From d89027be61c0eaf1567332564e78a412d7c3c8eb Mon Sep 17 00:00:00 2001 From: Masahiro Tanaka <81312776+tohtana@users.noreply.github.com> Date: Sun, 5 Nov 2023 10:23:46 -0800 Subject: [PATCH 1/8] Fix figure in FlexGen blog (#4624) Fix the latency-throughput figure for 13B. --- blogs/deepspeed-fastgen/README.md | 2 +- .../assets/images/throughput_latency_13B.png | Bin 339033 -> 0 bytes .../images/throughput_latency_13B_no_arrow.png | Bin 0 -> 176008 bytes 3 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 blogs/deepspeed-fastgen/assets/images/throughput_latency_13B.png create mode 100644 blogs/deepspeed-fastgen/assets/images/throughput_latency_13B_no_arrow.png diff --git a/blogs/deepspeed-fastgen/README.md b/blogs/deepspeed-fastgen/README.md index 6b440e35b675..b271c112a762 100644 --- a/blogs/deepspeed-fastgen/README.md +++ b/blogs/deepspeed-fastgen/README.md @@ -145,7 +145,7 @@ In this experiment, DeepSpeed-FastGen outperforms vLLM in both throughput and la
-
+
*Figure 3: Throughput and latency of text generation using Llama 2 13B (A100-80GB GPU, no tensor parallelism). A normal distribution was applied to prompt and generation lengths with averages of 1200/2600 and 60/128, respectively, and a 30% variance*
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b/blogs/deepspeed-offloadpp/README.md index 22910ffef7b1..1441da5a35c0 100644 --- a/blogs/deepspeed-offloadpp/README.md +++ b/blogs/deepspeed-offloadpp/README.md @@ -43,7 +43,7 @@ We conduct our performance evaluations over both A100 and H100 DGX machine and t ## Tutorials -Examples and Tutorials are [here](https://github.com/microsoft/Megatron-DeepSpeed/blob/guanhua/partial-offload/examples_deepspeed/offload_pp/README.md) +Examples and Tutorials are [here](https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples_deepspeed/offload_pp/README.md) ## Contributors: From cbec96b00ec16bd7c724dcb7ae00ceb1d319cefe Mon Sep 17 00:00:00 2001 From: Jeff Rasley Date: Mon, 6 Nov 2023 15:55:25 -0800 Subject: [PATCH 7/8] [docs] update news items (#4640) Co-authored-by: Guanhua Wang --- README.md | 18 ++++++++++++++---- docs/index.md | 21 ++++++++++++++++----- 2 files changed, 30 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 721ba62cee37..e0463beb1a77 100755 --- a/README.md +++ b/README.md @@ -15,13 +15,23 @@ ## Latest News DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat). +* [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) * [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] * [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] -* [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) -* [2023/08] [DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md) -* [2023/08] [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses) [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md)] -* [2023/06] [ZeRO++: A leap in speed for LLM and chat model training with 4X less communication](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)[[English](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/japanese/README.md)] +* [2023/08] [DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) + + +

    + More news + +
    --- diff --git a/docs/index.md b/docs/index.md index 60bcf19b84da..a4027ca1e52e 100755 --- a/docs/index.md +++ b/docs/index.md @@ -7,13 +7,24 @@ title: "Latest News" --- DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat). +* [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) * [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] -* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] -* [2023/08] [DeepSpeed ZeRO-Inference: 20X faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) -* [2023/08] [DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md) -* [2023/08] [DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses) [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md)] -* [2023/06] [ZeRO++: A leap in speed for LLM and chat model training with 4X less communication](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)[[English](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/zeropp/japanese/README.md)] +* [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] +* [2023/08] [DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) + + + +
    + More news + +
    # Extreme Speed and Scale for DL Training and Inference From 00df0c1998ab84aaa7ceaae2b9e04857923c7fa2 Mon Sep 17 00:00:00 2001 From: Heyang Qin Date: Mon, 6 Nov 2023 21:16:53 -0800 Subject: [PATCH 8/8] DeepSpeed-FastGen Chinese Blog (#4642) Thanks @xiaoxiawu-microsoft and @conglongli for reviewing and improving it! --------- Co-authored-by: Xiaoxia (Shirley) Wu <94406484+xiaoxiawu-microsoft@users.noreply.github.com> Co-authored-by: Conglong Li --- README.md | 2 +- blogs/deepspeed-fastgen/chinese/README.md | 299 ++++++++++++++++++ .../2023-11-06-deepspeed-fastgen-chinese.md | 7 + docs/_posts/2023-11-06-deepspeed-fastgen.md | 7 + docs/index.md | 2 +- 5 files changed, 315 insertions(+), 2 deletions(-) create mode 100644 blogs/deepspeed-fastgen/chinese/README.md create mode 100644 docs/_posts/2023-11-06-deepspeed-fastgen-chinese.md create mode 100644 docs/_posts/2023-11-06-deepspeed-fastgen.md diff --git a/README.md b/README.md index e0463beb1a77..06d244801bc4 100755 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat). * [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) -* [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) +* [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md)] * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] * [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] * [2023/08] [DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md) diff --git a/blogs/deepspeed-fastgen/chinese/README.md b/blogs/deepspeed-fastgen/chinese/README.md new file mode 100644 index 000000000000..4fcb1f00f8f2 --- /dev/null +++ b/blogs/deepspeed-fastgen/chinese/README.md @@ -0,0 +1,299 @@ +
    + +# DeepSpeed-FastGen:通过 MII 和 DeepSpeed-Inference 实现 LLM 高吞吐量文本生成 + +
    + +
    + + +
    + +## 目录 +1. [引言](#introduction) +2. [关键的 LLM 服务技术](#background) +3. [动态 SplitFuse:一种新颖的提示和生成组合策略](#technical-approach) +4. [性能评估](#performance-evaluation) +5. [DeepSpeed-FastGen:实现与使用](#using-deepspeed-fastgen) +6. [尝试 DeepSpeed-FastGen](#try) +7. [致谢](#acknowledgements) + + +## 1. 引言 + +GPT-4 和 LLaMA 这样的大型语言模型(LLMs)已在各个层次上成为了集成 AI 的主流服务应用。从常规聊天模型到文档摘要,从自动驾驶到各个软件中的Copilot功能,这些模型的部署和服务需求正在迅速增加。像 DeepSpeed、PyTorch 和其他几个框架可以在 LLM 训练期间实现良好的硬件利用率。但它们在与用户互动及处理开放式文本生成等任务时,受限于这些操作的计算密集度相对较低,现有系统往往在推理吞吐量上遇到瓶颈。 + +为了解决这一问题, [vLLM](https://arxiv.org/pdf/2309.06180.pdf) 这样由 PagedAttention 驱动的框架和 [Orca](https://www.usenix.org/system/files/osdi22-yu.pdf) 这样的系统显著提高了 LLM 推理的性能。然而,这些系统在面对长提示的工作负载时,依旧难以提供良好的服务质量。随着越来越多的模型(例如 [MPT-StoryWriter](https://www.mosaicml.com/blog/mpt-7b))和系统(例如[DeepSpeed Ulysses](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-ulysses))支持延伸到数万个令牌的上下文窗口,这些长提示工作负载变得越来越重要。为了更好地理解问题,我们在下文中提供了详细的示例来说明 LLM 的文本生成是如何在“提示处理”和“生成”的这两个阶段中工作的。当系统将它们视为不同的阶段时,生成阶段将被提示处理所抢占,这可能会破坏服务级别协议(SLAs)。 + +今天,我们很高兴地介绍 DeepSpeed-FastGen 框架,它通过采用我们提出的动态 SplitFuse 技术,能够提供比vLLM 等先进系统高出多达 2.3 倍的有效吞吐量。DeepSpeed-FastGen 是 DeepSpeed-MII 和 DeepSpeed-Inference 的结合,提供了一个易于使用的服务系统。 + +**快速开始:** 要使用 DeepSpeed-FastGen 只需安装最新的 [DeepSpeed-MII](https://github.com/microsoft/DeepSpeed-MII) 发行版: + +```bash +pip install deepspeed-mii +``` + +要使用简单的非持久性管道部署并生成文本,请运行以下代码。更多详情,请参见[第 5 节](#using-deepspeed-fastgen)。 + +```python +from mii import pipeline +pipe = pipeline("mistralai/Mistral-7B-v0.1") +output = pipe(["Hello, my name is", "DeepSpeed is"], max_new_tokens=128) +print(output) +``` + +## 2. 现有 LLM 服务技术 + +单个序列的文本生成工作负载包含两个阶段:1)提示处理,此阶段系统处理用户输入的文本,将其转换成一系列令牌并构建用于注意力机制的键值(KV)缓存;2)生成令牌,即向缓存中添加单个令牌并产生新的令牌。在生成文本序列的过程中,系统将对模型进行多次前向调用以生成完整的文本序列。现有文献和系统中已经提出了两种主要技术,它们解决了这些阶段中可能出现的各种限制和瓶颈。 + +_ 分块 KV 缓存:_ + +vLLM识别出大型单体KV缓存导致的内存碎片化显著降低了大型语言模型服务系统的并发性,并提出了“分页注意力”[Paged Attention](https://arxiv.org/pdf/2309.06180.pdf) 机制来实现非连续KV缓存,并增加整个系统的总吞吐量。此技术采用分页缓存机制,从而提升了系统的整体吞吐量。不同于之前分配各个不同大小的连续内存块的做法,分块 KV 缓存中的底层存储是固定大小的块(也称为页面)。分块 KV 缓存通过消除 KV 缓存引起的内存碎片化,增加了潜在的序列并发量,从而增加了系统吞吐量。非连续 KV 缓存也被 [HuggingFace TGI](https://github.com/huggingface/text-generation-inference) 和 [NVIDIA TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) 等框架所实现。 + +_ 连续批处理:_ + +过去,动态批处理(服务器等待多个请求以同步处理)被用来提高 GPU 利用率。然而,这种方法有缺点,因为它通常需要将输入填充到相同长度或使系统等待以构建更大的批次(batch)。 + +近期大型语言模型(LLM)推理和服务的优化一直专注于细粒度调度和优化内存效率。例如,Orca 提出了 _迭代级调度_(也称为连续批处理),它在模型的每次前向传递时作出独特的调度决策。这允许请求根据需要加入/离开批次,从而消除了填充请求的需要,提高了总体吞吐量。除了 Orca,NVIDIA TRT-LLM、HuggingFace TGI 和 vLLM 也实现了连续批处理。 + +在当前系统中,有两种主要方法来实现连续批处理。在 TGI 和 vLLM 中,生成阶段被抢占以执行提示处理(在 TGI 中称为填充)然后继续生成。在 Orca 中,这些阶段不被区分;相反,只要总序列数没有达到固定限制,Orca 就会将提示加入正在运行的批次中。这两种方法都在不同程度上需要暂停生成以处理长提示(参见[第 3B 节](#splitfuse))。 + +为了解决这些缺点,我们提出了一种新颖的提示和生成组合策略,动态 SplitFuse。 + +## 3. 动态 SplitFuse:一种新颖的提示和生成组合策略 + +类似于现有的框架如 TRT-LLM、TGI 和 vLLM,DeepSpeed-FastGen 的目标是利用连续批处理和非连续 KV 缓存技术,以提升数据中心服务大型语言模型(LLM)的硬件利用率和响应速度。为了实现更高的性能,DeepSpeed-FastGen 提出了 SplitFuse 技术,它利用动态提示和生成分解, 统一来进一步改善连续批处理和系统吞吐量。 + +### A. 三个性能见解 +在描述动态 SplitFuse 之前,我们回答三个关键的性能问题,这些问题解释了SplitFuse背后的逻辑。 + +*__1. 哪些因素影响单个 LLM 的前向传递?__* 为了有效地调度,我们必须首先了解调度过程中应考虑的独立变量有哪些。我们观察到,在前向传递中序列的组成(序列中的批次大小)对性能的影响可以忽略不计。这意味着我们可以围绕单一变量--即前向传递中的令牌数量--构建一个高效的调度器。 + +
    +
    +
    + +*__2. 模型的吞吐量与前向传递中令牌数量的关系如何?__* 一个 LLM 有两个关键的运行区间,并且过渡相对陡峭。当令牌数量较少时,GPU 的瓶颈是从内存中读取模型,因此吞吐量会随着令牌数量的增加而上升,而当令牌数量很多时,模型的吞吐量受GPU计算能力限制,吞吐量近乎恒定。因此如果我们能将所有前向传递都保持在吞吐量饱和区间,则模型运行效率最高。 + +
    +
    +
    + +*__3. 如何在多个前向传递中调度一组令牌?__* 我们在上图中观察到,对于对齐良好的输入,令牌吞吐量曲线是凹的,这意味着第二导数必定小于或等于 0。设 $f(x)$ 为给定模型的延迟至吞吐量的凹函数。则对于凹函数 $f(x)$,以下关系成立: + + $$0 \geq \lim_{h \to 0} \frac{f(x + h) - 2f(x) + f(x - h)}{h^2}$$ + + $$0 \geq f(x + h) - 2f(x) + f(x - h)$$ + + $$2f(x) \geq f(x + h) + f(x - h)$$ + +这表明,对于给定的 `2x` 个总令牌来说,最大化吞吐量的方式是将它们均匀分割到两个批次之间。更一般地说,在一个系统中,如果要在 F 个前向传递中处理 P 个令牌,最理想的分区方案是均匀分配它们。 + +### B. 动态分割融合(Dynamic SplitFuse) + +动态分割融合是一种用于提示处理和令牌生成的新型令牌组成策略。DeepSpeed-FastGen 利用动态分割融合策略,通过从提示中取出部分令牌并与生成过程相结合,使得模型可以保持一致的前向传递大小(forward size)。具体来说,动态分割融合执行两个关键行为: + +1. 将长提示分解成更小的块,并在多个前向传递(迭代)中进行调度,只有在最后一个传递中才执行生成。 +2. 短提示将被组合以精确填满目标令牌预算。即使是短提示也可能被分解,以确保预算被精确满足,前向大小(forward sizes)保持良好对齐。 + +动态分割融合(Dynamic SplitFuse)提升了以下性能指标: + +1. **更好的响应性:** 由于长提示不再需要极长的前向传递来处理,模型将提供更低的客户端延迟。在同一时间窗口内执行的前向传递更多。 +2. **更高的效率:** 短提示的融合到更大的令牌预算使模型能够持续运行在高吞吐量状态。 +3. **更低的波动和更好的一致性:** 由于前向传递的大小一致,且前向传递大小是性能的主要决定因素,每个前向传递的延迟比其他系统更加一致。生成频率也是如此,因为DeepSpeed-FastGen不需要像其他先前的系统那样抢占或长时间运行提示,因此延迟会更低。 + +因此,与现有最先进的服务系统相比,DeepSpeed-FastGen 将以允许快速、持续生成的速率消耗来自提示的令牌,同时向系统添加令牌,提高系统利用率,提供更低的延迟和更高的吞吐量流式生成给所有客户端。 + +
    + +
    + + *图 1: 连续批处理策略的示意图。每个块显示一个前向传递的执行。箭头表示前向传递有一个或多个生成的令牌序列。vLLM 在一个前向传递中要么生成令牌,要么处理提示;令牌生成抢占提示处理。Orca 在生成过程中以完整长度处理提示。DeepSpeed-FastGen动态分割融合则执行固定大小批次的动态组合,包括生成和提示令牌。* + +
    + +## 4. 性能评估 + +DeepSpeed-FastGen 利用分块 KV 缓存和动态分割融合连续批处理,提供了最先进的 LLM 服务性能。我们以下述的基准测试方法对 DeepSpeed-FastGen 和 vLLM 在一系列模型和硬件配置上进行评估。 + +### A. 基准测试方法论 + +我们采用两种主要的定量方法来衡量性能。 + +**吞吐量-延迟曲线:** 生产环境的两个关键指标是吞吐量(以每秒请求计)和延迟(每个请求的响应性)。为了衡量这一点,我们模拟了多个客户端(数量从 1 到 32 不等)同时向服务器发送请求(总计 512 个)的情况。每个请求的结果延迟在端点测量,吞吐量通过完成实验的端到端时间来测量。 + +**有效吞吐量:** 诸如聊天应用程序之类的交互式应用程序可能有比上述指标(如端到端延迟)更严格和复杂的要求。以越来越受欢迎的聊天应用为例: + + 1. 用户通过发送提示(输入)来开始对话。 + 2. 系统处理提示并返回第一个令牌。 + 3. 随着生成的进行,后续令牌被流式传输给用户。 + +在这个过程的每个阶段,系统都有可能提供不利的用户体验;例如,第一个令牌到达得太慢;或生成似乎停止了一段时间。我们提出了一个考虑这两个维度的 SLA 框架。 + +由于提示和生成文本的长度差异很大,影响计算成本,因此设定同一个 SLA 值对于吞吐量和延迟是不切实际的。因此,我们将提示延迟的 SLA 定义为 “|提示中的令牌|/512” 秒(= 512 令牌/秒)。此外,考虑到人类的阅读速度,我们将生成延迟的 SLA 设置在指数移动平均(EMA)上为 2、4 或 6 令牌/秒。能够达到这些 SLA 的请求被认为是成功的,这些成功请求的吞吐量被称为**有效吞吐量**。 + +我们通过在 NVIDIA A100、H100 和 A6000 上运行 Llama-2 7B、Llama-2 13B 和 Llama-2 70B 对 vLLM 和 DeepSpeed-FastGen进行了评估。 + +### B. 吞吐量-延迟分析 + +在这个实验中,DeepSpeed-FastGen 在吞吐量和延迟方面都优于 vLLM,在相同的延迟下DeepSpeed-FastGen的吞吐量更大;在相同的吞吐量下DeepSpeed-FastGen的响应延迟更小。如图 2 所示,在 Llama-2 70B 运行于 4 个 A100x80GB 的情况下,DeepSpeed-FastGen 展示了高达 2 倍的吞吐量(1.36 rps 对比 0.67 rps)在相同的延迟(9 秒)下;或高达 50% 的延迟减少(7 秒对比 14 秒)同时实现相同的吞吐量(1.2 rps)。评估 Llama-2 13B 时DeepSpeed-FastGen也呈现了这些趋势,如图 3 所示。 + +
    +
    + + *图 2: 使用 Llama 2 70B 进行文本生成的吞吐量和延迟(使用 4 个 A100-80GB GPU 的张量并行)。提示和生成长度遵循正态分布,平均值分别为 1200/2600 和 128/60,方差为 30%* +

    + +
    +
    + + *图 3: 使用 Llama 2 13B 进行文本生成的吞吐量和延迟(A100-80GB GPU,无张量并行)。提示和生成长度遵循正态分布,平均值分别为 1200/2600 和 60/128,并且有 30% 的方差* +
    + +### C. 有效吞吐量分析 + +在考虑了首个令牌的延迟和生成速率的有效吞吐量分析下,DeepSpeed-FastGen 提供的吞吐量比 vLLM 高出多达 2.3 倍。图 4 展示了 DeepSpeed-FastGen 和 vLLM 的有效吞吐量的比较分析。每个绘制的点表示从特定数量的客户端得出的有效吞吐量。当我们扩大客户端数量时,我们最初观察到有效吞吐量的增加。然而,当客户端数量接近系统容量时,延迟也显著增加,导致许多请求未能满足 SLA。因此,有效吞吐量将在某个点上饱和或减少。从可用性角度来看,达到最大有效吞吐量所需的客户端数量并不特别重要;线条的最高点是最优的服务点。 + +
    + + + *图 4: DeepSpeed-FastGen 和 vLLM 的有效吞吐量(Llama 2 70B/A100-80GB 使用张量并行在 4 个 A100-80GB GPU 上。提示和生成长度遵循正态分布,平均值分别为 2600 和 60,并且有 30% 的方差)* +

    + +当 vLLM 抢占正在进行的先前请求的生成时,生成延迟会明显增加。这导致 vLLM 的有效吞吐量看起来低于其直接测量的吞吐量。在 vLLM 的峰值时,有效吞吐量为 0.63 查询/秒,大约 28% 的请求未能满足 4 令牌/秒的 SLA。在相同的 SLA 下,DeepSpeed-FastGen 达到了 1.42 查询/秒(不到 1% 的请求未能满足 SLA),这是 vLLM 的 2.3 倍。 + +### D. 令牌级时间分析 + +图 5 显示了生成过程的 P50、P90 和 P95 延迟。vLLM 和 DeepSpeed-FlexGen 展示了类似的 P50 延迟,但 vLLM 的 P90 和 P95 延迟显著更高。 + +这种差异是由于 vLLM 在抢占正在进行的生成以处理新提示时,生成延迟出现显著增加所导致的。 +相比之下,DeepSpeed-FastGen 通常会同时处理之前请求的提示和生成,导致生成延迟更加一致。 + +
    +
    + + *图 5: 使用张量并行在 4 个 A100-80GB GPU 上的 Llama 2 70B/A100-80GB 的每令牌生成延迟,16 客户端。提示和生成长度遵循正态分布,平均值分别为 2600 和 128,并且有 30% 的方差。 +

    + + +### E. 使用负载均衡的可扩展性 + +DeepSpeed-FastGen 提供了副本级负载均衡,可以将请求均匀分布在多个服务器上,让您轻松扩展应用程序。 + +图 6 展示了 DeepSpeed-FastGen 在使用负载均衡器和最多 16 个副本时的可扩展性。请注意,我们使用了 4 个 A100 GPU 来计算每个 Llama 2 70B 模型。总共,我们使用了 8 个节点来运行 16 个副本。结果展示了 DeepSpeed-FastGen 几乎完美的可扩展性。 +单个副本时DeepSpeed-FastGen的吞吐量为 1.46 查询/秒,而16 个副本的吞吐量达到了 23.7 查询/秒,与单个副本相比标志着线性的 16 倍增长。 + +
    +
    + + *图 6: 使用负载均衡功能的可扩展性。提示和生成长度遵循正态分布,平均值分别为 2600 和 60,并且有 30% 的方差*
    +
    + +### F. 其他硬件平台 + +除了对 A100 的深入分析,我们还提供了 H100 和 A6000 的基准测试结果。在 A6000 和 H100 上观察到的性能趋势与 A100 相同。 + +
    +
    + + *图 7: 使用 8 个 H100 GPU 的 Llama 2 70b 的吞吐量-延迟曲线和有效吞吐量。提示和生成长度遵循正态分布,平均值分别为 2600 和 60,并且有 30% 的方差*
    +
    + +
    +
    + + *图 8: 使用 A6000 的 Llama 2 7b 的吞吐量-延迟曲线和有效吞吐量。提示和生成长度遵循正态分布,平均值分别为 2600 和 60,并且有 30% 的方差*
    +
    + +## 5. DeepSpeed-FastGen:软件实现与使用指南 + +DeepSpeed-FastGen 是 [DeepSpeed-MII](https://github.com/microsoft/DeepSpeed-MII) 和 [DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed) 的协同组合,如下图所示。这两个软件包共同提供了系统的各个组成部分,包括前端 API、用于使用动态 SplitFuse 调度批次的主机和设备基础设施、优化的内核实现,以及构建新模型实现的工具。 + +
    + + +
    + + +使用我们的 alpha 版 DeepSpeed-FastGen 最快的入门方式是:`pip install deepspeed-mii`。 + +请按照我们的 [入门指南](https://github.com/microsoft/deepspeed-mii#getting-started-with-mii) 获取更多细节。如需使用和报告问题,请使用 [DeepSpeed-MII Github 仓库](https://github.com/microsoft/DeepSpeed-MII)。 + +### A. 支持的模型 + +在 DeepSpeed-FastGen 的当前 alpha 版本中,我们目前支持以下模型架构: + +* [LLaMA](https://huggingface.co/models?other=llama) 和 [LLaMA-2](https://huggingface.co/models?other=llama-2) +* [Mistral](https://huggingface.co/models?other=mistral) +* [OPT](https://huggingface.co/models?other=opt) + +所有当前模型都利用了后端的 [HuggingFace](https://github.com/huggingface) API 来提供模型权重和模型对应的分词器。 + +> 我们计划在最初发布后的几周和几个月内添加更多模型。如果您希望支持特定的模型架构,请[提交问题](https://github.com/microsoft/DeepSpeed-MII/issues)来让我们知道。 + +### B. 部署选项 +以下所有示例均可在 [DeepSpeedExamples](https://github.com/microsoft/DeepSpeedExamples/tree/master/inference/mii) 中运行。安装后,您有两种部署方式:交互式非持久管道或持久化服务部署: + +#### 非持久管道 + +非持久管道部署是快速入门的好方法,只需几行代码即可完成。非持久模型只在您运行的 python 脚本期间存在,适用于临时交互式会话。 + +```python +from mii import pipeline +pipe = pipeline("mistralai/Mistral-7B-v0.1") +output = pipe(["Hello, my name is", "DeepSpeed is"], max_new_tokens=128) +print(output) +``` + +#### 持久部署 + +持久部署非常适合用于长时间运行和生产的应用。持久部署使用了轻量级的 GRPC 服务器,可以使用以下两行代码创建: + +```python +import mii +mii.serve("mistralai/Mistral-7B-v0.1") +``` + +上述服务器可以同时被多个客户端查询,这要归功于 DeepSpeed-MII 内置的负载平衡器。创建客户端也只需要两行代码: + +```python +client = mii.client("mistralai/Mistral-7B-v0.1") +output = client.generate("Deepspeed is", max_new_tokens=128) +print(output) +``` + +持久部署可以在不再需要时终止: + +```python +client.terminate_server() +``` + +### C. 高级安装方式 + +为了使用方便并显著减少许多其他框架所需的冗长编译时间,我们通过名为 [DeepSpeed-Kernels](https://github.com/microsoft/DeepSpeed-Kernels) 的新库分发了覆盖我们大部分自定义内核的预编译 Python wheel。我们发现这个库在环境中非常便携,只要这些环境具有 NVIDIA GPU 计算能力 8.0+(Ampere+)、CUDA 11.6+ 和 Ubuntu 20+。在大多数情况下,您甚至不需要知道这个库的存在,因为它是 DeepSpeed-MII 的依赖项,并将自动与之一起安装。然而,如果您因任何原因需要手动编译我们的内核,请参阅我们的[高级安装文档](https://github.com/microsoft/DeepSpeed-Kernels#source)。 + + +# 6. 尝试 DeepSpeed-FastGen +我们非常高兴分享 DeepSpeed-FastGen 的首个 alpha 版本。 + +* 要开始,请访问我们的 DeepSpeed-MII GitHub 页面: [GitHub 登陆页面](https://github.com/microsoft/DeepSpeed-MII) + +DeepSpeed-FastGen 是更大的 DeepSpeed 生态系统的一部分,该生态系统包含了多种深度学习系统和建模技术。要了解更多, + +* 请访问我们的[网站](https://www.deepspeed.ai/),详细查看博客文章、教程和有用的文档。 +* 您也可以通过我们的[英文 Twitter](https://twitter.com/MSFTDeepSpeed)、[日本 Twitter](https://twitter.com/MSFTDeepSpeedJP) 和[中文知乎](https://www.zhihu.com/people/deepspeed) 关注我们,以获取 DeepSpeed 的最新消息。 + +DeepSpeed 欢迎您的贡献!我们鼓励您在 [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) 页面上报告问题、贡献 PR,并参与讨论。有关更多详细信息,请参见我们的[贡献指南](https://github.com/microsoft/DeepSpeed/blob/master/CONTRIBUTING.md)。我们愿意与大学、研究实验室和公司合作,比如那些在深度学习研究上共同工作,应用 DeepSpeed 来赋能真实世界的 AI 模型和应用等。对于那些不适合在 GitHub 上提出的请求(以及其他请求),请直接发送电子邮件至 deepspeed-info@microsoft.com。 + +以下项目在我们的路线图上,我们计划通过我们的 GitHub 问题和 PR 与我们的社区在这些项目上进行交流: + +- 性能改进 +- 更广泛的模型支持 +- 通过与合作伙伴的合作支持新硬件后端 +- 发布性能测试套件(例如此博客中生成的图表) + +如果您喜欢我们的工作,请为我们的 [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) 和 [DeepSpeedMII GitHub](https://github.com/microsoft/DeepSpeed-MII/) 仓库打上“星标”! + +# 7. 致谢 + +我们要对包括 HuggingFace、vLLM 和 HuggingFace TGI 在内的多个开源社区项目表示感谢。在 alpha 版本中, 我们利用 HF API 来调用模型和分词器,并计划未来添加更多模型。我们特别感谢 [Flash Attention](https://github.com/Dao-AILab/flash-attention) 开发者的出色工作。我们在系统中广泛利用了 FlashAttention 内核,并已经在我们的代码库的对应的文件头部进行了致谢。最后,我们要感谢我们在 MoE 内核(作为 DeepSpeed-Kernels 仓库的一部分发布)中使用的 [FasterTransformer](https://github.com/NVIDIA/FasterTransformer) 内核的开发者。 diff --git a/docs/_posts/2023-11-06-deepspeed-fastgen-chinese.md b/docs/_posts/2023-11-06-deepspeed-fastgen-chinese.md new file mode 100644 index 000000000000..cc259c20361a --- /dev/null +++ b/docs/_posts/2023-11-06-deepspeed-fastgen-chinese.md @@ -0,0 +1,7 @@ +--- +title: "DeepSpeed-FastGen:通过 MII 和 DeepSpeed-Inference 实现 LLM 高吞吐量文本生成" +excerpt: "" +link: https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md +date: 2023-11-06 00:00:00 +tags: inference Chinese +--- diff --git a/docs/_posts/2023-11-06-deepspeed-fastgen.md b/docs/_posts/2023-11-06-deepspeed-fastgen.md new file mode 100644 index 000000000000..2af103e81f15 --- /dev/null +++ b/docs/_posts/2023-11-06-deepspeed-fastgen.md @@ -0,0 +1,7 @@ +--- +title: "DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference" +excerpt: "" +link: https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen +date: 2023-11-06 00:00:00 +tags: inference English +--- diff --git a/docs/index.md b/docs/index.md index a4027ca1e52e..cc2def722b5a 100755 --- a/docs/index.md +++ b/docs/index.md @@ -8,7 +8,7 @@ title: "Latest News" DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; [learn how](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat). * [2023/11] [DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-offloadpp) -* [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) +* [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen)] [[中文](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md)] * [2023/10] [DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md) [[English](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md)] * [2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [[DeepSpeed4Science website](https://deepspeed4science.ai/)] [[Tutorials](https://www.deepspeed.ai/deepspeed4science/)] [[White paper](https://arxiv.org/abs/2310.04610)] [[Blog](https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/)] [[中文](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md)] [[日本語](https://github.com/microsoft/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md)] * [2023/08] [DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading](https://github.com/microsoft/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md)

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zhM$rfzRt1|*-N8SfrEbjv*OQ4{>papf17|W4QJR$odyO}?>S)igaLMEYy z@~6B40}R_gZqEq20D{crZH<0_gP?r%e~M&{7-=a9RO|2{y4d1MIrnbIW95uQ`)`Q6 zP+9%`(*W%*9dE+0p@4Kq>bJU^?Q5JOk`NbnFq^F*8eq?m+gIR4lAhwoI`+@vAkHcwyq53I=WdJE-oy0ufVmo{WDtL69 zEa1HN&ZTZ~V0aTz%<8k1+ZMxhuM~37gvbD-fFjJfd&N4uGe^Jvsb1Y~oo8Rp^P=Q= z%}l+Fx_b1nV}v`8@&19Ct8>6S?>n!LQPvv&Vw3<|XDN-ON+w}2!1;8c>BtmUh1qz51dk{!v~2-}LH$Bt4|~KZW}Lb^+4%_ Date: Tue, 7 Nov 2023 03:22:54 +0800 Subject: [PATCH 3/8] Fix config format (#4594) Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> --- deepspeed/runtime/activation_checkpointing/config.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepspeed/runtime/activation_checkpointing/config.py b/deepspeed/runtime/activation_checkpointing/config.py index 13df4b981298..dc07388a95da 100755 --- a/deepspeed/runtime/activation_checkpointing/config.py +++ b/deepspeed/runtime/activation_checkpointing/config.py @@ -17,7 +17,7 @@ "partitioned_activations": [true|false], "number_checkpoints": 100, "contiguous_memory_optimization": [true|false], - "cpu_checkpointing": [true|false] + "cpu_checkpointing": [true|false], "profile": [true|false], "synchronize_checkpoint_boundary": [true|false], } From b1cb0dfc4623fc44e9931d1481c246bd8a59b778 Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Mon, 6 Nov 2023 14:15:16 -0800 Subject: [PATCH 4/8] Guanhua/partial offload rebase v2 (#590) (#4636) This PR introduces Twin-Flow feature of ZeRO-Offload++, which improves e2e training iteration time by up to 6x on DGX-H100s. This PR includes: * Twin-Flow implementation inside ZeRO optimizer * json config tutorial * example using deepspeed * unit tests cc @jeffra @awan-10 @tjruwase @mrwyattii Co-authored-by: Olatunji Ruwase Co-authored-by: Jeff Rasley --- deepspeed/runtime/engine.py | 4 + deepspeed/runtime/zero/config.py | 9 +- deepspeed/runtime/zero/offload_config.py | 3 + deepspeed/runtime/zero/stage3.py | 60 ++++++-- docs/_pages/config-json.md | 7 + .../partial_offload_test.py | 128 ++++++++++++++++++ tests/unit/ops/adam/test_hybrid_adam.py | 78 +++++++++++ .../unit/runtime/zero/test_zero_offloadpp.py | 75 ++++++++++ 8 files changed, 355 insertions(+), 9 deletions(-) create mode 100644 tests/small_model_debugging/partial_offload_test.py create mode 100644 tests/unit/ops/adam/test_hybrid_adam.py create mode 100644 tests/unit/runtime/zero/test_zero_offloadpp.py diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py index 04611671fe06..3d75022ccd86 100644 --- a/deepspeed/runtime/engine.py +++ b/deepspeed/runtime/engine.py @@ -700,6 +700,9 @@ def zero_cpu_offload(self): return self._config.zero_config.offload_optimizer.device == OffloadDeviceEnum.cpu return False + def zero_partial_offload(self): + return getattr(self._config.zero_config.offload_optimizer, "ratio", 1.0) + def zero_sub_group_size(self): return self._config.zero_config.sub_group_size @@ -1565,6 +1568,7 @@ def _configure_zero_optimizer(self, optimizer): offload_optimizer_config=self.zero_offload_optimizer(), offload_param_config=self.zero_offload_param(), sub_group_size=self.zero_sub_group_size(), + offload_ratio=self.zero_partial_offload(), mpu=self.mpu, postscale_gradients=self.postscale_gradients(), gradient_predivide_factor=self.gradient_predivide_factor(), diff --git a/deepspeed/runtime/zero/config.py b/deepspeed/runtime/zero/config.py index 35d60b5b3290..f16dfd7ac4c0 100644 --- a/deepspeed/runtime/zero/config.py +++ b/deepspeed/runtime/zero/config.py @@ -6,7 +6,7 @@ import sys from typing import Optional from enum import Enum -from deepspeed.pydantic_v1 import Field, validator +from deepspeed.pydantic_v1 import Field, validator, root_validator from deepspeed.runtime.config_utils import get_scalar_param, pp_int, DeepSpeedConfigModel from deepspeed.utils import logger from .offload_config import DeepSpeedZeroOffloadParamConfig, DeepSpeedZeroOffloadOptimizerConfig, OffloadDeviceEnum @@ -300,3 +300,10 @@ def overlap_comm_valid(cls, field_value, values): assert ("stage" in values), "DeepSpeedZeroConfig: 'stage' must be defined before 'overlap_comm'" field_value = values["stage"] == ZeroStageEnum.weights return field_value + + @root_validator + def offload_ratio_check(cls, values): + offload_config = getattr(values, "offload_optimizer", {}) + if offload_config and offload_config.ratio < 1.0: + assert values.get("stage") == ZeroStageEnum.weights, "Partial offloading only supported for ZeRO Stage 3." + return values diff --git a/deepspeed/runtime/zero/offload_config.py b/deepspeed/runtime/zero/offload_config.py index 1bd79412d39f..b7adc13a0ea2 100644 --- a/deepspeed/runtime/zero/offload_config.py +++ b/deepspeed/runtime/zero/offload_config.py @@ -92,3 +92,6 @@ class DeepSpeedZeroOffloadOptimizerConfig(DeepSpeedConfigModel): def set_pipeline(cls, field_value, values): values["pipeline"] = field_value or values.get("pipeline", False) return field_value + + ratio: float = Field(1.0, ge=0.0, le=1.0) + """ Percentage of offloaded optimizer states to CPU Adam. Only valid with ZeRO Stage 3.""" diff --git a/deepspeed/runtime/zero/stage3.py b/deepspeed/runtime/zero/stage3.py index 1030ec3e1120..7fdf4e4d9998 100644 --- a/deepspeed/runtime/zero/stage3.py +++ b/deepspeed/runtime/zero/stage3.py @@ -20,7 +20,7 @@ from deepspeed.runtime.zero.config import ZeroStageEnum from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum from deepspeed.runtime.zero.parameter_offload import DeepSpeedZeRoOffload -from deepspeed.ops.adam import DeepSpeedCPUAdam +from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam from deepspeed.runtime.swap_tensor.partitioned_param_swapper import PartitionedParamStatus from deepspeed.runtime.swap_tensor.partitioned_optimizer_swapper import PartitionedOptimizerSwapper from deepspeed.runtime.swap_tensor.pipelined_optimizer_swapper import PipelinedOptimizerSwapper @@ -104,6 +104,7 @@ def __init__( offload_optimizer_config=None, offload_param_config=None, sub_group_size=1000000000000, + offload_ratio=0.0, mpu=None, clip_grad=0.0, gradient_accumulation_dtype=torch.float32, @@ -159,6 +160,7 @@ def __init__( self.offload_param_pin_memory = False self.params_in_nvme_and_cpu = False self.max_params_in_cpu = 0 + self.partial_offload = offload_ratio #num of ranks in a ZeRO param partitioning group self.zero_hpz_partition_size = zero_hpz_partition_size @@ -191,6 +193,23 @@ def __init__( self.persistent_parameters = self.parameter_offload.persistent_parameters self._configure_offloading(offload_optimizer_config, offload_param_config) + # backup fused_adam optimizer init + if self.offload_optimizer and self.partial_offload != 1.0: + backup_gpu_tensor = torch.randn(1, device='cuda').to(self.dtype) + backup_gpu_param = torch.nn.Parameter(backup_gpu_tensor) + assert type(init_optimizer) == DeepSpeedCPUAdam, 'Hybrid Optimizer Only Supports DeepSpeedCPUAdam' + self.backup_optimizer = FusedAdam([backup_gpu_param], + lr=self.optimizer.param_groups[0]["lr"], + bias_correction=self.optimizer.param_groups[0]["bias_correction"], + betas=self.optimizer.param_groups[0]["betas"], + eps=self.optimizer.param_groups[0]["eps"], + weight_decay=self.optimizer.param_groups[0]["weight_decay"], + amsgrad=self.optimizer.param_groups[0]["amsgrad"]) + # Multiple param_groups configs for back-up optimizer + if len(self.optimizer.param_groups) > 1: + for i in range(1, len(self.optimizer.param_groups)): + self.backup_optimizer.add_param_group(self.optimizer.param_groups[i]) + self.module = module self.elastic_checkpoint = elastic_checkpoint @@ -780,6 +799,17 @@ def _create_fp32_partitions(self): nvme_fp32_dest_tensors = [] fp32_element_size = torch.tensor([], dtype=torch.float32).element_size() + # Assign portion of subgroup to cpu, the other to gpu. + if self.offload_optimizer: + self.subgroup_to_device = {} + sub_group_size = len(self.fp16_partitioned_groups_flat) + # print(f"Partial offload sub_group_size is {sub_group_size}, ratio is {self.partial_offload}\n") + for i in range(sub_group_size): + if i < int(self.partial_offload * sub_group_size): + self.subgroup_to_device[i] = 'cpu' + else: + self.subgroup_to_device[i] = get_accelerator()._name + for i, tensor in enumerate(self.fp16_partitioned_groups_flat): num_elements = self.fp16_partitioned_groups_flat_numel[i] @@ -816,8 +846,12 @@ def _create_fp32_partitions(self): self._swap_in_sub_group_to_flat_buffer(unpinned_fp32_buffer, i) self.fp32_partitioned_groups_flat.append(unpinned_fp32_buffer) else: - self.fp32_partitioned_groups_flat.append(self.fp16_partitioned_groups_flat[i].to( - self.device).clone().float().detach()) + if self.offload_optimizer: + self.fp32_partitioned_groups_flat.append(self.fp16_partitioned_groups_flat[i].to( + self.subgroup_to_device[i]).clone().float().detach()) + else: + self.fp32_partitioned_groups_flat.append(self.fp16_partitioned_groups_flat[i].to( + self.device).clone().float().detach()) self.fp32_partitioned_groups_flat[i].requires_grad = True # keep this in case internal optimizer uses it @@ -885,10 +919,20 @@ def _release_ipg_buffers(self): def _optimizer_step(self, sub_group_id): param_group_id = self.sub_group_to_group_id[sub_group_id] fp32_param = self.fp32_partitioned_groups_flat[sub_group_id] - self.optimizer.param_groups[param_group_id]['params'] = [fp32_param] - - self.optimizer.step() - self.optimizer.param_groups[param_group_id]['params'] = [] + if self.offload_optimizer: + cur_device = self.subgroup_to_device[sub_group_id] + if cur_device == 'cpu': + self.optimizer.param_groups[param_group_id]['params'] = [fp32_param] + cpu_loss = self.optimizer.step() + self.optimizer.param_groups[param_group_id]['params'] = [] + else: + self.backup_optimizer.param_groups[param_group_id]['params'] = [fp32_param] + gpu_loss = self.backup_optimizer.step() + self.backup_optimizer.param_groups[param_group_id]['params'] = [] + else: + self.optimizer.param_groups[param_group_id]['params'] = [fp32_param] + self.optimizer.step() + self.optimizer.param_groups[param_group_id]['params'] = [] def _swappable_optimizer_subgroup(self, sub_group_id): if not self.swap_optimizer: @@ -955,7 +999,7 @@ def initialize_optimizer_states(self): if self.offload_optimizer_pin_memory: subgroup_gradient_buffer = get_accelerator().pin_memory(subgroup_gradient_buffer) - self.fp32_partitioned_groups_flat[i].grad = subgroup_gradient_buffer + self.fp32_partitioned_groups_flat[i].grad = subgroup_gradient_buffer.to(self.subgroup_to_device[i]) else: self.fp32_partitioned_groups_flat[i].grad = gradient_buffer.narrow(0, 0, num_elements) diff --git a/docs/_pages/config-json.md b/docs/_pages/config-json.md index cdd18e62a29e..e9d7166b05b3 100755 --- a/docs/_pages/config-json.md +++ b/docs/_pages/config-json.md @@ -576,6 +576,7 @@ Note that if the value of "device" is not specified or not supported, an asserti "device": "[cpu|nvme]", "nvme_path": "/local_nvme", "pin_memory": [true|false], + "ratio": 0.3, "buffer_count": 4, "fast_init": false } @@ -598,6 +599,12 @@ Note that if the value of "device" is not specified or not supported, an asserti | ---------------------------------------------------------------------------------------------------- | ------- | | Offload to page-locked CPU memory. This could boost throughput at the cost of extra memory overhead. | `false` | +***ratio***: [float] + +| Description | Default | +| ------------------------------------------------------------------- | ------- | +| the ratio of parameters updating (i.e. optimizer step) on CPU side. | 1 | + ***buffer_count***: [integer] | Description | Default | diff --git a/tests/small_model_debugging/partial_offload_test.py b/tests/small_model_debugging/partial_offload_test.py new file mode 100644 index 000000000000..2094448d534d --- /dev/null +++ b/tests/small_model_debugging/partial_offload_test.py @@ -0,0 +1,128 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import os +import json +import argparse +import torch +import deepspeed +from torch.utils.data.distributed import DistributedSampler +import deepspeed.comm as dist + + +class SimpleModel(torch.nn.Module): + + def __init__(self, hidden_dim, empty_grad=False): + super(SimpleModel, self).__init__() + self.linear = torch.nn.Linear(hidden_dim, hidden_dim) + self.linear2 = torch.nn.Linear(hidden_dim, hidden_dim) + self.linear3 = torch.nn.Linear(hidden_dim, hidden_dim) + self.linear4 = torch.nn.Linear(hidden_dim, hidden_dim) + if empty_grad: + self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hidden_dim, hidden_dim)]) + self.cross_entropy_loss = torch.nn.CrossEntropyLoss() + + def forward(self, x, y): + hidden = x + hidden = self.linear(hidden) + hidden = self.linear2(hidden) + hidden = self.linear3(hidden) + hidden = self.linear4(hidden) + return self.cross_entropy_loss(hidden, y) + + +def create_config_from_dict(tmpdir, config_dict): + config_path = os.path.join(tmpdir, 'temp_config.json') + with open(config_path, 'w') as fd: + json.dump(config_dict, fd) + return config_path + + +def get_data_loader(model, total_samples, hidden_dim, device): + batch_size = model.train_micro_batch_size_per_gpu() + train_data = torch.randn(total_samples, hidden_dim, device=device, dtype=torch.half) + train_label = torch.empty(total_samples, dtype=torch.long, device=device).random_(hidden_dim) + train_dataset = torch.utils.data.TensorDataset(train_data, train_label) + sampler = DistributedSampler(train_dataset) + train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=sampler) + return train_loader + + +def get_args(tmpdir, config_dict): + parser = argparse.ArgumentParser() + parser.add_argument("--local_rank", type=int, default=0) + parser.add_argument('--zero', type=int, default=0) + args = parser.parse_args() #args='' + + config_dict["zero_optimization"]["stage"] = args.zero + print('config_dict["zero_optimization"]', config_dict["zero_optimization"]) + config_path = create_config_from_dict(tmpdir, config_dict) + + args.deepspeed_config = config_path + return args + + +def print0(msg): + if dist.get_rank() == 0: + print(msg, flush=True) + + +rank = int(os.environ['RANK']) +print('seed:', 2222 + rank) +torch.random.manual_seed(2222 + rank) + +config_dict = { + "train_batch_size": 256, + "steps_per_print": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00015, + } + }, + "fp16": { + "enabled": True, + "initial_scale_power": 15 + }, + "zero_optimization": { + "stage": 0, + "sub_group_size": 8, + "reduce_bucket_size": 20, + "offload_optimizer": { + "device": "cpu", + "pin_memory": True, + "ratio": 0.3 + } + } +} +# "initial_scale_power": 15 +args = get_args('/tmp/', config_dict) +hidden_dim = 4 * 1024 + +model = SimpleModel(hidden_dim, empty_grad=False) + +model, _, _, _ = deepspeed.initialize(args=args, + model=model, + model_parameters=model.parameters(), + dist_init_required=True) + + +def print_params(tag, model): + if dist.get_rank() == 0: + for n, p in model.named_parameters(): + print0("{} {}:{}".format(tag, n, p)) + + +data_loader = get_data_loader(model=model, total_samples=1000, hidden_dim=hidden_dim, device=model.device) +#print_params('pre-train', model) +#while True: +for n, batch in enumerate(data_loader): + loss = model(batch[0], batch[1]) + if dist.get_rank() == 0: + print("LOSS:", loss.item()) + model.backward(loss) + model.step() + #print_params('step={}'.format(n), model) + if n == 2: break diff --git a/tests/unit/ops/adam/test_hybrid_adam.py b/tests/unit/ops/adam/test_hybrid_adam.py new file mode 100644 index 000000000000..c7ef4890b322 --- /dev/null +++ b/tests/unit/ops/adam/test_hybrid_adam.py @@ -0,0 +1,78 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +import torch +import numpy as np +import pytest + +from cpuinfo import get_cpu_info + +import deepspeed +from deepspeed.accelerator import get_accelerator +from deepspeed.ops.adam import FusedAdam, DeepSpeedCPUAdam +from deepspeed.ops.op_builder import CPUAdamBuilder +from unit.common import DistributedTest + +if not deepspeed.ops.__compatible_ops__[CPUAdamBuilder.NAME]: + pytest.skip("hybrid-adam is not compatible", allow_module_level=True) + +pytest.cpu_vendor = get_cpu_info()["vendor_id_raw"].lower() + + +def check_equal(first, second, atol=1e-2, verbose=False): + x = first.detach().numpy() + y = second.detach().numpy() + print("ATOL", atol) + if verbose: + print("x = {}".format(x.flatten())) + print("y = {}".format(y.flatten())) + print('-' * 80) + np.testing.assert_allclose(x, y, err_msg="param-update mismatch!", atol=atol) + + +@pytest.mark.parametrize('dtype', [torch.half, torch.float], ids=["fp16", "fp32"]) +@pytest.mark.parametrize('model_size', [8, 16]) +class TestHybridAdam(DistributedTest): + world_size = 1 + reuse_dist_env = True + requires_cuda_env = False + if not get_accelerator().is_available(): + init_distributed = False + set_dist_env = False + + @pytest.mark.skipif(not get_accelerator().is_available(), reason="only supported in CUDA environments.") + def test_hybrid_adam_equal(self, dtype, model_size): + if ("amd" in pytest.cpu_vendor) and (dtype == torch.half): + pytest.skip("cpu-adam with half precision not supported on AMD CPUs") + + ref_data = torch.randn(model_size).to(dtype) + total_data = ref_data.clone().detach() + + ref_param = torch.nn.Parameter(ref_data) + ref_optimizer = DeepSpeedCPUAdam([ref_param]) + + cpu_data, cuda_data = total_data.chunk(2) + cpu_param = torch.nn.Parameter(cpu_data) + cuda_param = torch.nn.Parameter(cuda_data.to(get_accelerator().device_name())) + + cpu_optimizer = DeepSpeedCPUAdam([cpu_param]) + cuda_optimizer = FusedAdam([cuda_param]) + + ref_grad = torch.randn(model_size).to(dtype) + cpu_grad, cuda_grad = ref_grad.clone().detach().chunk(2) + + ref_param.grad = ref_grad + cpu_param.grad = cpu_grad + cuda_param.grad = cuda_grad.to(get_accelerator().device_name()) + + ref_optimizer.step() + cpu_optimizer.step() + cuda_optimizer.step() + + cuda_param_copy = cuda_param.cpu() + + total_param = torch.cat((cpu_param, cuda_param_copy)) + + check_equal(ref_param, total_param) diff --git a/tests/unit/runtime/zero/test_zero_offloadpp.py b/tests/unit/runtime/zero/test_zero_offloadpp.py new file mode 100644 index 000000000000..c376686f8052 --- /dev/null +++ b/tests/unit/runtime/zero/test_zero_offloadpp.py @@ -0,0 +1,75 @@ +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team +import pytest +import deepspeed.comm as dist +from unit.common import DistributedTest +from unit.simple_model import random_dataloader + +import deepspeed + +from deepspeed.runtime.zero.offload_config import DeepSpeedZeroOffloadOptimizerConfig + +import torch.nn as nn + + +class NNModel(nn.Module): + + def __init__(self, h_dim=1024, n_layers=2): + super(NNModel, self).__init__() + self.layers = nn.ModuleList([nn.Linear(h_dim, h_dim) for i in range(n_layers)]) + self.cross_entropy_loss = nn.CrossEntropyLoss() + + def forward(self, x, y): + for layer in self.layers: + x = layer(x) + return self.cross_entropy_loss(x, y) + + +def test_zero_partial_offload_config(): + config = DeepSpeedZeroOffloadOptimizerConfig(**{"ratio": 0.3}) + assert config.ratio == 0.3 + + +#Large sweep along hidden dim, num_layers of different sizes +@pytest.mark.parametrize("h_dim", [1024]) +@pytest.mark.parametrize("n_layers", [4, 8]) +class TestZeroPartialOffloadConfigSweep(DistributedTest): + world_size = 4 + + def test(self, h_dim: int, n_layers: int) -> None: + config_dict = { + "train_batch_size": 256, + "steps_per_print": 1, + "optimizer": { + "type": "Adam", + "params": { + "lr": 0.00015, + } + }, + "fp16": { + "enabled": True, + "initial_scale_power": 15 + }, + "zero_optimization": { + "stage": 3, + "sub_group_size": 8, + "reduce_bucket_size": 20, + "offload_optimizer": { + "device": "cpu", + "pin_memory": True, + "ratio": 0.3 + } + } + } + + model = NNModel(h_dim, n_layers) + model, _, _, _ = deepspeed.initialize(model=model, model_parameters=model.parameters(), config=config_dict) + data_loader = random_dataloader(model=model, total_samples=20, hidden_dim=h_dim, device=model.device) + dist.barrier() + + for n, batch in enumerate(data_loader): + loss = model(batch[0], batch[1]) + model.backward(loss) + model.step() From 7e480ea02fa1837aad0e9d3eb8056da3055a392c Mon Sep 17 00:00:00 2001 From: Guanhua Wang Date: Mon, 6 Nov 2023 14:17:15 -0800 Subject: [PATCH 5/8] offload++ blog (#623) (#4637) This PR is the blog for ZeRO-Offload++, it describes the details of how our new Twin-Flow feature works and its performance numbers on both DGX-A100 and DGX-H100 machines. Corresponding code PR is https://github.com/microsoft/DeepSpeed/pull/4636 cc @jeffra @awan-10 @tjruwase @mrwyattii --------- Co-authored-by: Jeff Rasley --- blogs/deepspeed-offloadpp/README.md | 52 ++++++++++++++++++ blogs/deepspeed-offloadpp/images/a100-8.png | Bin 0 -> 22879 bytes .../images/cpu-offload.png | Bin 0 -> 42294 bytes blogs/deepspeed-offloadpp/images/h100-8.png | Bin 0 -> 20621 bytes .../images/twin-offload.png | Bin 0 -> 59949 bytes 5 files changed, 52 insertions(+) create mode 100644 blogs/deepspeed-offloadpp/README.md create mode 100644 blogs/deepspeed-offloadpp/images/a100-8.png create mode 100644 blogs/deepspeed-offloadpp/images/cpu-offload.png create mode 100644 blogs/deepspeed-offloadpp/images/h100-8.png create mode 100644 blogs/deepspeed-offloadpp/images/twin-offload.png diff --git a/blogs/deepspeed-offloadpp/README.md b/blogs/deepspeed-offloadpp/README.md new file mode 100644 index 000000000000..22910ffef7b1 --- /dev/null +++ b/blogs/deepspeed-offloadpp/README.md @@ -0,0 +1,52 @@ +# DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow + +Deep learning has been successfully adopted in a wide range of applications such as speech recognition, chatbot, text and image generation, etc. To achieve better model serving accuracy, model size grows significantly. Take language models as example, from BERT with 110 million parameters to Megatron-Turing NLG with 530 billion parameters, the model size grows almost 5000x. Given limited GPU memory size, we need to efficiently utilize GPU memory to achieve good system throughput. + +ZeRO offers memory efficient data parallel training scheme. For training large models like LLMs using ZeRO, GPU memory size is still often insufficient to hold all the model parameters. Thus, ZeRO-Offload is introduced to solve this insufficient GPU memory issue. ZeRO-Offload releases GPU memory pressure by offloading data and compute to the CPU side while minimizing CPU-GPU data copy overhead. Given CPU memory is often orders-of-magnitude larger than GPU memory, ZeRO-Offload was the first piece of work that enables billion-level parameter training even with very limited GPU memory resources (e.g., to an extreme: single GPU). ZeRO-Offload provides excellent performance when model size is multiple times larger than total GPU memory size. + +However, system efficiency is still far from optimal when adopting ZeRO-Offload in some scenarios. Especially in the cases like small batch training, model that could not fit into GPU memory but not orders-of-magnitude bigger than GPU memory capacity, CPU offload not only introduce long end-to-end latency, but also underutilize GPU computation resources. To reduce memory copy latency as well as inefficient utilization of GPU introduced in these offload cases, we propose ZeRO-Offload++, which leverages both CPU and GPU coherently. ZeRO-Offload++ mainly includes 3 new features as _Twin-Flow_, MemCpy reduction, CPUAdam optimization. Now we release our __Twin-Flow__ feature. + +The key benefits are: +* With _Twin-Flow_, ZeRO-Offload++ achieves up to **6x** training speedup compared with ZeRO-Offload. +* High-level API provided in DeepSpeed config JSON makes it easy to use and fine-tune. + +![h100-img](./images/h100-8.png) + +## Twin-Flow + +In DeepSpeed, when training using popular optimizer like Adam, optimizer offloading follows an all-or-nothing policy. For simplifed example shown as Figure below, without offloading, all the parameters will be updated using GPU adam as FusedAdam optimizer. On the other hand, if offloading is enabled, all model weights use CPUAdam to update. + +![cpu-offload-img](./images/cpu-offload.png) + +The major downside of this all-or-nothing offloading is, when offload all optimizer states to CPU side, both GPU memory and compute resources remain under-utilized. Although increasing batch size improves GPU utilization rate, each training iteration time is still super long compared with no-offloading case. To improve GPU compute and memory utilization rate as well as decrease training iteration time, we introduce a new feature in our DeepSpeed training engine called _Twin-Flow_. + +In comparison, _Twin-Flow_ allows a portion of optimizer states to be held in CPU memory and the other portion of optimizer states remaining in GPU memory. When optimization step is triggered, both CPU and GPU can do parameter updates simultaneously. Once offloading is enabled, we provide an offload ratio configuration which allows users to adjust how many percentages of model weights are updated on CPU side and the rest are happened on GPU side. "_Twin_" comes from the idea that both CPU and GPU are using the same optimizer function here. "_Flow_" means parameters are not only hold in both host and device memory, but also computed using both CPU and GPU cores. + +As shown in Figure below, with ZeRO-Offload enabled and we set _Twin-Flow_ ratio of 0.4 (40%). DeepSpeed Training engine will automatically assign first 40% (i.e. 0-40%) of weights step procedure on the CPU side using CPUAdam, and use GPU side FusedAdam to update the rest 60% (i.e., 40-100%) model parameters jointly. Therefore, with _Twin-Flow_, we can achieve decent GPU memory and core utilization rate, at the same time reduce training iteation time in optimizer offloading cases. + +![_Twin-Flow_-img](./images/twin-offload.png) + +Note that this _Twin-Flow_ ratio can be adjusted based on how much GPU idle memory is available. The smaller this ratio is, the more GPU memory and cores are used and the shorter training iteration time it achieves. The ideal case is to be as near as GPU memory upper bound in order to minimize training iteration time. +Note that _Twin-Flow_ is not limited to Adam optimizer only, it can be applied to any optimizer (e.g., AdaGrad) from the user side. + +## Performance Evaluation + +We conduct our performance evaluations over both A100 and H100 DGX machine and test for OPT model with 13B and 30B parameters. We run 13B OPT model training on a 8 A100 DGX machine, and run OPT-30B model training using a 8 H100 DGX machine. With some tuning on offload ratio in ZeRO-Offload++, we achieve 6x and 3x training speedup of Meta OPT models on single DGX-H100-80GB and DGX-A100-40GB, respectively (top-most figure and bottom figure here). + +![a100-img](./images/a100-8.png) + +## On-going Optimizations + +* Reduce uncessary D2H/H2D memcpy + +* On-the-fly fp16 to fp32 casting for CPUAdam + +## Tutorials + +Examples and Tutorials are [here](https://github.com/microsoft/Megatron-DeepSpeed/blob/guanhua/partial-offload/examples_deepspeed/offload_pp/README.md) + +## Contributors: + +This project was made possible by the contributions of the following people from DeepSpeed Team: + +[Guanhua Wang](https://www.microsoft.com/en-us/research/people/guanhuawang/), Masahiro Tanaka, Xiaoxia Wu, Lok Chand Koppaka, Samyam Rajbhandari, [Olatunji Ruwase](https://www.microsoft.com/en-us/research/people/olruwase/), [Yuxiong He](https://www.microsoft.com/en-us/research/people/yuxhe/) (team lead) diff --git 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