add Incremental Pre-training Guide
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</table>
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### 🎇最近更新
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- 【2024.5.7】[增量预训练指南](xtuner_config/pt/README.md)
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- 【2024.4.20】[LLAMA3微调指南](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md)及基于[LLaMA3_8b_instruct的艾薇](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei)开源
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- 【2023.4.14】新增[快速开始](docs/quick_start.md)和保姆级教程[BabyEmoLLM](Baby_EmoLLM.ipynb)
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- 【2024.4.2】在 Huggingface 上传[老母亲心理咨询师](https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main)
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@ -101,6 +101,7 @@ The Model aims to fully understand and promote the mental health of individuals,
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</table>
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### Recent Updates
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- [2024.5.7][Incremental Pre-training Guide](xtuner_config/pt/README.md)
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- [2024.4.20] [LLAMA3 fine-tuning guide](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md) and based on [LLaMA3_8b_instruct's aiwei](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei) open source
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- [2023.4.14] Added [Quick Start](docs/quick_start_EN.md) and Nanny level tutorial [BabyEmoLLM](Baby_EmoLLM.ipynb)
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- [2024.4.2] Uploaded at Huggingface [Old Mother Counsellor](https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main)
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36
xtuner_config/pt/README.md
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36
xtuner_config/pt/README.md
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# 增量预训练教程
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# 增量预训练简介
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增量预训练旨在提升模型在特定领域或任务的能力。
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# 预训练流程
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- Step1 处理数据
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- Step2 配置config（全量、Lora、Qlora）
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- Step3 启动训练（单卡、多卡、是否使用deepspeed）
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- Step4 模型合成
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- Step5 模型测试
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- Step6 模型上传
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# EmoLLM增量预训练教程
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基于微调中的数据集[datasets](../../datasets)修改而来
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- Step1 修改`ft2pt.py`中的文件路径
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这里以[output2.json](../../datasets/processed/output2.json)为例，运行脚本生成[pt.json](../../datasets/pt/pt.json)
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- Step2 [config](./internlm2_chat_1_8b_qlora_e3_pt.py)
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注意：本config采用了**变长注意力 (Variable Length Attention)**
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需要安装flash_attn
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`MAX_JOBS=4 pip install flash-attn --no-build-isolation`
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- Step3 训练：
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```
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# On a single GPU
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xtuner train internlm2_chat_1_8b_qlora_e3_pt.py --deepspeed deepspeed_zero2
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# On multiple GPUs
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(DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_chat_1_8b_qlora_e3_pt.py --deepspeed deepspeed_zero2
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(SLURM) srun ${SRUN_ARGS} xtuner train internlm2_chat_1_8b_qlora_e3_pt.py --launcher slurm --deepspeed deepspeed_zero2
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```
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- 其余流程请参考[微调教程](../../xtuner_config/README.md)
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48
xtuner_config/pt/ft2pt.py
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xtuner_config/pt/ft2pt.py
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# 将微调的数据格式转为预训练的格式
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import json
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def convert(data_path:str, target_path:str):
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# 假设原始JSON数据存储在名为'data.json'的文件中
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filename = data_path
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# 读取文件内容
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with open(filename, 'rt', encoding='utf-8') as file:
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original_json = file.read()
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# 将原始JSON字符串解析为Python对象
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data = json.loads(original_json)
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# 遍历每个对话
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converted_data = []
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# 遍历原始数据中的每个对话对象
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for conversation_group in data:
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# 遍历每个对话
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for dialog in conversation_group["conversation"]:
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# 创建一个新的对话对象，用于存储转换后的对话
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new_conversation_group = {
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"conversation": []
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}
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# 创建一个新的对话，其中输出被替换为"xxx"
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new_dialog = {
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"input": '',
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"output": f'问题:{dialog["input"]}\n答案:{dialog["output"]}',
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}
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# 将新的对话添加到新对话对象的列表中
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new_conversation_group["conversation"].append(new_dialog)
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# 将新对话对象添加到转换后的数据列表中
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converted_data.append(new_conversation_group)
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# 将更新后的数据转换回JSON字符串，并格式化输出
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updated_json = json.dumps(converted_data, indent=4, ensure_ascii=False)
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# 将更新后的JSON数据写入到新的文件中
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with open(f'{target_path}', 'wt', encoding='utf-8') as file:
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file.write(updated_json)
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if __name__ == '__main__':
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convert(data_path='./output2.json', target_path='pt.json')
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xtuner_config/pt/internlm2_chat_1_8b_qlora_e3_pt.py
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xtuner_config/pt/internlm2_chat_1_8b_qlora_e3_pt.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from datasets import load_dataset
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from mmengine.dataset import DefaultSampler
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from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
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LoggerHook, ParamSchedulerHook)
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from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
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from peft import LoraConfig
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from torch.optim import AdamW
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from transformers import (AutoModelForCausalLM, AutoTokenizer,
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BitsAndBytesConfig)
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from xtuner.dataset import process_hf_dataset
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from xtuner.dataset.collate_fns import default_collate_fn
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from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory
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from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
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VarlenAttnArgsToMessageHubHook)
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from xtuner.engine.runner import TrainLoop
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from xtuner.model import SupervisedFinetune
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from xtuner.parallel.sequence import SequenceParallelSampler
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from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
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#######################################################################
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# PART 1 Settings #
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#######################################################################
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# Model
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pretrained_model_name_or_path = '/root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b'
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use_varlen_attn = True # True
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# Data
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data_path = '/root/wxz/work/pt/pt.json'
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prompt_template = PROMPT_TEMPLATE.internlm2_chat
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max_length = 2048
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pack_to_max_length = True
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# parallel
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sequence_parallel_size = 1
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# Scheduler & Optimizer
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batch_size = 1 # per_device
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accumulative_counts = 1
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accumulative_counts *= sequence_parallel_size
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dataloader_num_workers = 0
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max_epochs = 3
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optim_type = AdamW
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lr = 2e-4
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betas = (0.9, 0.999)
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weight_decay = 0
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max_norm = 1 # grad clip
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warmup_ratio = 0.03
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# Save
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save_steps = 500
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save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
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# Evaluate the generation performance during the training
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evaluation_freq = 500
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SYSTEM = SYSTEM_TEMPLATE.alpaca
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evaluation_inputs = [
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'我们经常因为一些小事争吵，他总是忽略我的感受。我感到很孤独，'
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]
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#######################################################################
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# PART 2 Model & Tokenizer #
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#######################################################################
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tokenizer = dict(
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type=AutoTokenizer.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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padding_side='right')
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model = dict(
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type=SupervisedFinetune,
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use_varlen_attn=use_varlen_attn,
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llm=dict(
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type=AutoModelForCausalLM.from_pretrained,
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pretrained_model_name_or_path=pretrained_model_name_or_path,
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trust_remote_code=True,
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torch_dtype=torch.float16,),
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lora=dict(
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type=LoraConfig,
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r=64,
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lora_alpha=16,
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lora_dropout=0.1,
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bias='none',
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task_type='CAUSAL_LM'))
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#######################################################################
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# PART 3 Dataset & Dataloader #
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#######################################################################
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alpaca_en = dict(
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type=process_hf_dataset,
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dataset=dict(type=load_dataset, path='json', data_files=dict(train=data_path)),
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tokenizer=tokenizer,
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max_length=max_length,
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dataset_map_fn=None,
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template_map_fn=None,
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remove_unused_columns=True,
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shuffle_before_pack=True,
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pack_to_max_length=pack_to_max_length,
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use_varlen_attn=use_varlen_attn)
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sampler = SequenceParallelSampler \
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if sequence_parallel_size > 1 else DefaultSampler
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train_dataloader = dict(
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batch_size=batch_size,
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num_workers=dataloader_num_workers,
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dataset=alpaca_en,
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sampler=dict(type=sampler, shuffle=True),
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collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
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#######################################################################
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# PART 4 Scheduler & Optimizer #
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#######################################################################
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# optimizer
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optim_wrapper = dict(
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type=AmpOptimWrapper,
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optimizer=dict(
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type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
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clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
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accumulative_counts=accumulative_counts,
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loss_scale='dynamic',
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dtype='float16')
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# learning policy
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# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
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param_scheduler = [
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dict(
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type=LinearLR,
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start_factor=1e-5,
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by_epoch=True,
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begin=0,
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end=warmup_ratio * max_epochs,
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convert_to_iter_based=True),
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dict(
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type=CosineAnnealingLR,
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eta_min=0.0,
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by_epoch=True,
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begin=warmup_ratio * max_epochs,
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end=max_epochs,
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convert_to_iter_based=True)
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]
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# train, val, test setting
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train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
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#######################################################################
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# PART 5 Runtime #
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#######################################################################
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# Log the dialogue periodically during the training process, optional
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custom_hooks = [
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dict(type=DatasetInfoHook, tokenizer=tokenizer),
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]
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if use_varlen_attn:
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custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
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# configure default hooks
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default_hooks = dict(
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# record the time of every iteration.
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timer=dict(type=IterTimerHook),
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# print log every 10 iterations.
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logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
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# enable the parameter scheduler.
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param_scheduler=dict(type=ParamSchedulerHook),
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# save checkpoint per `save_steps`.
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checkpoint=dict(
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type=CheckpointHook,
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by_epoch=False,
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interval=save_steps,
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max_keep_ckpts=save_total_limit),
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# set sampler seed in distributed evrionment.
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sampler_seed=dict(type=DistSamplerSeedHook),
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)
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# configure environment
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env_cfg = dict(
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# whether to enable cudnn benchmark
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cudnn_benchmark=False,
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# set multi process parameters
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
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# set distributed parameters
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dist_cfg=dict(backend='nccl'),
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)
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# set visualizer
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visualizer = None
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# set log level
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log_level = 'INFO'
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# load from which checkpoint
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load_from = None
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# whether to resume training from the loaded checkpoint
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resume = False
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# Defaults to use random seed and disable `deterministic`
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randomness = dict(seed=None, deterministic=False)
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# set log processor
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log_processor = dict(by_epoch=False)
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