From 64a1e9e46d029d010faf097729e0a47d52815f4b Mon Sep 17 00:00:00 2001 From: Alex-TG001 <60740185+Alex-TG001@users.noreply.github.com> Date: Fri, 21 Jun 2024 23:00:37 +0800 Subject: [PATCH] feat-dpo-words-count-1.0 (#598) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * feat-dpo-words-count-1.0 字数控制训练样本间cookbook，包括微调，评估 * Update main.ipynb * feat_dpo_words_count 实现字数控制dpo微调任务，完成相应实验以及验证 * Update main.ipynb (修改了代码bug) sft+dpo训练，验证 * Update main.ipynb * Update main.ipynb * Update load_statistics.py * Update main.ipynb * fix * 修改eval逻辑 * Update main.ipynb * Update main.ipynb * Update load_statistics.py --------- Co-authored-by: zhonghanjun --- .../dpo_words_count_control/eval.py | 39 + .../dpo_words_count_control/main.ipynb | 721 ++++++++++++++++++ 2 files changed, 760 insertions(+) create mode 100644 cookbook/awesome_demo/dpo_words_count_control/eval.py create mode 100644 cookbook/awesome_demo/dpo_words_count_control/main.ipynb diff --git a/cookbook/awesome_demo/dpo_words_count_control/eval.py b/cookbook/awesome_demo/dpo_words_count_control/eval.py new file mode 100644 index 00000000..779de10f --- /dev/null +++ b/cookbook/awesome_demo/dpo_words_count_control/eval.py @@ -0,0 +1,39 @@ +from qianfan.model import Model +from qianfan.common import Prompt +from qianfan import Completion +import re +from qianfan.dataset import Dataset + +def eval(version_id, ds): + result_ds = ds.test_using_llm(model_version_id=version_id) + res = [] + for i in result_ds: + + # 提取字数限制 + char_limit_match = re.search(r"(\d+)字", i['prompt']) + if not char_limit_match: + raise ValueError("输入中未找到字数限制") + + limit = int(char_limit_match.group(1)) + fact = len(i['llm_output']) + + # 计算比例 + ratio = fact / limit + abs_diff = abs(ratio - 1) + + # 根据绝对值的大小返回相应的得分 + if abs_diff <= 0.05: + score = 1 + elif abs_diff <= 0.10: + score = 0.75 + elif abs_diff <= 0.15: + score = 0.5 + elif abs_diff <= 0.20: + score = 0.25 + elif abs_diff <= 0.25: + score = 0.1 + else: + score = 0 + res.append(score) + avg = sum(res) / len(res) + return avg \ No newline at end of file diff --git a/cookbook/awesome_demo/dpo_words_count_control/main.ipynb b/cookbook/awesome_demo/dpo_words_count_control/main.ipynb new file mode 100644 index 00000000..58ebf249 --- /dev/null +++ b/cookbook/awesome_demo/dpo_words_count_control/main.ipynb @@ -0,0 +1,721 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 文本创作字数控制\n", + "文本创作场景中，大模型可以扮演高效的写作助手角色。大模型具备文本理解、生成及编辑能力，我们可以通过调优场景的Prompt或者通过精调的方式，使得大模型生成精准且符合主题的内容。\n", + "\n", + "在利用大模型进行文本创作时，实现字数控制的功能同等重要，尤其在新闻摘要、微博等社交媒体内容创作，都需要精确地控制文本的长度。\n", + "\n", + "本文将通过对千帆sdk平台的ERNIE-Speed模型进行微调，优化模型对于文本创作字数控制的功能，并且在实验中验证微调实现的有效性。\n", + "\n", + "参考本文的sft,dpo以及sft+dpo实践案例，您也可以更好地上手千帆sdk平台的训练功能。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 0. 环境准备" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "在此部分，我们将讨论使用千帆平台进行推理任务之前的准备工作。这包括获取访问权限、安装 SDK 等内容。\n", + "\n", + "然后通过如下方式设置鉴权所需的 Access Key 和 Secret Key，相关 Key 可以从 [百度智能云控制台 - 安全认证](https://console.bce.baidu.com/iam/#/iam/accesslist) 页面获得。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install 'qianfan>=0.3.16'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "#此处请您根据 SDK 文档获取自己的 access key 和 secret key\n", + "os.environ[\"QIANFAN_ACCESS_KEY\"] = \"your_qianfan_console_access_key\"\n", + "os.environ[\"QIANFAN_SECRET_KEY\"] = \"your_qianfan_console_secret_key\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from qianfan.dataset import Dataset\n", + "from qianfan.trainer.configs import TrainConfig\n", + "from qianfan.trainer import DPO\n", + "from qianfan import Completion\n", + "from qianfan.common import Prompt\n", + "from qianfan.trainer import LLMFinetune\n", + "from qianfan.trainer.consts import PeftType\n", + "from eval import eval\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. 基座模型效果示例" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "首先，我们用本次实验采用的ERNIE-Speed-8K模型进文本创作字数控制的任务。直观感受一下模型微调前的效果。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "导入评估集" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO] [06-19 11:50:44] dataset.py:407 [t:8335662592]: no data source was provided, construct\n", + "[INFO] [06-19 11:50:44] dataset.py:281 [t:8335662592]: construct a qianfan data source from existed id: ds-2hdewmq2w2yw8dz7, with args: {}\n", + "[INFO] [06-19 11:50:44] dataset.py:480 [t:8335662592]: no destination data source was provided, construct\n", + "[INFO] [06-19 11:50:44] dataset.py:275 [t:8335662592]: construct a file data source from path: data_file/dpo_test.jsonl, with args: {}\n", + "[INFO] [06-19 11:50:44] file.py:293 [t:8335662592]: use format type FormatType.Jsonl\n", + "[INFO] [06-19 11:50:45] utils.py:348 [t:8335662592]: start to get memory_map from /Users/jianruitian/.qianfan_cache/dataset/Users/jianruitian/.qianfan_cache/dataset/.qianfan_download_cache/dg-i3vsgebdzvtec7zb/ds-2hdewmq2w2yw8dz7/1/content/1.arrow\n", + "[INFO] [06-19 11:50:45] utils.py:276 [t:8335662592]: has got a memory-mapped table\n", + "[INFO] [06-19 11:50:45] dataset.py:213 [t:8335662592]: change local file format FormatType.Jsonl to qianfan file format FormatType.Jsonl\n" + ] + } + ], + "source": [ + "ds_test = Dataset.load(qianfan_dataset_id = \"ds-2hdewmq2w2yw8dz7\")\n", + "ds_test = ds_test.save(data_file=\"data_file/dpo_test.jsonl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这里我们用一个文本归纳总结的任务展示基座模型优化前的效果：\n", + "* 阅读文本后，提炼出一个30字以内的简洁总结。\n", + "\n", + "* 夏日炎炎，一场别开生面的社区环保创意大赛即将拉开帷幕！这不仅仅是一场智慧的较量，更是一次社区凝聚力的提升。让我们一起走进这场充满欢乐与创意的“夏日环保嘉年华——社区篇”。\n", + "在这个充满活力的季节，我们邀请每个家庭派出2-4名代表，组成充满激情的战队。你们将共同面对一个有趣的挑战：运用生活中的废旧物品或可回收材料，创造出一个个体现夏日风情的环保艺术品。这些作品不仅要体现循环再用的环保精神，还要展现独特创意和社区文化的融合。\n", + "想象一下，你们的作品或许是一个由废旧塑料瓶打造而成的别致花器，或许是一个用废旧轮胎改造而成的时尚户外座椅，又或许是一盏由废弃玻璃瓶制作而成的别致风灯……无论是什么，它们都将是你们家庭创意的结晶，都将在这个夏天里焕发出新的生机。\n", + "当然，想要在这场比赛中脱颖而出需要付出不少努力。我们的评委团将根据作品的创新性、实用性和美观性进行细致的评选。所以，请尽情挥洒你们的创意，用双手赋予废旧物品新的生命吧！\n", + "参与方式也非常便捷。只需通过社区平台在线报名，并上传你们的作品照片。经过评委团的线上初选后，入围的家庭将有机会在线下展示环节大放异彩。最终，我们将评选出冠军1名、亚军2名、季军3名，并为他们颁发精美的证书和丰厚的奖品。\n", + "这场“夏日环保嘉年华——社区篇”不仅是一次智慧的较量，更是一次社区的欢聚。在这个热情的季节里，让我们共同感受创意的无限可能，体验社区大家庭的温暖，一起度过这个充满欢乐与创意的夏日时光吧！\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO] [06-19 09:28:42] dataset.py:993 [t:8335662592]: list local dataset data by 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "社区环保创意大赛邀家庭代表创造环保艺术品，激发创意和凝聚力，线上线下评比颁奖，共庆夏日环保嘉年华。\n" + ] + } + ], + "source": [ + "p = Prompt(ds_test[5][0]['prompt'])\n", + "#将任务描述输入到模型进行优化\n", + "comp = Completion(model=\"ERNIE-Speed-8K\")\n", + "r = comp.do(prompt=p.render()[0])\n", + "output = r['result']\n", + "print(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* ERNIE-Speed的回答长度达到了45个词，超出了30字的限制的50%，与预期效果相差甚远。\n", + "\n", + "* 该现象属于大模型满足某些特定约束的能力较弱。在此处即字数约束能力不强，出于SFT能够通过对模型输出的概率分布进行精细调整，从而更有效地控制生成文本长度的优势，该问题可以考虑通过准备一些包含明确Prompt和对应Response的语料数据，使用SFT的能力解决。\n", + "\n", + "* 因此，我们进行了微调实验，提升生成的效果。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2.数据集准备" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "从千帆平台导入sft训练和dpo训练的数据集" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO] [06-19 11:54:48] dataset.py:407 [t:8335662592]: no data source was provided, construct\n", + "[INFO] [06-19 11:54:48] dataset.py:281 [t:8335662592]: construct a qianfan data source from existed id: ds-sjv3xchndftmg2fu, with args: {}\n" + ] + } + ], + "source": [ + "#enable_log(logging.INFO)\n", + "ds_sft = Dataset.load(qianfan_dataset_id = \"ds-sjv3xchndftmg2fu\")#sft训练集\n", + "#ds_sft = ds_sft.save(data_file=\"data_file/sft_train.jsonl\")\n", + "#print(new_ds[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO] [06-19 11:54:50] dataset.py:407 [t:8335662592]: no data source was provided, construct\n", + "[INFO] [06-19 11:54:50] dataset.py:281 [t:8335662592]: construct a qianfan data source from existed id: ds-ca94jxph35qp1ks3, with args: {}\n" + ] + } + ], + "source": [ + "ds_dpo = Dataset.load(qianfan_dataset_id = \"ds-ca94jxph35qp1ks3\")#dpo训练集\n", + "#ds_dpo = ds_dpo.save(data_file=\"data_file/dpo_train.jsonl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们从实际业务场景中得到数据后，需要对样本进行分析和处理。一般包括对原始数据进行清洗、分析数据质量和分布、对数据进行扩充。\n", + "\n", + "此处我们的数据已经处理完成，您可以参考[如何使用千帆进行数据处理](https://github.com/baidubce/bce-qianfan-sdk/blob/main/cookbook/dataset/how_to_use_qianfan_operator.ipynb)，来处理您的原始数据数据。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. 微调训练与测试\n", + "针对我们的任务，此处设计了六组实验，进行了相应的sft训练，dpo训练以及sft+dpo训练。\n", + "\n", + "实验数据如下：\n", + "| | 实验1 | 实验2 | 实验3 | 实验4(基于实验1) | 实验5(基于实验2) |\n", + "|-|-|-|-|-|-|\n", + "| 精调方法 | sft | sft | dpo | sft+dpo | sft+dpo |\n", + "| Epoch | 1 | 3 | 1 | 1 | 1 |\n", + "| Learning Rate | 1e-5 | 1e-5 | 1e-6 | 1e-6 | 1e-6 |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.1 sft精调\n", + "\n", + "使用千帆sdk平台的微调功能，此处选择sft训练，设置相应的参数执行训练任务。这里以第二组参数的实验为例，展示平台高效的训练能力。\n", + "\n", + "* 以此为例，您可以修改参数，执行其他组别的实验。" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# 默认参数\n", + "trainer2 = LLMFinetune( #sft训练\n", + " train_type=\"ERNIE-Speed\",\n", + " name = \"dpo_words_2_fin\",\n", + " train_config=TrainConfig(\n", + " epoch=3,\n", + " learning_rate=1e-5,\n", + " #max_seq_len=4096,\n", + " peft_type=PeftType.ALL,\n", + " #logging_steps=1,\n", + " #warmup_ratio=0.10,\n", + " #weight_decay=0.0100,\n", + " #lora_rank=8,\n", + " #lora_all_linear=\"True\",\n", + " ),\n", + " dataset=ds_sft\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO] [06-19 09:51:31] actions.py:667 [t:10754224128]: [train_action] training ... job_name:dpo_words_2_fin current status: Running, 1% check train task log in https://console.bce.baidu.com/qianfan/train/sft/job-c9qm8jeiy1q7/task-yuc4f8mt3a7y/detail/traininglog\n", + "[INFO] [06-19 09:52:01] actions.py:667 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], + "source": [ + "trainer2.run()\n", + "print(trainer2.result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 dpo精调\n", + "\n", + "使用千帆sdk平台的微调功能，此处选择dpo训练，设置相应的参数执行训练任务。这里以第二组参数的实验为例，展示平台高效的训练能力。\n", + "\n", + "* 以此为例，您可以修改参数，执行其他组别的实验。" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "trainer6 = DPO( #dpo训练\n", + " #train_type=\"ERNIE-Speed-8K\", ###如果没有前置任务，则不需要设置previous_task_id，改在此处设置基座模型\n", + " name = \"dpo_words_6\",\n", + " previous_task_id = \"task-tdng641si3it\",#第六组实验是基于第二组实验的sft结果进行dpo微调，此处需要设置前序实验的task_id\n", + " train_config=TrainConfig(\n", + " epoch=1,\n", + " learning_rate=1e-6,\n", + " max_seq_len=4096,\n", + " peft_type=PeftType.ALL,\n", + " ), \n", + " dataset=ds_dpo,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer6.run()#执行训练任务" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.3 实验评估\n", + "\n", + "进行完六组实验后，进行评估。此处使用千帆平台的批量推理能力进行评估。\n", + "\n", + "评分标准如下：\n", + "\n", + " * 长度控制得分：针对不同参数设置的输出进行字符数统计，定义以下长度控制得分规则，对不同参数设置的训练方式求一个平均分：\n", + " \n", + " 首先计算预测字符数与输入字符限制的比例减一的绝对值，明确两者之间的差距，然后根据这个绝对值的大小返回不同的得分。\n", + " 如果这个绝对值在0到0.05之间（也就是说，预测字符数与输入字符限制的比例在1±0.05之间），则得分为1；随着这个绝对值的增大，得分逐渐降低，当绝对值超过0.25时，得分为0。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "导入评估集" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO] [06-18 23:42:30] dataset.py:407 [t:8335662592]: no data source was provided, construct\n", + "[INFO] [06-18 23:42:30] dataset.py:281 [t:8335662592]: construct a qianfan data source from existed id: ds-2hdewmq2w2yw8dz7, with args: {}\n" + ] + } + ], + "source": [ + "dpo_test = Dataset.load(qianfan_dataset_id = \"ds-2hdewmq2w2yw8dz7\")#dpo评估集\n", + "# dpo_test = dpo_test.save(data_file=\"data_file/dpo_test.jsonl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "此处，我们以第六组实验的训练结果为例，展示批量离线推理和评估的过程。" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.5899999999999999\n" + ] + } + ], + "source": [ + "#第一个参数填写对应实验的模型版本id，第二个参数填写用于评估的数据集id\n", + "res = eval(\"amv-wsr34aidpqcr\", dpo_test)\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "最终的评估结果如下：\n", + "| | 实验1 | 实验2 | 实验3 | 实验4(基于实验1) | 实验5(基于实验2) |\n", + "|-|-|-|-|-|-|\n", + "| 精调方法 | sft | sft | dpo | sft+dpo | sft+dpo |\n", + "| Epoch | 1 | 3 | 1 | 1 | 1 |\n", + "| Learning Rate | 1e-5 | 1e-5 | 1e-6 | 1e-6 | 1e-6 |\n", + "| 长度得分 | 0.629 | 0.468 | 0.733 | 0.744 | 0.590|" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "可以看到，对于文本的字数控制任务，dpo的表现远远优于sft。\n", + "\n", + "单独使用dpo训练以及在sft基础上进行dpo训练的效果都好于单独sft训练的效果。\n", + "\n", + "您可以参考本文的方法，修改训练方法，尝试训练您自己的模型。" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}