Efficient LLM inference solution on Intel GPU

• URL: http://arxiv.org/abs/2401.05391v2
• Date: Sun, 23 Jun 2024 13:03:32 GMT
• Title: Efficient LLM inference solution on Intel GPU
• Authors: Hui Wu, Yi Gan, Feng Yuan, Jing Ma, Wei Zhu, Yutao Xu, Hong Zhu, Yuhua Zhu, Xiaoli Liu, Jinghui Gu, Peng Zhao,
• Abstract summary: 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.
• Score: 19.154403468201924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer based Large Language Models (LLMs) have been widely used in many fields, and the efficiency of LLM inference becomes hot topic in real applications. However, LLMs are usually complicatedly designed in model structure with massive operations and perform inference in the auto-regressive mode, making it a challenging task to design a system with high efficiency. In this paper, we propose an efficient LLM inference solution with low latency and high throughput. Firstly, we simplify the LLM decoder layer by fusing data movement and element-wise operations to reduce the memory access frequency and lower system latency. We also propose a segment KV cache policy to keep key/value of the request and response tokens in separate physical memory for effective device memory management, helping enlarge the runtime batch size and improve system throughput. A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution. We implement our LLM inference solution on Intel GPU and publish it publicly. Compared with the standard HuggingFace implementation, the proposed solution achieves up to 7x lower token latency and 27x higher throughput for some popular LLMs on Intel GPU.

Related papers

• Fast Inference for Augmented Large Language Models [14.195265302357148]
  Augmented Large Language Models (LLMs) enhance the capabilities of standalone LLMs by integrating external data sources through API calls. Traditional size-based scheduling algorithms, such as Shortest Job First (SJF), become less effective at minimizing completion times. We propose LAMPS, a novel LLM inference framework for augmented LLMs.
  arXiv  Detail & Related papers  (2024-10-23T19:53:30Z)
• Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores [3.6385567224218556]
  Large language models (LLMs) have been widely applied but face challenges in efficient inference. We introduce a novel bipolar-INT data format that facilitates parallel computing and supports symmetric quantization. We implement an arbitrary precision matrix multiplication scheme that decomposes and recovers at the bit level, enabling flexible precision.
  arXiv  Detail & Related papers  (2024-09-26T14:17:58Z)
• Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction [47.38471103190534]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long context inputs, but this comes at the cost of increased computational resources and latency. Our research introduces a novel approach for the long context bottleneck to accelerate LLM inference and reduce GPU memory consumption. We propose an algorithm that uses early layers of an LLM as filters to select and compress input tokens, significantly reducing the context length for subsequent processing.
  arXiv  Detail & Related papers  (2024-09-25T23:14:47Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping [49.66872823080736]
  Autoregressive Large Language Models (e.g., LLaMa, GPTs) are omnipresent achieving remarkable success in language understanding and generation. To mitigate overload incurred during generation, several early-exit and layer-dropping strategies have been proposed. We propose FFN-SkipLLM, which is an input-adaptive feed-forward skipping strategy.
  arXiv  Detail & Related papers  (2024-04-05T02:35:43Z)
• An Embarrassingly Simple Approach for LLM with Strong ASR Capacity [56.30595787061546]
  We focus on solving one of the most important tasks in the field of speech processing, with speech foundation encoders and large language models (LLM) Recent works have complex designs such as compressing the output temporally for the speech encoder, tackling modal alignment for the projector, and utilizing parameter-efficient fine-tuning for the LLM. We found that delicate designs are not necessary, while an embarrassingly simple composition of off-the-shelf speech encoder, LLM, and the only trainable linear projector is competent for the ASR task.
  arXiv  Detail & Related papers  (2024-02-13T23:25:04Z)
• Anchor-based Large Language Models [33.86392289481657]
  This study introduces Anchor-based LLMs (AnLLMs), which utilize an anchor-based self-attention network (AnSAN) and also an anchor-based inference strategy. AnLLMs maintain similar accuracy levels while achieving up to 99% keys/values cache reduction and up to 3.5 times faster inference.
  arXiv  Detail & Related papers  (2024-02-12T12:48:02Z)
• Efficient LLM Inference on CPUs [8.802223672775844]
  Large language models (LLMs) have demonstrated remarkable performance and tremendous potential across a wide range of tasks. deploying these models has been challenging due to the astronomical amount of model parameters. We propose an effective approach that can make the deployment of LLMs more efficiently.
  arXiv  Detail & Related papers  (2023-11-01T13:08:50Z)
• 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)
• Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline [22.08897444328099]
  Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs.
  arXiv  Detail & Related papers  (2023-05-22T15:36:06Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

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.