From 0b0fef3d41feeb550845ef4349b36c7f2dd7e840 Mon Sep 17 00:00:00 2001 From: Sam Ade Jacobs Date: Thu, 5 Dec 2024 08:39:44 -0800 Subject: [PATCH] Ulyssess offload blog (#6814) Ulysses-Offload (FPDT) blog, please see corresponding tutorial page at [link](https://github.com/microsoft/DeepSpeed/pull/6813). --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Logan Adams --- blogs/ulysses-offload/README.md | 263 +++++++++++++++++++++++++ blogs/ulysses-offload/media/image1.png | Bin 0 -> 1314185 bytes blogs/ulysses-offload/media/image2.png | Bin 0 -> 134983 bytes blogs/ulysses-offload/media/image3.png | Bin 0 -> 187041 bytes blogs/ulysses-offload/media/image4.png | Bin 0 -> 319690 bytes blogs/ulysses-offload/media/image5.png | Bin 0 -> 91541 bytes 6 files changed, 263 insertions(+) create mode 100644 blogs/ulysses-offload/README.md create mode 100644 blogs/ulysses-offload/media/image1.png create mode 100644 blogs/ulysses-offload/media/image2.png create mode 100644 blogs/ulysses-offload/media/image3.png create mode 100644 blogs/ulysses-offload/media/image4.png create mode 100644 blogs/ulysses-offload/media/image5.png diff --git a/blogs/ulysses-offload/README.md b/blogs/ulysses-offload/README.md new file mode 100644 index 000000000000..740c07229d9f --- /dev/null +++ b/blogs/ulysses-offload/README.md @@ -0,0 +1,263 @@ +# Ulysses-Offload: Democratizing Long Context LLM Training + + + +Figure 1: Ulysses-Offload supports 16x longer sequence lengths at 55% +Model FLOPs Utilization (MFU) than NVIDIA Megatron-SP and DeepSpeed Ulysses. + + +To cite and for more technical in depth of this release, please see +our [arxiv report](https://arxiv.org/abs/2408.16978): + +@article{yao2024ulysses, + +title={ Training Ultra Long Context Language Model with Fully Pipelined +Distributed Transformer}, + +author={Jinghan Yao and Sam Ade Jacobs and Masahiro Tanaka and Olatunji +Ruwase and Aamir Shafi and Hari Subramoni and Dhabaleswar K. (DK) Panda +}, + +journal={https://arxiv.org/abs/2408.16978}, + +year={2024} + +} + +## Introduction + +In the rapidly evolving field of generative AI and scientific ML, the +ability to train large (language) models with ultra-long context +capabilities is becoming increasingly important. These models are +essential for a variety of complex tasks, such as understanding +lengthy documents, generating images and videos, and processing extensive +sequences in computational biology. However, training such models +efficiently poses significant challenges due to the enormous GPU +memory required. + +Building DeepSpeed Ulysses, our previous project, which developed +system optimizations for training extremely long sequence transformer +models, we are excited to present Ulysses-Offload, in this release. Ulysses-Offload +is an innovative, resource-efficient technique that offers comparable +benefits to DeepSpeed Ulysses and other previous long-context +optimization methods, but with a lower hardware budget. Ulysses-Offload makes +ultra long-context large language models (LLM) training and finetuning +accessible to everyone, including those with limited GPU resources. Ulysses-Offload enables +training with context lengths of up to 2 million tokens using just 4 +NVIDIA A100-40GB GPUs. Ulysses-Offload supports 16x longer sequence lengths at 55% +Model FLOPs Utilization (MFU) than NVIDIA Megatron-SP and DeepSpeed Ulysses +(see Figure 1). The next section highlights the key innovations of Ulysses-Offload, +and subsequent sections provide additional details on the design and +usability of Ulysses-Offload, followed by experimental results. + +## Key Innovations + +### 1. Fully Pipelined Distributed Transformer (FPDT) + +The core innovation of our work is the Fully Pipelined Distributed +Transformer (FPDT). This approach leverages a pipelined sequence +chunking, which allows for the training of LLMs with sequence lengths up +to 2 million tokens on just 4 A100-40GB GPUs. By breaking down the +sequence into manageable chunks and processing them in a pipelined +manner, Ulysses-Offload significantly reduces the memory footprint while +maintaining high computational efficiency. This method ensures that the +GPUs are utilized effectively, even when dealing with extremely long +sequences. + +### 2. Memory Optimization + +One of the critical aspects of our approach is the comprehensive +analysis and optimization of the memory footprint during LLM training. +We target the reduction of redundant intermediate buffers in both the +forward and backward passes of the training process. By optimizing the +use of GPU and host CPU memory, we can train larger models with longer +sequences without running into GPU memory limitations. This optimization +is crucial for enabling the training of ultra-long context models on a +limited number of GPUs. It is worth noting that Ulysses-Offload memory optimization +is orthogonal and complementary to model- parameter-focused memory +optimization techniques used by DeepSpeed ZeRO and PyTorch FSDP. Ulysses-Offload optimizes memory footprint of activations associated with long sequences while ZeRO and FSDP optimize memory footprint of model parameters. + +### 3. Compatibility and Flexibility + +Ulysses-Offload is designed to be agnostic to existing training techniques and +works efficiently across different LLM models, including popular +architecture like GPT and Llama. This flexibility ensures that our +approach can be easily integrated into various training workflows. +Additionally, Ulysses-Offload is compatible with advanced memory optimization +techniques such as DeepSpeed ZeRO and PyTorch FSDP, further enhancing +its usability and performance. + +## Core Design of Ulysses-Offload + +Figure 2 illustrates the core structure of Ulysses-Offload. Ulysses-Offload leverages multiple +memory hierarchies in modern GPU clusters, thus boosting hardware +efficiency and cost-effectiveness while achieving very high model FLOP +utilization (MFU). The design of Ulysses-Offload centers around pipelining, +scheduling, and memory management. These well-known optimization +techniques are essential for scaling LLM context length to a million +scale with a few GPUs and will be discussed in the subsequent +subsections. + + + +Figure 2: Core design + +### + +### Pipelining and Scheduling + +Ulysses-Offload employs sequence chunking and pipelined computation design to manage the memory +and computational load efficiently. In traditional Transformer model, +input (hidden state) tensor is projected to q, k, v tensors. Each of these tensors can be denoted *\[B, S, H, D\]*, where *B* is batch +size, *S* is sequence length, *H* is number of heads and *D* is hidden +dimension per head. With sequence parallelism such as DeepSpeed Ulysses, +input tensor is partitioned along sequence dimension across sequence +parallel group P, that is *\[B, S/P, H, D\]* prior to alltoall collective +communication. The alltoall collective communication gathers partitioned tensors +along sequence dimension and scatter them along head dimension essentially +transforming tensor from *\[B, S/P, H, D\]* to *\[B, S, H/P, D\]*. Post attention computation, a second alltoall communication transforms *\[B, S, H/P, D\]* back to *\[B, S/P, H, D\]* + +In our Ulysses-Offload design, input sequence are partitioned at a much finer granularity than DeepSpeed Ulysses. In other words, we made changes to sequence partitioning such that we further subdivide per GPU *S/P* sequence into smaller *u* +chunks. Thus, the input tensors are now represented as \[*B, S/uP, H, +D*\]. We denote these chunks as *Ti*, +where$\ i\ \in \ 0,1,\ldots,\ u - 1.$ As shown in Figure 1, +*Ti* is projected to query *qi*, key +*ki*, and value *vi*. Then, similar to DeepSpeed Ulysses, an alltoall collective communication gathers partitioned tensor +along sequence dimension and scatter them along head dimension. In our chunk +design, the sequence length for each chunk is reduced by a factor of *u* +compared to Ulysses. Please note that our Ulysses-Offload chunking procedure is generally applicable to other sequence parallelism techniques. + + + +Figure 3: Core design with offload description + +Figure 3 gives an example of how to perform the computation of chunk +*Tm*. After the alltoall collective communication, +*GPUj* receives +$\widehat{q}m,\ \widehat{k}m,\ and\ \widehat{v}m$*.* We then fetch the +previous sequence chunk by chunk from the host memory to +GPUj, and perform online attention with the current +$\widehat{q}m$ and update the output chunk accordingly. Note that, in a +strict manner, at any given time, only one set of chunks +$\widehat{k}i,\ and\ \widehat{v}i$ is placed on GPU's HBM, reducing the +memory footprint to $\frac{1}{u}$ compared to the non-offloading version +without double buffering. With double buffering, memory footprint is +reduced by *2/u*. + +### Memory Management + +Ulysses-Offload optimizes memory usage by carefully managing the allocation and +deallocation of buffers during training. This involves: + +1. Double Buffering: + + - Two sets of buffers are maintained to overlap computation with + data transfer. + + - While one set of buffers is used for computation, the other set is + preloaded with the next chunk of data. + +2. Hierarchical Memory Utilization: + + - GPU High Bandwidth Memory (HBM) is used for active computation. + + - Host memory is used to store intermediate results that are not + immediately needed, reducing the pressure on GPU memory. + +## Integration with Existing Frameworks + +Ulysses-Offload is designed to integrate seamlessly with popular deep learning +frameworks such as PyTorch. Ulysses-Offload provides user-friendly APIs that +abstract the complexities of pipelined training and memory management. +Users can adopt Ulysses-Offload with minimal changes to existing codebases. + +## Experimental Results + + + +Figure 4: Supported sequence lengths and corresponding Model FLOPs +Utilization (MFU) using Megatron-SP, Ulysses, and our proposed Ulysses-Offload (FPDT). OOM +denotes the point where increasing sequence length will cause memory +issues. We show Ulysses-Offload's performance when the sequence length is larger +than 128K, as shorter sequences can be properly handled by existing +strategies. + +### Extended Sequence Lengths + +In our experimental setup, we compare Ulysses-Offload with two existing methods: +Microsoft DeepSpeed Ulysses and NVIDIA Megatron-SP. Both DeepSpeed +Ulysses and Megatron-SP employ similar approaches to sequence +parallelism but differ in the collective communication used for +gathering sequences before the attention block. The former utilizes +alltoall communication, whereas the latter employs allgather. Ulysses-Offload +builds upon the DeepSpeed Ulysses approach. The primary advantage of +Ulysses-Offload is its capability to support the training of large language models +(LLMs) with ultra-long sequence lengths using fewer GPUs. As shown in +Figure 4, our method enables the training of 8B parameter models with +sequence lengths of 2 million tokens using only 4 GPUs. For even larger +models, such as GPT-30B and Llama-70B parameter models, Ulysses-Offload supports +sequence lengths up to 3 million and 4 million tokens using 16 GPUs and +32 GPUs respectively. This represents a 16x increase in sequence length +compared to current state-of-the-art solutions (see Figure 5), making +Ulysses-Offload a game-changer for tasks that require processing long sequences. + +### High Hardware Efficiency + +As shown in Figure 4 with different model sizes ranging from GPT-2.7B to +Llama-80B parameters, Ulysses-Offload achieves over 55% Model FLOPs Utilization +(MFU), ensuring that the hardware resources are utilized effectively. +This high level of efficiency is maintained even when dealing with +extremely long sequences (up to 4 million context length), making Ulysses-Offload +an ideal solution for training large-scale LLMs. By maximizing the use +of available hardware, Ulysses-Offload reduces the overall cost and complexity of +training long-context models. Our [technical report](https://arxiv.org/abs/2408.16978) offers +further insights into optimizing sequence chunks to balance the +trade-off between memory usage and MFU. + + + +Figure 5: A comprehensive analysis on long-context LLM training with +different training techniques: tensor parallelism (TP), activation +checkpoint (AC), activation checkpoint with CPU offloading (OC), Ulysses +(UL), and our approach Ulysses-Offload (FPDT). + +## Implementation and Usability + +Ulysses-Offload is designed to be easily integrated with popular deep learning +frameworks such as DeepSpeed, Megatron-DeepSpeed and PyTorch. Users can +adopt our approach with minimal changes to their existing training +pipeline, making it accessible to a broad audience. The integration +process involves setting up the sequence chunk pipeline and configuring +the memory optimization techniques, both of which are straightforward +and well-documented (see tutorial). + +Our pipeline design and memory optimization techniques are +straightforward to implement, making Ulysses-Offload accessible to researchers and +practitioners aiming to train long-context LLMs efficiently. We provide +detailed [technical report](https://arxiv.org/abs/2408.16978), +documentation and examples to guide users through the setup process, +ensuring a smooth transition to using Ulysses-Offload. Additionally, Ulysses-Offload, in the +tradition of DeepSpeed provides user-friendly API which abstracts the +complexities of mixed precision training and memory optimization, +allowing users to focus on their research and development tasks. + +## General Availability of DeepSpeed Ulysses-Offload + +We are excited to release Ulysses-Offload. Ulysses-Offload has been +fully integrated with Megatron-DeepSpeed and accessible through both +DeepSpeed and Megatron-DeepSpeed GitHub repos. Click here for detailed +[tutorial](https://www.deepspeed.ai/tutorials/fpdt/) on usage. + +We invite the community to explore our implementation, contribute to +further advancements, and join us in pushing the boundaries of what is +possible in LLM and AI. This release is part of the bigger DeepSpeed +ecosystem of large-scale AI training, finetuning and inference. For more +details on all DeepSpeed technologies and innovations, please visit our +[website]((https://www.deepspeed.ai/)) and follow us +on X, formerly Twitter, ([English](https://twitter.com/MSFTDeepSpeed), +[Japanese](https://twitter.com/MSFTDeepSpeedJP)) and +[Chinese Zhihu](https://www.zhihu.com/people/deepspeed). diff --git a/blogs/ulysses-offload/media/image1.png b/blogs/ulysses-offload/media/image1.png new file mode 100644 index 0000000000000000000000000000000000000000..df63ef8197633bc1b1055af3b905793538a7be7a GIT binary patch literal 1314185 zcmeFa$+Dy97A|%?adRW?eE^52Cwx6P74*%-5g-HtL?aN5M4mz7zrtjt|o2+{oi`q%o_x4wn`tEo%>@jv{}|KT71_{V>g zSAzAAfBdKa>mUF4AO6e#_}{~p|M!3YU;j_|-+yo@&_y6<1{I`GnPyfe%>z96C zPRF!}|M5?J@$%b0F(j$a{XcQxpBNT2%}-GpoVuVq|5U|2Y=ZZLQ(QfE(CS-S6v00+ zk^BjPxBj!2R#82k;a7bNKTrfV<2?KZZ&1X`uM{IVH;*>b46DDGZ1!98IA#Fzg>IM{Xmi!UDc4=<_!&V3&j6Ncbl z=*!2`=w+L2t0sC&C+4Ei$;2W@Ca*s}+Vhy@By~ZPXmu19aNuZqIRr`~H1TrO==!n= 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