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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Training Pipeline\n",
"[run_training_dpo_pipeline.ipynb](https://github.com/shibing624/MedicalGPT/blob/main/run_training_dpo_pipeline.ipynb) | [Open In Colab](https://colab.research.google.com/github/shibing624/MedicalGPT/blob/main/run_training_dpo_pipeline.ipynb)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"# Stage 1: Continue Pretraining\n",
"\n",
"第一阶段：PT(Continue PreTraining)增量预训练，在海量领域文本数据上二次预训练GPT模型，以注入领域知识\n",
"\n",
"| Stage 1: Continue Pretraining | [pretraining.py](https://github.com/shibing624/MedicalGPT/blob/main/pretraining.py) | [run_pt.sh](https://github.com/shibing624/MedicalGPT/blob/main/run_pt.sh) |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 说明：\n",
"以下 notebook/colab 代码为了快速验证训练代码可用，我们使用了小size的生成模型和小样本数据集，实际使用时，需要使用更大的模型和数据集，以获得更好的效果。\n",
"\n",
"1. 生成模型：使用的是Bloom的`bigscience/bloomz-560m`\n",
"2. 数据集：PT阶段使用的是中文天龙八部小说部分文本和英文书籍部分文本，位于`data/pretrain`文件夹"
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 配置运行环境\n",
"\n",
"本地执行可注释以下配置环境的命令，colab执行要打开注释，用于配置环境\n",
"\n",
"colab建议使用T4 GPU训练，设置方式：`代码执行程序 -> 更改运行时类型 -> 运行时类型：Python3，硬件加速器：GPU，GPU类型：T4 -> 保存`\n",
"\n",
"步骤：\n",
"1. 下载最新代码到本地\n",
"2. 安装依赖包\n",
"\n",
"依赖包如下，保证最新版本：\n",
"\n",
"```\n",
"loguru\n",
"transformers\n",
"sentencepiece\n",
"datasets\n",
"tensorboard\n",
"tqdm\n",
"peft\n",
"trl\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!git clone --depth 1 https://github.com/shibing624/MedicalGPT.git\n",
"%cd MedicalGPT\n",
"%ls\n",
"!pip install -r requirements.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stage1 咱们开始吧\n",
"\n",
"训练步骤如下：\n",
"\n",
"1. 确认训练集\n",
"2. 执行训练脚本\n",
"\n",
"训练脚本的执行逻辑如下：\n",
"1. 导入依赖包\n",
"2. 设置参数\n",
"3. 定义各函数并加载训练集\n",
"4. 加载模型和tokenizer\n",
"5. 开始训练并评估\n",
"6. 查看训练结果\n",
"\n",
"**以下参数可以根据你的GPU实际情况修改，当前参数是根据Colab的T4单卡GPU（16GB显存）配置的**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%ls ./data/pretrain/"
]
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python pretraining.py \\\n",
" --model_type bloom \\\n",
" --model_name_or_path bigscience/bloomz-560m \\\n",
" --train_file_dir ./data/pretrain \\\n",
" --validation_file_dir ./data/pretrain \\\n",
" --per_device_train_batch_size 3 \\\n",
" --per_device_eval_batch_size 3 \\\n",
" --do_train \\\n",
" --do_eval \\\n",
" --use_peft True \\\n",
" --seed 42 \\\n",
" --fp16 \\\n",
" --max_train_samples 10000 \\\n",
" --max_eval_samples 10 \\\n",
" --num_train_epochs 1 \\\n",
" --learning_rate 2e-4 \\\n",
" --warmup_ratio 0.05 \\\n",
" --weight_decay 0.01 \\\n",
" --logging_strategy steps \\\n",
" --logging_steps 10 \\\n",
" --eval_steps 50 \\\n",
" --evaluation_strategy steps \\\n",
" --save_steps 500 \\\n",
" --save_strategy steps \\\n",
" --save_total_limit 3 \\\n",
" --gradient_accumulation_steps 1 \\\n",
" --preprocessing_num_workers 1 \\\n",
" --block_size 1024 \\\n",
" --output_dir outputs-pt-v1 \\\n",
" --overwrite_output_dir \\\n",
" --ddp_timeout 30000 \\\n",
" --logging_first_step True \\\n",
" --target_modules all \\\n",
" --lora_rank 8 \\\n",
" --lora_alpha 16 \\\n",
" --lora_dropout 0.05 \\\n",
" --torch_dtype float16 \\\n",
" --device_map auto \\\n",
" --report_to tensorboard \\\n",
" --ddp_find_unused_parameters False \\\n",
" --gradient_checkpointing True"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%ls -lh outputs-pt-v1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"模型训练结果：\n",
"- 使用lora训练模型，则保存的lora权重是`adapter_model.bin`, lora配置文件是`adapter_config.json`，合并到base model的方法见`merge_peft_adapter.py`\n",
"- 日志保存在`output_dir/runs`目录下，可以使用tensorboard查看，启动tensorboard方式如下：`tensorboard --logdir output_dir/runs --host 0.0.0.0 --port 8009`"
]
},
{
"cell_type": "markdown",
"source": [
"lora模型权重合并到base model，合并后的模型保存在`--output_dir`目录下，合并方法如下："
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python merge_peft_adapter.py --model_type bloom \\\n",
" --base_model_name_or_path bigscience/bloomz-560m --peft_model_path outputs-pt-v1 --output_dir merged-pt/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls -lh merged-pt/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%cat merged-pt/config.json"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stage1 增量预训练完成。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2023-06-15T13:56:17.032821Z",
"end_time": "2023-06-15T13:56:17.081153Z"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"source": [
"# Stage 2: Supervised FineTuning\n",
"\n",
"第二阶段：SFT(Supervised Fine-tuning)有监督微调，构造指令微调数据集，在预训练模型基础上做指令精调，以对齐指令意图\n",
"\n",
"| Stage 2: Supervised Fine-tuning | [supervised_finetuning.py](https://github.com/shibing624/MedicalGPT/blob/main/supervised_finetuning.py) | [run_sft.sh](https://github.com/shibing624/MedicalGPT/blob/main/run_sft.sh) |"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"#### 说明：\n",
"以下 notebook/colab 代码为了快速验证训练代码可用，我们使用了小size的生成模型和小样本数据集，实际使用时，需要使用更大的模型和数据集，以获得更好的效果。\n",
"\n",
"1. 生成模型：使用的是Bloom的`bigscience/bloomz-560m` 或者 Stage1得到的预训练模型\n",
"2. 数据集：SFT阶段使用的是使用的是Belle的1千条抽样数据，位于`data/finetune`文件夹"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Stage2 咱们开始吧\n",
"\n",
"训练步骤如下：\n",
"\n",
"1. 确认训练集\n",
"2. 执行训练脚本\n",
"\n",
"训练脚本的执行逻辑如下：\n",
"1. 导入依赖包\n",
"2. 设置参数\n",
"3. 定义各函数并加载训练集\n",
"4. 加载模型和tokenizer\n",
"5. 开始训练并评估\n",
"6. 查看训练结果"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls ./data/finetune"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-15T13:58:38.778132Z",
"end_time": "2023-06-15T13:58:38.966506Z"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python supervised_finetuning.py \\\n",
" --model_type bloom \\\n",
" --model_name_or_path merged-pt \\\n",
" --train_file_dir ./data/finetune \\\n",
" --validation_file_dir ./data/finetune \\\n",
" --per_device_train_batch_size 4 \\\n",
" --per_device_eval_batch_size 4 \\\n",
" --do_train \\\n",
" --do_eval \\\n",
" --use_peft True \\\n",
" --fp16 \\\n",
" --max_train_samples 1000 \\\n",
" --max_eval_samples 10 \\\n",
" --num_train_epochs 1 \\\n",
" --learning_rate 2e-5 \\\n",
" --warmup_ratio 0.05 \\\n",
" --weight_decay 0.05 \\\n",
" --logging_strategy steps \\\n",
" --logging_steps 10 \\\n",
" --eval_steps 50 \\\n",
" --evaluation_strategy steps \\\n",
" --save_steps 500 \\\n",
" --save_strategy steps \\\n",
" --save_total_limit 3 \\\n",
" --gradient_accumulation_steps 1 \\\n",
" --preprocessing_num_workers 1 \\\n",
" --output_dir outputs-sft-v1 \\\n",
" --overwrite_output_dir \\\n",
" --ddp_timeout 30000 \\\n",
" --logging_first_step True \\\n",
" --target_modules all \\\n",
" --lora_rank 8 \\\n",
" --lora_alpha 16 \\\n",
" --lora_dropout 0.05 \\\n",
" --torch_dtype float16 \\\n",
" --device_map auto \\\n",
" --report_to tensorboard \\\n",
" --ddp_find_unused_parameters False \\\n",
" --gradient_checkpointing True"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls -lh outputs-sft-v1"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"模型训练结果：\n",
"- 使用lora训练模型，则保存的lora权重是`adapter_model.bin`, lora配置文件是`adapter_config.json`，合并到base model的方法见`merge_peft_adapter.py`\n",
"- 日志保存在`output_dir/runs`目录下，可以使用tensorboard查看，启动tensorboard方式如下：`tensorboard --logdir output_dir/runs --host 0.0.0.0 --port 8009`"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"lora模型权重合并到base model，合并后的模型保存在`--output_dir`目录下，合并方法如下："
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python merge_peft_adapter.py --model_type bloom \\\n",
" --base_model_name_or_path merged-pt --peft_model_path outputs-sft-v1 --output_dir merged-sft/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls -lh merged-sft/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%cat merged-sft/config.json"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Stage2 SFT训练完成。"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-15T14:07:40.731186Z",
"end_time": "2023-06-15T14:07:40.752635Z"
}
}
},
{
"cell_type": "markdown",
"source": [
"# Stage 3: DPO(Direct Preference Optimization)\n",
"\n",
"第三阶段：DPO(Direct Preference Optimization)直接偏好优化，DPO通过直接优化语言模型来实现对其行为的精确控制，而无需使用复杂的强化学习，也可以有效学习到人类偏好，DPO相较于RLHF更容易实现且易于训练，效果更好\n",
"\n",
"| Stage 3: Direct Preference Optimization | [dpo_training.py](https://github.com/shibing624/MedicalGPT/blob/main/dpo_training.py) | [run_dpo.sh](https://github.com/shibing624/MedicalGPT/blob/main/run_dpo.sh) |"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"#### 说明：\n",
"以下 notebook/colab 代码为了快速验证训练代码可用，我们使用了小size的生成模型和小样本数据集，实际使用时，需要使用更大的模型和数据集，以获得更好的效果。\n",
"\n",
"1. 生成模型：使用的是Bloom的`bigscience/bloomz-560m` 或者 Stage2得到的SFT模型\n",
"2. 数据集：DPO阶段使用的是医疗reward数据，抽样了500条，位于`data/reward`文件夹"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Stage3 咱们开始吧\n",
"\n",
"训练步骤如下：\n",
"\n",
"1. 确认训练集\n",
"2. 执行训练脚本\n",
"\n",
"训练脚本的执行逻辑如下：\n",
"1. 导入依赖包\n",
"2. 设置参数\n",
"3. 定义各函数并加载训练集\n",
"4. 加载模型和tokenizer\n",
"5. 开始训练并评估\n",
"6. 查看训练结果"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls ./data/reward/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python dpo_training.py \\\n",
" --model_type bloom \\\n",
" --model_name_or_path merged-sft \\\n",
" --train_file_dir ./data/reward \\\n",
" --validation_file_dir ./data/reward \\\n",
" --per_device_train_batch_size 3 \\\n",
" --per_device_eval_batch_size 1 \\\n",
" --do_train \\\n",
" --do_eval \\\n",
" --use_peft True \\\n",
" --max_train_samples 1000 \\\n",
" --max_eval_samples 10 \\\n",
" --max_steps 100 \\\n",
" --eval_steps 10 \\\n",
" --save_steps 50 \\\n",
" --max_source_length 128 \\\n",
" --max_target_length 128 \\\n",
" --output_dir outputs-dpo-v1 \\\n",
" --target_modules all \\\n",
" --lora_rank 8 \\\n",
" --lora_alpha 16 \\\n",
" --lora_dropout 0.05 \\\n",
" --torch_dtype float16 \\\n",
" --fp16 True \\\n",
" --device_map auto \\\n",
" --report_to tensorboard \\\n",
" --remove_unused_columns False \\\n",
" --gradient_checkpointing True \\\n",
" --cache_dir ./cache"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls -lh outputs-dpo-v1"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"模型训练结果：\n",
"- 使用lora训练模型，则保存的lora权重是`adapter_model.bin`, lora配置文件是`adapter_config.json`，合并到base model的方法见`merge_peft_adapter.py`\n",
"- 日志保存在`output_dir/runs`目录下，可以使用tensorboard查看，启动tensorboard方式如下：`tensorboard --logdir output_dir/runs --host 0.0.0.0 --port 8009`"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"lora模型权重合并到base model，合并后的模型保存在`--output_dir`目录下，合并方法如下："
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python merge_peft_adapter.py --model_type bloom \\\n",
" --base_model_name_or_path merged-sft --peft_model_path outputs-dpo-v1 --output_dir merged-dpo/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%ls -lh merged-dpo/"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"%cat merged-dpo/config.json"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Stage3 偏好建模第一次训练完成。"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"**至此一个完整的训练流程演示完成。**"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-26T12:34:29.620609Z",
"end_time": "2023-06-26T12:34:29.658428Z"
}
}
},
{
"cell_type": "markdown",
"source": [
"# Test"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"!python inference.py --model_type bloom --base_model merged-dpo --interactive"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"start_time": "2023-06-26T12:34:47.802087Z",
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{
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"Input:介绍下南京\n",
"Response: 南京市位于江苏省西南部，是全国首批历史文化名城、国家中心城市和自由贸易试验区。\n",
"\n",
"完。\n"
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|