{ "cells": [ { "cell_type": "markdown", "id": "44db347d-fef7-4e56-aeab-ce55bdab8eab", "metadata": {}, "source": [ "# 2-Dataset\n", "\n", "到这里我们便完成了对于 MiniMind Tokenizer 和 Model 部分的全部了解，我们所熟悉的大语言模型正是由这个组件构成的，接下来，我们需要对大模型训练所使用的数据集结构有个基本的认识。\n", "\n", "想要训练一个能够正常对话，并且符合人类对话偏好的大模型一般需要经过以下几个训练阶段：\n", "\n", "- 预训练（Pre-training）\n", "- 有监督微调（Supervised Fine-tuning，SFT）\n", "- 人类反馈强化学习（Reinforcement Learning from Human Feedback，RLHF）\n", "\n", "在不同训练阶段使用的数据集有所不同，下面会从 MiniMind 代码出发进行介绍和解读。" ] }, { "cell_type": "code", "execution_count": 1, "id": "e41156fd-ca0e-4083-a7a4-a5f98019ab88", "metadata": {}, "outputs": [], "source": [ "import json\n", "import random\n", "import re\n", "\n", "import pandas as pd\n", "import numpy as np\n", "from torch.utils.data import Dataset, DataLoader\n", "import torch\n", "from sklearn.model_selection import train_test_split\n", "import os\n", "import ast\n", "from transformers import AutoTokenizer\n", "\n", "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "27d18ad2-3f71-435c-af03-ad9d347aa048", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6400\n" ] } ], "source": [ "# 从 ../model 目录加载分词器\n", "\n", "tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer')\n", "print(tokenizer.vocab_size)" ] }, { "cell_type": "markdown", "id": "ad08fe92-1d04-4b9c-b689-6ad9d763a7c2", "metadata": {}, "source": [ "## 预训练数据集\n", "\n", "预训练是模型在大规模语料上进行无监督学习的训练阶段，在该阶段，模型主要学习下一词预测的能力，简单的来说就是学会说话，而不是胡言乱语。因此，该阶段训练的模型不会具有问答能力，而是根据用户输入进行简单的词语接龙。\n", "\n", "我们可以看一看预训练的数据集格式：\n", "\n", "```\n", "{\"text\": \"如何才能摆脱拖延症？治愈拖延症并不容易，但以下建议可能有所帮助...\"}\n", "```\n", "\n", "为了降低该 demo 的运行门槛，在 `./demo` 文件夹下提供了包含两条训练数据的 `pretrain_data.jsonl` 文件作为熟悉训练流程的数据集 demo。" ] }, { "cell_type": "code", "execution_count": 3, "id": "cdcac4aa-0ced-4a74-bba0-bc248923c7bc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Row 1: {'text': 'LLM首先要学习的并非直接与人交流，而是让网络参数中充满知识的墨水，“墨水” 理论上喝的越饱越好，产生大量的对世界的知识积累。预训练就是让Model先埋头苦学大量基本的知识，例如从Wiki百科、新闻、书籍整理大规模的高质量训练数据。这个过程是“无监督”的，即人类不需要在过程中做任何“有监督”的校正，而是由模型自己从大量文本中总结规律学习知识点。模型此阶段目的只有一个：学会词语接龙。例如我们输入“秦始皇”四个字，它可以接龙“是中国的第一位皇帝”。'}\n", "\n", "Row 2: {'text': '经过预训练，LLM此时已经掌握了大量知识，然而此时它只会无脑地词语接龙，还不会与人聊天。 SFT阶段就需要把半成品LLM施加一个自定义的聊天模板进行微调。例如模型遇到这样的模板【问题->回答，问题->回答】后不再无脑接龙，而是意识到这是一段完整的对话结束。称这个过程为指令微调，就如同让已经学富五车的「牛顿」先生适应21世纪智能手机的聊天习惯，学习屏幕左侧是对方消息，右侧是本人消息这个规律。在训练时，MiniMind的指令和回答长度被截断在512，是为了节省显存空间。就像我们学习时，会先从短的文章开始，当学会写作200字作文后，800字文章也可以手到擒来。在需要长度拓展时，只需要准备少量的2k/4k/8k长度对话数据进行进一步微调即可（此时最好配合RoPE-NTK的基准差值）。'}\n", "\n" ] } ], "source": [ "# 我们可以查看一下 demo 中提供的数据\n", "path_pretrain = './toydata/pretrain_data.jsonl'\n", "\n", "with open(path_pretrain, 'r', encoding='utf-8') as f:\n", " for line_num, line in enumerate(f, 1):\n", " data = json.loads(line.strip())\n", " print(f'Row {line_num}: {data}\\n')" ] }, { "cell_type": "markdown", "id": "0c0707e3-3ff9-424b-a5be-dab9db55aa44", "metadata": {}, "source": [ "我们知道，构建一个深度学习数据集需要继承 `torch.utils.data.dataset`，并构建 DataLoader 数据迭代器进行迭代访问。下面，我们来看看 MiniMind 是如何抽象一个预训练数据集的。" ] }, { "cell_type": "code", "execution_count": 4, "id": "fb2458e3-3661-40de-96be-aa036291a8fb", "metadata": {}, "outputs": [], "source": [ "class PretrainDataset(Dataset):\n", " def __init__(self, data_path, tokenizer, max_length=512):\n", " super().__init__()\n", " self.tokenizer = tokenizer\n", " self.max_length = max_length\n", " self.samples = self.load_data(data_path)\n", "\n", " def load_data(self, path):\n", " \"\"\"按行读取 jsonl 文件，并存储在列表中\"\"\"\n", " samples = []\n", " with open(path, 'r', encoding='utf-8') as f:\n", " for line_num, line in enumerate(f, 1):\n", " data = json.loads(line.strip())\n", " samples.append(data)\n", " return samples\n", "\n", " def __len__(self):\n", " return len(self.samples)\n", "\n", " def __getitem__(self, index):\n", " sample = self.samples[index]\n", "\n", " text = f\"{self.tokenizer.bos_token}{str(sample['text'])}{self.tokenizer.eos_token}\"\n", " # print(text) # uncomment to see formating prompt\n", " encoding = self.tokenizer(\n", " text,\n", " max_length=self.max_length,\n", " padding='max_length',\n", " truncation=True,\n", " return_tensors='pt'\n", " )\n", " # print(encoding) # uncomment to see encoding result\n", " input_ids = encoding.input_ids.squeeze()\n", " loss_mask = (input_ids != self.tokenizer.pad_token_id) # mask to ignore loss on pad token\n", "\n", " X = torch.tensor(input_ids[:-1], dtype=torch.long) # token not included\n", " Y = torch.tensor(input_ids[1:], dtype=torch.long) # token not included\n", " loss_mask = torch.tensor(loss_mask[1:], dtype=torch.long) # align to tensor X\n", " return X, Y, loss_mask" ] }, { "cell_type": "code", "execution_count": 5, "id": "677a1bbb-0df7-45e5-a3ed-d4431cddf43e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "预训练数据集长度2\n" ] } ], "source": [ "pretrain_dataset = PretrainDataset(path_pretrain, tokenizer)\n", "print(f'预训练数据集长度{len(pretrain_dataset)}')\n", "# x, y, lm = pretrain_dataset[0]\n", "# print(x.shape, y.shape, lm.shape)" ] }, { "cell_type": "markdown", "id": "ebb160cb-0e96-45a7-a1c0-be145895e70f", "metadata": {}, "source": [ "## 有监督微调数据集\n", "\n", "有监督微调（Supervised Fine Tuning，SFT）对预训练后得到的基座 LLM 施加一个自定义聊天模板进行微调，由于在这一阶段，模型训练的目标是根据用户指令生成响应（构建问答体系），故又称为指令微调。\n", "\n", "我们可以看一看有监督微调的数据集格式：\n", "\n", "```\n", "{\n", " \"conversations\": [\n", " {\"role\": \"user\", \"content\": \"你好\"},\n", " {\"role\": \"assistant\", \"content\": \"你好！\"},\n", " {\"role\": \"user\", \"content\": \"再见\"},\n", " {\"role\": \"assistant\", \"content\": \"再见！\"}\n", " ]\n", "}\n", "```\n", "\n", "为了降低该 demo 的运行门槛，在 `./demo` 文件夹下提供了包含两条 conversation 问答数据的 `sft_data.jsonl` 文件作为熟悉训练流程的数据集 demo。" ] }, { "cell_type": "code", "execution_count": 6, "id": "7e025e25-c937-4468-ae66-b32447440658", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Row 1: {'conversations': [{'role': 'user', 'content': '你好吗？'}, {'role': 'assistant', 'content': '我很好，谢谢！你呢？'}, {'role': 'user', 'content': '我也很好，谢谢！'}, {'role': 'assistant', 'content': '太好了！祝你今天愉快！'}]}\n", "\n", "Row 2: {'conversations': [{'role': 'user', 'content': '你喜欢什么运动？'}, {'role': 'assistant', 'content': '我喜欢跑步和游泳。你呢？'}, {'role': 'user', 'content': '我喜欢打篮球！'}, {'role': 'assistant', 'content': '篮球很棒！是一个很好的团队运动。'}]}\n", "\n" ] } ], "source": [ "# 我们可以查看一下 demo 中提供的数据\n", "path_sft = './toydata/sft_data.jsonl'\n", "\n", "with open(path_sft, 'r', encoding='utf-8') as f:\n", " for line_num, line in enumerate(f, 1):\n", " data = json.loads(line.strip())\n", " print(f'Row {line_num}: {data}\\n')" ] }, { "cell_type": "markdown", "id": "5f988233-530f-4ddf-8d64-9532442a2535", "metadata": {}, "source": [ "接下来，我们尝试构造一个数据集对象，实现对 sft 格式数据的读取与处理。" ] }, { "cell_type": "code", "execution_count": 7, "id": "8b25f0c6-b5b5-47b1-974f-15a2f0d2760e", "metadata": {}, "outputs": [], "source": [ "class SFTDataset(Dataset):\n", " def __init__(self, jsonl_path, tokenizer, max_length=512):\n", " super().__init__()\n", " self.tokenizer = tokenizer\n", " self.max_length = max_length\n", " self.samples = self.load_data(jsonl_path)\n", " self.bos_id = tokenizer('~~assistant\\n', add_special_tokens=False).input_ids # set bos token\n", " self.eos_id = tokenizer('~~\\n', add_special_tokens=False).input_ids # set eos token\n", "\n", " def load_data(self, path):\n", " samples = []\n", " with open(path, 'r', encoding='utf-8') as f:\n", " for line_num, line in enumerate(f, 1):\n", " data = json.loads(line.strip())\n", " samples.append(data)\n", " return samples\n", " \n", " def __len__(self):\n", " return len(self.samples)\n", "\n", " def _create_chat_prompt(self, conversations):\n", " \"\"\"构建符合 ChatML 格式的对话\"\"\"\n", " messages = []\n", " for i, turn in enumerate(conversations): # for each speaker in one conversation\n", " role = 'user' if i % 2 == 0 else 'assistant'\n", " messages.append({\"role\": role, \"content\": turn['content']})\n", " return self.tokenizer.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=False\n", " )\n", "\n", " def _generate_loss_mask(self, input_ids):\n", " loss_mask = [0] * len(input_ids)\n", " i = 0\n", " while i < len(input_ids):\n", " if input_ids[i:i + len(self.bos_id)] == self.bos_id: # check if reach bos token\n", " ########### find content start & end point ###########\n", " start = i + len(self.bos_id) # set start point at the end of bos token\n", " end = start\n", " while end < len(input_ids):\n", " if input_ids[end:end + len(self.eos_id)] == self.eos_id: # check if reach eos token\n", " break\n", " end += 1\n", " ######################################################\n", " for j in range(start + 1, min(end + len(self.eos_id) + 1, self.max_length)): # ignore tokens that reach max input length\n", " loss_mask[j] = 1\n", " i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids) # update i to exit current conversation turn\n", " else:\n", " i += 1\n", " return loss_mask\n", " \n", " def __getitem__(self, index):\n", " sample = self.samples[index]\n", " prompt = self._create_chat_prompt(sample['conversations'])\n", " # print(prompt) # uncomment to see formating prompt\n", " input_ids = self.tokenizer(prompt).input_ids[:self.max_length] # encode input\n", " input_ids += [self.tokenizer.pad_token_id] * (self.max_length - len(input_ids)) # encode padding\n", " # print(input_ids) # uncomment to see encoded prompt\n", " \n", " loss_mask = self._generate_loss_mask(input_ids)\n", "\n", " X = torch.tensor(input_ids[:-1], dtype=torch.long)\n", " Y = torch.tensor(input_ids[1:], dtype=torch.long)\n", " loss_mask = torch.tensor(loss_mask[1:], dtype=torch.long) # align with pred pos\n", "\n", " return X, Y, loss_mask" ] }, { "cell_type": "code", "execution_count": 8, "id": "67a62dbf-6a50-4faf-8ad1-5228eb8aa7c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "样本 shape = torch.Size([511]), 标签 shape = torch.Size([511]), loss_mask shape torch.Size([511])\n" ] } ], "source": [ "sft_dataset = SFTDataset(path_sft, tokenizer)\n", "print(len(sft_dataset))\n", "x, y, lm = sft_dataset[0]\n", "print(f'样本 shape = {x.shape}, 标签 shape = {y.shape}, loss_mask shape {lm.shape}')\n", "# print(lm) # 打印 loss mask，你会发现在序列中有两处以 1 填充的序列，这是因为我们在一个 conversation 中开展了两轮对话，其中只有 assistant 回复计算损失" ] }, { "cell_type": "markdown", "id": "a7094ff6-4657-4483-8c34-785ffe9adbd4", "metadata": {}, "source": [ "## 人类反馈强化学习数据集\n", "\n", "在 MiniMind 项目中，采用直接偏好优化（Direct Parameter Optimization，DPO）训练大模型对齐人类偏好。在这一训练阶段，模型将会根据提供的问答正反例进行偏好优化，从而降低让人类不满意的答案出现的几率。\n", "\n", "与PPO(Proximal Policy Optimization)这种需要奖励模型、价值模型的RL算法不同； DPO通过推导PPO奖励模型的显式解，把在线奖励模型换成离线数据，Ref模型输出可以提前保存。 DPO性能几乎不变，只用跑 actor_model 和 ref_model 两个模型，大大节省显存开销和增加训练稳定性。\n", "\n", "我们可以看一看有监督微调的数据集格式：\n", "\n", "```\n", "{\n", " \"chosen\": [\n", " {\"content\": \"Query\", \"role\": \"user\"}, \n", " {\"content\": \"good answer\", \"role\": \"assistant\"}\n", " ], \n", " \"rejected\": [\n", " {\"content\": \"Query\", \"role\": \"user\"}, \n", " {\"content\": \"bad answer\", \"role\": \"assistant\"}\n", " ]\n", "}\n", "```\n", "\n", "为了降低该 demo 的运行门槛，在 ./demo 文件夹下提供了包含两条 conversation 问答数据的 sft_data.jsonl 文件作为熟悉训练流程的数据集 demo。" ] }, { "cell_type": "code", "execution_count": 9, "id": "81682b5f-0d3e-4fa2-aec4-0be2b19d5c05", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Row 1: {'conversations': [{'role': 'user', 'content': '你好吗？'}, {'role': 'assistant', 'content': '我很好，谢谢！你呢？'}, {'role': 'user', 'content': '我也很好，谢谢！'}, {'role': 'assistant', 'content': '太好了！祝你今天愉快！'}]}\n", "\n", "Row 2: {'conversations': [{'role': 'user', 'content': '你喜欢什么运动？'}, {'role': 'assistant', 'content': '我喜欢跑步和游泳。你呢？'}, {'role': 'user', 'content': '我喜欢打篮球！'}, {'role': 'assistant', 'content': '篮球很棒！是一个很好的团队运动。'}]}\n", "\n" ] } ], "source": [ "# 我们可以查看一下 demo 中提供的数据\n", "path_dpo = './toydata/dpo_data.jsonl'\n", "\n", "with open(path_sft, 'r', encoding='utf-8') as f:\n", " for line_num, line in enumerate(f, 1):\n", " data = json.loads(line.strip())\n", " print(f'Row {line_num}: {data}\\n')" ] }, { "cell_type": "markdown", "id": "ebb54557-54f4-4004-8a7b-443b8671002b", "metadata": {}, "source": [ "接下来，我们尝试构造 json 对象，实现对 dpo 格式数据的读取和处理" ] }, { "cell_type": "code", "execution_count": 10, "id": "efa98777-5d3a-4b3f-a9fb-e0f05e4af81d", "metadata": {}, "outputs": [], "source": [ "class DPODataset(Dataset):\n", " def __init__(self, file_path, tokenizer, max_length=512):\n", " super().__init__()\n", " self.tokenizer = tokenizer\n", " self.max_length = max_length\n", " self.padding = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0\n", " self.bos_id = tokenizer('~~assistant\\n', add_special_tokens=False).input_ids # content prefix\n", " self.eos_id = tokenizer('~~\\n', add_special_tokens=False).input_ids # content suffix\n", " with open(file_path, 'r', encoding='utf-8') as f: # load data\n", " self.data = []\n", " for line in f:\n", " line = line.strip()\n", " obj = json.loads(line)\n", " self.data.append(obj)\n", "\n", " def __len__(self):\n", " return len(self.data)\n", "\n", " def _generate_loss_mask(self, input_ids):\n", " \"\"\"此处的损失掩码生成函数与 SFT 阶段逻辑一致\"\"\"\n", " loss_mask = [0] * len(input_ids)\n", " i = 0\n", " while i < len(input_ids):\n", " if input_ids[i:i + len(self.bos_id)] == self.bos_id:\n", " start = i + len(self.bos_id)\n", " end = start\n", " while end < len(input_ids):\n", " if input_ids[end:end + len(self.eos_id)] == self.eos_id:\n", " break\n", " end += 1\n", " for j in range(start + 1, min(end + len(self.eos_id) + 1, self.max_length)):\n", " loss_mask[j] = 1\n", " i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)\n", " else:\n", " i += 1\n", " return loss_mask\n", " \n", " def __getitem__(self, index):\n", " item = self.data[index]\n", " chosen = item['chosen'] # 一个 list，里面包含若干 {role, content}\n", " rejected = item['rejected'] # 同上\n", " # format prompt\n", " chosen_prompt = self.tokenizer.apply_chat_template(\n", " chosen, tokenize=False, add_generation_prompt=False\n", " )\n", " print(chosen_prompt) # uncomment to see formating prompt\n", " rejected_prompt = self.tokenizer.apply_chat_template(\n", " rejected, tokenize = False, add_gerneration_prompt=False\n", " )\n", " print(rejected_prompt) # uncomment to see formating prompt\n", " # tokenize\n", " chosen_encoding = self.tokenizer(\n", " chosen_prompt, truncation=True, max_length=self.max_length, padding='max_length'\n", " )\n", " rejected_encoding = self.tokenizer(\n", " rejected_prompt, truncation=True, max_length=self.max_length, padding='max_length'\n", " )\n", " # generate loss mask\n", " chosen_input_ids = chosen_encoding['input_ids']\n", " chosen_loss_mask = self._generate_loss_mask(chosen_input_ids)\n", " rejected_input_ids = rejected_encoding['input_ids']\n", " rejected_loss_mask = self._generate_loss_mask(rejected_input_ids)\n", " # same as sft / pretrain\n", " x_chosen = torch.tensor(chosen_input_ids[:-1], dtype=torch.long)\n", " y_chosen = torch.tensor(chosen_input_ids[1:], dtype=torch.long)\n", " mask_chosen = torch.tensor(chosen_loss_mask[1:], dtype=torch.long)\n", " x_rejected = torch.tensor(rejected_input_ids[:-1], dtype=torch.long)\n", " y_rejected = torch.tensor(rejected_input_ids[1:], dtype=torch.long)\n", " mask_rejected = torch.tensor(rejected_loss_mask[1:], dtype=torch.long)\n", "\n", " return {\n", " 'x_chosen': x_chosen,\n", " 'y_chosen': y_chosen,\n", " 'mask_chosen': mask_chosen,\n", " 'x_rejected': x_rejected,\n", " 'y_rejected': y_rejected,\n", " 'mask_rejected': mask_rejected\n", " }" ] }, { "cell_type": "code", "execution_count": 11, "id": "5ba205c4-0dd2-48fd-8aa2-26a42afc90d8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DPO 数据集长度：2\n", "~~system\n", "你是 MiniMind，是一个有用的人工智能助手。~~\n", "~~user\n", "你好吗？~~\n", "~~assistant\n", "我很好，谢谢！你呢？~~\n", "~~user\n", "今天过得怎么样？~~\n", "~~assistant\n", "挺好的，去跑步了，心情不错。~~\n", "\n", "~~system\n", "你是 MiniMind，是一个有用的人工智能助手。~~\n", "~~user\n", "你好吗？~~\n", "~~assistant\n", "不好，我很累。~~\n", "~~user\n", "你喜欢什么运动？~~\n", "~~assistant\n", "我不喜欢运动，没兴趣。~~\n", "\n" ] } ], "source": [ "dpo_dataset = DPODataset(path_dpo, tokenizer)\n", "print(f'DPO 数据集长度：{len(dpo_dataset)}')\n", "res = dpo_dataset[0]\n", "# 如有需要，请自主查看 res 中的元素" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }