FP6 blog (#5235)

Co-authored-by: Zhen Zheng
[[email protected]](mailto:[email protected])
Co-authored-by: Xiaoxia Wu
[[email protected]](mailto:[email protected])
Co-authored-by: Haojun Xia
[[email protected]](mailto:[email protected])
Co-authored-by: Olatunji Ruwase
[[email protected]](mailto:[email protected])
Co-authored-by: Leon Song
[[email protected]](mailto:[email protected])

---------

Co-authored-by: xiaoxiawu-microsoft <[email protected]>
Co-authored-by: Arash Bakhtiari <[email protected]>
Co-authored-by: Xiaoxia (Shirley) Wu <[email protected]>
Co-authored-by: ZHENG, Zhen <[email protected]>
Co-authored-by: Michael Wyatt <[email protected]>

README.md

@@ -15,6 +15,7 @@
 ## Latest News
 <b> <span style="color:orange" > 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)</span>.</b>
 
+* [2024/03] [DeepSpeed-FP6:The power of FP6-Centric Serving for Large Language Models](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024)
 * [2024/01] [DeepSpeed-FastGen: Introducting Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19)
 * [2023/11] [Llama 2 Inference on 4th Gen Intel® Xeon® Scalable Processor with DeepSpeed](https://github.com/microsoft/DeepSpeed/tree/master/blogs/intel-inference) [[Intel version]](https://www.intel.com/content/www/us/en/developer/articles/technical/xllama-2-on-xeon-scalable-processor-with-deepspeed.html)
 * [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)
@@ -254,6 +255,11 @@ Conduct](https://opensource.microsoft.com/codeofconduct/). For more information
 28. Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [arXiv:2310.04610](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/)
 29. Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers [arXiv:2310.17723](https://arxiv.org/abs/2310.17723)
 
+30. Xiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng, Shiyang Chen, Arash Bakhtiari, Michael Wyatt, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase, Leon Song, Zhewei Yao (2023) ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks [arXiv:2312.08583](https://arxiv.org/abs/2312.08583)
+
+31. Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song. (2024) FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design  [arXiv:2401.14112](https://arxiv.org/abs/2401.14112)
+
+
 
 # Videos
 1. DeepSpeed KDD 2020 Tutorial

blogs/deepspeed-fp6/03-05-2024/README.md

@@ -0,0 +1,147 @@
+<div align="center">
+
+# DeepSpeed-FP6: The Power of FP6-Centric Serving for Large Language Models
+
+</div>
+
+<div align="center">
+
+<img src="./assets/hero-figure.png" width="1000px" alt="DeepSpeed-VisualChat!"/>
+
+</div>
+
+
+To cite DeepSpeed-FP6, please cite the following two arxiv reports - ZeroQuant(4+2) and FP6-LLM:
+
+```
+@article{wu2023zeroquant,
+  title={Zeroquant(4+2): Redefining llms quantization with a new fp6-centric strategy for diverse generative tasks},
+  author={Wu, Xiaoxia and Xia, Haojun and Youn, Stephen and Zheng, Zhen and Chen, Shiyang and Bakhtiari, Arash and Wyatt, Michael and Aminabadi, Reza Yazdani and He, Yuxiong and Ruwase, Olatunji and Song, Leon and others},
+  journal={arXiv preprint arXiv:2312.08583},
+  year={2023}
+}
+
+@article{xia2024fp6,
+  title={FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design},
+  author={Xia, Haojun and Zheng, Zhen and Wu, Xiaoxia and Chen, Shiyang and Yao, Zhewei and Youn, Stephen and Bakhtiari, Arash and Wyatt, Michael and Zhuang, Donglin and Zhou, Zhongzhu and others},
+  journal={arXiv preprint arXiv:2401.14112},
+  year={2024}
+}
+```
+
+
+# Table of Contents
+1. [Why 6-bit Floating Point (FP6)](#introduction)
+2. [System Support for FP6](#system-fp6)
+3. [LLMs Serving with FP6](#serving-llm)
+4. [How to Start](#how-to-start)
+5. [Software Improvements](#software-improvements)
+6. [Acknowledgments and Contributions](#ac)
+
+# 1. Why 6-bit Floating Point (FP6) <a name="introduction"></a>
+
+
+In the evolving landscape of Large Language Models (LLMs) like GPT, our research aims to boost computational efficiency and storage while preserving model quality. This focus brings us to tackle the complex challenges of 4-bit quantization, where optimizing performance, efficiency, and accuracy is crucial.
+
+**Exploring the Challenges of 4-bit Quantization** In our recent research findings -- ZeroQuant (4+2)[1], we explore the capabilities of INT4 quantization techniques (like the GPTQ algorithm) for serving Large Language Models (LLMs). While these techniques reduce memory and computational requirements, they often perform poorly on a broad array of tasks, including generative tasks such as code generation and summarization, due to overfitting issues. This highlights the urgent need for new quantization approaches that simultanenously improve both the efficiency and effectiveness of LLMs.
+
+**Breakthroughs with FP6 Precision** Our exploration of different quantization methods led us to the FP6 precision standard. Despite the challenges in integrating and accelerating FP6 with current AI hardware -- which we will address in the next section - this format excels in performance and flexibility across various tasks. Notably, we observe that for generative tasks, FP6 quantization can match the performance of the half-precision (FP16) format. For example, with FP6 quantization, StarCoder-15B achieves comparable code generation results to the FP16 variant, while a smaller model, such as BART-460M, achieves comparable summarization performance to the standard FP16 equivalent. In order to preserve these quality gains, while matching the system efficiency of INT4 quantization on AI hardware, we propose a novel 4+2 FP6 scheme. This innovation makes FP6 a promising direction for improving the efficiency of LLMs, marking a significant leap in AI technology advancement. For more details, please refer to our research paper - ZeroQuant (4+2)[1].
+
+
+# 2. System Support for FP6 <a name="system-fp6"></a>
+
+**Pioneering Full-Stack GPU Kernel Design** A key challenge of FP6 quantization is the lack of efficient GPU kernel designs for this irregular, i.e., "non-power of 2", bit-width. In our recent research — FP6-LLM [2], we introduce TC-FPx, the first full-stack GPU system design scheme with unified Tensor Core support of floating point weights for FP6 and other irregular quantization bit-widths (6-bit, 5-bit, 3-bit, etc.). TC-FPx breaks the limitations of the underlying GPU hardware, allowing the GPU to support linear layer calculations on model weights of arbitrary bit width. By increasing the number of bit-width options for efficient quantization, TC-FPx significantly mitigates the "memory wall" challenges of LLM inference. In TC-FPx, Tensor Cores are utilized for intensive computation of matrix multiplications, while SIMT cores are effectively leveraged for weight dequantization, transforming the x-bit model weights to FP16 type during runtime before feeding them to Tensor Cores. It has the following key innovations:
+<div align="center">
+  <img src="./assets/fp6-design.png" alt="fp6 design" width="600"/>
+
+</div>
+
+* *Ahead-of-time Bit-level Pre-packing*: resolve the challenge of unfriendly memory access for weights with irregular bit-width, and enable optimal GPU memory access.
+
+* *SIMT-Efficient GPU Runtime*: minimize the runtime overhead of weight de-quantization.
+
+* *The software pipeline of TC-FPx kernel*: efficiently utilize SIMT cores, Tensor Cores, and the GPU memory hierarchy for high performance.
+
+
+
+On average, the TC-FPx kernel demonstrates a 2.1-fold improvement in processing speed over the FP16 cuBLAS benchmark during memory-intensive General Matrix Multiply (GEMM) operations on NVIDIA A100 GPUs. Notably, the implementation of the FP6 kernel through FP6 quantization facilitates the operation of LLaMA-70b on a solitary A100 GPU. This remarkable feat results in a normalized inference throughput that is 1.69 to 2.65 times superior to the FP16 benchmark when conducting inference tasks with batch-size under 32. Currently, TC-FPx kernel only supports NVIDIA Ampere GPUs and is only tested and verified on A100 GPUs
+
+
+# 3. LLMs serving with FP6 <a name="serving-llm"></a>
+
+We have successfully integrated the FP6 quantization kernel [3] into DeepSpeed-FastGen, facilitating on-the-fly, weight-only quantization. This enhancement permits the efficient quantization and deployment of large language models (LLMs) through a unified configuration option within DeepSpeed-FastGen. Detailed information regarding this feature will be provided in due course. Through our interface, users have the flexibility to load a model checkpoint from either HuggingFace hub or a local directory. While loading the checkpoint, our system applies FP6 round-to-nearest quantization on each linear layer, and transforms the quantized weights into 6-bit prepacked tensors. These tensors will serve as the model weights for inference, while the original FP16 weights are discarded to release memory. Throughout the inference stage, the FP6 kernels leverage the 6-bit prepacked weights, ensuring a seamless experience for users engaging with our platform.
+
+We assessed the LLaMA-70b model's serving performance using FP6 quantization on two A100 GPUs-80G, and observed a *1.5x* reduction in inference latency and a *3.5x* increase in inference throughput compared to the FP16 baseline. FP6 quantization offers two key benefits for model inference: it enables the deployment of large language models (LLMs) on fewer GPUs — for instance, LLaMA-70b fits on a single A100-80G GPU with FP6, versus at least two GPUs required for the FP16 baseline. Additionally, it significantly accelerates linear layers in memory-bound scenarios, which are common in LLM inference. Moreover, FP6 quantization reduces the GPU memory requirements for model weights, allowing for more queries to be served simultaneously, and thus increasing serving throughput.
+
+Our system demonstrates exceptional efficiency in handling long generation sequences. As illustrated in Figure 1, for generation lengths surpassing the prompt length, our system exhibits a notable performance superiority. The disparity in performance between FP6 and the FP16 baseline widens with the extension of the generation sequence length. This trend is primarily attributed to the inference process becoming increasingly memory-constrained as the decoding length expands, favoring our weight-quantized GPU kernels by facilitating faster compute compared to the FP16 baseline. It is important to highlight two factors contributing to the increased memory constraints in longer decoding scenarios.
+ - Firstly, the memory usage for the KV cache escalates with the sequence length, reducing the feasible batch sizes and leading to memory-bound GEMM  operations.
+ - Secondly, within the context of DeepSpeed-FastGen's prefill-decoding-mixed-batch technique, scenarios involving extended token generation encounter a reduction in prefill-chunks available for mixing with decodings. This results in a higher frequency of batches dedicated solely to decodings, further intensifying the memory-bound conditions.
+
+<p align="center">
+  <img src="./assets/servingllm/100-250.png" alt="Caption1" width="30%">
+  <img src="./assets/servingllm/100-500.png" alt="Caption2" width="30%">
+  <img src="./assets/servingllm/100-1000.png" alt="Caption3" width="30%">
+</p>
+
+  *Figure 1*:  End-to-end serving performances in DeepSpeed-MII with 32 clients and total of 128 requests, for LLaMA-2-70B model on 2xA100-80g with two-way tensor parallelism. We experimented with different number of requests between 128, 256 and 512 and found that the speedup is simillar.
+
+Despite the significant benefits of FP6 quantization, the current implementation faces limitations. Notably, in scenarios where GEMM operations become compute-bound due to large batch sizes or sufficient GPU memory, our weight-only quantization kernel may not sustain its latency advantage, especially against optimized libraries like cuBlas. However, our system's memory efficiency remains a key benefit. Currently, support is limited to Non-Mixture of Experts (Non-MoE) structures, with efforts underway to extend support to MoE structures. Additionally, the system is compatible only with FP16 input models, as the FP6 kernel processes FP16 activations exclusively.
+
+</div>
+
+# 4. How to begin with DeepSpeed-FP6  <a name="how-to-start"></a>
+
+The quantization-and-inference experience of DeepSpeed-FP6 is straightforward and convenient. Here we give an example based on LLaMa-2-70B model:
+
+```python
+import mii
+pipe = mii.pipeline("NousResearch/Llama-2-70b-hf", quantization_mode='wf6af16')
+response = pipe(["DeepSpeed is", "Seattle is"], max_new_tokens=128)
+print(response)
+```
+
+You need to install the following:
+```
+pip install deepspeed-mii
+pip install qtorch
+```
+
+To benchmark with our DeepSpeed-FP6, please visit the following script:
+```bash
+https://github.com/microsoft/DeepSpeedExamples/blob/master/benchmarks/inference/mii/run_fp6.sh
+```
+
+Please also visit the [FP6-LLM github](https://github.com/usyd-fsalab/fp6_llm) for the standalone kernel of FP6.  Don't forget to star the repo to show your support!
+
+
+# 5. Software Improvements  <a name="software-improvements"></a>
+
+
+Currently, DeepSpeed-FP6 supports only dense models with MoE models support upcoming. We will continue to improve DeepSpeed-FP6 with your feedback and support. DeepSpeed-FP6 is a component of the larger DeepSpeed ecosystem, which includes a range of Deep Learning systems and modeling technologies. To learn more,
+
+* Please visit our [website](https://www.deepspeed.ai/) for detailed blog posts, tutorials, and helpful documentation.
+* Follow us on our [English X(Twitter)](https://twitter.com/MSFTDeepSpeed), [Japanese X(Twitter)](https://twitter.com/MSFTDeepSpeedJP), and [Chinese Zhihu](https://www.zhihu.com/people/deepspeed) for latest news on DeepSpeed.
+
+We welcome your contributions to DeepSpeed! We encourage you to report issues, contribute PRs, and join discussions on the [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) page. Please see our [contributing guide](https://github.com/microsoft/DeepSpeed/blob/master/CONTRIBUTING.md) for more details. We are open to collaborations with universities, research labs, companies, such as those working together on deep learning research, applying DeepSpeed to empower real-world AI models and applications, and so on. For such requests (and other requests unsuitable for GitHub), please directly email to [email protected].
+
+* "Star" our [DeepSpeed GitHub](https://github.com/microsoft/DeepSpeed/) and [DeepSpeed-MII GitHub](https://github.com/microsoft/DeepSpeed-MII/) and [DeepSpeedExamples GitHub](https://github.com/microsoft/DeepSpeedExamples/)  repositories if you like our work!
+
+
+# 6. Acknowledgments and Contributions <a name="ac"></a>
+We thank the collaboration of the University of Sydney and Rutgers University. We also thank the open-source library [aspuru-guzik-group/qtorch](https://github.com/aspuru-guzik-group/qtorch).
+
+Contributions:
+Xiaoxia Wu\* $^1$, Zhen Zheng\* $^1$, Haojun Xia\* $^2$, Arash Bakhtiari $^1$, Michael Wyatt $^1$, Shiyang Chen $^3$, Stephen Youn $^1$, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase $^1$,  Zhewei Yao, Leon Song $^1$ $^2$ (project lead)
+
+\* Equal Contribution
+1: Microsoft
+2: University of Sydney
+3: Rutgers University
+
+Reference:
+
+[1] ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks. arXiv. https://arxiv.org/abs/2312.08583
+
+[2] FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design. arXiv. https://arxiv.org/abs/2401.14112
+
+[3] FP6-LLM kernel release. GitHub. https://github.com/usyd-fsalab/fp6_llm