重磅！微软开源Deep Speed Chat，人人拥有ChatGPT！

AIGC开放社区2023-04-13

用户通过Deep Speed Chat提供的“傻瓜式”操作，能以最短的时间、最高效的成本训练类ChatGPT大语言模型，这标志着一个人手一个ChatGPT的时代要来了。4月12日，微软宣布开源了Deep Speed Chat，帮助用户轻松训练类ChatGPT等大语言模型，使得人人都能拥有自己的ChatGPT！（开源地址：https://github.com/microsoft/DeepSpeed）...

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·2023-04-13
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Microsoft Open Source Deep Speed Chat, everyone has ChatGPT!","themeId":"","isJumpTheme":false,"ttsUrl":"https://static.tigerbbs.com/b08ed743dcd64a3d084e8094723b0673","symbols_score_info":{"MSFT":0.9},"content_text":"用户通过Deep Speed Chat提供的“傻瓜式”操作，能以最短的时间、最高效的成本训练类ChatGPT大语言模型，这标志着一个人手一个ChatGPT的时代要来了。4月12日，微软宣布开源了Deep Speed Chat，帮助用户轻松训练类ChatGPT等大语言模型，使得人人都能拥有自己的ChatGPT！（开源地址：https://github.com/microsoft/DeepSpeed） 据悉，Deep Speed Chat是基于微软Deep Speed深度学习优化库开发而成，具备训练、强化推理等功能，还使用了RLHF（人工反馈机制的强化学习）技术，可将训练速度提升15倍以上，成本却大幅度降低。例如，一个130亿参数的类ChatGPT模型，只需1.25小时就能完成训练。简单来说，用户通过Deep Speed Chat提供的“傻瓜式”操作，能以最短的时间、最高效的成本训练类ChatGPT大语言模型，这标志着一个人手一个ChatGPT的时代要来了。微软为什么开源Deep Speed ChatChatGPT只用了几个月的时间便席卷全球，成为史上用户增长速度最快的消费级应用，对世界的经济发展、科技研发起到了重要的推动作用。很多企业、个人用户纷纷想参与到这场AI技术变革中，但由于庞大的资源、资金投入只能望而却步。即便开源领域推出了LLaMa、Alpaca、Vicuna、Databricks-Dolly等优秀的类ChatGPT项目，仍缺乏一个支持端到端的基于RLHF的规模化系统，使得训练强大的类ChatGPT模型非常艰难。例如，使用上述开源项目训练一个67亿参数的类ChatGPT模型，通常需要昂贵的多卡至多节点的GPU集群，但这些资源对大多数科学家、小型企业、个人开发者而言难以获取。即便是拥有这样的资源，现有的开源系统的训练效率通常还不到，这些机器所能达到的最大效率的5%。现在，通过Deep Speed Chat解决资源、算法等难题，轻松、高效的训练数千亿参数的最先进的类ChatGPT模型。Deep Speed Chat技术简单介绍ChatGPT模型的训练是基于InstructGPT论文中的RLHF方式，这使得现有深度学习系统在训练类ChatGPT模型时存在种种局限。现在，通过Deep Speed Chat可以突破这些训练瓶颈，达到最佳效果。Deep Speed Chat拥有强化推理、RLHF模块、RLHF系统三大核心功能。简化 ChatGPT 类型模型的训练和强化推理：只需一个脚本即可实现多个训练步骤，包括使用Huggingface 预训练的模型、使用 DeepSpeed-RLHF 系统运行 InstructGPT 训练的所有三个步骤，生成属于自己的类ChatGPT模型。此外，还提供了一个易于使用的推理API，用于在模型训练后测试对话性能。Deep Speed-RLHF 模块：DeepSpeed-RLHF 复刻了InstructGPT 论文中的训练模式，并确保包括：a) 监督微调（SFT），b) 奖励模型微调和 c) 基于人类反馈的强化学习（RLHF）在内的三个步骤与其对应。此外，还提供了数据抽象和混合功能，以支持用户使用多个不同来源的数据源进行训练。DeepSpeed-RLHF系统：将DeepSpeed的训练和推理能力整合到统一的混合引擎（DeepSpeed Hybrid Engine or DeepSpeed-HE）中用于RLHF 训练。DeepSpeed-HE 能够在RLHF中无缝地在推理和训练模式之间切换，使其能够利用来自 DeepSpeed-Inference 的各种优化。例如，张量并行计算和高性能CUDA算子进行语言生成，同时对训练部分还能从 ZeRO- 和 LoRA-based 内存优化策略中受益。DeepSpeed-HE可以自动在 RLHF 的不同阶段进行智能的内存管理和数据缓存。此外，Deep Speed Chat在成本、效率、扩展性等方面非常优秀。高效且节省成本：在Azure云上只需9小时即可训练一个OPT-13B模型，只需18小时即可训练一个 OPT-30B模型。这两种训练分别花费不到300美元和600 美元。卓越扩展性：能够支持训练拥有数千亿参数的模型，并在多节点多 GPU 系统上展现出卓越的扩展性。因此，即使是一个拥有 130 亿参数的模型，也只需 1.25 小时就能完成训练。而对于拥1750亿参数的模型，使用Deep Speed Chat进行训练也只需不到一天的时间。注意：在Azure上训练时由于GPU不同，所以训练时间和费用也各不相同。全民ChatGPT时代来了做个不太恰当的比喻，微软此次开源Deep Speed Chat就像当年的互联网，为了提升发展速率降低了台式机的价格，使得普通用户也能拥有自己的电脑。只有参与的用户多了，整个行业生态才能快速壮大发展，所以，微软此次开源对整个ChatGPT生态发展起到了至关重要的推动作用，使得人人都能拥有自己的ChatGPT。目前，ChatGPT处于初级发展阶段，存在安全隐患、数据隐私、道德等问题。相信随着参与用户的增多，这些难题将会被有效的解决，从而为全球经济发展做出贡献。","kind":"news","is_publish_news":true,"is_publish_highlight":true,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":1},"commentList":[{"id":9945946651,"gmtCreate":1681359170074,"gmtModify":1681359173556,"author":{"id":"3548412292431483","authorId":"3548412292431483","name":"benben82","avatar":"https://community-static.tradeup.com/news/3d5066411f8c70a09959487fa1c126ab","vip":1,"hat":"https://static.tigerbbs.com/b0a2963eb37c60c5d6d4a8dbcd266952","crmLevel":3,"crmLevelSwitch":1,"idStr":"3548412292431483","authorIdStr":"3548412292431483"},"htmlText":"Good","listText":"Good","text":"Good","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9945946651","repostId":2327971959,"repostType":2,"isVote":1,"likeStatus":false,"favoriteStatus":false,"reportStatus":false,"tweetType":1,"langContent":"EN"}],"hasMoreComments":true,"newsSizeData":{"likeSize":15,"commentSize":1,"repostSize":1,"favoriteSize":1,"likeStatus":false,"favoriteStatus":false}}