Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
- URL: http://arxiv.org/abs/2308.12066v3
- Date: Sat, 27 Apr 2024 09:11:44 GMT
- Title: Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference
- Authors: Ranggi Hwang, Jianyu Wei, Shijie Cao, Changho Hwang, Xiaohu Tang, Ting Cao, Mao Yang,
- Abstract summary: Mixture-of-Experts (MoE) is able to scale its model size without proportionally scaling up its computational requirements.
Pre-gated MoE employs our novel pre-gating function which alleviates the dynamic nature of sparse expert activation.
We demonstrate that Pre-gated MoE is able to improve performance, reduce GPU memory consumption, while also maintaining the same level of model quality.
- Score: 23.207326766883405
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Large language models (LLMs) based on transformers have made significant strides in recent years, the success of which is driven by scaling up their model size. Despite their high algorithmic performance, the computational and memory requirements of LLMs present unprecedented challenges. To tackle the high compute requirements of LLMs, the Mixture-of-Experts (MoE) architecture was introduced which is able to scale its model size without proportionally scaling up its computational requirements. Unfortunately, MoE's high memory demands and dynamic activation of sparse experts restrict its applicability to real-world problems. Previous solutions that offload MoE's memory-hungry expert parameters to CPU memory fall short because the latency to migrate activated experts from CPU to GPU incurs high performance overhead. Our proposed Pre-gated MoE system effectively tackles the compute and memory challenges of conventional MoE architectures using our algorithm-system co-design. Pre-gated MoE employs our novel pre-gating function which alleviates the dynamic nature of sparse expert activation, allowing our proposed system to address the large memory footprint of MoEs while also achieving high performance. We demonstrate that Pre-gated MoE is able to improve performance, reduce GPU memory consumption, while also maintaining the same level of model quality. These features allow our Pre-gated MoE system to cost-effectively deploy large-scale LLMs using just a single GPU with high performance.
Related papers
- OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration [5.0389804644646174]
We introduce OPIMA, a processing-in-memory (PIM)-based machine learning accelerator.
PIM struggles to achieve high throughput and energy efficiency due to internal data movement bottlenecks.
We show that OPIMA can achieve 2.98x higher throughput and 137x better energy efficiency than the best-known prior work.
arXiv Detail & Related papers (2024-07-11T06:12:04Z) - Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference [2.9302211589186244]
Large language models (LLMs) have transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations.
Developments in computing and memory capabilities are lagging behind, exacerbated by the discontinuation of Moore's law.
compute-in-memory (CIM) technologies offer a promising solution for accelerating AI inference by directly performing analog computations in memory.
arXiv Detail & Related papers (2024-06-12T16:57:58Z) - Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment [56.44025052765861]
Large language models (LLMs) have revolutionized Natural Language Processing (NLP), but their size creates computational bottlenecks.
We introduce a novel approach to create accurate, sparse foundational versions of performant LLMs.
We show a total speedup on CPUs for sparse-quantized LLaMA models of up to 8.6x.
arXiv Detail & Related papers (2024-05-06T16:03:32Z) - SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts [49.01990048827639]
We introduce SEER-MoE, a framework for reducing both the memory footprint and compute requirements of pre-trained MoE models.
The first stage involves pruning the total number of experts using a heavy-hitters counting guidance, while the second stage employs a regularization-based fine-tuning strategy to recover accuracy loss.
Our empirical studies demonstrate the effectiveness of our method, resulting in a sparse MoEs model optimized for inference efficiency with minimal accuracy trade-offs.
arXiv Detail & Related papers (2024-04-07T22:13:43Z) - Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models [90.14693869269519]
MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes.
This paper mainly aims to enhance the deployment efficiency of MoE LLMs by introducing plug-and-play expert-level sparsification techniques.
arXiv Detail & Related papers (2024-02-22T18:56:07Z) - Efficient LLM inference solution on Intel GPU [19.154403468201924]
Transformer based Large Language Models (LLMs) have been widely used in many fields.
We propose an efficient LLM inference solution with low latency and high throughput.
Compared with the standard HuggingFace implementation, the proposed solution achieves up to 7x lower token latency and 27x higher throughput.
arXiv Detail & Related papers (2023-12-19T05:40:43Z) - FusionAI: Decentralized Training and Deploying LLMs with Massive
Consumer-Level GPUs [57.12856172329322]
We envision a decentralized system unlocking the potential vast untapped consumer-level GPU.
This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
arXiv Detail & Related papers (2023-09-03T13:27:56Z) - EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models [3.597163516372061]
EdgeMoE is an on-device inference engine tailored for mixture-of-expert (MoE) LLMs.
It achieves both memory and computational efficiency by strategically partitioning the model across the storage hierarchy.
It demonstrates substantial memory savings and performance improvements when compared to competitive baseline solutions.
arXiv Detail & Related papers (2023-08-28T06:56:08Z) - FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only
Quantization for LLMs [9.072821427818557]
Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment.
We propose an efficient weight-only quantization method that reduces memory consumption and accelerates inference for LLMs.
We evaluate our approach on large-scale open source models such as OPT-175B and internal MoE models, showcasing minimal accuracy loss while achieving up to 3.65 times higher throughput.
arXiv Detail & Related papers (2023-08-16T23:57:41Z) - Energy-efficient Task Adaptation for NLP Edge Inference Leveraging
Heterogeneous Memory Architectures [68.91874045918112]
adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks.
We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
arXiv Detail & Related papers (2023-03-25T14:40:59Z) - Memformer: A Memory-Augmented Transformer for Sequence Modeling [55.780849185884996]
We present Memformer, an efficient neural network for sequence modeling.
Our model achieves linear time complexity and constant memory space complexity when processing long sequences.
arXiv Detail & Related papers (2020-10-14T09:03:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.