[Update] modified and add files related to fintuning with internlm2_7b_base (#116)
This commit is contained in:
xzw
GitHub
commit
e0ec624943
@ -7552,9 +7552,6 @@
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]
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},
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{
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"conversation": []
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{
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"conversation": [
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{
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@ -8540,9 +8537,6 @@
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}
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]
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},
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{
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"conversation": []
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},
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{
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"conversation": [
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{
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@ -13389,9 +13383,6 @@
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}
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]
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},
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{
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"conversation": []
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},
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{
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"conversation": [
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{
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@ -19973,9 +19964,6 @@
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}
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]
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},
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{
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"conversation": []
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},
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{
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"conversation": [
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{
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@ -1,12 +1,25 @@
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import json
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# 打开JSON文件并读取其内容
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with open('/root/Emollm/datasets/multi_turn_dataset_2.json', 'rt', encoding='utf-8') as file:
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# file_name = 'multi_turn_dataset_1.json'
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# file_name = 'multi_turn_dataset_2.json'
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# file_name = 'data_pro.json'
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file_name = 'data.json'
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with open(f'/root/StableCascade/emollm2/EmoLLM/datasets/{file_name}', 'rt', encoding='utf-8') as file:
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data = json.load(file)
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n = 0
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for i in data:
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i['conversation'][0]['system'] = "你是心理健康助手EmoLLM，由EmoLLM团队打造。你旨在通过专业心理咨询，协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术，一步步帮助来访者解决心理问题。"
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try:
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i['conversation'][0]['system'] = "你是心理健康助手EmoLLM，由EmoLLM团队打造。你旨在通过专业心理咨询，协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术，一步步帮助来访者解决心理问题。"
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except:
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print(n,i) # 4 empty lines in data.json 425 483 742 1120
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n+=1
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with open('output2.json', 'wt', encoding='utf-8') as file:
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with open(f'processed_{file_name}', 'wt', encoding='utf-8') as file:
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json.dump(data, file, ensure_ascii=False, indent=4)
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print(data[0])
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34
datasets/processed/process_merge.py
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34
datasets/processed/process_merge.py
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@ -0,0 +1,34 @@
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import os
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import json
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# 设置目录路径，这里假设你的JSON文件都在当前目录下的directory_path文件夹中
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directory_path = './'
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# 初始化一个空列表，用于存储所有JSON文件的数据
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combined_list = []
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# 遍历指定目录下的所有文件
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for filename in os.listdir(directory_path):
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# 检查文件扩展名是否为.json
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if filename.endswith('.json'):
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# 构建文件的完整路径
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file_path = os.path.join(directory_path, filename)
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# 打开并读取JSON文件
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with open(file_path, 'r', encoding='utf-8') as json_file:
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# 加载JSON文件的内容
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data = json.load(json_file)
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# 将读取到的数据添加到combined_list中
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# 假设每个JSON文件包含的是一个列表，如果不是，可以根据实际情况调整
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if isinstance(data, list):
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combined_list.extend(data)
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else:
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combined_list.append(data)
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# 打印合并后的列表 very large and slow
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# print(combined_list)
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# 如果需要，可以将合并后的列表保存到一个新的JSON文件中
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with open('combined_data.json', 'w', encoding='utf-8') as combined_json_file:
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json.dump(combined_list, combined_json_file, ensure_ascii=False, indent=4)
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@ -0,0 +1,27 @@
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import json
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# 打开JSON文件并读取其内容
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# file_name = 'single_turn_dataset_1.json'
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file_name = 'single_turn_dataset_2.json'
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with open(f'/root/StableCascade/emollm2/EmoLLM/datasets/{file_name}', 'rt', encoding='utf-8') as file:
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format1_data = json.load(file)
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system = "你是心理健康助手EmoLLM，由EmoLLM团队打造。你旨在通过专业心理咨询，协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术，一步步帮助来访者解决心理问题。"
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# 转换为格式2的数据
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format2_data = []
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for item in format1_data:
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conversation = {
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"system": system,
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"input": item["prompt"],
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"output": item["completion"]
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}
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format2_data.append({"conversation": [conversation]})
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# 将转换后的数据转换为JSON格式
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with open(f'./processed_{file_name}', 'wt', encoding='utf-8') as file:
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json.dump(format2_data, file, ensure_ascii=False, indent=4)
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print(format2_data[0])
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203
xtuner_config/internlm2_7b_base_qlora_e3.py
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203
xtuner_config/internlm2_7b_base_qlora_e3.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 template_map_fn_factory
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from xtuner.engine import DatasetInfoHook, EvaluateChatHook
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from xtuner.model import SupervisedFinetune
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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/model_repos/internlm2-chat-7b'
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pretrained_model_name_or_path = '/root/share/model_repos/internlm2-base-7b'
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# Data
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# data_path = 'merge.json'
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data_path ='/root/StableCascade/emollm2/EmoLLM/datasets/processed/combined_data.json'
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# https://github.com/InternLM/xtuner/blob/main/xtuner/utils/templates.py#L24C25-L24C25
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prompt_template = PROMPT_TEMPLATE.internlm2_chat # there is No internlm2_base
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max_length = 2048
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pack_to_max_length = True
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# Scheduler & Optimizer
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# batch_size = 8 # per_device
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# accumulative_counts = 2
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batch_size = 16 # per_device
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accumulative_counts = 1
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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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# Evaluate the generation performance during the training
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evaluation_freq = 500
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# SYSTEM = "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。"
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SYSTEM = "你是心理健康助手EmoLLM，由EmoLLM团队打造。你旨在通过专业心理咨询，协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术，一步步帮助来访者解决心理问题。"
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evaluation_inputs = [
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'我最近总是感到很焦虑，尤其是在学业上。我有个特别崇拜的同学，他好像在各方面都比我优秀，我总觉得自己怎么努力也追不上他，这让我压力特别大。', '我知道应该理性看待，但就是忍不住会去比较。我甚至晚上会因为这个睡不着觉，总想着怎样才能像他那样出色。'
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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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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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quantization_config=dict(
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type=BitsAndBytesConfig,
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load_in_4bit=True,
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load_in_8bit=False,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type='nf4')),
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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=dict(
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type=template_map_fn_factory, template=prompt_template),
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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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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=DefaultSampler, shuffle=True),
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collate_fn=dict(type=default_collate_fn))
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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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T_max=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(by_epoch=True, max_epochs=max_epochs, val_interval=1)
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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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dict(
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type=EvaluateChatHook,
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tokenizer=tokenizer,
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every_n_iters=evaluation_freq,
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evaluation_inputs=evaluation_inputs,
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system=SYSTEM,
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prompt_template=prompt_template)
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]
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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 100 iterations.
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logger=dict(type=LoggerHook, 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 epoch.
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checkpoint=dict(type=CheckpointHook, interval=1),
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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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@ -1,11 +1,23 @@
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datasets==2.16.1
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deepspeed==0.13.1
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einops==0.7.0
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flash_attn==2.5.0
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mmengine==0.10.2
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openxlab==0.0.34
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peft==0.7.1
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sentencepiece==0.1.99
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torch==2.1.2
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transformers==4.36.2
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xtuner==0.1.11
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# modified version
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# xtuner==0.1.11
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# mmengine==0.10.2
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mmengine==0.10.3
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xtuner==0.1.15
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# flash_attn==2.5.0 # build is very slow about 2 hours?
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# method 1: https://github.com/Dao-AILab/flash-attention/releases
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# flash_attn-2.5.0+cu122torch2.1cxx11abiTRUE-cp310-cp310-linux_x86_64.whl
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# method 2:
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# pip install /root/share/wheels/flash_attn-2.4.2+cu118torch2.0cxx11abiTRUE-cp310-cp310-linux_x86_64.whl
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# mpi4py==3.1.5 # conda install mpi4py
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