383 lines
14 KiB
Plaintext
383 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "70560a4d-ff57-4699-afb2-52f6a145b97e",
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"metadata": {},
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"source": [
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"# 4-SFT\n",
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"\n",
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"在预训练阶段后，我们应该能够获得一个下一词预测模型，此时的模型已经掌握了大量的知识。不过，仅仅具备下一词预测能力是不够的，我们希望大模型能够获得问答能力，这一能力便是在有监督微调（Supervised Fine Tuning，SFT）阶段获得的。\n",
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"\n",
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"在这个笔记本中，我们仅对 SFT 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_full_sft.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": "2508c8c7-eb44-4ac0-918c-41479f41d1ac",
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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 demo.model import MiniMindLM\n",
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"from demo.LMConfig import LMConfig\n",
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"from demo.dataset import SFTDataset"
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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": "4a79e140-23c7-4498-9a4d-ef7459692996",
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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": "dbe1d40e-e074-4856-8c67-da8019f250ab",
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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": "98cbd6cf-081f-4c31-8dce-52566004801a",
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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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" 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 = 2 # MiniMind Block 数量 模型超参数\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 = './demo/sft_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": "642f5107-aa9b-49cc-9759-e8731326edc3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'cuda'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"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": "b70fadb6-f78f-45a0-ad3e-d41d7253c72b",
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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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"> 注意 与预训练阶段不同的是 在 sft 阶段 我们实际上是在上一阶段训练获得的模型的基础上修改数据集进行接续训练 因此需要载入上一阶段的模型权重 出于展示的目的 载入权重的代码在此笔记本中只作展示 并不执行"
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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": "c64a0e37-a9d0-4ce6-b5ee-7b2728de879b",
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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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" tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer')\n",
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" model = MiniMindLM(lm_config).to(args.device)\n",
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" moe_path = '_moe' if lm_config.use_moe else ''\n",
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" # ckp = f'./out/pretrain_{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.laod_state_dict(state_dict, strict=False) # 装入模型\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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" return 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": "2eec33f3-2ac2-4c75-b58e-844d89d8add9",
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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：8.915 百万\n",
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"cuda:0 6400 <torch.utils.data.dataloader.DataLoader object at 0x00000184B5C6B4F0>\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, tokenizer = init_model(lm_config)\n",
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"\n",
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"train_ds = SFTDataset(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(model.device, tokenizer.vocab_size, 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": "b02644e0-e61d-4df0-9203-fa841cd83635",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"([tensor([[ 1, 85, 736, ..., 0, 0, 0],\n",
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" [ 1, 85, 736, ..., 0, 0, 0]]),\n",
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" tensor([[ 85, 736, 201, ..., 0, 0, 0],\n",
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" [ 85, 736, 201, ..., 0, 0, 0]]),\n",
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" tensor([[0, 0, 0, ..., 0, 0, 0],\n",
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" [0, 0, 0, ..., 0, 0, 0]])],\n",
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" 2,\n",
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" 1)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"loader = iter(train_loader)\n",
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"next(loader), len(train_ds), 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": "13ac8a47-d26a-49da-82fb-468ef5ac91a4",
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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 都包含一个长度为 3 的张量列表，这是因为 train_dataset 每一次取数据都会返回三个张量，分别为:\n",
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"\n",
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"- 样本 X: 包含 \\<bos> 在内的输入 conversation\n",
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"- 标签 Y: 包含 \\<eos> 在内的输出 conversation\n",
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"- 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": "ee23f937-f333-4c02-a3b4-a768818c0e2b",
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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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"> 不难发现 pretrain 阶段和 sft 阶段的训练主体差不多 因为这两个阶段的差异体现在数据集格式 而数据集在经过 chat template 格式化后差异小了很多"
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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": "5c32a59a-be0b-4426-b47e-c522322bef69",
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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": "13963ec0-d22e-4120-b0f2-8f5e5abfcda1",
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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": 9,
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"id": "74499592-2065-4fff-97d1-762cd08b77b6",
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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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" loss_fct = nn.CrossEntropyLoss(reduction='none') # 损失函数 采用交叉熵损失\n",
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" start_time = time.time()\n",
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" for step, (X, Y, loss_mask) in enumerate(train_loader):\n",
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" X = X.to(args.device)\n",
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" Y = Y.to(args.device)\n",
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" loss_mask = loss_mask.to(args.device)\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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" res = model(X) # 前向推理\n",
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" loss = loss_fct(\n",
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" res.logits.view(-1, res.logits.size(-1)),\n",
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" Y.view(-1)\n",
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" ).view(Y.size()) # 取生成的最后一个 token 的 logits 计算损失\n",
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" loss = (loss * loss_mask).sum() / loss_mask.sum()\n",
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" loss += res.aux_loss # 若为混合专家 则将 MOE 辅助损失纳入考虑\n",
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" loss = loss / args.accumulation_steps # 梯度累积\n",
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"\n",
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" scaler.scale(loss).backward() # 梯度缩放\n",
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"\n",
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" if (step + 1) % args.accumulation_steps == 0:\n",
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" scaler.unscale_(optimizer)\n",
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" torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) # 梯度剪裁\n",
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"\n",
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" scaler.step(optimizer)\n",
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" scaler.update()\n",
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"\n",
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" optimizer.zero_grad(set_to_none=True) # 单步更新\n",
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"\n",
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" if step % args.log_interval == 0:\n",
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" spend_time = time.time() - start_time\n",
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" print(\n",
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" 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format(\n",
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" epoch + 1,\n",
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" args.epochs,\n",
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" step,\n",
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" iter_per_epoch,\n",
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" loss.item() * args.accumulation_steps,\n",
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" optimizer.param_groups[-1]['lr'],\n",
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" spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))\n",
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"\n",
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" # 到达指定保存步数时，save as PyTorch\n",
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" # if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):\n",
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" # model.eval()\n",
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" # moe_path = '_moe' if lm_config.use_moe else ''\n",
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" # ckp = f'{args.save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'\n",
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"\n",
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" # if isinstance(model, torch.nn.parallel.DistributedDataParallel):\n",
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" # state_dict = model.module.state_dict()\n",
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" # else:\n",
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" # state_dict = model.state_dict()\n",
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"\n",
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" # torch.save(state_dict, ckp)\n",
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" # model.train()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "546f28c2-418e-453c-88aa-695468f9216c",
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"metadata": {},
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"source": [
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"准备完毕，我们进行一轮长度 1 个 iter 的训练看看怎么个事。"
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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": "341200c9-0d3a-4ed1-9dd2-d2fc35d02564",
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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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"Epoch:[1/1](0/1) loss:8.865 lr:0.000550000000 epoch_Time:0.0min:\n"
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]
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}
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],
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"source": [
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"iter_per_epoch = len(train_loader)\n",
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"for epoch in range(args.epochs):\n",
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" train_epoch(epoch)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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