458 lines
17 KiB
Plaintext
458 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f53f31fb-4795-49cf-9a68-a13f8f7e5cae",
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"metadata": {},
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"source": [
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"# 5-DPO\n",
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"\n",
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"直接偏好优化（Direct Preference Optimization，DPO）是后训练阶段中，使用正反样例激励大模型产生符合人类偏好的回答的策略，为人类反馈强化学习（Reinforcement Learning from Human Feedback, RLHF）提供了一个高效简化的替代方案。通过这一阶段的训练，大模型将会学会依照人类的喜好生成回复.\n",
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"\n",
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"在这个笔记本中，我们仅对 DPO 的训练流程进行展示和学习，因此只给出必要的代码片段，如 wandb 和 ddp 不会在此笔记本中涉及.\n",
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"\n",
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"此笔记本的完整实现见主仓库 `/minimind/train_dpo.py`"
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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": "3e09f234-5b8b-4bfb-8d42-bb766a457acc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 导入依赖\n",
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"import os\n",
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"import platform\n",
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"import argparse\n",
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"import time\n",
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"import math\n",
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"import warnings\n",
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"\n",
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"import pandas as pd\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"import torch.distributed as dist\n",
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"from contextlib import nullcontext\n",
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"\n",
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"from torch import optim, nn\n",
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"from torch.nn.parallel import DistributedDataParallel\n",
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"from torch.utils.data import DataLoader, DistributedSampler\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
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"from model.model import MiniMindLM\n",
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"from model.LMConfig import LMConfig\n",
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"from model.dataset import DPODataset"
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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": "eb946301-eb7e-45c6-9e1c-16cc0ee4db82",
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"metadata": {},
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"outputs": [],
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"source": [
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"warnings.filterwarnings('ignore')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d70c65d3-14f7-4569-bebd-cddb48f1851f",
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"metadata": {},
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"source": [
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"## 可选参数设置\n",
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"\n",
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"首先，查看训练的可选参数，这些参数在实际使用时通过命令行导入，为了保持笔记本的易用性，选择用 class 进行包装."
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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": "b78b11a1-9189-4eaf-a094-07c0497c4d38",
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"metadata": {},
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"outputs": [],
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"source": [
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"class args:\n",
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" # out_dir: str = \"out\" # pytorch 格式权重文件保存位置 我们只展示训练过程 所以不使用\n",
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" epochs: int = 1 # 训练轮数\n",
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" batch_size: int = 2 # pretrain 数据集仅两个样本，设置 batch 为 2\n",
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" # sft阶段学习率为 「5e-6」->「5e-7」长度512，建议离线正负样本「概率」偏好对齐阶段lr <=「1e-8」长度3000，否则很容易遗忘训坏\n",
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" learning_rate: float = 5e-4 # 学习率\n",
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" device: str = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
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" dtype: str = 'bfloat16' # 16 bit 浮点数：8 bit 指数 + 7 bit 尾数\n",
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" # use_wandb: bool = False # 是否使用 wandb 我们不使用\n",
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" wandb_project: str = 'MiniMind-Notebook'\n",
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" num_workers: int = 1 # 工作进程数\n",
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" # ddp：bool = False # 单机多卡\n",
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" accumulation_steps: int = 1 # 梯度累积步数\n",
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" grad_clip: float = 1.0 # 梯度剪裁\n",
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" warmup_iters: int = 0 # 学习率热启动\n",
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" log_interval: int = 1 # 每一步打印日志 仅用于观察\n",
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" # save_interval: int = 100 # checkpoint 保存点 我们不使用\n",
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" local_rank: int = 1 # device 设备号\n",
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" dim: int = 512 # 词嵌入维度 模型超参数\n",
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" n_layers: int = 1 # MiniMind Block 数量 模型超参数 | 由于 dpo 要加载两个模型 我们出于演示目的设定 n_layers = 1\n",
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" max_seq_len: int = 512 # 序列长度阈值\n",
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" use_moe: bool = False # 是否启用混合专家\n",
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" data_path: str = './toydata/dpo_data.jsonl' # 数据集路径"
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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": "0a72c8aa-ca60-4b0d-aabb-2c9d16b48175",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"查看工作设备 cuda\n"
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]
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}
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],
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"source": [
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"print(f'查看工作设备 {args.device}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8fb31ff-524b-4788-a25f-699edb887704",
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"metadata": {},
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"source": [
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"## 初始化训练\n",
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"\n",
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"接下来，我们对一些重要模块进行初始化，我们已经了解过，分词器，模型和数据集是大模型的基本组件，我们对其进行初始化.\n",
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"\n",
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"> 在这一阶段 我们调整的是大模型的问答偏好 因此与 sft 阶段同理 我们需要载入在 sft 阶段微调好的问答模型"
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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": "5d5971fc-4794-4903-b418-76f266bf19d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def init_model(lm_config):\n",
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" \"\"\"注意 在此处我们注释了加载模型部分代码 但应当认识到 actor 和 ref 两个模型都具备 sft 阶段获得的权重\"\"\"\n",
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" tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer')\n",
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" model = MiniMindLM(lm_config)\n",
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" moe_path = '_moe' if lm_config.use_moe else ''\n",
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" # ckp = f'./out/full_sft_{lm_config.dim}{moe_path}.pth' # 指示上一阶段训练保存的模型文件位置\n",
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" # state_dict = torch.load(ckp, map_location=args.device) # 载入模型状态字典\n",
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" # model.load_state_dict(state_dict, strict=False) # 装入模型\n",
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" # 初始化参考模型\n",
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" ref_model = MiniMindLM(lm_config)\n",
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" # ref_model.load_state_dict(state_dict, strict=False)\n",
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" ref_model.eval()\n",
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" ref_model.requires_grad_(False)\n",
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"\n",
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" print(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')\n",
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" model = model.to(args.device)\n",
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" ref_model = ref_model.to(args.device)\n",
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"\n",
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" return model, ref_model, tokenizer"
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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": "fbd7bd15-a060-4aba-8c5d-277705ec6b52",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LLM总参数量：6.096 百万\n",
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"模型位于设备：cuda:0, 词表长度：6400, DataLoader：<torch.utils.data.dataloader.DataLoader object at 0x000001AF0BBB71C0>\n"
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]
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}
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],
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"source": [
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"lm_config = LMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len, use_moe=args.use_moe)\n",
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"model, ref_model ,tokenizer = init_model(lm_config)\n",
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"\n",
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"train_ds = DPODataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len)\n",
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"\n",
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"train_loader = DataLoader(\n",
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" train_ds,\n",
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" batch_size=args.batch_size,\n",
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" pin_memory=True,\n",
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" drop_last=False,\n",
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" shuffle=False,\n",
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" num_workers=args.num_workers,\n",
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")\n",
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"\n",
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"print(f'模型位于设备：{model.device}, 词表长度：{tokenizer.vocab_size}, DataLoader：{train_loader}')"
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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": "d807a2af-41e9-4115-b470-a05f6a04613c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"打印一个 iter 的数据:\n",
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"{'x_chosen': tensor([[ 1, 85, 736, ..., 0, 0, 0],\n",
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" [ 1, 85, 736, ..., 0, 0, 0]]), 'y_chosen': tensor([[ 85, 736, 201, ..., 0, 0, 0],\n",
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" [ 85, 736, 201, ..., 0, 0, 0]]), 'mask_chosen': tensor([[0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0]]), 'x_rejected': tensor([[ 1, 85, 736, ..., 0, 0, 0],\n",
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" [ 1, 85, 736, ..., 0, 0, 0]]), 'y_rejected': tensor([[ 85, 736, 201, ..., 0, 0, 0],\n",
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" [ 85, 736, 201, ..., 0, 0, 0]]), 'mask_rejected': tensor([[0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0]])}\n",
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"\n",
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"数据集大小：2, DataLoader 大小：1\n"
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]
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}
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],
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"source": [
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"loader = iter(train_loader)\n",
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"print(f'打印一个 iter 的数据:\\n{next(loader)}\\n')\n",
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"print(f'数据集大小：{len(train_ds)}, DataLoader 大小：{len(loader)}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "665ad506-ab12-4186-ad63-ada143a33d52",
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"metadata": {},
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"source": [
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"我们发现，train loader 的每一个 iter 都包含一个拥有六个键值对的字典，这是因为 train_dataset 每一次取数据都会返回:\n",
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"\n",
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"- chosen 样本 X: 包含 \\<bos> 在内的输入 content\n",
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"- chosen 标签 Y: 包含 \\<eos> 在内的输出 content\n",
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"- chosen 掩码 loss_mask: 指示需要计算损失的 token 位置\n",
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"- rejected 样本 X: 包含 \\<bos> 在内的输入 content\n",
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"- rejected 标签 Y: 包含 \\<eos> 在内的输出 content\n",
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"- rejected 掩码 loss_mask: 指示需要计算损失的 token 位置\n",
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"\n",
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"由于我们的数据集只有两条数据，而 batch size 设置为 2，因此我们的 dataloader 只有一个 iter."
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]
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},
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{
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"cell_type": "markdown",
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"id": "32462eb9-7008-4389-a54b-47ef1c1c4732",
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"metadata": {},
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"source": [
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"# 启动训练\n",
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"\n",
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"训练一个深度学习模型，还涉及到了优化器，损失函数和学习率调度. 接下来，我们查看 MiniMind 训练部分的代码，并进行一轮简单的训练.\n",
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"\n",
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"> DPO 阶段涉及 DPO 损失函数涉及 因此与前两个阶段相比内容略有增加 不过整体流程与逻辑类似"
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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": "1752f117-33c0-4810-886f-fbd1b4d44c28",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 学习率调度方面 采用余弦退火学习率\n",
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"def get_lr(current_step, total_steps, lr):\n",
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" return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))\n",
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"\n",
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"# 优化器方面 选择 AdamW 优化器 并在混精度场景下创建 scaler 进行梯度缩放避免数值下溢\n",
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"scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))\n",
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"optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)\n",
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"\n",
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"device_type = \"cuda\" if \"cuda\" in args.device else \"cpu\"\n",
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"ctx = nullcontext() if device_type == \"cpu\" else torch.cuda.amp.autocast() # 在 cuda 上启动混精度训练，否则空白上下文"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5f2c3ac1-92bf-4eae-8d22-7fac4cfa69df",
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"metadata": {},
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"source": [
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"DPO 的原理是增加偏好样本的对数概率与减小非偏好样本响应的对数概率.\n",
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"\n",
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"该阶段引入 DPO 损失函数，通过计算选择样本和拒绝样本的对数比率，然后基于这些比率计算 DPO 损失，适用于偏好学习任务."
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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": 9,
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"id": "2c07f54a-496e-4b1d-9394-dd2c266ab5d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"def logits_to_probs(logits, labels):\n",
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" # logits shape: (batch_size, seq_len, vocab_size)\n",
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" # labels shape: (batch_size, seq_len)\n",
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" # probs shape: (batch_size, seq_len)\n",
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" log_probs = F.log_softmax(logits, dim=2)\n",
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" probs = torch.gather(log_probs, dim=2, index=labels.unsqueeze(2)).squeeze(-1)\n",
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" return probs\n",
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"\n",
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"\n",
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"def dpo_loss(ref_probs, probs, beta):\n",
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" # ref_probs 和 probs 都是 shape: (batch_size, seq_len)\n",
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" # 计算每个样本的平均概率\n",
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" ref_probs = ref_probs.mean(dim=1)\n",
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" probs = probs.mean(dim=1)\n",
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"\n",
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" # 将 chosen 和 rejected 数据分开\n",
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" batch_size = ref_probs.shape[0]\n",
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" chosen_ref_probs = ref_probs[:batch_size // 2]\n",
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" reject_ref_probs = ref_probs[batch_size // 2:]\n",
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" chosen_probs = probs[:batch_size // 2]\n",
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" reject_probs = probs[batch_size // 2:]\n",
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"\n",
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" # 计算对数比率，比较偏好差异\n",
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" pi_logratios = chosen_probs - reject_probs\n",
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" ref_logratios = chosen_ref_probs - reject_ref_probs\n",
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" logits = pi_logratios - ref_logratios\n",
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" loss = -F.logsigmoid(beta * logits)\n",
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" return loss.mean()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e743562c-6e34-488d-bd84-f62eaee0ae20",
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"metadata": {},
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"source": [
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"接下来，我们来看看 MiniMind 的训练函数"
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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": 10,
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"id": "493b7368-d82f-48a6-b5bd-7737b7a23bee",
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_epoch(epoch):\n",
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" start_time = time.time()\n",
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" for step, batch in enumerate(train_loader):\n",
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" # 提取数据\n",
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" x_chosen = batch['x_chosen'].to(args.device)\n",
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" x_rejected = batch['x_rejected'].to(args.device)\n",
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" y_chosen = batch['y_chosen'].to(args.device)\n",
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" y_rejected = batch['y_rejected'].to(args.device)\n",
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" mask_chosen = batch['mask_chosen'].to(args.device)\n",
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" mask_rejected = batch['mask_rejected'].to(args.device)\n",
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" # 正反例拼接\n",
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" x = torch.cat([x_chosen, x_rejected], dim=0)\n",
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" y = torch.cat([y_chosen, y_rejected], dim=0)\n",
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" mask = torch.cat([mask_chosen, mask_rejected], dim=0)\n",
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"\n",
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" lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)\n",
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" for param_group in optimizer.param_groups:\n",
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" param_group['lr'] = lr\n",
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"\n",
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" with ctx:\n",
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" with torch.no_grad(): # 计算 ref 模型输出\n",
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" ref_outputs = ref_model(x)\n",
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" ref_logits = ref_outputs.logits\n",
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" ref_probs = logits_to_probs(ref_logits, y)\n",
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" ref_probs = ref_probs * mask # 得到 ref 概率\n",
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" outputs = model(x) # 计算 actor 模型输出\n",
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" logits = outputs.logits\n",
|
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" probs = logits_to_probs(logits, y)\n",
|
||
" probs = probs * mask # 得到 actor 概率\n",
|
||
" loss = dpo_loss(ref_probs, probs, beta=0.1) # dpo 损失\n",
|
||
" loss = loss / args.accumulation_steps\n",
|
||
"\n",
|
||
" scaler.scale(loss).backward() # 梯度缩放\n",
|
||
"\n",
|
||
" if (step + 1) % args.accumulation_steps == 0:\n",
|
||
" scaler.unscale_(optimizer)\n",
|
||
" torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) # 梯度剪裁\n",
|
||
" scaler.step(optimizer)\n",
|
||
" scaler.update()\n",
|
||
" optimizer.zero_grad(set_to_none=True)\n",
|
||
"\n",
|
||
" if step % args.log_interval == 0:\n",
|
||
" spend_time = time.time() - start_time\n",
|
||
" print(\n",
|
||
" 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format(\n",
|
||
" epoch + 1,\n",
|
||
" args.epochs,\n",
|
||
" step,\n",
|
||
" iter_per_epoch,\n",
|
||
" loss.item(),\n",
|
||
" optimizer.param_groups[-1]['lr'],\n",
|
||
" spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))\n",
|
||
"\n",
|
||
" # 到达指定保存步数时，save as PyTorch\n",
|
||
" # if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):\n",
|
||
" # model.eval()\n",
|
||
" # moe_path = '_moe' if lm_config.use_moe else ''\n",
|
||
" # ckp = f'{args.save_dir}/rlhf_{lm_config.dim}{moe_path}.pth'\n",
|
||
"\n",
|
||
" # if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n",
|
||
" # state_dict = model.module.state_dict()\n",
|
||
" # else:\n",
|
||
" # state_dict = model.state_dict()\n",
|
||
"\n",
|
||
" # torch.save(state_dict, ckp)\n",
|
||
" # model.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "49386dc7-2919-428a-a073-f37d576afbcf",
|
||
"metadata": {},
|
||
"source": [
|
||
"准备完毕，我们尝试一轮长度 1 个 iter 的训练."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "0ac86572-ee04-43b5-b5fa-b1c65cbaebec",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch:[1/1](0/1) loss:0.694 lr:0.000550000000 epoch_Time:0.0min:\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"iter_per_epoch = len(train_loader)\n",
|
||
"for epoch in range(args.epochs):\n",
|
||
" train_epoch(epoch)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "5215ef67-2647-4519-966b-074c46c225ac",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"del model"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|