ZeRO(Zero Redundancy Optimizer)是一种去除冗余的分布式数据并行(Data Parallel)方案,分为Stage 1, Stage 2, Stage 3,而Deepspeed就是论文中ZeRO方法的Microsoft官方的工程实现。 ZeRO-Offload为解决由于ZeRO而增加通信数据量的问题,提出将GPU转移到CPU ZeRO-Infinity同样是进行offload,ZeRO-Offload更侧重单卡场景,而ZeR...
先放一个官方无声的视频,本文主要围绕此视频理解zero的原理和实践。 zero的三个阶段Stage 1 p_{os} : 把 优化器状态 分片到每个数据并行的工作进程(每个GPU)下Stage 2 p_{os+g} : 把优化器状态+ 梯度分片到每个数…
4.2 DeepSpeedEngine 对ZERO实现的api 在简单了解了通讯相关的api后。我们会逐步的理解DeepSpeedEngine的相关代码 class DeepSpeedEngine(Module): def __init__(self): ... if self.stage <= 2: # deepspeed stage1 , stage2 self.optimizer = DeepSpeedZeroOptimizer(...) else: # stage3 self.optimizer =...
deepspeed zero1.json zero2.json zero3.json zero3_bf16.json docker docs examples image scripts src tests .bandit .editorconfig .flake8 .gitattributes .gitignore .isort.cfg .mypy.ini .pre-commit-config.yaml .pylintrc FAQS.md LICENSE README.md TODO.md docker-compose.yaml requirements-dev.txt...
deepspeed ZeRO-Inference 可在1-GPU上推理～100B的大模型,09/zero-inference.html原理:
Compared with ZeRO-1, ZeRO-2 doubles the model size that can be trained with DeepSpeed while significantly improving the training efficiency. With ZeRO-2, a 100-billion-parameter model can be trained 10x faster than with the state-of-art technology based on model parallelism alone. ZeRO-...
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DeepSpeed-Chat配备了(1)抽象数据集层,以统一不同数据集的格式;以及(2)数据拆分/混合功能,从而使多个数据集被适当地混合,然后在3个训练阶段进行分割。 DeepSpeed混合引擎 指示引导的RLHF管道的第1步和第2步,类似于大模型的常规微调,它们由基于ZeRO的优化和DeepSpeed训练中灵活的并行策略组合,来实现规模和速度。
ZeRO-Offload was co-developed with our intern Jie Ren from UC Merced. We would also like to thank Dong Li from UC Merced, as well as Bharadwaj Pudipeddi and Maral Mesmakhouroshahi from Microsoft L2L work (opens in new tab), for their discussions on the topic. 1-bit Adam was co-dev...
1. ZeRO++ 加速大型模型预训练和微调 每个GPU 上 batch size 较小时:无论是在数千个 GPU 上预训练大型模型,还是在数百个甚至数十个 GPU 上对其进行微调,当每个 GPU 的 batch size 较小时,ZeRO++ 提供比 ZeRO 高 2.2 倍的吞吐量,直接减少训练时间和成本。