "# # lr_lambda 自定义学习率随时间衰减（此处是简单的线性衰减）\n",
    "# lr_lambda = lambda step: max(0.0, 1.0 - step / total_steps)\n",
    "# scheduler = LambdaLR(optimizer, lr_lambda=lr_lambda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置 Accelerator\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 梯度累积（可选）####\n",
    "# 注意：train_batch_size * gradient_accumulation_steps = 有效批次大小\n",
    "# 如果 CUDA 内存不足，你可以降低 train_batch_size 并提高 gradient_accumulation_steps\n",
    "# 文档：https://huggingface.co/docs/accelerate/usage_guides/gradient_accumulation\n",
    "gradient_accumulation_steps = 1\n",
    "\n",
    "# 将 \"fp16_training\" 更改为 True 以支持自动混合精度训练（fp16）\n",
    "fp16_training = True\n",
    "if fp16_training:\n",
    "    accelerator = Accelerator(mixed_precision=\"fp16\", gradient_accumulation_steps=gradient_accumulation_steps)\n",
    "else:\n",
    "    accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps)\n",
    "\n",
    "model, optimizer, train_loader, scheduler = accelerator.prepare(model, optimizer, train_loader, scheduler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rzHQit6eMnKG"
   },
   "source": [
    "### 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.train()\n",
    "\n",
    "print(\"开始训练...\")\n",
    "\n",
    "for epoch in range(num_epoch):\n",
    "    step = 1\n",
    "    train_loss = train_acc = 0\n",
    "\n",
    "    for data in tqdm(train_loader):\n",
    "        with accelerator.accumulate(model):\n",
    "            # 数据已经通过 accelerator.prepare() 移动到设备\n",
    "            #data = [i.to(device) for i in data]\n",
    "\n",
    "            # 模型输入：input_ids, token_type_ids, attention_mask, start_positions, end_positions（注意：只有 \"input_ids\" 是必需的）\n",
    "            # 模型输出：start_logits, end_logits, loss（提供 start_positions/end_positions 时返回）\n",
    "            output = model(\n",
    "                input_ids=data[0],\n",
    "                token_type_ids=data[1],\n",
    "                attention_mask=data[2],\n",
    "                start_positions=data[3],\n",
    "                end_positions=data[4]\n",
    "            )\n",
    "            # 选择最可能的起始位置/结束位置\n",
    "            start_index = torch.argmax(output.start_logits, dim=1)\n",
    "            end_index = torch.argmax(output.end_logits, dim=1)\n",
    "\n",
    "            # 只有当 start_index 和 end_index 都正确时，预测才正确\n",
    "            train_acc += ((start_index == data[3]) & (end_index == data[4])).float().mean()\n",
    "\n",
    "            train_loss += output.loss\n",
    "\n",
    "            accelerator.backward(output.loss)\n",
    "\n",
    "            step += 1\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # 每经过 logging_step，打印训练损失和准确率（实际上，使用了梯度累积后的 step 应该除以对应的数量进行校正）\n",
    "            if step % logging_step == 0:\n",
    "                print(f\"Epoch {epoch + 1} | Step {step} | loss = {train_loss.item() / logging_step:.3f}, acc = {train_acc / logging_step:.3f}\")\n",
    "                train_loss = train_acc = 0\n",
    "\n",
    "    if validation:\n",
    "        print(\"评估开发集...\")\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            dev_acc = 0\n",
    "            for i, data in enumerate(tqdm(dev_loader)):\n",
    "                # 这里保留了 device 的使用\n",
    "                output = model(\n",
    "                    input_ids=data[0].squeeze(0).to(device),\n",
    "                    token_type_ids=data[1].squeeze(0).to(device),\n",
    "                    attention_mask=data[2].squeeze(0).to(device),\n",
    "                )\n",
    "                # 只有当答案文本完全匹配时，预测才正确\n",
    "                dev_acc += evaluate(data, output) == dev_questions[i][\"answer_text\"]\n",
    "            print(f\"Validation | Epoch {epoch + 1} | acc = {dev_acc / len(dev_loader):.3f}\")\n",
    "        model.train()\n",
    "\n",
    "# 将模型及其配置文件保存到目录「saved_model」\n",
    "# 即，在目录「saved_model」下有两个文件：「pytorch_model.bin」和「config.json」\n",
    "# 可以使用「model = BertForQuestionAnswering.from_pretrained(\"saved_model\")」重新加载保存的模型\n",
    "print(\"保存模型...\")\n",
    "model_save_dir = \"saved_model\"\n",
    "model.save_pretrained(model_save_dir)\n",
    "#tokenizer.save_pretrained(model_save_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kMmdLOKBMsdE"
   },
   "source": [
    "## 测试部分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84,
     "referenced_widgets": [
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    "outputId": "841c70f2-2f3c-4c0f-866e-143f127eb6b5"
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   "outputs": [],
   "source": [
    "model.eval()\n",
    "\n",
    "print(\"评估测试集...\")\n",
    "\n",
    "result = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for data in tqdm(test_loader):\n",
    "        output = model(\n",
    "            input_ids=data[0].squeeze(dim=0).to(device),\n",
    "            token_type_ids=data[1].squeeze(dim=0).to(device),\n",
    "            attention_mask=data[2].squeeze(dim=0).to(device)\n",
    "        )\n",
    "        result.append(evaluate(data, output))\n",
    "\n",
    "result_file = \"result.csv\"\n",
    "with open(result_file, 'w') as f:\n",
    "    f.write(\"ID,Answer\\n\")\n",
    "    for i, test_question in enumerate(test_questions):\n",
    "        # 将答案中的逗号替换为空字符串（因为 csv 以逗号分隔），这一步处理只是为了作业提交到 Kaggle 上，实际上不一定需要保存为 .csv 文件\n",
    "        f.write(f\"{test_question['id']},{result[i].replace(',','')}\\n\")\n",
    "\n",
    "print(f\"完成！结果保存在 {result_file}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "k3prlGCdjf38"
   },
   "outputs": [],
   "source": []
  }
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